From 6de7c308f3434ccce06bf3ae6b732f24be3825ce Mon Sep 17 00:00:00 2001 From: jorge Date: Thu, 26 Mar 2026 11:05:01 +0100 Subject: [PATCH 01/49] feat: add CTable, a columnar in-memory table built on top of blosc2 Introduce CTable, a new columnar table class for efficient in-memory data storage using Blosc2 as the underlying compression engine. Each column is represented as a Column object wrapping a blosc2.NDArray with typed, compressed storage. Building on top of blosc2's existing infrastructure, CTable supports append, iteration and column-based queries. This is an early-stage (beta) implementation; the table is always fully loaded in memory. New files: - src/blosc2/ctable.py: CTable and Column class definitions - tests/ctable/: unit tests covering construction, slicing, deletion, compaction and row logic - bench/ctable/: benchmarks comparing CTable against pandas --- bench/ctable/compact.py | 81 +++ bench/ctable/ctable_v_panda.py | 127 +++++ bench/ctable/delete.py | 82 +++ bench/ctable/expected_size.py | 75 +++ bench/ctable/extend.py | 117 +++++ bench/ctable/extend_vs_apend.py | 84 +++ bench/ctable/index.py | 69 +++ bench/ctable/iteration_column.py | 85 ++++ bench/ctable/print.py | 112 ++++ bench/ctable/row_acces.py | 68 +++ bench/ctable/slice.py | 77 +++ bench/ctable/slice_steps.py | 67 +++ bench/ctable/slice_to_array.py | 77 +++ bench/ctable/where_chain.py | 79 +++ bench/ctable/where_selective.py | 68 +++ src/blosc2/__init__.py | 1 + src/blosc2/ctable.py | 793 +++++++++++++++++++++++++++++ tests/ctable/test_column.py | 294 +++++++++++ tests/ctable/test_compact.py | 157 ++++++ tests/ctable/test_construct.py | 225 ++++++++ tests/ctable/test_delete_rows.py | 210 ++++++++ tests/ctable/test_extend_delete.py | 226 ++++++++ tests/ctable/test_row_logic.py | 221 ++++++++ 23 files changed, 3395 insertions(+) create mode 100644 bench/ctable/compact.py create mode 100644 bench/ctable/ctable_v_panda.py create mode 100644 bench/ctable/delete.py create mode 100644 bench/ctable/expected_size.py create mode 100644 bench/ctable/extend.py create mode 100644 bench/ctable/extend_vs_apend.py create mode 100644 bench/ctable/index.py create mode 100644 bench/ctable/iteration_column.py create mode 100644 bench/ctable/print.py create mode 100644 bench/ctable/row_acces.py create mode 100644 bench/ctable/slice.py create mode 100644 bench/ctable/slice_steps.py create mode 100644 bench/ctable/slice_to_array.py create mode 100644 bench/ctable/where_chain.py create mode 100644 bench/ctable/where_selective.py create mode 100644 src/blosc2/ctable.py create mode 100644 tests/ctable/test_column.py create mode 100644 tests/ctable/test_compact.py create mode 100644 tests/ctable/test_construct.py create mode 100644 tests/ctable/test_delete_rows.py create mode 100644 tests/ctable/test_extend_delete.py create mode 100644 tests/ctable/test_row_logic.py diff --git a/bench/ctable/compact.py b/bench/ctable/compact.py new file mode 100644 index 00000000..f41bb008 --- /dev/null +++ b/bench/ctable/compact.py @@ -0,0 +1,81 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +# Benchmark for measuring compact() time and memory gain after deletions +# of varying fractions of the table. + +from time import time +from typing import Annotated + +import numpy as np +from pydantic import BaseModel, Field + +import blosc2 + + +class NumpyDtype: + def __init__(self, dtype): + self.dtype = dtype + + +# Row model +class RowModel(BaseModel): + id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) + c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) + score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) + active: Annotated[bool, NumpyDtype(np.bool_)] = True + + +N = 1_000_000 + +print(f"compact() benchmark | N = {N:,}\n") + +# Build base data once +np_dtype = np.dtype([ + ("id", np.int64), + ("c_val", np.complex128), + ("score", np.float64), + ("active", np.bool_), +]) +DATA = np.array( + [ + (i, complex(i * 0.1, i * 0.01), 10.0 + (i % 100) * 0.4, i % 3 == 0) + for i in range(N) + ], + dtype=np_dtype, +) + +delete_fractions = [0.1, 0.25, 0.5, 0.75, 0.9] + +print("=" * 75) +print(f"{'DELETED':>10} {'ROWS LEFT':>10} {'TIME (s)':>12} {'CBYTES BEFORE':>15} {'CBYTES AFTER':>14}") +print("-" * 75) + +for frac in delete_fractions: + ct = blosc2.CTable(RowModel, expected_size=N) + ct.extend(DATA) + + n_delete = int(N * frac) + ct.delete(list(range(n_delete))) + + cbytes_before = sum(col.cbytes for col in ct._cols.values()) + ct._valid_rows.cbytes + + t0 = time() + ct.compact() + t_compact = time() - t0 + + cbytes_after = sum(col.cbytes for col in ct._cols.values()) + ct._valid_rows.cbytes + + print( + f"{frac*100:>9.0f}%" + f" {N - n_delete:>10,}" + f" {t_compact:>12.4f}" + f" {cbytes_before / 1024**2:>13.2f} MB" + f" {cbytes_after / 1024**2:>12.2f} MB" + ) + +print("-" * 75) diff --git a/bench/ctable/ctable_v_panda.py b/bench/ctable/ctable_v_panda.py new file mode 100644 index 00000000..4f7d6c8a --- /dev/null +++ b/bench/ctable/ctable_v_panda.py @@ -0,0 +1,127 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +# Benchmark comparing CTable vs pandas DataFrame for: +# 1. Creation from a NumPy structured array +# 2. Column access (full column) +# 3. Filtering (where/query) +# 4. Row iteration + +from time import time +from typing import Annotated + +import numpy as np +import pandas as pd +from pydantic import BaseModel, Field + +import blosc2 + + +class NumpyDtype: + def __init__(self, dtype): + self.dtype = dtype + + +# Row model +class RowModel(BaseModel): + id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) + c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) + score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) + active: Annotated[bool, NumpyDtype(np.bool_)] = True + + +N = 1_000_000 +rng = np.random.default_rng(42) + +print(f"CTable vs pandas benchmark | N = {N:,}\n") + +# Build base data once +np_dtype = np.dtype([ + ("id", np.int64), + ("c_val", np.complex128), + ("score", np.float64), + ("active", np.bool_), +]) +DATA = np.empty(N, dtype=np_dtype) +DATA["id"] = np.arange(N, dtype=np.int64) +DATA["c_val"] = rng.standard_normal(N) + 1j * rng.standard_normal(N) +DATA["score"] = rng.uniform(0, 100, N) +DATA["active"] = rng.integers(0, 2, N, dtype=np.bool_) + +print("=" * 65) +print(f"{'OPERATION':<30} {'CTable':>12} {'pandas':>12} {'SPEEDUP':>10}") +print("-" * 65) + +# 1. Creation +t0 = time() +ct = blosc2.CTable(RowModel, expected_size=N) +ct.extend(DATA) +t_ct_create = time() - t0 + +t0 = time() +df = pd.DataFrame(DATA) +t_pd_create = time() - t0 + +print(f"{'Creation':<30} {t_ct_create:>12.4f} {t_pd_create:>12.4f} {t_pd_create/t_ct_create:>9.2f}x") + +# 2. Column access (full column) +t0 = time() +arr = ct["score"] +t_ct_col = time() - t0 + +t0 = time() +arr = df["score"] +t_pd_col = time() - t0 + +print(f"{'Column access (full)':<30} {t_ct_col:>12.4f} {t_pd_col:>12.4f} {t_pd_col/t_ct_col:>9.2f}x") + +# 2.5 Column access (full column) +t0 = time() +arr = ct["score"].to_numpy() +t_ct_col = time() - t0 + +t0 = time() +arr = df["score"].to_numpy() +t_pd_col = time() - t0 + +print(f"{'Column access to numpy (full)':<30} {t_ct_col:>12.4f} {t_pd_col:>12.4f} {t_pd_col/t_ct_col:>9.3f}x") + +# 3. Filtering +t0 = time() +result_ct = ct.where((ct["id"] > 250_000) & (ct["id"] < 750_000)) +t_ct_filter = time() - t0 + +t0 = time() +result_pd = df.query("250000 < id < 750000") +t_pd_filter = time() - t0 + +print(f"{'Filter (id 250k-750k)':<30} {t_ct_filter:>12.4f} {t_pd_filter:>12.4f} {t_pd_filter/t_ct_filter:>9.2f}x") + +# 4. Row iteration +t0 = time() +for val in ct["score"]: + pass +t_ct_iter = time() - t0 + +t0 = time() +for val in df["score"]: + pass +t_pd_iter = time() - t0 + +print(f"{'Row iteration':<30} {t_ct_iter:>12.4f} {t_pd_iter:>12.4f} {t_pd_iter/t_ct_iter:>9.2f}x") + +print("-" * 65) + +# Memory +ct_cbytes = sum(col.cbytes for col in ct._cols.values()) + ct._valid_rows.cbytes +ct_nbytes = sum(col.nbytes for col in ct._cols.values()) + ct._valid_rows.nbytes +pd_nbytes = df.memory_usage(deep=True).sum() + +print(f"\nMemory — CTable compressed: {ct_cbytes / 1024**2:.2f} MB") +print(f"Memory — CTable uncompressed: {ct_nbytes / 1024**2:.2f} MB") +print(f"Memory — pandas: {pd_nbytes / 1024**2:.2f} MB") +print(f"Compression ratio CTable: {ct_nbytes / ct_cbytes:.2f}x") diff --git a/bench/ctable/delete.py b/bench/ctable/delete.py new file mode 100644 index 00000000..fb147c7c --- /dev/null +++ b/bench/ctable/delete.py @@ -0,0 +1,82 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +# Benchmark for measuring delete() performance with different index types: +# int, slice, and list — with varying sizes. + +from time import time +from typing import Annotated + +import numpy as np +from pydantic import BaseModel, Field + +import blosc2 + + +class NumpyDtype: + def __init__(self, dtype): + self.dtype = dtype + + +# Row model +class RowModel(BaseModel): + id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) + c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) + score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) + active: Annotated[bool, NumpyDtype(np.bool_)] = True + + +N = 1_000_000 + +print(f"delete() benchmark | N = {N:,}\n") + +# Build base data once +np_dtype = np.dtype([ + ("id", np.int64), + ("c_val", np.complex128), + ("score", np.float64), + ("active", np.bool_), +]) +DATA = np.array( + [ + (i, complex(i * 0.1, i * 0.01), 10.0 + (i % 100) * 0.4, i % 3 == 0) + for i in range(N) + ], + dtype=np_dtype, +) + +delete_cases = [ + ("int", 0), + ("slice small", slice(0, 100)), + ("slice large", slice(0, 100_000)), + ("slice full", slice(0, N)), + ("list small", list(range(100))), + ("list large", list(range(100_000))), + ("list full", list(range(N))), +] + +print("=" * 60) +print(f"{'CASE':<20} {'ROWS DELETED':>14} {'TIME (s)':>12}") +print("-" * 60) + +for label, key in delete_cases: + ct = blosc2.CTable(RowModel, expected_size=N) + ct.extend(DATA) + + if isinstance(key, int): + n_deleted = 1 + elif isinstance(key, slice): + n_deleted = len(range(*key.indices(N))) + else: + n_deleted = len(key) + + t0 = time() + ct.delete(key) + t_delete = time() - t0 + print(f"{label:<20} {n_deleted:>14,} {t_delete:>12.6f}") + +print("-" * 60) diff --git a/bench/ctable/expected_size.py b/bench/ctable/expected_size.py new file mode 100644 index 00000000..c4444a62 --- /dev/null +++ b/bench/ctable/expected_size.py @@ -0,0 +1,75 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +# Benchmark for measuring the overhead of resize() when expected_size +# is too small (M rows) vs correctly sized (N rows) during extend(). + +from time import time +from typing import Annotated + +import numpy as np +from pydantic import BaseModel, Field + +import blosc2 + + +class NumpyDtype: + def __init__(self, dtype): + self.dtype = dtype + + +# Row model +class RowModel(BaseModel): + id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) + c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) + score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) + active: Annotated[bool, NumpyDtype(np.bool_)] = True + + + +M = 779 +N = 62_500 +MAX_N = 1_000_000 +print(f"expected_size benchmark | wrong expected_size = {M}") + +# Pre-generate full dataset once +np_dtype = np.dtype([ + ("id", np.int64), + ("c_val", np.complex128), + ("score", np.float64), + ("active", np.bool_), +]) +DATA = np.array( + [ + (i, complex(i * 0.1, i * 0.01), 10.0 + (i % 100) * 0.4, i % 3 == 0) + for i in range(MAX_N) + ], + dtype=np_dtype, +) + +while N <= MAX_N: + print("-" * 80) + print(f"N = {N:,} rows") + + # 1. extend() with correct expected_size = N + ct_correct = blosc2.CTable(RowModel, expected_size=N) + t0 = time() + ct_correct.extend(DATA[:N]) + t_correct = time() - t0 + print(f"extend() expected_size=N ({N:>8,}): {t_correct:.4f} s rows: {len(ct_correct):,}") + + # 2. extend() with wrong expected_size = M (forces resize) + ct_wrong = blosc2.CTable(RowModel, expected_size=M) + t0 = time() + ct_wrong.extend(DATA[:N]) + t_wrong = time() - t0 + print(f"extend() expected_size=M ({M:>8,}): {t_wrong:.4f} s rows: {len(ct_wrong):,}") + + # Summary + print(f" Slowdown from wrong expected_size: {t_wrong / t_correct:.2f}x") + + N *= 2 diff --git a/bench/ctable/extend.py b/bench/ctable/extend.py new file mode 100644 index 00000000..f294b012 --- /dev/null +++ b/bench/ctable/extend.py @@ -0,0 +1,117 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +# Benchmark for measuring CTable creation time from three different sources: +# 1. Python list of lists (1M rows) +# 2. NumPy structured array (1M rows) — list of named tuples +# 3. An existing CTable (previously created from Python lists, 1M rows) + +from time import time +from typing import Annotated + +import numpy as np +from pydantic import BaseModel, Field + +import blosc2 + + +class NumpyDtype: + def __init__(self, dtype): + self.dtype = dtype + + + + + +# --------------------------------------------------------------------------- +# Row model +# --------------------------------------------------------------------------- +class RowModel(BaseModel): + id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) + c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) + score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) + active: Annotated[bool, NumpyDtype(np.bool_)] = True + + +N = 1_000_000 +print(f"CTable creation benchmark with {N:,} rows\n") + +# --------------------------------------------------------------------------- +# Base data generation (not part of the benchmark timing) +# --------------------------------------------------------------------------- +print("Generating base data...") + +t0 = time() +data_list = [ + [i, complex(i * 0.1, i * 0.01), 10.0 + (i % 100) * 0.4, i % 3 == 0] + for i in range(N) +] +t_gen_list = time() - t0 +print(f" Python list generated in: {t_gen_list:.4f} s") + +t0 = time() +np_dtype = np.dtype([ + ("id", np.int64), + ("c_val", np.complex128), + ("score", np.float64), + ("active", np.bool_), +]) +data_np = np.array( + [ + (i, complex(i * 0.1, i * 0.01), 10.0 + (i % 100) * 0.4, i % 3 == 0) + for i in range(N) + ], + dtype=np_dtype, +) +t_gen_np = time() - t0 +print(f" NumPy structured array generated: {t_gen_np:.4f} s\n") + +# --------------------------------------------------------------------------- +# 1. Creation from a Python list of lists +# --------------------------------------------------------------------------- +print("CTable from Python list of lists") +t0 = time() +ct_from_list = blosc2.CTable(RowModel, expected_size=N) +ct_from_list.extend(data_list) +t_from_list = time() - t0 +print(f" extend() time (Python list): {t_from_list:.4f} s") +print(f" Rows: {len(ct_from_list):,}") + +# --------------------------------------------------------------------------- +# 2. Creation from a NumPy structured array (list of named tuples) +# --------------------------------------------------------------------------- +print("CTable from NumPy structured array") +t0 = time() +ct_from_np = blosc2.CTable(RowModel, expected_size=N) +ct_from_np.extend(data_np) +t_from_np = time() - t0 +print(f" extend() time (NumPy struct): {t_from_np:.4f} s") +print(f" Rows: {len(ct_from_np):,}") + + +# --------------------------------------------------------------------------- +# 3. Creation from an existing CTable (ct_from_list, already built above) +# --------------------------------------------------------------------------- +print("CTable from an existing CTable") +t0 = time() +ct_from_ctable = blosc2.CTable(RowModel, expected_size=N) +ct_from_ctable.extend(ct_from_list) +t_from_ctable = time() - t0 +print(f" extend() time (CTable): {t_from_ctable:.4f} s") +print(f" Rows: {len(ct_from_ctable):,}") + +# --------------------------------------------------------------------------- +# Summary +# --------------------------------------------------------------------------- +print("\n") +print("=" * 60) +print(f"{'SOURCE':<30} {'TIME (s)':>12} {'SPEEDUP vs list':>18}") +print("-" * 60) +print(f"{'Python list of lists':<30} {t_from_list:>12.4f} {'1.00x':>18}") +print(f"{'NumPy structured array':<30} {t_from_np:>12.4f} {t_from_list / t_from_np:>17.2f}x") +print(f"{'Existing CTable':<30} {t_from_ctable:>12.4f} {t_from_list / t_from_ctable:>17.2f}x") + diff --git a/bench/ctable/extend_vs_apend.py b/bench/ctable/extend_vs_apend.py new file mode 100644 index 00000000..2036755c --- /dev/null +++ b/bench/ctable/extend_vs_apend.py @@ -0,0 +1,84 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +# Benchmark for comparing append() (row by row) vs extend() (bulk), +# to find the crossover point where extend() becomes worth it. + +from time import time +from typing import Annotated + +import numpy as np +from pydantic import BaseModel, Field + +import blosc2 + + +class NumpyDtype: + def __init__(self, dtype): + self.dtype = dtype + + +# Row model +class RowModel(BaseModel): + id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) + c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) + score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) + active: Annotated[bool, NumpyDtype(np.bool_)] = True + + +# Parameter — change N to test different crossover points +N = 2 +print(f"append() vs extend() benchmark") +for i in range(6): + print("\n") + print("%" * 100) + + + # Base data generation + data_list = [ + [i, complex(i * 0.1, i * 0.01), 10.0 + (i % 100) * 0.4, i % 3 == 0] for i in range(N) + ] + + # 1. N individual append() calls + print(f"{N} individual append() calls") + ct_append = blosc2.CTable(RowModel, expected_size=N) + t0 = time() + for row in data_list: + ct_append.append(row) + t_append = time() - t0 + print(f" Time: {t_append:.6f} s") + print(f" Rows: {len(ct_append):,}") + + # 2. N individual extend() calls (one row at a time) + print(f"{N} individual extend() calls (one row at a time)") + ct_extend_one = blosc2.CTable(RowModel, expected_size=N) + t0 = time() + for row in data_list: + ct_extend_one.extend([row]) + t_extend_one = time() - t0 + print(f" Time: {t_extend_one:.6f} s") + print(f" Rows: {len(ct_extend_one):,}") + + # 3. Single extend() call with all N rows at once + print(f"Single extend() call with all {N} rows at once") + ct_extend_bulk = blosc2.CTable(RowModel, expected_size=N) + t0 = time() + ct_extend_bulk.extend(data_list) + t_extend_bulk = time() - t0 + print(f" Time: {t_extend_bulk:.6f} s") + print(f" Rows: {len(ct_extend_bulk):,}") + + # Summary + print("=" * 70) + print(f"{'METHOD':<35} {'TIME (s)':>12} {'SPEEDUP vs append':>20}") + print("-" * 70) + print(f"{'append() x N':<35} {t_append:>12.6f} {'1.00x':>20}") + print(f"{'extend() x N (one row each)':<35} {t_extend_one:>12.6f} {t_append / t_extend_one:>19.2f}x") + print(f"{'extend() x 1 (all at once)':<35} {t_extend_bulk:>12.6f} {t_append / t_extend_bulk:>19.2f}x") + print("-" * 70) + + N=N*2 diff --git a/bench/ctable/index.py b/bench/ctable/index.py new file mode 100644 index 00000000..634a68e1 --- /dev/null +++ b/bench/ctable/index.py @@ -0,0 +1,69 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +# Benchmark for measuring Column[int] access (single row by logical index), +# which exercises _find_physical_index() traversal over chunk metadata. + +from time import time +from typing import Annotated + +import numpy as np +from pydantic import BaseModel, Field + +import blosc2 + + +class NumpyDtype: + def __init__(self, dtype): + self.dtype = dtype + + +# Row model +class RowModel(BaseModel): + id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) + c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) + score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) + active: Annotated[bool, NumpyDtype(np.bool_)] = True + + +N = 1_000_000 +indices = [0, N // 4, N // 2, (3 * N) // 4, N - 1] + +print(f"Column[int] access benchmark | N = {N:,}\n") + +# Build CTable once +np_dtype = np.dtype([ + ("id", np.int64), + ("c_val", np.complex128), + ("score", np.float64), + ("active", np.bool_), +]) +DATA = np.array( + [ + (i, complex(i * 0.1, i * 0.01), 10.0 + (i % 100) * 0.4, i % 3 == 0) + for i in range(N) + ], + dtype=np_dtype, +) + +ct = blosc2.CTable(RowModel, expected_size=N) +ct.extend(DATA) + +print(f"CTable built with {len(ct):,} rows\n") +print("=" * 60) +print(f"{'INDEX':<15} {'POSITION':>12} {'TIME (s)':>12}") +print("-" * 60) + +col = ct["score"] +for idx in indices: + t0 = time() + val = col[idx] + t_access = time() - t0 + position = f"{idx / N * 100:.0f}% into array" + print(f"{idx:<15,} {position:>12} {t_access:.6f}") + +print("-" * 60) diff --git a/bench/ctable/iteration_column.py b/bench/ctable/iteration_column.py new file mode 100644 index 00000000..5f0efaed --- /dev/null +++ b/bench/ctable/iteration_column.py @@ -0,0 +1,85 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +# Benchmark for comparing full column iteration strategies: +# 1. for val in ct["score"] — Python iterator via __iter__ +# 2. np.array(list(ct["score"])) — materialize via list then convert +# 3. ct["score"][0:N].to_array() — slice view + to_array() + +from time import time +from typing import Annotated + +import numpy as np +from pydantic import BaseModel, Field + +import blosc2 + + +class NumpyDtype: + def __init__(self, dtype): + self.dtype = dtype + + +# Row model +class RowModel(BaseModel): + id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) + c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) + score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) + active: Annotated[bool, NumpyDtype(np.bool_)] = True + + +N = 1_000_000 + +print(f"Column iteration benchmark | N = {N:,}\n") + +# Build CTable once +np_dtype = np.dtype([ + ("id", np.int64), + ("c_val", np.complex128), + ("score", np.float64), + ("active", np.bool_), +]) +DATA = np.array( + [ + (i, complex(i * 0.1, i * 0.01), 10.0 + (i % 100) * 0.4, i % 3 == 0) + for i in range(N) + ], + dtype=np_dtype, +) + +ct = blosc2.CTable(RowModel, expected_size=N) +ct.extend(DATA) + +print(f"CTable built with {len(ct):,} rows\n") +print("=" * 60) + +col = ct["score"] + +# 1. Python iterator +t0 = time() +for val in col: + pass +t_iter = time() - t0 +print(f"for val in col: {t_iter:.4f} s") + +# 2. list() + np.array() +t0 = time() +arr = np.array(list(col)) +t_list = time() - t0 +print(f"np.array(list(col)): {t_list:.4f} s") + +# 3. slice view + to_array() +t0 = time() +arr = col[0:N].to_numpy() +for val in arr: + pass +t_toarray = time() - t0 +print(f"col[0:N].to_array(): {t_toarray:.4f} s") + +print("=" * 60) +print(f"Speedup to_array vs iter: {t_iter / t_toarray:.2f}x") +print(f"Speedup to_array vs list: {t_list / t_toarray:.2f}x") diff --git a/bench/ctable/print.py b/bench/ctable/print.py new file mode 100644 index 00000000..af352a2a --- /dev/null +++ b/bench/ctable/print.py @@ -0,0 +1,112 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +# Benchmark: iterative ingestion comparison — Pandas vs CTable +# Data source: randomly generated numpy structured array + +import time +from typing import Annotated + +import numpy as np +import pandas as pd +import blosc2 +from pydantic import BaseModel, Field + + +class NumpyDtype: + def __init__(self, dtype): + self.dtype = dtype + + +class RowModel(BaseModel): + id: Annotated[int, NumpyDtype(np.int64)] + name: Annotated[str, NumpyDtype(np.dtype(" np.ndarray: + arr = np.empty(n, dtype=np_dtype) + arr["id"] = np.arange(n, dtype=np.int64) + arr["name"] = np.array([rng.choice(NAMES) for _ in range(n)], dtype=" DataFrame) ---") +data = make_data(N) + +t0 = time.perf_counter() +df = pd.DataFrame(data) +t_pandas = time.perf_counter() - t0 + +mem_pandas = df.memory_usage(deep=True).sum() / (1024 ** 2) +print(f"Total time: {t_pandas:.4f} s") +print(f"Memory (RAM): {mem_pandas:.2f} MB") + +print("\n--- PANDAS: First 10 rows ---") +t0_print = time.perf_counter() +print(df.head(10).to_string()) +t_print_pandas = time.perf_counter() - t0_print +print(f"\nPrint time: {t_print_pandas:.6f} s") + +# ───────────────────────────────────────────────────────────── +# 2. BLOSC2 CTable +# ───────────────────────────────────────────────────────────── +print("\n" + "=" * 60) +print("--- 2. BLOSC2 CTable (structured array -> extend) ---") +data = make_data(N) + +t0 = time.perf_counter() +ct = blosc2.CTable(RowModel, expected_size=N) +ct.extend(data) +t_blosc = time.perf_counter() - t0 + +fields = list(RowModel.model_fields.keys()) +mem_blosc_c = (sum(col.cbytes for col in ct._cols.values()) + ct._valid_rows.cbytes) / (1024 ** 2) +mem_blosc_uc = (sum(col.nbytes for col in ct._cols.values()) + ct._valid_rows.nbytes) / (1024 ** 2) + +print(f"Total time: {t_blosc:.4f} s") +print(f"Memory (uncompressed): {mem_blosc_uc:.2f} MB") +print(f"Memory (compressed): {mem_blosc_c:.2f} MB") + +print("\n--- BLOSC2: First 10 rows ---") +t0_print = time.perf_counter() +print(ct.head(10)) +t_print_blosc = time.perf_counter() - t0_print +print(f"\nPrint time: {t_print_blosc:.6f} s") + +# ───────────────────────────────────────────────────────────── +# SUMMARY +# ───────────────────────────────────────────────────────────── +print("\n" + "=" * 60) +print("--- SUMMARY ---") +speedup = t_pandas / t_blosc +direction = "faster" if t_blosc < t_pandas else "slower" + +print(f"{'METRIC':<30} {'Pandas':>12} {'Blosc2':>12}") +print("-" * 55) +print(f"{'Ingestion time (s)':<30} {t_pandas:>12.4f} {t_blosc:>12.4f}") +print(f"{'Memory (MB)':<30} {mem_pandas:>12.2f} {mem_blosc_c:>12.2f}") +print(f"{'Print time (s)':<30} {t_print_pandas:>12.6f} {t_print_blosc:>12.6f}") +print("-" * 55) +print(f"\nSpeedup: {speedup:.2f}x {direction}") +print(f"Compression ratio: {mem_blosc_uc / mem_blosc_c:.2f}x") +print(f"Blosc2 vs Pandas size: {mem_blosc_c / mem_pandas * 100:.1f}%") diff --git a/bench/ctable/row_acces.py b/bench/ctable/row_acces.py new file mode 100644 index 00000000..c44439e0 --- /dev/null +++ b/bench/ctable/row_acces.py @@ -0,0 +1,68 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +# Benchmark for measuring row[int] access (full row via _RowIndexer), +# testing access at different positions across the array. + +from time import time +from typing import Annotated + +import numpy as np +from pydantic import BaseModel, Field + +import blosc2 + + +class NumpyDtype: + def __init__(self, dtype): + self.dtype = dtype + + +# Row model +class RowModel(BaseModel): + id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) + c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) + score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) + active: Annotated[bool, NumpyDtype(np.bool_)] = True + + +N = 1_000_000 +indices = [0, N // 4, N // 2, (3 * N) // 4, N - 1] + +print(f"row[int] access benchmark | N = {N:,}\n") + +# Build CTable once +np_dtype = np.dtype([ + ("id", np.int64), + ("c_val", np.complex128), + ("score", np.float64), + ("active", np.bool_), +]) +DATA = np.array( + [ + (i, complex(i * 0.1, i * 0.01), 10.0 + (i % 100) * 0.4, i % 3 == 0) + for i in range(N) + ], + dtype=np_dtype, +) + +ct = blosc2.CTable(RowModel, expected_size=N) +ct.extend(DATA) + +print(f"CTable built with {len(ct):,} rows\n") +print("=" * 60) +print(f"{'INDEX':<15} {'POSITION':>12} {'TIME (s)':>12}") +print("-" * 60) + +for idx in indices: + t0 = time() + row = ct.row[idx] + t_access = time() - t0 + position = f"{idx / N * 100:.0f}% into array" + print(f"{idx:<15,} {position:>12} {t_access:.6f}") + +print("-" * 60) diff --git a/bench/ctable/slice.py b/bench/ctable/slice.py new file mode 100644 index 00000000..4976a8d8 --- /dev/null +++ b/bench/ctable/slice.py @@ -0,0 +1,77 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +# Benchmark for measuring Column[slice] access with slices of different +# sizes and positions: small, large, and middle of the array. + +from time import time +from typing import Annotated + +import numpy as np +from pydantic import BaseModel, Field + +import blosc2 + + +class NumpyDtype: + def __init__(self, dtype): + self.dtype = dtype + + +# Row model +class RowModel(BaseModel): + id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) + c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) + score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) + active: Annotated[bool, NumpyDtype(np.bool_)] = True + + +N = 1_000_000 +slices = [ + ("small — start", slice(0, 100)), + ("small — middle", slice(N // 2, N // 2 + 100)), + ("small — end", slice(N - 100, N)), + ("large — start", slice(0, 100_000)), + ("large — middle", slice(N // 2 - 50_000, N // 2 + 50_000)), + ("large — end", slice(N - 100_000, N)), + ("full — all", slice(0, N)), +] + +print(f"Column[slice] access benchmark | N = {N:,}\n") + +# Build CTable once +np_dtype = np.dtype([ + ("id", np.int64), + ("c_val", np.complex128), + ("score", np.float64), + ("active", np.bool_), +]) +DATA = np.array( + [ + (i, complex(i * 0.1, i * 0.01), 10.0 + (i % 100) * 0.4, i % 3 == 0) + for i in range(N) + ], + dtype=np_dtype, +) + +ct = blosc2.CTable(RowModel, expected_size=N) +ct.extend(DATA) + +print(f"CTable built with {len(ct):,} rows\n") +print("=" * 65) +print(f"{'SLICE':<25} {'ROWS':>8} {'TIME (s)':>12}") +print("-" * 65) + +col = ct["score"] +for label, s in slices: + t0 = time() + val = col[s] + t_access = time() - t0 + n_rows = s.stop - s.start + print(f"{label:<25} {n_rows:>8,} {t_access:>12.6f}") + +print("-" * 65) diff --git a/bench/ctable/slice_steps.py b/bench/ctable/slice_steps.py new file mode 100644 index 00000000..311b5f9c --- /dev/null +++ b/bench/ctable/slice_steps.py @@ -0,0 +1,67 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +# Benchmark for measuring Column[::step].to_array() with varying step sizes. + +from time import time +from typing import Annotated + +import numpy as np +from pydantic import BaseModel, Field + +import blosc2 + + +class NumpyDtype: + def __init__(self, dtype): + self.dtype = dtype + + +# Row model +class RowModel(BaseModel): + id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) + c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) + score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) + active: Annotated[bool, NumpyDtype(np.bool_)] = True + + +N = 1_000_000 +steps = [1, 2, 4, 8, 16, 100, 1000] + +print(f"Column[::step].to_array() benchmark | N = {N:,}\n") + +# Build CTable once +np_dtype = np.dtype([ + ("id", np.int64), + ("c_val", np.complex128), + ("score", np.float64), + ("active", np.bool_), +]) +DATA = np.array( + [ + (i, complex(i * 0.1, i * 0.01), 10.0 + (i % 100) * 0.4, i % 3 == 0) + for i in range(N) + ], + dtype=np_dtype, +) + +ct = blosc2.CTable(RowModel, expected_size=N) +ct.extend(DATA) + +print(f"CTable built with {len(ct):,} rows\n") +print("=" * 60) +print(f"{'STEP':<10} {'ROWS RETURNED':>15} {'TIME (s)':>12}") +print("-" * 60) + +col = ct["score"] +for step in steps: + t0 = time() + arr = col[::step].to_numpy() + t_total = time() - t0 + print(f"::{ step:<8} {len(arr):>15,} {t_total:>12.6f}") + +print("-" * 60) diff --git a/bench/ctable/slice_to_array.py b/bench/ctable/slice_to_array.py new file mode 100644 index 00000000..2b072c8c --- /dev/null +++ b/bench/ctable/slice_to_array.py @@ -0,0 +1,77 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +# Benchmark for measuring Column[slice] + to_array() with slices of +# different sizes and positions: small, large, and middle of the array. + +from time import time +from typing import Annotated + +import numpy as np +from pydantic import BaseModel, Field + +import blosc2 + + +class NumpyDtype: + def __init__(self, dtype): + self.dtype = dtype + + +# Row model +class RowModel(BaseModel): + id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) + c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) + score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) + active: Annotated[bool, NumpyDtype(np.bool_)] = True + + +N = 1_000_000 +slices = [ + ("small — start", slice(0, 100)), + ("small — middle", slice(N // 2, N // 2 + 100)), + ("small — end", slice(N - 100, N)), + ("large — start", slice(0, 100_000)), + ("large — middle", slice(N // 2 - 50_000, N // 2 + 50_000)), + ("large — end", slice(N - 100_000, N)), + ("full — all", slice(0, N)), +] + +print(f"Column[slice].to_array() benchmark | N = {N:,}\n") + +# Build CTable once +np_dtype = np.dtype([ + ("id", np.int64), + ("c_val", np.complex128), + ("score", np.float64), + ("active", np.bool_), +]) +DATA = np.array( + [ + (i, complex(i * 0.1, i * 0.01), 10.0 + (i % 100) * 0.4, i % 3 == 0) + for i in range(N) + ], + dtype=np_dtype, +) + +ct = blosc2.CTable(RowModel, expected_size=N) +ct.extend(DATA) + +print(f"CTable built with {len(ct):,} rows\n") +print("=" * 65) +print(f"{'SLICE':<25} {'ROWS':>8} {'TIME (s)':>12}") +print("-" * 65) + +col = ct["score"] +for label, s in slices: + t0 = time() + arr = col[s].to_numpy() + t_total = time() - t0 + n_rows = s.stop - s.start + print(f"{label:<25} {n_rows:>8,} {t_total:>12.6f}") + +print("-" * 65) diff --git a/bench/ctable/where_chain.py b/bench/ctable/where_chain.py new file mode 100644 index 00000000..ff8b9b30 --- /dev/null +++ b/bench/ctable/where_chain.py @@ -0,0 +1,79 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +# Benchmark for comparing chained where() calls vs a single combined filter. +# Filters: 250k < id < 750k, active == False, 25.0 < score < 75.0 + +from time import time +from typing import Annotated + +import numpy as np +from pydantic import BaseModel, Field + +import blosc2 + + +class NumpyDtype: + def __init__(self, dtype): + self.dtype = dtype + + +# Row model +class RowModel(BaseModel): + id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) + c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) + score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) + active: Annotated[bool, NumpyDtype(np.bool_)] = True + + +N = 1_000_000 + +print(f"where() chained vs combined benchmark | N = {N:,}") + +# Build CTable once +np_dtype = np.dtype([ + ("id", np.int64), + ("c_val", np.complex128), + ("score", np.float64), + ("active", np.bool_), +]) +DATA = np.array( + [ + (i, complex(i * 0.1, i * 0.01), 10.0 + (i % 100) * 0.4, i % 3 == 0) + for i in range(N) + ], + dtype=np_dtype, +) + +ct = blosc2.CTable(RowModel, expected_size=N) +ct.extend(DATA) + +print(f"CTable built with {len(ct):,} rows\n") +print("=" * 70) + +# 1. Three chained where() calls +t0 = time() +r1 = ct.where((ct["id"] > 250_000)) +r2 = r1.where((ct["id"] < 750_000)) +r3 = r2.where((ct["score"] > 25.0)) +r4 = r3.where((ct["score"] < 75.0)) +r5 = r4.where(ct["active"] == False) +t_chained = time() - t0 +print(f"Chained where() (5 calls): {t_chained:.6f} s rows: {len(r5):,}") + +# 2. Single combined where() call +t0 = time() +result = ct.where( + (ct["id"] > 250_000) & (ct["id"] < 750_000) & + (ct["active"] == False) & + (ct["score"] > 25.0) & (ct["score"] < 75.0) +) +t_combined = time() - t0 +print(f"Combined where() (1 call): {t_combined:.6f} s rows: {len(result):,}") + +print("=" * 70) +print(f"Speedup combined vs chained: {t_chained / t_combined:.2f}x") diff --git a/bench/ctable/where_selective.py b/bench/ctable/where_selective.py new file mode 100644 index 00000000..77767d45 --- /dev/null +++ b/bench/ctable/where_selective.py @@ -0,0 +1,68 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +# Benchmark for measuring where() performance with varying selectivity. +# Filter: id < threshold, with thresholds covering 1%, 10%, 50%, 90%, 100% + +from time import time +from typing import Annotated + +import numpy as np +from pydantic import BaseModel, Field + +import blosc2 + + +class NumpyDtype: + def __init__(self, dtype): + self.dtype = dtype + + +# Row model +class RowModel(BaseModel): + id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) + c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) + score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) + active: Annotated[bool, NumpyDtype(np.bool_)] = True + + +N = 1_000_000 +thresholds = [10,10_000, 100_000,250_000, 500_000,750_000 ,900_000, 999_990, 1_000_000] + +print(f"where() selectivity benchmark | N = {N:,}") + +# Build CTable once +np_dtype = np.dtype([ + ("id", np.int64), + ("c_val", np.complex128), + ("score", np.float64), + ("active", np.bool_), +]) +DATA = np.array( + [ + (i, complex(i * 0.1, i * 0.01), 10.0 + (i % 100) * 0.4, i % 3 == 0) + for i in range(N) + ], + dtype=np_dtype, +) + +ct = blosc2.CTable(RowModel, expected_size=N) +ct.extend(DATA) + +print(f"CTable built with {len(ct):,} rows\n") +print("=" * 70) +print(f"{'THRESHOLD':<15} {'ROWS RETURNED':>15} {'SELECTIVITY':>13} {'TIME (s)':>12}") +print("-" * 70) + +for threshold in thresholds: + t0 = time() + result = ct.where(ct["id"] < threshold) + t_where = time() - t0 + selectivity = threshold / N * 100 + print(f"id < {threshold:<10,} {len(result):>15,} {selectivity:>12.1f}% {t_where:>12.6f}") + +print("-" * 70) diff --git a/src/blosc2/__init__.py b/src/blosc2/__init__.py index e32b2f48..8afc0653 100644 --- a/src/blosc2/__init__.py +++ b/src/blosc2/__init__.py @@ -596,6 +596,7 @@ def _raise(exc): """ # Delayed imports for avoiding overwriting of python builtins +from .ctable import CTable, Column from .ndarray import ( abs, acos, diff --git a/src/blosc2/ctable.py b/src/blosc2/ctable.py new file mode 100644 index 00000000..3acb1731 --- /dev/null +++ b/src/blosc2/ctable.py @@ -0,0 +1,793 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# This source code is licensed under a BSD-style license (found in the +# LICENSE file in the root directory of this source tree) +####################################################################### + +"""Imports for CTable""" + +from __future__ import annotations + +from collections.abc import Iterable +from typing import Any, Generic, TypeVar + +import numpy as np + +from blosc2 import compute_chunks_blocks + +try: + from line_profiler import profile +except ImportError: + + def profile(func): + + def wrapper(*args, **kwargs): + return func(*args, **kwargs) + + wrapper.__name__ = func.__name__ + return wrapper + + +from pydantic import BaseModel + +import blosc2 + +RowT = TypeVar("RowT", bound=BaseModel) + + +class NumpyDtype: + def __init__(self, dtype): + self.dtype = dtype + + +class MaxLen: + def __init__(self, length: int): + self.length = int(length) + + +############################# +#### Row model examples ### +############################# +""" +class RowModel(BaseModel): + id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) + c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) + score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) + active: Annotated[bool, NumpyDtype(np.bool_)] = True + +class RowModel2(BaseModel): + id: Annotated[int, NumpyDtype(np.int16)] = Field(ge=0) + name: Annotated[str, MaxLen(10)] = Field(default="unknown") + # name: Annotated[bytes, MaxLen(10)] = Field(default=b"unknown") + score: Annotated[float, NumpyDtype(np.float32)] = Field(ge=0, le=100) + active: Annotated[bool, NumpyDtype(np.bool_)] = True + +class RowModel3(BaseModel): + id: Annotated[int, NumpyDtype(np.int16)] = Field(ge=0) + #name: Annotated[str, MaxLen(10)] = Field(default="unknown") + name: Annotated[bytes, MaxLen(10)] = Field(default=b"unknown")""" + + +class _RowIndexer: + def __init__(self, table): + self._table = table + + def __getitem__(self, item): + return self._table._run_row_logic(item) + + +def _resolve_field_dtype(field) -> tuple[np.dtype, int]: + """Return (numpy dtype, display_width) for a pydantic model field. + + Extracts dtype from NumpyDtype metadata when present, otherwise falls + back to a sensible default for each Python primitive type. + """ + annotation = field.annotation + origin = getattr(annotation, "__origin__", annotation) + + # str / bytes: look for MaxLen metadata, build fixed-width dtype + if origin in (str, bytes) or annotation in (str, bytes): + is_bytes = origin is bytes or annotation is bytes + max_len = 32 + if hasattr(annotation, "__metadata__"): + for meta in annotation.__metadata__: + if isinstance(meta, MaxLen): + max_len = meta.length + break + kind = "S" if is_bytes else "U" + dt = np.dtype(f"{kind}{max_len}") + display_width = max(10, min(max_len, 50)) + return dt, display_width + + # Check for explicit NumpyDtype metadata (overrides primitive defaults) + if hasattr(annotation, "__metadata__"): + for meta in annotation.__metadata__: + if isinstance(meta, NumpyDtype): + dt = np.dtype(meta.dtype) + display_width = _default_display_width(origin) + return dt, display_width + + # Primitive defaults + _PRIMITIVE_MAP = { + int: (np.int64, 12), + float: (np.float64, 15), + bool: (np.bool_, 6), + complex: (np.complex128, 25), + } + if origin in _PRIMITIVE_MAP: + dt_raw, display_width = _PRIMITIVE_MAP[origin] + return np.dtype(dt_raw), display_width + + return np.dtype(np.object_), 20 + + +def _default_display_width(origin) -> int: + """Return a sensible display column width for a given Python type.""" + return {int: 12, float: 15, bool: 6, complex: 25}.get(origin, 20) + + +def _find_physical_index(arr: blosc2.NDArray, logical_key: int) -> int: + """Translate a logical (valid-row) index into a physical array index. + + Iterates chunk metadata of the boolean *arr* (valid_rows) to locate the + *logical_key*-th True value without fully decompressing the array. + + Returns + ------- + int + Physical position in the underlying storage array. + + Raises + ------ + IndexError + If the logical index is out of range or the array is inconsistent. + """ + count = 0 + chunk_size = arr.chunks[0] + + for info in arr.iterchunks_info(): + actual_size = min(chunk_size, arr.shape[0] - info.nchunk * chunk_size) + chunk_start = info.nchunk * chunk_size + + if info.special == blosc2.SpecialValue.ZERO: + continue + + if info.special == blosc2.SpecialValue.VALUE: + val = np.frombuffer(info.repeated_value, dtype=arr.dtype)[0] + if not val: + continue + if count + actual_size <= logical_key: + count += actual_size + continue + return chunk_start + (logical_key - count) + + chunk_data = arr[chunk_start : chunk_start + actual_size] + n_true = int(np.count_nonzero(chunk_data)) + if count + n_true <= logical_key: + count += n_true + continue + + return chunk_start + int(np.flatnonzero(chunk_data)[logical_key - count]) + + raise IndexError("Unexpected error finding physical index.") + + +class Column: + def __init__(self, table: CTable, col_name: str, mask=None): + self._table = table + self._col_name = col_name + self._mask = mask + + @property + def _raw_col(self): + return self._table._cols[self._col_name] + + @property + def _valid_rows(self): + if self._mask is None: + return self._table._valid_rows + return (self._table._valid_rows & self._mask).compute() + + def __getitem__(self, key: int | slice | list | np.ndarray): + if isinstance(key, int): + n_rows = len(self) + if key < 0: + key += n_rows + if not (0 <= key < n_rows): + raise IndexError(f"index {key} is out of bounds for column with size {n_rows}") + pos_true = _find_physical_index(self._valid_rows, key) + return self._raw_col[int(pos_true)] + + + + elif isinstance(key, slice): + real_pos = blosc2.where(self._valid_rows, np.arange(len(self._valid_rows))).compute() + start, stop, step = key.indices(len(real_pos)) + mask = blosc2.zeros(len(self._table._valid_rows), dtype=np.bool_) + if step == 1: + phys_start = real_pos[start] + phys_stop = real_pos[stop - 1] + mask[phys_start: phys_stop + 1] = True + else: + lindices = np.arange(start, stop, step) + phys_indices = real_pos[lindices] + mask[phys_indices[:]] = True + return Column(self._table, self._col_name, mask=mask) + + + elif isinstance(key, (list, tuple, np.ndarray)): + real_pos = blosc2.where(self._valid_rows, np.arange(len(self._valid_rows))).compute() + phys_indices = np.array([real_pos[i] for i in key], dtype=np.int64) + return self._raw_col[phys_indices] + + raise TypeError(f"Invalid index type: {type(key)}") + + def __setitem__(self, key: int | slice | list | np.ndarray, value): + if isinstance(key, int): + n_rows = len(self) + if key < 0: + key += n_rows + if not (0 <= key < n_rows): + raise IndexError(f"index {key} is out of bounds for column with size {n_rows}") + pos_true = _find_physical_index(self._valid_rows, key) + self._raw_col[int(pos_true)] = value + + elif isinstance(key, (slice, list, tuple, np.ndarray)): + real_pos = blosc2.where(self._valid_rows, np.arange(len(self._valid_rows))).compute() + if isinstance(key, slice): + lindices = range(*key.indices(len(real_pos))) + phys_indices = np.array([real_pos[i] for i in lindices], dtype=np.int64) + else: + phys_indices = np.array([real_pos[i] for i in key], dtype=np.int64) + + if isinstance(value, (list, tuple)): + value = np.array(value, dtype=self._raw_col.dtype) + self._raw_col[phys_indices] = value + + else: + raise TypeError(f"Invalid index type: {type(key)}") + + def __iter__(self): + arr = self._valid_rows + chunk_size = arr.chunks[0] + + for info in arr.iterchunks_info(): + actual_size = min(chunk_size, arr.shape[0] - info.nchunk * chunk_size) + chunk_start = info.nchunk * chunk_size + + if info.special == blosc2.SpecialValue.ZERO: + continue + + if info.special == blosc2.SpecialValue.VALUE: + val = np.frombuffer(info.repeated_value, dtype=arr.dtype)[0] + if not val: + continue + yield from self._raw_col[chunk_start: chunk_start + actual_size] + continue + + mask_chunk = arr[chunk_start: chunk_start + actual_size] + data_chunk = self._raw_col[chunk_start: chunk_start + actual_size] + yield from data_chunk[mask_chunk] + + def __len__(self): + return blosc2.count_nonzero(self._valid_rows) + + def __lt__(self, other): + # < (Less than) + return self._raw_col < other + + def __le__(self, other): + # <= (Less than or equal to) + return self._raw_col <= other + + def __eq__(self, other): + # == (Equal to) + return self._raw_col == other + + def __ne__(self, other): + # != (Not equal to) + return self._raw_col != other + + def __gt__(self, other): + # > (Greater than) + return self._raw_col > other + + def __ge__(self, other): + # >= (Greater than or equal to) + return self._raw_col >= other + + @property + def dtype(self): + return self._raw_col.dtype + + def to_numpy(self): + real_pos = blosc2.where(self._valid_rows, np.arange(len(self._valid_rows))).compute() + return self._raw_col[real_pos[:]] + + +class CTable(Generic[RowT]): + def __init__(self, row_type: type[RowT], new_data=None, expected_size: int = 1_048_576, compact: bool = False) -> None: + self._row_type = row_type + self._cols: dict[str, blosc2.NDArray] = {} + self._n_rows: int = 0 + self._col_widths: dict[str, int] = {} + self.col_names = [] + self.row = _RowIndexer(self) + self.auto_compact = compact + self.base = None + + c, b = compute_chunks_blocks((expected_size,)) + self._valid_rows = blosc2.zeros(shape=(expected_size,), dtype=np.bool_, chunks=c, blocks=b) + + for name, field in row_type.model_fields.items(): + self.col_names.append(name) + dt, display_width = _resolve_field_dtype(field) + final_width = max(len(name), display_width) + self._col_widths[name] = final_width + self._cols[name] = blosc2.zeros(shape=(expected_size,), dtype=dt, chunks=c, blocks=b) + + if new_data is not None: + self._load_initial_data(new_data) + + def _load_initial_data(self, new_data) -> None: + """Dispatch new_data to append() or extend() as appropriate.""" + is_append = False + + if isinstance(new_data, (np.void, np.record)): + is_append = True + elif isinstance(new_data, np.ndarray): + if new_data.dtype.names is not None and new_data.ndim == 0: + is_append = True + elif isinstance(new_data, list) and len(new_data) > 0: + first_elem = new_data[0] + if isinstance(first_elem, (str, bytes, int, float, bool, complex)): + is_append = True + + if is_append: + self.append(new_data) + else: + self.extend(new_data) + + def __str__(self): + retval = [] + cont = 0 + + # We print the header + for name in self._cols: + retval.append(f"{name:^{self._col_widths[name]}} |") + cont += self._col_widths[name] + 2 + retval.append("\n") + for _i in range(cont): + retval.append("-") + retval.append("\n") + + # We print the rows + + """Change this. Use where""" + real_poss = blosc2.where(self._valid_rows, np.array(range(len(self._valid_rows)))).compute() + + for j in real_poss: + for name in self._cols: + retval.append(f"{self._cols[name][j]:^{self._col_widths[name]}}") + retval.append(" |") + retval.append("\n") + for _ in range(cont): + retval.append("-") + retval.append("\n") + return "".join(retval) + + def __len__(self): + return self._n_rows + + def view(self, new_valid_rows): + if not ( + isinstance(new_valid_rows, (blosc2.NDArray, blosc2.LazyExpr)) + and (getattr(new_valid_rows, "dtype", None) == np.bool_) + ): + raise TypeError( + f"Expected boolean blosc2.NDArray or LazyExpr, got {type(new_valid_rows).__name__}" + ) + + new_valid_rows = ( + new_valid_rows.compute() if isinstance(new_valid_rows, blosc2.LazyExpr) else new_valid_rows + ) + + if len(self._valid_rows) != len(new_valid_rows): + raise ValueError() + + retval = CTable(self._row_type, compact=self.auto_compact, expected_size=len(self._valid_rows)) + retval._cols = self._cols + retval._n_rows = blosc2.count_nonzero(new_valid_rows) + retval._col_widths = self._col_widths + retval.col_names = self.col_names + retval.base = self + retval._valid_rows = new_valid_rows + + return retval + + def head(self, N: int = 5) -> CTable: + """ + # Alternative code, slower with big data + if n <= 0: + return CTable(self._row_type, compact=self.auto_compact) + + real_poss = blosc2.where(self._valid_rows, np.array(range(len(self._valid_rows)))).compute() + n_take = min(n, self._n_rows) + + retval = CTable(self._row_type, compact=self.auto_compact) + retval._n_rows = n_take + retval._valid_rows[:n_take] = True + + for k in self._cols.keys(): + retval._cols[k][:n_take] = self._cols[k][real_poss[:n_take]] + + return retval""" + if N <= 0: + # If N is 0 or negative, return an empty table + return self.view(blosc2.zeros(shape=len(self._valid_rows), dtype=np.bool_)) + + arr = self._valid_rows + count = 0 + chunk_size = arr.chunks[0] + pos_N_true = -1 + if N <= 0: + return self.view(blosc2.zeros(shape=len(arr), dtype=np.bool_)) + for info in arr.iterchunks_info(): + actual_size = min(chunk_size, arr.shape[0] - info.nchunk * chunk_size) + chunk_start = info.nchunk * chunk_size + + # All False without decompressing -> skip + if info.special == blosc2.SpecialValue.ZERO: + continue + + # Repeated value -> check if True or False + if info.special == blosc2.SpecialValue.VALUE: + val = np.frombuffer(info.repeated_value, dtype=arr.dtype)[0] + if not val: + continue # all False, skip + # All True: target is at offset (N - count - 1) within the chunk + if count + actual_size < N: + count += actual_size + continue + pos_N_true = chunk_start + (N - count - 1) + break + + # General case: decompress only this chunk + chunk_data = arr[chunk_start : chunk_start + actual_size] + + n_true = int(np.count_nonzero(chunk_data)) + if count + n_true < N: + count += n_true + continue + + # The N-th True is in this chunk + pos_N_true = chunk_start + int(np.flatnonzero(chunk_data)[N - count - 1]) + break + + if pos_N_true == -1: + return self.view(self._valid_rows) + + if pos_N_true < len(self._valid_rows) // 2: + mask_arr = blosc2.zeros(shape=len(arr), dtype=np.bool_) + mask_arr[: pos_N_true + 1] = True + else: + mask_arr = blosc2.ones(shape=len(arr), dtype=np.bool_) + mask_arr[pos_N_true + 1 :] = False + + mask_arr = (mask_arr & self._valid_rows).compute() + return self.view(mask_arr) + + def tail(self, N: int = 5) -> CTable: + if N <= 0: + # If N is 0 or negative, return an empty table + return self.view(blosc2.zeros(shape=len(self._valid_rows), dtype=np.bool_)) + + arr = self._valid_rows + count = 0 + chunk_size = arr.chunks[0] + pos_N_true = -1 + + # Convert to list to iterate chunks in reverse order (metadata only, ~0 memory) + for info in reversed(list(arr.iterchunks_info())): + actual_size = min(chunk_size, arr.shape[0] - info.nchunk * chunk_size) + chunk_start = info.nchunk * chunk_size + + # All False without decompressing -> skip + if info.special == blosc2.SpecialValue.ZERO: + continue + + # Repeated value -> check if True or False + if info.special == blosc2.SpecialValue.VALUE: + val = np.frombuffer(info.repeated_value, dtype=arr.dtype)[0] + if not val: + continue # all False, skip + + # All True: target is at offset 'actual_size - (N - count)' from chunk start + if count + actual_size < N: + count += actual_size + continue + pos_N_true = chunk_start + actual_size - (N - count) + break + + # General case: decompress only this chunk + chunk_data = arr[chunk_start : chunk_start + actual_size] + + n_true = int(np.count_nonzero(chunk_data)) + if count + n_true < N: + count += n_true + continue + + # The N-th True from the end is in this chunk + # We use negative indexing [-(N - count)] to get elements from the back + pos_N_true = chunk_start + int(np.flatnonzero(chunk_data)[-(N - count)]) + break + + if pos_N_true == -1: + return self.view(self._valid_rows) + + # Mask creation logic reversed: keep everything from pos_N_true to the end + if pos_N_true > len(arr) // 2: + # We keep a small tail (less than half the array): start with zeros + mask_arr = blosc2.zeros(shape=len(arr), dtype=np.bool_) + mask_arr[pos_N_true:] = True + else: + # We keep a large tail (more than half the array): start with ones + mask_arr = blosc2.ones(shape=len(arr), dtype=np.bool_) + if pos_N_true > 0: + mask_arr[:pos_N_true] = False + + # Compute intersection with existing valid rows and creating view + mask_arr = (mask_arr & self._valid_rows).compute() + return self.view(mask_arr) + + def __getitem__(self, s: str): + if s in self._cols: + return Column(self, s) + return None + + def __getattr__(self, s: str): + if s in self._cols: + return Column(self, s) + return super().__getattribute__(s) + + def compact(self): + real_poss = blosc2.where(self._valid_rows, np.array(range(len(self._valid_rows)))).compute() + start = 0 + block_size = self._valid_rows.blocks[0] + end = min(block_size, self._n_rows) + while start < end: + for _k, v in self._cols.items(): + v[start:end] = v[real_poss[start:end]] + start += block_size + end = min(end + block_size, self._n_rows) + + self._valid_rows[: self._n_rows] = True + self._valid_rows[self._n_rows :] = False + + @property + def nrows(self) -> int: + return self._n_rows + + @property + def ncols(self) -> int: + return len(self._cols) + + def info(self) -> None: + """ + Prints a concise summary of the CTable, including the column names, + their data types, and memory layout. + """ + n_cols = len(self._cols) + n_rows = len(self) + + # Calculate global memory usage + cbytes = sum(col.cbytes for col in self._cols.values()) + self._valid_rows.cbytes + nbytes = sum(col.nbytes for col in self._cols.values()) + self._valid_rows.nbytes + + def format_bytes(bytes_size: float) -> str: + if bytes_size < 1024: + return f"{bytes_size} B" + elif bytes_size < 1024**2: + return f"{bytes_size / 1024:.2f} KB" + elif bytes_size < 1024**3: + return f"{bytes_size / (1024**2):.2f} MB" + else: + return f"{bytes_size / (1024**3):.2f} GB" + + ratio = (nbytes / cbytes) if cbytes > 0 else 0.0 + + lines = [] + lines.append("") + lines.append(f"nºColumns: {n_cols}") + lines.append(f"nºRows: {n_rows}") + lines.append("") + + # New Header: replaced "Non-Null Count" with internal Array length & Itemsize + header = f" {'#':>3} {'Column':<15} {'Itemsize':<12} {'Dtype':<15}" + lines.append(header) + lines.append(f" {'---':>3} {'------':<15} {'--------':<12} {'-----':<15}") + + for i, name in enumerate(self.col_names): + col_array = self._cols[name] + dtype_str = str(col_array.dtype) + itemsize = f"{col_array.dtype.itemsize} B" + + line = f" {i:>3} {name:<15} {itemsize:<12} {dtype_str:<15}" + lines.append(line) + + lines.append("") + lines.append(f"memory usage: {format_bytes(cbytes)}") + lines.append(f"uncompressed size: {format_bytes(nbytes)}") + lines.append(f"compression ratio: {ratio:.2f}x") + lines.append("") + + print("\n".join(lines)) + + def append(self, data: list | np.void | np.ndarray) -> None: + if self.base is not None: + raise TypeError("Cannot extend view.") + + is_list = isinstance(data, (list, tuple)) + + arr = self._valid_rows + chunk_size = arr.chunks[0] + last_true_pos = -1 + + for info in reversed(list(arr.iterchunks_info())): + actual_size = min(chunk_size, arr.shape[0] - info.nchunk * chunk_size) + chunk_start = info.nchunk * chunk_size + + if info.special == blosc2.SpecialValue.ZERO: + continue + + if info.special == blosc2.SpecialValue.VALUE: + val = np.frombuffer(info.repeated_value, dtype=arr.dtype)[0] + if not val: + continue + last_true_pos = chunk_start + actual_size - 1 + break + + chunk_data = arr[chunk_start : chunk_start + actual_size] + nonzero = np.flatnonzero(chunk_data) + if len(nonzero) == 0: + continue + last_true_pos = chunk_start + int(nonzero[-1]) + break + + pos = last_true_pos + 1 + + if pos >= len(self._valid_rows): + c = len(self._valid_rows) + for v in self._cols.values(): + v.resize((c * 2,)) + self._valid_rows.resize((c * 2,)) + + if is_list: + for i, col_array in enumerate(self._cols.values()): + col_array[pos] = data[i] + else: + for name, col_array in self._cols.items(): + col_array[pos] = data[name] + + self._valid_rows[pos] = True + self._n_rows += 1 + + def delete(self, ind: int | slice | str | Iterable) -> blosc2.NDArray: + valid_rows_np = self._valid_rows[:] + true_pos = np.where(valid_rows_np)[0] + + if isinstance(ind, Iterable) and not isinstance(ind, (str, bytes)): + ind = list(ind) + elif not isinstance(ind, int) and not isinstance(ind, slice): + raise TypeError(f"Invalid type '{type(ind)}'") + + false_pos = true_pos[ind] + + new_mask_np = valid_rows_np.copy() + new_mask_np[false_pos] = False + + new_mask = blosc2.asarray(new_mask_np) + self._valid_rows = new_mask + self._n_rows = blosc2.count_nonzero(self._valid_rows) + + def extend(self, data: list | CTable | Any) -> None: + if self.base is not None: + raise TypeError("Cannot extend view.") + if len(data) <= 0: + return + ultimas_validas = blosc2.where(self._valid_rows, np.array(range(len(self._valid_rows)))).compute() + start_pos = ultimas_validas[-1] + 1 if len(ultimas_validas) > 0 else 0 + + current_col_names = self.col_names + columns_to_insert = [] + new_nrows = 0 + + if hasattr(data, "_cols") and hasattr(data, "_n_rows"): + for name in current_col_names: + col = data._cols[name][: data._n_rows] + columns_to_insert.append(col) + new_nrows = data._n_rows + else: + if isinstance(data, np.ndarray) and data.dtype.names is not None: + for name in current_col_names: + columns_to_insert.append(data[name]) + new_nrows = len(data) + else: + columns_to_insert = list(zip(*data, strict=False)) + new_nrows = len(data) + + processed_cols = [] + for i, raw_col in enumerate(columns_to_insert): + target_dtype = self._cols[current_col_names[i]].dtype + b2_arr = blosc2.asarray(raw_col, dtype=target_dtype) + processed_cols.append(b2_arr) + + end_pos = start_pos + new_nrows + + if self.auto_compact and end_pos >= len(self._valid_rows): + self.compact() + ultimas_validas = blosc2.where( + self._valid_rows, np.array(range(len(self._valid_rows))) + ).compute() + start_pos = ultimas_validas[-1] + 1 if len(ultimas_validas) > 0 else 0 + end_pos = start_pos + new_nrows + + while end_pos > len(self._valid_rows): + c = len(self._valid_rows) + for name in current_col_names: + self._cols[name].resize((c * 2,)) + self._valid_rows.resize((c * 2,)) + + # Do this per chunks + for j, name in enumerate(current_col_names): + self._cols[name][start_pos:end_pos] = processed_cols[j][:] + + self._valid_rows[start_pos:end_pos] = True + self._n_rows = blosc2.count_nonzero(self._valid_rows) + + @profile + def where(self, expr_result) -> CTable: + if not ( + isinstance(expr_result, (blosc2.NDArray, blosc2.LazyExpr)) + and (getattr(expr_result, "dtype", None) == np.bool_) + ): + raise TypeError(f"Expected boolean blosc2.NDArray or LazyExpr, got {type(expr_result).__name__}") + + filter = expr_result.compute() if isinstance(expr_result, blosc2.LazyExpr) else expr_result + + target_len = len(self._valid_rows) + + if len(filter) > target_len: + filter = filter[:target_len] + elif len(filter) < target_len: + padding = blosc2.zeros(target_len, dtype=np.bool_) + padding[: len(filter)] = filter[:] + filter = padding + + filter = (filter & self._valid_rows).compute() + + return self.view(filter) + + def _run_row_logic(self, ind: int | slice | str | Iterable) -> CTable: + valid_rows_np = self._valid_rows[:] + true_pos = np.where(valid_rows_np)[0] + + if isinstance(ind, Iterable) and not isinstance(ind, (str, bytes)): + ind = list(ind) + + mant_pos = true_pos[ind] + + new_mask_np = np.zeros_like(valid_rows_np, dtype=bool) + new_mask_np[mant_pos] = True + + new_mask = blosc2.asarray(new_mask_np) + return self.view(new_mask) + + """Save & load are blank for now""" + + def save(self, urlpath: str, group: str = "table") -> None: ... + + @classmethod + def load(cls, urlpath: str, group: str = "table", row_type: type[RowT] | None = None) -> CTable: ... diff --git a/tests/ctable/test_column.py b/tests/ctable/test_column.py new file mode 100644 index 00000000..4f2e450b --- /dev/null +++ b/tests/ctable/test_column.py @@ -0,0 +1,294 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +from typing import Annotated + +import numpy as np +import pytest +from pydantic import BaseModel, Field +import blosc2 + +from blosc2 import CTable + + +class NumpyDtype: + def __init__(self, dtype): + self.dtype = dtype + + +class RowModel(BaseModel): + id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) + score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) + active: Annotated[bool, NumpyDtype(np.bool_)] = True + + +DATA20 = [(i, float(i * 10), True) for i in range(20)] + + +# ------------------------------------------------------------------- +# Tests +# ------------------------------------------------------------------- + + +def test_column_metadata(): + """dtype correctness, internal reference consistency, and mask defaults.""" + tabla = CTable(RowModel, new_data=DATA20) + + assert tabla.id.dtype == np.int64 + assert tabla.score.dtype == np.float64 + assert tabla.active.dtype == np.bool_ + + assert tabla.id._raw_col is tabla._cols["id"] + assert tabla.id._valid_rows is tabla._valid_rows + + # mask is None by default + assert tabla.id._mask is None + assert tabla.score._mask is None + + +def test_column_getitem_no_holes(): + """int, slice, and list indexing on a full table.""" + tabla = CTable(RowModel, new_data=DATA20) + col = tabla.id + + # int + assert col[0] == 0 + assert col[5] == 5 + assert col[19] == 19 + assert col[-1] == 19 + assert col[-5] == 15 + + # slice returns a Column view + assert isinstance(col[0:5], blosc2.Column) + assert isinstance(col[10:15], blosc2.Column) + + # list + assert list(col[[0, 5, 10, 15]]) == [0, 5, 10, 15] + assert list(col[[19, 0, 10]]) == [19, 0, 10] + + +def test_column_getitem_with_holes(): + """int, slice, and list indexing after deletions.""" + tabla = CTable(RowModel, new_data=DATA20) + tabla.delete([1, 3, 5, 7, 9]) + col = tabla.id + + assert col[0] == 0 + assert col[1] == 2 + assert col[2] == 4 + assert col[3] == 6 + assert col[4] == 8 + assert col[-1] == 19 + assert col[-2] == 18 + + assert list(col[[0, 2, 4]]) == [0, 4, 8] + assert list(col[[5, 3, 1]]) == [10, 6, 2] + + tabla2 = CTable(RowModel, new_data=DATA20) + tabla2.delete([1, 3, 5, 7, 9, 11, 13, 15, 17, 19]) + col2 = tabla2.id + + assert list(col2[0:5].to_numpy()) == [0, 2, 4, 6, 8] + assert list(col2[5:10].to_numpy()) == [10, 12, 14, 16, 18] + assert list(col2[::2].to_numpy()) == [0, 4, 8, 12, 16] + + +def test_column_getitem_out_of_range(): + """int and list indexing raise IndexError when out of bounds.""" + tabla = CTable(RowModel, new_data=DATA20) + tabla.delete([1, 3, 5, 7, 9]) + col = tabla.id + + with pytest.raises(IndexError): + _ = col[100] + with pytest.raises(IndexError): + _ = col[-100] + with pytest.raises(IndexError): + _ = col[[0, 1, 100]] + + +def test_column_setitem_no_holes(): + """int, slice, and list assignment on a full table.""" + tabla = CTable(RowModel, new_data=DATA20) + col = tabla.id + + col[0] = 999 + assert col[0] == 999 + col[10] = 888 + assert col[10] == 888 + col[-1] = 777 + assert col[-1] == 777 + + col[0:5] = [100, 101, 102, 103, 104] + assert list(col[0:5].to_numpy()) == [100, 101, 102, 103, 104] + + col[[0, 5, 10]] = [10, 50, 100] + assert col[0] == 10 + assert col[5] == 50 + assert col[10] == 100 + + +def test_column_setitem_with_holes(): + """int, slice, and list assignment after deletions.""" + tabla = CTable(RowModel, new_data=DATA20) + tabla.delete([1, 3, 5, 7, 9]) + col = tabla.id + + col[0] = 999 + assert col[0] == 999 + assert tabla._cols["id"][0] == 999 + + col[2] = 888 + assert col[2] == 888 + assert tabla._cols["id"][4] == 888 + + col[-1] = 777 + assert col[-1] == 777 + + col[0:3] = [100, 200, 300] + assert col[0] == 100 + assert col[1] == 200 + assert col[2] == 300 + + col[[0, 2, 4]] = [11, 22, 33] + assert col[0] == 11 + assert col[2] == 22 + assert col[4] == 33 + + +def test_column_iter(): + """Iteration over full table, with odd-index holes, and on score column.""" + tabla = CTable(RowModel, new_data=DATA20) + assert list(tabla.id) == list(range(20)) + + tabla2 = CTable(RowModel, new_data=DATA20) + tabla2.delete([1, 3, 5, 7, 9, 11, 13, 15, 17, 19]) + assert list(tabla2.id) == [0, 2, 4, 6, 8, 10, 12, 14, 16, 18] + + tabla3 = CTable(RowModel, new_data=DATA20) + tabla3.delete([0, 5, 10, 15]) + expected_score = [ + 10.0, 20.0, 30.0, 40.0, + 60.0, 70.0, 80.0, 90.0, + 110.0, 120.0, 130.0, 140.0, + 160.0, 170.0, 180.0, 190.0, + ] + assert list(tabla3.score) == expected_score + + +def test_column_len(): + """len() after no deletions, partial deletions, cumulative deletions, and cross-column.""" + tabla = CTable(RowModel, new_data=DATA20) + col = tabla.id + assert len(col) == 20 + + tabla.delete([1, 3, 5, 7, 9]) + assert len(col) == 15 + + tabla2 = CTable(RowModel, new_data=DATA20) + col2 = tabla2.id + tabla2.delete([0, 1, 2]) + assert len(col2) == 17 + tabla2.delete([0, 1, 2, 3, 4]) + assert len(col2) == 12 + + data = [(i, float(i * 10), i % 2 == 0) for i in range(10)] + tabla3 = CTable(RowModel, new_data=data, expected_size=10) + tabla3.delete([0, 1, 5, 6, 9]) + assert len(tabla3.id) == len(tabla3.score) == len(tabla3.active) == 5 + for i in range(len(tabla3.id)): + assert tabla3.score[i] == float(tabla3.id[i] * 10) + + +def test_column_edge_cases(): + """Empty table and fully-deleted table both behave as zero-length columns.""" + tabla = CTable(RowModel) + assert len(tabla.id) == 0 + assert list(tabla.id) == [] + + data = [(i, float(i * 10), True) for i in range(10)] + tabla2 = CTable(RowModel, new_data=data) + tabla2.delete(list(range(10))) + assert len(tabla2.id) == 0 + assert list(tabla2.id) == [] + + +# ------------------------------------------------------------------- +# New tests for Column view (mask) and to_array() +# ------------------------------------------------------------------- + + +def test_column_slice_returns_view(): + """Column[slice] returns a Column instance with a non-None mask.""" + tabla = CTable(RowModel, new_data=DATA20) + col = tabla.id + + view = col[0:5] + assert isinstance(view, blosc2.Column) + assert view._mask is not None + assert view._table is tabla + assert view._col_name == "id" + + +def test_to_array_no_holes(): + """to_array() on a slice view returns correct data on a full table.""" + tabla = CTable(RowModel, new_data=DATA20) + col = tabla.id + + np.testing.assert_array_equal(col[0:5].to_numpy(), np.array([0, 1, 2, 3, 4], dtype=np.int64)) + np.testing.assert_array_equal(col[5:10].to_numpy(), np.array([5, 6, 7, 8, 9], dtype=np.int64)) + np.testing.assert_array_equal(col[15:20].to_numpy(), np.array([15, 16, 17, 18, 19], dtype=np.int64)) + np.testing.assert_array_equal(col[0:20].to_numpy(), np.arange(20, dtype=np.int64)) + + +def test_to_array_with_holes(): + """to_array() on a slice view skips deleted rows correctly.""" + tabla = CTable(RowModel, new_data=DATA20) + tabla.delete([1, 3, 5, 7, 9, 11, 13, 15, 17, 19]) # keep evens: 0,2,4,...,18 + col = tabla.id + + # logical [0:5] → physical rows 0,2,4,6,8 + np.testing.assert_array_equal(col[0:5].to_numpy(), np.array([0, 2, 4, 6, 8], dtype=np.int64)) + # logical [5:10] → physical rows 10,12,14,16,18 + np.testing.assert_array_equal(col[5:10].to_numpy(), np.array([10, 12, 14, 16, 18], dtype=np.int64)) + + +def test_to_array_full_column(): + """to_array() with no slice (full column) returns all valid rows.""" + tabla = CTable(RowModel, new_data=DATA20) + tabla.delete([0, 10, 19]) + col = tabla.id + + expected = np.array([i for i in range(20) if i not in {0, 10, 19}], dtype=np.int64) + np.testing.assert_array_equal(col[0:len(col)].to_numpy(), expected) + + +def test_to_array_mask_does_not_include_deleted(): + """Mask & valid_rows intersection excludes deleted rows inside the slice range.""" + tabla = CTable(RowModel, new_data=DATA20) + # delete rows 2 and 3, which fall inside slice [0:5] + tabla.delete([2, 3]) + col = tabla.id + + # logical [0:5] should now map to physical rows 0,1,4,5,6 + result = col[0:5].to_numpy() + np.testing.assert_array_equal(result, np.array([0, 1, 4, 5, 6], dtype=np.int64)) + + +def test_column_view_mask_is_independent(): + """Two slice views on the same column have independent masks.""" + tabla = CTable(RowModel, new_data=DATA20) + col = tabla.id + + view_a = col[0:5] + + np.testing.assert_array_equal(view_a.to_numpy(), np.arange(0, 5, dtype=np.int64)) + + + +if __name__ == "__main__": + pytest.main(["-v", __file__]) diff --git a/tests/ctable/test_compact.py b/tests/ctable/test_compact.py new file mode 100644 index 00000000..a0eaebb5 --- /dev/null +++ b/tests/ctable/test_compact.py @@ -0,0 +1,157 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +from typing import Annotated + +import numpy as np +from pydantic import BaseModel, Field + +from blosc2 import CTable + + +# --- Basic model setup for tests --- +class NumpyDtype: + def __init__(self, dtype): + self.dtype = dtype + + +class RowModel(BaseModel): + id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) + score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) + + +def generate_test_data(n_rows: int) -> list: + return [(i, float(i)) for i in range(n_rows)] + + +def test_compact_empty_table(): + """Test compact() on a completely empty table (no data).""" + table = CTable(RowModel, expected_size=100) + + assert len(table) == 0 + + # Should not raise any error + table.compact() + + # Capacity might have drastically reduced, but the logical table must remain empty + assert len(table) == 0 + # Verify that if data is added later, it works correctly + table.append((1, 10.0)) + assert len(table) == 1 + assert table.id[0] == 1 + + +def test_compact_full_table(): + """Test compact() on a completely full table (no holes or free space).""" + data = generate_test_data(50) + table = CTable(RowModel, new_data=data, expected_size=50) + + assert len(table) == 50 + initial_capacity = len(table._valid_rows) + + # Should not raise any error or change the logical state + table.compact() + + assert len(table) == 50 + # Capacity should not have changed because it was already full + assert len(table._valid_rows) == initial_capacity + + # Verify data integrity + assert table.id[0] == 0 + assert table.id[-1] == 49 + + +def test_compact_already_compacted_table(): + """Test compact() on a table that has free space but no holes (contiguous data).""" + data = generate_test_data(20) + # Large expected_size to ensure free space at the end + table = CTable(RowModel, new_data=data, expected_size=100) + + assert len(table) == 20 + + # Execute compact. Since data is already contiguous, the table might reduce + # its size due to the < len//2 while loop, but it shouldn't fail. + table.compact() + + assert len(table) == 20 + + # Verify that data remains in place + for i in range(20): + assert table.id[i] == i + + # Validate that all True values are consecutive at the beginning + mask = table._valid_rows[: len(table._valid_rows)] + assert np.all(mask[:20]) + if len(mask) > 20: + assert not np.any(mask[20:]) + + +def test_compact_with_holes(): + """Test compact() on a table with high fragmentation (holes).""" + data = generate_test_data(30) + table = CTable(RowModel, new_data=data, expected_size=50) + + # Delete sparsely: leave only [0, 5, 10, 15, 20, 25] + to_delete = [i for i in range(30) if i % 5 != 0] + table.delete(to_delete) + + assert len(table) == 6 + + # Execute compact + table.compact() + + assert len(table) == 6 + + # Verify that the correct data survived and moved to the beginning + expected_ids = [0, 5, 10, 15, 20, 25] + for i, exp_id in enumerate(expected_ids): + # Through the logical view (Column wrapper) + assert table.id[i] == exp_id + # Through the physical blosc2 array (to ensure compact worked) + assert table._cols["id"][i] == exp_id + + # Verify physical mask: first 6 must be True, the rest False + mask = table._valid_rows[: len(table._valid_rows)] + assert np.all(mask[:6]) + if len(mask) > 6: + assert not np.any(mask[6:]) + + +def test_compact_all_deleted(): + """Test compact() on a table where absolutely all rows have been deleted.""" + data = generate_test_data(20) + table = CTable(RowModel, new_data=data, expected_size=20) + + # Delete everything + table.delete(list(range(20))) + assert len(table) == 0 + + # Should handle empty arrays correctly + table.compact() + + assert len(table) == 0 + + # Check that we can write to it again + table.append((99, 99.0)) + assert len(table) == 1 + assert table.id[0] == 99 + + +def test_compact_multiple_times(): + """Calling compact() multiple times in a row must not corrupt data or crash.""" + data = generate_test_data(10) + table = CTable(RowModel, new_data=data, expected_size=20) + + table.delete([1, 3, 5, 7, 9]) # 5 elements remaining + + # Compact 3 times in a row + table.compact() + table.compact() + table.compact() + + assert len(table) == 5 + assert list(table.id) == [0, 2, 4, 6, 8] diff --git a/tests/ctable/test_construct.py b/tests/ctable/test_construct.py new file mode 100644 index 00000000..63810076 --- /dev/null +++ b/tests/ctable/test_construct.py @@ -0,0 +1,225 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +from typing import Annotated, TypeVar + +import numpy as np +import pytest +from pydantic import BaseModel, Field + +import blosc2 +from blosc2 import CTable + +RowT = TypeVar("RowT", bound=BaseModel) + + +class NumpyDtype: + def __init__(self, dtype): + self.dtype = dtype + + +class MaxLen: + def __init__(self, length: int): + self.length = int(length) + + +class RowModel(BaseModel): + id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) + c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) + score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) + active: Annotated[bool, NumpyDtype(np.bool_)] = True + + +# ------------------------------------------------------------------- +# Predefined Test Data +# ------------------------------------------------------------------- +SMALL_DATA = [ + (1, 1 + 2j, 95.5, True), + (2, 3 - 4j, 80.0, False), + (3, 0j, 50.2, True), + (4, -1 + 1j, 12.3, False), + (5, 5j, 99.9, True), +] +SMALLEST_DATA = SMALL_DATA[:2] + +dtype_struct = [("id", "i8"), ("c_val", "c16"), ("score", "f8"), ("active", "?")] +SMALL_STRUCT = np.array(SMALL_DATA, dtype=dtype_struct) + + +# ------------------------------------------------------------------- +# Validation Utility +# ------------------------------------------------------------------- +def assert_table_equals_data(table: CTable, expected_data: list): + assert len(table) == len(expected_data), f"Expected length {len(expected_data)}, got {len(table)}" + col_names = table.col_names + for i, expected_row in enumerate(expected_data): + row_extracted = table.row[i] + for col_idx, expected_val in enumerate(expected_row): + col_name = col_names[col_idx] + extracted_val = getattr(row_extracted, col_name)[0] + if isinstance(expected_val, (float, complex)): + np.testing.assert_allclose( + extracted_val, expected_val, err_msg=f"Discrepancy at row {i}, col {col_name}" + ) + else: + assert extracted_val == expected_val, ( + f"Row {i}, col {col_name}: expected {expected_val}, got {extracted_val}" + ) + + +# ------------------------------------------------------------------- +# Tests +# ------------------------------------------------------------------- + + +def test_empty_table_variants(): + """Empty table: default, with expected_size, and with compact=True.""" + table = CTable(RowModel) + assert len(table) == 0 + assert table.nrows == 0 + assert table.ncols == 4 + for col_name in ["id", "c_val", "score", "active"]: + assert col_name in table._cols + assert isinstance(table._cols[col_name], blosc2.NDArray) + + table_sized = CTable(RowModel, expected_size=5000) + assert len(table_sized) == 0 + assert all(len(col) == 5000 for col in table_sized._cols.values()) + + table_compact = CTable(RowModel, compact=True) + assert len(table_compact) == 0 + assert table_compact.auto_compact is True + + +def test_empty_data_lifecycle(): + """Create from [], extend with [], then extend with real data.""" + table = CTable(RowModel, new_data=[]) + assert len(table) == 0 + + table.extend([]) + assert len(table) == 0 + + table.extend(SMALL_DATA) + assert_table_equals_data(table, SMALL_DATA) + + +def test_construction_sources(): + """List of tuples and structured array both produce identical tables.""" + assert_table_equals_data(CTable(RowModel, new_data=SMALL_DATA), SMALL_DATA) + assert_table_equals_data(CTable(RowModel, new_data=SMALL_STRUCT), SMALL_DATA) + + +def test_expected_size_variants(): + """expected_size smaller, exact, and larger than the inserted data.""" + for es in [1, 5]: + assert_table_equals_data(CTable(RowModel, new_data=SMALL_DATA, expected_size=es), SMALL_DATA) + + table_large = CTable(RowModel, new_data=SMALL_DATA, expected_size=1000) + assert_table_equals_data(table_large, SMALL_DATA) + assert all(len(col) == 1000 for col in table_large._cols.values()) + + +def test_compact_flag(): + """compact=False and compact=True both preserve data correctly.""" + table_false = CTable(RowModel, new_data=SMALL_DATA, compact=False) + assert table_false.auto_compact is False + assert_table_equals_data(table_false, SMALL_DATA) + + table_true = CTable(RowModel, new_data=SMALL_DATA, compact=True) + assert table_true.auto_compact is True + assert_table_equals_data(table_true, SMALL_DATA) + + +def test_append_and_clone(): + """Build table row by row, then clone it into a new CTable.""" + table = CTable(RowModel) + for row in SMALLEST_DATA: + table.append(row) + assert_table_equals_data(table, SMALLEST_DATA) + + cloned = CTable(RowModel, new_data=table) + assert_table_equals_data(cloned, SMALLEST_DATA) + assert table is not cloned + + +def test_invalid_append(): + """Wrong length, incompatible type, and dict all raise errors.""" + table = CTable(RowModel, expected_size=1) + + # Too few values → IndexError (NumPy raises natively after simplification) + with pytest.raises((IndexError, ValueError)): + table.append([1, 1 + 2j, 95.5]) # missing boolean + + # Incompatible type → TypeError or ValueError from NumPy + with pytest.raises((TypeError, ValueError)): + table.append(["invalid_text", 1 + 2j, 95.5, True]) + + +def test_extreme_values(): + """Extreme complex, float boundary, and large integer values.""" + extreme_complex = [ + (1, complex(1e308, -1e308), 50.0, True), + (2, complex(0, 0), 0.0, False), + (3, complex(-1e308, 1e308), 100.0, True), + ] + extreme_float = [ + (1, 0j, 0.0, True), + (2, 0j, 100.0, False), + (3, 0j, 0.0001, True), + (4, 0j, 99.9999, False), + ] + extreme_int = [ + (1, 0j, 50.0, True), + (2**32, 0j, 50.0, False), + (2**60, 0j, 50.0, True), + ] + for data in [extreme_complex, extreme_float, extreme_int]: + assert_table_equals_data(CTable(RowModel, new_data=data), data) + + +def test_extend_append_and_resize(): + """Auto-resize via append one-by-one, then extend+append beyond initial size.""" + # Append beyond expected_size triggers resize + table = CTable(RowModel, expected_size=2) + for row in SMALL_DATA: + table.append(row) + assert_table_equals_data(table, SMALL_DATA) + assert all(len(col) >= 5 for col in table._cols.values()) + + # Extend beyond expected_size, then append the last row + table2 = CTable(RowModel, expected_size=2) + table2.extend(SMALL_DATA[:4]) + assert len(table2) == 4 + table2.append(SMALL_DATA[4]) + assert_table_equals_data(table2, SMALL_DATA) + + +def test_column_integrity(): + """Column access via [] and getattr, and correct dtypes.""" + table = CTable(RowModel, new_data=SMALL_DATA) + + assert isinstance(table["id"], blosc2.ctable.Column) + assert isinstance(table.score, blosc2.ctable.Column) + + assert table._cols["id"].dtype == np.int64 + assert table._cols["c_val"].dtype == np.complex128 + assert table._cols["score"].dtype == np.float64 + assert table._cols["active"].dtype == np.bool_ + + +def test_valid_rows(): + """_valid_rows has exactly 5 True entries after creation and after extend.""" + table_direct = CTable(RowModel, new_data=SMALL_DATA) + assert blosc2.count_nonzero(table_direct._valid_rows) == 5 + + table_extended = CTable(RowModel) + table_extended.extend(SMALL_DATA) + assert blosc2.count_nonzero(table_extended._valid_rows) == 5 + + +if __name__ == "__main__": + pytest.main(["-v", __file__]) diff --git a/tests/ctable/test_delete_rows.py b/tests/ctable/test_delete_rows.py new file mode 100644 index 00000000..b6d04f59 --- /dev/null +++ b/tests/ctable/test_delete_rows.py @@ -0,0 +1,210 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +from typing import Annotated, TypeVar + +import numpy as np +import pytest +from pydantic import BaseModel, Field + +from blosc2 import CTable + +RowT = TypeVar("RowT", bound=BaseModel) + + +class NumpyDtype: + def __init__(self, dtype): + self.dtype = dtype + + +class RowModel(BaseModel): + id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) + c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) + score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) + active: Annotated[bool, NumpyDtype(np.bool_)] = True + + +def generate_test_data(n_rows: int) -> list: + return [(i, complex(i, -i), float((i * 7) % 100), bool(i % 2)) for i in range(1, n_rows + 1)] + + +# ------------------------------------------------------------------- +# Tests +# ------------------------------------------------------------------- + + +def test_delete_single_element(): + """First, last, middle deletion once; and repeated deletion from front/back.""" + data = generate_test_data(50) + + # Delete first + t = CTable(RowModel, new_data=data, expected_size=50) + t.delete(0) + assert len(t) == 49 + assert not t._valid_rows[0] + + # Delete last + t2 = CTable(RowModel, new_data=data, expected_size=50) + t2.delete(-1) + assert len(t2) == 49 + + # Delete middle + t3 = CTable(RowModel, new_data=data, expected_size=50) + t3.delete(25) + assert len(t3) == 49 + + # Delete first 10 times in a row + t4 = CTable(RowModel, new_data=data, expected_size=50) + for i in range(10): + t4.delete(0) + assert len(t4) == 50 - (i + 1) + assert len(t4) == 40 + + # Delete last 10 times in a row + t5 = CTable(RowModel, new_data=data, expected_size=50) + for i in range(10): + t5.delete(-1) + assert len(t5) == 50 - (i + 1) + assert len(t5) == 40 + + +def test_delete_list_of_positions(): + """Scattered, consecutive, even, odd, and slice-equivalent list deletions.""" + data = generate_test_data(50) + + # Scattered + t = CTable(RowModel, new_data=data, expected_size=50) + t.delete([0, 10, 20, 30, 40]) + assert len(t) == 45 + + # Consecutive block + t2 = CTable(RowModel, new_data=data, expected_size=50) + t2.delete([5, 6, 7, 8, 9]) + assert len(t2) == 45 + + # All even positions + t3 = CTable(RowModel, new_data=data, expected_size=50) + t3.delete(list(range(0, 50, 2))) + assert len(t3) == 25 + + # All odd positions + t4 = CTable(RowModel, new_data=data, expected_size=50) + t4.delete(list(range(1, 50, 2))) + assert len(t4) == 25 + + # Slice-equivalent: range(10, 20) + t5 = CTable(RowModel, new_data=data, expected_size=50) + t5.delete(list(range(10, 20))) + assert len(t5) == 40 + + # Slice with step: range(0, 20, 2) + t6 = CTable(RowModel, new_data=data, expected_size=50) + t6.delete(list(range(0, 20, 2))) + assert len(t6) == 40 + + # First 10 rows + t7 = CTable(RowModel, new_data=data, expected_size=50) + t7.delete(list(range(0, 10))) + assert len(t7) == 40 + + # Last 10 rows + t8 = CTable(RowModel, new_data=data, expected_size=50) + t8.delete(list(range(40, 50))) + assert len(t8) == 40 + + +def test_delete_out_of_bounds(): + """All IndexError scenarios: full table, partial table, empty table, negative.""" + data = generate_test_data(50) + + # Beyond length on full table + t = CTable(RowModel, new_data=data, expected_size=50) + with pytest.raises(IndexError): + t.delete(60) + with pytest.raises(IndexError): + t.delete(-60) + + # Beyond nrows on partial table (capacity 50, only 25 rows) + t2 = CTable(RowModel, new_data=generate_test_data(25), expected_size=50) + assert len(t2) == 25 + with pytest.raises(IndexError): + t2.delete(35) + + # Empty table: positions 0, 25, -1 all raise + for pos in [0, 25, -1]: + empty = CTable(RowModel, expected_size=50) + assert len(empty) == 0 + with pytest.raises(IndexError): + empty.delete(pos) + + +def test_delete_edge_cases(): + """Same position twice, all rows front/back, negative and mixed indices.""" + data = generate_test_data(50) + + # Same logical position twice: second delete hits what was position 11 + t = CTable(RowModel, new_data=data, expected_size=50) + t.delete(10) + assert len(t) == 49 + t.delete(10) + assert len(t) == 48 + + # Delete all rows from the front one by one + t2 = CTable(RowModel, new_data=data, expected_size=50) + for _ in range(50): + t2.delete(0) + assert len(t2) == 0 + + # Delete all rows from the back one by one + t3 = CTable(RowModel, new_data=data, expected_size=50) + for _ in range(50): + t3.delete(-1) + assert len(t3) == 0 + + # Negative indices list + t4 = CTable(RowModel, new_data=data, expected_size=50) + t4.delete([-1, -5, -10]) + assert len(t4) == 47 + + # Mixed positive and negative indices + t5 = CTable(RowModel, new_data=data, expected_size=50) + t5.delete([0, -1, 25]) + assert len(t5) == 47 + + +def test_delete_invalid_types(): + """string, float, and list-with-strings all raise errors.""" + data = generate_test_data(50) + + t = CTable(RowModel, new_data=data, expected_size=50) + with pytest.raises(TypeError): + t.delete("invalid") + with pytest.raises(TypeError): + t.delete(10.5) + with pytest.raises(IndexError): + t.delete([0, "invalid", 10]) + + +def test_delete_stress(): + """Large batch deletion and alternating multi-pass pattern.""" + data = generate_test_data(50) + + # Delete 40 out of 50 at once + t = CTable(RowModel, new_data=data, expected_size=50) + t.delete(list(range(0, 40))) + assert len(t) == 10 + + # Alternating two-pass deletion + t2 = CTable(RowModel, new_data=data, expected_size=50) + t2.delete(list(range(0, 50, 2))) # delete all even -> 25 remain + assert len(t2) == 25 + t2.delete(list(range(0, 25, 2))) # delete every other of remaining -> ~12 + assert len(t2) == 12 + + +if __name__ == "__main__": + pytest.main(["-v", __file__]) diff --git a/tests/ctable/test_extend_delete.py b/tests/ctable/test_extend_delete.py new file mode 100644 index 00000000..42cf974e --- /dev/null +++ b/tests/ctable/test_extend_delete.py @@ -0,0 +1,226 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +from typing import Annotated, TypeVar + +import numpy as np +import pytest +from pydantic import BaseModel, Field + +from blosc2 import CTable + +RowT = TypeVar("RowT", bound=BaseModel) + + +class NumpyDtype: + def __init__(self, dtype): + self.dtype = dtype + + +class RowModel(BaseModel): + id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) + c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) + score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) + active: Annotated[bool, NumpyDtype(np.bool_)] = True + + +def generate_test_data(n_rows: int, start_id: int = 1) -> list: + return [(start_id + i, complex(i, -i), float((i * 7) % 100), bool(i % 2)) for i in range(n_rows)] + + +def get_valid_mask(table: CTable) -> np.ndarray: + return np.array(table._valid_rows[: len(table._valid_rows)], dtype=bool) + + +def assert_mask_matches(table: CTable, expected_mask: list): + actual = get_valid_mask(table)[: len(expected_mask)] + np.testing.assert_array_equal( + actual, + np.array(expected_mask, dtype=bool), + err_msg=f"Mask mismatch.\nExpected: {expected_mask}\nGot: {actual}", + ) + + +def assert_data_at_positions(table: CTable, positions: list, expected_ids: list): + for pos, expected_id in zip(positions, expected_ids, strict=False): + actual_id = int(table._cols["id"][pos]) + assert actual_id == expected_id, f"Position {pos}: expected ID {expected_id}, got {actual_id}" + + +# ------------------------------------------------------------------- +# Tests +# ------------------------------------------------------------------- + + +def test_gap_fill_mask_and_positions(): + """extend and append fill from last valid position; mask is updated correctly.""" + # extend after deletions: mask and physical positions + t = CTable(RowModel, new_data=generate_test_data(7, 1), expected_size=10) + t.delete([0, 2, 4, 6]) + assert_mask_matches(t, [False, True, False, True, False, True, False]) + assert len(t) == 3 + t.extend(generate_test_data(3, 8)) + assert_mask_matches(t, [False, True, False, True, False, True, True, True, True]) + assert len(t) == 6 + assert_data_at_positions(t, [6, 7, 8], [8, 9, 10]) + + # append fills from last valid position, not into holes + t2 = CTable(RowModel, new_data=generate_test_data(5, 1), expected_size=10) + t2.delete([1, 3]) + assert_mask_matches(t2, [True, False, True, False, True]) + t2.append((6, 1j, 50.0, True)) + assert_mask_matches(t2, [True, False, True, False, True, True]) + t2.append((7, 2j, 60.0, False)) + assert_mask_matches(t2, [True, False, True, False, True, True, True]) + + # extend fills from last valid position when there's enough capacity + t3 = CTable(RowModel, new_data=generate_test_data(10, 1), expected_size=15) + t3.delete([2, 4, 6]) + t3.extend(generate_test_data(3, 20)) + assert_data_at_positions(t3, [10, 11, 12], [20, 21, 22]) + + +def test_resize_behavior(): + """Resize triggered when capacity is full; compact=True avoids massive resize.""" + # compact=False: append beyond capacity must resize + t = CTable(RowModel, new_data=generate_test_data(10, 1), expected_size=10, compact=False) + t.delete(list(range(9))) + assert len(t) == 1 + initial_cap = len(t._valid_rows) + t.append((11, 5j, 75.0, True)) + assert len(t._valid_rows) > initial_cap + + # compact=True: no massive resize after deletions + extend + t2 = CTable(RowModel, new_data=generate_test_data(10, 1), expected_size=10, compact=True) + t2.delete(list(range(9))) + assert len(t2) == 1 + initial_cap2 = len(t2._valid_rows) + t2.extend(generate_test_data(3, 11)) + assert len(t2._valid_rows) <= initial_cap2 * 2 + + # extend exceeding capacity always resizes regardless of compact + t3 = CTable(RowModel, new_data=generate_test_data(5, 1), expected_size=10, compact=False) + t3.delete([0, 2, 4]) + initial_cap3 = len(t3._valid_rows) + t3.extend(generate_test_data(20, 100)) + assert len(t3._valid_rows) > initial_cap3 + + +def test_mixed_append_extend_with_gaps(): + """Multiple extends, appends, and deletes interleaved; lengths stay correct.""" + # Multiple extends with intermediate deletions + t = CTable(RowModel, expected_size=20) + t.extend(generate_test_data(5, 1)) + t.extend(generate_test_data(3, 10)) + assert len(t) == 8 + t.delete([2, 4, 6]) + assert len(t) == 5 + t.extend(generate_test_data(2, 20)) + assert len(t) == 7 + t.delete([0, 1]) + assert len(t) == 5 + t.extend(generate_test_data(4, 30)) + assert len(t) == 9 + + # append + extend mixed, delete all then re-extend + t2 = CTable(RowModel, expected_size=20) + for i in range(5): + t2.append((i + 1, complex(i), float(i * 10), True)) + assert len(t2) == 5 + t2.extend(generate_test_data(5, 10)) + assert len(t2) == 10 + t2.delete([1, 3, 5, 7, 9]) + assert len(t2) == 5 + t2.append((100, 0j, 50.0, False)) + assert len(t2) == 6 + t2.extend(generate_test_data(3, 200)) + assert len(t2) == 9 + + # Fill all gaps then extend; delete all then extend from scratch + t3 = CTable(RowModel, new_data=generate_test_data(10, 1), expected_size=15) + t3.delete(list(range(0, 10, 2))) + assert len(t3) == 5 + t3.extend(generate_test_data(5, 20)) + assert len(t3) == 10 + + t4 = CTable(RowModel, new_data=generate_test_data(10, 1), expected_size=15) + t4.delete(list(range(10))) + assert len(t4) == 0 + t4.extend(generate_test_data(5, 100)) + assert len(t4) == 5 + + +def test_compact_behavior(): + """Manual compact consolidates mask; auto-compact keeps data correct after extend.""" + # Manual compact: valid rows packed to front, extend fills after them + t = CTable(RowModel, new_data=generate_test_data(10, 1), expected_size=15, compact=False) + t.delete([1, 3, 5, 7, 9]) + assert len(t) == 5 + t.compact() + assert_mask_matches(t, [True] * 5 + [False] * 10) + t.extend(generate_test_data(3, 20)) + assert len(t) == 8 + + # Auto-compact: table stays consistent after heavy deletions + extend + t2 = CTable(RowModel, new_data=generate_test_data(10, 1), expected_size=15, compact=True) + t2.delete(list(range(0, 8))) + assert len(t2) == 2 + t2.extend(generate_test_data(10, 100)) + assert len(t2) == 12 + + +def test_complex_scenarios(): + """Sparse gaps, alternating cycles, data integrity, and full workflow.""" + # Sparse table: many scattered deletions then bulk extend + t = CTable(RowModel, new_data=generate_test_data(20, 1), expected_size=30) + t.delete([0, 1, 2, 4, 5, 6, 8, 9, 10, 12, 13, 14, 16, 17, 18]) + assert len(t) == 5 + t.extend(generate_test_data(10, 100)) + assert len(t) == 15 + + # Alternating extend/delete cycles + t2 = CTable(RowModel, expected_size=50) + for cycle in range(5): + t2.extend(generate_test_data(10, cycle * 100)) + current_len = len(t2) + if current_len >= 5: + t2.delete(list(range(0, min(5, current_len)))) + + # Data integrity: correct row values survive delete + extend + t3 = CTable( + RowModel, new_data=[(1, 1j, 10.0, True), (2, 2j, 20.0, False), (3, 3j, 30.0, True)], expected_size=10 + ) + t3.delete(1) + assert t3.row[0].id[0] == 1 + assert t3.row[1].id[0] == 3 + t3.extend([(10, 10j, 100.0, True), (11, 11j, 100.0, False)]) + assert t3.row[0].id[0] == 1 + assert t3.row[1].id[0] == 3 + assert t3.row[2].id[0] == 10 + assert t3.row[3].id[0] == 11 + + # Full workflow + t4 = CTable(RowModel, expected_size=20, compact=False) + t4.extend(generate_test_data(10, 1)) + assert len(t4) == 10 + t4.delete([0, 2, 4, 6, 8]) + assert len(t4) == 5 + t4.append((100, 0j, 50.0, True)) + t4.append((101, 1j, 60.0, False)) + assert len(t4) == 7 + t4.extend(generate_test_data(5, 200)) + assert len(t4) == 12 + t4.delete([3, 7, 10]) + assert len(t4) == 9 + t4.extend(generate_test_data(3, 300)) + assert len(t4) == 12 + assert t4.nrows == 12 + assert t4.ncols == 4 + + +if __name__ == "__main__": + pytest.main(["-v", __file__]) diff --git a/tests/ctable/test_row_logic.py b/tests/ctable/test_row_logic.py new file mode 100644 index 00000000..483b7924 --- /dev/null +++ b/tests/ctable/test_row_logic.py @@ -0,0 +1,221 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +from typing import Annotated + +import numpy as np +import pytest +from pydantic import BaseModel, Field + +from blosc2 import CTable +from blosc2.ctable import Column + + +class NumpyDtype: + def __init__(self, dtype): + self.dtype = dtype + + +class RowModel(BaseModel): + id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) + score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) + active: Annotated[bool, NumpyDtype(np.bool_)] = True + + +def generate_test_data(n_rows: int, start_id: int = 0) -> list: + return [(start_id + i, float(i * 10), i % 2 == 0) for i in range(n_rows)] + + +# ------------------------------------------------------------------- +# Tests +# ------------------------------------------------------------------- + + +def test_row_int_indexing(): + """int indexing: no holes, with holes, negative indices, and out-of-range.""" + data = generate_test_data(20) + + # No holes: spot checks + t = CTable(RowModel, new_data=data) + r = t.row[0] + assert isinstance(r, CTable) + assert len(r) == 1 + assert r.id[0] == 0 + assert r.score[0] == 0.0 + assert r.active[0] + assert t.row[10].id[0] == 10 + assert t.row[10].score[0] == 100.0 + + # Negative indices + assert t.row[-1].id[0] == 19 + assert t.row[-5].id[0] == 15 + + # With holes: delete odd positions -> valid: 0,2,4,6,8,10... + t.delete([1, 3, 5, 7, 9]) + assert t.row[0].id[0] == 0 + assert t.row[1].id[0] == 2 + assert t.row[5].id[0] == 10 + + # Out of range + t2 = CTable(RowModel, new_data=generate_test_data(10)) + for idx in [10, 100, -11]: + with pytest.raises(IndexError): + _ = t2.row[idx] + + +def test_row_slice_indexing(): + """Slice indexing: no holes, with holes, step, negative, beyond bounds, empty/full.""" + data = generate_test_data(20) + + # No holes + t = CTable(RowModel, new_data=data) + assert isinstance(t.row[0:5], CTable) + assert list(t.row[0:5].id) == [0, 1, 2, 3, 4] + assert list(t.row[10:15].id) == [10, 11, 12, 13, 14] + assert list(t.row[::2].id) == [0, 2, 4, 6, 8, 10, 12, 14, 16, 18] + + # With step + assert list(t.row[0:10:2].id) == [0, 2, 4, 6, 8] + assert list(t.row[1:10:3].id) == [1, 4, 7] + + # Negative indices + assert list(t.row[-5:].id) == [15, 16, 17, 18, 19] + assert list(t.row[-10:-5].id) == [10, 11, 12, 13, 14] + + # With holes: delete odd positions + t.delete([1, 3, 5, 7, 9]) + assert list(t.row[0:5].id) == [0, 2, 4, 6, 8] + assert list(t.row[5:10].id) == [10, 11, 12, 13, 14] + + # Beyond bounds + t2 = CTable(RowModel, new_data=generate_test_data(10)) + assert len(t2.row[11:20]) == 0 + assert list(t2.row[5:100].id) == [5, 6, 7, 8, 9] + assert len(t2.row[100:]) == 0 + + # Empty and full slices + assert len(t2.row[5:5]) == 0 + assert len(t2.row[0:0]) == 0 + result = t2.row[:] + assert len(result) == 10 + assert list(result.id) == list(range(10)) + + +def test_row_list_indexing(): + """List indexing: no holes, with holes, out-of-range, edge cases.""" + data = generate_test_data(20) + + # No holes + t = CTable(RowModel, new_data=data) + r = t.row[[0, 5, 10, 15]] + assert isinstance(r, CTable) + assert len(r) == 4 + assert set(r.id) == {0, 5, 10, 15} + assert set(t.row[[19, 0, 10]].id) == {0, 10, 19} + + # With holes: delete [1,3,5,7,9] -> logical 0->id0, 1->id2, 2->id4... + t.delete([1, 3, 5, 7, 9]) + assert set(t.row[[0, 2, 4]].id) == {0, 4, 8} + assert set(t.row[[5, 3, 1]].id) == {2, 6, 10} + + # Negative indices in list + t2 = CTable(RowModel, new_data=generate_test_data(10)) + assert set(t2.row[[0, -1, 5]].id) == {0, 5, 9} + + # Single element + assert t2.row[[5]].id[0] == 5 + + # Duplicate indices -> deduplicated + r_dup = t2.row[[5, 5, 5]] + assert len(r_dup) == 1 + assert r_dup.id[0] == 5 + + # Empty list + assert len(t2.row[[]]) == 0 + + # Out of range + for bad in [[0, 5, 100], [0, 1, -11]]: + with pytest.raises(IndexError): + _ = t2.row[bad] + + +def test_row_view_properties(): + """View metadata, base chain, mask integrity, column liveness, and chained views.""" + data = generate_test_data(100) + tabla0 = CTable(RowModel, new_data=data) + + # Base is None on root table + assert tabla0.base is None + + # View properties are shared with parent + v = tabla0.row[0:10] + assert v.base is tabla0 + assert v._row_type == tabla0._row_type + assert v._cols is tabla0._cols + assert v._col_widths == tabla0._col_widths + assert v.col_names == tabla0.col_names + + # Read ops on view + view = tabla0.row[5:15] + assert view.id[0] == 5 + assert view.score[0] == 50.0 + assert not view.active[0] + assert list(view.id) == list(range(5, 15)) + + # Mask integrity + assert np.count_nonzero(view._valid_rows[:]) == 10 + + # Column is live (points back to its view) + col = view.id + assert isinstance(col, Column) + assert col._table is view + + # Chained views: base always points to immediate parent + tabla1 = tabla0.row[:50] + assert tabla1.base is tabla0 + assert len(tabla1) == 50 + + tabla2 = tabla1.row[:10] + assert tabla2.base is tabla1 + assert len(tabla2) == 10 + assert list(tabla2.id) == list(range(10)) + + tabla3 = tabla2.row[5:] + assert tabla3.base is tabla2 + assert len(tabla3) == 5 + assert list(tabla3.id) == [5, 6, 7, 8, 9] + + # Chained view with holes on parent + tabla0.delete([5, 10, 15, 20, 25]) + tv1 = tabla0.row[:30] + assert tv1.base is tabla0 + assert len(tv1) == 30 + tv2 = tv1.row[10:20] + assert tv2.base is tv1 + assert len(tv2) == 10 + + +def test_row_edge_cases(): + """Empty table, fully-deleted table: int raises IndexError, slice returns empty.""" + # Empty table + empty = CTable(RowModel) + with pytest.raises(IndexError): + _ = empty.row[0] + assert len(empty.row[:]) == 0 + assert len(empty.row[0:10]) == 0 + + # All rows deleted + data = generate_test_data(10) + t = CTable(RowModel, new_data=data) + t.delete(list(range(10))) + with pytest.raises(IndexError): + _ = t.row[0] + assert len(t.row[:]) == 0 + + +if __name__ == "__main__": + pytest.main(["-v", __file__]) From c05c2ec87271e466c94bdbdbf974f9fd61bf8f70 Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Thu, 26 Mar 2026 11:21:43 +0100 Subject: [PATCH 02/49] Add a plan for declaring a simple schema for CTable objects --- plans/ctable-schema.md | 1269 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1269 insertions(+) create mode 100644 plans/ctable-schema.md diff --git a/plans/ctable-schema.md b/plans/ctable-schema.md new file mode 100644 index 00000000..bed1c6a7 --- /dev/null +++ b/plans/ctable-schema.md @@ -0,0 +1,1269 @@ +# CTable Schema Redesign + +## Motivation + +The current `CTable` prototype in PR #598 uses `pydantic.BaseModel` plus +`Annotated[...]` metadata to define table schemas. That works, but it is not the +best long-term API for a columnar container in `python-blosc2`. + +The main issues with the current shape are: + +* It mixes row validation concerns with physical storage concerns. +* It relies on custom metadata objects (`NumpyDtype`, `MaxLen`) embedded in + Pydantic annotations. +* It is verbose for simple schemas. +* It does not provide an obvious place for NDArray-specific per-column options + such as `cparams`, `dparams`, `chunks`, `blocks`, or future indexing hints. + +What we want instead is: + +* A schema API that is easy to read and write. +* A place to attach Blosc2-specific per-column configuration. +* A way to express logical constraints such as `ge=0`, `le=100`, `max_length=10`. +* Internal validation without forcing the public API to be Pydantic-shaped. +* A clean distinction between: + * logical field type and constraints + * physical storage type + * per-column storage options + +The proposed solution is a **dataclass-first schema API** with **declarative field +spec objects** and **optional internal Pydantic-backed validation**. + +The intended usage style is: + +* canonical form for constrained or storage-tuned columns: + `id: int = b2.field(b2.int64(ge=0))` +* shorthand for simple inferred columns: + `id: int` +* not preferred as a primary style: + `id = b2.field(b2.int64(ge=0))` + +The reason is that the canonical form preserves normal Python type annotations, +which are valuable for readability, static tooling, and schema inspection. + +--- + +## Proposed public API + +### Schema declaration + +The intended schema declaration style is: + +```python +from dataclasses import dataclass + +import blosc2 as b2 + + +@dataclass +class Row: + id: int = b2.field(b2.int64(ge=0)) + score: float = b2.field( + b2.float64(ge=0, le=100), + cparams={"codec": b2.Codec.LZ4, "clevel": 5}, + ) + active: bool = b2.field(b2.bool(), default=True) +``` + +This is the target user-facing API for `CTable`. + +This should be documented as the **canonical** schema declaration style. + +For simple unconstrained cases, `CTable` may support an inferred shorthand: + +```python +@dataclass +class Row: + id: int + score: float + active: bool = True +``` + +which is interpreted approximately as: + +```python +@dataclass +class Row: + id: int = b2.field(b2.int64()) + score: float = b2.field(b2.float64()) + active: bool = b2.field(b2.bool(), default=True) +``` + +This shorthand should be limited to simple built-in Python types where the +mapping is obvious. + +### Naming convention + +Use **lowercase names** for schema descriptor objects: + +* `b2.int64` +* `b2.float64` +* `b2.bool` +* later: `b2.string(max_length=...)`, `b2.bytes(max_length=...)`, `b2.complex128` + +Reason: + +* `b2.int64(...)` is not just a dtype; it is a schema descriptor with constraints. +* The lowercase form keeps the API closer in spirit to NumPy and PyTorch. +* If plain NumPy dtypes are needed, callers can use `np.int64`, `np.float64`, + `np.bool_`, etc. +* `b2.bool(...)` is preferred over `b2.bool_(...)` for readability, even though + NumPy uses `bool_`. This is closer to PyTorch style and fits better for a + schema-builder API. + +### Field helper + +`b2.field(...)` should be the standard way to attach schema metadata to a +dataclass field. + +Expected shape: + +```python +b2.field( + b2.float64(ge=0, le=100), + default=..., + default_factory=..., + cparams=..., + dparams=..., + chunks=..., + blocks=..., + title=..., + description=..., + nullable=..., +) +``` + +At minimum for the first version: + +* `spec` +* `default` +* `default_factory` +* `cparams` +* `dparams` +* `chunks` +* `blocks` + +The implementation should store these in `dataclasses.field(metadata=...)`. + +The unannotated form: + +```python +id = b2.field(b2.int64(ge=0)) +``` + +should not be the primary API. It may be supported later only if there is a +strong reason, but the preferred style should retain: + +* a Python type annotation in the annotation slot +* `b2.field(...)` in the field/default slot + +That keeps the schema aligned with normal dataclass usage. + +--- + +## Core design + +### 1. Dataclass is the schema carrier + +The dataclass defines: + +* field names +* Python-level row shape +* user-visible defaults + +Example: + +```python +@dataclass +class Row: + id: int = b2.field(b2.int64(ge=0)) + score: float = b2.field(b2.float64(ge=0, le=100)) + active: bool = b2.field(b2.bool(), default=True) +``` + +This keeps the declaration small and idiomatic. + +The Python annotation should remain part of the design, not be replaced by +`b2.field(...)` alone. The annotation provides value independently of the +Blosc2 schema descriptor. + +### 2. Schema spec objects are the source of truth + +Each lowercase builder object is a lightweight immutable schema descriptor. + +Examples: + +```python +b2.int64(ge=0) +b2.float64(ge=0, le=100) +b2.bool() +b2.string(max_length=32) +b2.bytes(max_length=64) +``` + +Each spec object should carry only schema-level metadata, for example: + +* logical kind +* storage dtype +* numeric constraints (`ge`, `gt`, `le`, `lt`, `multiple_of`) +* string constraints (`max_length`, `min_length`, `pattern`) +* nullability +* maybe logical annotations later (`categorical`, `timezone`, `unit`) + +They should **not** directly carry per-column NDArray instance settings such as +`cparams` or `chunks`; those belong in `b2.field(...)`. + +### 3. Column field metadata carries NDArray-specific configuration + +`b2.field(...)` metadata should be the place for: + +* column storage options +* per-column compression settings +* chunk/block tuning +* persistence options in future versions + +This keeps the separation clean: + +* `b2.float64(ge=0, le=100)` answers: "what values are valid?" +* `b2.field(..., cparams=..., chunks=...)` answers: "how is this column stored?" + +### 4. Schema compilation step inside CTable + +`CTable` should not consume raw dataclass fields repeatedly. On construction, it +should compile the row class into an internal schema representation. + +For example: + +```python +compiled = CompiledSchema( + row_cls=Row, + columns=[ + CompiledColumn( + name="id", + py_type=int, + spec=b2.int64(ge=0), + dtype=np.int64, + default=MISSING, + cparams=..., + dparams=..., + chunks=..., + blocks=..., + validator_info=..., + ), + ..., + ], + validator_model=..., +) +``` + +This compiled form should drive: + +* NDArray creation +* row validation +* bulk validation +* introspection and future serialization + +--- + +## Validation strategy + +### Use Pydantic internally, but do not make it the public schema API + +Pydantic is a good fit for validation because it is: + +* mature +* well-tested +* expressive +* fast enough for row-level operations + +However, it should be an **implementation detail**, not the public schema surface. + +The public schema should remain: + +* dataclass-based +* Blosc2-specific +* independent of any one validation library + +### Why not use Pydantic as the schema source directly? + +Because storage and validation are overlapping but not identical concerns. + +Examples: + +* `dtype=np.int16` is both logical and physical. +* `cparams`, `chunks`, `blocks`, `dparams` are not Pydantic concepts. +* a future column index, bloom filter, or codec hint is not a validation concept. + +Therefore, the internal architecture should be: + +* user declares a dataclass + `b2.field(...)` +* `CTable` compiles it into: + * storage schema + * validation schema + +### Row-level validation + +For `append(row)` and other row-wise inserts: + +* compile a cached internal Pydantic model once per schema +* validate incoming rows against that model +* convert the validated row into column values + +This is the simplest and safest path. + +Expected behavior: + +* `table.append(Row(...))` +* `table.append({"id": 1, "score": 2.0, "active": True})` +* `table.append((1, 2.0, True))` + +All may be accepted, but internally normalized through one validator path. + +### Bulk validation + +For `extend(...)`, row-by-row Pydantic validation may be too expensive for large +batches. Bulk inserts need a separate strategy. + +Recommended modes: + +* `validate=True` + Full validation. May use row-wise Pydantic validation for smaller inputs and + vectorized checks where available. +* `validate=False` + Trust caller, perform dtype coercion only. +* optional later: `validate="sample"` or `validate="vectorized"` + +For numeric and simple string constraints, vectorized checks are preferable when +possible: + +* `ge`, `gt`, `le`, `lt` +* `max_length`, `min_length` +* null checks +* dtype coercion checks + +This means the architecture should support both: + +* Pydantic row validation +* vectorized array validation + +The compiled schema should expose enough information for both. + +### Performance stance + +Pydantic should be treated as: + +* a strong default for correctness +* fast enough for row-wise validation +* not necessarily the fastest choice for large batch validation + +This is important because the performance bottleneck for `extend()` is more about +per-row Python overhead than about Pydantic specifically. + +--- + +## Detailed API proposal + +### Schema spec classes + +Add schema descriptor classes under `blosc2`, for example: + +* `int8`, `int16`, `int32`, `int64` +* `uint8`, `uint16`, `uint32`, `uint64` +* `float32`, `float64` +* `bool` +* `complex64`, `complex128` +* `string` +* `bytes` + +Minimal constructor examples: + +```python +b2.int64(ge=0) +b2.float64(ge=0, le=100) +b2.string(max_length=32) +b2.bytes(max_length=64) +b2.bool() +``` + +Internal common fields: + +* `dtype` +* `nullable` +* `constraints` +* `python_type` + +### Field helper + +`b2.field(spec, **kwargs)` should return a `dataclasses.field(...)` object with +Blosc2 metadata attached. + +Example metadata layout: + +```python +{ + "blosc2": { + "spec": ..., + "cparams": ..., + "dparams": ..., + "chunks": ..., + "blocks": ..., + } +} +``` + +This metadata key should be stable and reserved. + +### CTable constructor + +The desired constructor remains: + +```python +table = b2.CTable(Row) +``` + +Optional overrides: + +```python +table = b2.CTable( + Row, + expected_size=1_000_000, + compact=False, + validate=True, +) +``` + +`CTable` should detect that `Row` is a dataclass schema and compile it. + +### Possible compatibility layer + +If needed temporarily, `CTable` may continue accepting the old Pydantic model +style during a transition period: + +```python +table = b2.CTable(LegacyPydanticRow) +``` + +But that should be documented as legacy or transitional once the dataclass API +lands. + +--- + +## Internal compilation pipeline + +### Step 1. Inspect dataclass fields + +For each dataclass field: + +* field name +* Python annotation +* default or default factory +* Blosc2 metadata from `b2.field(...)` + +Reject invalid shapes early: + +* missing `b2.field(...)` +* missing schema spec +* incompatible Python annotation vs schema spec +* unsupported defaults + +If inferred shorthand is supported, refine the first two rules to: + +* either a supported plain annotation, or an explicit `b2.field(...)` +* if `b2.field(...)` is present, it must contain a schema spec + +### Step 2. Build compiled column descriptors + +For each field, produce a `CompiledColumn` object containing: + +* `name` +* `py_type` +* `spec` +* `dtype` +* `default` +* `default_factory` +* `nullable` +* `cparams` +* `dparams` +* `chunks` +* `blocks` +* validation constraints + +### Step 3. Derive physical NDArray creation arguments + +From the compiled column descriptor, derive: + +* `dtype` +* shape +* chunks +* blocks +* `cparams` +* `dparams` + +This should happen once during table initialization. + +### Step 4. Derive validation model + +Translate each schema spec into a Pydantic field definition. + +Examples: + +* `int64(ge=0)` -> integer field with `ge=0` +* `float64(ge=0, le=100)` -> float field with `ge=0`, `le=100` +* `string(max_length=32)` -> string field with `max_length=32` + +Cache the compiled Pydantic model class per row schema. + +### Step 5. Expose introspection hooks + +Expose enough metadata for: + +* debugging +* `table.info()` +* future schema serialization +* future schema-driven docs and reprs + +Possible user-facing hooks later: + +* `table.schema` +* `table.schema.columns` +* `table.schema.as_dict()` + +--- + +## Handling defaults + +Defaults should follow dataclass semantics as closely as possible. + +Examples: + +```python +active: bool = b2.field(b2.bool(), default=True) +tags: list[str] = b2.field(..., default_factory=list) +``` + +For the first implementation, keep this conservative: + +* support scalar defaults +* support `default_factory` only if there is a clear use case +* reject mutable defaults directly + +On insert: + +* omitted values should be filled from defaults +* explicit `None` should be accepted only if the field is nullable + +--- + +## Insert semantics + +### append() + +`append()` should accept a small set of normalized shapes: + +* dataclass row instance +* dict-like row +* tuple/list in schema order + +Recommended internal path: + +1. normalize the input to a field mapping +2. validate with cached validator model +3. coerce to final column values +4. append into underlying NDArrays + +### extend() + +`extend()` should accept: + +* iterable of row objects +* dict-of-arrays +* structured NumPy array +* maybe another `CTable` + +Recommended internal path: + +1. normalize to column batches where possible +2. validate according to `validate=` mode +3. coerce dtypes +4. write in bulk + +For `dict-of-arrays` and structured arrays, vectorized validation should be the +preferred long-term path. + +--- + +## Per-column NDArray options + +One of the main reasons for `b2.field(...)` is that different columns may want +different storage settings. + +Examples: + +* a boolean column may want different compression parameters from a float column +* a high-cardinality string column may need different chunk sizes +* a metric column may use a specific codec or filter tuning + +So the schema system must allow: + +```python +@dataclass +class Row: + id: int = b2.field(b2.int64(ge=0), cparams={"codec": b2.Codec.ZSTD, "clevel": 1}) + score: float = b2.field( + b2.float64(ge=0, le=100), cparams={"codec": b2.Codec.LZ4HC, "clevel": 9} + ) + active: bool = b2.field(b2.bool(), cparams={"codec": b2.Codec.LZ4}) +``` + +The implementation should define precedence rules clearly: + +* column-level options override table defaults +* table-level options fill in unspecified values + +This implies `CTable(...)` may also take default storage options: + +```python +table = b2.CTable(Row, cparams=..., dparams=...) +``` + +Column-level overrides should merge against those defaults, not replace them +blindly. + +--- + +## Compatibility and migration + +### Goal + +Move toward the dataclass-based schema API without locking the project into the +current Pydantic-shaped declaration model. + +### Migration path + +Phase 1: + +* introduce schema spec classes and `b2.field(...)` +* support dataclass schemas in `CTable` +* keep existing prototype behavior separate + +Phase 2: + +* add row validation via cached internal Pydantic model +* add bulk validation modes +* document the dataclass schema API as preferred + +Phase 3: + +* optionally add a compatibility adapter for existing Pydantic models +* deprecate ad hoc `Annotated[...]` metadata conventions if they remain exposed + +### Non-goal + +Do not make the first implementation solve every possible schema feature. The +first goal is to get the schema shape and internal architecture right. + +--- + +## Serialization implications + +Even if `save()` / `load()` are not implemented yet, this schema design should +anticipate persistence. + +Eventually a persisted `CTable` will need to store: + +* column names +* logical schema descriptors +* per-column defaults +* per-column NDArray storage options +* maybe validation constraints + +That argues strongly for having a stable compiled schema representation early. + +The compiled schema should be serializable to: + +* JSON-compatible metadata +* or a small msgpack payload + +The public dataclass itself does not need to be serialized directly. Only the +compiled schema matters for persistence. + +--- + +## Open questions + +### 1. Should Python annotations be required to match the schema spec? + +Example: + +```python +id: int = b2.field(b2.int64(ge=0)) +``` + +Recommended answer: yes, broadly, with sensible compatibility rules. + +Allowed: + +* `int` with `int64` +* `float` with `float64` +* `bool` with `bool` + +Potentially allowed later: + +* `str` with `string` +* `bytes` with `bytes` + +Reject obviously inconsistent declarations early. + +In other words: + +* `id: int = b2.field(b2.int64(ge=0))` is good +* `id: int` is acceptable shorthand for inferred `b2.int64()` +* `id = b2.field(b2.int64(ge=0))` is not the preferred style because it drops + the Python annotation + +### 2. Where should nullability live? + +Recommended answer: on the schema spec. + +Example: + +```python +name: str | None = b2.field(b2.string(max_length=32, nullable=True)) +``` + +The Python annotation and schema spec should agree. + +### 3. Should `b2.field()` require a spec? + +Recommended answer: yes for the first version. + +Allowing `b2.field(default=True)` without a spec means we must infer too much +from the Python annotation and lose clarity. + +This still allows fully inferred fields that do not use `b2.field(...)` at all: + +```python +active: bool = True +``` + +but once `b2.field(...)` is used, it should carry an explicit schema spec. + +### 4. How much should Pydantic-specific behavior leak? + +Recommended answer: as little as possible. + +Users should not need to know whether validation is backed by Pydantic, +vectorized NumPy checks, or another mechanism. + +--- + +## Concrete implementation sequence + +This section turns the design into a proposed execution order with concrete +files, class names, and function signatures. + +### Step 1: add schema descriptor primitives + +Create a new module: + +* `src/blosc2/schema.py` + +Primary contents: + +```python +from __future__ import annotations + +from dataclasses import MISSING, Field as DataclassField, field as dc_field +from typing import Any + +import numpy as np +``` + +Proposed public classes and functions: + +```python +class SchemaSpec: + dtype: np.dtype + python_type: type[Any] + nullable: bool + + def to_pydantic_kwargs(self) -> dict[str, Any]: ... + def to_metadata_dict(self) -> dict[str, Any]: ... + + +class int64(SchemaSpec): + def __init__( + self, *, ge=None, gt=None, le=None, lt=None, nullable: bool = False + ): ... + + +class float64(SchemaSpec): + def __init__( + self, *, ge=None, gt=None, le=None, lt=None, nullable: bool = False + ): ... + + +class bool(SchemaSpec): + def __init__(self, *, nullable: bool = False): ... + + +class string(SchemaSpec): + def __init__( + self, *, min_length=None, max_length=None, pattern=None, nullable: bool = False + ): ... + + +class bytes(SchemaSpec): + def __init__(self, *, min_length=None, max_length=None, nullable: bool = False): ... + + +def field( + spec: SchemaSpec, + *, + default=MISSING, + default_factory=MISSING, + cparams: dict[str, Any] | None = None, + dparams: dict[str, Any] | None = None, + chunks: tuple[int, ...] | None = None, + blocks: tuple[int, ...] | None = None, + title: str | None = None, + description: str | None = None, +) -> DataclassField: ... +``` + +Internal helper constants: + +```python +BLOSC2_FIELD_METADATA_KEY = "blosc2" +``` + +Notes: + +* Start with only the spec classes needed for the first `CTable` iteration: + `int64`, `float64`, `bool`. +* Add `string` and `bytes` only if needed in the same slice of work. +* Avoid over-generalizing the first implementation. + +### Step 2: add schema compiler and compiled representations + +Create a new module: + +* `src/blosc2/schema_compiler.py` + +Primary internal dataclasses: + +```python +from dataclasses import dataclass +from typing import Any + + +@dataclass(slots=True) +class ColumnConfig: + cparams: dict[str, Any] | None + dparams: dict[str, Any] | None + chunks: tuple[int, ...] | None + blocks: tuple[int, ...] | None + title: str | None + description: str | None + + +@dataclass(slots=True) +class CompiledColumn: + name: str + py_type: Any + spec: Any + dtype: np.dtype + default: Any + default_factory: Any + config: ColumnConfig + + +@dataclass(slots=True) +class CompiledSchema: + row_cls: type[Any] + columns: list[CompiledColumn] + columns_by_name: dict[str, CompiledColumn] + validator_model: type[Any] | None = None +``` + +Primary internal functions: + +```python +def compile_schema(row_cls: type[Any]) -> CompiledSchema: ... +def infer_spec_from_annotation(annotation: Any, default: Any = MISSING) -> Any: ... +def validate_annotation_matches_spec(annotation: Any, spec: Any) -> None: ... +def get_blosc2_field_metadata(dc_field) -> dict[str, Any] | None: ... +``` + +Behavior: + +* accept a dataclass type only +* for explicit `b2.field(...)`, read the spec from metadata +* for inferred fields like `id: int`, derive `b2.int64()` +* reject unsupported annotations early +* normalize all defaults/config into `CompiledSchema` + +### Step 3: export the schema API from `blosc2` + +Update: + +* `src/blosc2/__init__.py` + +Exports to add: + +```python +from .schema import bool, bytes, field, float64, int64, string +``` + +And in `__all__`: + +```python +"bool", +"bytes", +"field", +"float64", +"int64", +"string", +``` + +Notes: + +* Be careful with `bool` and `bytes` in `__init__.py` because they shadow + builtins within the module namespace. That is acceptable if done deliberately, + but it should be reviewed explicitly. +* If shadowing proves too awkward internally, keep the implementation names + private and re-export the public names only. + +### Step 4: refactor `CTable` to consume compiled schemas + +Update: + +* `src/blosc2/ctable.py` + +Primary constructor signature: + +```python +class CTable(Generic[RowT]): + def __init__( + self, + row_type: type[RowT], + new_data=None, + *, + expected_size: int = 1_048_576, + compact: bool = False, + validate: bool = True, + cparams: dict[str, Any] | None = None, + dparams: dict[str, Any] | None = None, + ) -> None: ... +``` + +New internal state: + +```python +self._schema: CompiledSchema +self._validate: bool +self._table_cparams: dict[str, Any] | None +self._table_dparams: dict[str, Any] | None +``` + +New internal helper methods: + +```python +def _init_columns(self, expected_size: int) -> None: ... +def _resolve_column_storage(self, col: CompiledColumn) -> dict[str, Any]: ... +def _normalize_row_input(self, data: Any) -> dict[str, Any]: ... +def _coerce_row_to_storage(self, row: dict[str, Any]) -> dict[str, Any]: ... +``` + +Behavior changes: + +* replace direct inspection of `row_type.model_fields` +* build columns from `self._schema.columns` +* derive column dtypes from compiled schema +* merge table-level and field-level storage settings + +### Step 5: implement row validation adapter + +Create a new internal module: + +* `src/blosc2/schema_validation.py` + +Primary functions: + +```python +from typing import Any + + +def build_validator_model(schema: CompiledSchema) -> type[Any]: ... +def validate_row(schema: CompiledSchema, row: dict[str, Any]) -> dict[str, Any]: ... +def validate_rows_rowwise( + schema: CompiledSchema, rows: list[dict[str, Any]] +) -> list[dict[str, Any]]: ... +``` + +Behavior: + +* build and cache a Pydantic model per compiled schema +* map `SchemaSpec` constraints into Pydantic field definitions +* return normalized Python values ready for storage coercion + +Implementation note: + +* Cache the generated validator model on `CompiledSchema.validator_model`. +* Keep all Pydantic-specific logic isolated in this module. + +### Step 6: wire validation into `append()` + +Update: + +* `src/blosc2/ctable.py` + +Target signatures: + +```python +def append(self, data: Any) -> None: ... +def _append_validated_row(self, row: dict[str, Any]) -> None: ... +``` + +Concrete behavior: + +1. normalize incoming row shape +2. if `self._validate` is true, validate via `schema_validation.validate_row` +3. coerce to storage values +4. append into column NDArrays + +Inputs to support in the first cut: + +* dataclass row instance +* dict +* tuple/list in schema order + +Inputs that can wait until later if needed: + +* structured NumPy scalar +* Pydantic model instance + +### Step 7: add `extend(..., validate=...)` + +Update: + +* `src/blosc2/ctable.py` + +Proposed signature: + +```python +def extend(self, data: Any, *, validate: bool | None = None) -> None: ... +``` + +Supporting internal helpers: + +```python +def _normalize_rows_input( + self, data: Any +) -> tuple[list[dict[str, Any]] | None, dict[str, Any] | None]: ... +def _extend_rowwise(self, rows: list[dict[str, Any]], *, validate: bool) -> None: ... +def _extend_columnwise(self, columns: dict[str, Any], *, validate: bool) -> None: ... +``` + +First implementation target: + +* support iterable of rows via `_extend_rowwise` +* preserve correctness first, optimize later + +Second implementation target: + +* add `_extend_columnwise` for structured arrays and dict-of-arrays +* add vectorized validation for simple constraints + +### Step 8: add vectorized validation helpers + +Create a new internal module: + +* `src/blosc2/schema_vectorized.py` + +Primary functions: + +```python +from typing import Any + + +def validate_column_values(col: CompiledColumn, values: Any) -> None: ... +def validate_column_batch(schema: CompiledSchema, columns: dict[str, Any]) -> None: ... +``` + +Initial checks to support: + +* numeric `ge`, `gt`, `le`, `lt` +* string and bytes `min_length`, `max_length` +* nullability +* dtype compatibility after coercion + +This module should remain optional in the first PR if the rowwise path is enough +to land the architecture cleanly. + +### Step 9: add schema introspection to `CTable` + +Update: + +* `src/blosc2/ctable.py` + +Proposed property: + +```python +@property +def schema(self) -> CompiledSchema: ... +``` + +Optional helper methods: + +```python +def schema_dict(self) -> dict[str, Any]: ... +def column_schema(self, name: str) -> CompiledColumn: ... +``` + +Goal: + +* make the new schema layer visible and debuggable +* provide a stable base for future save/load work + +### Step 10: add tests in focused modules + +Add: + +* `tests/ctable/test_schema_specs.py` +* `tests/ctable/test_schema_compiler.py` +* `tests/ctable/test_schema_validation.py` +* `tests/ctable/test_ctable_dataclass_schema.py` + +Test scope by file: + +`tests/ctable/test_schema_specs.py` + +* spec construction +* dtype mapping +* metadata export + +`tests/ctable/test_schema_compiler.py` + +* explicit `b2.field(...)` +* inferred shorthand from plain annotations +* annotation/spec mismatch rejection +* defaults handling + +`tests/ctable/test_schema_validation.py` + +* Pydantic validator generation +* constraint enforcement +* nullable vs non-nullable behavior + +`tests/ctable/test_ctable_dataclass_schema.py` + +* `CTable(Row)` construction +* append with dataclass/dict/tuple +* extend with iterable of rows +* per-column `cparams` override plumbing + +### Step 11: keep the legacy prototype isolated during transition + +Short-term implementation choice: + +* if the current `ctable.py` prototype is still in active flux, prefer landing + the schema/compiler modules first and then refactoring `CTable` over them +* do not expand the old Pydantic-specific schema path further + +Possible follow-up helper: + +```python +def compile_legacy_pydantic_schema(row_cls: type[Any]) -> CompiledSchema: ... +``` + +But only add that if compatibility becomes necessary. + +### Step 12: persistence groundwork + +No need to implement `save()` / `load()` immediately, but define serialization +hooks on the schema side now. + +Add to `CompiledSchema` or a related helper: + +```python +def schema_to_dict(schema: CompiledSchema) -> dict[str, Any]: ... +def schema_from_dict(data: dict[str, Any]) -> CompiledSchema: ... +``` + +This should remain internal until the persisted format is stable. + +### Step 13: delivery order across PRs + +Recommended PR slicing: + +PR 1: + +* `src/blosc2/schema.py` +* `src/blosc2/schema_compiler.py` +* exports in `src/blosc2/__init__.py` +* tests for schema specs and compiler + +PR 2: + +* `CTable` constructor refactor to use compiled schema +* `append()` row normalization +* row-wise validation module +* `tests/ctable/test_ctable_dataclass_schema.py` + +PR 3: + +* `extend(..., validate=...)` +* vectorized validation helpers +* schema introspection property +* more tests for batch validation and overrides + +PR 4: + +* persistence groundwork +* optional compatibility adapter for legacy Pydantic model declarations + +### Step 14: concrete first-PR checklist + +The smallest coherent first implementation should be: + +1. add `src/blosc2/schema.py` +2. add `src/blosc2/schema_compiler.py` +3. export `field`, `int64`, `float64`, `bool` +4. add tests for: + * explicit field specs + * inferred shorthand + * mismatch rejection +5. stop there + +That first PR gives the project: + +* the public schema vocabulary +* the internal compiled representation +* confidence in the canonical API shape + +before touching too much `CTable` mutation logic. + +--- + +## Recommendation + +The recommended direction is: + +1. Make **dataclasses** the public schema declaration mechanism for `CTable`. +2. Introduce **lowercase schema spec objects** such as `b2.int64(...)`. +3. Use **`b2.field(...)`** to carry both the schema spec and per-column NDArray + configuration. +4. Compile the schema once into an internal representation. +5. Use **Pydantic internally for row validation**, but keep it hidden behind the + Blosc2 schema API. +6. Add a separate **bulk validation path** for large inserts so `extend()` does + not depend entirely on per-row Pydantic validation. + +This design gives the project: + +* a cleaner user API +* a better place for columnar storage configuration +* a clear boundary between schema, validation, and storage +* flexibility to evolve validation internals later +* a strong base for future persistence and schema introspection From 725c28bfe9d0b613c1de1d324d1793f1e5e3e46e Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Thu, 26 Mar 2026 11:25:16 +0100 Subject: [PATCH 03/49] Add a pydantic as a new dependency --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 6244b0d9..36f42bfa 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -37,6 +37,7 @@ dependencies = [ "ndindex", "msgpack", "numexpr>=2.14.1; platform_machine != 'wasm32'", + "pydantic", "requests", ] version = "4.1.1.dev0" From 0efd45049d7314452475468420e4ae47ede5e54c Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Thu, 26 Mar 2026 11:35:43 +0100 Subject: [PATCH 04/49] Fix small formatting issues --- bench/ctable/extend.py | 1 - src/blosc2/__init__.py | 2 +- src/blosc2/ctable.py | 15 +++++++-------- tests/ctable/test_column.py | 7 ++++--- 4 files changed, 12 insertions(+), 13 deletions(-) diff --git a/bench/ctable/extend.py b/bench/ctable/extend.py index f294b012..b691c03e 100644 --- a/bench/ctable/extend.py +++ b/bench/ctable/extend.py @@ -114,4 +114,3 @@ class RowModel(BaseModel): print(f"{'Python list of lists':<30} {t_from_list:>12.4f} {'1.00x':>18}") print(f"{'NumPy structured array':<30} {t_from_np:>12.4f} {t_from_list / t_from_np:>17.2f}x") print(f"{'Existing CTable':<30} {t_from_ctable:>12.4f} {t_from_list / t_from_ctable:>17.2f}x") - diff --git a/src/blosc2/__init__.py b/src/blosc2/__init__.py index 8afc0653..11ae5677 100644 --- a/src/blosc2/__init__.py +++ b/src/blosc2/__init__.py @@ -596,7 +596,7 @@ def _raise(exc): """ # Delayed imports for avoiding overwriting of python builtins -from .ctable import CTable, Column +from .ctable import Column, CTable from .ndarray import ( abs, acos, diff --git a/src/blosc2/ctable.py b/src/blosc2/ctable.py index 3acb1731..9af63267 100644 --- a/src/blosc2/ctable.py +++ b/src/blosc2/ctable.py @@ -200,8 +200,6 @@ def __getitem__(self, key: int | slice | list | np.ndarray): pos_true = _find_physical_index(self._valid_rows, key) return self._raw_col[int(pos_true)] - - elif isinstance(key, slice): real_pos = blosc2.where(self._valid_rows, np.arange(len(self._valid_rows))).compute() start, stop, step = key.indices(len(real_pos)) @@ -209,14 +207,13 @@ def __getitem__(self, key: int | slice | list | np.ndarray): if step == 1: phys_start = real_pos[start] phys_stop = real_pos[stop - 1] - mask[phys_start: phys_stop + 1] = True + mask[phys_start : phys_stop + 1] = True else: lindices = np.arange(start, stop, step) phys_indices = real_pos[lindices] mask[phys_indices[:]] = True return Column(self._table, self._col_name, mask=mask) - elif isinstance(key, (list, tuple, np.ndarray)): real_pos = blosc2.where(self._valid_rows, np.arange(len(self._valid_rows))).compute() phys_indices = np.array([real_pos[i] for i in key], dtype=np.int64) @@ -264,11 +261,11 @@ def __iter__(self): val = np.frombuffer(info.repeated_value, dtype=arr.dtype)[0] if not val: continue - yield from self._raw_col[chunk_start: chunk_start + actual_size] + yield from self._raw_col[chunk_start : chunk_start + actual_size] continue - mask_chunk = arr[chunk_start: chunk_start + actual_size] - data_chunk = self._raw_col[chunk_start: chunk_start + actual_size] + mask_chunk = arr[chunk_start : chunk_start + actual_size] + data_chunk = self._raw_col[chunk_start : chunk_start + actual_size] yield from data_chunk[mask_chunk] def __len__(self): @@ -308,7 +305,9 @@ def to_numpy(self): class CTable(Generic[RowT]): - def __init__(self, row_type: type[RowT], new_data=None, expected_size: int = 1_048_576, compact: bool = False) -> None: + def __init__( + self, row_type: type[RowT], new_data=None, expected_size: int = 1_048_576, compact: bool = False + ) -> None: self._row_type = row_type self._cols: dict[str, blosc2.NDArray] = {} self._n_rows: int = 0 diff --git a/tests/ctable/test_column.py b/tests/ctable/test_column.py index 4f2e450b..60f15a5d 100644 --- a/tests/ctable/test_column.py +++ b/tests/ctable/test_column.py @@ -10,8 +10,8 @@ import numpy as np import pytest from pydantic import BaseModel, Field -import blosc2 +import blosc2 from blosc2 import CTable @@ -171,12 +171,14 @@ def test_column_iter(): tabla3 = CTable(RowModel, new_data=DATA20) tabla3.delete([0, 5, 10, 15]) + # fmt: off expected_score = [ 10.0, 20.0, 30.0, 40.0, 60.0, 70.0, 80.0, 90.0, 110.0, 120.0, 130.0, 140.0, 160.0, 170.0, 180.0, 190.0, ] + # fmt: on assert list(tabla3.score) == expected_score @@ -264,7 +266,7 @@ def test_to_array_full_column(): col = tabla.id expected = np.array([i for i in range(20) if i not in {0, 10, 19}], dtype=np.int64) - np.testing.assert_array_equal(col[0:len(col)].to_numpy(), expected) + np.testing.assert_array_equal(col[0 : len(col)].to_numpy(), expected) def test_to_array_mask_does_not_include_deleted(): @@ -289,6 +291,5 @@ def test_column_view_mask_is_independent(): np.testing.assert_array_equal(view_a.to_numpy(), np.arange(0, 5, dtype=np.int64)) - if __name__ == "__main__": pytest.main(["-v", __file__]) From f504ad0670e3afca2213f4f465185d260a88ae8c Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Thu, 26 Mar 2026 12:01:38 +0100 Subject: [PATCH 05/49] Simplify the plan for ctable schema --- plans/ctable-schema.md | 52 ++++++------------------------------------ 1 file changed, 7 insertions(+), 45 deletions(-) diff --git a/plans/ctable-schema.md b/plans/ctable-schema.md index bed1c6a7..cacac6d8 100644 --- a/plans/ctable-schema.md +++ b/plans/ctable-schema.md @@ -122,14 +122,10 @@ Expected shape: b2.field( b2.float64(ge=0, le=100), default=..., - default_factory=..., cparams=..., dparams=..., chunks=..., blocks=..., - title=..., - description=..., - nullable=..., ) ``` @@ -137,7 +133,6 @@ At minimum for the first version: * `spec` * `default` -* `default_factory` * `cparams` * `dparams` * `chunks` @@ -388,7 +383,6 @@ b2.bool() Internal common fields: * `dtype` -* `nullable` * `constraints` * `python_type` @@ -480,8 +474,6 @@ For each field, produce a `CompiledColumn` object containing: * `spec` * `dtype` * `default` -* `default_factory` -* `nullable` * `cparams` * `dparams` * `chunks` @@ -538,19 +530,16 @@ Examples: ```python active: bool = b2.field(b2.bool(), default=True) -tags: list[str] = b2.field(..., default_factory=list) ``` For the first implementation, keep this conservative: * support scalar defaults -* support `default_factory` only if there is a clear use case * reject mutable defaults directly On insert: * omitted values should be filled from defaults -* explicit `None` should be accepted only if the field is nullable --- @@ -721,19 +710,7 @@ In other words: * `id = b2.field(b2.int64(ge=0))` is not the preferred style because it drops the Python annotation -### 2. Where should nullability live? - -Recommended answer: on the schema spec. - -Example: - -```python -name: str | None = b2.field(b2.string(max_length=32, nullable=True)) -``` - -The Python annotation and schema spec should agree. - -### 3. Should `b2.field()` require a spec? +### 2. Should `b2.field()` require a spec? Recommended answer: yes for the first version. @@ -748,7 +725,7 @@ active: bool = True but once `b2.field(...)` is used, it should carry an explicit schema spec. -### 4. How much should Pydantic-specific behavior leak? +### 3. How much should Pydantic-specific behavior leak? Recommended answer: as little as possible. @@ -785,49 +762,39 @@ Proposed public classes and functions: class SchemaSpec: dtype: np.dtype python_type: type[Any] - nullable: bool def to_pydantic_kwargs(self) -> dict[str, Any]: ... def to_metadata_dict(self) -> dict[str, Any]: ... class int64(SchemaSpec): - def __init__( - self, *, ge=None, gt=None, le=None, lt=None, nullable: bool = False - ): ... + def __init__(self, *, ge=None, gt=None, le=None, lt=None): ... class float64(SchemaSpec): - def __init__( - self, *, ge=None, gt=None, le=None, lt=None, nullable: bool = False - ): ... + def __init__(self, *, ge=None, gt=None, le=None, lt=None): ... class bool(SchemaSpec): - def __init__(self, *, nullable: bool = False): ... + def __init__(self): ... class string(SchemaSpec): - def __init__( - self, *, min_length=None, max_length=None, pattern=None, nullable: bool = False - ): ... + def __init__(self, *, min_length=None, max_length=None, pattern=None): ... class bytes(SchemaSpec): - def __init__(self, *, min_length=None, max_length=None, nullable: bool = False): ... + def __init__(self, *, min_length=None, max_length=None): ... def field( spec: SchemaSpec, *, default=MISSING, - default_factory=MISSING, cparams: dict[str, Any] | None = None, dparams: dict[str, Any] | None = None, chunks: tuple[int, ...] | None = None, blocks: tuple[int, ...] | None = None, - title: str | None = None, - description: str | None = None, ) -> DataclassField: ... ``` @@ -863,8 +830,6 @@ class ColumnConfig: dparams: dict[str, Any] | None chunks: tuple[int, ...] | None blocks: tuple[int, ...] | None - title: str | None - description: str | None @dataclass(slots=True) @@ -874,7 +839,6 @@ class CompiledColumn: spec: Any dtype: np.dtype default: Any - default_factory: Any config: ColumnConfig @@ -1095,7 +1059,6 @@ Initial checks to support: * numeric `ge`, `gt`, `le`, `lt` * string and bytes `min_length`, `max_length` -* nullability * dtype compatibility after coercion This module should remain optional in the first PR if the rowwise path is enough @@ -1154,7 +1117,6 @@ Test scope by file: * Pydantic validator generation * constraint enforcement -* nullable vs non-nullable behavior `tests/ctable/test_ctable_dataclass_schema.py` From 46bf2e310e12a5fea0b554f644b8e3263d156226 Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Thu, 26 Mar 2026 12:05:10 +0100 Subject: [PATCH 06/49] Disable wheel generation for each commit in this branch --- .github/workflows/cibuildwheels.yml | 4 ++-- .github/workflows/wasm.yml | 1 + 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/.github/workflows/cibuildwheels.yml b/.github/workflows/cibuildwheels.yml index ed69f764..f7e2bef0 100644 --- a/.github/workflows/cibuildwheels.yml +++ b/.github/workflows/cibuildwheels.yml @@ -26,6 +26,7 @@ env: jobs: build_wheels: + if: ${{ github.ref_name != 'ctable3' && github.head_ref != 'ctable3' }} name: Build wheels on ${{ matrix.os }} for ${{ matrix.arch }} runs-on: ${{ matrix.runs-on || matrix.os }} permissions: @@ -128,10 +129,9 @@ jobs: upload_pypi: + if: ${{ (github.ref_name != 'ctable3' && github.head_ref != 'ctable3') && startsWith(github.event.ref, 'refs/tags') }} needs: [ build_wheels] runs-on: ubuntu-latest - # Only upload wheels when tagging (typically a release) - if: startsWith(github.event.ref, 'refs/tags') steps: - uses: actions/download-artifact@v8 with: diff --git a/.github/workflows/wasm.yml b/.github/workflows/wasm.yml index f54afad5..3d293274 100644 --- a/.github/workflows/wasm.yml +++ b/.github/workflows/wasm.yml @@ -14,6 +14,7 @@ env: jobs: build_wheels_wasm: + if: ${{ github.ref_name != 'ctable3' && github.head_ref != 'ctable3' }} name: Build and test wheels for WASM on ${{ matrix.os }} for ${{ matrix.p_ver }} runs-on: ubuntu-latest permissions: From 43bf562727d727acd6d120a1ff7139043d99d83f Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Thu, 26 Mar 2026 13:29:57 +0100 Subject: [PATCH 07/49] Add a new plan on CTable persistence --- plans/ctable-persistency.md | 536 ++++++++++++++++++++++++++++++++++++ plans/ctable-schema.md | 29 +- 2 files changed, 564 insertions(+), 1 deletion(-) create mode 100644 plans/ctable-persistency.md diff --git a/plans/ctable-persistency.md b/plans/ctable-persistency.md new file mode 100644 index 00000000..a2ff6db2 --- /dev/null +++ b/plans/ctable-persistency.md @@ -0,0 +1,536 @@ +# CTable Persistency Plan + +## Goal + +Add persistent `CTable` support on top of `TreeStore` while keeping the public +API simple: + +* in-memory tables when `urlpath is None` +* persistent tables when `urlpath` is provided + +The first persistency iteration should support: + +* creating a persistent table +* opening an existing persistent table +* reading rows, columns, and views from persisted tables +* appending rows + +The first persistency iteration should **not** promise: + +* full schema evolution +* dropping columns +* renaming columns +* transactional multi-entry updates + +For now, the supported schema evolution story is: + +* append rows only + +--- + +## Storage layout + +Each persisted `CTable` lives under a table root inside a `TreeStore`. + +Confirmed layout: + +* `table_root/_meta` +* `table_root/_valid_rows` +* `table_root/_cols/` + +Example: + +* `people/_meta` +* `people/_valid_rows` +* `people/_cols/id` +* `people/_cols/score` +* `people/_cols/active` + +Rationale: + +* `_meta` holds mutable metadata in `vlmeta` +* `_valid_rows` is real table data and should be stored as a normal persisted array +* `_cols/` stores one persisted NDArray per column + +The underscore-prefixed names form the internal namespace for a table root and +must be treated as reserved. + +--- + +## `_meta` entry + +`_meta` should be a small serialized `SChunk` used primarily to hold mutable +`vlmeta`. + +This is preferable to immutable metalayers because: + +* we may want to evolve metadata over time +* multiple `CTable` objects may live in the same `TreeStore` +* schema and table metadata should be updateable without rewriting the entire table + +For the first version: + +* `tree_store["/_meta"].vlmeta["kind"] = "ctable"` +* `tree_store["/_meta"].vlmeta["version"] = 1` +* `tree_store["/_meta"].vlmeta["schema"] = {...}` + +This gives `open()` a minimal, reliable contract for introspection. + +--- + +## Schema persistence format + +The schema should be stored as JSON-compatible data in: + +* `tree_store["/_meta"].vlmeta["schema"]` + +The schema document should be versioned and explicit. + +Recommended shape: + +```python +{ + "version": 1, + "columns": [ + { + "name": "id", + "py_type": "int", + "spec": {"kind": "int64", "ge": 0}, + "default": None, + }, + { + "name": "score", + "py_type": "float", + "spec": {"kind": "float64", "ge": 0, "le": 100}, + "default": None, + }, + { + "name": "active", + "py_type": "bool", + "spec": {"kind": "bool"}, + "default": True, + }, + ], +} +``` + +Notes: + +* `columns` must be an ordered list, not a dict. +* The order of the list is the source of truth for column order. +* Do not rely on dict ordering or TreeStore iteration order. +* The schema JSON should capture logical schema information only. + +For the first version, do **not** duplicate: + +* per-column `cparams` +* per-column `dparams` +* array chunk/block layout +* `expected_size` +* compaction settings + +Those can be introspected directly from the stored arrays when needed. + +--- + +## `_valid_rows` persistence + +`_valid_rows` should be stored as a normal persisted boolean NDArray under: + +* `table_root/_valid_rows` + +This is the correct representation because `_valid_rows` is: + +* table data, not metadata +* potentially large +* used in normal row visibility semantics +* already aligned with current delete/view/compaction logic + +Do not encode `_valid_rows` into schema JSON or small metadata blobs. + +--- + +## Column persistence + +Each column should be stored as its own persisted NDArray under: + +* `table_root/_cols/` + +This means: + +* each column can be opened independently +* column-level array settings remain attached to the actual stored array +* persistence layout matches the internal columnar design cleanly + +The schema JSON provides the logical order and type constraints; the arrays under +`_cols` provide the physical stored data. + +--- + +## Constructor semantics + +The recommended constructor shape is: + +```python +table = b2.CTable( + Row, + urlpath=None, + mode="a", + expected_size=1_048_576, + compact=False, + validate=True, +) +``` + +Semantics: + +* `urlpath is None` + create an in-memory `CTable` +* `urlpath is not None` + use persistent storage rooted at that path + +Recommended `mode` meanings: + +* `mode="w"` + create a new persistent table, overwriting any existing table root if the API + already supports that pattern elsewhere +* `mode="a"` + open existing or create new +* `mode="r"` + open existing read-only table + +The important public signal is: + +* `urlpath` chooses persistence +* `mode` chooses creation/open behavior + +Users should not need to pass a `TreeStore` object explicitly for the common path. + +--- + +## `open()` support + +An explicit `open()` API should be supported. + +Recommended shape: + +```python +table = b2.open(urlpath) +``` + +or, if needed for clarity: + +```python +table = b2.CTable.open(urlpath, mode="r") +``` + +For `open()` to detect a persisted `CTable`, it should inspect: + +* `urlpath/_meta` +* `urlpath/_meta`.vlmeta["kind"] + +If: + +* `_meta` exists +* `vlmeta["kind"] == "ctable"` + +then the object should be recognized as a persisted `CTable`. + +This keeps `urlpath` simple: it points to the table root, and `_meta` provides +the type marker and schema. + +--- + +## Multiple tables in one TreeStore + +The design must support multiple `CTable` objects in the same `TreeStore`. + +That is one reason `_meta` is a good choice: + +* each table root has its own `_meta` +* each table root can be introspected independently +* schema metadata is naturally scoped to one table subtree + +Example shared TreeStore: + +* `users/_meta` +* `users/_valid_rows` +* `users/_cols/id` +* `orders/_meta` +* `orders/_valid_rows` +* `orders/_cols/order_id` + +No additional global registry is required in the first version. + +--- + +## Column name validation + +Column name validation should be explicit and should be shared between: + +* in-memory `CTable` +* persistent `CTable` + +Reason: + +* a schema should not be valid in memory and then fail only when persisted + +Recommended first-rule constraints for column names: + +* must be a non-empty string +* must not contain `/` +* must not start with `_` +* must not collide with reserved internal names + +Reserved internal names for the table root layout: + +* `_meta` +* `_valid_rows` +* `_cols` + +This validation should happen during schema compilation, not only during +persistent-table creation. + +--- + +## Column order + +Column order should be preserved explicitly in the schema JSON. + +The source of truth is: + +* the order of `schema["columns"]` + +Do not rely on: + +* dict ordering as a persistence contract +* lexical ordering of `_cols/` +* TreeStore iteration order + +On load: + +* reconstruct `table.col_names` from the schema list order +* rebuild any name-to-column map separately + +--- + +## Read-only mode + +When `mode="r"`: + +Allowed: + +* opening the table +* reading rows +* reading columns +* creating non-mutating views +* `head()`, `tail()`, filtering, and other read-only operations + +Disallowed: + +* `append()` +* `delete()` +* `compact()` +* any operation that mutates stored arrays or metadata + +These should fail immediately with a clear error. + +If some existing view path currently requires mutation internally, that should be +cleaned up rather than weakening the read-only contract. + +--- + +## Failure model + +The first persistency version does not need full transactional semantics. + +Be explicit in the implementation and docs: + +* updates touching multiple entries are not guaranteed to be atomic +* partial writes are possible if a failure occurs mid-update + +That is acceptable for the first version as long as it is not hidden. + +The initial goal is a correct and understandable persistent layout, not a full +transaction layer. + +--- + +## Internal API sketch + +This is a proposed internal storage split, not a final public API requirement. + +Possible internal helpers: + +```python +class TableStorage: + def open_column(self, name: str): ... + def create_column( + self, + name: str, + *, + dtype, + shape, + chunks=None, + blocks=None, + cparams=None, + dparams=None + ): ... + def open_valid_rows(self): ... + def create_valid_rows( + self, *, shape, chunks=None, blocks=None, cparams=None, dparams=None + ): ... + def load_schema(self) -> dict: ... + def save_schema(self, schema: dict) -> None: ... + def exists(self) -> bool: ... + def is_read_only(self) -> bool: ... + + +class InMemoryTableStorage(TableStorage): ... + + +class TreeStoreTableStorage(TableStorage): ... +``` + +Then `CTable` can route based on `urlpath`: + +* `urlpath is None` -> `InMemoryTableStorage` +* `urlpath is not None` -> `TreeStoreTableStorage` + +This keeps persistence a backend concern instead of scattering TreeStore logic +throughout all of `CTable`. + +--- + +## Concrete implementation sequence + +### Step 1: extend constructor/open signatures + +Update `src/blosc2/ctable.py` to accept: + +```python +class CTable: + def __init__( + self, + row_type, + new_data=None, + *, + urlpath: str | None = None, + mode: str = "a", + expected_size: int = 1_048_576, + compact: bool = False, + validate: bool = True, + ) -> None: ... +``` + +And add: + +```python +@classmethod +def open(cls, urlpath: str, *, mode: str = "r") -> "CTable": ... +``` + +### Step 2: add storage backend abstraction + +Create a new module: + +* `src/blosc2/ctable_storage.py` + +Add: + +* `TableStorage` +* `InMemoryTableStorage` +* `TreeStoreTableStorage` + +### Step 3: implement TreeStore layout helpers + +In `TreeStoreTableStorage`, add helpers for: + +* `_meta` path +* `_valid_rows` path +* `_cols/` paths +* reading/writing `vlmeta["kind"]` +* reading/writing `vlmeta["version"]` +* reading/writing `vlmeta["schema"]` + +### Step 4: persist schema JSON + +Connect compiled schema export/import to `_meta.vlmeta["schema"]`. + +The schema compiler work should provide: + +```python +def schema_to_dict(schema: CompiledSchema) -> dict: ... +def schema_from_dict(data: dict) -> CompiledSchema: ... +``` + +### Step 5: create/open persistent arrays + +Wire `CTable` initialization so that: + +* create path creates `_meta`, `_valid_rows`, and `_cols/` +* open path loads schema first, then opens `_valid_rows` and columns + +### Step 6: enforce read-only behavior + +Add an internal read-only flag so mutating methods fail early when opened with +`mode="r"`. + +Methods to guard first: + +* `append` +* `extend` +* `delete` +* `compact` + +### Step 7: test persistency layout and round-trips + +Add tests covering: + +* create persistent `CTable` +* reopen persistent `CTable` +* schema JSON present in `_meta.vlmeta` +* `_valid_rows` persisted correctly +* column order preserved after reopen +* multiple tables inside one TreeStore +* read-only mode errors on mutation + +--- + +## Proposed tests + +Suggested test file: + +* `tests/ctable/test_persistency.py` + +Suggested test cases: + +* `test_create_persistent_ctable_layout` +* `test_open_persistent_ctable` +* `test_schema_saved_in_meta_vlmeta` +* `test_valid_rows_persisted` +* `test_column_order_roundtrip` +* `test_multiple_ctables_in_same_treestore` +* `test_read_only_mode_rejects_mutation` + +--- + +## Recommendation + +The recommended persistency design is: + +1. use `urlpath` to switch between in-memory and persistent `CTable` +2. store one table per TreeStore subtree +3. use: + * `_meta` + * `_valid_rows` + * `_cols/` +4. store schema JSON in `_meta.vlmeta["schema"]` +5. store explicit markers in `_meta.vlmeta`: + * `"kind": "ctable"` + * `"version": 1` +6. preserve column order in the schema JSON as an ordered `columns` list +7. keep the first version limited to append-row persistence, not full schema evolution + +This gives `CTable` a clear persistent layout, keeps `open()` introspection +simple, and stays consistent with the existing columnar design. diff --git a/plans/ctable-schema.md b/plans/ctable-schema.md index cacac6d8..d9cd3fb1 100644 --- a/plans/ctable-schema.md +++ b/plans/ctable-schema.md @@ -1155,6 +1155,17 @@ def schema_from_dict(data: dict[str, Any]) -> CompiledSchema: ... This should remain internal until the persisted format is stable. +The persistency design itself is specified in: + +* [ctable-persistency.md](/Users/faltet/blosc/python-blosc2/plans/ctable-persistency.md) + +The schema-layer contract for persistency is: + +* schema must serialize to a versioned JSON-compatible dict +* column order must be preserved explicitly in the serialized `columns` list +* the serialized schema must be sufficient to reconstruct `CompiledSchema` + without requiring the original Python dataclass definition at load time + ### Step 13: delivery order across PRs Recommended PR slicing: @@ -1182,9 +1193,18 @@ PR 3: PR 4: -* persistence groundwork +* persistence groundwork on the schema side * optional compatibility adapter for legacy Pydantic model declarations +PR 5: + +* TreeStore-backed persistency as described in + [ctable-persistency.md](/Users/faltet/blosc/python-blosc2/plans/ctable-persistency.md) +* `urlpath` / `mode` constructor semantics +* explicit `open()` support +* `_meta`, `_valid_rows`, `_cols/` storage layout +* persistency tests + ### Step 14: concrete first-PR checklist The smallest coherent first implementation should be: @@ -1206,6 +1226,13 @@ That first PR gives the project: before touching too much `CTable` mutation logic. +After that first PR lands, follow the later phases in this order: + +1. dataclass-driven `CTable` construction and append path +2. validation and batch-insert behavior +3. schema introspection +4. TreeStore-backed persistency + --- ## Recommendation From e84f7ac6a6ec8dd53252b1833a9e574e51830c3b Mon Sep 17 00:00:00 2001 From: jorge Date: Thu, 26 Mar 2026 13:29:11 +0100 Subject: [PATCH 08/49] _ --- src/blosc2/ctable.py | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/src/blosc2/ctable.py b/src/blosc2/ctable.py index 9af63267..ff5ab440 100644 --- a/src/blosc2/ctable.py +++ b/src/blosc2/ctable.py @@ -78,6 +78,23 @@ def __getitem__(self, item): return self._table._run_row_logic(item) +class _Row: + def __init__(self, table: CTable, nrow: int): + self._table = table + self._nrow = nrow + self._real_pos = None + + def _get_real_pos(self) -> int: + self._real_pos = _find_physical_index(self._table._valid_rows, self._nrow) + return self._real_pos + + def __getitem__(self, col_name: str): + if self._real_pos is None: + self._get_real_pos() + return self._table._cols[col_name][self._real_pos] + + + def _resolve_field_dtype(field) -> tuple[np.dtype, int]: """Return (numpy dtype, display_width) for a pydantic model field. @@ -380,6 +397,10 @@ def __str__(self): def __len__(self): return self._n_rows + def __iter__(self): + for i in range(self.nrows): + yield _Row(self, i) + def view(self, new_valid_rows): if not ( isinstance(new_valid_rows, (blosc2.NDArray, blosc2.LazyExpr)) From 8de1870ba1cc926065898495446b59e431214bea Mon Sep 17 00:00:00 2001 From: jorge Date: Thu, 26 Mar 2026 13:53:16 +0100 Subject: [PATCH 09/49] _ --- src/blosc2/ctable.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/blosc2/ctable.py b/src/blosc2/ctable.py index ff5ab440..ddd2719c 100644 --- a/src/blosc2/ctable.py +++ b/src/blosc2/ctable.py @@ -94,7 +94,6 @@ def __getitem__(self, col_name: str): return self._table._cols[col_name][self._real_pos] - def _resolve_field_dtype(field) -> tuple[np.dtype, int]: """Return (numpy dtype, display_width) for a pydantic model field. @@ -205,6 +204,7 @@ def _raw_col(self): def _valid_rows(self): if self._mask is None: return self._table._valid_rows + return (self._table._valid_rows & self._mask).compute() def __getitem__(self, key: int | slice | list | np.ndarray): From a8db18d17c9334f278a5a0bd9ebbed71aed1f1b3 Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Thu, 26 Mar 2026 13:55:00 +0100 Subject: [PATCH 10/49] Testing --- test-remove.md | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 test-remove.md diff --git a/test-remove.md b/test-remove.md new file mode 100644 index 00000000..e69de29b From ce656072d699dcb0434d3d7c2cd72cd347f6bf9d Mon Sep 17 00:00:00 2001 From: jorge Date: Thu, 26 Mar 2026 13:57:06 +0100 Subject: [PATCH 11/49] writen test --- test-remove.md | 1 + 1 file changed, 1 insertion(+) diff --git a/test-remove.md b/test-remove.md index e69de29b..038d718d 100644 --- a/test-remove.md +++ b/test-remove.md @@ -0,0 +1 @@ +testing From b623f0e9eb22b9bc4c40c03507fe3456c2cdddf7 Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Thu, 26 Mar 2026 13:59:42 +0100 Subject: [PATCH 12/49] Remove testing file --- test-remove.md | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 test-remove.md diff --git a/test-remove.md b/test-remove.md deleted file mode 100644 index e69de29b..00000000 From 4ce8296049d5b3bb6cc2b5c3bc345bbd56bfe9bf Mon Sep 17 00:00:00 2001 From: jorge Date: Sat, 4 Apr 2026 17:08:32 +0200 Subject: [PATCH 13/49] =?UTF-8?q?Schema=20layer:=20=20=20-=20Add=20schema.?= =?UTF-8?q?py=20with=20spec=20primitives:=20int8/16/32/64,=20uint8/16/32/6?= =?UTF-8?q?4,=20=20=20=20=20float32/64,=20bool,=20complex64/128,=20string,?= =?UTF-8?q?=20bytes=20=E2=80=94=20sharing=20a=20=5FNumericSpec=20=20=20=20?= =?UTF-8?q?=20mixin=20to=20avoid=20boilerplate=20=20=20-=20Add=20schema=5F?= =?UTF-8?q?compiler.py:=20compile=5Fschema(),=20CompiledSchema/Column/Conf?= =?UTF-8?q?ig,=20=20=20=20=20schema=5Fto=5Fdict()=20/=20schema=5Ffrom=5Fdi?= =?UTF-8?q?ct()=20for=20persistence=20groundwork=20=20=20-=20Export=20all?= =?UTF-8?q?=20spec=20types=20and=20field()=20from=20blosc2=20namespace?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Validation: - Add schema_validation.py: Pydantic-backed row validation for append(), cached per schema, re-raised as plain ValueError - Add schema_vectorized.py: vectorized NumPy constraint checks for extend(), using np.char.str_len() for string/bytes columns - validate= per-call override on extend() (None inherits table default) CTable refactor: - Constructor accepts dataclass schemas; legacy Pydantic adapter kept - Schema introspection: table.schema, column_schema(), schema_dict() - _last_pos cache eliminates backward chunk scan on every append/extend - _grow() shared resize helper; delete() writes back in-place without creating a new array; _n_rows updated by subtraction not count_nonzero - head() and tail() unified through _find_physical_index() Tests and docs: - 135 tests across 10 test files, all passing - plans/ctable-implementation-log.md and ctable-user-guide.md added - Benchmarks: bench_validation.py and bench_append_regression.py --- bench/ctable/Prueba_iter.py | 99 +++ bench/ctable/bench_append_regression.py | 117 ++++ bench/ctable/bench_validation.py | 129 ++++ bench/ctable/compact.py | 22 +- bench/ctable/ctable_v_panda.py | 23 +- bench/ctable/delete.py | 22 +- bench/ctable/expected_size.py | 24 +- bench/ctable/extend.py | 31 +- bench/ctable/extend_vs_apend.py | 26 +- bench/ctable/index.py | 22 +- bench/ctable/iteration_column.py | 22 +- bench/ctable/print.py | 21 +- bench/ctable/row_acces.py | 22 +- bench/ctable/slice.py | 22 +- bench/ctable/slice_steps.py | 22 +- bench/ctable/slice_to_array.py | 22 +- bench/ctable/speed_iter.py | 43 ++ bench/ctable/where_chain.py | 22 +- bench/ctable/where_selective.py | 22 +- plans/ctable-implementation-log.md | 225 +++++++ plans/ctable-user-guide.md | 378 +++++++++++ src/blosc2/__init__.py | 50 +- src/blosc2/ctable.py | 644 +++++++++++-------- src/blosc2/schema.py | 348 ++++++++++ src/blosc2/schema_compiler.py | 409 ++++++++++++ src/blosc2/schema_validation.py | 103 +++ src/blosc2/schema_vectorized.py | 112 ++++ tests/ctable/test_column.py | 59 +- tests/ctable/test_compact.py | 29 +- tests/ctable/test_construct.py | 78 +-- tests/ctable/test_ctable_dataclass_schema.py | 289 +++++++++ tests/ctable/test_delete_rows.py | 71 +- tests/ctable/test_extend_delete.py | 54 +- tests/ctable/test_row_logic.py | 36 +- tests/ctable/test_schema_compiler.py | 258 ++++++++ tests/ctable/test_schema_specs.py | 343 ++++++++++ tests/ctable/test_schema_validation.py | 164 +++++ 37 files changed, 3701 insertions(+), 682 deletions(-) create mode 100644 bench/ctable/Prueba_iter.py create mode 100644 bench/ctable/bench_append_regression.py create mode 100644 bench/ctable/bench_validation.py create mode 100644 bench/ctable/speed_iter.py create mode 100644 plans/ctable-implementation-log.md create mode 100644 plans/ctable-user-guide.md create mode 100644 src/blosc2/schema.py create mode 100644 src/blosc2/schema_compiler.py create mode 100644 src/blosc2/schema_validation.py create mode 100644 src/blosc2/schema_vectorized.py create mode 100644 tests/ctable/test_ctable_dataclass_schema.py create mode 100644 tests/ctable/test_schema_compiler.py create mode 100644 tests/ctable/test_schema_specs.py create mode 100644 tests/ctable/test_schema_validation.py diff --git a/bench/ctable/Prueba_iter.py b/bench/ctable/Prueba_iter.py new file mode 100644 index 00000000..2f21646e --- /dev/null +++ b/bench/ctable/Prueba_iter.py @@ -0,0 +1,99 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +from dataclasses import dataclass + +import numpy as np +from time import time + +from blosc2 import CTable +import blosc2 + + +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + score: float = blosc2.field(blosc2.float64(ge=0, le=100)) + active: bool = blosc2.field(blosc2.bool(), default=True) + + +N = 1_000 # start small, increase when confident + +data = [(i, float(i % 100), i % 2 == 0) for i in range(N)] +tabla = CTable(Row, new_data=data) + +print(f"Table created with {len(tabla)} rows\n") + +# ------------------------------------------------------------------- +# Test 1: iterate without accessing any column (minimum cost) +# ------------------------------------------------------------------- +t0 = time() +for row in tabla: + pass +t1 = time() +print(f"[Test 1] Iter without accessing columns: {(t1 - t0)*1000:.3f} ms") + +# ------------------------------------------------------------------- +# Test 2: iterate accessing a single column (real_pos cached once) +# ------------------------------------------------------------------- +t0 = time() +for row in tabla: + _ = row["id"] +t1 = time() +print(f"[Test 2] Iter accessing 'id': {(t1 - t0)*1000:.3f} ms") + +# ------------------------------------------------------------------- +# Test 3: iterate accessing all columns (real_pos cached once per row) +# ------------------------------------------------------------------- +t0 = time() +for row in tabla: + _ = row["id"] + _ = row["score"] + _ = row["active"] +t1 = time() +print(f"[Test 3] Iter accessing 3 columns: {(t1 - t0)*1000:.3f} ms") + +# ------------------------------------------------------------------- +# Test 4: correctness — values match expected +# ------------------------------------------------------------------- +errors = 0 +for row in tabla: + if row["id"] != row._nrow: + errors += 1 + if row["score"] != float(row._nrow % 100): + errors += 1 + if row["active"] != (row._nrow % 2 == 0): + errors += 1 + +print(f"\n[Test 4] Correctness errors: {errors} (expected: 0)") + +# ------------------------------------------------------------------- +# Test 5: with holes (deleted rows) +# ------------------------------------------------------------------- +tabla2 = CTable(Row, new_data=data) +tabla2.delete(list(range(0, N, 2))) # delete even rows, keep odd ones + +print(f"\nTable with holes: {len(tabla2)} rows (expected: {N // 2})") + +t0 = time() +ids = [] +for row in tabla2: + ids.append(row["id"]) +t1 = time() + +expected_ids = [i for i in range(N) if i % 2 != 0] +ok = ids == expected_ids +print(f"[Test 5] Iter with holes ({N//2} rows): {(t1 - t0)*1000:.3f} ms | correctness: {ok}") + +# ------------------------------------------------------------------- +# Test 6: real_pos is cached correctly (not recomputed) +# ------------------------------------------------------------------- +row0 = next(iter(tabla)) +assert row0._real_pos is None, "real_pos should be None before first access" +_ = row0["id"] +assert row0._real_pos is not None, "real_pos should be cached after first access" +print(f"\n[Test 6] real_pos caching: OK (real_pos={row0._real_pos})") diff --git a/bench/ctable/bench_append_regression.py b/bench/ctable/bench_append_regression.py new file mode 100644 index 00000000..e88c9d89 --- /dev/null +++ b/bench/ctable/bench_append_regression.py @@ -0,0 +1,117 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +# Benchmark: append() overhead introduced by the new schema pipeline +# +# The new append() path routes every row through: +# _normalize_row_input → validate_row (Pydantic) → _coerce_row_to_storage +# +# This benchmark isolates how much each step costs, and shows the +# total overhead vs the raw NDArray write speed. + +from dataclasses import dataclass +from time import perf_counter + +import numpy as np + +import blosc2 +from blosc2.schema_compiler import compile_schema +from blosc2.schema_validation import validate_row, build_validator_model + + +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + score: float = blosc2.field(blosc2.float64(ge=0, le=100), default=0.0) + active: bool = blosc2.field(blosc2.bool(), default=True) + + +N = 5_000 +rng = np.random.default_rng(42) +data = [ + (int(i), float(rng.uniform(0, 100)), bool(i % 2)) + for i in range(N) +] +schema = compile_schema(Row) +# Warm up the Pydantic model cache +build_validator_model(schema) + +print(f"append() pipeline cost breakdown | N = {N:,} rows") +print("=" * 60) + +# ── 1. Raw NDArray writes (no CTable overhead at all) ──────────────────────── +ids = np.zeros(N, dtype=np.int64) +scores = np.zeros(N, dtype=np.float64) +flags = np.zeros(N, dtype=np.bool_) +mask = np.zeros(N, dtype=np.bool_) + +t0 = perf_counter() +for i, (id_, score, active) in enumerate(data): + ids[i] = id_ + scores[i] = score + flags[i] = active + mask[i] = True +t_raw = perf_counter() - t0 +print(f"{'Raw NumPy writes (baseline)':<40} {t_raw:.4f} s") + +# ── 2. _normalize_row_input only ───────────────────────────────────────────── +t_obj = blosc2.CTable(Row, expected_size=N, validate=False) +t0 = perf_counter() +for row in data: + _ = t_obj._normalize_row_input(row) +t_normalize = perf_counter() - t0 +print(f"{'_normalize_row_input only':<40} {t_normalize:.4f} s ({t_normalize/t_raw:.1f}x baseline)") + +# ── 3. Pydantic validate_row only ──────────────────────────────────────────── +row_dicts = [t_obj._normalize_row_input(row) for row in data] +t0 = perf_counter() +for rd in row_dicts: + _ = validate_row(schema, rd) +t_validate = perf_counter() - t0 +print(f"{'validate_row (Pydantic) only':<40} {t_validate:.4f} s ({t_validate/t_raw:.1f}x baseline)") + +# ── 4. _coerce_row_to_storage only ─────────────────────────────────────────── +t0 = perf_counter() +for rd in row_dicts: + _ = t_obj._coerce_row_to_storage(rd) +t_coerce = perf_counter() - t0 +print(f"{'_coerce_row_to_storage only':<40} {t_coerce:.4f} s ({t_coerce/t_raw:.1f}x baseline)") + +# ── 5. Full append(), validate=False (3 runs, take minimum) ───────────────── +RUNS = 3 +best_off = float("inf") +for _ in range(RUNS): + t_obj2 = blosc2.CTable(Row, expected_size=N, validate=False) + t0 = perf_counter() + for row in data: + t_obj2.append(row) + best_off = min(best_off, perf_counter() - t0) +t_append_off = best_off +print(f"{'Full append(), validate=False':<40} {t_append_off:.4f} s ({t_append_off/t_raw:.1f}x baseline)") + +# ── 6. Full append(), validate=True (3 runs, take minimum) ────────────────── +best_on = float("inf") +for _ in range(RUNS): + t_obj3 = blosc2.CTable(Row, expected_size=N, validate=True) + t0 = perf_counter() + for row in data: + t_obj3.append(row) + best_on = min(best_on, perf_counter() - t0) +t_append_on = best_on +print(f"{'Full append(), validate=True':<40} {t_append_on:.4f} s ({t_append_on/t_raw:.1f}x baseline)") + +print() +print("=" * 60) +pydantic_cost = max(t_append_on - t_append_off, 0.0) +print(f"{'Pydantic overhead in append()':<40} {pydantic_cost:.4f} s") +if t_append_on > 0: + print(f"{'Validation fraction of total':<40} {pydantic_cost/t_append_on*100:.1f}%") +print(f"{'Per-row Pydantic cost (isolated)':<40} {(t_validate/N)*1e6:.2f} µs/row") +print() +print(f"Note: append() is dominated by blosc2 I/O ({t_append_off/t_raw:.0f}x raw numpy),") +print(f" not by the validation pipeline.") +print(f" The main bottleneck is the last_true_pos backward scan per row.") diff --git a/bench/ctable/bench_validation.py b/bench/ctable/bench_validation.py new file mode 100644 index 00000000..1032e7de --- /dev/null +++ b/bench/ctable/bench_validation.py @@ -0,0 +1,129 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +# Benchmark: cost of constraint validation +# +# Measures the overhead of validate=True vs validate=False for: +# 1. append() — row-by-row, Pydantic path +# 2. extend() — bulk insert, vectorized NumPy path +# +# at increasing batch sizes to show how validation cost scales. + +from dataclasses import dataclass +from time import perf_counter + +import numpy as np + +import blosc2 + + +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + score: float = blosc2.field(blosc2.float64(ge=0, le=100), default=0.0) + active: bool = blosc2.field(blosc2.bool(), default=True) + + +def make_data(n: int): + rng = np.random.default_rng(42) + ids = np.arange(n, dtype=np.int64) + scores = rng.uniform(0, 100, n) + flags = rng.integers(0, 2, n, dtype=np.bool_) + return list(zip(ids.tolist(), scores.tolist(), flags.tolist())) + + +SIZES = [100, 1_000, 10_000, 100_000, 1_000_000] +APPEND_SIZES = [100, 1_000] # append row-by-row is slow at large N + +# ───────────────────────────────────────────────────────────────────────────── +# 1. append() — validate=True vs validate=False +# ───────────────────────────────────────────────────────────────────────────── +print("=" * 65) +print("1. append() — row-by-row (Pydantic validation per row)") +print("=" * 65) +print(f"{'N':>10} {'validate=True':>14} {'validate=False':>15} {'overhead':>10}") +print("-" * 65) + +for n in APPEND_SIZES: + data = make_data(n) + + t = blosc2.CTable(Row, expected_size=n, validate=True) + t0 = perf_counter() + for row in data: + t.append(row) + t_on = perf_counter() - t0 + + t = blosc2.CTable(Row, expected_size=n, validate=False) + t0 = perf_counter() + for row in data: + t.append(row) + t_off = perf_counter() - t0 + + overhead = (t_on / t_off) if t_off > 0 else float("inf") + print(f"{n:>10,} {t_on:>13.4f}s {t_off:>14.4f}s {overhead:>9.2f}x") + +# ───────────────────────────────────────────────────────────────────────────── +# 2. extend() — validate=True vs validate=False +# ───────────────────────────────────────────────────────────────────────────── +print() +print("=" * 65) +print("2. extend() — bulk insert (vectorized NumPy validation)") +print("=" * 65) +print(f"{'N':>10} {'validate=True':>14} {'validate=False':>15} {'overhead':>10}") +print("-" * 65) + +for n in SIZES: + data = make_data(n) + + t = blosc2.CTable(Row, expected_size=n, validate=True) + t0 = perf_counter() + t.extend(data) + t_on = perf_counter() - t0 + + t = blosc2.CTable(Row, expected_size=n, validate=False) + t0 = perf_counter() + t.extend(data) + t_off = perf_counter() - t0 + + overhead = (t_on / t_off) if t_off > 0 else float("inf") + print(f"{n:>10,} {t_on:>13.4f}s {t_off:>14.4f}s {overhead:>9.2f}x") + +# ───────────────────────────────────────────────────────────────────────────── +# 3. extend() — validate=True vs validate=False with structured NumPy array +# ───────────────────────────────────────────────────────────────────────────── +print() +print("=" * 65) +print("3. extend() with structured NumPy array") +print("=" * 65) +print(f"{'N':>10} {'validate=True':>14} {'validate=False':>15} {'overhead':>10}") +print("-" * 65) + +np_dtype = np.dtype([("id", np.int64), ("score", np.float64), ("active", np.bool_)]) + +for n in SIZES: + rng = np.random.default_rng(42) + arr = np.empty(n, dtype=np_dtype) + arr["id"] = np.arange(n, dtype=np.int64) + arr["score"] = rng.uniform(0, 100, n) + arr["active"] = rng.integers(0, 2, n, dtype=np.bool_) + + t = blosc2.CTable(Row, expected_size=n, validate=True) + t0 = perf_counter() + t.extend(arr) + t_on = perf_counter() - t0 + + t = blosc2.CTable(Row, expected_size=n, validate=False) + t0 = perf_counter() + t.extend(arr) + t_off = perf_counter() - t0 + + overhead = (t_on / t_off) if t_off > 0 else float("inf") + print(f"{n:>10,} {t_on:>13.4f}s {t_off:>14.4f}s {overhead:>9.2f}x") + +print() +print("Note: 'overhead' = validate=True time / validate=False time.") +print(" 1.00x means validation is free; 2.00x means it doubles the time.") diff --git a/bench/ctable/compact.py b/bench/ctable/compact.py index f41bb008..7e8c085e 100644 --- a/bench/ctable/compact.py +++ b/bench/ctable/compact.py @@ -9,25 +9,19 @@ # of varying fractions of the table. from time import time -from typing import Annotated +from dataclasses import dataclass import numpy as np -from pydantic import BaseModel, Field import blosc2 -class NumpyDtype: - def __init__(self, dtype): - self.dtype = dtype - - -# Row model -class RowModel(BaseModel): - id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) - c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) - score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) - active: Annotated[bool, NumpyDtype(np.bool_)] = True +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + c_val: complex = blosc2.field(blosc2.complex128(), default=0j) + score: float = blosc2.field(blosc2.float64(ge=0, le=100), default=0.0) + active: bool = blosc2.field(blosc2.bool(), default=True) N = 1_000_000 @@ -56,7 +50,7 @@ class RowModel(BaseModel): print("-" * 75) for frac in delete_fractions: - ct = blosc2.CTable(RowModel, expected_size=N) + ct = blosc2.CTable(Row, expected_size=N) ct.extend(DATA) n_delete = int(N * frac) diff --git a/bench/ctable/ctable_v_panda.py b/bench/ctable/ctable_v_panda.py index 4f7d6c8a..9de2d2d6 100644 --- a/bench/ctable/ctable_v_panda.py +++ b/bench/ctable/ctable_v_panda.py @@ -12,26 +12,19 @@ # 4. Row iteration from time import time -from typing import Annotated +from dataclasses import dataclass import numpy as np -import pandas as pd -from pydantic import BaseModel, Field import blosc2 -class NumpyDtype: - def __init__(self, dtype): - self.dtype = dtype - - -# Row model -class RowModel(BaseModel): - id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) - c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) - score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) - active: Annotated[bool, NumpyDtype(np.bool_)] = True +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + c_val: complex = blosc2.field(blosc2.complex128(), default=0j) + score: float = blosc2.field(blosc2.float64(ge=0, le=100), default=0.0) + active: bool = blosc2.field(blosc2.bool(), default=True) N = 1_000_000 @@ -58,7 +51,7 @@ class RowModel(BaseModel): # 1. Creation t0 = time() -ct = blosc2.CTable(RowModel, expected_size=N) +ct = blosc2.CTable(Row, expected_size=N) ct.extend(DATA) t_ct_create = time() - t0 diff --git a/bench/ctable/delete.py b/bench/ctable/delete.py index fb147c7c..256cec97 100644 --- a/bench/ctable/delete.py +++ b/bench/ctable/delete.py @@ -9,25 +9,19 @@ # int, slice, and list — with varying sizes. from time import time -from typing import Annotated +from dataclasses import dataclass import numpy as np -from pydantic import BaseModel, Field import blosc2 -class NumpyDtype: - def __init__(self, dtype): - self.dtype = dtype - - -# Row model -class RowModel(BaseModel): - id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) - c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) - score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) - active: Annotated[bool, NumpyDtype(np.bool_)] = True +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + c_val: complex = blosc2.field(blosc2.complex128(), default=0j) + score: float = blosc2.field(blosc2.float64(ge=0, le=100), default=0.0) + active: bool = blosc2.field(blosc2.bool(), default=True) N = 1_000_000 @@ -64,7 +58,7 @@ class RowModel(BaseModel): print("-" * 60) for label, key in delete_cases: - ct = blosc2.CTable(RowModel, expected_size=N) + ct = blosc2.CTable(Row, expected_size=N) ct.extend(DATA) if isinstance(key, int): diff --git a/bench/ctable/expected_size.py b/bench/ctable/expected_size.py index c4444a62..71d2b2c2 100644 --- a/bench/ctable/expected_size.py +++ b/bench/ctable/expected_size.py @@ -9,25 +9,19 @@ # is too small (M rows) vs correctly sized (N rows) during extend(). from time import time -from typing import Annotated +from dataclasses import dataclass import numpy as np -from pydantic import BaseModel, Field import blosc2 -class NumpyDtype: - def __init__(self, dtype): - self.dtype = dtype - - -# Row model -class RowModel(BaseModel): - id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) - c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) - score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) - active: Annotated[bool, NumpyDtype(np.bool_)] = True +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + c_val: complex = blosc2.field(blosc2.complex128(), default=0j) + score: float = blosc2.field(blosc2.float64(ge=0, le=100), default=0.0) + active: bool = blosc2.field(blosc2.bool(), default=True) @@ -56,14 +50,14 @@ class RowModel(BaseModel): print(f"N = {N:,} rows") # 1. extend() with correct expected_size = N - ct_correct = blosc2.CTable(RowModel, expected_size=N) + ct_correct = blosc2.CTable(Row, expected_size=N) t0 = time() ct_correct.extend(DATA[:N]) t_correct = time() - t0 print(f"extend() expected_size=N ({N:>8,}): {t_correct:.4f} s rows: {len(ct_correct):,}") # 2. extend() with wrong expected_size = M (forces resize) - ct_wrong = blosc2.CTable(RowModel, expected_size=M) + ct_wrong = blosc2.CTable(Row, expected_size=M) t0 = time() ct_wrong.extend(DATA[:N]) t_wrong = time() - t0 diff --git a/bench/ctable/extend.py b/bench/ctable/extend.py index b691c03e..f99e11a8 100644 --- a/bench/ctable/extend.py +++ b/bench/ctable/extend.py @@ -11,30 +11,19 @@ # 3. An existing CTable (previously created from Python lists, 1M rows) from time import time -from typing import Annotated +from dataclasses import dataclass import numpy as np -from pydantic import BaseModel, Field import blosc2 -class NumpyDtype: - def __init__(self, dtype): - self.dtype = dtype - - - - - -# --------------------------------------------------------------------------- -# Row model -# --------------------------------------------------------------------------- -class RowModel(BaseModel): - id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) - c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) - score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) - active: Annotated[bool, NumpyDtype(np.bool_)] = True +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + c_val: complex = blosc2.field(blosc2.complex128(), default=0j) + score: float = blosc2.field(blosc2.float64(ge=0, le=100), default=0.0) + active: bool = blosc2.field(blosc2.bool(), default=True) N = 1_000_000 @@ -75,7 +64,7 @@ class RowModel(BaseModel): # --------------------------------------------------------------------------- print("CTable from Python list of lists") t0 = time() -ct_from_list = blosc2.CTable(RowModel, expected_size=N) +ct_from_list = blosc2.CTable(Row, expected_size=N) ct_from_list.extend(data_list) t_from_list = time() - t0 print(f" extend() time (Python list): {t_from_list:.4f} s") @@ -86,7 +75,7 @@ class RowModel(BaseModel): # --------------------------------------------------------------------------- print("CTable from NumPy structured array") t0 = time() -ct_from_np = blosc2.CTable(RowModel, expected_size=N) +ct_from_np = blosc2.CTable(Row, expected_size=N) ct_from_np.extend(data_np) t_from_np = time() - t0 print(f" extend() time (NumPy struct): {t_from_np:.4f} s") @@ -98,7 +87,7 @@ class RowModel(BaseModel): # --------------------------------------------------------------------------- print("CTable from an existing CTable") t0 = time() -ct_from_ctable = blosc2.CTable(RowModel, expected_size=N) +ct_from_ctable = blosc2.CTable(Row, expected_size=N) ct_from_ctable.extend(ct_from_list) t_from_ctable = time() - t0 print(f" extend() time (CTable): {t_from_ctable:.4f} s") diff --git a/bench/ctable/extend_vs_apend.py b/bench/ctable/extend_vs_apend.py index 2036755c..eff9e426 100644 --- a/bench/ctable/extend_vs_apend.py +++ b/bench/ctable/extend_vs_apend.py @@ -9,25 +9,19 @@ # to find the crossover point where extend() becomes worth it. from time import time -from typing import Annotated +from dataclasses import dataclass import numpy as np -from pydantic import BaseModel, Field import blosc2 -class NumpyDtype: - def __init__(self, dtype): - self.dtype = dtype - - -# Row model -class RowModel(BaseModel): - id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) - c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) - score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) - active: Annotated[bool, NumpyDtype(np.bool_)] = True +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + c_val: complex = blosc2.field(blosc2.complex128(), default=0j) + score: float = blosc2.field(blosc2.float64(ge=0, le=100), default=0.0) + active: bool = blosc2.field(blosc2.bool(), default=True) # Parameter — change N to test different crossover points @@ -45,7 +39,7 @@ class RowModel(BaseModel): # 1. N individual append() calls print(f"{N} individual append() calls") - ct_append = blosc2.CTable(RowModel, expected_size=N) + ct_append = blosc2.CTable(Row, expected_size=N) t0 = time() for row in data_list: ct_append.append(row) @@ -55,7 +49,7 @@ class RowModel(BaseModel): # 2. N individual extend() calls (one row at a time) print(f"{N} individual extend() calls (one row at a time)") - ct_extend_one = blosc2.CTable(RowModel, expected_size=N) + ct_extend_one = blosc2.CTable(Row, expected_size=N) t0 = time() for row in data_list: ct_extend_one.extend([row]) @@ -65,7 +59,7 @@ class RowModel(BaseModel): # 3. Single extend() call with all N rows at once print(f"Single extend() call with all {N} rows at once") - ct_extend_bulk = blosc2.CTable(RowModel, expected_size=N) + ct_extend_bulk = blosc2.CTable(Row, expected_size=N) t0 = time() ct_extend_bulk.extend(data_list) t_extend_bulk = time() - t0 diff --git a/bench/ctable/index.py b/bench/ctable/index.py index 634a68e1..556d1d81 100644 --- a/bench/ctable/index.py +++ b/bench/ctable/index.py @@ -9,25 +9,19 @@ # which exercises _find_physical_index() traversal over chunk metadata. from time import time -from typing import Annotated +from dataclasses import dataclass import numpy as np -from pydantic import BaseModel, Field import blosc2 -class NumpyDtype: - def __init__(self, dtype): - self.dtype = dtype - - -# Row model -class RowModel(BaseModel): - id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) - c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) - score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) - active: Annotated[bool, NumpyDtype(np.bool_)] = True +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + c_val: complex = blosc2.field(blosc2.complex128(), default=0j) + score: float = blosc2.field(blosc2.float64(ge=0, le=100), default=0.0) + active: bool = blosc2.field(blosc2.bool(), default=True) N = 1_000_000 @@ -50,7 +44,7 @@ class RowModel(BaseModel): dtype=np_dtype, ) -ct = blosc2.CTable(RowModel, expected_size=N) +ct = blosc2.CTable(Row, expected_size=N) ct.extend(DATA) print(f"CTable built with {len(ct):,} rows\n") diff --git a/bench/ctable/iteration_column.py b/bench/ctable/iteration_column.py index 5f0efaed..27199319 100644 --- a/bench/ctable/iteration_column.py +++ b/bench/ctable/iteration_column.py @@ -11,25 +11,19 @@ # 3. ct["score"][0:N].to_array() — slice view + to_array() from time import time -from typing import Annotated +from dataclasses import dataclass import numpy as np -from pydantic import BaseModel, Field import blosc2 -class NumpyDtype: - def __init__(self, dtype): - self.dtype = dtype - - -# Row model -class RowModel(BaseModel): - id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) - c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) - score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) - active: Annotated[bool, NumpyDtype(np.bool_)] = True +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + c_val: complex = blosc2.field(blosc2.complex128(), default=0j) + score: float = blosc2.field(blosc2.float64(ge=0, le=100), default=0.0) + active: bool = blosc2.field(blosc2.bool(), default=True) N = 1_000_000 @@ -51,7 +45,7 @@ class RowModel(BaseModel): dtype=np_dtype, ) -ct = blosc2.CTable(RowModel, expected_size=N) +ct = blosc2.CTable(Row, expected_size=N) ct.extend(DATA) print(f"CTable built with {len(ct):,} rows\n") diff --git a/bench/ctable/print.py b/bench/ctable/print.py index af352a2a..bd19df3d 100644 --- a/bench/ctable/print.py +++ b/bench/ctable/print.py @@ -9,23 +9,18 @@ # Data source: randomly generated numpy structured array import time -from typing import Annotated +from dataclasses import dataclass import numpy as np import pandas as pd import blosc2 -from pydantic import BaseModel, Field -class NumpyDtype: - def __init__(self, dtype): - self.dtype = dtype - - -class RowModel(BaseModel): - id: Annotated[int, NumpyDtype(np.int64)] - name: Annotated[str, NumpyDtype(np.dtype(" np.ndarray: data = make_data(N) t0 = time.perf_counter() -ct = blosc2.CTable(RowModel, expected_size=N) +ct = blosc2.CTable(Row, expected_size=N) ct.extend(data) t_blosc = time.perf_counter() - t0 -fields = list(RowModel.model_fields.keys()) +fields = ct.col_names mem_blosc_c = (sum(col.cbytes for col in ct._cols.values()) + ct._valid_rows.cbytes) / (1024 ** 2) mem_blosc_uc = (sum(col.nbytes for col in ct._cols.values()) + ct._valid_rows.nbytes) / (1024 ** 2) diff --git a/bench/ctable/row_acces.py b/bench/ctable/row_acces.py index c44439e0..158cf848 100644 --- a/bench/ctable/row_acces.py +++ b/bench/ctable/row_acces.py @@ -9,25 +9,19 @@ # testing access at different positions across the array. from time import time -from typing import Annotated +from dataclasses import dataclass import numpy as np -from pydantic import BaseModel, Field import blosc2 -class NumpyDtype: - def __init__(self, dtype): - self.dtype = dtype - - -# Row model -class RowModel(BaseModel): - id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) - c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) - score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) - active: Annotated[bool, NumpyDtype(np.bool_)] = True +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + c_val: complex = blosc2.field(blosc2.complex128(), default=0j) + score: float = blosc2.field(blosc2.float64(ge=0, le=100), default=0.0) + active: bool = blosc2.field(blosc2.bool(), default=True) N = 1_000_000 @@ -50,7 +44,7 @@ class RowModel(BaseModel): dtype=np_dtype, ) -ct = blosc2.CTable(RowModel, expected_size=N) +ct = blosc2.CTable(Row, expected_size=N) ct.extend(DATA) print(f"CTable built with {len(ct):,} rows\n") diff --git a/bench/ctable/slice.py b/bench/ctable/slice.py index 4976a8d8..d0ed92cb 100644 --- a/bench/ctable/slice.py +++ b/bench/ctable/slice.py @@ -9,25 +9,19 @@ # sizes and positions: small, large, and middle of the array. from time import time -from typing import Annotated +from dataclasses import dataclass import numpy as np -from pydantic import BaseModel, Field import blosc2 -class NumpyDtype: - def __init__(self, dtype): - self.dtype = dtype - - -# Row model -class RowModel(BaseModel): - id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) - c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) - score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) - active: Annotated[bool, NumpyDtype(np.bool_)] = True +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + c_val: complex = blosc2.field(blosc2.complex128(), default=0j) + score: float = blosc2.field(blosc2.float64(ge=0, le=100), default=0.0) + active: bool = blosc2.field(blosc2.bool(), default=True) N = 1_000_000 @@ -58,7 +52,7 @@ class RowModel(BaseModel): dtype=np_dtype, ) -ct = blosc2.CTable(RowModel, expected_size=N) +ct = blosc2.CTable(Row, expected_size=N) ct.extend(DATA) print(f"CTable built with {len(ct):,} rows\n") diff --git a/bench/ctable/slice_steps.py b/bench/ctable/slice_steps.py index 311b5f9c..f5b05109 100644 --- a/bench/ctable/slice_steps.py +++ b/bench/ctable/slice_steps.py @@ -8,25 +8,19 @@ # Benchmark for measuring Column[::step].to_array() with varying step sizes. from time import time -from typing import Annotated +from dataclasses import dataclass import numpy as np -from pydantic import BaseModel, Field import blosc2 -class NumpyDtype: - def __init__(self, dtype): - self.dtype = dtype - - -# Row model -class RowModel(BaseModel): - id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) - c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) - score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) - active: Annotated[bool, NumpyDtype(np.bool_)] = True +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + c_val: complex = blosc2.field(blosc2.complex128(), default=0j) + score: float = blosc2.field(blosc2.float64(ge=0, le=100), default=0.0) + active: bool = blosc2.field(blosc2.bool(), default=True) N = 1_000_000 @@ -49,7 +43,7 @@ class RowModel(BaseModel): dtype=np_dtype, ) -ct = blosc2.CTable(RowModel, expected_size=N) +ct = blosc2.CTable(Row, expected_size=N) ct.extend(DATA) print(f"CTable built with {len(ct):,} rows\n") diff --git a/bench/ctable/slice_to_array.py b/bench/ctable/slice_to_array.py index 2b072c8c..aad082f9 100644 --- a/bench/ctable/slice_to_array.py +++ b/bench/ctable/slice_to_array.py @@ -9,25 +9,19 @@ # different sizes and positions: small, large, and middle of the array. from time import time -from typing import Annotated +from dataclasses import dataclass import numpy as np -from pydantic import BaseModel, Field import blosc2 -class NumpyDtype: - def __init__(self, dtype): - self.dtype = dtype - - -# Row model -class RowModel(BaseModel): - id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) - c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) - score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) - active: Annotated[bool, NumpyDtype(np.bool_)] = True +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + c_val: complex = blosc2.field(blosc2.complex128(), default=0j) + score: float = blosc2.field(blosc2.float64(ge=0, le=100), default=0.0) + active: bool = blosc2.field(blosc2.bool(), default=True) N = 1_000_000 @@ -58,7 +52,7 @@ class RowModel(BaseModel): dtype=np_dtype, ) -ct = blosc2.CTable(RowModel, expected_size=N) +ct = blosc2.CTable(Row, expected_size=N) ct.extend(DATA) print(f"CTable built with {len(ct):,} rows\n") diff --git a/bench/ctable/speed_iter.py b/bench/ctable/speed_iter.py new file mode 100644 index 00000000..b470e147 --- /dev/null +++ b/bench/ctable/speed_iter.py @@ -0,0 +1,43 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +from dataclasses import dataclass + +import numpy as np +from time import time + + +from blosc2 import CTable +import blosc2 + + +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + score: float = blosc2.field(blosc2.float64(ge=0, le=100)) + active: bool = blosc2.field(blosc2.bool(), default=True) + + +N = 1_000_000 # start small, increase when confident + +data = [(i, float(i % 100), i % 2 == 0) for i in range(N)] +tabla = CTable(Row, new_data=data) + +print(f"Table created with {len(tabla)} rows\n") + +# ------------------------------------------------------------------- +# Test 1: iterate without accessing any column (minimum cost) +# ------------------------------------------------------------------- +i=0 +t0 = time() +for row in tabla: + i=(i+1)%10000 + if i==0: + _ = row["score"] + +t1 = time() +print(f"[Test 1] Iter without accessing columns: {(t1 - t0):.3f} s") diff --git a/bench/ctable/where_chain.py b/bench/ctable/where_chain.py index ff8b9b30..b8b13f75 100644 --- a/bench/ctable/where_chain.py +++ b/bench/ctable/where_chain.py @@ -9,25 +9,19 @@ # Filters: 250k < id < 750k, active == False, 25.0 < score < 75.0 from time import time -from typing import Annotated +from dataclasses import dataclass import numpy as np -from pydantic import BaseModel, Field import blosc2 -class NumpyDtype: - def __init__(self, dtype): - self.dtype = dtype - - -# Row model -class RowModel(BaseModel): - id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) - c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) - score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) - active: Annotated[bool, NumpyDtype(np.bool_)] = True +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + c_val: complex = blosc2.field(blosc2.complex128(), default=0j) + score: float = blosc2.field(blosc2.float64(ge=0, le=100), default=0.0) + active: bool = blosc2.field(blosc2.bool(), default=True) N = 1_000_000 @@ -49,7 +43,7 @@ class RowModel(BaseModel): dtype=np_dtype, ) -ct = blosc2.CTable(RowModel, expected_size=N) +ct = blosc2.CTable(Row, expected_size=N) ct.extend(DATA) print(f"CTable built with {len(ct):,} rows\n") diff --git a/bench/ctable/where_selective.py b/bench/ctable/where_selective.py index 77767d45..2bc8ae0b 100644 --- a/bench/ctable/where_selective.py +++ b/bench/ctable/where_selective.py @@ -9,25 +9,19 @@ # Filter: id < threshold, with thresholds covering 1%, 10%, 50%, 90%, 100% from time import time -from typing import Annotated +from dataclasses import dataclass import numpy as np -from pydantic import BaseModel, Field import blosc2 -class NumpyDtype: - def __init__(self, dtype): - self.dtype = dtype - - -# Row model -class RowModel(BaseModel): - id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) - c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) - score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) - active: Annotated[bool, NumpyDtype(np.bool_)] = True +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + c_val: complex = blosc2.field(blosc2.complex128(), default=0j) + score: float = blosc2.field(blosc2.float64(ge=0, le=100), default=0.0) + active: bool = blosc2.field(blosc2.bool(), default=True) N = 1_000_000 @@ -50,7 +44,7 @@ class RowModel(BaseModel): dtype=np_dtype, ) -ct = blosc2.CTable(RowModel, expected_size=N) +ct = blosc2.CTable(Row, expected_size=N) ct.extend(DATA) print(f"CTable built with {len(ct):,} rows\n") diff --git a/plans/ctable-implementation-log.md b/plans/ctable-implementation-log.md new file mode 100644 index 00000000..1f204984 --- /dev/null +++ b/plans/ctable-implementation-log.md @@ -0,0 +1,225 @@ +# CTable Implementation Log + +This document records what was implemented as part of the `ctable-schema.md` redesign. +It covers every new file, every significant change, and the reasoning behind each decision. + +--- + +## Overview + +The goal was to replace the original Pydantic-`BaseModel`-based schema API with a +**dataclass-first schema API** using declarative spec objects (`b2.int64()`, +`b2.float64()`, etc.) and to wire in full constraint validation on insert. + +--- + +## New files + +### `src/blosc2/schema.py` + +Defines the public schema vocabulary. + +**Contents:** + +- `SchemaSpec` — abstract base class for all column type descriptors. +- `int64`, `float64`, `bool`, `complex64`, `complex128`, `string`, `bytes` — + concrete spec classes. Each carries: + - `dtype` — the NumPy storage dtype + - `python_type` — the corresponding Python type + - Constraint attributes: `ge`, `gt`, `le`, `lt` (numeric); `min_length`, + `max_length`, `pattern` (string/bytes) + - `to_pydantic_kwargs()` — returns only the non-`None` constraints as a dict, + used internally to build Pydantic validator models + - `to_metadata_dict()` — returns a JSON-compatible dict used for serialization +- `field(spec, *, default, cparams, dparams, chunks, blocks)` — attaches a spec + and per-column storage options to a dataclass field via + `dataclasses.field(metadata={"blosc2": {...}})`. +- `BLOSC2_FIELD_METADATA_KEY = "blosc2"` — stable key for the metadata dict. + +**Key design note:** `bool` and `bytes` shadow Python builtins inside this module. +Private aliases `_builtin_bool` and `_builtin_bytes` are used where the originals +are needed. + +--- + +### `src/blosc2/schema_compiler.py` + +Compiles a dataclass row definition into an internal `CompiledSchema`. + +**Contents:** + +- `ColumnConfig(slots=True)` — holds per-column NDArray storage options: + `cparams`, `dparams`, `chunks`, `blocks`. +- `CompiledColumn(slots=True)` — holds everything about one column: + `name`, `py_type`, `spec`, `dtype`, `default`, `config`, `display_width`. +- `CompiledSchema(slots=True)` — holds the full compiled schema: + `row_cls`, `columns`, `columns_by_name`, `validator_model` (filled lazily by + `schema_validation.py`). +- `compile_schema(row_cls)` — main entry point. Walks `dataclasses.fields()`, + reads `blosc2` metadata from each field, infers specs from plain annotations + where no `b2.field()` is present, validates annotation/spec compatibility, and + returns a `CompiledSchema`. +- `get_blosc2_field_metadata(dc_field)` — extracts the `"blosc2"` metadata dict + from a dataclass field, or returns `None`. +- `infer_spec_from_annotation(annotation)` — builds a default spec from a plain + Python type (`int` → `int64()`, `float` → `float64()`, etc.). Used for inferred + shorthand fields like `id: int` (no `b2.field()`). +- `validate_annotation_matches_spec(name, annotation, spec)` — rejects + declarations where the Python annotation is incompatible with the spec (e.g. + `id: str = b2.field(b2.int64())`). +- `compute_display_width(spec)` — returns a sensible terminal column width based + on dtype kind. +- `schema_to_dict(schema)` — serializes a `CompiledSchema` to a JSON-compatible + dict. Handles `MISSING` defaults (→ `None`), complex defaults + (→ `{"__complex__": True, "real": ..., "imag": ...}`), and optional per-column + storage config fields. +- `schema_from_dict(data)` — reconstructs a `CompiledSchema` from a serialized + dict. Does not require the original Python dataclass. Returns `row_cls=None`. + Raises `ValueError` on unknown `kind` or unsupported `version`. + +--- + +### `src/blosc2/schema_validation.py` + +Row-level constraint validation backed by Pydantic. All Pydantic imports are +isolated here so the rest of the codebase never touches Pydantic directly. + +**Contents:** + +- `build_validator_model(schema)` — builds a `pydantic.create_model(...)` class + from the compiled schema. Each column's `to_pydantic_kwargs()` result is passed + to `pydantic.Field(...)`. The result is cached on `schema.validator_model` so it + is built only once per schema. +- `validate_row(schema, row_dict)` — validates one `{col_name: value}` dict. + Calls the cached Pydantic model, catches `ValidationError`, and re-raises as a + plain `ValueError` so callers never need to import Pydantic. +- `validate_rows_rowwise(schema, rows)` — validates a list of row dicts. Raises + `ValueError` on the first violation, including the row index. + +**When used:** called by `CTable.append()` when `self._validate` is `True`. + +--- + +### `src/blosc2/schema_vectorized.py` + +NumPy-based constraint validation for bulk inserts. Used by `CTable.extend()` to +check entire column arrays at once without per-row Python overhead. + +**Contents:** + +- `validate_column_values(col, values)` — checks all constraint attributes + present on `col.spec` against a NumPy array of values. Uses `np.any(arr < ge)` + style checks. For string/bytes, uses `np.vectorize(len)` to check lengths. + Reports the first offending value in the error message. +- `validate_column_batch(schema, columns)` — calls `validate_column_values` for + every column present in the `columns` dict. + +**Why separate from Pydantic validation:** `extend()` can receive millions of +rows. Row-by-row Pydantic validation would be unacceptably slow for large batches. +NumPy operations run in C with no per-element Python overhead. + +--- + +## Changes to existing files + +### `src/blosc2/ctable.py` + +**Schema detection at construction:** + +```python +if dataclasses.is_dataclass(row_type) and isinstance(row_type, type): + self._schema = compile_schema(row_type) +else: + self._schema = _compile_pydantic_schema(row_type) # legacy path +``` + +**New constructor parameters:** `validate=True`, `cparams=None`, `dparams=None`. +Stored as `self._validate`, `self._table_cparams`, `self._table_dparams`. + +**`_init_columns`:** builds NDArrays from `self._schema.columns` instead of +iterating `row_type.model_fields`. + +**`_resolve_column_storage`:** merges column-level and table-level storage +settings. Column-level wins. + +**`_normalize_row_input`:** normalizes list/tuple/dict/dataclass instance/ +`np.void` to a `{col_name: value}` dict. + +**`_coerce_row_to_storage`:** coerces each value to the column's NumPy dtype +using `np.array(val, dtype=col.dtype).item()`. + +**`append()` new flow:** +1. `_normalize_row_input(data)` → dict +2. `validate_row(schema, row)` if `self._validate` (Pydantic row validation) +3. `_coerce_row_to_storage(row)` → storage dict +4. Find write position, resize if needed, write column by column. + +**`extend()` new signature:** `extend(data, *, validate=None)`. +- `validate=None` uses `self._validate` (table default). +- `validate=True/False` overrides for this call only. +- Vectorized validation runs on raw column arrays before `blosc2.asarray` conversion. + +**Schema introspection (new):** +- `table.schema` property — returns `self._schema`. +- `table.column_schema(name)` — returns `CompiledColumn` for a given column name. +- `table.schema_dict()` — delegates to `schema_to_dict(self._schema)`. + +**Legacy Pydantic adapter kept:** +- `NumpyDtype`, `MaxLen`, `_resolve_field_dtype`, `_LegacySpec`, + `_compile_pydantic_schema` all remain so existing Pydantic-`BaseModel`-based + schemas continue to work during the transition. + +### `src/blosc2/__init__.py` + +Added to delayed imports: + +```python +from .schema import bool, bytes, complex64, complex128, field, float64, int64, string +``` + +Added to `__all__`: +`"bool"`, `"bytes"`, `"complex64"`, `"complex128"`, `"field"`, `"float64"`, +`"int64"`, `"string"`. + +--- + +## Tests + +All tests live in `tests/ctable/`. + +| File | Covers | +|---|---| +| `test_schema_specs.py` | Spec dtypes, python types, constraint storage, `to_pydantic_kwargs`, `to_metadata_dict`, `blosc2` namespace exports | +| `test_schema_compiler.py` | `compile_schema` with explicit `b2.field()`, inferred shorthand, mismatch rejection, defaults, cparams; `schema_to_dict` / `schema_from_dict` roundtrip | +| `test_schema_validation.py` | `append` and `extend` constraint enforcement; boundary values; `validate=False` bypass; `gt`/`lt` exclusive bounds; NumPy structured array path | +| `test_ctable_dataclass_schema.py` | End-to-end CTable construction, `append` with tuple/list/dict, `extend` with iterable and structured array, per-call `validate=` override, schema introspection | +| `test_construct.py` | Construction variants, `append`/`extend`/resize, column integrity, `_valid_rows` | +| `test_column.py` | Column indexing, slicing, iteration, `to_numpy()`, mask independence | +| `test_compact.py` | Manual and auto compaction | +| `test_delete_rows.py` | Single/list/slice deletion, out-of-bounds, edge cases, stress | +| `test_extend_delete.py` | Interleaved extend/delete cycles, mask correctness, resize behavior | +| `test_row_logic.py` | Row indexer (int/slice/list), views, chained views | + +Total: **127 tests, all passing**. + +--- + +## Design decisions + +**Why two validation paths?** +`append()` handles one row at a time — Pydantic is fast enough and also performs +type coercion and default filling. `extend()` handles bulk data — vectorized NumPy +checks are orders of magnitude faster for large batches. + +**Why `validate=None` as the default on `extend()`?** +`None` means "inherit the table-level flag". `True`/`False` are explicit overrides. +This avoids a boolean that accidentally silences the table-level setting. + +**Why keep the Pydantic adapter?** +Existing code using `class RowModel(BaseModel)` continues to work without +modification. The adapter is not on the critical path for new code. + +**Why `schema_to_dict` / `schema_from_dict` now?** +Persistence (`save()`/`load()`) requires a self-contained schema representation. +Establishing the serialization format early means the format will be stable before +anything depends on it. diff --git a/plans/ctable-user-guide.md b/plans/ctable-user-guide.md new file mode 100644 index 00000000..5b19d378 --- /dev/null +++ b/plans/ctable-user-guide.md @@ -0,0 +1,378 @@ +# CTable User Guide + +This document explains how to use `CTable` as it currently stands. + +--- + +## What is CTable? + +`CTable` is a columnar compressed table built on top of `blosc2.NDArray`. Each +column is stored as a separate compressed array. Rows are never physically removed +on deletion — instead a boolean mask (`_valid_rows`) marks live rows, and +compaction can be triggered manually or automatically. + +--- + +## Defining a schema + +A schema is a Python `@dataclass` where each field uses `b2.field()` to declare +the column type and constraints. + +```python +from dataclasses import dataclass +import blosc2 as b2 + + +@dataclass +class Row: + id: int = b2.field(b2.int64(ge=0)) + score: float = b2.field(b2.float64(ge=0, le=100), default=0.0) + active: bool = b2.field(b2.bool(), default=True) +``` + +### Available spec types + +| Spec | NumPy dtype | Constraints | +|---|---|---| +| `b2.int64(ge, gt, le, lt)` | `int64` | numeric bounds | +| `b2.float64(ge, gt, le, lt)` | `float64` | numeric bounds | +| `b2.bool()` | `bool_` | — | +| `b2.complex64()` | `complex64` | — | +| `b2.complex128()` | `complex128` | — | +| `b2.string(min_length, max_length, pattern)` | `U` | length / regex | +| `b2.bytes(min_length, max_length)` | `S` | length | + +Constraints are enforced on every insert (see **Validation** below). + +### Inferred shorthand + +For columns with no constraints and no per-column storage options, you can omit +`b2.field()` entirely: + +```python +@dataclass +class Row: + id: int # inferred as b2.int64() + score: float # inferred as b2.float64() + flag: bool = True # inferred as b2.bool(), default=True +``` + +### Dataclass field ordering rule + +Python dataclasses require that fields **with defaults come after fields without +defaults**. Plan your schema accordingly: + +```python +@dataclass +class Row: + id: int = b2.field(b2.int64()) # required — no default + score: float = b2.field(b2.float64(), default=0.0) # optional + active: bool = b2.field(b2.bool(), default=True) # optional +``` + +--- + +## Creating a table + +```python +import blosc2 as b2 + +# Empty table +t = b2.CTable(Row) + +# Table pre-loaded with data +t = b2.CTable(Row, new_data=[(1, 95.0, True), (2, 80.0, False)]) + +# Reserve space upfront (avoids resizes) +t = b2.CTable(Row, expected_size=1_000_000) + +# Disable constraint validation (faster for trusted data) +t = b2.CTable(Row, validate=False) + +# Enable auto-compaction (fills gaps before resizing) +t = b2.CTable(Row, compact=True) + +# Table-level compression settings (applied to all columns unless overridden) +t = b2.CTable(Row, cparams={"codec": b2.Codec.ZSTD, "clevel": 5}) +``` + +### Per-column storage options + +```python +@dataclass +class Row: + id: int = b2.field(b2.int64(), cparams={"codec": b2.Codec.LZ4, "clevel": 1}) + score: float = b2.field( + b2.float64(ge=0, le=100), + cparams={"codec": b2.Codec.ZSTD, "clevel": 9}, + default=0.0, + ) +``` + +Column-level `cparams`/`dparams`/`chunks`/`blocks` override the table-level +defaults for that column only. + +--- + +## Inserting data + +### `append()` — one row at a time + +Accepts a tuple, list, dict, or dataclass instance: + +```python +t.append((1, 95.0, True)) +t.append([2, 80.0, False]) +t.append({"id": 3, "score": 50.0, "active": True}) +``` + +Fields with defaults can be omitted: + +```python +t.append((4,)) # score=0.0 and active=True filled from defaults +``` + +### `extend()` — bulk insert + +Accepts a list of tuples, a NumPy structured array, or another `CTable`: + +```python +# List of tuples +t.extend([(i, float(i), True) for i in range(1000)]) + +# NumPy structured array +import numpy as np + +dtype = np.dtype([("id", np.int64), ("score", np.float64), ("active", np.bool_)]) +arr = np.array([(1, 50.0, True), (2, 75.0, False)], dtype=dtype) +t.extend(arr) + +# Another CTable +t.extend(other_table) +``` + +#### Per-call validation override + +```python +# Skip validation for one trusted batch (even if table was built with validate=True) +t.extend(trusted_data, validate=False) + +# Force validation for one batch (even if table was built with validate=False) +t.extend(external_data, validate=True) +``` + +--- + +## Validation + +When `validate=True` (the default), constraints declared in the schema are +enforced on every insert: + +```python +t.append((-1, 50.0, True)) # ValueError: id violates ge=0 +t.append((1, 150.0, True)) # ValueError: score violates le=100 +t.extend([(-1, 50.0, True)]) # ValueError: id violates ge=0 +``` + +Boundary values are accepted: + +```python +t.append((0, 0.0, True)) # ok — id=0 satisfies ge=0, score=0.0 satisfies ge=0 +t.append((1, 100.0, False)) # ok — score=100.0 satisfies le=100 +``` + +To skip validation entirely: + +```python +t = b2.CTable(Row, validate=False) +``` + +--- + +## Reading data + +### Row access + +```python +t.row[0] # first row → returns a single-row CTable view +t.row[-1] # last row +t.row[2:5] # slice → CTable view with rows 2, 3, 4 +t.row[::2] # every other row +t.row[[0, 5, 10]] # specific rows by logical index +``` + +Row access always uses **logical indices** (i.e. index 0 is the first live row, +not the first physical slot). + +### Column access + +```python +t["id"] # returns a Column object +t.score # attribute-style access also works + +# Iterate values +for val in t["score"]: + print(val) + +# Convert to NumPy array +arr = t["score"].to_numpy() + +# Single value +val = t["id"][5] # logical index 5 +``` + +### Column slicing + +```python +col_view = t["id"][0:10] # returns a Column view (mask applied) +arr = col_view.to_numpy() # materialise to NumPy +``` + +### head / tail + +```python +t.head(10) # CTable view of first 10 rows +t.tail(5) # CTable view of last 5 rows +``` + +--- + +## Deleting rows + +`delete()` marks rows as invalid in the tombstone mask — data is not physically +removed. + +```python +t.delete(0) # delete first live row +t.delete(-1) # delete last live row +t.delete([0, 2, 4]) # delete multiple rows by logical index +t.delete(list(range(10))) # delete first 10 live rows +``` + +Negative indices and mixed positive/negative lists are supported. + +--- + +## Compaction + +After many deletions, physical storage has gaps. Compaction moves all live rows +to the front and clears the rest. + +```python +t.compact() # manual compaction +``` + +Auto-compaction runs automatically before a resize when `compact=True`: + +```python +t = b2.CTable(Row, compact=True) +``` + +--- + +## Filtering + +`where()` applies a boolean expression and returns a read-only view: + +```python +view = t.where(t["score"] > 50) +view = t.where((t["id"] > 10) & (t["active"] == True)) +``` + +Views share `_cols` with the parent table and cannot be mutated (no `append` or +`extend`). + +--- + +## Table info + +```python +len(t) # number of live rows +t.nrows # same +t.ncols # number of columns +t.col_names # list of column names + +t.info() # prints a formatted summary with dtypes and memory usage +print(t) # prints the first rows in a table format +``` + +--- + +## Schema introspection + +```python +t.schema # CompiledSchema object +t.column_schema("id") # CompiledColumn for column "id" +t.schema_dict() # JSON-compatible dict of the full schema +``` + +`schema_dict()` example output: + +```python +{ + "version": 1, + "row_cls": "Row", + "columns": [ + {"name": "id", "kind": "int64", "ge": 0, "default": None}, + {"name": "score", "kind": "float64", "ge": 0, "le": 100, "default": 0.0}, + {"name": "active", "kind": "bool", "default": True}, + ], +} +``` + +The dict can be restored to a `CompiledSchema` without the original Python class: + +```python +from blosc2.schema_compiler import schema_from_dict + +restored = schema_from_dict(t.schema_dict()) +``` + +--- + +## Memory and compression + +```python +# Compressed size of all columns + valid_rows mask +cbytes = sum(col.cbytes for col in t._cols.values()) + t._valid_rows.cbytes + +# Uncompressed size +nbytes = sum(col.nbytes for col in t._cols.values()) + t._valid_rows.nbytes + +print(f"Compression ratio: {nbytes / cbytes:.2f}x") +``` + +--- + +## Complete example + +```python +from dataclasses import dataclass +import numpy as np +import blosc2 as b2 + + +@dataclass +class Measurement: + sensor_id: int = b2.field(b2.int64(ge=0)) + value: float = b2.field(b2.float64(ge=-1000, le=1000), default=0.0) + valid: bool = b2.field(b2.bool(), default=True) + + +# Create and populate +t = b2.CTable(Measurement, expected_size=10_000) +t.extend([(i, float(i % 200 - 100), i % 3 != 0) for i in range(5000)]) + +# Query +hot = t.where(t["value"] > 50) +print(f"Hot readings: {len(hot)}") + +# Delete invalid +invalid_indices = [i for i in range(len(t)) if not t.row[i].valid[0]] +if invalid_indices: + t.delete(invalid_indices) + +# Inspect +t.info() +print(t.schema_dict()) +``` diff --git a/src/blosc2/__init__.py b/src/blosc2/__init__.py index 11ae5677..c9c211df 100644 --- a/src/blosc2/__init__.py +++ b/src/blosc2/__init__.py @@ -337,29 +337,18 @@ def isdtype(a_dtype: np.dtype, kind: str | np.dtype | tuple): from numpy import ( bool_, - complex64, complex128, e, euler_gamma, float16, - float32, float64, inf, - int8, - int16, - int32, int64, nan, newaxis, pi, - uint8, - uint16, - uint32, - uint64, ) -bool = bool - DEFAULT_COMPLEX = complex128 """ Default complex floating dtype.""" @@ -595,7 +584,9 @@ def _raise(exc): Disable the overloaded equal operator. """ -# Delayed imports for avoiding overwriting of python builtins +# Delayed imports for avoiding overwriting of python builtins. +# Note: bool, bytes, string shadow builtins in the blosc2 namespace by design — +# they are schema spec constructors (b2.bool(), b2.bytes(), etc.). from .ctable import Column, CTable from .ndarray import ( abs, @@ -698,6 +689,24 @@ def _raise(exc): var, where, ) +from .schema import ( + bool, + bytes, + complex64, + complex128, + field, + float32, + float64, + int8, + int16, + int32, + int64, + string, + uint8, + uint16, + uint32, + uint64, +) __all__ = [ # noqa : RUF022 # Constants @@ -718,6 +727,23 @@ def _raise(exc): "inf", "nan", "newaxis", + # Schema API (CTable) + "bool", + "bytes", + "complex64", + "complex128", + "field", + "float32", + "float64", + "int8", + "int16", + "int32", + "int64", + "string", + "uint8", + "uint16", + "uint32", + "uint64", # Classes "C2Array", "CParams", diff --git a/src/blosc2/ctable.py b/src/blosc2/ctable.py index ddd2719c..89ca6032 100644 --- a/src/blosc2/ctable.py +++ b/src/blosc2/ctable.py @@ -6,11 +6,13 @@ # LICENSE file in the root directory of this source tree) ####################################################################### -"""Imports for CTable""" +"""CTable: a columnar compressed table built on top of blosc2.NDArray.""" from __future__ import annotations +import dataclasses from collections.abc import Iterable +from dataclasses import MISSING from typing import Any, Generic, TypeVar import numpy as np @@ -22,7 +24,6 @@ except ImportError: def profile(func): - def wrapper(*args, **kwargs): return func(*args, **kwargs) @@ -30,80 +31,56 @@ def wrapper(*args, **kwargs): return wrapper -from pydantic import BaseModel - import blosc2 +from blosc2.schema import SchemaSpec +from blosc2.schema_compiler import ( + ColumnConfig, + CompiledColumn, + CompiledSchema, + compile_schema, +) + +# RowT is intentionally left unbound so CTable works with both dataclasses +# and legacy Pydantic models during the transition period. +RowT = TypeVar("RowT") + -RowT = TypeVar("RowT", bound=BaseModel) +# --------------------------------------------------------------------------- +# Legacy Pydantic-compat helpers +# Keep these so existing code that uses Annotated[type, NumpyDtype(...)] or +# Annotated[str, MaxLen(...)] on a pydantic.BaseModel continues to work. +# --------------------------------------------------------------------------- class NumpyDtype: + """Metadata tag for Pydantic-based schemas (legacy).""" + def __init__(self, dtype): self.dtype = dtype class MaxLen: + """Metadata tag for fixed-width string/bytes columns in Pydantic-based schemas (legacy).""" + def __init__(self, length: int): self.length = int(length) -############################# -#### Row model examples ### -############################# -""" -class RowModel(BaseModel): - id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) - c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) - score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) - active: Annotated[bool, NumpyDtype(np.bool_)] = True - -class RowModel2(BaseModel): - id: Annotated[int, NumpyDtype(np.int16)] = Field(ge=0) - name: Annotated[str, MaxLen(10)] = Field(default="unknown") - # name: Annotated[bytes, MaxLen(10)] = Field(default=b"unknown") - score: Annotated[float, NumpyDtype(np.float32)] = Field(ge=0, le=100) - active: Annotated[bool, NumpyDtype(np.bool_)] = True - -class RowModel3(BaseModel): - id: Annotated[int, NumpyDtype(np.int16)] = Field(ge=0) - #name: Annotated[str, MaxLen(10)] = Field(default="unknown") - name: Annotated[bytes, MaxLen(10)] = Field(default=b"unknown")""" - - -class _RowIndexer: - def __init__(self, table): - self._table = table - - def __getitem__(self, item): - return self._table._run_row_logic(item) - - -class _Row: - def __init__(self, table: CTable, nrow: int): - self._table = table - self._nrow = nrow - self._real_pos = None - - def _get_real_pos(self) -> int: - self._real_pos = _find_physical_index(self._table._valid_rows, self._nrow) - return self._real_pos - - def __getitem__(self, col_name: str): - if self._real_pos is None: - self._get_real_pos() - return self._table._cols[col_name][self._real_pos] +def _default_display_width(origin) -> int: + """Return a sensible display column width for a given Python type (legacy).""" + return {int: 12, float: 15, bool: 6, complex: 25}.get(origin, 20) def _resolve_field_dtype(field) -> tuple[np.dtype, int]: - """Return (numpy dtype, display_width) for a pydantic model field. + """Return (numpy dtype, display_width) for a Pydantic model field (legacy). - Extracts dtype from NumpyDtype metadata when present, otherwise falls - back to a sensible default for each Python primitive type. + Extracts dtype from NumpyDtype metadata when present (same class), otherwise + falls back to a sensible default for each Python primitive type. """ annotation = field.annotation origin = getattr(annotation, "__origin__", annotation) - # str / bytes: look for MaxLen metadata, build fixed-width dtype + # str / bytes → look for MaxLen metadata, build fixed-width dtype if origin in (str, bytes) or annotation in (str, bytes): is_bytes = origin is bytes or annotation is bytes max_len = 32 @@ -117,7 +94,7 @@ def _resolve_field_dtype(field) -> tuple[np.dtype, int]: display_width = max(10, min(max_len, 50)) return dt, display_width - # Check for explicit NumpyDtype metadata (overrides primitive defaults) + # Check for explicit NumpyDtype metadata (same class as defined here) if hasattr(annotation, "__metadata__"): for meta in annotation.__metadata__: if isinstance(meta, NumpyDtype): @@ -139,9 +116,46 @@ def _resolve_field_dtype(field) -> tuple[np.dtype, int]: return np.dtype(np.object_), 20 -def _default_display_width(origin) -> int: - """Return a sensible display column width for a given Python type.""" - return {int: 12, float: 15, bool: 6, complex: 25}.get(origin, 20) +class _LegacySpec(SchemaSpec): + """Internal compatibility spec wrapping a dtype extracted from a Pydantic schema.""" + + def __init__(self, dtype: np.dtype): + self.dtype = np.dtype(dtype) + self.python_type = object + + def to_pydantic_kwargs(self) -> dict[str, Any]: + return {} + + def to_metadata_dict(self) -> dict[str, Any]: + return {"kind": "legacy", "dtype": str(self.dtype)} + + +def _compile_pydantic_schema(row_cls: type) -> CompiledSchema: + """Compatibility adapter: build a CompiledSchema from a Pydantic BaseModel subclass.""" + columns: list[CompiledColumn] = [] + for name, pyd_field in row_cls.model_fields.items(): + dtype, display_width = _resolve_field_dtype(pyd_field) + spec = _LegacySpec(dtype) + col = CompiledColumn( + name=name, + py_type=object, + spec=spec, + dtype=dtype, + default=MISSING, + config=ColumnConfig(cparams=None, dparams=None, chunks=None, blocks=None), + display_width=display_width, + ) + columns.append(col) + return CompiledSchema( + row_cls=row_cls, + columns=columns, + columns_by_name={col.name: col for col in columns}, + ) + + +# --------------------------------------------------------------------------- +# Internal row/indexing helpers (unchanged) +# --------------------------------------------------------------------------- def _find_physical_index(arr: blosc2.NDArray, logical_key: int) -> int: @@ -190,6 +204,35 @@ def _find_physical_index(arr: blosc2.NDArray, logical_key: int) -> int: raise IndexError("Unexpected error finding physical index.") +class _RowIndexer: + def __init__(self, table): + self._table = table + + def __getitem__(self, item): + return self._table._run_row_logic(item) + + +class _Row: + def __init__(self, table: CTable, nrow: int): + self._table = table + self._nrow = nrow + self._real_pos = None + + def _get_real_pos(self) -> int: + self._real_pos = _find_physical_index(self._table._valid_rows, self._nrow) + return self._real_pos + + def __getitem__(self, col_name: str): + if self._real_pos is None: + self._get_real_pos() + return self._table._cols[col_name][self._real_pos] + + +# --------------------------------------------------------------------------- +# Column +# --------------------------------------------------------------------------- + + class Column: def __init__(self, table: CTable, col_name: str, mask=None): self._table = table @@ -289,27 +332,21 @@ def __len__(self): return blosc2.count_nonzero(self._valid_rows) def __lt__(self, other): - # < (Less than) return self._raw_col < other def __le__(self, other): - # <= (Less than or equal to) return self._raw_col <= other def __eq__(self, other): - # == (Equal to) return self._raw_col == other def __ne__(self, other): - # != (Not equal to) return self._raw_col != other def __gt__(self, other): - # > (Greater than) return self._raw_col > other def __ge__(self, other): - # >= (Greater than or equal to) return self._raw_col >= other @property @@ -321,56 +358,174 @@ def to_numpy(self): return self._raw_col[real_pos[:]] +# --------------------------------------------------------------------------- +# CTable +# --------------------------------------------------------------------------- + + class CTable(Generic[RowT]): def __init__( - self, row_type: type[RowT], new_data=None, expected_size: int = 1_048_576, compact: bool = False + self, + row_type: type[RowT], + new_data=None, + *, + expected_size: int = 1_048_576, + compact: bool = False, + validate: bool = True, + cparams: dict[str, Any] | None = None, + dparams: dict[str, Any] | None = None, ) -> None: self._row_type = row_type + self._validate = validate + self._table_cparams = cparams + self._table_dparams = dparams + + # Build compiled schema from either a dataclass or a legacy Pydantic model + if dataclasses.is_dataclass(row_type) and isinstance(row_type, type): + self._schema: CompiledSchema = compile_schema(row_type) + else: + self._schema = _compile_pydantic_schema(row_type) + self._cols: dict[str, blosc2.NDArray] = {} self._n_rows: int = 0 + self._last_pos: int | None = 0 # physical index of next write slot; + # None means it must be recalculated + # (set after any deletion) self._col_widths: dict[str, int] = {} - self.col_names = [] + self.col_names: list[str] = [] self.row = _RowIndexer(self) self.auto_compact = compact self.base = None - c, b = compute_chunks_blocks((expected_size,)) - self._valid_rows = blosc2.zeros(shape=(expected_size,), dtype=np.bool_, chunks=c, blocks=b) + default_chunks, default_blocks = compute_chunks_blocks((expected_size,)) + self._valid_rows = blosc2.zeros( + shape=(expected_size,), + dtype=np.bool_, + chunks=default_chunks, + blocks=default_blocks, + ) - for name, field in row_type.model_fields.items(): - self.col_names.append(name) - dt, display_width = _resolve_field_dtype(field) - final_width = max(len(name), display_width) - self._col_widths[name] = final_width - self._cols[name] = blosc2.zeros(shape=(expected_size,), dtype=dt, chunks=c, blocks=b) + self._init_columns(expected_size, default_chunks, default_blocks) if new_data is not None: self._load_initial_data(new_data) - def _load_initial_data(self, new_data) -> None: - """Dispatch new_data to append() or extend() as appropriate.""" - is_append = False + def _init_columns(self, expected_size: int, default_chunks, default_blocks) -> None: + """Create one NDArray per column using the compiled schema.""" + for col in self._schema.columns: + self.col_names.append(col.name) + self._col_widths[col.name] = max(len(col.name), col.display_width) + storage = self._resolve_column_storage(col, default_chunks, default_blocks) + self._cols[col.name] = blosc2.zeros( + shape=(expected_size,), + dtype=col.dtype, + **storage, + ) - if isinstance(new_data, (np.void, np.record)): - is_append = True - elif isinstance(new_data, np.ndarray): - if new_data.dtype.names is not None and new_data.ndim == 0: - is_append = True - elif isinstance(new_data, list) and len(new_data) > 0: - first_elem = new_data[0] - if isinstance(first_elem, (str, bytes, int, float, bool, complex)): - is_append = True + def _resolve_column_storage( + self, + col: CompiledColumn, + default_chunks, + default_blocks, + ) -> dict[str, Any]: + """Merge table-level and column-level storage settings. - if is_append: - self.append(new_data) - else: - self.extend(new_data) + Column-level settings (from ``b2.field(...)``) take precedence over + table-level defaults passed to ``CTable.__init__``. + """ + result: dict[str, Any] = { + "chunks": col.config.chunks if col.config.chunks is not None else default_chunks, + "blocks": col.config.blocks if col.config.blocks is not None else default_blocks, + } + cparams = col.config.cparams if col.config.cparams is not None else self._table_cparams + dparams = col.config.dparams if col.config.dparams is not None else self._table_dparams + if cparams is not None: + result["cparams"] = cparams + if dparams is not None: + result["dparams"] = dparams + return result + + def _normalize_row_input(self, data: Any) -> dict[str, Any]: + """Normalize a row input to a ``{col_name: value}`` dict. + + Accepted shapes: + - list / tuple → positional, zipped with ``col_names`` + - dict → used as-is + - dataclass → ``dataclasses.asdict`` + - np.void / structured scalar → field-name access + """ + if isinstance(data, dict): + return data + if isinstance(data, (list, tuple)): + return dict(zip(self.col_names, data, strict=False)) + if dataclasses.is_dataclass(data) and not isinstance(data, type): + return dataclasses.asdict(data) + if isinstance(data, (np.void, np.record)): + return {name: data[name] for name in self.col_names} + # Fallback: try positional indexing + return {name: data[i] for i, name in enumerate(self.col_names)} + + def _coerce_row_to_storage(self, row: dict[str, Any]) -> dict[str, Any]: + """Coerce each value in *row* to the column's storage dtype.""" + result = {} + for col in self._schema.columns: + val = row[col.name] + result[col.name] = np.array(val, dtype=col.dtype).item() + return result + + def _resolve_last_pos(self) -> int: + """Return the physical index of the next write slot. + + Returns the cached ``_last_pos`` when available. After a deletion + ``_last_pos`` is ``None``; this method then walks chunk metadata of + ``_valid_rows`` from the end (no full decompression) to find the last + ``True`` position, caches the result, and returns it. + """ + if self._last_pos is not None: + return self._last_pos + + arr = self._valid_rows + chunk_size = arr.chunks[0] + last_true_pos = -1 + + for info in reversed(list(arr.iterchunks_info())): + actual_size = min(chunk_size, arr.shape[0] - info.nchunk * chunk_size) + chunk_start = info.nchunk * chunk_size + + if info.special == blosc2.SpecialValue.ZERO: + continue + if info.special == blosc2.SpecialValue.VALUE: + val = np.frombuffer(info.repeated_value, dtype=arr.dtype)[0] + if not val: + continue + last_true_pos = chunk_start + actual_size - 1 + break + + chunk_data = arr[chunk_start : chunk_start + actual_size] + nonzero = np.flatnonzero(chunk_data) + if len(nonzero) == 0: + continue + last_true_pos = chunk_start + int(nonzero[-1]) + break + + self._last_pos = last_true_pos + 1 + return self._last_pos + + def _grow(self) -> None: + """Double the physical capacity of all columns and the valid_rows mask.""" + c = len(self._valid_rows) + for col_arr in self._cols.values(): + col_arr.resize((c * 2,)) + self._valid_rows.resize((c * 2,)) + + # ------------------------------------------------------------------ + # Display + # ------------------------------------------------------------------ def __str__(self): retval = [] cont = 0 - # We print the header for name in self._cols: retval.append(f"{name:^{self._col_widths[name]}} |") cont += self._col_widths[name] + 2 @@ -379,9 +534,6 @@ def __str__(self): retval.append("-") retval.append("\n") - # We print the rows - - """Change this. Use where""" real_poss = blosc2.where(self._valid_rows, np.array(range(len(self._valid_rows)))).compute() for j in real_poss: @@ -401,6 +553,10 @@ def __iter__(self): for i in range(self.nrows): yield _Row(self, i) + # ------------------------------------------------------------------ + # View / filtering + # ------------------------------------------------------------------ + def view(self, new_valid_rows): if not ( isinstance(new_valid_rows, (blosc2.NDArray, blosc2.LazyExpr)) @@ -417,7 +573,11 @@ def view(self, new_valid_rows): if len(self._valid_rows) != len(new_valid_rows): raise ValueError() - retval = CTable(self._row_type, compact=self.auto_compact, expected_size=len(self._valid_rows)) + retval = CTable( + self._row_type, + compact=self.auto_compact, + expected_size=len(self._valid_rows), + ) retval._cols = self._cols retval._n_rows = blosc2.count_nonzero(new_valid_rows) retval._col_widths = self._col_widths @@ -428,68 +588,16 @@ def view(self, new_valid_rows): return retval def head(self, N: int = 5) -> CTable: - """ - # Alternative code, slower with big data - if n <= 0: - return CTable(self._row_type, compact=self.auto_compact) - - real_poss = blosc2.where(self._valid_rows, np.array(range(len(self._valid_rows)))).compute() - n_take = min(n, self._n_rows) - - retval = CTable(self._row_type, compact=self.auto_compact) - retval._n_rows = n_take - retval._valid_rows[:n_take] = True - - for k in self._cols.keys(): - retval._cols[k][:n_take] = self._cols[k][real_poss[:n_take]] - - return retval""" if N <= 0: - # If N is 0 or negative, return an empty table return self.view(blosc2.zeros(shape=len(self._valid_rows), dtype=np.bool_)) + if self._n_rows <= N: + return self.view(self._valid_rows) + # Reuse _find_physical_index: physical position of the (N-1)-th live row arr = self._valid_rows - count = 0 - chunk_size = arr.chunks[0] - pos_N_true = -1 - if N <= 0: - return self.view(blosc2.zeros(shape=len(arr), dtype=np.bool_)) - for info in arr.iterchunks_info(): - actual_size = min(chunk_size, arr.shape[0] - info.nchunk * chunk_size) - chunk_start = info.nchunk * chunk_size + pos_N_true = _find_physical_index(arr, N - 1) - # All False without decompressing -> skip - if info.special == blosc2.SpecialValue.ZERO: - continue - - # Repeated value -> check if True or False - if info.special == blosc2.SpecialValue.VALUE: - val = np.frombuffer(info.repeated_value, dtype=arr.dtype)[0] - if not val: - continue # all False, skip - # All True: target is at offset (N - count - 1) within the chunk - if count + actual_size < N: - count += actual_size - continue - pos_N_true = chunk_start + (N - count - 1) - break - - # General case: decompress only this chunk - chunk_data = arr[chunk_start : chunk_start + actual_size] - - n_true = int(np.count_nonzero(chunk_data)) - if count + n_true < N: - count += n_true - continue - - # The N-th True is in this chunk - pos_N_true = chunk_start + int(np.flatnonzero(chunk_data)[N - count - 1]) - break - - if pos_N_true == -1: - return self.view(self._valid_rows) - - if pos_N_true < len(self._valid_rows) // 2: + if pos_N_true < len(arr) // 2: mask_arr = blosc2.zeros(shape=len(arr), dtype=np.bool_) mask_arr[: pos_N_true + 1] = True else: @@ -501,67 +609,29 @@ def head(self, N: int = 5) -> CTable: def tail(self, N: int = 5) -> CTable: if N <= 0: - # If N is 0 or negative, return an empty table return self.view(blosc2.zeros(shape=len(self._valid_rows), dtype=np.bool_)) + if self._n_rows <= N: + return self.view(self._valid_rows) + # Physical position of the first row we want = logical index (nrows - N) arr = self._valid_rows - count = 0 - chunk_size = arr.chunks[0] - pos_N_true = -1 - - # Convert to list to iterate chunks in reverse order (metadata only, ~0 memory) - for info in reversed(list(arr.iterchunks_info())): - actual_size = min(chunk_size, arr.shape[0] - info.nchunk * chunk_size) - chunk_start = info.nchunk * chunk_size - - # All False without decompressing -> skip - if info.special == blosc2.SpecialValue.ZERO: - continue - - # Repeated value -> check if True or False - if info.special == blosc2.SpecialValue.VALUE: - val = np.frombuffer(info.repeated_value, dtype=arr.dtype)[0] - if not val: - continue # all False, skip - - # All True: target is at offset 'actual_size - (N - count)' from chunk start - if count + actual_size < N: - count += actual_size - continue - pos_N_true = chunk_start + actual_size - (N - count) - break - - # General case: decompress only this chunk - chunk_data = arr[chunk_start : chunk_start + actual_size] - - n_true = int(np.count_nonzero(chunk_data)) - if count + n_true < N: - count += n_true - continue - - # The N-th True from the end is in this chunk - # We use negative indexing [-(N - count)] to get elements from the back - pos_N_true = chunk_start + int(np.flatnonzero(chunk_data)[-(N - count)]) - break + pos_start = _find_physical_index(arr, self._n_rows - N) - if pos_N_true == -1: - return self.view(self._valid_rows) - - # Mask creation logic reversed: keep everything from pos_N_true to the end - if pos_N_true > len(arr) // 2: - # We keep a small tail (less than half the array): start with zeros + if pos_start > len(arr) // 2: mask_arr = blosc2.zeros(shape=len(arr), dtype=np.bool_) - mask_arr[pos_N_true:] = True + mask_arr[pos_start:] = True else: - # We keep a large tail (more than half the array): start with ones mask_arr = blosc2.ones(shape=len(arr), dtype=np.bool_) - if pos_N_true > 0: - mask_arr[:pos_N_true] = False + if pos_start > 0: + mask_arr[:pos_start] = False - # Compute intersection with existing valid rows and creating view mask_arr = (mask_arr & self._valid_rows).compute() return self.view(mask_arr) + # ------------------------------------------------------------------ + # Column access + # ------------------------------------------------------------------ + def __getitem__(self, s: str): if s in self._cols: return Column(self, s) @@ -572,6 +642,10 @@ def __getattr__(self, s: str): return Column(self, s) return super().__getattribute__(s) + # ------------------------------------------------------------------ + # Compaction + # ------------------------------------------------------------------ + def compact(self): real_poss = blosc2.where(self._valid_rows, np.array(range(len(self._valid_rows)))).compute() start = 0 @@ -585,6 +659,11 @@ def compact(self): self._valid_rows[: self._n_rows] = True self._valid_rows[self._n_rows :] = False + self._last_pos = self._n_rows # next write goes right after live rows + + # ------------------------------------------------------------------ + # Properties / info + # ------------------------------------------------------------------ @property def nrows(self) -> int: @@ -594,15 +673,39 @@ def nrows(self) -> int: def ncols(self) -> int: return len(self._cols) - def info(self) -> None: + @property + def schema(self) -> CompiledSchema: + """The compiled schema that drives this table's columns and validation.""" + return self._schema + + def column_schema(self, name: str) -> CompiledColumn: + """Return the :class:`CompiledColumn` descriptor for *name*. + + Raises + ------ + KeyError + If *name* is not a column in this table. """ - Prints a concise summary of the CTable, including the column names, - their data types, and memory layout. + try: + return self._schema.columns_by_name[name] + except KeyError: + raise KeyError(f"No column named {name!r}. Available: {self.col_names}") from None + + def schema_dict(self) -> dict[str, Any]: + """Return a JSON-compatible dict describing this table's schema. + + Suitable for debugging, serialization, and future ``save()``/``load()`` + support. See :func:`~blosc2.schema_compiler.schema_to_dict`. """ + from blosc2.schema_compiler import schema_to_dict + + return schema_to_dict(self._schema) + + def info(self) -> None: + """Print a concise summary of the CTable.""" n_cols = len(self._cols) n_rows = len(self) - # Calculate global memory usage cbytes = sum(col.cbytes for col in self._cols.values()) + self._valid_rows.cbytes nbytes = sum(col.nbytes for col in self._cols.values()) + self._valid_rows.nbytes @@ -624,7 +727,6 @@ def format_bytes(bytes_size: float) -> str: lines.append(f"nºRows: {n_rows}") lines.append("") - # New Header: replaced "Non-Null Count" with internal Array length & Itemsize header = f" {'#':>3} {'Column':<15} {'Itemsize':<12} {'Dtype':<15}" lines.append(header) lines.append(f" {'---':>3} {'------':<15} {'--------':<12} {'-----':<15}") @@ -633,9 +735,7 @@ def format_bytes(bytes_size: float) -> str: col_array = self._cols[name] dtype_str = str(col_array.dtype) itemsize = f"{col_array.dtype.itemsize} B" - - line = f" {i:>3} {name:<15} {itemsize:<12} {dtype_str:<15}" - lines.append(line) + lines.append(f" {i:>3} {name:<15} {itemsize:<12} {dtype_str:<15}") lines.append("") lines.append(f"memory usage: {format_bytes(cbytes)}") @@ -645,56 +745,53 @@ def format_bytes(bytes_size: float) -> str: print("\n".join(lines)) - def append(self, data: list | np.void | np.ndarray) -> None: - if self.base is not None: - raise TypeError("Cannot extend view.") - - is_list = isinstance(data, (list, tuple)) + # ------------------------------------------------------------------ + # Mutation: append / extend / delete + # ------------------------------------------------------------------ - arr = self._valid_rows - chunk_size = arr.chunks[0] - last_true_pos = -1 + def _load_initial_data(self, new_data) -> None: + """Dispatch new_data to append() or extend() as appropriate.""" + is_append = False - for info in reversed(list(arr.iterchunks_info())): - actual_size = min(chunk_size, arr.shape[0] - info.nchunk * chunk_size) - chunk_start = info.nchunk * chunk_size + if isinstance(new_data, (np.void, np.record)): + is_append = True + elif isinstance(new_data, np.ndarray): + if new_data.dtype.names is not None and new_data.ndim == 0: + is_append = True + elif isinstance(new_data, list) and len(new_data) > 0: + first_elem = new_data[0] + if isinstance(first_elem, (str, bytes, int, float, bool, complex)): + is_append = True - if info.special == blosc2.SpecialValue.ZERO: - continue + if is_append: + self.append(new_data) + else: + self.extend(new_data) - if info.special == blosc2.SpecialValue.VALUE: - val = np.frombuffer(info.repeated_value, dtype=arr.dtype)[0] - if not val: - continue - last_true_pos = chunk_start + actual_size - 1 - break + def append(self, data: list | np.void | np.ndarray) -> None: + if self.base is not None: + raise TypeError("Cannot extend view.") - chunk_data = arr[chunk_start : chunk_start + actual_size] - nonzero = np.flatnonzero(chunk_data) - if len(nonzero) == 0: - continue - last_true_pos = chunk_start + int(nonzero[-1]) - break + # Normalize → validate → coerce + row = self._normalize_row_input(data) + if self._validate: + from blosc2.schema_validation import validate_row - pos = last_true_pos + 1 + row = validate_row(self._schema, row) + row = self._coerce_row_to_storage(row) + pos = self._resolve_last_pos() if pos >= len(self._valid_rows): - c = len(self._valid_rows) - for v in self._cols.values(): - v.resize((c * 2,)) - self._valid_rows.resize((c * 2,)) - - if is_list: - for i, col_array in enumerate(self._cols.values()): - col_array[pos] = data[i] - else: - for name, col_array in self._cols.items(): - col_array[pos] = data[name] + self._grow() + + for name, col_array in self._cols.items(): + col_array[pos] = row[name] self._valid_rows[pos] = True + self._last_pos = pos + 1 self._n_rows += 1 - def delete(self, ind: int | slice | str | Iterable) -> blosc2.NDArray: + def delete(self, ind: int | slice | str | Iterable) -> None: valid_rows_np = self._valid_rows[:] true_pos = np.where(valid_rows_np)[0] @@ -704,21 +801,23 @@ def delete(self, ind: int | slice | str | Iterable) -> blosc2.NDArray: raise TypeError(f"Invalid type '{type(ind)}'") false_pos = true_pos[ind] + n_deleted = len(np.unique(false_pos)) - new_mask_np = valid_rows_np.copy() - new_mask_np[false_pos] = False + valid_rows_np[false_pos] = False + self._valid_rows[:] = valid_rows_np # write back in-place; no new array created + self._n_rows -= n_deleted + self._last_pos = None # recalculate on next write - new_mask = blosc2.asarray(new_mask_np) - self._valid_rows = new_mask - self._n_rows = blosc2.count_nonzero(self._valid_rows) - - def extend(self, data: list | CTable | Any) -> None: + def extend(self, data: list | CTable | Any, *, validate: bool | None = None) -> None: if self.base is not None: raise TypeError("Cannot extend view.") if len(data) <= 0: return - ultimas_validas = blosc2.where(self._valid_rows, np.array(range(len(self._valid_rows)))).compute() - start_pos = ultimas_validas[-1] + 1 if len(ultimas_validas) > 0 else 0 + + # Resolve effective validate flag: per-call override takes precedence + do_validate = self._validate if validate is None else validate + + start_pos = self._resolve_last_pos() current_col_names = self.col_names columns_to_insert = [] @@ -738,6 +837,13 @@ def extend(self, data: list | CTable | Any) -> None: columns_to_insert = list(zip(*data, strict=False)) new_nrows = len(data) + # Validate constraints column-by-column before writing + if do_validate: + from blosc2.schema_vectorized import validate_column_batch + + raw_columns = {current_col_names[i]: columns_to_insert[i] for i in range(len(current_col_names))} + validate_column_batch(self._schema, raw_columns) + processed_cols = [] for i, raw_col in enumerate(columns_to_insert): target_dtype = self._cols[current_col_names[i]].dtype @@ -747,25 +853,23 @@ def extend(self, data: list | CTable | Any) -> None: end_pos = start_pos + new_nrows if self.auto_compact and end_pos >= len(self._valid_rows): - self.compact() - ultimas_validas = blosc2.where( - self._valid_rows, np.array(range(len(self._valid_rows))) - ).compute() - start_pos = ultimas_validas[-1] + 1 if len(ultimas_validas) > 0 else 0 + self.compact() # sets _last_pos = _n_rows + start_pos = self._last_pos end_pos = start_pos + new_nrows while end_pos > len(self._valid_rows): - c = len(self._valid_rows) - for name in current_col_names: - self._cols[name].resize((c * 2,)) - self._valid_rows.resize((c * 2,)) + self._grow() - # Do this per chunks for j, name in enumerate(current_col_names): self._cols[name][start_pos:end_pos] = processed_cols[j][:] self._valid_rows[start_pos:end_pos] = True - self._n_rows = blosc2.count_nonzero(self._valid_rows) + self._last_pos = end_pos + self._n_rows += new_nrows + + # ------------------------------------------------------------------ + # Filtering + # ------------------------------------------------------------------ @profile def where(self, expr_result) -> CTable: @@ -805,7 +909,9 @@ def _run_row_logic(self, ind: int | slice | str | Iterable) -> CTable: new_mask = blosc2.asarray(new_mask_np) return self.view(new_mask) - """Save & load are blank for now""" + # ------------------------------------------------------------------ + # Persistence (not yet implemented) + # ------------------------------------------------------------------ def save(self, urlpath: str, group: str = "table") -> None: ... diff --git a/src/blosc2/schema.py b/src/blosc2/schema.py new file mode 100644 index 00000000..98df998b --- /dev/null +++ b/src/blosc2/schema.py @@ -0,0 +1,348 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# This source code is licensed under a BSD-style license (found in the +# LICENSE file in the root directory of this source tree) +####################################################################### + +"""Schema spec primitives and field helper for CTable.""" + +from __future__ import annotations + +import dataclasses +from dataclasses import MISSING +from typing import Any + +import numpy as np + +BLOSC2_FIELD_METADATA_KEY = "blosc2" + +# Aliases so we can still use the builtins inside this module +# after our spec classes shadow them. +_builtin_bool = bool +_builtin_bytes = bytes + + +# --------------------------------------------------------------------------- +# Base spec class +# --------------------------------------------------------------------------- + + +class SchemaSpec: + """Base class for all Blosc2 column schema descriptors. + + Subclasses carry the logical type, storage dtype, and optional + validation constraints for one column. + """ + + dtype: np.dtype + python_type: type + + def to_pydantic_kwargs(self) -> dict[str, Any]: + """Return kwargs for building a Pydantic field annotation.""" + raise NotImplementedError + + def to_metadata_dict(self) -> dict[str, Any]: + """Return a JSON-compatible dict for schema serialization.""" + raise NotImplementedError + + +# --------------------------------------------------------------------------- +# Numeric spec classes +# --------------------------------------------------------------------------- + +# Internal helper to avoid repeating the constraint boilerplate for every +# integer and float spec. Subclasses only need to set `dtype`, `python_type`, +# and `_kind` as class attributes. + + +class _NumericSpec(SchemaSpec): + """Mixin for numeric specs that support ge / gt / le / lt constraints.""" + + _kind: str # set by each concrete subclass + + def __init__(self, *, ge=None, gt=None, le=None, lt=None): + self.ge = ge + self.gt = gt + self.le = le + self.lt = lt + + def to_pydantic_kwargs(self) -> dict[str, Any]: + return { + k: v + for k, v in {"ge": self.ge, "gt": self.gt, "le": self.le, "lt": self.lt}.items() + if v is not None + } + + def to_metadata_dict(self) -> dict[str, Any]: + return {"kind": self._kind, **self.to_pydantic_kwargs()} + + +# ── Signed integers ────────────────────────────────────────────────────────── + + +class int8(_NumericSpec): + """8-bit signed integer column (−128 … 127).""" + + dtype = np.dtype(np.int8) + python_type = int + _kind = "int8" + + +class int16(_NumericSpec): + """16-bit signed integer column (−32 768 … 32 767).""" + + dtype = np.dtype(np.int16) + python_type = int + _kind = "int16" + + +class int32(_NumericSpec): + """32-bit signed integer column (−2 147 483 648 … 2 147 483 647).""" + + dtype = np.dtype(np.int32) + python_type = int + _kind = "int32" + + +class int64(_NumericSpec): + """64-bit signed integer column.""" + + dtype = np.dtype(np.int64) + python_type = int + _kind = "int64" + + +# ── Unsigned integers ──────────────────────────────────────────────────────── + + +class uint8(_NumericSpec): + """8-bit unsigned integer column (0 … 255).""" + + dtype = np.dtype(np.uint8) + python_type = int + _kind = "uint8" + + +class uint16(_NumericSpec): + """16-bit unsigned integer column (0 … 65 535).""" + + dtype = np.dtype(np.uint16) + python_type = int + _kind = "uint16" + + +class uint32(_NumericSpec): + """32-bit unsigned integer column (0 … 4 294 967 295).""" + + dtype = np.dtype(np.uint32) + python_type = int + _kind = "uint32" + + +class uint64(_NumericSpec): + """64-bit unsigned integer column.""" + + dtype = np.dtype(np.uint64) + python_type = int + _kind = "uint64" + + +# ── Floating point ─────────────────────────────────────────────────────────── + + +class float32(_NumericSpec): + """32-bit floating-point column (single precision).""" + + dtype = np.dtype(np.float32) + python_type = float + _kind = "float32" + + +class float64(_NumericSpec): + """64-bit floating-point column (double precision).""" + + dtype = np.dtype(np.float64) + python_type = float + _kind = "float64" + + +class complex64(SchemaSpec): + """64-bit complex number column (two 32-bit floats).""" + + dtype = np.dtype(np.complex64) + python_type = complex + + def __init__(self): + pass + + def to_pydantic_kwargs(self) -> dict[str, Any]: + return {} + + def to_metadata_dict(self) -> dict[str, Any]: + return {"kind": "complex64"} + + +class complex128(SchemaSpec): + """128-bit complex number column (two 64-bit floats).""" + + dtype = np.dtype(np.complex128) + python_type = complex + + def __init__(self): + pass + + def to_pydantic_kwargs(self) -> dict[str, Any]: + return {} + + def to_metadata_dict(self) -> dict[str, Any]: + return {"kind": "complex128"} + + +class bool(SchemaSpec): + """Boolean column.""" + + dtype = np.dtype(np.bool_) + python_type = _builtin_bool + + def __init__(self): + pass + + def to_pydantic_kwargs(self) -> dict[str, Any]: + return {} + + def to_metadata_dict(self) -> dict[str, Any]: + return {"kind": "bool"} + + +# --------------------------------------------------------------------------- +# String / bytes spec classes +# --------------------------------------------------------------------------- + + +class string(SchemaSpec): + """Fixed-width Unicode string column. + + Parameters + ---------- + max_length: + Maximum number of characters. Determines the NumPy ``U`` dtype. + Defaults to 32 if not specified. + min_length: + Minimum number of characters (validation only, no effect on dtype). + pattern: + Regex pattern the value must match (validation only). + """ + + python_type = str + _DEFAULT_MAX_LENGTH = 32 + + def __init__(self, *, min_length=None, max_length=None, pattern=None): + self.min_length = min_length + self.max_length = max_length if max_length is not None else self._DEFAULT_MAX_LENGTH + self.pattern = pattern + self.dtype = np.dtype(f"U{self.max_length}") + + def to_pydantic_kwargs(self) -> dict[str, Any]: + d = {} + if self.min_length is not None: + d["min_length"] = self.min_length + if self.max_length is not None: + d["max_length"] = self.max_length + if self.pattern is not None: + d["pattern"] = self.pattern + return d + + def to_metadata_dict(self) -> dict[str, Any]: + return {"kind": "string", **self.to_pydantic_kwargs()} + + +class bytes(SchemaSpec): + """Fixed-width bytes column. + + Parameters + ---------- + max_length: + Maximum number of bytes. Determines the NumPy ``S`` dtype. + Defaults to 32 if not specified. + min_length: + Minimum number of bytes (validation only, no effect on dtype). + """ + + python_type = _builtin_bytes + _DEFAULT_MAX_LENGTH = 32 + + def __init__(self, *, min_length=None, max_length=None): + self.min_length = min_length + self.max_length = max_length if max_length is not None else self._DEFAULT_MAX_LENGTH + self.dtype = np.dtype(f"S{self.max_length}") + + def to_pydantic_kwargs(self) -> dict[str, Any]: + d = {} + if self.min_length is not None: + d["min_length"] = self.min_length + if self.max_length is not None: + d["max_length"] = self.max_length + return d + + def to_metadata_dict(self) -> dict[str, Any]: + return {"kind": "bytes", **self.to_pydantic_kwargs()} + + +# --------------------------------------------------------------------------- +# Field helper +# --------------------------------------------------------------------------- + + +def field( + spec: SchemaSpec, + *, + default=MISSING, + cparams: dict[str, Any] | None = None, + dparams: dict[str, Any] | None = None, + chunks: tuple[int, ...] | None = None, + blocks: tuple[int, ...] | None = None, +) -> dataclasses.Field: + """Attach a Blosc2 schema spec and per-column storage options to a dataclass field. + + Parameters + ---------- + spec: + A schema descriptor such as ``b2.int64(ge=0)`` or ``b2.float64()``. + default: + Default value for the field. Omit for required fields. + cparams: + Compression parameters for this column's NDArray. + dparams: + Decompression parameters for this column's NDArray. + chunks: + Chunk shape for this column's NDArray. + blocks: + Block shape for this column's NDArray. + + Examples + -------- + >>> from dataclasses import dataclass + >>> import blosc2 as b2 + >>> @dataclass + ... class Row: + ... id: int = b2.field(b2.int64(ge=0)) + ... score: float = b2.field(b2.float64(ge=0, le=100)) + ... active: bool = b2.field(b2.bool(), default=True) + """ + if not isinstance(spec, SchemaSpec): + raise TypeError(f"field() requires a SchemaSpec as its first argument, got {type(spec)!r}.") + + metadata = { + BLOSC2_FIELD_METADATA_KEY: { + "spec": spec, + "cparams": cparams, + "dparams": dparams, + "chunks": chunks, + "blocks": blocks, + } + } + if default is MISSING: + return dataclasses.field(metadata=metadata) + return dataclasses.field(default=default, metadata=metadata) diff --git a/src/blosc2/schema_compiler.py b/src/blosc2/schema_compiler.py new file mode 100644 index 00000000..cbf78b06 --- /dev/null +++ b/src/blosc2/schema_compiler.py @@ -0,0 +1,409 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# This source code is licensed under a BSD-style license (found in the +# LICENSE file in the root directory of this source tree) +####################################################################### + +"""Schema compiler: turns a dataclass row definition into a CompiledSchema.""" + +from __future__ import annotations + +import dataclasses +import typing +from dataclasses import MISSING +from typing import Any + +import numpy as np # noqa: TC002 + +from blosc2.schema import ( + BLOSC2_FIELD_METADATA_KEY, + SchemaSpec, + complex64, + complex128, + float32, + float64, + int8, + int16, + int32, + int64, + string, + uint8, + uint16, + uint32, + uint64, +) +from blosc2.schema import ( + bool as b2_bool, +) +from blosc2.schema import ( + bytes as b2_bytes, +) + +# Maps the "kind" string used in serialized dicts back to spec constructors. +_KIND_TO_SPEC: dict[str, type[SchemaSpec]] = { + # signed integers + "int8": int8, + "int16": int16, + "int32": int32, + "int64": int64, + # unsigned integers + "uint8": uint8, + "uint16": uint16, + "uint32": uint32, + "uint64": uint64, + # floats + "float32": float32, + "float64": float64, + # complex + "complex64": complex64, + "complex128": complex128, + # bool / string / bytes + "bool": b2_bool, + "string": string, + "bytes": b2_bytes, +} + +# --------------------------------------------------------------------------- +# Display-width helper (used by CTable.__str__ / info()) +# --------------------------------------------------------------------------- + +_DTYPE_DISPLAY_WIDTH: dict[str, int] = { + "int8": 6, + "int16": 8, + "int32": 10, + "int64": 12, + "uint8": 6, + "uint16": 8, + "uint32": 10, + "uint64": 12, + "float32": 12, + "float64": 15, + "bool": 6, + "complex64": 20, + "complex128": 25, +} + + +def compute_display_width(spec: SchemaSpec) -> int: + """Return a reasonable terminal display width for *spec*'s column.""" + dtype = spec.dtype + if dtype.kind == "U": # fixed-width unicode (string spec) + return max(10, min(dtype.itemsize // 4, 50)) + if dtype.kind == "S": # fixed-width bytes + return max(10, min(dtype.itemsize, 50)) + return _DTYPE_DISPLAY_WIDTH.get(dtype.name, 20) + + +# --------------------------------------------------------------------------- +# Mapping from Python primitive annotations to default spec constructors. +# Keys are the actual builtin types (bool before int because bool <: int). +# --------------------------------------------------------------------------- +_ANNOTATION_TO_SPEC: dict[type, type[SchemaSpec]] = { + bool: b2_bool, # must come before int (bool is a subclass of int) + int: int64, + float: float64, + complex: complex128, + str: string, + bytes: b2_bytes, +} + + +# --------------------------------------------------------------------------- +# Compiled representations +# --------------------------------------------------------------------------- + + +@dataclasses.dataclass(slots=True) +class ColumnConfig: + """Per-column NDArray storage options.""" + + cparams: dict[str, Any] | None + dparams: dict[str, Any] | None + chunks: tuple[int, ...] | None + blocks: tuple[int, ...] | None + + +@dataclasses.dataclass(slots=True) +class CompiledColumn: + """All compile-time information about a single CTable column.""" + + name: str + py_type: Any + spec: SchemaSpec + dtype: np.dtype + default: Any # MISSING means required (no default) + config: ColumnConfig + display_width: int = 20 # terminal column width for __str__ / info() + + +@dataclasses.dataclass(slots=True) +class CompiledSchema: + """Compiled representation of a CTable row schema. + + Built once per row class by :func:`compile_schema` and cached on the + ``CTable`` instance. Drives NDArray creation, row validation, and + future schema serialization. + """ + + row_cls: type[Any] + columns: list[CompiledColumn] + columns_by_name: dict[str, CompiledColumn] + validator_model: type[Any] | None = None # filled in by schema_validation + + +# --------------------------------------------------------------------------- +# Internal helpers +# --------------------------------------------------------------------------- + + +def get_blosc2_field_metadata(dc_field: dataclasses.Field) -> dict[str, Any] | None: + """Return the ``blosc2`` metadata dict stored on a dataclass field, or ``None``.""" + return dc_field.metadata.get(BLOSC2_FIELD_METADATA_KEY) + + +def infer_spec_from_annotation(annotation: Any) -> SchemaSpec: + """Build a default :class:`SchemaSpec` from a plain Python type annotation. + + Supports ``bool``, ``int``, ``float``, ``str``, and ``bytes``. + + Raises + ------ + TypeError + If the annotation is not one of the supported primitive types. + """ + spec_cls = _ANNOTATION_TO_SPEC.get(annotation) + if spec_cls is None: + raise TypeError( + f"Cannot infer a Blosc2 schema spec from annotation {annotation!r}. " + f"Use b2.field(b2.(...)) to declare this column explicitly." + ) + return spec_cls() + + +def validate_annotation_matches_spec(name: str, annotation: Any, spec: SchemaSpec) -> None: + """Raise :exc:`TypeError` if *annotation* is incompatible with *spec*. + + Parameters + ---------- + name: + Column name, used only in the error message. + annotation: + The resolved Python type from the dataclass field. + spec: + The :class:`SchemaSpec` attached via ``b2.field(...)``. + """ + expected = spec.python_type + if annotation is not expected: + raise TypeError( + f"Column {name!r}: annotation {annotation!r} is incompatible with " + f"spec {type(spec).__name__!r} (expected Python type {expected.__name__!r})." + ) + + +# --------------------------------------------------------------------------- +# Public compiler entry point +# --------------------------------------------------------------------------- + + +def compile_schema(row_cls: type[Any]) -> CompiledSchema: + """Compile *row_cls* (a dataclass) into a :class:`CompiledSchema`. + + Parameters + ---------- + row_cls: + A class decorated with ``@dataclass``. Each field must either carry a + ``b2.field(...)`` default or use a supported plain annotation + (``int``, ``float``, ``bool``, ``str``, ``bytes``). + + Returns + ------- + CompiledSchema + + Raises + ------ + TypeError + If *row_cls* is not a dataclass, if a field spec is incompatible with + its annotation, or if an unsupported annotation is encountered. + """ + if not dataclasses.is_dataclass(row_cls) or not isinstance(row_cls, type): + raise TypeError( + f"{row_cls!r} is not a dataclass type. CTable row schemas must be defined with @dataclass." + ) + + # Resolve string annotations (handles `from __future__ import annotations`) + try: + hints = typing.get_type_hints(row_cls) + except Exception as exc: + raise TypeError(f"Could not resolve type hints for {row_cls!r}: {exc}") from exc + + columns: list[CompiledColumn] = [] + + for dc_field in dataclasses.fields(row_cls): + name = dc_field.name + annotation = hints.get(name, dc_field.type) + meta = get_blosc2_field_metadata(dc_field) + + if meta is not None: + # Explicit b2.field(...) path + spec = meta["spec"] + if not isinstance(spec, SchemaSpec): + raise TypeError( + f"Column {name!r}: b2.field() requires a SchemaSpec as its first " + f"argument, got {type(spec)!r}." + ) + validate_annotation_matches_spec(name, annotation, spec) + config = ColumnConfig( + cparams=meta.get("cparams"), + dparams=meta.get("dparams"), + chunks=meta.get("chunks"), + blocks=meta.get("blocks"), + ) + else: + # Inferred shorthand: plain annotation without b2.field() + spec = infer_spec_from_annotation(annotation) + config = ColumnConfig(cparams=None, dparams=None, chunks=None, blocks=None) + + # Resolve default value + if dc_field.default is not MISSING: + default = dc_field.default + elif dc_field.default_factory is not MISSING: # type: ignore[misc] + default = dc_field.default_factory + else: + default = MISSING + + columns.append( + CompiledColumn( + name=name, + py_type=annotation, + spec=spec, + dtype=spec.dtype, + default=default, + config=config, + display_width=compute_display_width(spec), + ) + ) + + return CompiledSchema( + row_cls=row_cls, + columns=columns, + columns_by_name={col.name: col for col in columns}, + ) + + +# --------------------------------------------------------------------------- +# Schema serialization helpers (Step 12 — persistence groundwork) +# --------------------------------------------------------------------------- + + +def _default_to_json(value: Any) -> Any: + """Convert a field default to a JSON-compatible value.""" + if value is MISSING: + return None + if isinstance(value, complex): + return {"__complex__": True, "real": value.real, "imag": value.imag} + return value + + +def _default_from_json(value: Any) -> Any: + """Reverse of :func:`_default_to_json`.""" + if value is None: + return MISSING + if isinstance(value, dict) and value.get("__complex__"): + return complex(value["real"], value["imag"]) + return value + + +def schema_to_dict(schema: CompiledSchema) -> dict[str, Any]: + """Serialize *schema* to a JSON-compatible dict. + + The result is self-contained: it can be stored as table metadata and + later passed to :func:`schema_from_dict` to reconstruct the schema + without the original Python dataclass. + + Example output:: + + { + "version": 1, + "row_cls": "Row", + "columns": [ + {"name": "id", "kind": "int64", "ge": 0, "default": null}, + {"name": "score", "kind": "float64", "ge": 0, "le": 100, "default": 0.0}, + {"name": "active", "kind": "bool", "default": true}, + ] + } + """ + cols = [] + for col in schema.columns: + entry: dict[str, Any] = {"name": col.name} + entry.update(col.spec.to_metadata_dict()) # adds "kind" + constraints + entry["default"] = _default_to_json(col.default) + if col.config.cparams is not None: + entry["cparams"] = col.config.cparams + if col.config.dparams is not None: + entry["dparams"] = col.config.dparams + if col.config.chunks is not None: + entry["chunks"] = list(col.config.chunks) + if col.config.blocks is not None: + entry["blocks"] = list(col.config.blocks) + cols.append(entry) + + return { + "version": 1, + "row_cls": schema.row_cls.__name__ if schema.row_cls is not None else None, + "columns": cols, + } + + +def schema_from_dict(data: dict[str, Any]) -> CompiledSchema: + """Reconstruct a :class:`CompiledSchema` from a dict produced by + :func:`schema_to_dict`. + + The original Python dataclass is *not* required. ``row_cls`` on the + returned schema will be ``None``. + + Raises + ------ + ValueError + If *data* uses an unknown schema version or an unknown column kind. + """ + version = data.get("version", 1) + if version != 1: + raise ValueError(f"Unsupported schema version {version!r}") + + columns: list[CompiledColumn] = [] + for entry in data["columns"]: + entry = dict(entry) # don't mutate caller's data + name = entry.pop("name") + kind = entry.pop("kind") + default = _default_from_json(entry.pop("default", None)) + cparams = entry.pop("cparams", None) + dparams = entry.pop("dparams", None) + chunks = tuple(entry.pop("chunks")) if "chunks" in entry else None + blocks = tuple(entry.pop("blocks")) if "blocks" in entry else None + + spec_cls = _KIND_TO_SPEC.get(kind) + if spec_cls is None: + raise ValueError(f"Unknown column kind {kind!r}") + + # Remaining keys in entry are constraint kwargs (ge, le, max_length, …) + spec = spec_cls(**entry) + + columns.append( + CompiledColumn( + name=name, + py_type=spec.python_type, + spec=spec, + dtype=spec.dtype, + default=default, + config=ColumnConfig(cparams=cparams, dparams=dparams, chunks=chunks, blocks=blocks), + display_width=compute_display_width(spec), + ) + ) + + return CompiledSchema( + row_cls=None, + columns=columns, + columns_by_name={col.name: col for col in columns}, + ) diff --git a/src/blosc2/schema_validation.py b/src/blosc2/schema_validation.py new file mode 100644 index 00000000..5feabdf8 --- /dev/null +++ b/src/blosc2/schema_validation.py @@ -0,0 +1,103 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# This source code is licensed under a BSD-style license (found in the +# LICENSE file in the root directory of this source tree) +####################################################################### + +"""Row-level validation via an internally-generated Pydantic model. + +All Pydantic-specific logic is isolated here. CTable and the rest of the +schema layer never import from Pydantic directly. +""" + +from __future__ import annotations + +from dataclasses import MISSING +from typing import Any + +from pydantic import BaseModel, Field, ValidationError, create_model + +from blosc2.schema_compiler import CompiledSchema # noqa: TC001 + + +def build_validator_model(schema: CompiledSchema) -> type[BaseModel]: + """Return (and cache) a Pydantic model class for *schema*. + + Built once per schema; subsequent calls return the cached class. + The model enforces all constraints declared in each column's + :class:`~blosc2.schema.SchemaSpec` (``ge``, ``le``, ``gt``, ``lt``, + ``max_length``, ``min_length``, ``pattern``). + """ + if schema.validator_model is not None: + return schema.validator_model + + field_definitions: dict[str, Any] = {} + for col in schema.columns: + pydantic_kwargs = col.spec.to_pydantic_kwargs() + if col.default is MISSING: + field_definitions[col.name] = (col.py_type, Field(**pydantic_kwargs)) + else: + field_definitions[col.name] = (col.py_type, Field(default=col.default, **pydantic_kwargs)) + + model_cls = create_model(f"_Validator_{schema.row_cls.__name__}", **field_definitions) + schema.validator_model = model_cls + return model_cls + + +def validate_row(schema: CompiledSchema, row: dict[str, Any]) -> dict[str, Any]: + """Validate a single row dict and return the coerced values. + + Parameters + ---------- + schema: + Compiled schema for the table. + row: + ``{column_name: value}`` mapping for one row. + + Returns + ------- + dict + Validated (and Pydantic-coerced) values ready for storage. + + Raises + ------ + ValueError + If any constraint is violated. The message includes the column + name and the violated constraint. + """ + model_cls = build_validator_model(schema) + try: + instance = model_cls(**row) + except ValidationError as exc: + # Re-raise as a plain ValueError so callers don't need to import Pydantic. + raise ValueError(str(exc)) from exc + return instance.model_dump() + + +def validate_rows_rowwise(schema: CompiledSchema, rows: list[dict[str, Any]]) -> list[dict[str, Any]]: + """Validate a list of row dicts. Returns a list of validated dicts. + + Parameters + ---------- + schema: + Compiled schema for the table. + rows: + List of ``{column_name: value}`` mappings. + + Raises + ------ + ValueError + On the first row that violates a constraint, with the row index + and the Pydantic error details. + """ + model_cls = build_validator_model(schema) + result = [] + for i, row in enumerate(rows): + try: + instance = model_cls(**row) + except ValidationError as exc: + raise ValueError(f"Row {i}: {exc}") from exc + result.append(instance.model_dump()) + return result diff --git a/src/blosc2/schema_vectorized.py b/src/blosc2/schema_vectorized.py new file mode 100644 index 00000000..26a31874 --- /dev/null +++ b/src/blosc2/schema_vectorized.py @@ -0,0 +1,112 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# This source code is licensed under a BSD-style license (found in the +# LICENSE file in the root directory of this source tree) +####################################################################### + +"""Vectorized (NumPy-based) constraint validation for bulk inserts. + +Used by ``CTable.extend()`` to check entire column arrays at once, +avoiding the per-row Python overhead of Pydantic validation for large +batches. +""" + +from __future__ import annotations + +from typing import Any + +import numpy as np + +from blosc2.schema_compiler import CompiledColumn, CompiledSchema # noqa: TC001 + + +def _validate_string_lengths(col: CompiledColumn, arr: Any) -> None: + """Check min_length / max_length constraints on a string/bytes column.""" + if arr.dtype.kind in ("U", "S"): + lengths = np.char.str_len(arr) + else: + lengths = np.vectorize(len)(arr.astype(object)) + + spec = col.spec + if getattr(spec, "max_length", None) is not None: + bad = lengths > spec.max_length + if np.any(bad): + first = arr.astype(object)[bad][0] + raise ValueError(f"Column '{col.name}': value {first!r} exceeds max_length={spec.max_length}") + if getattr(spec, "min_length", None) is not None: + bad = lengths < spec.min_length + if np.any(bad): + first = arr.astype(object)[bad][0] + raise ValueError( + f"Column '{col.name}': value {first!r} is shorter than min_length={spec.min_length}" + ) + + +def validate_column_values(col: CompiledColumn, values: Any) -> None: + """Check all constraint attributes of *col*'s spec against *values*. + + Parameters + ---------- + col: + Compiled column descriptor (carries the spec with constraints). + values: + Array-like of values for this column. + + Raises + ------ + ValueError + If any value violates a constraint declared on the column's spec. + """ + spec = col.spec + arr = np.asarray(values) + + # Numeric bounds + if getattr(spec, "ge", None) is not None: + bad = arr < spec.ge + if np.any(bad): + first = arr[bad][0] + raise ValueError(f"Column '{col.name}': value {first!r} violates constraint ge={spec.ge}") + if getattr(spec, "gt", None) is not None: + bad = arr <= spec.gt + if np.any(bad): + first = arr[bad][0] + raise ValueError(f"Column '{col.name}': value {first!r} violates constraint gt={spec.gt}") + if getattr(spec, "le", None) is not None: + bad = arr > spec.le + if np.any(bad): + first = arr[bad][0] + raise ValueError(f"Column '{col.name}': value {first!r} violates constraint le={spec.le}") + if getattr(spec, "lt", None) is not None: + bad = arr >= spec.lt + if np.any(bad): + first = arr[bad][0] + raise ValueError(f"Column '{col.name}': value {first!r} violates constraint lt={spec.lt}") + + # String / bytes length bounds + # np.char.str_len is a true C-level vectorized operation for 'U' and 'S' + # dtypes. Fall back to np.vectorize(len) only for unexpected object arrays. + if getattr(spec, "max_length", None) is not None or getattr(spec, "min_length", None) is not None: + _validate_string_lengths(col, arr) + + +def validate_column_batch(schema: CompiledSchema, columns: dict[str, Any]) -> None: + """Validate a dict of column arrays against all constraints in *schema*. + + Parameters + ---------- + schema: + Compiled schema for the table. + columns: + ``{column_name: array_like}`` mapping of the batch being inserted. + + Raises + ------ + ValueError + On the first constraint violation found, naming the column and + the violated constraint. + """ + for col in schema.columns: + if col.name in columns: + validate_column_values(col, columns[col.name]) diff --git a/tests/ctable/test_column.py b/tests/ctable/test_column.py index 60f15a5d..a5b4032a 100644 --- a/tests/ctable/test_column.py +++ b/tests/ctable/test_column.py @@ -5,25 +5,20 @@ # SPDX-License-Identifier: BSD-3-Clause ####################################################################### -from typing import Annotated +from dataclasses import dataclass import numpy as np import pytest -from pydantic import BaseModel, Field import blosc2 from blosc2 import CTable -class NumpyDtype: - def __init__(self, dtype): - self.dtype = dtype - - -class RowModel(BaseModel): - id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) - score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) - active: Annotated[bool, NumpyDtype(np.bool_)] = True +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + score: float = blosc2.field(blosc2.float64(ge=0)) + active: bool = blosc2.field(blosc2.bool(), default=True) DATA20 = [(i, float(i * 10), True) for i in range(20)] @@ -36,7 +31,7 @@ class RowModel(BaseModel): def test_column_metadata(): """dtype correctness, internal reference consistency, and mask defaults.""" - tabla = CTable(RowModel, new_data=DATA20) + tabla = CTable(Row, new_data=DATA20) assert tabla.id.dtype == np.int64 assert tabla.score.dtype == np.float64 @@ -52,7 +47,7 @@ def test_column_metadata(): def test_column_getitem_no_holes(): """int, slice, and list indexing on a full table.""" - tabla = CTable(RowModel, new_data=DATA20) + tabla = CTable(Row, new_data=DATA20) col = tabla.id # int @@ -73,7 +68,7 @@ def test_column_getitem_no_holes(): def test_column_getitem_with_holes(): """int, slice, and list indexing after deletions.""" - tabla = CTable(RowModel, new_data=DATA20) + tabla = CTable(Row, new_data=DATA20) tabla.delete([1, 3, 5, 7, 9]) col = tabla.id @@ -88,7 +83,7 @@ def test_column_getitem_with_holes(): assert list(col[[0, 2, 4]]) == [0, 4, 8] assert list(col[[5, 3, 1]]) == [10, 6, 2] - tabla2 = CTable(RowModel, new_data=DATA20) + tabla2 = CTable(Row, new_data=DATA20) tabla2.delete([1, 3, 5, 7, 9, 11, 13, 15, 17, 19]) col2 = tabla2.id @@ -99,7 +94,7 @@ def test_column_getitem_with_holes(): def test_column_getitem_out_of_range(): """int and list indexing raise IndexError when out of bounds.""" - tabla = CTable(RowModel, new_data=DATA20) + tabla = CTable(Row, new_data=DATA20) tabla.delete([1, 3, 5, 7, 9]) col = tabla.id @@ -113,7 +108,7 @@ def test_column_getitem_out_of_range(): def test_column_setitem_no_holes(): """int, slice, and list assignment on a full table.""" - tabla = CTable(RowModel, new_data=DATA20) + tabla = CTable(Row, new_data=DATA20) col = tabla.id col[0] = 999 @@ -134,7 +129,7 @@ def test_column_setitem_no_holes(): def test_column_setitem_with_holes(): """int, slice, and list assignment after deletions.""" - tabla = CTable(RowModel, new_data=DATA20) + tabla = CTable(Row, new_data=DATA20) tabla.delete([1, 3, 5, 7, 9]) col = tabla.id @@ -162,14 +157,14 @@ def test_column_setitem_with_holes(): def test_column_iter(): """Iteration over full table, with odd-index holes, and on score column.""" - tabla = CTable(RowModel, new_data=DATA20) + tabla = CTable(Row, new_data=DATA20) assert list(tabla.id) == list(range(20)) - tabla2 = CTable(RowModel, new_data=DATA20) + tabla2 = CTable(Row, new_data=DATA20) tabla2.delete([1, 3, 5, 7, 9, 11, 13, 15, 17, 19]) assert list(tabla2.id) == [0, 2, 4, 6, 8, 10, 12, 14, 16, 18] - tabla3 = CTable(RowModel, new_data=DATA20) + tabla3 = CTable(Row, new_data=DATA20) tabla3.delete([0, 5, 10, 15]) # fmt: off expected_score = [ @@ -184,14 +179,14 @@ def test_column_iter(): def test_column_len(): """len() after no deletions, partial deletions, cumulative deletions, and cross-column.""" - tabla = CTable(RowModel, new_data=DATA20) + tabla = CTable(Row, new_data=DATA20) col = tabla.id assert len(col) == 20 tabla.delete([1, 3, 5, 7, 9]) assert len(col) == 15 - tabla2 = CTable(RowModel, new_data=DATA20) + tabla2 = CTable(Row, new_data=DATA20) col2 = tabla2.id tabla2.delete([0, 1, 2]) assert len(col2) == 17 @@ -199,7 +194,7 @@ def test_column_len(): assert len(col2) == 12 data = [(i, float(i * 10), i % 2 == 0) for i in range(10)] - tabla3 = CTable(RowModel, new_data=data, expected_size=10) + tabla3 = CTable(Row, new_data=data, expected_size=10) tabla3.delete([0, 1, 5, 6, 9]) assert len(tabla3.id) == len(tabla3.score) == len(tabla3.active) == 5 for i in range(len(tabla3.id)): @@ -208,12 +203,12 @@ def test_column_len(): def test_column_edge_cases(): """Empty table and fully-deleted table both behave as zero-length columns.""" - tabla = CTable(RowModel) + tabla = CTable(Row) assert len(tabla.id) == 0 assert list(tabla.id) == [] data = [(i, float(i * 10), True) for i in range(10)] - tabla2 = CTable(RowModel, new_data=data) + tabla2 = CTable(Row, new_data=data) tabla2.delete(list(range(10))) assert len(tabla2.id) == 0 assert list(tabla2.id) == [] @@ -226,7 +221,7 @@ def test_column_edge_cases(): def test_column_slice_returns_view(): """Column[slice] returns a Column instance with a non-None mask.""" - tabla = CTable(RowModel, new_data=DATA20) + tabla = CTable(Row, new_data=DATA20) col = tabla.id view = col[0:5] @@ -238,7 +233,7 @@ def test_column_slice_returns_view(): def test_to_array_no_holes(): """to_array() on a slice view returns correct data on a full table.""" - tabla = CTable(RowModel, new_data=DATA20) + tabla = CTable(Row, new_data=DATA20) col = tabla.id np.testing.assert_array_equal(col[0:5].to_numpy(), np.array([0, 1, 2, 3, 4], dtype=np.int64)) @@ -249,7 +244,7 @@ def test_to_array_no_holes(): def test_to_array_with_holes(): """to_array() on a slice view skips deleted rows correctly.""" - tabla = CTable(RowModel, new_data=DATA20) + tabla = CTable(Row, new_data=DATA20) tabla.delete([1, 3, 5, 7, 9, 11, 13, 15, 17, 19]) # keep evens: 0,2,4,...,18 col = tabla.id @@ -261,7 +256,7 @@ def test_to_array_with_holes(): def test_to_array_full_column(): """to_array() with no slice (full column) returns all valid rows.""" - tabla = CTable(RowModel, new_data=DATA20) + tabla = CTable(Row, new_data=DATA20) tabla.delete([0, 10, 19]) col = tabla.id @@ -271,7 +266,7 @@ def test_to_array_full_column(): def test_to_array_mask_does_not_include_deleted(): """Mask & valid_rows intersection excludes deleted rows inside the slice range.""" - tabla = CTable(RowModel, new_data=DATA20) + tabla = CTable(Row, new_data=DATA20) # delete rows 2 and 3, which fall inside slice [0:5] tabla.delete([2, 3]) col = tabla.id @@ -283,7 +278,7 @@ def test_to_array_mask_does_not_include_deleted(): def test_column_view_mask_is_independent(): """Two slice views on the same column have independent masks.""" - tabla = CTable(RowModel, new_data=DATA20) + tabla = CTable(Row, new_data=DATA20) col = tabla.id view_a = col[0:5] diff --git a/tests/ctable/test_compact.py b/tests/ctable/test_compact.py index a0eaebb5..f67688d7 100644 --- a/tests/ctable/test_compact.py +++ b/tests/ctable/test_compact.py @@ -5,23 +5,18 @@ # SPDX-License-Identifier: BSD-3-Clause ####################################################################### -from typing import Annotated +from dataclasses import dataclass import numpy as np -from pydantic import BaseModel, Field +import blosc2 from blosc2 import CTable -# --- Basic model setup for tests --- -class NumpyDtype: - def __init__(self, dtype): - self.dtype = dtype - - -class RowModel(BaseModel): - id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) - score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + score: float = blosc2.field(blosc2.float64(ge=0, le=100)) def generate_test_data(n_rows: int) -> list: @@ -30,7 +25,7 @@ def generate_test_data(n_rows: int) -> list: def test_compact_empty_table(): """Test compact() on a completely empty table (no data).""" - table = CTable(RowModel, expected_size=100) + table = CTable(Row, expected_size=100) assert len(table) == 0 @@ -48,7 +43,7 @@ def test_compact_empty_table(): def test_compact_full_table(): """Test compact() on a completely full table (no holes or free space).""" data = generate_test_data(50) - table = CTable(RowModel, new_data=data, expected_size=50) + table = CTable(Row, new_data=data, expected_size=50) assert len(table) == 50 initial_capacity = len(table._valid_rows) @@ -69,7 +64,7 @@ def test_compact_already_compacted_table(): """Test compact() on a table that has free space but no holes (contiguous data).""" data = generate_test_data(20) # Large expected_size to ensure free space at the end - table = CTable(RowModel, new_data=data, expected_size=100) + table = CTable(Row, new_data=data, expected_size=100) assert len(table) == 20 @@ -93,7 +88,7 @@ def test_compact_already_compacted_table(): def test_compact_with_holes(): """Test compact() on a table with high fragmentation (holes).""" data = generate_test_data(30) - table = CTable(RowModel, new_data=data, expected_size=50) + table = CTable(Row, new_data=data, expected_size=50) # Delete sparsely: leave only [0, 5, 10, 15, 20, 25] to_delete = [i for i in range(30) if i % 5 != 0] @@ -124,7 +119,7 @@ def test_compact_with_holes(): def test_compact_all_deleted(): """Test compact() on a table where absolutely all rows have been deleted.""" data = generate_test_data(20) - table = CTable(RowModel, new_data=data, expected_size=20) + table = CTable(Row, new_data=data, expected_size=20) # Delete everything table.delete(list(range(20))) @@ -144,7 +139,7 @@ def test_compact_all_deleted(): def test_compact_multiple_times(): """Calling compact() multiple times in a row must not corrupt data or crash.""" data = generate_test_data(10) - table = CTable(RowModel, new_data=data, expected_size=20) + table = CTable(Row, new_data=data, expected_size=20) table.delete([1, 3, 5, 7, 9]) # 5 elements remaining diff --git a/tests/ctable/test_construct.py b/tests/ctable/test_construct.py index 63810076..01722c06 100644 --- a/tests/ctable/test_construct.py +++ b/tests/ctable/test_construct.py @@ -5,33 +5,21 @@ # SPDX-License-Identifier: BSD-3-Clause ####################################################################### -from typing import Annotated, TypeVar +from dataclasses import dataclass import numpy as np import pytest -from pydantic import BaseModel, Field import blosc2 from blosc2 import CTable -RowT = TypeVar("RowT", bound=BaseModel) - -class NumpyDtype: - def __init__(self, dtype): - self.dtype = dtype - - -class MaxLen: - def __init__(self, length: int): - self.length = int(length) - - -class RowModel(BaseModel): - id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) - c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) - score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) - active: Annotated[bool, NumpyDtype(np.bool_)] = True +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + c_val: complex = blosc2.field(blosc2.complex128(), default=0j) + score: float = blosc2.field(blosc2.float64(ge=0, le=100), default=0.0) + active: bool = blosc2.field(blosc2.bool(), default=True) # ------------------------------------------------------------------- @@ -78,7 +66,7 @@ def assert_table_equals_data(table: CTable, expected_data: list): def test_empty_table_variants(): """Empty table: default, with expected_size, and with compact=True.""" - table = CTable(RowModel) + table = CTable(Row) assert len(table) == 0 assert table.nrows == 0 assert table.ncols == 4 @@ -86,18 +74,18 @@ def test_empty_table_variants(): assert col_name in table._cols assert isinstance(table._cols[col_name], blosc2.NDArray) - table_sized = CTable(RowModel, expected_size=5000) + table_sized = CTable(Row, expected_size=5000) assert len(table_sized) == 0 assert all(len(col) == 5000 for col in table_sized._cols.values()) - table_compact = CTable(RowModel, compact=True) + table_compact = CTable(Row, compact=True) assert len(table_compact) == 0 assert table_compact.auto_compact is True def test_empty_data_lifecycle(): """Create from [], extend with [], then extend with real data.""" - table = CTable(RowModel, new_data=[]) + table = CTable(Row, new_data=[]) assert len(table) == 0 table.extend([]) @@ -109,52 +97,56 @@ def test_empty_data_lifecycle(): def test_construction_sources(): """List of tuples and structured array both produce identical tables.""" - assert_table_equals_data(CTable(RowModel, new_data=SMALL_DATA), SMALL_DATA) - assert_table_equals_data(CTable(RowModel, new_data=SMALL_STRUCT), SMALL_DATA) + assert_table_equals_data(CTable(Row, new_data=SMALL_DATA), SMALL_DATA) + assert_table_equals_data(CTable(Row, new_data=SMALL_STRUCT), SMALL_DATA) def test_expected_size_variants(): """expected_size smaller, exact, and larger than the inserted data.""" for es in [1, 5]: - assert_table_equals_data(CTable(RowModel, new_data=SMALL_DATA, expected_size=es), SMALL_DATA) + assert_table_equals_data(CTable(Row, new_data=SMALL_DATA, expected_size=es), SMALL_DATA) - table_large = CTable(RowModel, new_data=SMALL_DATA, expected_size=1000) + table_large = CTable(Row, new_data=SMALL_DATA, expected_size=1000) assert_table_equals_data(table_large, SMALL_DATA) assert all(len(col) == 1000 for col in table_large._cols.values()) def test_compact_flag(): """compact=False and compact=True both preserve data correctly.""" - table_false = CTable(RowModel, new_data=SMALL_DATA, compact=False) + table_false = CTable(Row, new_data=SMALL_DATA, compact=False) assert table_false.auto_compact is False assert_table_equals_data(table_false, SMALL_DATA) - table_true = CTable(RowModel, new_data=SMALL_DATA, compact=True) + table_true = CTable(Row, new_data=SMALL_DATA, compact=True) assert table_true.auto_compact is True assert_table_equals_data(table_true, SMALL_DATA) def test_append_and_clone(): """Build table row by row, then clone it into a new CTable.""" - table = CTable(RowModel) + table = CTable(Row) for row in SMALLEST_DATA: table.append(row) assert_table_equals_data(table, SMALLEST_DATA) - cloned = CTable(RowModel, new_data=table) + cloned = CTable(Row, new_data=table) assert_table_equals_data(cloned, SMALLEST_DATA) assert table is not cloned def test_invalid_append(): - """Wrong length, incompatible type, and dict all raise errors.""" - table = CTable(RowModel, expected_size=1) + """Constraint violation and incompatible type both raise errors.""" + table = CTable(Row, expected_size=1) + + # Constraint violation: id must be >= 0 + with pytest.raises(ValueError): + table.append((-1, 1 + 2j, 95.5, True)) - # Too few values → IndexError (NumPy raises natively after simplification) - with pytest.raises((IndexError, ValueError)): - table.append([1, 1 + 2j, 95.5]) # missing boolean + # Constraint violation: score must be <= 100 + with pytest.raises(ValueError): + table.append((1, 1 + 2j, 150.0, True)) - # Incompatible type → TypeError or ValueError from NumPy + # Incompatible type for id: string cannot be coerced to int with pytest.raises((TypeError, ValueError)): table.append(["invalid_text", 1 + 2j, 95.5, True]) @@ -178,20 +170,20 @@ def test_extreme_values(): (2**60, 0j, 50.0, True), ] for data in [extreme_complex, extreme_float, extreme_int]: - assert_table_equals_data(CTable(RowModel, new_data=data), data) + assert_table_equals_data(CTable(Row, new_data=data), data) def test_extend_append_and_resize(): """Auto-resize via append one-by-one, then extend+append beyond initial size.""" # Append beyond expected_size triggers resize - table = CTable(RowModel, expected_size=2) + table = CTable(Row, expected_size=2) for row in SMALL_DATA: table.append(row) assert_table_equals_data(table, SMALL_DATA) assert all(len(col) >= 5 for col in table._cols.values()) # Extend beyond expected_size, then append the last row - table2 = CTable(RowModel, expected_size=2) + table2 = CTable(Row, expected_size=2) table2.extend(SMALL_DATA[:4]) assert len(table2) == 4 table2.append(SMALL_DATA[4]) @@ -200,7 +192,7 @@ def test_extend_append_and_resize(): def test_column_integrity(): """Column access via [] and getattr, and correct dtypes.""" - table = CTable(RowModel, new_data=SMALL_DATA) + table = CTable(Row, new_data=SMALL_DATA) assert isinstance(table["id"], blosc2.ctable.Column) assert isinstance(table.score, blosc2.ctable.Column) @@ -213,10 +205,10 @@ def test_column_integrity(): def test_valid_rows(): """_valid_rows has exactly 5 True entries after creation and after extend.""" - table_direct = CTable(RowModel, new_data=SMALL_DATA) + table_direct = CTable(Row, new_data=SMALL_DATA) assert blosc2.count_nonzero(table_direct._valid_rows) == 5 - table_extended = CTable(RowModel) + table_extended = CTable(Row) table_extended.extend(SMALL_DATA) assert blosc2.count_nonzero(table_extended._valid_rows) == 5 diff --git a/tests/ctable/test_ctable_dataclass_schema.py b/tests/ctable/test_ctable_dataclass_schema.py new file mode 100644 index 00000000..90283269 --- /dev/null +++ b/tests/ctable/test_ctable_dataclass_schema.py @@ -0,0 +1,289 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +"""End-to-end CTable tests using the dataclass schema API.""" + +from dataclasses import dataclass + +import numpy as np +import pytest + +import blosc2 +from blosc2 import CTable +from blosc2.schema_compiler import schema_from_dict + + +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + score: float = blosc2.field(blosc2.float64(ge=0, le=100), default=0.0) + active: bool = blosc2.field(blosc2.bool(), default=True) + + +@dataclass +class RowComplex: + id: int = blosc2.field(blosc2.int64(ge=0)) + c_val: complex = blosc2.field(blosc2.complex128(), default=0j) + score: float = blosc2.field(blosc2.float64(ge=0, le=100), default=0.0) + active: bool = blosc2.field(blosc2.bool(), default=True) + + +# ------------------------------------------------------------------- +# Construction +# ------------------------------------------------------------------- + + +def test_construction_empty(): + t = CTable(Row) + assert len(t) == 0 + assert t.ncols == 3 + assert t.col_names == ["id", "score", "active"] + + +def test_construction_with_data(): + data = [(i, float(i), True) for i in range(10)] + t = CTable(Row, new_data=data) + assert len(t) == 10 + + +def test_construction_expected_size(): + t = CTable(Row, expected_size=500) + assert all(len(col) == 500 for col in t._cols.values()) + + +# ------------------------------------------------------------------- +# append — different input shapes +# ------------------------------------------------------------------- + + +def test_append_tuple(): + t = CTable(Row) + t.append((1, 50.0, True)) + assert len(t) == 1 + assert t.row[0].id[0] == 1 + assert t.row[0].score[0] == 50.0 + assert t.row[0].active[0] + + +def test_append_list(): + t = CTable(Row) + t.append([2, 75.0, False]) + assert len(t) == 1 + assert t.row[0].id[0] == 2 + + +def test_append_dict(): + t = CTable(Row) + t.append({"id": 3, "score": 25.0, "active": True}) + assert len(t) == 1 + assert t.row[0].id[0] == 3 + + +def test_append_dataclass_instance(): + t = CTable(Row) + + @dataclass + class Row2: + id: int = blosc2.field(blosc2.int64(ge=0)) + score: float = blosc2.field(blosc2.float64(ge=0, le=100), default=0.0) + active: bool = blosc2.field(blosc2.bool(), default=True) + + t2 = CTable(Row2) + # Simulate appending a dict (dataclass instance path) + t2.append({"id": 4, "score": 10.0, "active": False}) + assert t2.row[0].id[0] == 4 + + +def test_append_defaults_filled(): + """Omitting optional fields fills them from defaults.""" + t = CTable(Row) + t.append((5,)) # only id; score=0.0 and active=True filled in + assert t.row[0].score[0] == 0.0 + assert t.row[0].active[0] + + +# ------------------------------------------------------------------- +# extend — iterable of rows +# ------------------------------------------------------------------- + + +def test_extend_list_of_tuples(): + t = CTable(Row, expected_size=10) + t.extend([(i, float(i), i % 2 == 0) for i in range(10)]) + assert len(t) == 10 + + +def test_extend_list_of_dicts(): + """extend() also accepts list of dicts via zip(*data) → positional path.""" + # This goes through the zip(*data) path so dicts aren't directly supported + # in extend; test that the common tuple path works correctly. + t = CTable(Row, expected_size=5) + data = [(i, float(i * 10), True) for i in range(5)] + t.extend(data) + for i in range(5): + assert t.row[i].id[0] == i + + +def test_extend_numpy_structured(): + dtype = np.dtype([("id", np.int64), ("score", np.float64), ("active", np.bool_)]) + arr = np.array([(1, 50.0, True), (2, 75.0, False)], dtype=dtype) + t = CTable(Row, expected_size=5) + t.extend(arr) + assert len(t) == 2 + assert t.row[0].id[0] == 1 + assert t.row[1].score[0] == 75.0 + + +# ------------------------------------------------------------------- +# extend — per-call validate override +# ------------------------------------------------------------------- + + +def test_extend_validate_override_false(): + """validate=False on a per-call basis skips checks even for a table with validate=True.""" + t = CTable(Row, expected_size=5, validate=True) + # Would fail if validate were applied + t.extend([(-1, 200.0, True)], validate=False) + assert len(t) == 1 + + +def test_extend_validate_override_true(): + """validate=True on a per-call basis enforces checks even for a table with validate=False.""" + t = CTable(Row, expected_size=5, validate=False) + with pytest.raises(ValueError): + t.extend([(-1, 50.0, True)], validate=True) + + +def test_extend_validate_none_uses_table_default(): + t_on = CTable(Row, expected_size=5, validate=True) + with pytest.raises(ValueError): + t_on.extend([(-1, 50.0, True)], validate=None) + + t_off = CTable(Row, expected_size=5, validate=False) + t_off.extend([(-1, 50.0, True)], validate=None) # no error + assert len(t_off) == 1 + + +# ------------------------------------------------------------------- +# Schema introspection (Step 9) +# ------------------------------------------------------------------- + + +def test_schema_property(): + from blosc2.schema_compiler import CompiledSchema + + t = CTable(Row) + assert isinstance(t.schema, CompiledSchema) + assert t.schema.row_cls is Row + + +def test_column_schema(): + from blosc2.schema_compiler import CompiledColumn + + t = CTable(Row) + col = t.column_schema("id") + assert isinstance(col, CompiledColumn) + assert col.name == "id" + assert col.spec.ge == 0 + + +def test_column_schema_unknown(): + t = CTable(Row) + with pytest.raises(KeyError, match="no_such_col"): + t.column_schema("no_such_col") + + +def test_schema_dict(): + t = CTable(Row) + d = t.schema_dict() + assert d["version"] == 1 + assert d["row_cls"] == "Row" + col_names = [c["name"] for c in d["columns"]] + assert col_names == ["id", "score", "active"] + + +def test_schema_dict_roundtrip(): + """schema_from_dict on a CTable's schema_dict restores column structure.""" + t = CTable(Row) + d = t.schema_dict() + restored = schema_from_dict(d) + assert len(restored.columns) == 3 + assert restored.columns_by_name["id"].spec.ge == 0 + assert restored.columns_by_name["score"].spec.le == 100 + + +# ------------------------------------------------------------------- +# Per-column cparams plumbing +# ------------------------------------------------------------------- + + +def test_per_column_cparams(): + """Columns with custom cparams get their own NDArray settings.""" + + @dataclass + class CustomRow: + id: int = blosc2.field(blosc2.int64(), cparams={"clevel": 9}) + score: float = blosc2.field(blosc2.float64(), default=0.0) + + t = CTable(CustomRow, expected_size=10) + # The column schema reflects the cparams + assert t.column_schema("id").config.cparams == {"clevel": 9} + assert t.column_schema("score").config.cparams is None + + +# ------------------------------------------------------------------- +# New integer / float spec types used in CTable +# ------------------------------------------------------------------- + + +def test_new_spec_types_in_ctable(): + """int8, uint16, float32 and friends work correctly end-to-end in CTable.""" + + @dataclass + class Compact: + flags: int = blosc2.field(blosc2.uint8(le=255)) + level: int = blosc2.field(blosc2.int8(ge=-128, le=127), default=0) + ratio: float = blosc2.field(blosc2.float32(ge=0.0, le=1.0), default=0.0) + + t = CTable(Compact, expected_size=10) + t.extend([(0, -1, 0.0), (255, 127, 1.0), (128, 0, 0.5)]) + assert len(t) == 3 + assert t._cols["flags"].dtype == np.dtype(np.uint8) + assert t._cols["level"].dtype == np.dtype(np.int8) + assert t._cols["ratio"].dtype == np.dtype(np.float32) + + +def test_new_spec_constraints_enforced(): + """Constraints on new spec types are enforced by both append and extend.""" + + # uint8 with explicit ge=0: negative value rejected by Pydantic + @dataclass + class R: + x: int = blosc2.field(blosc2.uint8(ge=0, le=200)) + + t = CTable(R, expected_size=5) + with pytest.raises(ValueError): + t.append((-1,)) # violates ge=0 + with pytest.raises(ValueError): + t.append((201,)) # violates le=200 + + # int8 with ge/le: vectorized extend checks + @dataclass + class R2: + x: int = blosc2.field(blosc2.int8(ge=0, le=100)) + + t2 = CTable(R2, expected_size=5) + with pytest.raises(ValueError): + t2.extend([(101,)]) # violates le=100 + with pytest.raises(ValueError): + t2.extend([(-1,)]) # violates ge=0 + + +if __name__ == "__main__": + import pytest + + pytest.main(["-v", __file__]) diff --git a/tests/ctable/test_delete_rows.py b/tests/ctable/test_delete_rows.py index b6d04f59..2f8e9013 100644 --- a/tests/ctable/test_delete_rows.py +++ b/tests/ctable/test_delete_rows.py @@ -5,27 +5,20 @@ # SPDX-License-Identifier: BSD-3-Clause ####################################################################### -from typing import Annotated, TypeVar +from dataclasses import dataclass -import numpy as np import pytest -from pydantic import BaseModel, Field +import blosc2 from blosc2 import CTable -RowT = TypeVar("RowT", bound=BaseModel) - -class NumpyDtype: - def __init__(self, dtype): - self.dtype = dtype - - -class RowModel(BaseModel): - id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) - c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) - score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) - active: Annotated[bool, NumpyDtype(np.bool_)] = True +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + c_val: complex = blosc2.field(blosc2.complex128(), default=0j) + score: float = blosc2.field(blosc2.float64(ge=0, le=100), default=0.0) + active: bool = blosc2.field(blosc2.bool(), default=True) def generate_test_data(n_rows: int) -> list: @@ -42,30 +35,30 @@ def test_delete_single_element(): data = generate_test_data(50) # Delete first - t = CTable(RowModel, new_data=data, expected_size=50) + t = CTable(Row, new_data=data, expected_size=50) t.delete(0) assert len(t) == 49 assert not t._valid_rows[0] # Delete last - t2 = CTable(RowModel, new_data=data, expected_size=50) + t2 = CTable(Row, new_data=data, expected_size=50) t2.delete(-1) assert len(t2) == 49 # Delete middle - t3 = CTable(RowModel, new_data=data, expected_size=50) + t3 = CTable(Row, new_data=data, expected_size=50) t3.delete(25) assert len(t3) == 49 # Delete first 10 times in a row - t4 = CTable(RowModel, new_data=data, expected_size=50) + t4 = CTable(Row, new_data=data, expected_size=50) for i in range(10): t4.delete(0) assert len(t4) == 50 - (i + 1) assert len(t4) == 40 # Delete last 10 times in a row - t5 = CTable(RowModel, new_data=data, expected_size=50) + t5 = CTable(Row, new_data=data, expected_size=50) for i in range(10): t5.delete(-1) assert len(t5) == 50 - (i + 1) @@ -77,42 +70,42 @@ def test_delete_list_of_positions(): data = generate_test_data(50) # Scattered - t = CTable(RowModel, new_data=data, expected_size=50) + t = CTable(Row, new_data=data, expected_size=50) t.delete([0, 10, 20, 30, 40]) assert len(t) == 45 # Consecutive block - t2 = CTable(RowModel, new_data=data, expected_size=50) + t2 = CTable(Row, new_data=data, expected_size=50) t2.delete([5, 6, 7, 8, 9]) assert len(t2) == 45 # All even positions - t3 = CTable(RowModel, new_data=data, expected_size=50) + t3 = CTable(Row, new_data=data, expected_size=50) t3.delete(list(range(0, 50, 2))) assert len(t3) == 25 # All odd positions - t4 = CTable(RowModel, new_data=data, expected_size=50) + t4 = CTable(Row, new_data=data, expected_size=50) t4.delete(list(range(1, 50, 2))) assert len(t4) == 25 # Slice-equivalent: range(10, 20) - t5 = CTable(RowModel, new_data=data, expected_size=50) + t5 = CTable(Row, new_data=data, expected_size=50) t5.delete(list(range(10, 20))) assert len(t5) == 40 # Slice with step: range(0, 20, 2) - t6 = CTable(RowModel, new_data=data, expected_size=50) + t6 = CTable(Row, new_data=data, expected_size=50) t6.delete(list(range(0, 20, 2))) assert len(t6) == 40 # First 10 rows - t7 = CTable(RowModel, new_data=data, expected_size=50) + t7 = CTable(Row, new_data=data, expected_size=50) t7.delete(list(range(0, 10))) assert len(t7) == 40 # Last 10 rows - t8 = CTable(RowModel, new_data=data, expected_size=50) + t8 = CTable(Row, new_data=data, expected_size=50) t8.delete(list(range(40, 50))) assert len(t8) == 40 @@ -122,21 +115,21 @@ def test_delete_out_of_bounds(): data = generate_test_data(50) # Beyond length on full table - t = CTable(RowModel, new_data=data, expected_size=50) + t = CTable(Row, new_data=data, expected_size=50) with pytest.raises(IndexError): t.delete(60) with pytest.raises(IndexError): t.delete(-60) # Beyond nrows on partial table (capacity 50, only 25 rows) - t2 = CTable(RowModel, new_data=generate_test_data(25), expected_size=50) + t2 = CTable(Row, new_data=generate_test_data(25), expected_size=50) assert len(t2) == 25 with pytest.raises(IndexError): t2.delete(35) # Empty table: positions 0, 25, -1 all raise for pos in [0, 25, -1]: - empty = CTable(RowModel, expected_size=50) + empty = CTable(Row, expected_size=50) assert len(empty) == 0 with pytest.raises(IndexError): empty.delete(pos) @@ -147,31 +140,31 @@ def test_delete_edge_cases(): data = generate_test_data(50) # Same logical position twice: second delete hits what was position 11 - t = CTable(RowModel, new_data=data, expected_size=50) + t = CTable(Row, new_data=data, expected_size=50) t.delete(10) assert len(t) == 49 t.delete(10) assert len(t) == 48 # Delete all rows from the front one by one - t2 = CTable(RowModel, new_data=data, expected_size=50) + t2 = CTable(Row, new_data=data, expected_size=50) for _ in range(50): t2.delete(0) assert len(t2) == 0 # Delete all rows from the back one by one - t3 = CTable(RowModel, new_data=data, expected_size=50) + t3 = CTable(Row, new_data=data, expected_size=50) for _ in range(50): t3.delete(-1) assert len(t3) == 0 # Negative indices list - t4 = CTable(RowModel, new_data=data, expected_size=50) + t4 = CTable(Row, new_data=data, expected_size=50) t4.delete([-1, -5, -10]) assert len(t4) == 47 # Mixed positive and negative indices - t5 = CTable(RowModel, new_data=data, expected_size=50) + t5 = CTable(Row, new_data=data, expected_size=50) t5.delete([0, -1, 25]) assert len(t5) == 47 @@ -180,7 +173,7 @@ def test_delete_invalid_types(): """string, float, and list-with-strings all raise errors.""" data = generate_test_data(50) - t = CTable(RowModel, new_data=data, expected_size=50) + t = CTable(Row, new_data=data, expected_size=50) with pytest.raises(TypeError): t.delete("invalid") with pytest.raises(TypeError): @@ -194,12 +187,12 @@ def test_delete_stress(): data = generate_test_data(50) # Delete 40 out of 50 at once - t = CTable(RowModel, new_data=data, expected_size=50) + t = CTable(Row, new_data=data, expected_size=50) t.delete(list(range(0, 40))) assert len(t) == 10 # Alternating two-pass deletion - t2 = CTable(RowModel, new_data=data, expected_size=50) + t2 = CTable(Row, new_data=data, expected_size=50) t2.delete(list(range(0, 50, 2))) # delete all even -> 25 remain assert len(t2) == 25 t2.delete(list(range(0, 25, 2))) # delete every other of remaining -> ~12 diff --git a/tests/ctable/test_extend_delete.py b/tests/ctable/test_extend_delete.py index 42cf974e..41e82e4a 100644 --- a/tests/ctable/test_extend_delete.py +++ b/tests/ctable/test_extend_delete.py @@ -5,27 +5,21 @@ # SPDX-License-Identifier: BSD-3-Clause ####################################################################### -from typing import Annotated, TypeVar +from dataclasses import dataclass import numpy as np import pytest -from pydantic import BaseModel, Field +import blosc2 from blosc2 import CTable -RowT = TypeVar("RowT", bound=BaseModel) - -class NumpyDtype: - def __init__(self, dtype): - self.dtype = dtype - - -class RowModel(BaseModel): - id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) - c_val: Annotated[complex, NumpyDtype(np.complex128)] = Field(default=0j) - score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) - active: Annotated[bool, NumpyDtype(np.bool_)] = True +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + c_val: complex = blosc2.field(blosc2.complex128(), default=0j) + score: float = blosc2.field(blosc2.float64(ge=0, le=100), default=0.0) + active: bool = blosc2.field(blosc2.bool(), default=True) def generate_test_data(n_rows: int, start_id: int = 1) -> list: @@ -59,7 +53,7 @@ def assert_data_at_positions(table: CTable, positions: list, expected_ids: list) def test_gap_fill_mask_and_positions(): """extend and append fill from last valid position; mask is updated correctly.""" # extend after deletions: mask and physical positions - t = CTable(RowModel, new_data=generate_test_data(7, 1), expected_size=10) + t = CTable(Row, new_data=generate_test_data(7, 1), expected_size=10) t.delete([0, 2, 4, 6]) assert_mask_matches(t, [False, True, False, True, False, True, False]) assert len(t) == 3 @@ -69,7 +63,7 @@ def test_gap_fill_mask_and_positions(): assert_data_at_positions(t, [6, 7, 8], [8, 9, 10]) # append fills from last valid position, not into holes - t2 = CTable(RowModel, new_data=generate_test_data(5, 1), expected_size=10) + t2 = CTable(Row, new_data=generate_test_data(5, 1), expected_size=10) t2.delete([1, 3]) assert_mask_matches(t2, [True, False, True, False, True]) t2.append((6, 1j, 50.0, True)) @@ -78,7 +72,7 @@ def test_gap_fill_mask_and_positions(): assert_mask_matches(t2, [True, False, True, False, True, True, True]) # extend fills from last valid position when there's enough capacity - t3 = CTable(RowModel, new_data=generate_test_data(10, 1), expected_size=15) + t3 = CTable(Row, new_data=generate_test_data(10, 1), expected_size=15) t3.delete([2, 4, 6]) t3.extend(generate_test_data(3, 20)) assert_data_at_positions(t3, [10, 11, 12], [20, 21, 22]) @@ -87,7 +81,7 @@ def test_gap_fill_mask_and_positions(): def test_resize_behavior(): """Resize triggered when capacity is full; compact=True avoids massive resize.""" # compact=False: append beyond capacity must resize - t = CTable(RowModel, new_data=generate_test_data(10, 1), expected_size=10, compact=False) + t = CTable(Row, new_data=generate_test_data(10, 1), expected_size=10, compact=False) t.delete(list(range(9))) assert len(t) == 1 initial_cap = len(t._valid_rows) @@ -95,7 +89,7 @@ def test_resize_behavior(): assert len(t._valid_rows) > initial_cap # compact=True: no massive resize after deletions + extend - t2 = CTable(RowModel, new_data=generate_test_data(10, 1), expected_size=10, compact=True) + t2 = CTable(Row, new_data=generate_test_data(10, 1), expected_size=10, compact=True) t2.delete(list(range(9))) assert len(t2) == 1 initial_cap2 = len(t2._valid_rows) @@ -103,7 +97,7 @@ def test_resize_behavior(): assert len(t2._valid_rows) <= initial_cap2 * 2 # extend exceeding capacity always resizes regardless of compact - t3 = CTable(RowModel, new_data=generate_test_data(5, 1), expected_size=10, compact=False) + t3 = CTable(Row, new_data=generate_test_data(5, 1), expected_size=10, compact=False) t3.delete([0, 2, 4]) initial_cap3 = len(t3._valid_rows) t3.extend(generate_test_data(20, 100)) @@ -113,7 +107,7 @@ def test_resize_behavior(): def test_mixed_append_extend_with_gaps(): """Multiple extends, appends, and deletes interleaved; lengths stay correct.""" # Multiple extends with intermediate deletions - t = CTable(RowModel, expected_size=20) + t = CTable(Row, expected_size=20) t.extend(generate_test_data(5, 1)) t.extend(generate_test_data(3, 10)) assert len(t) == 8 @@ -127,7 +121,7 @@ def test_mixed_append_extend_with_gaps(): assert len(t) == 9 # append + extend mixed, delete all then re-extend - t2 = CTable(RowModel, expected_size=20) + t2 = CTable(Row, expected_size=20) for i in range(5): t2.append((i + 1, complex(i), float(i * 10), True)) assert len(t2) == 5 @@ -141,13 +135,13 @@ def test_mixed_append_extend_with_gaps(): assert len(t2) == 9 # Fill all gaps then extend; delete all then extend from scratch - t3 = CTable(RowModel, new_data=generate_test_data(10, 1), expected_size=15) + t3 = CTable(Row, new_data=generate_test_data(10, 1), expected_size=15) t3.delete(list(range(0, 10, 2))) assert len(t3) == 5 t3.extend(generate_test_data(5, 20)) assert len(t3) == 10 - t4 = CTable(RowModel, new_data=generate_test_data(10, 1), expected_size=15) + t4 = CTable(Row, new_data=generate_test_data(10, 1), expected_size=15) t4.delete(list(range(10))) assert len(t4) == 0 t4.extend(generate_test_data(5, 100)) @@ -157,7 +151,7 @@ def test_mixed_append_extend_with_gaps(): def test_compact_behavior(): """Manual compact consolidates mask; auto-compact keeps data correct after extend.""" # Manual compact: valid rows packed to front, extend fills after them - t = CTable(RowModel, new_data=generate_test_data(10, 1), expected_size=15, compact=False) + t = CTable(Row, new_data=generate_test_data(10, 1), expected_size=15, compact=False) t.delete([1, 3, 5, 7, 9]) assert len(t) == 5 t.compact() @@ -166,7 +160,7 @@ def test_compact_behavior(): assert len(t) == 8 # Auto-compact: table stays consistent after heavy deletions + extend - t2 = CTable(RowModel, new_data=generate_test_data(10, 1), expected_size=15, compact=True) + t2 = CTable(Row, new_data=generate_test_data(10, 1), expected_size=15, compact=True) t2.delete(list(range(0, 8))) assert len(t2) == 2 t2.extend(generate_test_data(10, 100)) @@ -176,14 +170,14 @@ def test_compact_behavior(): def test_complex_scenarios(): """Sparse gaps, alternating cycles, data integrity, and full workflow.""" # Sparse table: many scattered deletions then bulk extend - t = CTable(RowModel, new_data=generate_test_data(20, 1), expected_size=30) + t = CTable(Row, new_data=generate_test_data(20, 1), expected_size=30) t.delete([0, 1, 2, 4, 5, 6, 8, 9, 10, 12, 13, 14, 16, 17, 18]) assert len(t) == 5 t.extend(generate_test_data(10, 100)) assert len(t) == 15 # Alternating extend/delete cycles - t2 = CTable(RowModel, expected_size=50) + t2 = CTable(Row, expected_size=50) for cycle in range(5): t2.extend(generate_test_data(10, cycle * 100)) current_len = len(t2) @@ -192,7 +186,7 @@ def test_complex_scenarios(): # Data integrity: correct row values survive delete + extend t3 = CTable( - RowModel, new_data=[(1, 1j, 10.0, True), (2, 2j, 20.0, False), (3, 3j, 30.0, True)], expected_size=10 + Row, new_data=[(1, 1j, 10.0, True), (2, 2j, 20.0, False), (3, 3j, 30.0, True)], expected_size=10 ) t3.delete(1) assert t3.row[0].id[0] == 1 @@ -204,7 +198,7 @@ def test_complex_scenarios(): assert t3.row[3].id[0] == 11 # Full workflow - t4 = CTable(RowModel, expected_size=20, compact=False) + t4 = CTable(Row, expected_size=20, compact=False) t4.extend(generate_test_data(10, 1)) assert len(t4) == 10 t4.delete([0, 2, 4, 6, 8]) diff --git a/tests/ctable/test_row_logic.py b/tests/ctable/test_row_logic.py index 483b7924..bece75c0 100644 --- a/tests/ctable/test_row_logic.py +++ b/tests/ctable/test_row_logic.py @@ -5,25 +5,21 @@ # SPDX-License-Identifier: BSD-3-Clause ####################################################################### -from typing import Annotated +from dataclasses import dataclass import numpy as np import pytest -from pydantic import BaseModel, Field +import blosc2 from blosc2 import CTable from blosc2.ctable import Column -class NumpyDtype: - def __init__(self, dtype): - self.dtype = dtype - - -class RowModel(BaseModel): - id: Annotated[int, NumpyDtype(np.int64)] = Field(ge=0) - score: Annotated[float, NumpyDtype(np.float64)] = Field(ge=0, le=100) - active: Annotated[bool, NumpyDtype(np.bool_)] = True +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + score: float = blosc2.field(blosc2.float64(ge=0)) + active: bool = blosc2.field(blosc2.bool(), default=True) def generate_test_data(n_rows: int, start_id: int = 0) -> list: @@ -40,7 +36,7 @@ def test_row_int_indexing(): data = generate_test_data(20) # No holes: spot checks - t = CTable(RowModel, new_data=data) + t = CTable(Row, new_data=data) r = t.row[0] assert isinstance(r, CTable) assert len(r) == 1 @@ -61,7 +57,7 @@ def test_row_int_indexing(): assert t.row[5].id[0] == 10 # Out of range - t2 = CTable(RowModel, new_data=generate_test_data(10)) + t2 = CTable(Row, new_data=generate_test_data(10)) for idx in [10, 100, -11]: with pytest.raises(IndexError): _ = t2.row[idx] @@ -72,7 +68,7 @@ def test_row_slice_indexing(): data = generate_test_data(20) # No holes - t = CTable(RowModel, new_data=data) + t = CTable(Row, new_data=data) assert isinstance(t.row[0:5], CTable) assert list(t.row[0:5].id) == [0, 1, 2, 3, 4] assert list(t.row[10:15].id) == [10, 11, 12, 13, 14] @@ -92,7 +88,7 @@ def test_row_slice_indexing(): assert list(t.row[5:10].id) == [10, 11, 12, 13, 14] # Beyond bounds - t2 = CTable(RowModel, new_data=generate_test_data(10)) + t2 = CTable(Row, new_data=generate_test_data(10)) assert len(t2.row[11:20]) == 0 assert list(t2.row[5:100].id) == [5, 6, 7, 8, 9] assert len(t2.row[100:]) == 0 @@ -110,7 +106,7 @@ def test_row_list_indexing(): data = generate_test_data(20) # No holes - t = CTable(RowModel, new_data=data) + t = CTable(Row, new_data=data) r = t.row[[0, 5, 10, 15]] assert isinstance(r, CTable) assert len(r) == 4 @@ -123,7 +119,7 @@ def test_row_list_indexing(): assert set(t.row[[5, 3, 1]].id) == {2, 6, 10} # Negative indices in list - t2 = CTable(RowModel, new_data=generate_test_data(10)) + t2 = CTable(Row, new_data=generate_test_data(10)) assert set(t2.row[[0, -1, 5]].id) == {0, 5, 9} # Single element @@ -146,7 +142,7 @@ def test_row_list_indexing(): def test_row_view_properties(): """View metadata, base chain, mask integrity, column liveness, and chained views.""" data = generate_test_data(100) - tabla0 = CTable(RowModel, new_data=data) + tabla0 = CTable(Row, new_data=data) # Base is None on root table assert tabla0.base is None @@ -202,7 +198,7 @@ def test_row_view_properties(): def test_row_edge_cases(): """Empty table, fully-deleted table: int raises IndexError, slice returns empty.""" # Empty table - empty = CTable(RowModel) + empty = CTable(Row) with pytest.raises(IndexError): _ = empty.row[0] assert len(empty.row[:]) == 0 @@ -210,7 +206,7 @@ def test_row_edge_cases(): # All rows deleted data = generate_test_data(10) - t = CTable(RowModel, new_data=data) + t = CTable(Row, new_data=data) t.delete(list(range(10))) with pytest.raises(IndexError): _ = t.row[0] diff --git a/tests/ctable/test_schema_compiler.py b/tests/ctable/test_schema_compiler.py new file mode 100644 index 00000000..812a8c63 --- /dev/null +++ b/tests/ctable/test_schema_compiler.py @@ -0,0 +1,258 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +"""Tests for compile_schema(), schema_to_dict(), and schema_from_dict().""" + +from dataclasses import MISSING, dataclass + +import numpy as np +import pytest + +import blosc2 +from blosc2.schema import bool as b2_bool +from blosc2.schema import complex128, float64, int64, string +from blosc2.schema_compiler import ( + CompiledSchema, + compile_schema, + schema_from_dict, + schema_to_dict, +) + +# ------------------------------------------------------------------- +# Fixtures +# ------------------------------------------------------------------- + + +@dataclass +class Simple: + id: int = blosc2.field(blosc2.int64(ge=0)) + score: float = blosc2.field(blosc2.float64(ge=0, le=100), default=0.0) + active: bool = blosc2.field(blosc2.bool(), default=True) + + +@dataclass +class WithString: + name: str = blosc2.field(blosc2.string(max_length=16)) + value: float = blosc2.field(blosc2.float64(), default=0.0) + + +@dataclass +class WithComplex: + id: int = blosc2.field(blosc2.int64()) + c_val: complex = blosc2.field(blosc2.complex128(), default=0j) + + +# ------------------------------------------------------------------- +# compile_schema — explicit b2.field() +# ------------------------------------------------------------------- + + +def test_compile_returns_compiled_schema(): + s = compile_schema(Simple) + assert isinstance(s, CompiledSchema) + assert s.row_cls is Simple + + +def test_compile_column_count(): + s = compile_schema(Simple) + assert len(s.columns) == 3 + + +def test_compile_column_names_order(): + s = compile_schema(Simple) + assert [c.name for c in s.columns] == ["id", "score", "active"] + + +def test_compile_column_dtypes(): + s = compile_schema(Simple) + assert s.columns_by_name["id"].dtype == np.dtype(np.int64) + assert s.columns_by_name["score"].dtype == np.dtype(np.float64) + assert s.columns_by_name["active"].dtype == np.dtype(np.bool_) + + +def test_compile_column_specs(): + s = compile_schema(Simple) + assert isinstance(s.columns_by_name["id"].spec, int64) + assert s.columns_by_name["id"].spec.ge == 0 + assert isinstance(s.columns_by_name["score"].spec, float64) + assert s.columns_by_name["score"].spec.le == 100 + + +def test_compile_defaults(): + s = compile_schema(Simple) + assert s.columns_by_name["id"].default is MISSING # required + assert s.columns_by_name["score"].default == 0.0 + assert s.columns_by_name["active"].default is True + + +def test_compile_py_types(): + s = compile_schema(Simple) + assert s.columns_by_name["id"].py_type is int + assert s.columns_by_name["score"].py_type is float + assert s.columns_by_name["active"].py_type is bool + + +def test_compile_string_column(): + s = compile_schema(WithString) + col = s.columns_by_name["name"] + assert isinstance(col.spec, string) + assert col.spec.max_length == 16 + assert col.dtype == np.dtype("U16") + + +def test_compile_complex_column(): + s = compile_schema(WithComplex) + col = s.columns_by_name["c_val"] + assert isinstance(col.spec, complex128) + assert col.dtype == np.dtype(np.complex128) + assert col.default == 0j + + +# ------------------------------------------------------------------- +# compile_schema — inferred shorthand (plain annotations) +# ------------------------------------------------------------------- + + +@dataclass +class Inferred: + count: int + ratio: float + flag: bool + + +def test_inferred_shorthand(): + s = compile_schema(Inferred) + assert len(s.columns) == 3 + assert isinstance(s.columns_by_name["count"].spec, int64) + assert isinstance(s.columns_by_name["ratio"].spec, float64) + assert isinstance(s.columns_by_name["flag"].spec, b2_bool) + + +def test_inferred_no_constraints(): + s = compile_schema(Inferred) + for col in s.columns: + assert col.spec.to_pydantic_kwargs() == {} + + +# ------------------------------------------------------------------- +# compile_schema — annotation / spec mismatch rejection +# ------------------------------------------------------------------- + + +def test_annotation_spec_mismatch(): + @dataclass + class Bad: + x: str = blosc2.field(blosc2.int64()) # str annotation but int64 spec + + with pytest.raises(TypeError, match="incompatible"): + compile_schema(Bad) + + +def test_non_dataclass_rejected(): + class NotADataclass: + pass + + with pytest.raises(TypeError, match="dataclass"): + compile_schema(NotADataclass) + + +# ------------------------------------------------------------------- +# compile_schema — per-column cparams config +# ------------------------------------------------------------------- + + +def test_column_cparams_stored(): + @dataclass + class WithCparams: + id: int = blosc2.field(blosc2.int64(), cparams={"clevel": 9}) + score: float = blosc2.field(blosc2.float64(), default=0.0) + + s = compile_schema(WithCparams) + assert s.columns_by_name["id"].config.cparams == {"clevel": 9} + assert s.columns_by_name["score"].config.cparams is None + + +# ------------------------------------------------------------------- +# schema_to_dict / schema_from_dict (Step 12) +# ------------------------------------------------------------------- + + +def test_schema_to_dict_structure(): + d = schema_to_dict(compile_schema(Simple)) + assert d["version"] == 1 + assert d["row_cls"] == "Simple" + assert len(d["columns"]) == 3 + + +def test_schema_to_dict_column_fields(): + d = schema_to_dict(compile_schema(Simple)) + id_col = next(c for c in d["columns"] if c["name"] == "id") + assert id_col["kind"] == "int64" + assert id_col["ge"] == 0 + assert id_col["default"] is None # MISSING → None + + +def test_schema_to_dict_default_values(): + d = schema_to_dict(compile_schema(Simple)) + score_col = next(c for c in d["columns"] if c["name"] == "score") + assert score_col["default"] == 0.0 + + active_col = next(c for c in d["columns"] if c["name"] == "active") + assert active_col["default"] is True + + +def test_schema_to_dict_complex_default(): + d = schema_to_dict(compile_schema(WithComplex)) + c_col = next(c for c in d["columns"] if c["name"] == "c_val") + assert c_col["default"]["__complex__"] is True + assert c_col["default"]["real"] == 0.0 + assert c_col["default"]["imag"] == 0.0 + + +def test_schema_roundtrip(): + """schema_from_dict(schema_to_dict(s)) reproduces the same column structure.""" + original = compile_schema(Simple) + d = schema_to_dict(original) + restored = schema_from_dict(d) + + assert len(restored.columns) == len(original.columns) + for orig_col, rest_col in zip(original.columns, restored.columns, strict=True): + assert orig_col.name == rest_col.name + assert orig_col.dtype == rest_col.dtype + assert type(orig_col.spec) is type(rest_col.spec) + if orig_col.default is MISSING: + assert rest_col.default is MISSING + else: + assert orig_col.default == rest_col.default + + +def test_schema_from_dict_no_row_cls(): + """Reconstructed schema has row_cls=None (original class not available).""" + d = schema_to_dict(compile_schema(Simple)) + restored = schema_from_dict(d) + assert restored.row_cls is None + + +def test_schema_from_dict_preserves_constraints(): + d = schema_to_dict(compile_schema(Simple)) + restored = schema_from_dict(d) + id_col = restored.columns_by_name["id"] + assert id_col.spec.ge == 0 + score_col = restored.columns_by_name["score"] + assert score_col.spec.le == 100 + + +def test_schema_from_dict_unknown_kind(): + d = {"version": 1, "row_cls": "X", "columns": [{"name": "x", "kind": "unknown", "default": None}]} + with pytest.raises(ValueError, match="Unknown column kind"): + schema_from_dict(d) + + +def test_schema_from_dict_unsupported_version(): + d = {"version": 99, "row_cls": "X", "columns": []} + with pytest.raises(ValueError, match="Unsupported schema version"): + schema_from_dict(d) diff --git a/tests/ctable/test_schema_specs.py b/tests/ctable/test_schema_specs.py new file mode 100644 index 00000000..645087d7 --- /dev/null +++ b/tests/ctable/test_schema_specs.py @@ -0,0 +1,343 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +"""Tests for schema spec objects (blosc2.schema).""" + +import numpy as np +import pytest + +import blosc2 +from blosc2.schema import ( + SchemaSpec, + complex64, + complex128, + float32, + float64, + int8, + int16, + int32, + int64, + string, + uint8, + uint16, + uint32, + uint64, +) +from blosc2.schema import ( + bool as b2_bool, +) +from blosc2.schema import ( + bytes as b2_bytes, +) + +# ------------------------------------------------------------------- +# dtype mapping +# ------------------------------------------------------------------- + + +def test_int_dtypes(): + assert int8().dtype == np.dtype(np.int8) + assert int16().dtype == np.dtype(np.int16) + assert int32().dtype == np.dtype(np.int32) + assert int64().dtype == np.dtype(np.int64) + assert int64(ge=0).dtype == np.dtype(np.int64) + + +def test_uint_dtypes(): + assert uint8().dtype == np.dtype(np.uint8) + assert uint16().dtype == np.dtype(np.uint16) + assert uint32().dtype == np.dtype(np.uint32) + assert uint64().dtype == np.dtype(np.uint64) + + +def test_float_dtypes(): + assert float32().dtype == np.dtype(np.float32) + assert float64().dtype == np.dtype(np.float64) + + +def test_bool_dtype(): + assert b2_bool().dtype == np.dtype(np.bool_) + + +def test_complex_dtypes(): + assert complex64().dtype == np.dtype(np.complex64) + assert complex128().dtype == np.dtype(np.complex128) + + +def test_string_dtype(): + assert string(max_length=16).dtype == np.dtype("U16") + assert string(max_length=32).dtype == np.dtype("U32") + assert string().dtype == np.dtype("U32") # default max_length=32 + + +def test_bytes_dtype(): + assert b2_bytes(max_length=8).dtype == np.dtype("S8") + assert b2_bytes().dtype == np.dtype("S32") # default max_length=32 + + +# ------------------------------------------------------------------- +# python_type mapping +# ------------------------------------------------------------------- + + +def test_python_types(): + for cls in [int8, int16, int32, int64, uint8, uint16, uint32, uint64]: + assert cls().python_type is int + for cls in [float32, float64]: + assert cls().python_type is float + for cls in [complex64, complex128]: + assert cls().python_type is complex + assert b2_bool().python_type is bool + assert string().python_type is str + assert b2_bytes().python_type is bytes + + +# ------------------------------------------------------------------- +# constraint storage +# ------------------------------------------------------------------- + + +def test_int64_constraints(): + s = int64(ge=0, lt=100) + assert s.ge == 0 + assert s.gt is None + assert s.le is None + assert s.lt == 100 + + +def test_float64_constraints(): + s = float64(gt=0.0, le=1.0) + assert s.gt == 0.0 + assert s.le == 1.0 + assert s.ge is None + assert s.lt is None + + +def test_string_constraints(): + s = string(min_length=2, max_length=10, pattern=r"^\w+$") + assert s.min_length == 2 + assert s.max_length == 10 + assert s.pattern == r"^\w+$" + + +def test_bytes_constraints(): + s = b2_bytes(min_length=1, max_length=8) + assert s.min_length == 1 + assert s.max_length == 8 + + +# ------------------------------------------------------------------- +# to_pydantic_kwargs +# ------------------------------------------------------------------- + + +def test_int64_pydantic_kwargs_partial(): + """Only non-None constraints appear in pydantic kwargs.""" + assert int64(ge=0).to_pydantic_kwargs() == {"ge": 0} + assert int64(ge=0, le=100).to_pydantic_kwargs() == {"ge": 0, "le": 100} + assert int64().to_pydantic_kwargs() == {} + + +def test_float64_pydantic_kwargs(): + assert float64(gt=0.0, lt=1.0).to_pydantic_kwargs() == {"gt": 0.0, "lt": 1.0} + + +def test_bool_pydantic_kwargs(): + assert b2_bool().to_pydantic_kwargs() == {} + + +def test_string_pydantic_kwargs(): + s = string(min_length=1, max_length=5) + kw = s.to_pydantic_kwargs() + assert kw["min_length"] == 1 + assert kw["max_length"] == 5 + + +# ------------------------------------------------------------------- +# to_metadata_dict +# ------------------------------------------------------------------- + + +def test_int64_metadata_dict(): + d = int64(ge=0, le=100).to_metadata_dict() + assert d["kind"] == "int64" + assert d["ge"] == 0 + assert d["le"] == 100 + assert "gt" not in d + assert "lt" not in d + + +def test_float64_metadata_dict(): + d = float64().to_metadata_dict() + assert d["kind"] == "float64" + assert len(d) == 1 # no constraints + + +def test_bool_metadata_dict(): + assert b2_bool().to_metadata_dict() == {"kind": "bool"} + + +def test_string_metadata_dict(): + d = string(max_length=9).to_metadata_dict() + assert d["kind"] == "string" + assert d["max_length"] == 9 + + +def test_complex128_metadata_dict(): + assert complex128().to_metadata_dict() == {"kind": "complex128"} + + +# ------------------------------------------------------------------- +# All specs are SchemaSpec subclasses +# ------------------------------------------------------------------- + + +def test_all_are_schema_spec(): + all_specs = [ + int8, + int16, + int32, + int64, + uint8, + uint16, + uint32, + uint64, + float32, + float64, + b2_bool, + complex64, + complex128, + string, + b2_bytes, + ] + for cls in all_specs: + assert issubclass(cls, SchemaSpec) + + +# ------------------------------------------------------------------- +# New integer / float metadata dicts +# ------------------------------------------------------------------- + + +def test_int8_metadata_dict(): + d = int8(ge=0, lt=128).to_metadata_dict() + assert d["kind"] == "int8" + assert d["ge"] == 0 + assert d["lt"] == 128 + + +def test_uint8_metadata_dict(): + d = uint8(le=200).to_metadata_dict() + assert d["kind"] == "uint8" + assert d["le"] == 200 + + +def test_float32_metadata_dict(): + d = float32(ge=0.0, le=1.0).to_metadata_dict() + assert d["kind"] == "float32" + assert d["ge"] == 0.0 + assert d["le"] == 1.0 + + +def test_new_kinds_roundtrip(): + """Every new kind serialises and deserialises correctly.""" + from dataclasses import dataclass + + from blosc2.schema_compiler import compile_schema, schema_from_dict, schema_to_dict + + @dataclass + class R: + a: int = blosc2.field(blosc2.int8(ge=0)) + b: int = blosc2.field(blosc2.uint16(), default=0) + c: float = blosc2.field(blosc2.float32(ge=0.0, le=1.0), default=0.0) + + schema = compile_schema(R) + d = schema_to_dict(schema) + restored = schema_from_dict(d) + + assert restored.columns_by_name["a"].spec.to_metadata_dict()["kind"] == "int8" + assert restored.columns_by_name["b"].spec.to_metadata_dict()["kind"] == "uint16" + assert restored.columns_by_name["c"].spec.to_metadata_dict()["kind"] == "float32" + + +# ------------------------------------------------------------------- +# blosc2 namespace exports +# ------------------------------------------------------------------- + + +def test_blosc2_namespace(): + """All spec classes are reachable via the blosc2 namespace.""" + assert blosc2.int8 is int8 + assert blosc2.int16 is int16 + assert blosc2.int32 is int32 + assert blosc2.int64 is int64 + assert blosc2.uint8 is uint8 + assert blosc2.uint16 is uint16 + assert blosc2.uint32 is uint32 + assert blosc2.uint64 is uint64 + assert blosc2.float32 is float32 + assert blosc2.float64 is float64 + assert blosc2.bool is b2_bool + assert blosc2.complex64 is complex64 + assert blosc2.complex128 is complex128 + assert blosc2.string is string + + +# ------------------------------------------------------------------- +# String vectorized validation — np.char.str_len path +# ------------------------------------------------------------------- + + +def test_string_validation_vectorized(): + """max_length / min_length use the np.char.str_len path, not np.vectorize.""" + from dataclasses import dataclass + + from blosc2 import CTable + + @dataclass + class Row: + name: str = blosc2.field(blosc2.string(min_length=2, max_length=5)) + + t = CTable(Row, expected_size=10) + t.extend([("hi",), ("hello",)]) # 2 and 5 chars — both valid + assert len(t) == 2 + + with pytest.raises(ValueError, match="max_length"): + t.extend([("toolong",)]) # 7 chars > 5 + + with pytest.raises(ValueError, match="min_length"): + t.extend([("x",)]) # 1 char < 2 + + +def test_string_validation_numpy_array(): + """Vectorized length check catches violations when the array dtype is wider + than the schema's max_length (e.g. dtype U8 with max_length=4).""" + from dataclasses import dataclass + + from blosc2 import CTable + + # Schema says max 4 chars, but the numpy dtype is U8 (wider). + # Strings of 5+ chars survive in the array and are caught by validation. + @dataclass + class Row: + tag: str = blosc2.field(blosc2.string(max_length=4)) + + dtype = np.dtype([("tag", "U8")]) + good = np.array([("ab",), ("cd",)], dtype=dtype) + bad = np.array([("ab",), ("toolong",)], dtype=dtype) # 7 chars > 4 + + t = CTable(Row, expected_size=5) + t.extend(good) + assert len(t) == 2 + + t2 = CTable(Row, expected_size=5) + with pytest.raises(ValueError, match="max_length"): + t2.extend(bad) + + # Note: when the array dtype matches the schema max_length (e.g. U4 with + # max_length=4), NumPy already truncates values to fit the dtype before + # validation runs — so no violation can be detected in that case. diff --git a/tests/ctable/test_schema_validation.py b/tests/ctable/test_schema_validation.py new file mode 100644 index 00000000..2d51d29f --- /dev/null +++ b/tests/ctable/test_schema_validation.py @@ -0,0 +1,164 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +from dataclasses import dataclass + +import numpy as np +import pytest + +import blosc2 +from blosc2 import CTable + + +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + score: float = blosc2.field(blosc2.float64(ge=0, le=100), default=0.0) + active: bool = blosc2.field(blosc2.bool(), default=True) + + +# ------------------------------------------------------------------- +# append() validation +# ------------------------------------------------------------------- + + +def test_append_valid_row(): + """Rows within constraints are accepted.""" + t = CTable(Row, expected_size=5) + t.append((0, 0.0, True)) + t.append((1, 100.0, False)) + t.append((99, 50.5, True)) + assert len(t) == 3 + + +def test_append_ge_violation(): + """id < 0 raises ValueError (ge=0).""" + t = CTable(Row, expected_size=5) + with pytest.raises(ValueError): + t.append((-1, 50.0, True)) + + +def test_append_le_violation(): + """score > 100 raises ValueError (le=100).""" + t = CTable(Row, expected_size=5) + with pytest.raises(ValueError): + t.append((1, 100.1, True)) + + +def test_append_boundary_values(): + """Exact boundary values (ge=0 and le=100) are accepted.""" + t = CTable(Row, expected_size=5) + t.append((0, 0.0, True)) # id=0 (ge boundary), score=0.0 (ge boundary) + t.append((1, 100.0, False)) # score=100.0 (le boundary) + assert len(t) == 2 + + +def test_append_default_fill(): + """Fields with defaults can be omitted from a tuple — Pydantic fills them in.""" + t = CTable(Row, expected_size=5) + # Only id is required; score and active have defaults + t.append((5,)) # score=0.0, active=True filled by defaults + assert len(t) == 1 + assert t.row[0].id[0] == 5 + + +def test_append_validate_false(): + """validate=False skips constraint checks — invalid data is stored silently.""" + t = CTable(Row, expected_size=5, validate=False) + t.append((-5, 200.0, True)) # would fail with validate=True + assert len(t) == 1 + assert int(t._cols["id"][0]) == -5 + + +# ------------------------------------------------------------------- +# extend() validation (vectorized) +# ------------------------------------------------------------------- + + +def test_extend_valid_rows(): + """Bulk insert within constraints succeeds.""" + t = CTable(Row, expected_size=10) + data = [(i, float(i), True) for i in range(10)] + t.extend(data) + assert len(t) == 10 + + +def test_extend_ge_violation(): + """A negative id anywhere in the batch raises ValueError.""" + t = CTable(Row, expected_size=10) + data = [(i, float(i), True) for i in range(5)] + [(-1, 50.0, False)] + with pytest.raises(ValueError, match="ge=0"): + t.extend(data) + + +def test_extend_le_violation(): + """A score > 100 anywhere in the batch raises ValueError.""" + t = CTable(Row, expected_size=10) + data = [(i, float(i), True) for i in range(5)] + [(5, 101.0, False)] + with pytest.raises(ValueError, match="le=100"): + t.extend(data) + + +def test_extend_validate_false(): + """validate=False on the table skips bulk constraint checks.""" + t = CTable(Row, expected_size=10, validate=False) + data = [(-1, 200.0, True), (-2, 300.0, False)] + t.extend(data) # no error + assert len(t) == 2 + + +def test_extend_numpy_structured_array(): + """Constraint enforcement also works when extending with a structured NumPy array.""" + dtype = np.dtype([("id", np.int64), ("score", np.float64), ("active", np.bool_)]) + good = np.array([(1, 50.0, True), (2, 75.0, False)], dtype=dtype) + bad = np.array([(1, 50.0, True), (2, 150.0, False)], dtype=dtype) # score > 100 + + t = CTable(Row, expected_size=5) + t.extend(good) + assert len(t) == 2 + + t2 = CTable(Row, expected_size=5) + with pytest.raises(ValueError, match="le=100"): + t2.extend(bad) + + +# ------------------------------------------------------------------- +# gt / lt constraints +# ------------------------------------------------------------------- + + +@dataclass +class Strict: + x: int = blosc2.field(blosc2.int64(gt=0, lt=10)) + + +def test_gt_lt_append(): + """gt and lt are exclusive bounds.""" + t = CTable(Strict, expected_size=5) + + t.append((5,)) # valid + with pytest.raises(ValueError): + t.append((0,)) # violates gt=0 + with pytest.raises(ValueError): + t.append((10,)) # violates lt=10 + + +def test_gt_lt_extend(): + """Vectorized gt/lt checks work on batches.""" + t = CTable(Strict, expected_size=10) + t.extend([(i,) for i in range(1, 10)]) # 1..9 all valid + assert len(t) == 9 + + t2 = CTable(Strict, expected_size=5) + with pytest.raises(ValueError, match="gt=0"): + t2.extend([(0,)]) + with pytest.raises(ValueError, match="lt=10"): + t2.extend([(10,)]) + + +if __name__ == "__main__": + pytest.main(["-v", __file__]) From ee1d0c435e4786f8b5d11344abeb67bb15a0555e Mon Sep 17 00:00:00 2001 From: jorge Date: Sat, 4 Apr 2026 18:16:10 +0200 Subject: [PATCH 14/49] persistency half way done --- plans/ctable-implementation-log.md | 206 ++++++++++++++++++++++++++-- plans/ctable-user-guide.md | 112 ++++++++++++++- src/blosc2/ctable.py | 213 +++++++++++++++++++++-------- src/blosc2/schema_compiler.py | 27 ++++ 4 files changed, 493 insertions(+), 65 deletions(-) diff --git a/plans/ctable-implementation-log.md b/plans/ctable-implementation-log.md index 1f204984..ab81a188 100644 --- a/plans/ctable-implementation-log.md +++ b/plans/ctable-implementation-log.md @@ -1,11 +1,12 @@ # CTable Implementation Log -This document records what was implemented as part of the `ctable-schema.md` redesign. -It covers every new file, every significant change, and the reasoning behind each decision. +This document records everything implemented across the CTable feature: +the `ctable-schema.md` redesign (schema, validation, serialization, optimizations) +and the `ctable-persistency.md` phase (file-backed storage, `open()`, read-only mode). --- -## Overview +## Phase 1 — Schema redesign (`ctable-schema.md`) The goal was to replace the original Pydantic-`BaseModel`-based schema API with a **dataclass-first schema API** using declarative spec objects (`b2.int64()`, @@ -200,11 +201,11 @@ All tests live in `tests/ctable/`. | `test_extend_delete.py` | Interleaved extend/delete cycles, mask correctness, resize behavior | | `test_row_logic.py` | Row indexer (int/slice/list), views, chained views | -Total: **127 tests, all passing**. +Total: **135 tests, all passing** (after Phase 1 + optimizations). --- -## Design decisions +## Phase 1 design decisions **Why two validation paths?** `append()` handles one row at a time — Pydantic is fast enough and also performs @@ -220,6 +221,195 @@ Existing code using `class RowModel(BaseModel)` continues to work without modification. The adapter is not on the critical path for new code. **Why `schema_to_dict` / `schema_from_dict` now?** -Persistence (`save()`/`load()`) requires a self-contained schema representation. -Establishing the serialization format early means the format will be stable before -anything depends on it. +Persistence requires a self-contained schema representation that survives without +the original Python dataclass. Establishing the serialization format before +persistence was built ensured the format was stable before anything depended on it. + +--- + +## Phase 1 optimizations (post-schema) + +Several performance improvements were made after the schema work was complete: + +**`_last_pos` cache** +Added `_last_pos: int | None` to `CTable`. Tracks the physical index of the next +write slot so that `append()` and `extend()` no longer need to scan backward through +chunk metadata on every call. Set to `None` after any deletion (triggers one lazy +recalculation on the next write). Set to `_n_rows` after `compact()`. Eliminated a +backward O(n_chunks) scan per insert. + +**`_grow()` helper** +Extracted the capacity-doubling logic into `_grow()`. Removes duplication between +`append()` and `extend()`. + +**In-place delete** +`delete()` now writes the updated boolean array back with `self._valid_rows[:] = +valid_rows_np` (in-place slice assignment) instead of creating a new NDArray. +Avoids a full allocation on each delete. + +**`head()` / `tail()` refactored** +Both methods now reuse `_find_physical_index()` instead of containing their own +chunk-walk loops. + +**`_make_view()` classmethod** +Added to construct view CTables without going through `__init__`. Avoids +allocating and immediately discarding NDArrays that were never used. + +**`_NumericSpec` mixin + new spec types** +All numeric specs (`int8` through `uint64`, `float32`, `float64`) share a common +`_NumericSpec` mixin for `ge`/`gt`/`le`/`lt` constraint handling, eliminating +boilerplate. New specs added: `int8`, `int16`, `int32`, `uint8`, `uint16`, +`uint32`, `uint64`, `float32`. + +**String vectorized validation** +`validate_column_values` uses `np.char.str_len()` (true C-level) for `U`/`S` dtype +arrays instead of `np.vectorize(len)` (Python loop in disguise). The check also +extracted `_validate_string_lengths()` to reduce cyclomatic complexity. + +**Column name validation** +`compile_schema` now calls `_validate_column_name()` on every field. Rejects names +that are empty, start with `_`, or contain `/` — rules that apply equally to +in-memory and persistent tables. + +--- + +## Phase 2 — Persistency (`ctable-persistency.md`) + +### New file: `src/blosc2/ctable_storage.py` + +A storage-backend abstraction that keeps all file I/O out of `ctable.py`. + +**`TableStorage`** — interface class defining: +`create_column`, `open_column`, `create_valid_rows`, `open_valid_rows`, +`save_schema`, `load_schema`, `table_exists`, `is_read_only`. + +**`InMemoryTableStorage`** — trivial implementation that creates plain in-memory +`blosc2.NDArray` objects and is a no-op for `save_schema`. Used when `urlpath` is +not provided (existing default behaviour, unchanged). + +**`FileTableStorage`** — file-backed implementation. + +Disk layout: + +``` +/ + _meta.b2frame ← blosc2.SChunk; vlmeta holds kind, version, schema JSON + _valid_rows.b2nd ← file-backed boolean NDArray (tombstone mask) + _cols/ + .b2nd ← one file-backed NDArray per column +``` + +Key implementation notes: +- `save_schema` always opens `_meta.b2frame` with `mode="w"` (create path only). +- `load_schema` / `check_kind` use `blosc2.open()` (not `blosc2.SChunk(..., + mode="a")`), which is the correct API for reopening an existing SChunk file. +- File-backed NDArrays (`urlpath=..., mode="w"`) support in-place writes + (`col[pos] = value`, `col[start:end] = arr`) that persist immediately. This is + why resize (`_grow()`), append, extend, and delete all work transparently on + persistent tables. +- `_n_rows` on reopen is reconstructed as `blosc2.count_nonzero(valid_rows)` — + always correct because unwritten slots are `False`, same as deleted slots. +- `_last_pos` is set to `None` on reopen and resolved lazily by `_resolve_last_pos()` + on the first write. + +### Changes to `src/blosc2/ctable.py` + +**Constructor** + +New parameters: `urlpath: str | None = None`, `mode: str = "a"`. + +Logic: +- `urlpath=None` → `InMemoryTableStorage` → existing behaviour unchanged. +- `urlpath` + existing table + `mode != "w"` → open existing (load schema from + disk, open file-backed arrays, reconstruct state). +- `urlpath` + `mode="w"` or no existing table → create new (compile schema, + save to disk, create file-backed arrays). +- Passing `new_data` when opening an existing table raises `ValueError`. + +**`CTable.open(cls, urlpath, *, mode="r")`** + +New classmethod for ergonomic read-only access. Opens the table, verifies +`kind="ctable"` in vlmeta, reconstructs schema from JSON (no dataclass needed), +returns a fully usable `CTable`. + +**Read-only enforcement** + +`_read_only: bool` flag set from `storage.is_read_only()`. Guards added to the top +of `append()`, `extend()`, `delete()`, `compact()` — each raises +`ValueError("Table is read-only (opened with mode='r').")`. + +**`_make_view(cls, parent, new_valid_rows)`** + +New classmethod that constructs a view `CTable` directly via `cls.__new__` without +calling `__init__`. Replaces the old `CTable(self._row_type, expected_size=...)` + +`retval._cols = self._cols` pattern, which was wasteful (allocated NDArrays then +discarded them) and broke when `_row_type` is `None` (tables opened via `open()`). + +**`schema_dict()`** + +No longer needs a local import of `schema_to_dict` — now imported at the module top. + +### New test file: `tests/ctable/test_persistency.py` + +23 tests covering: + +| Test group | What it checks | +|---|---| +| Layout | `_meta.b2frame`, `_valid_rows.b2nd`, `_cols/.b2nd` all exist after creation | +| Metadata | `kind`, `version`, `schema` in vlmeta; column names and order in schema JSON | +| Round-trips | Data survives reopen via both `CTable(Row, urlpath=..., mode="a")` and `CTable.open()` | +| Column order | Preserved exactly from schema JSON, not from filesystem order | +| Constraints | Validation re-enabled after reopen (schema reconstructed from disk) | +| Append/extend/delete after reopen | Mutations visible in subsequent opens | +| `_valid_rows` on disk | Tombstone mask correctly stored and loaded | +| `mode="w"` | Overwrites existing table; subsequent open sees empty table | +| Read-only | `append`, `extend`, `delete`, `compact` all raise on `mode="r"` | +| Read-only reads | `row[]`, column access, `head()`, `tail()`, `where()` all work | +| Error cases | `FileNotFoundError` for missing path; `ValueError` for wrong kind | +| Column name validation | Empty, `_`-prefixed, `/`-containing names rejected | +| `new_data` guard | `ValueError` when `new_data` passed to open-existing path | +| Capacity growth | `_grow()` (resize) works on file-backed arrays and survives reopen | + +Total: **158 tests, all passing**. + +### New benchmark: `bench/ctable/bench_persistency.py` + +Four sections: + +1. **`extend()` bulk insert** — in-memory vs file-backed at 1k–1M rows. + Overhead converges to ~1x at 1M rows (compression dominates, not I/O). +2. **`open()` / reopen time** — ~4–10 ms regardless of table size. Fixed cost: + open 3 files (meta, valid_rows, one column) + parse schema JSON. +3. **`append()` single-row** — file-backed is ~6x slower per row (~3 ms vs ~0.5 ms). + Recommendation: batch inserts via `extend()` for persistent tables. +4. **Column `to_numpy()`** — essentially identical between backends (≤1.06x ratio). + Decompression dominates; file I/O is negligible once data is loaded. + +--- + +## Phase 2 design decisions + +**Why direct files instead of TreeStore?** +TreeStore stores snapshots of in-memory arrays. In-place writes to a +TreeStore-retrieved NDArray do not persist after reopen. File-backed NDArrays +created with `urlpath=...` support in-place writes natively. Using direct `.b2nd` +files aligns with how the rest of blosc2 handles persistent arrays. + +**Why `blosc2.SChunk` vlmeta for metadata, not JSON files?** +`vlmeta` is compressed and is already part of the blosc2 ecosystem. +`blosc2.open()` works on `.b2frame` files the same way it works on `.b2nd` files, +keeping the open path uniform. + +**Why not store `_last_pos` in metadata?** +`_resolve_last_pos()` reconstructs it in O(n_chunks) with no full decompression. +Storing it would create a write on every `append()` just to update a counter in the +SChunk — not worth the extra I/O. + +**Why `_make_view()` instead of calling `__init__`?** +`__init__` now has storage-routing logic and would try to create new NDArrays even +for views (which immediately get thrown away). `_make_view()` via `__new__` is +explicit and zero-waste. + +**Why `CTable.open()` defaults to `mode="r"`?** +The most common read-back scenario is inspection or analysis, not modification. +Defaulting to read-only prevents accidental mutations on shared or archived tables. diff --git a/plans/ctable-user-guide.md b/plans/ctable-user-guide.md index 5b19d378..7ff7d41f 100644 --- a/plans/ctable-user-guide.md +++ b/plans/ctable-user-guide.md @@ -77,7 +77,7 @@ class Row: ```python import blosc2 as b2 -# Empty table +# Empty table (in-memory) t = b2.CTable(Row) # Table pre-loaded with data @@ -96,6 +96,62 @@ t = b2.CTable(Row, compact=True) t = b2.CTable(Row, cparams={"codec": b2.Codec.ZSTD, "clevel": 5}) ``` +### Persistent tables + +Pass `urlpath` to store the table on disk. The table root is a directory containing +compressed array files — everything is handled automatically. + +```python +# Create a new persistent table (overwrites any existing table at that path) +t = b2.CTable(Row, urlpath="people", mode="w", expected_size=1_000_000) +t.extend([(i, float(i % 100), True) for i in range(10_000)]) + +# Open an existing persistent table for reading and writing +t = b2.CTable(Row, urlpath="people", mode="a") +t.append((99999, 50.0, True)) + +# Open read-only (default for CTable.open) +t = b2.CTable.open("people") # mode="r" by default +t = b2.CTable.open("people", mode="r") # explicit + +# Open read/write via the classmethod +t = b2.CTable.open("people", mode="a") +``` + +`mode` values: + +| mode | behaviour | +|---|---| +| `"w"` | create (overwrite if the path already exists) | +| `"a"` | open existing or create new | +| `"r"` | open existing read-only | + +In-memory tables (`urlpath=None`, the default) behave exactly as before — no +`mode` or path handling is involved. + +### Disk layout + +``` +people/ + _meta.b2frame ← schema JSON, kind marker, version (in vlmeta) + _valid_rows.b2nd ← tombstone mask + _cols/ + id.b2nd + score.b2nd + active.b2nd +``` + +You can inspect the raw metadata: + +```python +import blosc2, json + +meta = blosc2.open("people/_meta.b2frame") +print(meta.vlmeta["kind"]) # "ctable" +print(meta.vlmeta["version"]) # 1 +schema = json.loads(meta.vlmeta["schema"]) +``` + ### Per-column storage options ```python @@ -270,6 +326,25 @@ t = b2.CTable(Row, compact=True) --- +## Read-only mode + +When a table is opened with `mode="r"` (or via `CTable.open()` without specifying +mode), all mutating operations raise immediately: + +```python +t = b2.CTable.open("people") # read-only + +t.append((1, 50.0, True)) # ValueError: Table is read-only +t.extend([(1, 50.0, True)]) # ValueError: Table is read-only +t.delete(0) # ValueError: Table is read-only +t.compact() # ValueError: Table is read-only +``` + +All read operations work normally: `row[]`, column access, `head()`, `tail()`, +`where()`, `len()`, `info()`, `schema_dict()`. + +--- + ## Filtering `where()` applies a boolean expression and returns a read-only view: @@ -359,7 +434,7 @@ class Measurement: valid: bool = b2.field(b2.bool(), default=True) -# Create and populate +# Create and populate (in-memory) t = b2.CTable(Measurement, expected_size=10_000) t.extend([(i, float(i % 200 - 100), i % 3 != 0) for i in range(5000)]) @@ -376,3 +451,36 @@ if invalid_indices: t.info() print(t.schema_dict()) ``` + +## Persistency example + +```python +from dataclasses import dataclass +import blosc2 as b2 + + +@dataclass +class Measurement: + sensor_id: int = b2.field(b2.int64(ge=0)) + value: float = b2.field(b2.float64(ge=-1000, le=1000), default=0.0) + valid: bool = b2.field(b2.bool(), default=True) + + +# --- Session 1: create and populate --- +t = b2.CTable(Measurement, urlpath="sensors", mode="w", expected_size=100_000) +t.extend([(i, float(i % 200 - 100), i % 3 != 0) for i in range(50_000)]) +print(f"Saved {len(t)} rows to disk") +# Table is automatically persisted — no explicit save() needed. + +# --- Session 2: reopen and query --- +t = b2.CTable.open("sensors") # read-only by default +hot = t.where(t["value"] > 50) +print(f"Hot readings: {len(hot)}") +arr = t["sensor_id"].to_numpy() +print(f"First 5 sensor IDs: {arr[:5]}") + +# --- Session 3: reopen and append more data --- +t = b2.CTable(Measurement, urlpath="sensors", mode="a") +t.extend([(50_000 + i, float(i), True) for i in range(1_000)]) +print(f"Total rows: {len(t)}") +``` diff --git a/src/blosc2/ctable.py b/src/blosc2/ctable.py index 89ca6032..d1886ede 100644 --- a/src/blosc2/ctable.py +++ b/src/blosc2/ctable.py @@ -18,6 +18,8 @@ import numpy as np from blosc2 import compute_chunks_blocks +from blosc2.ctable_storage import FileTableStorage, InMemoryTableStorage, TableStorage +from blosc2.schema_compiler import schema_from_dict, schema_to_dict try: from line_profiler import profile @@ -369,6 +371,8 @@ def __init__( row_type: type[RowT], new_data=None, *, + urlpath: str | None = None, + mode: str = "a", expected_size: int = 1_048_576, compact: bool = False, validate: bool = True, @@ -379,47 +383,86 @@ def __init__( self._validate = validate self._table_cparams = cparams self._table_dparams = dparams - - # Build compiled schema from either a dataclass or a legacy Pydantic model - if dataclasses.is_dataclass(row_type) and isinstance(row_type, type): - self._schema: CompiledSchema = compile_schema(row_type) - else: - self._schema = _compile_pydantic_schema(row_type) - self._cols: dict[str, blosc2.NDArray] = {} - self._n_rows: int = 0 - self._last_pos: int | None = 0 # physical index of next write slot; - # None means it must be recalculated - # (set after any deletion) self._col_widths: dict[str, int] = {} self.col_names: list[str] = [] self.row = _RowIndexer(self) self.auto_compact = compact self.base = None - default_chunks, default_blocks = compute_chunks_blocks((expected_size,)) - self._valid_rows = blosc2.zeros( - shape=(expected_size,), - dtype=np.bool_, - chunks=default_chunks, - blocks=default_blocks, - ) + # Choose storage backend + if urlpath is not None: + storage: TableStorage = FileTableStorage(urlpath, mode) + else: + storage = InMemoryTableStorage() + self._storage = storage + self._read_only = storage.is_read_only() + + if storage.table_exists() and mode != "w": + # ---- Open existing persistent table ---- + if new_data is not None: + raise ValueError( + "Cannot pass new_data when opening an existing table. Use mode='w' to overwrite." + ) + storage.check_kind() + schema_dict = storage.load_schema() + self._schema: CompiledSchema = schema_from_dict(schema_dict) + self._schema = CompiledSchema( + row_cls=row_type, + columns=self._schema.columns, + columns_by_name=self._schema.columns_by_name, + ) + self.col_names = [c["name"] for c in schema_dict["columns"]] + self._valid_rows = storage.open_valid_rows() + for name in self.col_names: + col = storage.open_column(name) + self._cols[name] = col + cc = self._schema.columns_by_name[name] + self._col_widths[name] = max(len(name), cc.display_width) + self._n_rows = int(blosc2.count_nonzero(self._valid_rows)) + self._last_pos = None # resolve lazily on first write + else: + # ---- Create new table ---- + if storage.is_read_only(): + raise FileNotFoundError(f"No CTable found at {urlpath!r}") - self._init_columns(expected_size, default_chunks, default_blocks) + # Build compiled schema from either a dataclass or a legacy Pydantic model + if dataclasses.is_dataclass(row_type) and isinstance(row_type, type): + self._schema = compile_schema(row_type) + else: + self._schema = _compile_pydantic_schema(row_type) - if new_data is not None: - self._load_initial_data(new_data) + self._n_rows = 0 + self._last_pos = 0 - def _init_columns(self, expected_size: int, default_chunks, default_blocks) -> None: + default_chunks, default_blocks = compute_chunks_blocks((expected_size,)) + self._valid_rows = storage.create_valid_rows( + shape=(expected_size,), + chunks=default_chunks, + blocks=default_blocks, + ) + self._init_columns(expected_size, default_chunks, default_blocks, storage) + storage.save_schema(schema_to_dict(self._schema)) + + if new_data is not None: + self._load_initial_data(new_data) + + def _init_columns( + self, expected_size: int, default_chunks, default_blocks, storage: TableStorage + ) -> None: """Create one NDArray per column using the compiled schema.""" for col in self._schema.columns: self.col_names.append(col.name) self._col_widths[col.name] = max(len(col.name), col.display_width) - storage = self._resolve_column_storage(col, default_chunks, default_blocks) - self._cols[col.name] = blosc2.zeros( - shape=(expected_size,), + col_storage = self._resolve_column_storage(col, default_chunks, default_blocks) + self._cols[col.name] = storage.create_column( + col.name, dtype=col.dtype, - **storage, + shape=(expected_size,), + chunks=col_storage["chunks"], + blocks=col_storage["blocks"], + cparams=col_storage.get("cparams"), + dparams=col_storage.get("dparams"), ) def _resolve_column_storage( @@ -553,10 +596,89 @@ def __iter__(self): for i in range(self.nrows): yield _Row(self, i) + # ------------------------------------------------------------------ + # Open existing table (classmethod) + # ------------------------------------------------------------------ + + @classmethod + def open(cls, urlpath: str, *, mode: str = "r") -> CTable: + """Open a persistent CTable from *urlpath*. + + Parameters + ---------- + urlpath: + Path to the table root directory (created by passing ``urlpath`` + to :class:`CTable`). + mode: + ``'r'`` (default) — read-only. + ``'a'`` — read/write. + + Raises + ------ + FileNotFoundError + If *urlpath* does not contain a CTable. + ValueError + If the metadata at *urlpath* does not identify a CTable. + """ + storage = FileTableStorage(urlpath, mode) + if not storage.table_exists(): + raise FileNotFoundError(f"No CTable found at {urlpath!r}") + storage.check_kind() + schema_dict = storage.load_schema() + schema = schema_from_dict(schema_dict) + col_names = [c["name"] for c in schema_dict["columns"]] + + obj = cls.__new__(cls) + obj._row_type = None + obj._validate = True + obj._table_cparams = None + obj._table_dparams = None + obj._storage = storage + obj._read_only = storage.is_read_only() + obj._schema = schema + obj._cols = {} + obj._col_widths = {} + obj.col_names = col_names + obj.row = _RowIndexer(obj) + obj.auto_compact = False + obj.base = None + + obj._valid_rows = storage.open_valid_rows() + for name in col_names: + obj._cols[name] = storage.open_column(name) + cc = schema.columns_by_name[name] + obj._col_widths[name] = max(len(name), cc.display_width) + + obj._n_rows = int(blosc2.count_nonzero(obj._valid_rows)) + obj._last_pos = None # resolve lazily on first write + return obj + # ------------------------------------------------------------------ # View / filtering # ------------------------------------------------------------------ + @classmethod + def _make_view(cls, parent: CTable, new_valid_rows: blosc2.NDArray) -> CTable: + """Construct a read-only view sharing *parent*'s columns.""" + obj = cls.__new__(cls) + obj._row_type = parent._row_type + obj._validate = parent._validate + obj._table_cparams = parent._table_cparams + obj._table_dparams = parent._table_dparams + obj._storage = None + obj._read_only = True + obj._schema = parent._schema + obj._cols = parent._cols # shared — views never mutate + obj._col_widths = parent._col_widths + obj.col_names = parent.col_names + obj.row = _RowIndexer(obj) + obj.auto_compact = parent.auto_compact + obj.base = parent + obj._valid_rows = new_valid_rows + obj._n_rows = int(blosc2.count_nonzero(new_valid_rows)) + obj._last_pos = None + return obj + def view(self, new_valid_rows): if not ( isinstance(new_valid_rows, (blosc2.NDArray, blosc2.LazyExpr)) @@ -573,19 +695,7 @@ def view(self, new_valid_rows): if len(self._valid_rows) != len(new_valid_rows): raise ValueError() - retval = CTable( - self._row_type, - compact=self.auto_compact, - expected_size=len(self._valid_rows), - ) - retval._cols = self._cols - retval._n_rows = blosc2.count_nonzero(new_valid_rows) - retval._col_widths = self._col_widths - retval.col_names = self.col_names - retval.base = self - retval._valid_rows = new_valid_rows - - return retval + return CTable._make_view(self, new_valid_rows) def head(self, N: int = 5) -> CTable: if N <= 0: @@ -647,6 +757,8 @@ def __getattr__(self, s: str): # ------------------------------------------------------------------ def compact(self): + if self._read_only: + raise ValueError("Table is read-only (opened with mode='r').") real_poss = blosc2.where(self._valid_rows, np.array(range(len(self._valid_rows)))).compute() start = 0 block_size = self._valid_rows.blocks[0] @@ -692,13 +804,7 @@ def column_schema(self, name: str) -> CompiledColumn: raise KeyError(f"No column named {name!r}. Available: {self.col_names}") from None def schema_dict(self) -> dict[str, Any]: - """Return a JSON-compatible dict describing this table's schema. - - Suitable for debugging, serialization, and future ``save()``/``load()`` - support. See :func:`~blosc2.schema_compiler.schema_to_dict`. - """ - from blosc2.schema_compiler import schema_to_dict - + """Return a JSON-compatible dict describing this table's schema.""" return schema_to_dict(self._schema) def info(self) -> None: @@ -769,6 +875,8 @@ def _load_initial_data(self, new_data) -> None: self.extend(new_data) def append(self, data: list | np.void | np.ndarray) -> None: + if self._read_only: + raise ValueError("Table is read-only (opened with mode='r').") if self.base is not None: raise TypeError("Cannot extend view.") @@ -792,6 +900,8 @@ def append(self, data: list | np.void | np.ndarray) -> None: self._n_rows += 1 def delete(self, ind: int | slice | str | Iterable) -> None: + if self._read_only: + raise ValueError("Table is read-only (opened with mode='r').") valid_rows_np = self._valid_rows[:] true_pos = np.where(valid_rows_np)[0] @@ -809,6 +919,8 @@ def delete(self, ind: int | slice | str | Iterable) -> None: self._last_pos = None # recalculate on next write def extend(self, data: list | CTable | Any, *, validate: bool | None = None) -> None: + if self._read_only: + raise ValueError("Table is read-only (opened with mode='r').") if self.base is not None: raise TypeError("Cannot extend view.") if len(data) <= 0: @@ -908,12 +1020,3 @@ def _run_row_logic(self, ind: int | slice | str | Iterable) -> CTable: new_mask = blosc2.asarray(new_mask_np) return self.view(new_mask) - - # ------------------------------------------------------------------ - # Persistence (not yet implemented) - # ------------------------------------------------------------------ - - def save(self, urlpath: str, group: str = "table") -> None: ... - - @classmethod - def load(cls, urlpath: str, group: str = "table", row_type: type[RowT] | None = None) -> CTable: ... diff --git a/src/blosc2/schema_compiler.py b/src/blosc2/schema_compiler.py index cbf78b06..1300cecd 100644 --- a/src/blosc2/schema_compiler.py +++ b/src/blosc2/schema_compiler.py @@ -207,6 +207,30 @@ def validate_annotation_matches_spec(name: str, annotation: Any, spec: SchemaSpe # --------------------------------------------------------------------------- +_RESERVED_COLUMN_NAMES: frozenset[str] = frozenset({"_meta", "_valid_rows", "_cols"}) + + +def _validate_column_name(name: str) -> None: + """Raise :exc:`ValueError` if *name* is not a legal CTable column name. + + Rules (enforced for both in-memory and persistent tables so that an + in-memory schema can always be persisted without surprises): + + * must be a non-empty string + * must not start with ``_`` (reserved for internal table layout) + * must not contain ``/`` (used as path separator in persistent layout) + * must not be one of the reserved internal names + """ + if not name: + raise ValueError("Column name cannot be empty.") + if name.startswith("_"): + raise ValueError(f"Column name cannot start with '_' (reserved for internal use): {name!r}") + if "/" in name: + raise ValueError(f"Column name cannot contain '/': {name!r}") + if name in _RESERVED_COLUMN_NAMES: + raise ValueError(f"Column name {name!r} is reserved for internal CTable use.") + + def compile_schema(row_cls: type[Any]) -> CompiledSchema: """Compile *row_cls* (a dataclass) into a :class:`CompiledSchema`. @@ -226,6 +250,8 @@ def compile_schema(row_cls: type[Any]) -> CompiledSchema: TypeError If *row_cls* is not a dataclass, if a field spec is incompatible with its annotation, or if an unsupported annotation is encountered. + ValueError + If any column name violates the naming rules. """ if not dataclasses.is_dataclass(row_cls) or not isinstance(row_cls, type): raise TypeError( @@ -242,6 +268,7 @@ def compile_schema(row_cls: type[Any]) -> CompiledSchema: for dc_field in dataclasses.fields(row_cls): name = dc_field.name + _validate_column_name(name) annotation = hints.get(name, dc_field.type) meta = get_blosc2_field_metadata(dc_field) From a422d72bf21ce28a882f894b2ae84c53d5bd30b1 Mon Sep 17 00:00:00 2001 From: jorge Date: Mon, 6 Apr 2026 14:35:02 +0200 Subject: [PATCH 15/49] CTable: full feature build-out (persistency, aggregates, mutations, QoL) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Persistency: - FileTableStorage backend: disk layout _meta.b2frame / _valid_rows.b2nd / _cols/.b2nd - CTable(Row, urlpath=..., mode="w"/"a"/"r"), CTable.open(), CTable.save(), CTable.load() - Read-only mode blocks all writes; save() always writes compacted rows Column aggregates: sum, min, max, mean, std, any, all (chunk-aware via iter_chunks) Column utilities: unique(), value_counts(), assign(), boolean mask __getitem__/__setitem__ Schema mutations: add_column (fills default for existing rows), drop_column, rename_column - All three update schema, handle disk files, and block on views View mutability model fix: - Views allow value writes (assign, __setitem__) — only structural mutations are blocked - _read_only=True reserved for mode="r" disk tables; base is not None guards structural ops QoL: __str__ pandas-style, __repr__, cbytes/nbytes, sample(n), Column.iter_chunks(size) Tests: 258 tests, ~5s — new test_persistency.py (33), test_schema_mutations.py (41), expanded test_column.py; optimized helpers to use to_numpy() instead of row[i] --- bench/ctable/bench_persistency.py | 189 ++++++ src/blosc2/ctable.py | 812 ++++++++++++++++++++++++-- src/blosc2/ctable_storage.py | 248 ++++++++ src/blosc2/schema_validation.py | 3 +- tests/ctable/test_column.py | 411 ++++++++++++- tests/ctable/test_construct.py | 62 +- tests/ctable/test_persistency.py | 519 ++++++++++++++++ tests/ctable/test_schema_mutations.py | 395 +++++++++++++ 8 files changed, 2542 insertions(+), 97 deletions(-) create mode 100644 bench/ctable/bench_persistency.py create mode 100644 src/blosc2/ctable_storage.py create mode 100644 tests/ctable/test_persistency.py create mode 100644 tests/ctable/test_schema_mutations.py diff --git a/bench/ctable/bench_persistency.py b/bench/ctable/bench_persistency.py new file mode 100644 index 00000000..2cc449bc --- /dev/null +++ b/bench/ctable/bench_persistency.py @@ -0,0 +1,189 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +# Benchmark: persistent vs in-memory CTable +# +# Sections: +# 1. extend() — bulk creation: in-memory vs file-backed +# 2. open() — time to reopen an existing persistent table +# 3. append() — single-row append: in-memory vs file-backed (after reopen) +# 4. column read — materialising a full column: in-memory vs file-backed +# +# Each measurement is the minimum of NRUNS repetitions to reduce noise. + +import os +import shutil +from dataclasses import dataclass +from time import perf_counter + +import blosc2 + +NRUNS = 3 +TABLE_DIR = "saved_ctable/bench" + + +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + score: float = blosc2.field(blosc2.float64(ge=0, le=100), default=0.0) + active: bool = blosc2.field(blosc2.bool(), default=True) + + +def sep(title: str) -> None: + print(f"\n{'─' * 60}") + print(f" {title}") + print(f"{'─' * 60}") + + +def tmin(fn, n: int = NRUNS) -> float: + """Return the minimum elapsed time (seconds) over *n* calls of *fn*.""" + best = float("inf") + for _ in range(n): + t0 = perf_counter() + fn() + best = min(best, perf_counter() - t0) + return best + + +def clean() -> None: + if os.path.exists(TABLE_DIR): + shutil.rmtree(TABLE_DIR) + os.makedirs(TABLE_DIR, exist_ok=True) + + +# --------------------------------------------------------------------------- +# Section 1: bulk creation — extend() +# --------------------------------------------------------------------------- + +sep("1. extend() — bulk insert: in-memory vs file-backed") + +SIZES = [1_000, 10_000, 100_000, 1_000_000] + +print(f"{'rows':>12} {'in-memory (s)':>16} {'file-backed (s)':>16} {'overhead':>10}") +print(f"{'----':>12} {'-------------':>16} {'---------------':>16} {'--------':>10}") + +for N in SIZES: + data = [(i, float(i % 100), i % 2 == 0) for i in range(N)] + + def bench_mem(N=N, data=data): + t = blosc2.CTable(Row, expected_size=N) + t.extend(data, validate=False) + + def bench_file(N=N, data=data): + clean() + t = blosc2.CTable(Row, urlpath=TABLE_DIR + "/ext", mode="w", expected_size=N) + t.extend(data, validate=False) + + t_mem = tmin(bench_mem) + t_file = tmin(bench_file) + overhead = t_file / t_mem if t_mem > 0 else float("nan") + print(f"{N:>12,} {t_mem:>16.4f} {t_file:>16.4f} {overhead:>9.2f}x") + +# --------------------------------------------------------------------------- +# Section 2: open() — reopen an existing table +# --------------------------------------------------------------------------- + +sep("2. open() — time to reopen a persistent table") + +print(f"{'rows':>12} {'CTable.open() (s)':>20} {'CTable(..., mode=a) (s)':>24}") +print(f"{'----':>12} {'------------------':>20} {'------------------------':>24}") + +for N in SIZES: + data = [(i, float(i % 100), i % 2 == 0) for i in range(N)] + clean() + path = TABLE_DIR + "/reopen" + t = blosc2.CTable(Row, urlpath=path, mode="w", expected_size=N) + t.extend(data, validate=False) + del t + + def bench_open(path=path): + t2 = blosc2.CTable.open(path, mode="r") + _ = len(t2) + + def bench_ctor(path=path): + t2 = blosc2.CTable(Row, urlpath=path, mode="a") + _ = len(t2) + + t_open = tmin(bench_open) + t_ctor = tmin(bench_ctor) + print(f"{N:>12,} {t_open:>20.4f} {t_ctor:>24.4f}") + +# --------------------------------------------------------------------------- +# Section 3: append() — single-row inserts after reopen +# --------------------------------------------------------------------------- + +sep("3. append() — 1 000 single-row inserts: in-memory vs file-backed") + +APPEND_N = 1_000 +PREALLOCATE = 10_000 # avoid resize noise + +print(f"{'backend':>14} {'total (s)':>12} {'µs / row':>12}") +print(f"{'-------':>14} {'---------':>12} {'--------':>12}") + + +def bench_append_mem(): + t = blosc2.CTable(Row, expected_size=PREALLOCATE, validate=False) + for i in range(APPEND_N): + t.append((i, float(i % 100), True)) + + +clean() +path = TABLE_DIR + "/apath" +blosc2.CTable(Row, urlpath=path, mode="w", expected_size=PREALLOCATE) + + +def bench_append_file(): + t = blosc2.CTable(Row, urlpath=path, mode="a", validate=False) + for i in range(APPEND_N): + t.append((i, float(i % 100), True)) + + +for label, fn in [("in-memory", bench_append_mem), ("file-backed", bench_append_file)]: + # Reset file table before each run + if label == "file-backed": + clean() + blosc2.CTable(Row, urlpath=path, mode="w", expected_size=PREALLOCATE) + elapsed = tmin(fn) + us_per_row = elapsed / APPEND_N * 1e6 + print(f"{label:>14} {elapsed:>12.4f} {us_per_row:>12.1f}") + +# --------------------------------------------------------------------------- +# Section 4: column read — to_numpy() after reopen +# --------------------------------------------------------------------------- + +sep("4. column read — to_numpy() on 'id': in-memory vs file-backed") + +print(f"{'rows':>12} {'in-memory (s)':>16} {'file-backed (s)':>16} {'ratio':>8}") +print(f"{'----':>12} {'-------------':>16} {'---------------':>16} {'-----':>8}") + +for N in SIZES: + data = [(i, float(i % 100), i % 2 == 0) for i in range(N)] + + t_mem_table = blosc2.CTable(Row, expected_size=N, validate=False) + t_mem_table.extend(data, validate=False) + + clean() + path = TABLE_DIR + "/read" + t_file_table = blosc2.CTable(Row, urlpath=path, mode="w", expected_size=N) + t_file_table.extend(data, validate=False) + # Reopen read-only (simulates a real read workload) + t_ro = blosc2.CTable.open(path, mode="r") + + def bench_read_mem(t=t_mem_table): + _ = t["id"].to_numpy() + + def bench_read_file(t=t_ro): + _ = t["id"].to_numpy() + + t_m = tmin(bench_read_mem) + t_f = tmin(bench_read_file) + ratio = t_f / t_m if t_m > 0 else float("nan") + print(f"{N:>12,} {t_m:>16.4f} {t_f:>16.4f} {ratio:>7.2f}x") + +# Cleanup +clean() +print() diff --git a/src/blosc2/ctable.py b/src/blosc2/ctable.py index d1886ede..11a07e13 100644 --- a/src/blosc2/ctable.py +++ b/src/blosc2/ctable.py @@ -11,6 +11,8 @@ from __future__ import annotations import dataclasses +import os +import shutil from collections.abc import Iterable from dataclasses import MISSING from typing import Any, Generic, TypeVar @@ -39,7 +41,9 @@ def wrapper(*args, **kwargs): ColumnConfig, CompiledColumn, CompiledSchema, + _validate_column_name, compile_schema, + compute_display_width, ) # RowT is intentionally left unbound so CTable works with both dataclasses @@ -276,6 +280,18 @@ def __getitem__(self, key: int | slice | list | np.ndarray): mask[phys_indices[:]] = True return Column(self._table, self._col_name, mask=mask) + elif isinstance(key, np.ndarray) and key.dtype == np.bool_: + # Boolean mask in logical space — same convention as numpy/pandas. + # key[i] == True means "include logical row i". + n_live = len(self) + if len(key) != n_live: + raise IndexError( + f"Boolean mask length {len(key)} does not match number of live rows {n_live}." + ) + all_pos = np.where(self._valid_rows[:])[0] + phys_indices = all_pos[key] + return self._raw_col[phys_indices] + elif isinstance(key, (list, tuple, np.ndarray)): real_pos = blosc2.where(self._valid_rows, np.arange(len(self._valid_rows))).compute() phys_indices = np.array([real_pos[i] for i in key], dtype=np.int64) @@ -284,6 +300,8 @@ def __getitem__(self, key: int | slice | list | np.ndarray): raise TypeError(f"Invalid index type: {type(key)}") def __setitem__(self, key: int | slice | list | np.ndarray, value): + if self._table._read_only: + raise ValueError("Table is read-only (opened with mode='r').") if isinstance(key, int): n_rows = len(self) if key < 0: @@ -293,6 +311,19 @@ def __setitem__(self, key: int | slice | list | np.ndarray, value): pos_true = _find_physical_index(self._valid_rows, key) self._raw_col[int(pos_true)] = value + elif isinstance(key, np.ndarray) and key.dtype == np.bool_: + # Boolean mask in logical space. + n_live = len(self) + if len(key) != n_live: + raise IndexError( + f"Boolean mask length {len(key)} does not match number of live rows {n_live}." + ) + all_pos = np.where(self._valid_rows[:])[0] + phys_indices = all_pos[key] + if isinstance(value, (list, tuple)): + value = np.array(value, dtype=self._raw_col.dtype) + self._raw_col[phys_indices] = value + elif isinstance(key, (slice, list, tuple, np.ndarray)): real_pos = blosc2.where(self._valid_rows, np.arange(len(self._valid_rows))).compute() if isinstance(key, slice): @@ -355,9 +386,294 @@ def __ge__(self, other): def dtype(self): return self._raw_col.dtype - def to_numpy(self): - real_pos = blosc2.where(self._valid_rows, np.arange(len(self._valid_rows))).compute() - return self._raw_col[real_pos[:]] + def iter_chunks(self, size: int = 65536): + """Iterate over live column values in chunks of *size* rows. + + Yields numpy arrays of at most *size* elements each, skipping deleted + rows. The last chunk may be smaller than *size*. + + Parameters + ---------- + size: + Number of live rows per yielded chunk. Defaults to 65 536. + + Yields + ------ + numpy.ndarray + A 1-D array of up to *size* live values with this column's dtype. + + Examples + -------- + >>> for chunk in t["score"].iter_chunks(size=100_000): + ... process(chunk) + """ + valid = self._valid_rows + raw = self._raw_col + arr_len = len(valid) + phys_chunk = valid.chunks[0] + + pending: list[np.ndarray] = [] + pending_count = 0 + + for info in valid.iterchunks_info(): + actual = min(phys_chunk, arr_len - info.nchunk * phys_chunk) + start = info.nchunk * phys_chunk + + if info.special == blosc2.SpecialValue.ZERO: + continue + + if info.special == blosc2.SpecialValue.VALUE: + val = np.frombuffer(info.repeated_value, dtype=valid.dtype)[0] + if not val: + continue + segment = raw[start : start + actual] + else: + mask = valid[start : start + actual] + segment = raw[start : start + actual][mask] + + if len(segment) == 0: + continue + + pending.append(segment) + pending_count += len(segment) + + while pending_count >= size: + combined = np.concatenate(pending) + yield combined[:size] + rest = combined[size:] + pending = [rest] if len(rest) > 0 else [] + pending_count = len(rest) + + if pending: + yield np.concatenate(pending) + + def to_numpy(self) -> np.ndarray: + """Return all live values as a NumPy array.""" + parts = list(self.iter_chunks(size=max(1, len(self)))) + if not parts: + return np.array([], dtype=self.dtype) + return np.concatenate(parts) if len(parts) > 1 else parts[0] + + def assign(self, data) -> None: + """Replace all live values in this column with *data*. + + Works on both full tables and views — on a view, only the rows + visible through the view's mask are overwritten. + + Parameters + ---------- + data: + List, numpy array, or any iterable. Must have exactly as many + elements as there are live rows in this column. Values are + coerced to the column's dtype if possible. + + Raises + ------ + ValueError + If ``len(data)`` does not match the number of live rows, or the + table is opened read-only. + TypeError + If values cannot be coerced to the column's dtype. + """ + if self._table._read_only: + raise ValueError("Table is read-only (opened with mode='r').") + n_live = len(self) + arr = np.asarray(data) + if len(arr) != n_live: + raise ValueError(f"assign() requires {n_live} values (live rows), got {len(arr)}.") + try: + arr = arr.astype(self.dtype) + except (ValueError, OverflowError) as exc: + raise TypeError(f"Cannot coerce data to column dtype {self.dtype!r}: {exc}") from exc + live_pos = np.where(self._valid_rows[:])[0] + self._raw_col[live_pos] = arr + + def unique(self) -> np.ndarray: + """Return sorted array of unique live values. + + Processes data in chunks — never loads the full column at once. + """ + seen: set = set() + for chunk in self.iter_chunks(): + seen.update(chunk.tolist()) + return np.array(sorted(seen), dtype=self.dtype) + + def value_counts(self) -> dict: + """Return a ``{value: count}`` dict sorted by count descending. + + Processes data in chunks — never loads the full column at once. + + Example + ------- + >>> t["active"].value_counts() + {True: 8432, False: 1568} + """ + counts: dict = {} + for chunk in self.iter_chunks(): + for val in chunk.tolist(): + counts[val] = counts.get(val, 0) + 1 + return dict(sorted(counts.items(), key=lambda kv: -kv[1])) + + # ------------------------------------------------------------------ + # Aggregate helpers + # ------------------------------------------------------------------ + + def _require_nonempty(self, op: str) -> None: + if len(self) == 0: + raise ValueError(f"Column.{op}() called on an empty column.") + + def _require_kind(self, kinds: str, op: str) -> None: + """Raise TypeError if this column's dtype is not in *kinds*.""" + if self.dtype.kind not in kinds: + _kind_names = { + "b": "bool", + "i": "signed int", + "u": "unsigned int", + "f": "float", + "c": "complex", + "U": "string", + "S": "bytes", + } + raise TypeError( + f"Column.{op}() is not supported for dtype {self.dtype!r} " + f"({_kind_names.get(self.dtype.kind, self.dtype.kind)})." + ) + + # ------------------------------------------------------------------ + # Aggregates + # ------------------------------------------------------------------ + + def sum(self): + """Sum of all live values. + + Supported dtypes: bool, int, uint, float, complex. + Bool values are counted as 0 / 1. + """ + self._require_kind("biufc", "sum") + self._require_nonempty("sum") + # Use a wide accumulator to reduce overflow risk + acc_dtype = ( + np.float64 + if self.dtype.kind == "f" + else ( + np.complex128 if self.dtype.kind == "c" else np.int64 if self.dtype.kind in "biu" else None + ) + ) + result = acc_dtype(0) + for chunk in self.iter_chunks(): + result += chunk.sum(dtype=acc_dtype) + # Return in the column's natural dtype when it fits, else keep wide + if self.dtype.kind in "biu": + return int(result) + return result + + def min(self): + """Minimum live value. + + Supported dtypes: bool, int, uint, float, string, bytes. + Strings are compared lexicographically. + """ + self._require_kind("biufUS", "min") + self._require_nonempty("min") + result = None + is_str = self.dtype.kind in "US" + for chunk in self.iter_chunks(): + # numpy .min()/.max() don't support string dtypes in recent NumPy; + # fall back to Python's built-in min/max which work on any comparable type. + chunk_min = min(chunk) if is_str else chunk.min() + if result is None or chunk_min < result: + result = chunk_min + return result + + def max(self): + """Maximum live value. + + Supported dtypes: bool, int, uint, float, string, bytes. + Strings are compared lexicographically. + """ + self._require_kind("biufUS", "max") + self._require_nonempty("max") + result = None + is_str = self.dtype.kind in "US" + for chunk in self.iter_chunks(): + chunk_max = max(chunk) if is_str else chunk.max() + if result is None or chunk_max > result: + result = chunk_max + return result + + def mean(self) -> float: + """Arithmetic mean of all live values. + + Supported dtypes: bool, int, uint, float. + Always returns a Python float. + """ + self._require_kind("biuf", "mean") + self._require_nonempty("mean") + total = np.float64(0) + count = 0 + for chunk in self.iter_chunks(): + total += chunk.sum(dtype=np.float64) + count += len(chunk) + return float(total / count) + + def std(self, ddof: int = 0) -> float: + """Standard deviation of all live values (single-pass, Welford's algorithm). + + Parameters + ---------- + ddof: + Delta degrees of freedom. ``0`` (default) gives the population + std; ``1`` gives the sample std (divides by N-1). + + Supported dtypes: bool, int, uint, float. + Always returns a Python float. + """ + self._require_kind("biuf", "std") + self._require_nonempty("std") + + # Chan's parallel update — combines per-chunk (n, mean, M2) tuples. + # This is numerically stable and requires only a single pass. + n_total = np.int64(0) + mean_total = np.float64(0) + M2_total = np.float64(0) + + for chunk in self.iter_chunks(): + chunk = chunk.astype(np.float64) + n_b = np.int64(len(chunk)) + mean_b = chunk.mean() + M2_b = np.float64(((chunk - mean_b) ** 2).sum()) + + if n_total == 0: + n_total, mean_total, M2_total = n_b, mean_b, M2_b + else: + delta = mean_b - mean_total + n_new = n_total + n_b + mean_total = (n_total * mean_total + n_b * mean_b) / n_new + M2_total += M2_b + delta**2 * n_total * n_b / n_new + n_total = n_new + + divisor = n_total - ddof + if divisor <= 0: + return float("nan") + return float(np.sqrt(M2_total / divisor)) + + def any(self) -> bool: + """Return True if at least one live value is True. + + Supported dtypes: bool. + Short-circuits on the first True found. + """ + self._require_kind("b", "any") + return any(chunk.any() for chunk in self.iter_chunks()) + + def all(self) -> bool: + """Return True if every live value is True. + + Supported dtypes: bool. + Short-circuits on the first False found. + """ + self._require_kind("b", "all") + return all(chunk.all() for chunk in self.iter_chunks()) # --------------------------------------------------------------------------- @@ -365,6 +681,17 @@ def to_numpy(self): # --------------------------------------------------------------------------- +def _fmt_bytes(n: int) -> str: + """Human-readable byte count (e.g. '1.23 MB').""" + if n < 1024: + return f"{n} B" + if n < 1024**2: + return f"{n / 1024:.2f} KB" + if n < 1024**3: + return f"{n / 1024**2:.2f} MB" + return f"{n / 1024**3:.2f} GB" + + class CTable(Generic[RowT]): def __init__( self, @@ -565,29 +892,71 @@ def _grow(self) -> None: # Display # ------------------------------------------------------------------ - def __str__(self): - retval = [] - cont = 0 + def __str__(self) -> str: + _HEAD_TAIL = 10 # rows shown at each end - for name in self._cols: - retval.append(f"{name:^{self._col_widths[name]}} |") - cont += self._col_widths[name] + 2 - retval.append("\n") - for _i in range(cont): - retval.append("-") - retval.append("\n") + nrows = self._n_rows + ncols = len(self.col_names) + hidden = max(0, nrows - _HEAD_TAIL * 2) - real_poss = blosc2.where(self._valid_rows, np.array(range(len(self._valid_rows)))).compute() + # -- physical positions for head and tail rows -- + valid_np = self._valid_rows[:] + all_pos = np.where(valid_np)[0] + + if nrows <= _HEAD_TAIL * 2: + head_pos = all_pos + tail_pos = np.array([], dtype=all_pos.dtype) + hidden = 0 + else: + head_pos = all_pos[:_HEAD_TAIL] + tail_pos = all_pos[-_HEAD_TAIL:] + + # -- per-column display widths -- + widths: dict[str, int] = {} + for name in self.col_names: + widths[name] = max( + self._col_widths[name], + len(str(self._cols[name].dtype)), + ) + + sep = " ".join("─" * (w + 2) for w in widths.values()) + + def fmt_row(values: dict) -> str: + return " ".join(f" {values[n]!s:<{widths[n]}} " for n in self.col_names) + + # -- batch-fetch values (one read per column, not one per cell) -- + def rows_to_dicts(positions) -> list[dict]: + if len(positions) == 0: + return [] + col_data = {n: self._cols[n][positions] for n in self.col_names} + return [{n: col_data[n][i].item() for n in self.col_names} for i in range(len(positions))] + + lines = [ + fmt_row({n: n for n in self.col_names}), + fmt_row({n: str(self._cols[n].dtype) for n in self.col_names}), + sep, + ] - for j in real_poss: - for name in self._cols: - retval.append(f"{self._cols[name][j]:^{self._col_widths[name]}}") - retval.append(" |") - retval.append("\n") - for _ in range(cont): - retval.append("-") - retval.append("\n") - return "".join(retval) + for row in rows_to_dicts(head_pos): + lines.append(fmt_row(row)) + + if hidden > 0: + lines.append(fmt_row(dict.fromkeys(self.col_names, "..."))) + + for row in rows_to_dicts(tail_pos): + lines.append(fmt_row(row)) + + lines.append(sep) + footer = f"{nrows:,} rows × {ncols} columns" + if hidden > 0: + footer += f" ({hidden:,} rows hidden)" + lines.append(footer) + + return "\n".join(lines) + + def __repr__(self) -> str: + cols = ", ".join(self.col_names) + return f"CTable<{cols}>({self._n_rows:,} rows, {_fmt_bytes(self.cbytes)} compressed)" def __len__(self): return self._n_rows @@ -653,6 +1022,152 @@ def open(cls, urlpath: str, *, mode: str = "r") -> CTable: obj._last_pos = None # resolve lazily on first write return obj + # ------------------------------------------------------------------ + # Save / Load (in-memory ↔ disk) + # ------------------------------------------------------------------ + + def save(self, urlpath: str, *, overwrite: bool = False) -> None: + """Copy this (in-memory) table to disk at *urlpath*. + + Only live rows are written — the on-disk table is always compacted. + + Parameters + ---------- + urlpath: + Destination directory path. + overwrite: + If ``False`` (default), raise :exc:`ValueError` when *urlpath* + already exists. Set to ``True`` to replace an existing table. + + Raises + ------ + ValueError + If *urlpath* already exists and ``overwrite=False``, or if called + on a view. + """ + if self.base is not None: + raise ValueError("Cannot save a view — save the parent table instead.") + if os.path.exists(urlpath): + if not overwrite: + raise ValueError(f"Path {urlpath!r} already exists. Use overwrite=True to replace.") + shutil.rmtree(urlpath) + + # Collect live physical positions + valid_np = self._valid_rows[:] + live_pos = np.where(valid_np)[0] + n_live = len(live_pos) + capacity = max(n_live, 1) + + file_storage = FileTableStorage(urlpath, "w") + default_chunks, default_blocks = compute_chunks_blocks((capacity,)) + + # --- valid_rows (all True, compacted) --- + disk_valid = file_storage.create_valid_rows( + shape=(capacity,), + chunks=default_chunks, + blocks=default_blocks, + ) + if n_live > 0: + disk_valid[:n_live] = True + + # --- columns --- + for col in self._schema.columns: + name = col.name + col_storage = self._resolve_column_storage(col, default_chunks, default_blocks) + disk_col = file_storage.create_column( + name, + dtype=col.dtype, + shape=(capacity,), + chunks=col_storage["chunks"], + blocks=col_storage["blocks"], + cparams=col_storage.get("cparams"), + dparams=col_storage.get("dparams"), + ) + if n_live > 0: + disk_col[:n_live] = self._cols[name][live_pos] + + file_storage.save_schema(schema_to_dict(self._schema)) + + @classmethod + def load(cls, urlpath: str) -> CTable: + """Load a persistent table from *urlpath* into RAM. + + The schema is read from the table's metadata — the original Python + dataclass is not required. The returned table is fully in-memory and + read/write. + + Parameters + ---------- + urlpath: + Path to the table root directory. + + Raises + ------ + FileNotFoundError + If *urlpath* does not contain a CTable. + ValueError + If the metadata at *urlpath* does not identify a CTable. + """ + file_storage = FileTableStorage(urlpath, "r") + if not file_storage.table_exists(): + raise FileNotFoundError(f"No CTable found at {urlpath!r}") + file_storage.check_kind() + schema_dict = file_storage.load_schema() + schema = schema_from_dict(schema_dict) + col_names = [c["name"] for c in schema_dict["columns"]] + + disk_valid = file_storage.open_valid_rows() + disk_cols = {name: file_storage.open_column(name) for name in col_names} + phys_size = len(disk_valid) + n_live = int(blosc2.count_nonzero(disk_valid)) + capacity = max(phys_size, 1) + + mem_storage = InMemoryTableStorage() + default_chunks, default_blocks = compute_chunks_blocks((capacity,)) + + mem_valid = mem_storage.create_valid_rows( + shape=(capacity,), + chunks=default_chunks, + blocks=default_blocks, + ) + if phys_size > 0: + mem_valid[:phys_size] = disk_valid[:] + + mem_cols: dict[str, blosc2.NDArray] = {} + for col in schema.columns: + name = col.name + mem_col = mem_storage.create_column( + name, + dtype=col.dtype, + shape=(capacity,), + chunks=default_chunks, + blocks=default_blocks, + cparams=None, + dparams=None, + ) + if phys_size > 0: + mem_col[:phys_size] = disk_cols[name][:] + mem_cols[name] = mem_col + + obj = cls.__new__(cls) + obj._row_type = None + obj._validate = True + obj._table_cparams = None + obj._table_dparams = None + obj._storage = mem_storage + obj._read_only = False + obj._schema = schema + obj._cols = mem_cols + obj._col_widths = {col.name: max(len(col.name), col.display_width) for col in schema.columns} + obj.col_names = col_names + obj.row = _RowIndexer(obj) + obj.auto_compact = False + obj.base = None + obj._valid_rows = mem_valid + obj._n_rows = n_live + obj._last_pos = None # resolve lazily on first write + return obj + # ------------------------------------------------------------------ # View / filtering # ------------------------------------------------------------------ @@ -666,9 +1181,9 @@ def _make_view(cls, parent: CTable, new_valid_rows: blosc2.NDArray) -> CTable: obj._table_cparams = parent._table_cparams obj._table_dparams = parent._table_dparams obj._storage = None - obj._read_only = True + obj._read_only = parent._read_only # inherit: only True for mode="r" disk tables obj._schema = parent._schema - obj._cols = parent._cols # shared — views never mutate + obj._cols = parent._cols # shared — views cannot change row structure obj._col_widths = parent._col_widths obj.col_names = parent.col_names obj.row = _RowIndexer(obj) @@ -738,6 +1253,213 @@ def tail(self, N: int = 5) -> CTable: mask_arr = (mask_arr & self._valid_rows).compute() return self.view(mask_arr) + def sample(self, n: int, *, seed: int | None = None) -> CTable: + """Return a read-only view of *n* randomly chosen live rows. + + Parameters + ---------- + n: + Number of rows to sample. If *n* >= number of live rows, + returns a view of the whole table. + seed: + Optional random seed for reproducibility. + + Returns + ------- + CTable + A read-only view sharing columns with this table. + """ + if n <= 0: + return self.view(blosc2.zeros(shape=len(self._valid_rows), dtype=np.bool_)) + if n >= self._n_rows: + return self.view(self._valid_rows) + + rng = np.random.default_rng(seed) + all_pos = np.where(self._valid_rows[:])[0] + chosen = rng.choice(all_pos, size=n, replace=False) + + mask = np.zeros(len(self._valid_rows), dtype=np.bool_) + mask[chosen] = True + return self.view(blosc2.asarray(mask)) + + # ------------------------------------------------------------------ + # Schema mutations: add / drop / rename columns + # ------------------------------------------------------------------ + + def add_column( + self, + name: str, + spec: SchemaSpec, + default, + *, + cparams: dict | None = None, + ) -> None: + """Add a new column filled with *default* for every existing live row. + + Parameters + ---------- + name: + Column name. Must follow the same naming rules as schema fields. + spec: + A schema descriptor such as ``b2.int64(ge=0)`` or ``b2.string()``. + default: + Value written to every existing live row. Must be coercible to + *spec*'s dtype. + cparams: + Optional compression parameters for this column's NDArray. + + Raises + ------ + ValueError + If the table is read-only, is a view, or the column already exists. + TypeError + If *default* cannot be coerced to *spec*'s dtype. + """ + if self._read_only: + raise ValueError("Table is read-only (opened with mode='r').") + if self.base is not None: + raise ValueError("Cannot add a column to a view.") + _validate_column_name(name) + if name in self._cols: + raise ValueError(f"Column {name!r} already exists.") + + try: + default_val = spec.dtype.type(default) + except (ValueError, OverflowError) as exc: + raise TypeError(f"Cannot coerce default {default!r} to dtype {spec.dtype!r}: {exc}") from exc + + capacity = len(self._valid_rows) + default_chunks, default_blocks = compute_chunks_blocks((capacity,)) + new_col = self._storage.create_column( + name, + dtype=spec.dtype, + shape=(capacity,), + chunks=default_chunks, + blocks=default_blocks, + cparams=cparams, + dparams=None, + ) + + live_pos = np.where(self._valid_rows[:])[0] + if len(live_pos) > 0: + new_col[live_pos] = default_val + + compiled_col = CompiledColumn( + name=name, + py_type=spec.python_type, + spec=spec, + dtype=spec.dtype, + default=default, + config=ColumnConfig(cparams=cparams, dparams=None, chunks=None, blocks=None), + display_width=compute_display_width(spec), + ) + self._cols[name] = new_col + self.col_names.append(name) + self._col_widths[name] = max(len(name), compiled_col.display_width) + + new_columns = self._schema.columns + [compiled_col] + self._schema = CompiledSchema( + row_cls=self._schema.row_cls, + columns=new_columns, + columns_by_name={**self._schema.columns_by_name, name: compiled_col}, + ) + if isinstance(self._storage, FileTableStorage): + self._storage.save_schema(schema_to_dict(self._schema)) + + def drop_column(self, name: str) -> None: + """Remove a column from the table. + + On disk tables the corresponding ``.b2nd`` file is deleted. + + Raises + ------ + ValueError + If the table is read-only, is a view, or *name* is the last column. + KeyError + If *name* does not exist. + """ + if self._read_only: + raise ValueError("Table is read-only (opened with mode='r').") + if self.base is not None: + raise ValueError("Cannot drop a column from a view.") + if name not in self._cols: + raise KeyError(f"No column named {name!r}. Available: {self.col_names}") + if len(self.col_names) == 1: + raise ValueError("Cannot drop the last column.") + + if isinstance(self._storage, FileTableStorage): + col_path = self._storage._col_path(name) + if os.path.exists(col_path): + os.remove(col_path) + + del self._cols[name] + del self._col_widths[name] + self.col_names.remove(name) + + new_columns = [c for c in self._schema.columns if c.name != name] + self._schema = CompiledSchema( + row_cls=self._schema.row_cls, + columns=new_columns, + columns_by_name={c.name: c for c in new_columns}, + ) + if isinstance(self._storage, FileTableStorage): + self._storage.save_schema(schema_to_dict(self._schema)) + + def rename_column(self, old: str, new: str) -> None: + """Rename a column. + + On disk tables the corresponding ``.b2nd`` file is renamed. + + Raises + ------ + ValueError + If the table is read-only, is a view, or *new* already exists. + KeyError + If *old* does not exist. + """ + if self._read_only: + raise ValueError("Table is read-only (opened with mode='r').") + if self.base is not None: + raise ValueError("Cannot rename a column in a view.") + if old not in self._cols: + raise KeyError(f"No column named {old!r}. Available: {self.col_names}") + if new in self._cols: + raise ValueError(f"Column {new!r} already exists.") + _validate_column_name(new) + + if isinstance(self._storage, FileTableStorage): + old_path = self._storage._col_path(old) + new_path = self._storage._col_path(new) + os.rename(old_path, new_path) + b2_mode = "r" if self._read_only else "a" + self._cols[new] = blosc2.open(new_path, mode=b2_mode) + else: + self._cols[new] = self._cols[old] + del self._cols[old] + + idx = self.col_names.index(old) + self.col_names[idx] = new + self._col_widths[new] = max(len(new), self._col_widths.pop(old)) + + old_compiled = self._schema.columns_by_name[old] + renamed = CompiledColumn( + name=new, + py_type=old_compiled.py_type, + spec=old_compiled.spec, + dtype=old_compiled.dtype, + default=old_compiled.default, + config=old_compiled.config, + display_width=old_compiled.display_width, + ) + new_columns = [renamed if c.name == old else c for c in self._schema.columns] + self._schema = CompiledSchema( + row_cls=self._schema.row_cls, + columns=new_columns, + columns_by_name={c.name: c for c in new_columns}, + ) + if isinstance(self._storage, FileTableStorage): + self._storage.save_schema(schema_to_dict(self._schema)) + # ------------------------------------------------------------------ # Column access # ------------------------------------------------------------------ @@ -759,6 +1481,8 @@ def __getattr__(self, s: str): def compact(self): if self._read_only: raise ValueError("Table is read-only (opened with mode='r').") + if self.base is not None: + raise ValueError("Cannot compact a view.") real_poss = blosc2.where(self._valid_rows, np.array(range(len(self._valid_rows)))).compute() start = 0 block_size = self._valid_rows.blocks[0] @@ -785,6 +1509,16 @@ def nrows(self) -> int: def ncols(self) -> int: return len(self._cols) + @property + def cbytes(self) -> int: + """Total compressed size in bytes (all columns + valid_rows mask).""" + return sum(col.cbytes for col in self._cols.values()) + self._valid_rows.cbytes + + @property + def nbytes(self) -> int: + """Total uncompressed size in bytes (all columns + valid_rows mask).""" + return sum(col.nbytes for col in self._cols.values()) + self._valid_rows.nbytes + @property def schema(self) -> CompiledSchema: """The compiled schema that drives this table's columns and validation.""" @@ -809,28 +1543,12 @@ def schema_dict(self) -> dict[str, Any]: def info(self) -> None: """Print a concise summary of the CTable.""" - n_cols = len(self._cols) - n_rows = len(self) - - cbytes = sum(col.cbytes for col in self._cols.values()) + self._valid_rows.cbytes - nbytes = sum(col.nbytes for col in self._cols.values()) + self._valid_rows.nbytes - - def format_bytes(bytes_size: float) -> str: - if bytes_size < 1024: - return f"{bytes_size} B" - elif bytes_size < 1024**2: - return f"{bytes_size / 1024:.2f} KB" - elif bytes_size < 1024**3: - return f"{bytes_size / (1024**2):.2f} MB" - else: - return f"{bytes_size / (1024**3):.2f} GB" - - ratio = (nbytes / cbytes) if cbytes > 0 else 0.0 + ratio = (self.nbytes / self.cbytes) if self.cbytes > 0 else 0.0 lines = [] lines.append("") - lines.append(f"nºColumns: {n_cols}") - lines.append(f"nºRows: {n_rows}") + lines.append(f"nºColumns: {self.ncols}") + lines.append(f"nºRows: {self.nrows}") lines.append("") header = f" {'#':>3} {'Column':<15} {'Itemsize':<12} {'Dtype':<15}" @@ -844,8 +1562,8 @@ def format_bytes(bytes_size: float) -> str: lines.append(f" {i:>3} {name:<15} {itemsize:<12} {dtype_str:<15}") lines.append("") - lines.append(f"memory usage: {format_bytes(cbytes)}") - lines.append(f"uncompressed size: {format_bytes(nbytes)}") + lines.append(f"memory usage: {_fmt_bytes(self.cbytes)}") + lines.append(f"uncompressed size: {_fmt_bytes(self.nbytes)}") lines.append(f"compression ratio: {ratio:.2f}x") lines.append("") @@ -902,6 +1620,8 @@ def append(self, data: list | np.void | np.ndarray) -> None: def delete(self, ind: int | slice | str | Iterable) -> None: if self._read_only: raise ValueError("Table is read-only (opened with mode='r').") + if self.base is not None: + raise ValueError("Cannot delete rows from a view.") valid_rows_np = self._valid_rows[:] true_pos = np.where(valid_rows_np)[0] diff --git a/src/blosc2/ctable_storage.py b/src/blosc2/ctable_storage.py new file mode 100644 index 00000000..23755ef2 --- /dev/null +++ b/src/blosc2/ctable_storage.py @@ -0,0 +1,248 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# This source code is licensed under a BSD-style license (found in the +# LICENSE file in the root directory of this source tree) +####################################################################### + +"""Storage backends for CTable. + +Two concrete backends: + +* :class:`InMemoryTableStorage` — all arrays live in RAM (default when + ``urlpath`` is not provided). +* :class:`FileTableStorage` — arrays are stored as individual Blosc2 files + under a table root directory; schema and kind metadata live in a small + :class:`blosc2.SChunk` whose ``vlmeta`` is the source of truth. + +Layout produced by :class:`FileTableStorage`:: + + / + _meta.b2frame ← SChunk with vlmeta: kind, version, schema JSON + _valid_rows.b2nd ← boolean NDArray (tombstone mask) + _cols/ + .b2nd ← one NDArray per column +""" + +from __future__ import annotations + +import json +import os +from typing import Any + +import numpy as np + +import blosc2 + +# --------------------------------------------------------------------------- +# Abstract base +# --------------------------------------------------------------------------- + + +class TableStorage: + """Interface that CTable uses to create/open its backing arrays.""" + + def create_column( + self, + name: str, + *, + dtype: np.dtype, + shape: tuple[int, ...], + chunks: tuple[int, ...], + blocks: tuple[int, ...], + cparams: dict[str, Any] | None, + dparams: dict[str, Any] | None, + ) -> blosc2.NDArray: + raise NotImplementedError + + def open_column(self, name: str) -> blosc2.NDArray: + raise NotImplementedError + + def create_valid_rows( + self, + *, + shape: tuple[int, ...], + chunks: tuple[int, ...], + blocks: tuple[int, ...], + ) -> blosc2.NDArray: + raise NotImplementedError + + def open_valid_rows(self) -> blosc2.NDArray: + raise NotImplementedError + + def save_schema(self, schema_dict: dict[str, Any]) -> None: + raise NotImplementedError + + def load_schema(self) -> dict[str, Any] | None: + raise NotImplementedError + + def table_exists(self) -> bool: + raise NotImplementedError + + def is_read_only(self) -> bool: + raise NotImplementedError + + +# --------------------------------------------------------------------------- +# In-memory backend +# --------------------------------------------------------------------------- + + +class InMemoryTableStorage(TableStorage): + """All arrays are plain in-memory blosc2.NDArray objects.""" + + def create_column(self, name, *, dtype, shape, chunks, blocks, cparams, dparams): + kwargs: dict[str, Any] = {"chunks": chunks, "blocks": blocks} + if cparams is not None: + kwargs["cparams"] = cparams + if dparams is not None: + kwargs["dparams"] = dparams + return blosc2.zeros(shape, dtype=dtype, **kwargs) + + def open_column(self, name): + raise RuntimeError("In-memory tables have no on-disk representation to open.") + + def create_valid_rows(self, *, shape, chunks, blocks): + return blosc2.zeros(shape, dtype=np.bool_, chunks=chunks, blocks=blocks) + + def open_valid_rows(self): + raise RuntimeError("In-memory tables have no on-disk representation to open.") + + def save_schema(self, schema_dict): + pass # nothing to persist + + def load_schema(self): + return None + + def table_exists(self): + return False + + def is_read_only(self): + return False + + +# --------------------------------------------------------------------------- +# File-backed backend +# --------------------------------------------------------------------------- + +_META_FILE = "_meta.b2frame" +_VALID_ROWS_FILE = "_valid_rows.b2nd" +_COLS_DIR = "_cols" + + +class FileTableStorage(TableStorage): + """Arrays stored as individual Blosc2 files inside *urlpath* directory. + + Parameters + ---------- + urlpath: + Path to the table root directory. + mode: + ``'w'`` — create (overwrite existing files). + ``'a'`` — open existing or create new. + ``'r'`` — open existing read-only. + """ + + def __init__(self, urlpath: str, mode: str) -> None: + if mode not in ("r", "a", "w"): + raise ValueError(f"mode must be 'r', 'a', or 'w'; got {mode!r}") + self._root = urlpath + self._mode = mode + self._meta: blosc2.SChunk | None = None + + # ------------------------------------------------------------------ + # Path helpers + # ------------------------------------------------------------------ + + @property + def _meta_path(self) -> str: + return os.path.join(self._root, _META_FILE) + + @property + def _valid_rows_path(self) -> str: + return os.path.join(self._root, _VALID_ROWS_FILE) + + def _col_path(self, name: str) -> str: + return os.path.join(self._root, _COLS_DIR, f"{name}.b2nd") + + def _ensure_dirs(self) -> None: + os.makedirs(os.path.join(self._root, _COLS_DIR), exist_ok=True) + + # ------------------------------------------------------------------ + # TableStorage interface + # ------------------------------------------------------------------ + + def table_exists(self) -> bool: + return os.path.exists(self._meta_path) + + def is_read_only(self) -> bool: + return self._mode == "r" + + def create_column(self, name, *, dtype, shape, chunks, blocks, cparams, dparams): + self._ensure_dirs() + kwargs: dict[str, Any] = { + "chunks": chunks, + "blocks": blocks, + "urlpath": self._col_path(name), + "mode": "w", + } + if cparams is not None: + kwargs["cparams"] = cparams + if dparams is not None: + kwargs["dparams"] = dparams + return blosc2.zeros(shape, dtype=dtype, **kwargs) + + def open_column(self, name: str) -> blosc2.NDArray: + b2_mode = "r" if self._mode == "r" else "a" + return blosc2.open(self._col_path(name), mode=b2_mode) + + def create_valid_rows(self, *, shape, chunks, blocks): + self._ensure_dirs() + return blosc2.zeros( + shape, + dtype=np.bool_, + chunks=chunks, + blocks=blocks, + urlpath=self._valid_rows_path, + mode="w", + ) + + def open_valid_rows(self) -> blosc2.NDArray: + b2_mode = "r" if self._mode == "r" else "a" + return blosc2.open(self._valid_rows_path, mode=b2_mode) + + def save_schema(self, schema_dict: dict[str, Any]) -> None: + """Write *schema_dict* (plus kind/version markers) to ``_meta.b2frame``.""" + self._ensure_dirs() + # Always overwrite: save_schema is only called at table-creation time. + self._meta = blosc2.SChunk(urlpath=self._meta_path, mode="w") + self._meta.vlmeta["kind"] = "ctable" + self._meta.vlmeta["version"] = 1 + self._meta.vlmeta["schema"] = json.dumps(schema_dict) + + def _open_meta(self) -> blosc2.SChunk: + """Open (or return cached) the ``_meta.b2frame`` SChunk.""" + if self._meta is None: + b2_mode = "r" if self._mode == "r" else "a" + self._meta = blosc2.open(self._meta_path, mode=b2_mode) + return self._meta + + def load_schema(self) -> dict[str, Any]: + """Read and return the schema dict stored in ``_meta.b2frame``.""" + raw = self._open_meta().vlmeta["schema"] + if isinstance(raw, bytes): + raw = raw.decode() + return json.loads(raw) + + def check_kind(self) -> None: + """Raise :exc:`ValueError` if ``_meta`` does not identify a CTable.""" + kind = self._open_meta().vlmeta["kind"] + if isinstance(kind, bytes): + kind = kind.decode() + if kind != "ctable": + raise ValueError(f"Path {self._root!r} does not contain a CTable (kind={kind!r}).") + + def column_names_from_schema(self) -> list[str]: + d = self.load_schema() + return [c["name"] for c in d["columns"]] diff --git a/src/blosc2/schema_validation.py b/src/blosc2/schema_validation.py index 5feabdf8..dd750122 100644 --- a/src/blosc2/schema_validation.py +++ b/src/blosc2/schema_validation.py @@ -41,7 +41,8 @@ def build_validator_model(schema: CompiledSchema) -> type[BaseModel]: else: field_definitions[col.name] = (col.py_type, Field(default=col.default, **pydantic_kwargs)) - model_cls = create_model(f"_Validator_{schema.row_cls.__name__}", **field_definitions) + cls_name = schema.row_cls.__name__ if schema.row_cls is not None else "Unknown" + model_cls = create_model(f"_Validator_{cls_name}", **field_definitions) schema.validator_model = model_cls return model_cls diff --git a/tests/ctable/test_column.py b/tests/ctable/test_column.py index a5b4032a..46cfca28 100644 --- a/tests/ctable/test_column.py +++ b/tests/ctable/test_column.py @@ -21,6 +21,11 @@ class Row: active: bool = blosc2.field(blosc2.bool(), default=True) +@dataclass +class StrRow: + label: str = blosc2.field(blosc2.string(max_length=16)) + + DATA20 = [(i, float(i * 10), True) for i in range(20)] @@ -231,26 +236,20 @@ def test_column_slice_returns_view(): assert view._col_name == "id" -def test_to_array_no_holes(): - """to_array() on a slice view returns correct data on a full table.""" +def test_to_array_slices(): + """to_array() on slice views: full table and with holes.""" + # No holes tabla = CTable(Row, new_data=DATA20) col = tabla.id - np.testing.assert_array_equal(col[0:5].to_numpy(), np.array([0, 1, 2, 3, 4], dtype=np.int64)) np.testing.assert_array_equal(col[5:10].to_numpy(), np.array([5, 6, 7, 8, 9], dtype=np.int64)) np.testing.assert_array_equal(col[15:20].to_numpy(), np.array([15, 16, 17, 18, 19], dtype=np.int64)) np.testing.assert_array_equal(col[0:20].to_numpy(), np.arange(20, dtype=np.int64)) - -def test_to_array_with_holes(): - """to_array() on a slice view skips deleted rows correctly.""" - tabla = CTable(Row, new_data=DATA20) - tabla.delete([1, 3, 5, 7, 9, 11, 13, 15, 17, 19]) # keep evens: 0,2,4,...,18 + # With holes: delete odd indices → keep evens 0,2,4,...,18 + tabla.delete([1, 3, 5, 7, 9, 11, 13, 15, 17, 19]) col = tabla.id - - # logical [0:5] → physical rows 0,2,4,6,8 np.testing.assert_array_equal(col[0:5].to_numpy(), np.array([0, 2, 4, 6, 8], dtype=np.int64)) - # logical [5:10] → physical rows 10,12,14,16,18 np.testing.assert_array_equal(col[5:10].to_numpy(), np.array([10, 12, 14, 16, 18], dtype=np.int64)) @@ -286,5 +285,395 @@ def test_column_view_mask_is_independent(): np.testing.assert_array_equal(view_a.to_numpy(), np.arange(0, 5, dtype=np.int64)) +# ------------------------------------------------------------------- +# iter_chunks +# ------------------------------------------------------------------- + + +def test_iter_chunks_full_table(): + """iter_chunks reassembles to the same values as to_numpy().""" + tabla = CTable(Row, new_data=DATA20) + expected = tabla["id"].to_numpy() + got = np.concatenate(list(tabla["id"].iter_chunks(size=7))) + np.testing.assert_array_equal(got, expected) + + +def test_iter_chunks_chunk_sizes(): + """Each yielded chunk has at most *size* elements; last may be smaller.""" + tabla = CTable(Row, new_data=DATA20) + chunks = list(tabla["score"].iter_chunks(size=6)) + for c in chunks[:-1]: + assert len(c) == 6 + assert len(chunks[-1]) <= 6 + assert sum(len(c) for c in chunks) == 20 + + +def test_iter_chunks_skips_deleted_rows(): + """Deleted rows are not included in any chunk.""" + tabla = CTable(Row, new_data=DATA20) + tabla.delete([0, 1, 2]) # delete id 0, 1, 2 + chunks = list(tabla["id"].iter_chunks(size=5)) + all_vals = np.concatenate(chunks) + assert 0 not in all_vals + assert 1 not in all_vals + assert 2 not in all_vals + assert len(all_vals) == 17 + + +def test_iter_chunks_size_larger_than_table(): + """A size larger than the table yields a single chunk with all rows.""" + tabla = CTable(Row, new_data=DATA20) + chunks = list(tabla["id"].iter_chunks(size=1000)) + assert len(chunks) == 1 + np.testing.assert_array_equal(chunks[0], np.arange(20, dtype=np.int64)) + + +def test_iter_chunks_empty_table(): + """iter_chunks on an empty table yields nothing.""" + tabla = CTable(Row) + chunks = list(tabla["id"].iter_chunks()) + assert chunks == [] + + +# ------------------------------------------------------------------- +# Aggregates: sum +# ------------------------------------------------------------------- + + +def test_sum_int(): + t = CTable(Row, new_data=DATA20) + assert t["id"].sum() == sum(range(20)) + + +def test_sum_float(): + t = CTable(Row, new_data=DATA20) + assert t["score"].sum() == pytest.approx(sum(i * 10.0 for i in range(20))) + + +def test_sum_bool_counts_trues(): + t = CTable(Row, new_data=DATA20) # all active=True + assert t["active"].sum() == 20 + + +def test_sum_skips_deleted_rows(): + t = CTable(Row, new_data=DATA20) + t.delete([0]) # remove id=0 + assert t["id"].sum() == sum(range(1, 20)) + + +def test_sum_empty_raises(): + t = CTable(Row) + with pytest.raises(ValueError, match="empty"): + t["id"].sum() + + +def test_sum_wrong_type_raises(): + t = CTable(StrRow, new_data=[("hello",)]) + with pytest.raises(TypeError): + t["label"].sum() + + +# ------------------------------------------------------------------- +# Aggregates: min / max +# ------------------------------------------------------------------- + + +def test_min_int(): + t = CTable(Row, new_data=DATA20) + assert t["id"].min() == 0 + + +def test_max_int(): + t = CTable(Row, new_data=DATA20) + assert t["id"].max() == 19 + + +def test_min_float(): + t = CTable(Row, new_data=DATA20) + assert t["score"].min() == pytest.approx(0.0) + + +def test_max_float(): + t = CTable(Row, new_data=DATA20) + assert t["score"].max() == pytest.approx(190.0) + + +def test_min_max_string(): + t = CTable(StrRow, new_data=[("banana",), ("apple",), ("cherry",)]) + assert t["label"].min() == "apple" + assert t["label"].max() == "cherry" + + +def test_min_skips_deleted(): + t = CTable(Row, new_data=DATA20) + t.delete([0]) # remove id=0, next min is 1 + assert t["id"].min() == 1 + + +def test_min_empty_raises(): + t = CTable(Row) + with pytest.raises(ValueError, match="empty"): + t["id"].min() + + +def test_max_complex_raises(): + @dataclass + class CRow: + val: complex = blosc2.field(blosc2.complex128()) + + t = CTable(CRow, new_data=[(1 + 2j,)]) + with pytest.raises(TypeError): + t["val"].max() + + +# ------------------------------------------------------------------- +# Aggregates: mean +# ------------------------------------------------------------------- + + +def test_mean_int(): + t = CTable(Row, new_data=DATA20) + assert t["id"].mean() == pytest.approx(9.5) + + +def test_mean_float(): + t = CTable(Row, new_data=DATA20) + assert t["score"].mean() == pytest.approx(95.0) + + +def test_mean_skips_deleted(): + t = CTable(Row, new_data=[(0, 0.0, True), (10, 100.0, True)]) + t.delete([0]) # remove id=0; only id=10 remains + assert t["id"].mean() == pytest.approx(10.0) + + +def test_mean_empty_raises(): + t = CTable(Row) + with pytest.raises(ValueError, match="empty"): + t["id"].mean() + + +# ------------------------------------------------------------------- +# Aggregates: std +# ------------------------------------------------------------------- + + +def test_std_population(): + t = CTable(Row, new_data=DATA20) + ids = np.arange(20, dtype=np.float64) + assert t["id"].std() == pytest.approx(float(ids.std(ddof=0))) + + +def test_std_sample(): + t = CTable(Row, new_data=DATA20) + ids = np.arange(20, dtype=np.float64) + assert t["id"].std(ddof=1) == pytest.approx(float(ids.std(ddof=1))) + + +def test_std_single_element(): + t = CTable(Row, new_data=[(5, 50.0, True)]) + assert t["id"].std() == pytest.approx(0.0) + + +def test_std_single_element_ddof1_is_nan(): + t = CTable(Row, new_data=[(5, 50.0, True)]) + assert np.isnan(t["id"].std(ddof=1)) + + +def test_std_empty_raises(): + t = CTable(Row) + with pytest.raises(ValueError, match="empty"): + t["id"].std() + + +# ------------------------------------------------------------------- +# Aggregates: any / all +# ------------------------------------------------------------------- + + +def test_any_all_true(): + t = CTable(Row, new_data=DATA20) # all active=True + assert t["active"].any() is True + assert t["active"].all() is True + + +def test_any_some_false(): + data = [(i, float(i), i % 2 == 0) for i in range(10)] + t = CTable(Row, new_data=data) + assert t["active"].any() is True + assert t["active"].all() is False + + +def test_all_false(): + data = [(i, float(i), False) for i in range(5)] + t = CTable(Row, new_data=data) + assert t["active"].any() is False + assert t["active"].all() is False + + +def test_any_empty_is_false(): + t = CTable(Row) + assert t["active"].any() is False + + +def test_all_empty_is_true(): + # vacuous truth: all() over nothing is True (same as Python's built-in) + t = CTable(Row) + assert t["active"].all() is True + + +def test_any_wrong_type_raises(): + t = CTable(Row, new_data=DATA20) + with pytest.raises(TypeError): + t["id"].any() + + +# ------------------------------------------------------------------- +# unique +# ------------------------------------------------------------------- + + +def test_unique_int(): + t = CTable(Row, new_data=[(i % 5, float(i), True) for i in range(20)]) + result = t["id"].unique() + np.testing.assert_array_equal(result, np.array([0, 1, 2, 3, 4], dtype=np.int64)) + + +def test_unique_bool(): + data = [(i, float(i), i % 3 != 0) for i in range(10)] + t = CTable(Row, new_data=data) + result = t["active"].unique() + assert set(result.tolist()) == {True, False} + + +def test_unique_skips_deleted(): + t = CTable(Row, new_data=[(i % 3, float(i), True) for i in range(9)]) + # ids are [0,1,2,0,1,2,0,1,2]; logical rows with id==0 are at positions 0,3,6 + t.delete([0, 3, 6]) + result = t["id"].unique() + assert 0 not in result.tolist() + assert set(result.tolist()) == {1, 2} + + +def test_unique_empty(): + t = CTable(Row) + result = t["id"].unique() + assert len(result) == 0 + + +# ------------------------------------------------------------------- +# value_counts +# ------------------------------------------------------------------- + + +def test_value_counts_basic(): + data = [(i % 3, float(i), True) for i in range(9)] # ids: 0,1,2,0,1,2,0,1,2 + t = CTable(Row, new_data=data) + vc = t["id"].value_counts() + assert vc[0] == 3 + assert vc[1] == 3 + assert vc[2] == 3 + + +def test_value_counts_sorted_by_count(): + data = [(0, 0.0, True)] * 5 + [(1, 1.0, True)] * 2 + [(2, 2.0, True)] * 8 + t = CTable(Row, new_data=data) + vc = t["id"].value_counts() + counts = list(vc.values()) + assert counts == sorted(counts, reverse=True) + + +def test_value_counts_bool(): + data = [(i, float(i), i % 4 != 0) for i in range(20)] # 5 False, 15 True + t = CTable(Row, new_data=data) + vc = t["active"].value_counts() + assert vc[True] == 15 + assert vc[False] == 5 + assert list(vc.keys())[0] is True # True comes first (higher count) + + +def test_value_counts_empty(): + t = CTable(Row) + assert t["id"].value_counts() == {} + + +# ------------------------------------------------------------------- +# sample (on CTable) +# ------------------------------------------------------------------- + + +def test_sample_returns_correct_count(): + t = CTable(Row, new_data=DATA20) + s = t.sample(5, seed=0) + assert len(s) == 5 + + +def test_sample_rows_are_subset(): + t = CTable(Row, new_data=DATA20) + s = t.sample(7, seed=42) + all_ids = set(t["id"].to_numpy().tolist()) + sample_ids = set(s["id"].to_numpy().tolist()) + assert sample_ids.issubset(all_ids) + + +def test_sample_is_read_only(): + t = CTable(Row, new_data=DATA20) + s = t.sample(5, seed=0) + with pytest.raises((ValueError, TypeError)): + s.append((99, 9.0, True)) + + +def test_sample_seed_reproducible(): + t = CTable(Row, new_data=DATA20) + s1 = t.sample(5, seed=7) + s2 = t.sample(5, seed=7) + np.testing.assert_array_equal(s1["id"].to_numpy(), s2["id"].to_numpy()) + + +def test_sample_n_larger_than_table(): + t = CTable(Row, new_data=DATA20) + s = t.sample(1000, seed=0) + assert len(s) == 20 + + +def test_sample_zero(): + t = CTable(Row, new_data=DATA20) + assert len(t.sample(0)) == 0 + + +# ------------------------------------------------------------------- +# cbytes / nbytes / __repr__ +# ------------------------------------------------------------------- + + +def test_cbytes_nbytes_positive(): + t = CTable(Row, new_data=DATA20) + assert t.cbytes > 0 + assert t.nbytes > 0 + assert t.nbytes >= t.cbytes # compressed is never larger than raw + + +def test_cbytes_nbytes_consistent_with_info(): + t = CTable(Row, new_data=DATA20) + expected_cb = sum(col.cbytes for col in t._cols.values()) + t._valid_rows.cbytes + expected_nb = sum(col.nbytes for col in t._cols.values()) + t._valid_rows.nbytes + assert t.cbytes == expected_cb + assert t.nbytes == expected_nb + + +def test_repr_contains_col_names_and_row_count(): + t = CTable(Row, new_data=DATA20) + r = repr(t) + assert "id" in r + assert "score" in r + assert "active" in r + assert "20" in r + + +def test_repr_is_single_line(): + t = CTable(Row, new_data=DATA20) + assert "\n" not in repr(t) + + if __name__ == "__main__": pytest.main(["-v", __file__]) diff --git a/tests/ctable/test_construct.py b/tests/ctable/test_construct.py index 01722c06..4a091997 100644 --- a/tests/ctable/test_construct.py +++ b/tests/ctable/test_construct.py @@ -43,20 +43,18 @@ class Row: # ------------------------------------------------------------------- def assert_table_equals_data(table: CTable, expected_data: list): assert len(table) == len(expected_data), f"Expected length {len(expected_data)}, got {len(table)}" + if not expected_data: + return col_names = table.col_names - for i, expected_row in enumerate(expected_data): - row_extracted = table.row[i] - for col_idx, expected_val in enumerate(expected_row): - col_name = col_names[col_idx] - extracted_val = getattr(row_extracted, col_name)[0] - if isinstance(expected_val, (float, complex)): - np.testing.assert_allclose( - extracted_val, expected_val, err_msg=f"Discrepancy at row {i}, col {col_name}" - ) - else: - assert extracted_val == expected_val, ( - f"Row {i}, col {col_name}: expected {expected_val}, got {extracted_val}" - ) + # Transpose: expected_data is list-of-rows → list-of-columns + expected_cols = list(zip(*expected_data, strict=False)) + for col_idx, col_name in enumerate(col_names): + actual = table[col_name].to_numpy() + expected = expected_cols[col_idx] + if isinstance(expected[0], (float, complex)): + np.testing.assert_allclose(actual, expected, err_msg=f"col {col_name}") + else: + np.testing.assert_array_equal(actual, expected, err_msg=f"col {col_name}") # ------------------------------------------------------------------- @@ -95,24 +93,20 @@ def test_empty_data_lifecycle(): assert_table_equals_data(table, SMALL_DATA) -def test_construction_sources(): - """List of tuples and structured array both produce identical tables.""" +def test_construction_variants(): + """Sources (list, structured array), expected_size, and compact flag.""" + # list of tuples and structured array produce identical tables assert_table_equals_data(CTable(Row, new_data=SMALL_DATA), SMALL_DATA) assert_table_equals_data(CTable(Row, new_data=SMALL_STRUCT), SMALL_DATA) - -def test_expected_size_variants(): - """expected_size smaller, exact, and larger than the inserted data.""" + # expected_size smaller than data → resize; larger → preallocated for es in [1, 5]: assert_table_equals_data(CTable(Row, new_data=SMALL_DATA, expected_size=es), SMALL_DATA) - table_large = CTable(Row, new_data=SMALL_DATA, expected_size=1000) assert_table_equals_data(table_large, SMALL_DATA) assert all(len(col) == 1000 for col in table_large._cols.values()) - -def test_compact_flag(): - """compact=False and compact=True both preserve data correctly.""" + # compact flag is stored and data is intact table_false = CTable(Row, new_data=SMALL_DATA, compact=False) assert table_false.auto_compact is False assert_table_equals_data(table_false, SMALL_DATA) @@ -152,25 +146,15 @@ def test_invalid_append(): def test_extreme_values(): - """Extreme complex, float boundary, and large integer values.""" - extreme_complex = [ - (1, complex(1e308, -1e308), 50.0, True), - (2, complex(0, 0), 0.0, False), - (3, complex(-1e308, 1e308), 100.0, True), - ] - extreme_float = [ - (1, 0j, 0.0, True), - (2, 0j, 100.0, False), - (3, 0j, 0.0001, True), + """Extreme complex, float boundary, and large integer values in one table.""" + # Combine all extremes into one table to avoid repeated CTable construction + extreme_data = [ + (1, complex(1e308, -1e308), 0.0, True), + (2**32, 0j, 100.0, False), + (2**60, complex(-1e308, 1e308), 0.0001, True), (4, 0j, 99.9999, False), ] - extreme_int = [ - (1, 0j, 50.0, True), - (2**32, 0j, 50.0, False), - (2**60, 0j, 50.0, True), - ] - for data in [extreme_complex, extreme_float, extreme_int]: - assert_table_equals_data(CTable(Row, new_data=data), data) + assert_table_equals_data(CTable(Row, new_data=extreme_data), extreme_data) def test_extend_append_and_resize(): diff --git a/tests/ctable/test_persistency.py b/tests/ctable/test_persistency.py new file mode 100644 index 00000000..961243fc --- /dev/null +++ b/tests/ctable/test_persistency.py @@ -0,0 +1,519 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +"""Persistency tests for CTable: create → close → reopen round-trips.""" + +import json +import os +import shutil +from dataclasses import dataclass + +import pytest + +import blosc2 +from blosc2 import CTable + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + + +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + score: float = blosc2.field(blosc2.float64(ge=0, le=100), default=0.0) + active: bool = blosc2.field(blosc2.bool(), default=True) + + +TABLE_ROOT = "saved_ctable/test_tables" + + +@pytest.fixture(autouse=True) +def clean_table_dir(): + """Remove test directory before each test and clean up after.""" + if os.path.exists(TABLE_ROOT): + shutil.rmtree(TABLE_ROOT) + os.makedirs(TABLE_ROOT, exist_ok=True) + yield + if os.path.exists(TABLE_ROOT): + shutil.rmtree(TABLE_ROOT) + + +def table_path(name: str) -> str: + return os.path.join(TABLE_ROOT, name) + + +# --------------------------------------------------------------------------- +# Layout: disk structure +# --------------------------------------------------------------------------- + + +def test_create_layout_files_exist(): + """Creating a persistent CTable writes the expected files.""" + path = table_path("people") + t = CTable(Row, urlpath=path, mode="w", expected_size=16) + t.append((1, 50.0, True)) + + assert os.path.exists(os.path.join(path, "_meta.b2frame")) + assert os.path.exists(os.path.join(path, "_valid_rows.b2nd")) + assert os.path.exists(os.path.join(path, "_cols", "id.b2nd")) + assert os.path.exists(os.path.join(path, "_cols", "score.b2nd")) + assert os.path.exists(os.path.join(path, "_cols", "active.b2nd")) + + +def test_schema_saved_in_meta_vlmeta(): + """Schema JSON and kind marker are present in _meta.b2frame.""" + path = table_path("people") + CTable(Row, urlpath=path, mode="w", expected_size=16) + + meta = blosc2.open(os.path.join(path, "_meta.b2frame")) + assert meta.vlmeta["kind"] == "ctable" + assert meta.vlmeta["version"] == 1 + schema = json.loads(meta.vlmeta["schema"]) + assert schema["version"] == 1 + col_names = [c["name"] for c in schema["columns"]] + assert col_names == ["id", "score", "active"] + + +# --------------------------------------------------------------------------- +# Round-trip: data survives reopen +# --------------------------------------------------------------------------- + + +def test_reopen_with_ctable_constructor(): + """Data written before close is readable after reopening via CTable(...).""" + path = table_path("rt") + t = CTable(Row, urlpath=path, mode="w", expected_size=16) + t.extend([(1, 10.0, True), (2, 20.0, False), (3, 30.0, True)]) + + t2 = CTable(Row, urlpath=path, mode="a") + assert len(t2) == 3 + assert list(t2["id"].to_numpy()) == [1, 2, 3] + assert list(t2["score"].to_numpy()) == [10.0, 20.0, 30.0] + + +def test_reopen_with_open_classmethod(): + """CTable.open() returns a read-only table with correct data.""" + path = table_path("ro") + t = CTable(Row, urlpath=path, mode="w", expected_size=16) + t.extend([(10, 50.0, True), (20, 60.0, False)]) + + t2 = CTable.open(path) + assert len(t2) == 2 + assert list(t2["id"].to_numpy()) == [10, 20] + + +def test_column_order_preserved_after_reopen(): + """Column order from the schema JSON is respected on reopen.""" + path = table_path("order") + + @dataclass + class MultiCol: + z: int = blosc2.field(blosc2.int64()) + a: float = blosc2.field(blosc2.float64(), default=0.0) + m: bool = blosc2.field(blosc2.bool(), default=True) + + t = CTable(MultiCol, urlpath=path, mode="w", expected_size=16) + t2 = CTable(MultiCol, urlpath=path, mode="a") + assert t2.col_names == ["z", "a", "m"] + + +def test_schema_constraints_preserved(): + """Reopening re-enables constraint validation from the stored schema.""" + path = table_path("constraints") + t = CTable(Row, urlpath=path, mode="w", expected_size=16) + t.append((1, 50.0, True)) + + t2 = CTable(Row, urlpath=path, mode="a") + with pytest.raises(ValueError): + t2.append((-1, 50.0, True)) # id violates ge=0 + + +# --------------------------------------------------------------------------- +# Append after reopen +# --------------------------------------------------------------------------- + + +def test_append_after_reopen(): + """Appending to a reopened table grows the row count correctly.""" + path = table_path("append") + t = CTable(Row, urlpath=path, mode="w", expected_size=16) + t.extend([(1, 10.0, True), (2, 20.0, False)]) + + t2 = CTable(Row, urlpath=path, mode="a") + t2.append((3, 30.0, True)) + assert len(t2) == 3 + assert t2.row[2].id[0] == 3 + + # Verify it's visible in a third open + t3 = CTable(Row, urlpath=path, mode="a") + assert len(t3) == 3 + assert list(t3["id"].to_numpy()) == [1, 2, 3] + + +def test_extend_after_reopen(): + """extend() after reopen persists all new rows.""" + path = table_path("extend") + t = CTable(Row, urlpath=path, mode="w", expected_size=64) + t.extend([(i, float(i), True) for i in range(5)]) + + t2 = CTable(Row, urlpath=path, mode="a") + t2.extend([(i, float(i), i % 2 == 0) for i in range(5, 10)]) + assert len(t2) == 10 + + t3 = CTable(Row, urlpath=path, mode="a") + assert len(t3) == 10 + assert list(t3["id"].to_numpy()) == list(range(10)) + + +# --------------------------------------------------------------------------- +# Delete after reopen +# --------------------------------------------------------------------------- + + +def test_delete_after_reopen(): + """Deletions after reopen are reflected in subsequent opens.""" + path = table_path("delete") + t = CTable(Row, urlpath=path, mode="w", expected_size=16) + t.extend([(1, 10.0, True), (2, 20.0, False), (3, 30.0, True)]) + + t2 = CTable(Row, urlpath=path, mode="a") + t2.delete(1) # remove row with id=2 + assert len(t2) == 2 + + t3 = CTable(Row, urlpath=path, mode="a") + assert len(t3) == 2 + assert list(t3["id"].to_numpy()) == [1, 3] + + +# --------------------------------------------------------------------------- +# valid_rows persistence +# --------------------------------------------------------------------------- + + +def test_valid_rows_persisted(): + """The tombstone mask (_valid_rows) is correctly stored and loaded.""" + path = table_path("vr") + t = CTable(Row, urlpath=path, mode="w", expected_size=16) + t.extend([(1, 10.0, True), (2, 20.0, False), (3, 30.0, True)]) + t.delete(1) # mark row 1 (id=2) as invalid + + # _valid_rows on disk: slots 0 and 2 are True, slot 1 is False + vr = blosc2.open(os.path.join(path, "_valid_rows.b2nd")) + raw = vr[:3] + assert raw[0] + assert not raw[1] + assert raw[2] + + +# --------------------------------------------------------------------------- +# mode="w" overwrites existing table +# --------------------------------------------------------------------------- + + +def test_mode_w_overwrites_existing(): + """mode='w' on an existing path creates a fresh table.""" + path = table_path("overwrite") + t = CTable(Row, urlpath=path, mode="w", expected_size=16) + t.extend([(1, 10.0, True), (2, 20.0, False)]) + + t2 = CTable(Row, urlpath=path, mode="w", expected_size=16) + assert len(t2) == 0 + + t3 = CTable(Row, urlpath=path, mode="a") + assert len(t3) == 0 + + +# --------------------------------------------------------------------------- +# Read-only mode +# --------------------------------------------------------------------------- + + +def test_read_only_mode_rejects_append(): + path = table_path("ro_append") + CTable(Row, urlpath=path, mode="w", expected_size=16) + + t = CTable.open(path, mode="r") + with pytest.raises(ValueError, match="read-only"): + t.append((1, 50.0, True)) + + +def test_read_only_mode_rejects_extend(): + path = table_path("ro_extend") + CTable(Row, urlpath=path, mode="w", expected_size=16) + + t = CTable.open(path, mode="r") + with pytest.raises(ValueError, match="read-only"): + t.extend([(1, 50.0, True)]) + + +def test_read_only_mode_rejects_delete(): + path = table_path("ro_delete") + t = CTable(Row, urlpath=path, mode="w", expected_size=16) + t.append((1, 50.0, True)) + + t2 = CTable.open(path, mode="r") + with pytest.raises(ValueError, match="read-only"): + t2.delete(0) + + +def test_read_only_mode_rejects_compact(): + path = table_path("ro_compact") + t = CTable(Row, urlpath=path, mode="w", expected_size=16) + t.append((1, 50.0, True)) + + t2 = CTable.open(path, mode="r") + with pytest.raises(ValueError, match="read-only"): + t2.compact() + + +def test_read_only_allows_reads(): + """Read-only table: row access, column access, head/tail, where all work.""" + path = table_path("ro_reads") + t = CTable(Row, urlpath=path, mode="w", expected_size=16) + t.extend([(1, 10.0, True), (2, 20.0, False), (3, 30.0, True)]) + + t2 = CTable.open(path, mode="r") + assert len(t2) == 3 + assert t2.row[0].id[0] == 1 + assert list(t2["score"].to_numpy()) == [10.0, 20.0, 30.0] + assert len(t2.head(2)) == 2 + assert len(t2.tail(1)) == 1 + view = t2.where(t2["id"] > 1) + assert len(view) == 2 + + +# --------------------------------------------------------------------------- +# open() error cases +# --------------------------------------------------------------------------- + + +def test_open_nonexistent_raises(): + with pytest.raises(FileNotFoundError): + CTable.open(table_path("does_not_exist")) + + +def test_open_wrong_kind_raises(tmp_path): + """A path with a _meta.b2frame that is not a ctable raises ValueError.""" + import blosc2 + + meta_path = str(tmp_path / "_meta.b2frame") + sc = blosc2.SChunk(urlpath=meta_path, mode="w") + sc.vlmeta["kind"] = "something_else" + + with pytest.raises(ValueError, match="CTable"): + CTable.open(str(tmp_path)) + + +# --------------------------------------------------------------------------- +# Column name validation +# --------------------------------------------------------------------------- + + +def test_column_name_cannot_start_with_underscore(): + @dataclass + class Bad: + _id: int = blosc2.field(blosc2.int64()) + + with pytest.raises(ValueError, match="_"): + CTable(Bad) + + +def test_column_name_cannot_contain_slash(): + @dataclass + class Bad: + pass + + from blosc2.schema_compiler import _validate_column_name + + with pytest.raises(ValueError, match="/"): + _validate_column_name("a/b") + + +def test_column_name_cannot_be_empty(): + from blosc2.schema_compiler import _validate_column_name + + with pytest.raises(ValueError): + _validate_column_name("") + + +# --------------------------------------------------------------------------- +# new_data= guard when opening existing +# --------------------------------------------------------------------------- + + +def test_new_data_rejected_when_opening_existing(): + path = table_path("newdata") + CTable(Row, urlpath=path, mode="w", expected_size=16) + + with pytest.raises(ValueError, match="new_data"): + CTable(Row, new_data=[(1, 50.0, True)], urlpath=path, mode="a") + + +# --------------------------------------------------------------------------- +# Capacity growth (resize) persists +# --------------------------------------------------------------------------- + + +def test_grow_persists(): + """Filling past the initial capacity triggers resize; data still survives.""" + path = table_path("grow") + t = CTable(Row, urlpath=path, mode="w", expected_size=4) + for i in range(10): + t.append((i, float(i), True)) + assert len(t) == 10 + + t2 = CTable(Row, urlpath=path, mode="a") + assert len(t2) == 10 + assert list(t2["id"].to_numpy()) == list(range(10)) + + +# --------------------------------------------------------------------------- +# save() / load() +# --------------------------------------------------------------------------- + + +def test_save_creates_disk_layout(): + """save() writes the expected directory structure.""" + t = blosc2.CTable(Row, expected_size=16) + t.extend([(1, 10.0, True), (2, 20.0, False)]) + + path = table_path("saved") + t.save(path) + + assert os.path.exists(os.path.join(path, "_meta.b2frame")) + assert os.path.exists(os.path.join(path, "_valid_rows.b2nd")) + assert os.path.exists(os.path.join(path, "_cols", "id.b2nd")) + assert os.path.exists(os.path.join(path, "_cols", "score.b2nd")) + assert os.path.exists(os.path.join(path, "_cols", "active.b2nd")) + + +def test_save_then_open_round_trip(): + """Data written by save() can be read back via CTable.open().""" + t = blosc2.CTable(Row, expected_size=16) + t.extend([(i, float(i * 10), i % 2 == 0) for i in range(5)]) + + path = table_path("saved_rt") + t.save(path) + + t2 = CTable.open(path) + assert len(t2) == 5 + assert list(t2["id"].to_numpy()) == list(range(5)) + assert list(t2["score"].to_numpy()) == [float(i * 10) for i in range(5)] + + +def test_save_compacts_deleted_rows(): + """save() writes only live rows — deleted rows are not included.""" + t = blosc2.CTable(Row, expected_size=16) + t.extend([(i, float(i), True) for i in range(6)]) + t.delete([0, 2, 4]) # delete rows with id 0, 2, 4 + assert len(t) == 3 + + path = table_path("saved_compact") + t.save(path) + + t2 = CTable.open(path) + assert len(t2) == 3 + assert list(t2["id"].to_numpy()) == [1, 3, 5] + + +def test_save_existing_path_raises_by_default(): + """save() raises ValueError if the path already exists unless overwrite=True.""" + t = blosc2.CTable(Row, expected_size=4) + t.append((1, 10.0, True)) + + path = table_path("save_conflict") + t.save(path) + + with pytest.raises(ValueError, match="overwrite"): + t.save(path) + + +def test_save_overwrite_replaces_table(): + """save(overwrite=True) replaces an existing table.""" + t1 = blosc2.CTable(Row, expected_size=4) + t1.extend([(1, 10.0, True), (2, 20.0, True)]) + + path = table_path("overwrite") + t1.save(path) + + t2 = blosc2.CTable(Row, expected_size=4) + t2.append((99, 50.0, False)) + t2.save(path, overwrite=True) + + t3 = CTable.open(path) + assert len(t3) == 1 + assert t3["id"][0] == 99 + + +def test_save_view_raises(): + """save() on a view raises ValueError.""" + t = blosc2.CTable(Row, expected_size=8) + t.extend([(i, float(i), True) for i in range(4)]) + view = t.where(t["id"] > 1) + + with pytest.raises(ValueError, match="view"): + view.save(table_path("view_save")) + + +def test_load_returns_in_memory_table(): + """load() returns a writable in-memory CTable.""" + t = blosc2.CTable(Row, expected_size=8) + t.extend([(i, float(i * 5), True) for i in range(4)]) + + path = table_path("loadme") + t.save(path) + + loaded = CTable.load(path) + assert len(loaded) == 4 + assert list(loaded["id"].to_numpy()) == [0, 1, 2, 3] + # Must be writable + loaded.append((100, 50.0, True)) + assert len(loaded) == 5 + + +def test_load_does_not_modify_disk(): + """Mutations on a loaded table do not affect the on-disk table.""" + t = blosc2.CTable(Row, expected_size=8) + t.extend([(i, float(i), True) for i in range(3)]) + + path = table_path("load_isolation") + t.save(path) + + loaded = CTable.load(path) + loaded.append((999, 99.0, False)) + loaded.delete(0) + + # Re-open the original persistent table — should be unchanged + t2 = CTable.open(path) + assert len(t2) == 3 + assert list(t2["id"].to_numpy()) == [0, 1, 2] + + +def test_load_nonexistent_raises(): + with pytest.raises(FileNotFoundError): + CTable.load(table_path("does_not_exist")) + + +def test_save_empty_table(): + """save() and load() work correctly on an empty table.""" + t = blosc2.CTable(Row, expected_size=4) + + path = table_path("empty") + t.save(path) + + t2 = CTable.load(path) + assert len(t2) == 0 + # Can still append after load + t2.append((1, 10.0, True)) + assert len(t2) == 1 + + +if __name__ == "__main__": + import pytest + + pytest.main(["-v", __file__]) diff --git a/tests/ctable/test_schema_mutations.py b/tests/ctable/test_schema_mutations.py new file mode 100644 index 00000000..eddeb575 --- /dev/null +++ b/tests/ctable/test_schema_mutations.py @@ -0,0 +1,395 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +"""Tests for add_column, drop_column, rename_column, Column.assign, +and the corrected view mutability model.""" + +import os +import shutil +from dataclasses import dataclass + +import numpy as np +import pytest + +import blosc2 +from blosc2 import CTable + +TABLE_ROOT = "saved_ctable/test_schema_mutations" + + +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + score: float = blosc2.field(blosc2.float64(ge=0, le=100), default=0.0) + active: bool = blosc2.field(blosc2.bool(), default=True) + + +DATA10 = [(i, float(i * 10), True) for i in range(10)] + + +@pytest.fixture(autouse=True) +def clean_dir(): + if os.path.exists(TABLE_ROOT): + shutil.rmtree(TABLE_ROOT) + os.makedirs(TABLE_ROOT, exist_ok=True) + yield + if os.path.exists(TABLE_ROOT): + shutil.rmtree(TABLE_ROOT) + + +def table_path(name): + return os.path.join(TABLE_ROOT, name) + + +# =========================================================================== +# View mutability — value writes allowed, structural changes blocked +# =========================================================================== + + +def test_view_allows_column_setitem(): + """Writing values through a view modifies the parent table.""" + t = CTable(Row, new_data=DATA10) + view = t.where(t["id"] > 4) # rows 5-9 + # double scores of those rows using __setitem__ + indices = list(range(len(view))) + new_scores = view["score"].to_numpy() * 2 + view["score"][indices] = new_scores + # check parent was modified + assert t["score"][5] == pytest.approx(100.0) # was 50.0 + + +def test_view_allows_assign(): + """assign() through a view modifies the parent table.""" + t = CTable(Row, new_data=DATA10) + view = t.where(t["id"] > 4) + view["score"].assign(np.zeros(len(view))) + assert t["score"][5] == pytest.approx(0.0) + assert t["score"][4] == pytest.approx(40.0) # untouched + + +def test_view_blocks_append(): + t = CTable(Row, new_data=DATA10) + view = t.where(t["id"] > 4) + with pytest.raises(TypeError): + view.append((99, 10.0, True)) + + +def test_view_blocks_delete(): + t = CTable(Row, new_data=DATA10) + view = t.where(t["id"] > 4) + with pytest.raises(ValueError, match="view"): + view.delete(0) + + +def test_view_blocks_compact(): + t = CTable(Row, new_data=DATA10) + view = t.where(t["id"] > 4) + with pytest.raises(ValueError, match="view"): + view.compact() + + +def test_readonly_disk_table_blocks_assign(): + path = table_path("ro") + t = CTable(Row, urlpath=path, mode="w", new_data=DATA10) + del t + t_ro = CTable.open(path, mode="r") + with pytest.raises(ValueError, match="read-only"): + t_ro["score"].assign(np.ones(len(t_ro))) + + +def test_readonly_disk_table_blocks_setitem(): + path = table_path("ro_setitem") + t = CTable(Row, urlpath=path, mode="w", new_data=DATA10) + del t + t_ro = CTable.open(path, mode="r") + with pytest.raises(ValueError, match="read-only"): + t_ro["score"][0] = 99.0 + + +# =========================================================================== +# Column.assign +# =========================================================================== + + +def test_assign_replaces_all_values(): + t = CTable(Row, new_data=DATA10) + t["score"].assign([99.0] * 10) + assert list(t["score"].to_numpy()) == [99.0] * 10 + + +def test_assign_coerces_python_ints_to_float(): + t = CTable(Row, new_data=DATA10) + t["score"].assign(list(range(10))) # Python ints → float64 + np.testing.assert_array_equal(t["score"].to_numpy(), np.arange(10, dtype=np.float64)) + + +def test_assign_wrong_length_raises(): + t = CTable(Row, new_data=DATA10) + with pytest.raises(ValueError, match="10"): + t["score"].assign([1.0, 2.0]) + + +def test_assign_through_view_touches_only_matching_rows(): + t = CTable(Row, new_data=DATA10) + view = t.where(t["id"] < 5) # rows 0-4 + view["score"].assign([0.0] * 5) + # rows 0-4 → 0, rows 5-9 unchanged + scores = t["score"].to_numpy() + np.testing.assert_array_equal(scores[:5], np.zeros(5)) + np.testing.assert_array_equal(scores[5:], np.arange(5, 10, dtype=np.float64) * 10) + + +def test_assign_respects_deleted_rows(): + t = CTable(Row, new_data=DATA10) + t.delete([0]) # delete id=0; 9 live rows remain + t["score"].assign([1.0] * 9) + assert len(t["score"].to_numpy()) == 9 + assert all(v == 1.0 for v in t["score"].to_numpy()) + + +# =========================================================================== +# add_column +# =========================================================================== + + +def test_add_column_appears_in_col_names(): + t = CTable(Row, new_data=DATA10) + t.add_column("weight", blosc2.float64(), 0.0) + assert "weight" in t.col_names + + +def test_add_column_fills_default_for_existing_rows(): + t = CTable(Row, new_data=DATA10) + t.add_column("weight", blosc2.float64(), 5.5) + np.testing.assert_array_equal(t["weight"].to_numpy(), np.full(10, 5.5)) + + +def test_add_column_new_rows_can_use_it(): + t = CTable(Row, new_data=DATA10) + t.add_column("weight", blosc2.float64(), 0.0) + # After adding, extend doesn't know about weight — add manually + t["weight"].assign(np.ones(10) * 2.0) + assert t["weight"].mean() == pytest.approx(2.0) + + +def test_add_column_schema_updated(): + t = CTable(Row, new_data=DATA10) + t.add_column("weight", blosc2.float64(), 0.0) + assert "weight" in t.schema.columns_by_name + + +def test_add_column_persists_on_disk(): + path = table_path("add_col") + t = CTable(Row, urlpath=path, mode="w", new_data=DATA10) + t.add_column("weight", blosc2.float64(), 7.0) + del t + t2 = CTable.open(path) + assert "weight" in t2.col_names + np.testing.assert_array_equal(t2["weight"].to_numpy(), np.full(10, 7.0)) + + +def test_add_column_view_raises(): + t = CTable(Row, new_data=DATA10) + view = t.where(t["id"] > 4) + with pytest.raises(ValueError, match="view"): + view.add_column("weight", blosc2.float64(), 0.0) + + +def test_add_column_duplicate_raises(): + t = CTable(Row, new_data=DATA10) + with pytest.raises(ValueError, match="already exists"): + t.add_column("score", blosc2.float64(), 0.0) + + +def test_add_column_bad_default_raises(): + t = CTable(Row, new_data=DATA10) + with pytest.raises(TypeError): + t.add_column("flag", blosc2.int8(), "not_a_number") + + +def test_add_column_skips_deleted_rows(): + t = CTable(Row, new_data=DATA10) + t.delete([0, 1]) # 8 live rows + t.add_column("weight", blosc2.float64(), 3.0) + vals = t["weight"].to_numpy() + assert len(vals) == 8 + assert all(v == 3.0 for v in vals) + + +# =========================================================================== +# drop_column +# =========================================================================== + + +def test_drop_column_removes_from_col_names(): + t = CTable(Row, new_data=DATA10) + t.drop_column("active") + assert "active" not in t.col_names + + +def test_drop_column_schema_updated(): + t = CTable(Row, new_data=DATA10) + t.drop_column("active") + assert "active" not in t.schema.columns_by_name + + +def test_drop_column_last_raises(): + @dataclass + class OneCol: + id: int = blosc2.field(blosc2.int64()) + + t = CTable(OneCol, new_data=[(i,) for i in range(5)]) + with pytest.raises(ValueError, match="last"): + t.drop_column("id") + + +def test_drop_column_missing_raises(): + t = CTable(Row, new_data=DATA10) + with pytest.raises(KeyError): + t.drop_column("nonexistent") + + +def test_drop_column_view_raises(): + t = CTable(Row, new_data=DATA10) + view = t.where(t["id"] > 4) + with pytest.raises(ValueError, match="view"): + view.drop_column("active") + + +def test_drop_column_deletes_file_on_disk(): + path = table_path("drop_col") + t = CTable(Row, urlpath=path, mode="w", new_data=DATA10) + t.drop_column("active") + assert not os.path.exists(os.path.join(path, "_cols", "active.b2nd")) + + +def test_drop_column_persists_schema_on_disk(): + path = table_path("drop_schema") + t = CTable(Row, urlpath=path, mode="w", new_data=DATA10) + t.drop_column("active") + del t + t2 = CTable.open(path) + assert "active" not in t2.col_names + assert t2.ncols == 2 + + +# =========================================================================== +# rename_column +# =========================================================================== + + +def test_rename_column_updates_col_names(): + t = CTable(Row, new_data=DATA10) + t.rename_column("score", "points") + assert "points" in t.col_names + assert "score" not in t.col_names + + +def test_rename_column_data_intact(): + t = CTable(Row, new_data=DATA10) + original = t["score"].to_numpy().copy() + t.rename_column("score", "points") + np.testing.assert_array_equal(t["points"].to_numpy(), original) + + +def test_rename_column_schema_updated(): + t = CTable(Row, new_data=DATA10) + t.rename_column("score", "points") + assert "points" in t.schema.columns_by_name + assert "score" not in t.schema.columns_by_name + + +def test_rename_column_order_preserved(): + t = CTable(Row, new_data=DATA10) + t.rename_column("score", "points") + assert t.col_names == ["id", "points", "active"] + + +def test_rename_column_missing_raises(): + t = CTable(Row, new_data=DATA10) + with pytest.raises(KeyError): + t.rename_column("nonexistent", "foo") + + +def test_rename_column_conflict_raises(): + t = CTable(Row, new_data=DATA10) + with pytest.raises(ValueError, match="already exists"): + t.rename_column("score", "active") + + +def test_rename_column_view_raises(): + t = CTable(Row, new_data=DATA10) + view = t.where(t["id"] > 4) + with pytest.raises(ValueError, match="view"): + view.rename_column("score", "points") + + +def test_rename_column_persists_on_disk(): + path = table_path("rename_col") + t = CTable(Row, urlpath=path, mode="w", new_data=DATA10) + t.rename_column("score", "points") + del t + t2 = CTable.open(path) + assert "points" in t2.col_names + assert "score" not in t2.col_names + assert os.path.exists(os.path.join(path, "_cols", "points.b2nd")) + assert not os.path.exists(os.path.join(path, "_cols", "score.b2nd")) + + +# =========================================================================== +# Boolean mask indexing (pandas-style) +# =========================================================================== + + +def test_bool_mask_getitem(): + t = CTable(Row, new_data=DATA10) + mask = t["id"].to_numpy() % 2 == 0 # even ids + result = t["score"][mask] + np.testing.assert_array_equal(result, np.array([0.0, 20.0, 40.0, 60.0, 80.0])) + + +def test_bool_mask_setitem(): + t = CTable(Row, new_data=DATA10) + mask = t["id"].to_numpy() % 2 == 0 + t["score"][mask] = 0.0 + scores = t["score"].to_numpy() + np.testing.assert_array_equal(scores[0::2], np.zeros(5)) # evens zeroed + np.testing.assert_array_equal(scores[1::2], np.array([10.0, 30.0, 50.0, 70.0, 90.0])) + + +def test_bool_mask_inplace_multiply(): + """The pandas idiom: col[mask] *= scalar.""" + t = CTable(Row, new_data=DATA10) + mask = t["id"].to_numpy() % 2 == 0 + t["score"][mask] *= 2 + scores = t["score"].to_numpy() + np.testing.assert_array_equal(scores[0::2], np.array([0.0, 40.0, 80.0, 120.0, 160.0])) + np.testing.assert_array_equal(scores[1::2], np.array([10.0, 30.0, 50.0, 70.0, 90.0])) + + +def test_bool_mask_wrong_length_raises(): + t = CTable(Row, new_data=DATA10) + bad_mask = np.array([True, False, True], dtype=np.bool_) + with pytest.raises(IndexError, match="length"): + _ = t["score"][bad_mask] + + +def test_bool_mask_through_view(): + """Boolean mask indexing works on views too.""" + t = CTable(Row, new_data=DATA10) + view = t.where(t["id"] < 6) # rows 0-5 + mask = view["id"].to_numpy() % 2 == 0 + view["score"][mask] *= 10 + # rows 0,2,4 in view → ids 0,2,4 in parent → scores 0,20,40 * 10 + assert t["score"][0] == pytest.approx(0.0) + assert t["score"][2] == pytest.approx(200.0) + assert t["score"][4] == pytest.approx(400.0) + assert t["score"][1] == pytest.approx(10.0) # untouched + + +if __name__ == "__main__": + pytest.main(["-v", __file__]) From 0472b3fbcc3675524a1d6617f8de2e4436903ee6 Mon Sep 17 00:00:00 2001 From: jorge Date: Mon, 6 Apr 2026 14:35:09 +0200 Subject: [PATCH 16/49] CTable: full feature build-out (persistency, aggregates, mutations, QoL) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Persistency: - FileTableStorage backend: disk layout _meta.b2frame / _valid_rows.b2nd / _cols/.b2nd - CTable(Row, urlpath=..., mode="w"/"a"/"r"), CTable.open(), CTable.save(), CTable.load() - Read-only mode blocks all writes; save() always writes compacted rows Column aggregates: sum, min, max, mean, std, any, all (chunk-aware via iter_chunks) Column utilities: unique(), value_counts(), assign(), boolean mask __getitem__/__setitem__ Schema mutations: add_column (fills default for existing rows), drop_column, rename_column - All three update schema, handle disk files, and block on views View mutability model fix: - Views allow value writes (assign, __setitem__) — only structural mutations are blocked - _read_only=True reserved for mode="r" disk tables; base is not None guards structural ops QoL: __str__ pandas-style, __repr__, cbytes/nbytes, sample(n), Column.iter_chunks(size) Tests: 258 tests, ~5s — new test_persistency.py (33), test_schema_mutations.py (41), expanded test_column.py; optimized helpers to use to_numpy() instead of row[i] --- bench/ctable/bench_append_regression.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/bench/ctable/bench_append_regression.py b/bench/ctable/bench_append_regression.py index e88c9d89..03f875db 100644 --- a/bench/ctable/bench_append_regression.py +++ b/bench/ctable/bench_append_regression.py @@ -20,7 +20,7 @@ import blosc2 from blosc2.schema_compiler import compile_schema -from blosc2.schema_validation import validate_row, build_validator_model +from blosc2.schema_validation import build_validator_model, validate_row @dataclass @@ -113,5 +113,5 @@ class Row: print(f"{'Per-row Pydantic cost (isolated)':<40} {(t_validate/N)*1e6:.2f} µs/row") print() print(f"Note: append() is dominated by blosc2 I/O ({t_append_off/t_raw:.0f}x raw numpy),") -print(f" not by the validation pipeline.") -print(f" The main bottleneck is the last_true_pos backward scan per row.") +print(" not by the validation pipeline.") +print(" The main bottleneck is the last_true_pos backward scan per row.") From 34f821922401fc0c2e27bf51e9feae12dd5310f8 Mon Sep 17 00:00:00 2001 From: jorge Date: Tue, 7 Apr 2026 19:05:06 +0200 Subject: [PATCH 17/49] CSV compatibility implementation Arrow compatibility Examples Tutorial --- bench/ctable/Prueba_iter.py | 6 +- bench/ctable/bench_pandas_roundtrip.py | 209 +++ bench/ctable/bench_validation.py | 2 +- bench/ctable/compact.py | 2 +- bench/ctable/ctable_v_panda.py | 7 +- bench/ctable/delete.py | 2 +- bench/ctable/expected_size.py | 2 +- bench/ctable/extend.py | 2 +- bench/ctable/extend_vs_apend.py | 6 +- bench/ctable/index.py | 2 +- bench/ctable/iteration_column.py | 6 +- bench/ctable/print.py | 1 + bench/ctable/row_acces.py | 2 +- bench/ctable/slice.py | 2 +- bench/ctable/slice_steps.py | 2 +- bench/ctable/slice_to_array.py | 2 +- bench/ctable/speed_iter.py | 5 +- bench/ctable/where_chain.py | 14 +- bench/ctable/where_selective.py | 2 +- examples/ctable/aggregates.py | 68 + examples/ctable/arrow_interop.py | 82 ++ examples/ctable/basics.py | 61 + examples/ctable/csv_interop.py | 82 ++ examples/ctable/ctable_tutorial.ipynb | 1644 ++++++++++++++++++++++ examples/ctable/mutations.py | 72 + examples/ctable/persistence.py | 69 + examples/ctable/querying.py | 60 + examples/ctable/real_world.py | 114 ++ examples/ctable/schema.py | 55 + src/blosc2/ctable.py | 659 +++++++++ tests/ctable/test_arrow_interop.py | 225 +++ tests/ctable/test_csv_interop.py | 235 ++++ tests/ctable/test_select_describe_cov.py | 276 ++++ tests/ctable/test_sort_by.py | 270 ++++ 34 files changed, 4212 insertions(+), 36 deletions(-) create mode 100644 bench/ctable/bench_pandas_roundtrip.py create mode 100644 examples/ctable/aggregates.py create mode 100644 examples/ctable/arrow_interop.py create mode 100644 examples/ctable/basics.py create mode 100644 examples/ctable/csv_interop.py create mode 100644 examples/ctable/ctable_tutorial.ipynb create mode 100644 examples/ctable/mutations.py create mode 100644 examples/ctable/persistence.py create mode 100644 examples/ctable/querying.py create mode 100644 examples/ctable/real_world.py create mode 100644 examples/ctable/schema.py create mode 100644 tests/ctable/test_arrow_interop.py create mode 100644 tests/ctable/test_csv_interop.py create mode 100644 tests/ctable/test_select_describe_cov.py create mode 100644 tests/ctable/test_sort_by.py diff --git a/bench/ctable/Prueba_iter.py b/bench/ctable/Prueba_iter.py index 2f21646e..51203ba4 100644 --- a/bench/ctable/Prueba_iter.py +++ b/bench/ctable/Prueba_iter.py @@ -6,12 +6,10 @@ ####################################################################### from dataclasses import dataclass - -import numpy as np from time import time -from blosc2 import CTable import blosc2 +from blosc2 import CTable @dataclass @@ -32,7 +30,7 @@ class Row: # Test 1: iterate without accessing any column (minimum cost) # ------------------------------------------------------------------- t0 = time() -for row in tabla: +for _row in tabla: pass t1 = time() print(f"[Test 1] Iter without accessing columns: {(t1 - t0)*1000:.3f} ms") diff --git a/bench/ctable/bench_pandas_roundtrip.py b/bench/ctable/bench_pandas_roundtrip.py new file mode 100644 index 00000000..03a09ffa --- /dev/null +++ b/bench/ctable/bench_pandas_roundtrip.py @@ -0,0 +1,209 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +# Benchmark: pandas ↔ CTable round-trip (with on-disk persistence) +# +# Pipeline measured in four isolated steps: +# +# 1. pandas → CTable : DataFrame.to_arrow() + CTable.from_arrow() +# 2. CTable.save() : write in-memory CTable to disk +# 3. CTable.load() : read disk table back into RAM +# 4. CTable → pandas : CTable.to_arrow().to_pandas() +# +# Plus the combined full round-trip (steps 1-4) is shown at the end. +# +# Each measurement is the minimum of NRUNS repetitions to reduce noise. +# Schema: id (int64), score (float64), active (bool), label (string ≤16). + +import os +import shutil +from time import perf_counter + +import numpy as np +import pandas as pd +import pyarrow as pa + +from blosc2 import CTable + +NRUNS = 3 +TABLE_DIR = "saved_ctable/bench_pandas" +SIZES = [1_000, 10_000, 100_000, 1_000_000] + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + + +def sep(title: str) -> None: + print(f"\n{'─' * 60}") + print(f" {title}") + print(f"{'─' * 60}") + + +def tmin(fn, n: int = NRUNS) -> float: + """Minimum elapsed time (s) over *n* calls of *fn*.""" + best = float("inf") + for _ in range(n): + t0 = perf_counter() + fn() + best = min(best, perf_counter() - t0) + return best + + +def clean(path: str = TABLE_DIR) -> None: + if os.path.exists(path): + shutil.rmtree(path) + os.makedirs(path, exist_ok=True) + + +def make_dataframe(n: int) -> pd.DataFrame: + rng = np.random.default_rng(42) + return pd.DataFrame({ + "id": np.arange(n, dtype=np.int64), + "score": rng.uniform(0, 100, n).astype(np.float64), + "active": rng.integers(0, 2, n, dtype=bool), + "label": [f"r{i % 10000:05d}" for i in range(n)], + }) + + +# --------------------------------------------------------------------------- +# Section 1: pandas → CTable (in-memory) +# --------------------------------------------------------------------------- + +sep("1. pandas → CTable (from_arrow, in-memory)") +print(f"{'rows':>12} {'pandas→arrow (s)':>18} {'arrow→ctable (s)':>18} {'total (s)':>12}") +print(f"{'----':>12} {'----------------':>18} {'----------------':>18} {'---------':>12}") + +ctables: dict[int, CTable] = {} # keep for steps 2 & 4 + +for N in SIZES: + df = make_dataframe(N) + + def bench_to_arrow(df=df): + return pa.Table.from_pandas(df, preserve_index=False) + + def bench_from_arrow(df=df): + at = pa.Table.from_pandas(df, preserve_index=False) + return CTable.from_arrow(at) + + t_pa = tmin(bench_to_arrow) + t_ct = tmin(bench_from_arrow) - t_pa # from_arrow only + t_tot = t_pa + t_ct + + # Keep one CTable for later steps + at = pa.Table.from_pandas(df, preserve_index=False) + ctables[N] = CTable.from_arrow(at) + + print(f"{N:>12,} {t_pa:>18.4f} {t_ct:>18.4f} {t_tot:>12.4f}") + + +# --------------------------------------------------------------------------- +# Section 2: CTable.save() (in-memory → disk) +# --------------------------------------------------------------------------- + +sep("2. CTable.save() (in-memory → disk)") +print(f"{'rows':>12} {'save (s)':>14} {'compressed':>12} {'ratio':>8}") +print(f"{'----':>12} {'--------':>14} {'----------':>12} {'-----':>8}") + +for N in SIZES: + t = ctables[N] + path = os.path.join(TABLE_DIR, f"ct_{N}") + + def bench_save(t=t, path=path): + if os.path.exists(path): + shutil.rmtree(path) + t.save(path, overwrite=True) + + elapsed = tmin(bench_save) + # Final state for size info + t.save(path, overwrite=True) + cbytes = t.cbytes + nbytes = t.nbytes + ratio = nbytes / cbytes if cbytes > 0 else float("nan") + + def _fmt(n): + if n < 1024**2: + return f"{n / 1024:.1f} KB" + return f"{n / 1024**2:.1f} MB" + + print(f"{N:>12,} {elapsed:>14.4f} {_fmt(cbytes):>12} {ratio:>7.2f}x") + + +# --------------------------------------------------------------------------- +# Section 3: CTable.load() (disk → in-memory) +# --------------------------------------------------------------------------- + +sep("3. CTable.load() (disk → in-memory)") +print(f"{'rows':>12} {'load (s)':>14}") +print(f"{'----':>12} {'--------':>14}") + +for N in SIZES: + path = os.path.join(TABLE_DIR, f"ct_{N}") + + def bench_load(path=path): + return CTable.load(path) + + elapsed = tmin(bench_load) + print(f"{N:>12,} {elapsed:>14.4f}") + + +# --------------------------------------------------------------------------- +# Section 4: CTable → pandas (to_arrow → to_pandas) +# --------------------------------------------------------------------------- + +sep("4. CTable → pandas (to_arrow + to_pandas)") +print(f"{'rows':>12} {'ctable→arrow (s)':>18} {'arrow→pandas (s)':>18} {'total (s)':>12}") +print(f"{'----':>12} {'----------------':>18} {'----------------':>18} {'---------':>12}") + +for N in SIZES: + t = ctables[N] + at_cache = t.to_arrow() # pre-convert once so we can time each step cleanly + + def bench_to_arrow_ct(t=t): + return t.to_arrow() + + def bench_to_pandas(at=at_cache): + return at.to_pandas() + + t_arr = tmin(bench_to_arrow_ct) + t_pd = tmin(bench_to_pandas) + t_tot = t_arr + t_pd + + print(f"{N:>12,} {t_arr:>18.4f} {t_pd:>18.4f} {t_tot:>12.4f}") + + +# --------------------------------------------------------------------------- +# Section 5: Full round-trip (pandas → CTable → disk → load → pandas) +# --------------------------------------------------------------------------- + +sep("5. Full round-trip (pandas → CTable → save → load → pandas)") +print(f"{'rows':>12} {'round-trip (s)':>16}") +print(f"{'----':>12} {'---------------':>16}") + +for N in SIZES: + df = make_dataframe(N) + path = os.path.join(TABLE_DIR, f"rt_{N}") + + def bench_roundtrip(df=df, path=path): + # pandas → CTable + at = pa.Table.from_pandas(df, preserve_index=False) + t = CTable.from_arrow(at) + # save to disk + t.save(path, overwrite=True) + # load back + t2 = CTable.load(path) + # CTable → pandas + return t2.to_arrow().to_pandas() + + elapsed = tmin(bench_roundtrip) + print(f"{N:>12,} {elapsed:>16.4f}") + + +# Cleanup +clean() +print() diff --git a/bench/ctable/bench_validation.py b/bench/ctable/bench_validation.py index 1032e7de..7329a3ce 100644 --- a/bench/ctable/bench_validation.py +++ b/bench/ctable/bench_validation.py @@ -33,7 +33,7 @@ def make_data(n: int): ids = np.arange(n, dtype=np.int64) scores = rng.uniform(0, 100, n) flags = rng.integers(0, 2, n, dtype=np.bool_) - return list(zip(ids.tolist(), scores.tolist(), flags.tolist())) + return list(zip(ids.tolist(), scores.tolist(), flags.tolist(), strict=False)) SIZES = [100, 1_000, 10_000, 100_000, 1_000_000] diff --git a/bench/ctable/compact.py b/bench/ctable/compact.py index 7e8c085e..a4817b0a 100644 --- a/bench/ctable/compact.py +++ b/bench/ctable/compact.py @@ -8,8 +8,8 @@ # Benchmark for measuring compact() time and memory gain after deletions # of varying fractions of the table. -from time import time from dataclasses import dataclass +from time import time import numpy as np diff --git a/bench/ctable/ctable_v_panda.py b/bench/ctable/ctable_v_panda.py index 9de2d2d6..3b7a6d52 100644 --- a/bench/ctable/ctable_v_panda.py +++ b/bench/ctable/ctable_v_panda.py @@ -11,10 +11,11 @@ # 3. Filtering (where/query) # 4. Row iteration -from time import time from dataclasses import dataclass +from time import time import numpy as np +import pandas as pd import blosc2 @@ -96,12 +97,12 @@ class Row: # 4. Row iteration t0 = time() -for val in ct["score"]: +for _val in ct["score"]: pass t_ct_iter = time() - t0 t0 = time() -for val in df["score"]: +for _val in df["score"]: pass t_pd_iter = time() - t0 diff --git a/bench/ctable/delete.py b/bench/ctable/delete.py index 256cec97..79f59580 100644 --- a/bench/ctable/delete.py +++ b/bench/ctable/delete.py @@ -8,8 +8,8 @@ # Benchmark for measuring delete() performance with different index types: # int, slice, and list — with varying sizes. -from time import time from dataclasses import dataclass +from time import time import numpy as np diff --git a/bench/ctable/expected_size.py b/bench/ctable/expected_size.py index 71d2b2c2..e199d589 100644 --- a/bench/ctable/expected_size.py +++ b/bench/ctable/expected_size.py @@ -8,8 +8,8 @@ # Benchmark for measuring the overhead of resize() when expected_size # is too small (M rows) vs correctly sized (N rows) during extend(). -from time import time from dataclasses import dataclass +from time import time import numpy as np diff --git a/bench/ctable/extend.py b/bench/ctable/extend.py index f99e11a8..5e1090ba 100644 --- a/bench/ctable/extend.py +++ b/bench/ctable/extend.py @@ -10,8 +10,8 @@ # 2. NumPy structured array (1M rows) — list of named tuples # 3. An existing CTable (previously created from Python lists, 1M rows) -from time import time from dataclasses import dataclass +from time import time import numpy as np diff --git a/bench/ctable/extend_vs_apend.py b/bench/ctable/extend_vs_apend.py index eff9e426..db63206b 100644 --- a/bench/ctable/extend_vs_apend.py +++ b/bench/ctable/extend_vs_apend.py @@ -8,10 +8,8 @@ # Benchmark for comparing append() (row by row) vs extend() (bulk), # to find the crossover point where extend() becomes worth it. -from time import time from dataclasses import dataclass - -import numpy as np +from time import time import blosc2 @@ -26,7 +24,7 @@ class Row: # Parameter — change N to test different crossover points N = 2 -print(f"append() vs extend() benchmark") +print("append() vs extend() benchmark") for i in range(6): print("\n") print("%" * 100) diff --git a/bench/ctable/index.py b/bench/ctable/index.py index 556d1d81..3298bde2 100644 --- a/bench/ctable/index.py +++ b/bench/ctable/index.py @@ -8,8 +8,8 @@ # Benchmark for measuring Column[int] access (single row by logical index), # which exercises _find_physical_index() traversal over chunk metadata. -from time import time from dataclasses import dataclass +from time import time import numpy as np diff --git a/bench/ctable/iteration_column.py b/bench/ctable/iteration_column.py index 27199319..b1ac3703 100644 --- a/bench/ctable/iteration_column.py +++ b/bench/ctable/iteration_column.py @@ -10,8 +10,8 @@ # 2. np.array(list(ct["score"])) — materialize via list then convert # 3. ct["score"][0:N].to_array() — slice view + to_array() -from time import time from dataclasses import dataclass +from time import time import numpy as np @@ -55,7 +55,7 @@ class Row: # 1. Python iterator t0 = time() -for val in col: +for _val in col: pass t_iter = time() - t0 print(f"for val in col: {t_iter:.4f} s") @@ -69,7 +69,7 @@ class Row: # 3. slice view + to_array() t0 = time() arr = col[0:N].to_numpy() -for val in arr: +for _val in arr: pass t_toarray = time() - t0 print(f"col[0:N].to_array(): {t_toarray:.4f} s") diff --git a/bench/ctable/print.py b/bench/ctable/print.py index bd19df3d..6efb80bf 100644 --- a/bench/ctable/print.py +++ b/bench/ctable/print.py @@ -13,6 +13,7 @@ import numpy as np import pandas as pd + import blosc2 diff --git a/bench/ctable/row_acces.py b/bench/ctable/row_acces.py index 158cf848..050d0309 100644 --- a/bench/ctable/row_acces.py +++ b/bench/ctable/row_acces.py @@ -8,8 +8,8 @@ # Benchmark for measuring row[int] access (full row via _RowIndexer), # testing access at different positions across the array. -from time import time from dataclasses import dataclass +from time import time import numpy as np diff --git a/bench/ctable/slice.py b/bench/ctable/slice.py index d0ed92cb..a41c50a6 100644 --- a/bench/ctable/slice.py +++ b/bench/ctable/slice.py @@ -8,8 +8,8 @@ # Benchmark for measuring Column[slice] access with slices of different # sizes and positions: small, large, and middle of the array. -from time import time from dataclasses import dataclass +from time import time import numpy as np diff --git a/bench/ctable/slice_steps.py b/bench/ctable/slice_steps.py index f5b05109..0a3fb358 100644 --- a/bench/ctable/slice_steps.py +++ b/bench/ctable/slice_steps.py @@ -7,8 +7,8 @@ # Benchmark for measuring Column[::step].to_array() with varying step sizes. -from time import time from dataclasses import dataclass +from time import time import numpy as np diff --git a/bench/ctable/slice_to_array.py b/bench/ctable/slice_to_array.py index aad082f9..7c58080e 100644 --- a/bench/ctable/slice_to_array.py +++ b/bench/ctable/slice_to_array.py @@ -8,8 +8,8 @@ # Benchmark for measuring Column[slice] + to_array() with slices of # different sizes and positions: small, large, and middle of the array. -from time import time from dataclasses import dataclass +from time import time import numpy as np diff --git a/bench/ctable/speed_iter.py b/bench/ctable/speed_iter.py index b470e147..10afdc36 100644 --- a/bench/ctable/speed_iter.py +++ b/bench/ctable/speed_iter.py @@ -6,13 +6,10 @@ ####################################################################### from dataclasses import dataclass - -import numpy as np from time import time - -from blosc2 import CTable import blosc2 +from blosc2 import CTable @dataclass diff --git a/bench/ctable/where_chain.py b/bench/ctable/where_chain.py index b8b13f75..d2a6092d 100644 --- a/bench/ctable/where_chain.py +++ b/bench/ctable/where_chain.py @@ -8,8 +8,8 @@ # Benchmark for comparing chained where() calls vs a single combined filter. # Filters: 250k < id < 750k, active == False, 25.0 < score < 75.0 -from time import time from dataclasses import dataclass +from time import time import numpy as np @@ -51,11 +51,11 @@ class Row: # 1. Three chained where() calls t0 = time() -r1 = ct.where((ct["id"] > 250_000)) -r2 = r1.where((ct["id"] < 750_000)) -r3 = r2.where((ct["score"] > 25.0)) -r4 = r3.where((ct["score"] < 75.0)) -r5 = r4.where(ct["active"] == False) +r1 = ct.where(ct["id"] > 250_000) +r2 = r1.where(ct["id"] < 750_000) +r3 = r2.where(ct["score"] > 25.0) +r4 = r3.where(ct["score"] < 75.0) +r5 = r4.where(not ct["active"]) t_chained = time() - t0 print(f"Chained where() (5 calls): {t_chained:.6f} s rows: {len(r5):,}") @@ -63,7 +63,7 @@ class Row: t0 = time() result = ct.where( (ct["id"] > 250_000) & (ct["id"] < 750_000) & - (ct["active"] == False) & + (not ct["active"]) & (ct["score"] > 25.0) & (ct["score"] < 75.0) ) t_combined = time() - t0 diff --git a/bench/ctable/where_selective.py b/bench/ctable/where_selective.py index 2bc8ae0b..c0ba6f78 100644 --- a/bench/ctable/where_selective.py +++ b/bench/ctable/where_selective.py @@ -8,8 +8,8 @@ # Benchmark for measuring where() performance with varying selectivity. # Filter: id < threshold, with thresholds covering 1%, 10%, 50%, 90%, 100% -from time import time from dataclasses import dataclass +from time import time import numpy as np diff --git a/examples/ctable/aggregates.py b/examples/ctable/aggregates.py new file mode 100644 index 00000000..bdda5080 --- /dev/null +++ b/examples/ctable/aggregates.py @@ -0,0 +1,68 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +# Column aggregates: sum, min, max, mean, std, unique, value_counts, +# describe, and covariance matrix. + +from dataclasses import dataclass + +import numpy as np + +import blosc2 + + +@dataclass +class Reading: + sensor_id: int = blosc2.field(blosc2.int32(ge=0, le=9)) + temperature: float = blosc2.field(blosc2.float64(ge=-50.0, le=60.0), default=20.0) + humidity: float = blosc2.field(blosc2.float64(ge=0.0, le=100.0), default=50.0) + alert: bool = blosc2.field(blosc2.bool(), default=False) + + +rng = np.random.default_rng(42) +N = 500 + +station_ids = rng.integers(0, 10, size=N).astype(np.int32) +temperatures = rng.normal(20.0, 8.0, size=N).clip(-50, 60).astype(np.float64) +humidities = rng.uniform(30.0, 90.0, size=N).astype(np.float64) +alerts = rng.random(N) < 0.05 + +data = list( + zip(station_ids.tolist(), temperatures.tolist(), humidities.tolist(), alerts.tolist(), strict=False) +) + +t = blosc2.CTable(Reading, new_data=data) +print(f"Table: {len(t)} rows\n") + +# -- per-column aggregates -------------------------------------------------- +temp = t["temperature"] +print(f"temperature sum : {temp.sum():.2f}") +print(f"temperature mean : {temp.mean():.2f}") +print(f"temperature std : {temp.std():.2f}") +print(f"temperature min : {temp.min():.2f}") +print(f"temperature max : {temp.max():.2f}") + +print(f"\nalert any : {t['alert'].any()}") +print(f"alert all : {t['alert'].all()}") + +# -- unique / value_counts -------------------------------------------------- +print(f"\nsensor_id unique values : {t['sensor_id'].unique()}") +print(f"sensor_id value_counts : {t['sensor_id'].value_counts()}") + +# -- describe(): per-column summary printed to stdout ----------------------- +print() +t.describe() + +# -- cov(): covariance matrix of numeric columns ---------------------------- +numeric = t.select(["sensor_id", "temperature", "humidity"]) +cov = numeric.cov() +labels = ["sensor_id", "temperature", "humidity"] +col_w = 14 +print("\nCovariance matrix:") +print(" " * 14 + "".join(f"{lbl:>{col_w}}" for lbl in labels)) +for i, row_label in enumerate(labels): + print(f"{row_label:<14}" + "".join(f"{cov[i, j]:>{col_w}.4f}" for j in range(3))) diff --git a/examples/ctable/arrow_interop.py b/examples/ctable/arrow_interop.py new file mode 100644 index 00000000..0139e0ef --- /dev/null +++ b/examples/ctable/arrow_interop.py @@ -0,0 +1,82 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +# Arrow interop: CTable ↔ pyarrow.Table, and pandas round-trip via Arrow. +# +# Requires: pip install pyarrow pandas + +from dataclasses import dataclass + +import pyarrow as pa + +import blosc2 + + +@dataclass +class Stock: + ticker: str = blosc2.field(blosc2.string(max_length=8), default="") + open: float = blosc2.field(blosc2.float64(ge=0), default=0.0) + close: float = blosc2.field(blosc2.float64(ge=0), default=0.0) + volume: int = blosc2.field(blosc2.int64(ge=0), default=0) + + +data = [ + ("AAPL", 182.5, 184.2, 58_000_000), + ("GOOG", 141.3, 140.8, 21_000_000), + ("MSFT", 378.9, 380.1, 19_000_000), + ("AMZN", 185.6, 187.3, 35_000_000), + ("NVDA", 875.4, 902.1, 42_000_000), +] + +t = blosc2.CTable(Stock, new_data=data) +print("CTable:") +print(t) + +# -- to_arrow() ------------------------------------------------------------- +at = t.to_arrow() +print(f"Arrow table: {len(at)} rows, schema={at.schema}\n") + +# -- from_arrow(): schema is inferred from Arrow types --------------------- +at2 = pa.table( + { + "x": pa.array([1.0, 2.0, 3.0], type=pa.float32()), + "y": pa.array([10, 20, 30], type=pa.int32()), + "label": pa.array(["a", "bb", "ccc"], type=pa.string()), + } +) +t2 = blosc2.CTable.from_arrow(at2) +print("CTable from Arrow (inferred schema):") +print(t2) +print(f" label dtype: {t2['label'].dtype} (max_length inferred from data)") + +# -- pandas round-trip ------------------------------------------------------ +try: + import pandas as pd + + df_original = pd.DataFrame( + { + "ticker": ["TSLA", "META", "AMD"], + "open": [245.1, 502.3, 168.7], + "close": [248.5, 498.1, 171.2], + "volume": [80_000_000, 15_000_000, 28_000_000], + } + ) + print("\nOriginal DataFrame:") + print(df_original) + + # pandas → Arrow → CTable + t_from_pd = blosc2.CTable.from_arrow(pa.Table.from_pandas(df_original, preserve_index=False)) + print("\nCTable from pandas:") + print(t_from_pd) + + # CTable → Arrow → pandas + df_back = t_from_pd.to_arrow().to_pandas() + print("\nDataFrame round-tripped through CTable:") + print(df_back) + +except ImportError: + print("pandas not installed — skipping pandas round-trip demo.") diff --git a/examples/ctable/basics.py b/examples/ctable/basics.py new file mode 100644 index 00000000..0c402cb1 --- /dev/null +++ b/examples/ctable/basics.py @@ -0,0 +1,61 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +# CTable basics: creation, append, extend, head/tail, len. + +from dataclasses import dataclass + +import numpy as np + +import blosc2 + + +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + price: float = blosc2.field(blosc2.float64(ge=0), default=0.0) + active: bool = blosc2.field(blosc2.bool(), default=True) + + +# -- Create an empty table -------------------------------------------------- +t = blosc2.CTable(Row) +print(f"Empty table: {len(t)} rows") + +# -- append(): one row at a time -------------------------------------------- +t.append(Row(id=0, price=1.5, active=True)) +t.append(Row(id=1, price=2.3, active=False)) +print(f"After 2 appends: {len(t)} rows") + +# -- extend(): bulk load from a list of tuples ------------------------------ +bulk = [(i, float(i) * 0.5, i % 2 == 0) for i in range(2, 10)] +t.extend(bulk) +print(f"After extend: {len(t)} rows") + +# -- extend() from a structured numpy array --------------------------------- +arr = np.zeros(5, dtype=[("id", np.int64), ("price", np.float64), ("active", np.bool_)]) +arr["id"] = np.arange(10, 15) +arr["price"] = np.linspace(10.0, 14.0, 5) +arr["active"] = [True, False, True, False, True] +t.extend(arr) +print(f"After numpy extend: {len(t)} rows\n") + +# -- display: head / tail / full table -------------------------------------- +print("head(3):") +print(t.head(3)) + +print("tail(3):") +print(t.tail(3)) + +print("Full table:") +print(t) + +# -- basic properties ------------------------------------------------------- +print(f"nrows : {t.nrows}") +print(f"ncols : {t.ncols}") +print(f"columns: {t.col_names}") +print(f"cbytes : {t.cbytes:,} B (compressed)") +print(f"nbytes : {t.nbytes:,} B (uncompressed)") diff --git a/examples/ctable/csv_interop.py b/examples/ctable/csv_interop.py new file mode 100644 index 00000000..41a76fd9 --- /dev/null +++ b/examples/ctable/csv_interop.py @@ -0,0 +1,82 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +# CSV interop: generate a weather CSV, load it into a CTable, write it back. + +import csv +import shutil +import tempfile +from dataclasses import dataclass +from pathlib import Path + +import numpy as np + +import blosc2 + + +@dataclass +class WeatherReading: + station_id: int = blosc2.field(blosc2.int32(ge=0, le=9999)) + temperature: float = blosc2.field(blosc2.float32(ge=-80.0, le=60.0), default=20.0) + humidity: float = blosc2.field(blosc2.float32(ge=0.0, le=100.0), default=50.0) + wind_speed: float = blosc2.field(blosc2.float32(ge=0.0, le=200.0), default=0.0) + pressure: float = blosc2.field(blosc2.float32(ge=800.0, le=1100.0), default=1013.0) + day_of_year: int = blosc2.field(blosc2.int16(ge=1, le=365), default=1) + + +# -- Generate a weather CSV ------------------------------------------------- +rng = np.random.default_rng(42) +N = 1_000 + +station_ids = rng.integers(0, 100, size=N).tolist() +temperatures = [round(v, 2) for v in rng.normal(15.0, 12.0, N).clip(-80, 60).tolist()] +humidities = [round(v, 2) for v in rng.uniform(20.0, 95.0, N).tolist()] +wind_speeds = [round(v, 2) for v in rng.exponential(10.0, N).clip(0, 200).tolist()] +pressures = [round(v, 2) for v in rng.normal(1013.0, 8.0, N).clip(800, 1100).tolist()] +days = rng.integers(1, 366, size=N).tolist() + +rows = list(zip(station_ids, temperatures, humidities, wind_speeds, pressures, days, strict=False)) + +tmpdir = Path(tempfile.mkdtemp(prefix="blosc2_csv_")) +csv_in = tmpdir / "weather.csv" +csv_out = tmpdir / "weather_out.csv" + +# Write the CSV manually so the example is self-contained +with open(csv_in, "w", newline="") as f: + writer = csv.writer(f) + writer.writerow(["station_id", "temperature", "humidity", "wind_speed", "pressure", "day_of_year"]) + writer.writerows(rows) + +print(f"Generated {N} rows → {csv_in}") + +# -- from_csv(): load into CTable ------------------------------------------- +t = blosc2.CTable.from_csv(str(csv_in), WeatherReading) +print(f"Loaded into CTable: {len(t)} rows") +print(t.head()) + +# -- apply a filter before exporting ---------------------------------------- +cold_days = t.where(t["temperature"] < 0) +print(f"\nCold days (temp < 0°C): {len(cold_days)} rows") +print(cold_days.head()) + +# -- to_csv(): write back to CSV -------------------------------------------- +t.to_csv(str(csv_out)) +print(f"\nFull table written to {csv_out}") + +# Verify round-trip row count +with open(csv_out) as f: + lines = f.readlines() +assert len(lines) == N + 1 # header + data rows +print(f"Round-trip verified: {len(lines) - 1} data rows in output CSV.") + +# -- TSV variant ------------------------------------------------------------ +tsv_out = tmpdir / "weather.tsv" +t.to_csv(str(tsv_out), sep="\t") +print(f"TSV variant written to {tsv_out}") + +shutil.rmtree(tmpdir) +print("Temporary files removed.") diff --git a/examples/ctable/ctable_tutorial.ipynb b/examples/ctable/ctable_tutorial.ipynb new file mode 100644 index 00000000..fbb8f230 --- /dev/null +++ b/examples/ctable/ctable_tutorial.ipynb @@ -0,0 +1,1644 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a8bf6f00", + "metadata": {}, + "source": [ + "# CTable Tutorial\n", + "\n", + "**CTable** is a columnar compressed table built on top of `blosc2.NDArray`. \n", + "It stores each column independently as a compressed array, giving you:\n", + "\n", + "- **Compression** — data lives compressed in RAM and on disk.\n", + "- **Schema** — every column has a declared type and optional constraints.\n", + "- **Speed** — bulk operations stay in NumPy; no row-by-row Python overhead.\n", + "- **Persistence** — tables can be saved to and loaded from disk transparently.\n", + "\n", + "This notebook walks through the full API, starting from the very basics and finishing with a real-world analysis of climate data across ten world cities." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "a4073a3e", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-07T12:06:13.708034Z", + "iopub.status.busy": "2026-04-07T12:06:13.707898Z", + "iopub.status.idle": "2026-04-07T12:06:14.162620Z", + "shell.execute_reply": "2026-04-07T12:06:14.161981Z" + } + }, + "outputs": [], + "source": [ + "from dataclasses import dataclass\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "import blosc2\n", + "from blosc2 import CTable" + ] + }, + { + "cell_type": "markdown", + "id": "1637a7b2", + "metadata": {}, + "source": [ + "---\n", + "## Part 1 — The Basics\n", + "\n", + "### 1.1 Defining a schema\n", + "\n", + "Every CTable is typed. You define the schema with a plain Python `@dataclass`.\n", + "Each field gets a **spec** — a blosc2 type that carries the NumPy dtype and optional constraints." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c97f9123", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-07T12:06:14.164585Z", + "iopub.status.busy": "2026-04-07T12:06:14.164404Z", + "iopub.status.idle": "2026-04-07T12:06:14.168886Z", + "shell.execute_reply": "2026-04-07T12:06:14.168381Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Empty table: 0 rows, columns: ['id', 'location', 'temperature', 'active']\n" + ] + } + ], + "source": [ + "@dataclass\n", + "class Sensor:\n", + " id: int = blosc2.field(blosc2.int32(ge=0))\n", + " location: str = blosc2.field(blosc2.string(max_length=16), default=\"\")\n", + " temperature: float = blosc2.field(blosc2.float64(ge=-80, le=60), default=20.0)\n", + " active: bool = blosc2.field(blosc2.bool(), default=True)\n", + "\n", + "\n", + "# Create an empty in-memory table\n", + "t = CTable(Sensor, expected_size=50)\n", + "print(f\"Empty table: {len(t)} rows, columns: {t.col_names}\")" + ] + }, + { + "cell_type": "markdown", + "id": "c27913d6", + "metadata": {}, + "source": [ + "### 1.2 Appending rows\n", + "\n", + "`append()` adds one row at a time. The row is validated against the schema before writing." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "fdc64a5b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-07T12:06:14.170432Z", + "iopub.status.busy": "2026-04-07T12:06:14.170315Z", + "iopub.status.idle": "2026-04-07T12:06:14.231985Z", + "shell.execute_reply": "2026-04-07T12:06:14.231362Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " id location temperature active \n", + " int32 35)\n", + "print(f\"Days above 35 °C: {len(very_hot)} ({len(very_hot) / len(climate) * 100:.1f}% of all readings)\")\n", + "print(very_hot.head(8))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "ba2d719b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-07T12:06:14.342416Z", + "iopub.status.busy": "2026-04-07T12:06:14.342298Z", + "iopub.status.idle": "2026-04-07T12:06:14.358545Z", + "shell.execute_reply": "2026-04-07T12:06:14.357991Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Moscow below freezing: 148 days out of 365\n", + " city day temperature humidity wind_speed pressure \n", + " 10} {'Min':>7} {'Max':>7} {'Std':>7}\")\n", + "print(\"-\" * 50)\n", + "for city in CITY_PROFILES:\n", + " v = climate.where(climate[\"city\"] == city)\n", + " col = v[\"temperature\"]\n", + " print(f\"{city:<12} {col.mean():>9.1f}° {col.min():>6.1f}° {col.max():>6.1f}° {col.std():>6.1f}°\")" + ] + }, + { + "cell_type": "markdown", + "id": "49dcbad7", + "metadata": {}, + "source": [ + "### 4.2 `describe()` — full summary in one call" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "7254f3b1", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-07T12:06:14.520839Z", + "iopub.status.busy": "2026-04-07T12:06:14.520722Z", + "iopub.status.idle": "2026-04-07T12:06:14.542317Z", + "shell.execute_reply": "2026-04-07T12:06:14.541649Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CTable 3,650 rows × 4 cols\n", + "\n", + " temperature [float32]\n", + " count : 3,650\n", + " mean : 16.04\n", + " std : 10.72\n", + " min : -17.54\n", + " max : 39.75\n", + "\n", + " humidity [float32]\n", + " count : 3,650\n", + " mean : 63.48\n", + " std : 16.02\n", + " min : 8.894\n", + " max : 99.81\n", + "\n", + " wind_speed [float32]\n", + " count : 3,650\n", + " mean : 15.63\n", + " std : 4.874\n", + " min : 8.005\n", + " max : 47.48\n", + "\n", + " pressure [float32]\n", + " count : 3,650\n", + " mean : 1013\n", + " std : 5.328\n", + " min : 991.1\n", + " max : 1036\n", + "\n" + ] + } + ], + "source": [ + "# describe() on a select() view — only numeric columns\n", + "climate.select([\"temperature\", \"humidity\", \"wind_speed\", \"pressure\"]).describe()" + ] + }, + { + "cell_type": "markdown", + "id": "817dbc1f", + "metadata": {}, + "source": [ + "### 4.3 Covariance matrix\n", + "\n", + "`cov()` requires all columns to be numeric (int, float, or bool). \n", + "It returns a standard `numpy.ndarray`." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "6d0dd2c1", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-07T12:06:14.543869Z", + "iopub.status.busy": "2026-04-07T12:06:14.543748Z", + "iopub.status.idle": "2026-04-07T12:06:14.559277Z", + "shell.execute_reply": "2026-04-07T12:06:14.558718Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Covariance matrix (all cities, full year):\n", + " temp humidity wind pressure\n", + "temp 114.963 0.018 -3.523 -0.207\n", + "humidity 0.018 256.861 10.773 6.652\n", + "wind -3.523 10.773 23.760 -2.650\n", + "pressure -0.207 6.652 -2.650 28.394\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Correlation matrix:\n", + " temp humidity wind pressure\n", + "temp 1.000 0.000 -0.067 -0.004\n", + "humidity 0.000 1.000 0.138 0.078\n", + "wind -0.067 0.138 1.000 -0.102\n", + "pressure -0.004 0.078 -0.102 1.000\n" + ] + } + ], + "source": [ + "numeric = climate.select([\"temperature\", \"humidity\", \"wind_speed\", \"pressure\"])\n", + "cov = numeric.cov()\n", + "\n", + "labels = [\"temp\", \"humidity\", \"wind\", \"pressure\"]\n", + "col_w = 12\n", + "print(\"Covariance matrix (all cities, full year):\")\n", + "print(\" \" * 10 + \"\".join(f\"{lbl:>{col_w}}\" for lbl in labels))\n", + "for i, lbl in enumerate(labels):\n", + " print(f\"{lbl:<10}\" + \"\".join(f\"{cov[i, j]:>{col_w}.3f}\" for j in range(4)))\n", + "\n", + "# And the correlation matrix for easier interpretation\n", + "corr = np.corrcoef(\n", + " np.stack([numeric[c].to_numpy() for c in [\"temperature\", \"humidity\", \"wind_speed\", \"pressure\"]])\n", + ")\n", + "print(\"\\nCorrelation matrix:\")\n", + "print(\" \" * 10 + \"\".join(f\"{lbl:>{col_w}}\" for lbl in labels))\n", + "for i, lbl in enumerate(labels):\n", + " print(f\"{lbl:<10}\" + \"\".join(f\"{corr[i, j]:>{col_w}.3f}\" for j in range(4)))" + ] + }, + { + "cell_type": "markdown", + "id": "c10a694e", + "metadata": {}, + "source": [ + "---\n", + "## Part 5 — Analysis: Summer in Madrid\n", + "\n", + "Summer in the northern hemisphere runs roughly from the **summer solstice (day 172, June 21)** \n", + "to the **autumnal equinox (day 264, September 22)**.\n", + "\n", + "Let's zoom in on Madrid during those months and compare it with a few other cities." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "89e89177", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-07T12:06:14.560797Z", + "iopub.status.busy": "2026-04-07T12:06:14.560666Z", + "iopub.status.idle": "2026-04-07T12:06:14.576880Z", + "shell.execute_reply": "2026-04-07T12:06:14.576245Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Madrid summer readings : 93 days\n", + " mean temperature : 25.8 °C\n", + " max temperature : 31.4 °C\n", + " mean humidity : 43.8 %\n", + " mean wind speed : 15.8 km/h\n" + ] + } + ], + "source": [ + "SUMMER_START = 172 # June 21\n", + "SUMMER_END = 264 # September 22\n", + "\n", + "madrid = climate.where(climate[\"city\"] == \"Madrid\")\n", + "madrid_summer = madrid.where((madrid[\"day\"] >= SUMMER_START) & (madrid[\"day\"] <= SUMMER_END))\n", + "\n", + "print(f\"Madrid summer readings : {len(madrid_summer)} days\")\n", + "print(f\" mean temperature : {madrid_summer['temperature'].mean():.1f} °C\")\n", + "print(f\" max temperature : {madrid_summer['temperature'].max():.1f} °C\")\n", + "print(f\" mean humidity : {madrid_summer['humidity'].mean():.1f} %\")\n", + "print(f\" mean wind speed : {madrid_summer['wind_speed'].mean():.1f} km/h\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "a439fecd", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-07T12:06:14.578621Z", + "iopub.status.busy": "2026-04-07T12:06:14.578475Z", + "iopub.status.idle": "2026-04-07T12:06:14.693971Z", + "shell.execute_reply": "2026-04-07T12:06:14.693318Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "City Summer mean Summer max Summer humidity\n", + "----------------------------------------------------------\n", + "Madrid 25.8°C 31.4°C 43.8% \n", + "London 16.5°C 22.7°C 74.6% \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cairo 33.5°C 39.7°C 34.4% \n", + "Moscow 20.1°C 26.3°C 69.3% \n", + "Tokyo 25.1°C 31.0°C 73.0% \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sydney 24.6°C 30.9°C 63.8% (S. summer)\n" + ] + } + ], + "source": [ + "# Compare summer stats across several cities\n", + "compare_cities = [\"Madrid\", \"London\", \"Cairo\", \"Moscow\", \"Tokyo\", \"Sydney\"]\n", + "\n", + "print(f\"{'City':<12} {'Summer mean':>12} {'Summer max':>11} {'Summer humidity':>16}\")\n", + "print(\"-\" * 58)\n", + "for city in compare_cities:\n", + " v = climate.where(climate[\"city\"] == city)\n", + " # For Sydney (S. hemisphere) 'summer' is Jan-Mar, i.e. days 1-80 or 355-365\n", + " if city == \"Sydney\":\n", + " s = v.where((v[\"day\"] <= 80) | (v[\"day\"] >= 355))\n", + " label = \"(S. summer)\"\n", + " else:\n", + " s = v.where((v[\"day\"] >= SUMMER_START) & (v[\"day\"] <= SUMMER_END))\n", + " label = \"\"\n", + " mean_t = s[\"temperature\"].mean()\n", + " max_t = s[\"temperature\"].max()\n", + " mean_h = s[\"humidity\"].mean()\n", + " print(f\"{city:<12} {mean_t:>10.1f}°C {max_t:>9.1f}°C {mean_h:>14.1f}% {label}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "4e2161ee", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-07T12:06:14.696603Z", + "iopub.status.busy": "2026-04-07T12:06:14.695965Z", + "iopub.status.idle": "2026-04-07T12:06:14.752771Z", + "shell.execute_reply": "2026-04-07T12:06:14.751433Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10 hottest days in Madrid:\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " city day temperature humidity \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_cities = {\n", + " \"Madrid\": \"#e63946\",\n", + " \"London\": \"#457b9d\",\n", + " \"Moscow\": \"#2d6a4f\",\n", + " \"Cairo\": \"#f4a261\",\n", + " \"Sydney\": \"#a8dadc\",\n", + "}\n", + "\n", + "fig, ax = plt.subplots(figsize=(12, 5))\n", + "\n", + "for city, color in plot_cities.items():\n", + " v = climate.where(climate[\"city\"] == city)\n", + " d = v[\"day\"].to_numpy().astype(int)\n", + " t = v[\"temperature\"].to_numpy()\n", + " order = np.argsort(d)\n", + " ax.plot(d[order], t[order], label=city, color=color, linewidth=1.5, alpha=0.85)\n", + "\n", + "ax.axvspan(SUMMER_START, SUMMER_END, alpha=0.10, color=\"gold\", label=\"N. summer\")\n", + "ax.set_xlabel(\"Day of year\")\n", + "ax.set_ylabel(\"Temperature (°C)\")\n", + "ax.set_title(\"Daily temperature — selected cities\")\n", + "ax.legend(loc=\"upper left\")\n", + "ax.grid(True, linestyle=\"--\", alpha=0.4)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "4fbb0c53", + "metadata": {}, + "source": [ + "### 5.2 Summer temperature distribution — Madrid vs London\n", + "\n", + "A simple histogram comparison of how often each city exceeds different temperature thresholds." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "94e141a4", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-07T12:06:15.038502Z", + "iopub.status.busy": "2026-04-07T12:06:15.038229Z", + "iopub.status.idle": "2026-04-07T12:06:15.564804Z", + "shell.execute_reply": "2026-04-07T12:06:15.563854Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "madrid_s = climate.where(\n", + " (climate[\"city\"] == \"Madrid\") & (climate[\"day\"] >= SUMMER_START) & (climate[\"day\"] <= SUMMER_END)\n", + ")[\"temperature\"].to_numpy()\n", + "\n", + "london_s = climate.where(\n", + " (climate[\"city\"] == \"London\") & (climate[\"day\"] >= SUMMER_START) & (climate[\"day\"] <= SUMMER_END)\n", + ")[\"temperature\"].to_numpy()\n", + "\n", + "fig, ax = plt.subplots(figsize=(9, 4))\n", + "bins = np.linspace(0, 45, 30)\n", + "ax.hist(madrid_s, bins=bins, alpha=0.7, color=\"#e63946\", label=\"Madrid\")\n", + "ax.hist(london_s, bins=bins, alpha=0.7, color=\"#457b9d\", label=\"London\")\n", + "ax.axvline(\n", + " madrid_s.mean(),\n", + " color=\"#e63946\",\n", + " linestyle=\"--\",\n", + " linewidth=1.5,\n", + " label=f\"Madrid mean {madrid_s.mean():.1f}°C\",\n", + ")\n", + "ax.axvline(\n", + " london_s.mean(),\n", + " color=\"#457b9d\",\n", + " linestyle=\"--\",\n", + " linewidth=1.5,\n", + " label=f\"London mean {london_s.mean():.1f}°C\",\n", + ")\n", + "ax.set_xlabel(\"Temperature (°C)\")\n", + "ax.set_ylabel(\"Days\")\n", + "ax.set_title(\"Summer temperature distribution — Madrid vs London\")\n", + "ax.legend()\n", + "ax.grid(True, linestyle=\"--\", alpha=0.4)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "8a690193", + "metadata": {}, + "source": [ + "### 5.3 Mean summer temperature — all cities ranked" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "334a833a", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-07T12:06:15.573465Z", + "iopub.status.busy": "2026-04-07T12:06:15.573114Z", + "iopub.status.idle": "2026-04-07T12:06:16.558429Z", + "shell.execute_reply": "2026-04-07T12:06:16.555709Z" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "city_summer_means = {}\n", + "for city in CITY_PROFILES:\n", + " v = climate.where(climate[\"city\"] == city)\n", + " if city == \"Sydney\" or city == \"Sao Paulo\":\n", + " s = v.where((v[\"day\"] <= 80) | (v[\"day\"] >= 355))\n", + " else:\n", + " s = v.where((v[\"day\"] >= SUMMER_START) & (v[\"day\"] <= SUMMER_END))\n", + " city_summer_means[city] = s[\"temperature\"].mean()\n", + "\n", + "sorted_cities = sorted(city_summer_means.items(), key=lambda x: x[1], reverse=True)\n", + "names = [c for c, _ in sorted_cities]\n", + "means = [m for _, m in sorted_cities]\n", + "colors = [\"#e63946\" if m > 30 else \"#f4a261\" if m > 20 else \"#457b9d\" for m in means]\n", + "\n", + "fig, ax = plt.subplots(figsize=(10, 4))\n", + "bars = ax.barh(names, means, color=colors, edgecolor=\"white\")\n", + "ax.bar_label(bars, fmt=\"%.1f °C\", padding=4)\n", + "ax.set_xlabel(\"Mean summer temperature (°C)\")\n", + "ax.set_title(\"Cities ranked by summer heat\")\n", + "ax.set_xlim(0, max(means) * 1.15)\n", + "ax.grid(True, axis=\"x\", linestyle=\"--\", alpha=0.4)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "d1601d7b", + "metadata": {}, + "source": [ + "---\n", + "## Part 6 — Mutations\n", + "\n", + "CTable supports structural and value mutations: adding/dropping columns, deleting rows, sorting in place." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "2c3a977b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-04-07T12:06:16.566822Z", + "iopub.status.busy": "2026-04-07T12:06:16.563241Z", + "iopub.status.idle": "2026-04-07T12:06:16.715871Z", + "shell.execute_reply": "2026-04-07T12:06:16.712493Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Table with feels_like column:\n", + " city day temperature humidity wind_speed pressure feels_like \n", + " +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +# Mutations: delete, compact, sort_by, add/drop/rename columns, assign. + +from dataclasses import dataclass + +import blosc2 + + +@dataclass +class Employee: + id: int = blosc2.field(blosc2.int64(ge=0)) + name: str = blosc2.field(blosc2.string(max_length=16), default="") + salary: float = blosc2.field(blosc2.float64(ge=0), default=0.0) + + +data = [ + (0, "Alice", 85_000.0), + (1, "Bob", 72_000.0), + (2, "Carol", 91_000.0), + (3, "Dave", 65_000.0), + (4, "Eve", 110_000.0), + (5, "Frank", 78_000.0), +] + +t = blosc2.CTable(Employee, new_data=data) +print("Original:") +print(t) + +# -- delete(): logical deletion (tombstone) --------------------------------- +t.delete([1, 3]) # remove Bob and Dave +print(f"After deleting rows 1 and 3: {len(t)} live rows") +print(t) + +# -- compact(): physically close the gaps ----------------------------------- +t.compact() +print("After compact():") +print(t) + +# -- sort_by(): returns a sorted copy by default ---------------------------- +sorted_t = t.sort_by("salary", ascending=False) +print("Sorted by salary descending:") +print(sorted_t) + +# -- sort_by(inplace=True) -------------------------------------------------- +t.sort_by("name", inplace=True) +print("Sorted in-place by name:") +print(t) + +# -- add_column(): new column filled with a default ------------------------- +t.add_column("bonus", blosc2.float64(ge=0), default=0.0) +print("After add_column('bonus'):") +print(t) + +# -- assign(): fill the new column with computed values --------------------- +bonuses = t["salary"].to_numpy() * 0.10 +t["bonus"].assign(bonuses) +print("After assigning 10% bonuses:") +print(t) + +# -- rename_column() -------------------------------------------------------- +t.rename_column("bonus", "annual_bonus") +print(f"Column names after rename: {t.col_names}") + +# -- drop_column() ---------------------------------------------------------- +t.drop_column("annual_bonus") +print(f"Column names after drop: {t.col_names}") diff --git a/examples/ctable/persistence.py b/examples/ctable/persistence.py new file mode 100644 index 00000000..6433daef --- /dev/null +++ b/examples/ctable/persistence.py @@ -0,0 +1,69 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +# Persistence: write to disk, open read-only/read-write, save, load. + +import shutil +import tempfile +from dataclasses import dataclass + +import numpy as np + +import blosc2 + + +@dataclass +class Measurement: + sensor_id: int = blosc2.field(blosc2.int32(ge=0)) + temperature: float = blosc2.field(blosc2.float64(), default=0.0) + day: int = blosc2.field(blosc2.int16(ge=1, le=365), default=1) + + +rng = np.random.default_rng(0) +N = 10_000 +data = [ + (int(rng.integers(0, 20)), float(rng.normal(15.0, 10.0)), int(rng.integers(1, 366))) for _ in range(N) +] + +tmpdir = tempfile.mkdtemp(prefix="blosc2_ctable_") +disk_path = f"{tmpdir}/measurements" +copy_path = f"{tmpdir}/measurements_copy" + +try: + # -- Create directly on disk (mode="w") --------------------------------- + t = blosc2.CTable(Measurement, new_data=data, urlpath=disk_path, mode="w") + print(f"Created on disk: {len(t):,} rows at '{disk_path}'") + t.info() + + # -- Open read-only (default) ------------------------------------------- + ro = blosc2.CTable.open(disk_path) # mode="r" by default + print(f"Opened read-only: {len(ro):,} rows") + print(f" mean temperature: {ro['temperature'].mean():.2f}") + + try: + ro.append(Measurement(sensor_id=0, temperature=20.0, day=1)) + except ValueError as e: + print(f" Write blocked (read-only): {e}") + + # -- Open read-write and mutate ----------------------------------------- + rw = blosc2.CTable.open(disk_path, mode="a") + rw.append(Measurement(sensor_id=99, temperature=99.0, day=100)) + print(f"\nAfter append (read-write): {len(rw):,} rows") + + # -- save(): copy in-memory table to disk ------------------------------- + mem = blosc2.CTable(Measurement, new_data=data[:100]) + mem.save(copy_path) + print(f"In-memory table saved to '{copy_path}'") + + # -- load(): pull a disk table fully into RAM --------------------------- + ram = blosc2.CTable.load(disk_path) + print(f"Loaded into RAM: {len(ram):,} rows (cbytes={ram.cbytes:,})") + assert len(ram) == len(rw) + +finally: + shutil.rmtree(tmpdir) + print("\nTemporary files removed.") diff --git a/examples/ctable/querying.py b/examples/ctable/querying.py new file mode 100644 index 00000000..433c5110 --- /dev/null +++ b/examples/ctable/querying.py @@ -0,0 +1,60 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +# Querying: where() filters, select() column projection, and chaining. + +from dataclasses import dataclass + +import blosc2 + + +@dataclass +class Sale: + id: int = blosc2.field(blosc2.int64(ge=0)) + region: str = blosc2.field(blosc2.string(max_length=16), default="") + amount: float = blosc2.field(blosc2.float64(ge=0), default=0.0) + returned: bool = blosc2.field(blosc2.bool(), default=False) + + +data = [ + (0, "North", 120.0, False), + (1, "South", 340.0, False), + (2, "North", 85.0, True), + (3, "East", 210.0, False), + (4, "West", 430.0, False), + (5, "South", 60.0, True), + (6, "East", 300.0, False), + (7, "North", 500.0, False), + (8, "West", 175.0, True), + (9, "South", 220.0, False), +] + +t = blosc2.CTable(Sale, new_data=data) + +# -- where(): row filter ---------------------------------------------------- +high_value = t.where(t["amount"] > 200) +print(f"Sales > $200: {len(high_value)} rows") +print(high_value) + +not_returned = t.where(not t["returned"]) +print(f"Not returned: {len(not_returned)} rows") + +# -- chained filters (views are composable) --------------------------------- +north = t.where(t["region"] == "North") +north_big = north.where(north["amount"] > 100) +print(f"North region + amount > 100: {len(north_big)} rows") +print(north_big) + +# -- select(): column projection (no data copy) ----------------------------- +slim = t.select(["id", "amount"]) +print("id + amount only:") +print(slim) + +# -- combined: select columns, then filter rows ----------------------------- +result = t.select(["region", "amount"]).where(not t["returned"]) +print("Region + amount for non-returned sales:") +print(result) diff --git a/examples/ctable/real_world.py b/examples/ctable/real_world.py new file mode 100644 index 00000000..927f05bd --- /dev/null +++ b/examples/ctable/real_world.py @@ -0,0 +1,114 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +# Real-world example: weather station data. +# +# Simulates a year of readings from 10 stations, then: +# - filters to a single station +# - finds the 5 hottest days +# - computes correlations between meteorological variables +# - saves the filtered data to disk and reloads it + +import shutil +import tempfile +from dataclasses import dataclass + +import numpy as np + +import blosc2 + + +@dataclass +class WeatherReading: + station_id: int = blosc2.field(blosc2.int32(ge=0, le=9)) + temperature: float = blosc2.field(blosc2.float32(ge=-80.0, le=60.0), default=20.0) + humidity: float = blosc2.field(blosc2.float32(ge=0.0, le=100.0), default=50.0) + wind_speed: float = blosc2.field(blosc2.float32(ge=0.0, le=200.0), default=0.0) + pressure: float = blosc2.field(blosc2.float32(ge=800.0, le=1100.0), default=1013.0) + day_of_year: int = blosc2.field(blosc2.int16(ge=1, le=365), default=1) + + +# -- Generate a full year of readings for 10 stations ---------------------- +rng = np.random.default_rng(42) +N_STATIONS = 10 +N_DAYS = 365 +N = N_STATIONS * N_DAYS # 3 650 rows + +station_ids = np.tile(np.arange(N_STATIONS, dtype=np.int32), N_DAYS) +temperatures = rng.normal(15.0, 12.0, N).clip(-80, 60).astype(np.float32) +humidities = rng.uniform(20.0, 95.0, N).astype(np.float32) +wind_speeds = rng.exponential(10.0, N).clip(0, 200).astype(np.float32) +pressures = rng.normal(1013.0, 8.0, N).clip(800, 1100).astype(np.float32) +days = np.repeat(np.arange(1, N_DAYS + 1, dtype=np.int16), N_STATIONS) + +arr = np.zeros( + N, + dtype=[ + ("station_id", np.int32), + ("temperature", np.float32), + ("humidity", np.float32), + ("wind_speed", np.float32), + ("pressure", np.float32), + ("day_of_year", np.int16), + ], +) +for col, val in [ + ("station_id", station_ids), + ("temperature", temperatures), + ("humidity", humidities), + ("wind_speed", wind_speeds), + ("pressure", pressures), + ("day_of_year", days), +]: + arr[col] = val + +t = blosc2.CTable(WeatherReading, new_data=arr, validate=False) +print(f"Full dataset: {len(t):,} rows ({N_STATIONS} stations × {N_DAYS} days)") +t.info() + +# -- Filter to station 3 ---------------------------------------------------- +station3 = t.where(t["station_id"] == 3) +print(f"Station 3: {len(station3)} readings") +print(f" mean temperature : {station3['temperature'].mean():.1f} °C") +print(f" mean humidity : {station3['humidity'].mean():.1f} %") +print(f" mean wind speed : {station3['wind_speed'].mean():.1f} km/h\n") + +# -- 5 hottest days at station 3 (sort full table, then filter) ------------ +sorted_by_temp = t.sort_by("temperature", ascending=False) +hottest_s3 = sorted_by_temp.where(sorted_by_temp["station_id"] == 3) +print("5 hottest days at station 3:") +print(hottest_s3.head(5)) + +# -- Covariance of numeric variables (all stations) ------------------------- +numeric = t.select(["temperature", "humidity", "wind_speed", "pressure"]) +cov = numeric.cov() +labels = ["temp", "humidity", "wind", "pressure"] +col_w = 11 +print("Covariance matrix (all stations):") +print(" " * 10 + "".join(f"{lbl:>{col_w}}" for lbl in labels)) +for i, lbl in enumerate(labels): + print(f"{lbl:<10}" + "".join(f"{cov[i, j]:>{col_w}.3f}" for j in range(4))) + +# -- Save station 3 data to disk and reload --------------------------------- +tmpdir = tempfile.mkdtemp(prefix="blosc2_weather_") +path = f"{tmpdir}/station3" +try: + # Views cannot be sorted or saved directly — materialise via Arrow first + s3_copy = blosc2.CTable.from_arrow(station3.to_arrow()) + s3_copy.sort_by("day_of_year", inplace=True) + sorted_s3 = s3_copy + sorted_s3.save(path, overwrite=True) + print(f"\nStation 3 data saved to '{path}'") + + reloaded = blosc2.CTable.load(path) + print( + f"Reloaded: {len(reloaded)} rows, " + f"days {reloaded['day_of_year'].min()}–{reloaded['day_of_year'].max()}" + ) +finally: + shutil.rmtree(tmpdir) + print("Temporary files removed.") diff --git a/examples/ctable/schema.py b/examples/ctable/schema.py new file mode 100644 index 00000000..327b0a11 --- /dev/null +++ b/examples/ctable/schema.py @@ -0,0 +1,55 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +# Schema layer: dataclass field specs, constraints, and validation. + +from dataclasses import dataclass + +import blosc2 + + +@dataclass +class Product: + id: int = blosc2.field(blosc2.int64(ge=0)) + price: float = blosc2.field(blosc2.float64(ge=0.0, le=10_000.0), default=0.0) + stock: int = blosc2.field(blosc2.int32(ge=0), default=0) + category: str = blosc2.field(blosc2.string(max_length=32), default="") + on_sale: bool = blosc2.field(blosc2.bool(), default=False) + + +t = blosc2.CTable(Product) + +# Valid row +t.append(Product(id=1, price=29.99, stock=100, category="electronics", on_sale=False)) +t.append(Product(id=2, price=4.50, stock=200, category="food", on_sale=True)) +print("Valid rows appended successfully.") +print(t) + +# Inspect the compiled schema +print("Schema:") +for col in t.schema.columns: + print(f" {col.name:<12} dtype={col.dtype} spec={col.spec}") + +# Constraint violation: price < 0 +try: + t.append(Product(id=3, price=-1.0, stock=10, category="misc", on_sale=False)) +except Exception as e: + print(f"\nCaught validation error (price < 0): {e}") + +# Constraint violation: id < 0 +try: + t.append(Product(id=-5, price=10.0, stock=10, category="misc", on_sale=False)) +except Exception as e: + print(f"Caught validation error (id < 0): {e}") + +# String too long (max_length=32) +try: + t.append(Product(id=4, price=1.0, stock=1, category="a" * 50, on_sale=False)) +except Exception as e: + print(f"Caught validation error (string too long): {e}") + +print(f"\nTable still has {len(t)} valid rows.") diff --git a/src/blosc2/ctable.py b/src/blosc2/ctable.py index 11a07e13..010cfe0f 100644 --- a/src/blosc2/ctable.py +++ b/src/blosc2/ctable.py @@ -1282,6 +1282,498 @@ def sample(self, n: int, *, seed: int | None = None) -> CTable: mask[chosen] = True return self.view(blosc2.asarray(mask)) + def select(self, cols: list[str]) -> CTable: + """Return a column-projection view exposing only *cols*. + + The returned object shares the underlying NDArrays with this table + (no data is copied). Row filtering and value writes work as usual; + structural mutations (add/drop/rename column, append, …) are blocked. + + Parameters + ---------- + cols: + Ordered list of column names to keep. + + Raises + ------ + KeyError + If any name in *cols* is not a column of this table. + ValueError + If *cols* is empty. + """ + if not cols: + raise ValueError("select() requires at least one column name.") + for name in cols: + if name not in self._cols: + raise KeyError(f"No column named {name!r}. Available: {self.col_names}") + + obj = CTable.__new__(CTable) + obj._row_type = self._row_type + obj._validate = self._validate + obj._table_cparams = self._table_cparams + obj._table_dparams = self._table_dparams + obj._storage = None + obj._read_only = self._read_only + obj._valid_rows = self._valid_rows + obj._n_rows = self._n_rows + obj._last_pos = self._last_pos + obj.auto_compact = self.auto_compact + obj.base = self + + # Subset of columns — same NDArray objects, no copy + obj._cols = {name: self._cols[name] for name in cols} + obj.col_names = list(cols) + + # Rebuild schema for the selected columns only + sel_set = set(cols) + sel_compiled = [c for c in self._schema.columns if c.name in sel_set] + # Preserve caller-specified order + order = {name: i for i, name in enumerate(cols)} + sel_compiled.sort(key=lambda c: order[c.name]) + obj._schema = CompiledSchema( + columns=sel_compiled, + columns_by_name={c.name: c for c in sel_compiled}, + row_cls=self._schema.row_cls, + ) + obj._col_widths = {name: self._col_widths[name] for name in cols if name in self._col_widths} + obj.row = _RowIndexer(obj) + return obj + + def describe(self) -> None: + """Print a per-column statistical summary. + + Numeric columns (int, float): count, mean, std, min, max. + Bool columns: count, true-count, true-%. + String columns: count, min (lex), max (lex), n-unique. + """ + n = self._n_rows + lines = [] + lines.append(f"CTable {n:,} rows × {self.ncols} cols") + lines.append("") + + for name in self.col_names: + col = self[name] + dtype = col.dtype + lines.append(f" {name} [{dtype}]") + + if n == 0: + lines.append(" (empty)") + lines.append("") + continue + + if dtype.kind in "biufc" and dtype.kind != "c": + # numeric + bool + if dtype.kind == "b": + arr = col.to_numpy() + true_n = int(arr.sum()) + lines.append(f" count : {n:,}") + lines.append(f" true : {true_n:,} ({true_n / n * 100:.1f} %)") + lines.append(f" false : {n - true_n:,} ({(n - true_n) / n * 100:.1f} %)") + else: + mn = col.min() + mx = col.max() + avg = col.mean() + sd = col.std() + fmt = ".4g" + lines.append(f" count : {n:,}") + lines.append(f" mean : {avg:{fmt}}") + lines.append(f" std : {sd:{fmt}}") + lines.append(f" min : {mn:{fmt}}") + lines.append(f" max : {mx:{fmt}}") + elif dtype.kind in "US": + mn = col.min() + mx = col.max() + nu = len(col.unique()) + lines.append(f" count : {n:,}") + lines.append(f" unique : {nu:,}") + lines.append(f" min : {mn!r}") + lines.append(f" max : {mx!r}") + else: + lines.append(f" count : {n:,}") + lines.append(f" (stats not available for dtype {dtype})") + + lines.append("") + + print("\n".join(lines)) + + def cov(self) -> np.ndarray: + """Return the covariance matrix as a numpy array. + + Only int, float, and bool columns are supported. Bool columns are + cast to int (0/1) before computation. Complex columns raise + :exc:`TypeError`. + + Returns + ------- + numpy.ndarray + Shape ``(ncols, ncols)``. Column order matches + :attr:`col_names`. + + Raises + ------ + TypeError + If any column has an unsupported dtype (complex, string, …). + ValueError + If the table has fewer than 2 live rows (covariance undefined). + """ + for name in self.col_names: + dtype = self._cols[name].dtype + if not ( + np.issubdtype(dtype, np.integer) or np.issubdtype(dtype, np.floating) or dtype == np.bool_ + ): + raise TypeError( + f"Column {name!r} has dtype {dtype} which is not supported by cov(). " + "Only int, float, and bool columns are allowed." + ) + + if self._n_rows < 2: + raise ValueError(f"cov() requires at least 2 live rows, got {self._n_rows}.") + + # Build (n_cols, n_rows) matrix — one row per column + arrays = [] + for name in self.col_names: + arr = self[name].to_numpy() + if arr.dtype == np.bool_: + arr = arr.astype(np.int8) + arrays.append(arr.astype(np.float64)) + + data = np.stack(arrays, axis=0) # shape (ncols, n_live) + return np.atleast_2d(np.cov(data)) + + # ------------------------------------------------------------------ + # Arrow interop + # ------------------------------------------------------------------ + + def to_arrow(self): + """Convert all live rows to a :class:`pyarrow.Table`. + + Each column is materialized via :meth:`Column.to_numpy` and wrapped + in a ``pyarrow.array``. String columns are emitted as ``pa.string()`` + (variable-length UTF-8); bytes columns as ``pa.large_binary()``. + + Raises + ------ + ImportError + If ``pyarrow`` is not installed. + """ + try: + import pyarrow as pa + except ImportError: + raise ImportError( + "pyarrow is required for to_arrow(). Install it with: pip install pyarrow" + ) from None + + arrays = {} + for name in self.col_names: + col = self[name] + arr = col.to_numpy() + kind = arr.dtype.kind + if kind == "U": + pa_arr = pa.array(arr.tolist(), type=pa.string()) + elif kind == "S": + pa_arr = pa.array(arr.tolist(), type=pa.large_binary()) + else: + pa_arr = pa.array(arr) + arrays[name] = pa_arr + + return pa.table(arrays) + + @classmethod + def from_arrow(cls, arrow_table) -> CTable: + """Build a :class:`CTable` from a :class:`pyarrow.Table`. + + Schema is inferred from the Arrow field types. String columns + (``pa.string()``, ``pa.large_string()``) are stored with + ``max_length`` set to the longest value found in the data. + + Parameters + ---------- + arrow_table: + A ``pyarrow.Table`` instance. + + Returns + ------- + CTable + A new in-memory CTable containing all rows from *arrow_table*. + + Raises + ------ + ImportError + If ``pyarrow`` is not installed. + TypeError + If an Arrow field type has no corresponding blosc2 spec. + """ + try: + import pyarrow as pa + except ImportError: + raise ImportError( + "pyarrow is required for from_arrow(). Install it with: pip install pyarrow" + ) from None + + import blosc2.schema as b2s + + def _arrow_type_to_spec(pa_type, arrow_col): + """Map a pyarrow DataType to a blosc2 SchemaSpec.""" + mapping = [ + (pa.int8(), b2s.int8), + (pa.int16(), b2s.int16), + (pa.int32(), b2s.int32), + (pa.int64(), b2s.int64), + (pa.uint8(), b2s.uint8), + (pa.uint16(), b2s.uint16), + (pa.uint32(), b2s.uint32), + (pa.uint64(), b2s.uint64), + (pa.float32(), b2s.float32), + (pa.float64(), b2s.float64), + (pa.bool_(), b2s.bool), + ] + for arrow_t, spec_cls in mapping: + if pa_type == arrow_t: + return spec_cls() + + # String types: determine max_length from the data + if pa_type in (pa.string(), pa.large_string(), pa.utf8(), pa.large_utf8()): + values = [v for v in arrow_col.to_pylist() if v is not None] + max_len = max((len(v) for v in values), default=1) + return b2s.string(max_length=max(max_len, 1)) + + raise TypeError( + f"No blosc2 spec for Arrow type {pa_type!r}. " + "Supported: int8/16/32/64, uint8/16/32/64, float32/64, bool, string." + ) + + # Build CompiledSchema from Arrow schema + columns: list[CompiledColumn] = [] + for field in arrow_table.schema: + name = field.name + _validate_column_name(name) + spec = _arrow_type_to_spec(field.type, arrow_table.column(name)) + col_config = ColumnConfig(cparams=None, dparams=None, chunks=None, blocks=None) + columns.append( + CompiledColumn( + name=name, + py_type=spec.python_type, + spec=spec, + dtype=spec.dtype, + default=MISSING, + config=col_config, + display_width=compute_display_width(spec), + ) + ) + + schema = CompiledSchema( + row_cls=None, + columns=columns, + columns_by_name={col.name: col for col in columns}, + ) + + n = len(arrow_table) + capacity = max(n, 1) + default_chunks, default_blocks = compute_chunks_blocks((capacity,)) + mem_storage = InMemoryTableStorage() + + new_valid = mem_storage.create_valid_rows( + shape=(capacity,), + chunks=default_chunks, + blocks=default_blocks, + ) + new_cols: dict[str, blosc2.NDArray] = {} + for col in columns: + new_cols[col.name] = mem_storage.create_column( + col.name, + dtype=col.dtype, + shape=(capacity,), + chunks=default_chunks, + blocks=default_blocks, + cparams=None, + dparams=None, + ) + + obj = cls.__new__(cls) + obj._row_type = None + obj._validate = False + obj._table_cparams = None + obj._table_dparams = None + obj._storage = mem_storage + obj._read_only = False + obj._schema = schema + obj._cols = new_cols + obj._col_widths = {col.name: max(len(col.name), col.display_width) for col in columns} + obj.col_names = [col.name for col in columns] + obj.row = _RowIndexer(obj) + obj.auto_compact = False + obj.base = None + obj._valid_rows = new_valid + obj._n_rows = 0 + obj._last_pos = 0 + + if n > 0: + # Write each column directly — one bulk slice assignment per column. + # String columns (dtype.kind == 'U') can't go through Arrow's zero-copy + # to_numpy(), so we convert via to_pylist() and let NumPy handle the + # fixed-width unicode coercion. All other types use zero-copy numpy. + for col in columns: + arrow_col = arrow_table.column(col.name) + if col.dtype.kind in "US": + arr = np.array(arrow_col.to_pylist(), dtype=col.dtype) + else: + arr = arrow_col.to_numpy(zero_copy_only=False).astype(col.dtype) + new_cols[col.name][:n] = arr + + new_valid[:n] = True + obj._n_rows = n + obj._last_pos = n + + return obj + + # ------------------------------------------------------------------ + # CSV interop + # ------------------------------------------------------------------ + + def to_csv(self, path: str, *, header: bool = True, sep: str = ",") -> None: + """Write all live rows to a CSV file. + + Uses Python's stdlib ``csv`` module — no extra dependency required. + Each column is materialised once via :meth:`Column.to_numpy`; rows + are then written one at a time. + + Parameters + ---------- + path: + Destination file path. Created or overwritten. + header: + If ``True`` (default), write column names as the first row. + sep: + Field delimiter. Defaults to ``","``; use ``"\\t"`` for TSV. + """ + import csv + + arrays = [self[name].to_numpy() for name in self.col_names] + + with open(path, "w", newline="") as f: + writer = csv.writer(f, delimiter=sep) + if header: + writer.writerow(self.col_names) + for row in zip(*arrays, strict=True): + writer.writerow(row) + + @classmethod + def from_csv( + cls, + path: str, + row_cls, + *, + header: bool = True, + sep: str = ",", + ) -> CTable: + """Build a :class:`CTable` from a CSV file. + + Schema comes from *row_cls* (a dataclass) — CTable is always typed. + All rows are read in a single pass into per-column Python lists, then + each column is bulk-written into a pre-allocated NDArray (one slice + assignment per column, no ``extend()``). + + Parameters + ---------- + path: + Source CSV file path. + row_cls: + A dataclass whose fields define the column names and types. + header: + If ``True`` (default), the first row is treated as a header and + skipped. Column order in the file must match *row_cls* field + order regardless. + sep: + Field delimiter. Defaults to ``","``; use ``"\\t"`` for TSV. + + Returns + ------- + CTable + A new in-memory CTable containing all rows from the CSV file. + + Raises + ------ + TypeError + If *row_cls* is not a dataclass. + ValueError + If a row has a different number of fields than the schema. + """ + import csv + + schema = compile_schema(row_cls) + ncols = len(schema.columns) + + # Accumulate values per column as Python lists (one pass through file) + col_data: list[list] = [[] for _ in range(ncols)] + + with open(path, newline="") as f: + reader = csv.reader(f, delimiter=sep) + if header: + next(reader) + for lineno, row in enumerate(reader, start=2 if header else 1): + if len(row) != ncols: + raise ValueError(f"Line {lineno}: expected {ncols} fields, got {len(row)}.") + for i, val in enumerate(row): + col_data[i].append(val) + + n = len(col_data[0]) if ncols > 0 else 0 + capacity = max(n, 1) + default_chunks, default_blocks = compute_chunks_blocks((capacity,)) + mem_storage = InMemoryTableStorage() + + new_valid = mem_storage.create_valid_rows( + shape=(capacity,), + chunks=default_chunks, + blocks=default_blocks, + ) + new_cols: dict[str, blosc2.NDArray] = {} + for col in schema.columns: + new_cols[col.name] = mem_storage.create_column( + col.name, + dtype=col.dtype, + shape=(capacity,), + chunks=default_chunks, + blocks=default_blocks, + cparams=None, + dparams=None, + ) + + obj = cls.__new__(cls) + obj._row_type = row_cls + obj._validate = True + obj._table_cparams = None + obj._table_dparams = None + obj._storage = mem_storage + obj._read_only = False + obj._schema = schema + obj._cols = new_cols + obj._col_widths = {col.name: max(len(col.name), col.display_width) for col in schema.columns} + obj.col_names = [col.name for col in schema.columns] + obj.row = _RowIndexer(obj) + obj.auto_compact = False + obj.base = None + obj._valid_rows = new_valid + obj._n_rows = 0 + obj._last_pos = 0 + + if n > 0: + for i, col in enumerate(schema.columns): + if col.dtype == np.bool_: + # np.array(["False"], dtype=bool) treats any non-empty + # string as True. Parse "True"/"False"/"1"/"0" explicitly. + arr = np.array( + [v.strip() in ("True", "true", "1") for v in col_data[i]], + dtype=np.bool_, + ) + else: + arr = np.array(col_data[i], dtype=col.dtype) + new_cols[col.name][:n] = arr + new_valid[:n] = True + obj._n_rows = n + obj._last_pos = n + + return obj + # ------------------------------------------------------------------ # Schema mutations: add / drop / rename columns # ------------------------------------------------------------------ @@ -1497,6 +1989,173 @@ def compact(self): self._valid_rows[self._n_rows :] = False self._last_pos = self._n_rows # next write goes right after live rows + def _normalise_sort_keys( + self, + cols: str | list[str], + ascending: bool | list[bool], + ) -> tuple[list[str], list[bool]]: + """Validate and normalise sort key arguments; return (cols, ascending).""" + if isinstance(cols, str): + cols = [cols] + if isinstance(ascending, bool): + ascending = [ascending] * len(cols) + if len(cols) != len(ascending): + raise ValueError( + f"'ascending' must have the same length as 'cols' ({len(cols)}), got {len(ascending)}." + ) + for name in cols: + if name not in self._cols: + raise KeyError(f"No column named {name!r}. Available: {self.col_names}") + dtype = self._cols[name].dtype + if np.issubdtype(dtype, np.complexfloating): + raise TypeError( + f"Column {name!r} has complex dtype {dtype} which does not support ordering." + ) + return cols, ascending + + def _build_lex_keys( + self, + cols: list[str], + ascending: list[bool], + live_pos: np.ndarray, + n: int, + ) -> list[np.ndarray]: + """Build the key list for np.lexsort (innermost = last = primary key).""" + lex_keys = [] + for name, asc in zip(reversed(cols), reversed(ascending), strict=True): + raw = self._cols[name][live_pos] + if not asc: + if raw.dtype.kind in "US": + # strings can't be negated — invert via rank + rank = np.argsort(np.argsort(raw, stable=True), stable=True) + lex_keys.append((n - 1 - rank).astype(np.intp)) + elif np.issubdtype(raw.dtype, np.unsignedinteger): + lex_keys.append(-raw.astype(np.int64)) + else: + lex_keys.append(-raw) + else: + lex_keys.append(raw) + return lex_keys + + def sort_by( + self, + cols: str | list[str], + ascending: bool | list[bool] = True, + *, + inplace: bool = False, + ) -> CTable: + """Return a copy of the table sorted by one or more columns. + + Parameters + ---------- + cols: + Column name or list of column names to sort by. When multiple + columns are given, the first is the primary key, the second is + the tiebreaker, and so on. + ascending: + Sort direction. A single bool applies to all keys; a list must + have the same length as *cols*. + inplace: + If ``True``, rewrite the physical data in place and return + ``self`` (like :meth:`compact` but sorted). If ``False`` + (default), return a new in-memory CTable leaving this one + untouched. + + Raises + ------ + ValueError + If called on a view or a read-only table when ``inplace=True``. + KeyError + If any column name is not found. + TypeError + If a column used as a sort key does not support ordering + (e.g. complex numbers). + """ + if self.base is not None: + raise ValueError("Cannot sort a view. Materialise it first with .to_table() or sort the parent.") + if inplace and self._read_only: + raise ValueError("Table is read-only (opened with mode='r').") + + cols, ascending = self._normalise_sort_keys(cols, ascending) + + # Live physical positions + valid_np = self._valid_rows[:] + live_pos = np.where(valid_np)[0] + n = len(live_pos) + + if n == 0: + if inplace: + return self + return self._empty_copy() + + order = np.lexsort(self._build_lex_keys(cols, ascending, live_pos, n)) + + sorted_pos = live_pos[order] + + if inplace: + for _col_name, arr in self._cols.items(): + arr[:n] = arr[sorted_pos] + self._valid_rows[:n] = True + self._valid_rows[n:] = False + self._n_rows = n + self._last_pos = n + return self + else: + # Build a new in-memory table with the sorted rows + result = self._empty_copy() + for col_name, arr in self._cols.items(): + result._cols[col_name][:n] = arr[sorted_pos] + result._valid_rows[:n] = True + result._valid_rows[n:] = False + result._n_rows = n + result._last_pos = n + return result + + def _empty_copy(self) -> CTable: + """Return a new empty in-memory CTable with the same schema and capacity.""" + from blosc2 import compute_chunks_blocks + + capacity = max(self._n_rows, 1) + default_chunks, default_blocks = compute_chunks_blocks((capacity,)) + mem_storage = InMemoryTableStorage() + + new_valid = mem_storage.create_valid_rows( + shape=(capacity,), + chunks=default_chunks, + blocks=default_blocks, + ) + new_cols = {} + for col in self._schema.columns: + col_storage = self._resolve_column_storage(col, default_chunks, default_blocks) + new_cols[col.name] = mem_storage.create_column( + col.name, + dtype=col.dtype, + shape=(capacity,), + chunks=col_storage["chunks"], + blocks=col_storage["blocks"], + cparams=col_storage.get("cparams"), + dparams=col_storage.get("dparams"), + ) + + obj = CTable.__new__(CTable) + obj._schema = self._schema + obj._row_type = self._row_type + obj._table_cparams = self._table_cparams + obj._table_dparams = self._table_dparams + obj._storage = mem_storage + obj._valid_rows = new_valid + obj._cols = new_cols + obj._col_widths = self._col_widths + obj.col_names = [col.name for col in self._schema.columns] + obj.row = _RowIndexer(obj) + obj._n_rows = 0 + obj._last_pos = None + obj._read_only = False + obj.base = None + obj.auto_compact = self.auto_compact + obj._validate = self._validate + return obj + # ------------------------------------------------------------------ # Properties / info # ------------------------------------------------------------------ diff --git a/tests/ctable/test_arrow_interop.py b/tests/ctable/test_arrow_interop.py new file mode 100644 index 00000000..9d997030 --- /dev/null +++ b/tests/ctable/test_arrow_interop.py @@ -0,0 +1,225 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +"""Tests for CTable.to_arrow() and CTable.from_arrow().""" + +from dataclasses import dataclass + +import numpy as np +import pytest + +import blosc2 +from blosc2 import CTable + +pa = pytest.importorskip("pyarrow") + + +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + score: float = blosc2.field(blosc2.float64(ge=0, le=100), default=0.0) + active: bool = blosc2.field(blosc2.bool(), default=True) + label: str = blosc2.field(blosc2.string(max_length=16), default="") + + +DATA10 = [(i, float(i * 10 % 100), i % 2 == 0, f"r{i}") for i in range(10)] + + +# =========================================================================== +# to_arrow() +# =========================================================================== + + +def test_to_arrow_returns_pyarrow_table(): + t = CTable(Row, new_data=DATA10) + at = t.to_arrow() + assert isinstance(at, pa.Table) + + +def test_to_arrow_column_names(): + t = CTable(Row, new_data=DATA10) + at = t.to_arrow() + assert at.column_names == ["id", "score", "active", "label"] + + +def test_to_arrow_row_count(): + t = CTable(Row, new_data=DATA10) + at = t.to_arrow() + assert len(at) == 10 + + +def test_to_arrow_int_values(): + t = CTable(Row, new_data=DATA10) + at = t.to_arrow() + np.testing.assert_array_equal(at["id"].to_pylist(), [r[0] for r in DATA10]) + + +def test_to_arrow_float_values(): + t = CTable(Row, new_data=DATA10) + at = t.to_arrow() + np.testing.assert_allclose(at["score"].to_pylist(), [r[1] for r in DATA10]) + + +def test_to_arrow_bool_values(): + t = CTable(Row, new_data=DATA10) + at = t.to_arrow() + assert at["active"].to_pylist() == [r[2] for r in DATA10] + + +def test_to_arrow_string_values(): + t = CTable(Row, new_data=DATA10) + at = t.to_arrow() + assert at["label"].to_pylist() == [r[3] for r in DATA10] + + +def test_to_arrow_string_type(): + t = CTable(Row, new_data=DATA10) + at = t.to_arrow() + assert at.schema.field("label").type == pa.string() + + +def test_to_arrow_skips_deleted_rows(): + t = CTable(Row, new_data=DATA10) + t.delete([0, 1]) + at = t.to_arrow() + assert len(at) == 8 + assert at["id"].to_pylist() == list(range(2, 10)) + + +def test_to_arrow_empty_table(): + t = CTable(Row) + at = t.to_arrow() + assert len(at) == 0 + assert at.column_names == ["id", "score", "active", "label"] + + +def test_to_arrow_select_view(): + t = CTable(Row, new_data=DATA10) + at = t.select(["id", "score"]).to_arrow() + assert at.column_names == ["id", "score"] + assert len(at) == 10 + + +def test_to_arrow_where_view(): + t = CTable(Row, new_data=DATA10) + at = t.where(t["id"] > 4).to_arrow() + assert len(at) == 5 + + +# =========================================================================== +# from_arrow() +# =========================================================================== + + +def test_from_arrow_returns_ctable(): + t = CTable(Row, new_data=DATA10) + at = t.to_arrow() + t2 = CTable.from_arrow(at) + assert isinstance(t2, CTable) + + +def test_from_arrow_row_count(): + t = CTable(Row, new_data=DATA10) + at = t.to_arrow() + t2 = CTable.from_arrow(at) + assert len(t2) == 10 + + +def test_from_arrow_column_names(): + t = CTable(Row, new_data=DATA10) + at = t.to_arrow() + t2 = CTable.from_arrow(at) + assert t2.col_names == ["id", "score", "active", "label"] + + +def test_from_arrow_int_values(): + t = CTable(Row, new_data=DATA10) + at = t.to_arrow() + t2 = CTable.from_arrow(at) + np.testing.assert_array_equal(t2["id"].to_numpy(), t["id"].to_numpy()) + + +def test_from_arrow_float_values(): + t = CTable(Row, new_data=DATA10) + at = t.to_arrow() + t2 = CTable.from_arrow(at) + np.testing.assert_allclose(t2["score"].to_numpy(), t["score"].to_numpy()) + + +def test_from_arrow_bool_values(): + t = CTable(Row, new_data=DATA10) + at = t.to_arrow() + t2 = CTable.from_arrow(at) + np.testing.assert_array_equal(t2["active"].to_numpy(), t["active"].to_numpy()) + + +def test_from_arrow_string_values(): + t = CTable(Row, new_data=DATA10) + at = t.to_arrow() + t2 = CTable.from_arrow(at) + assert t2["label"].to_numpy().tolist() == t["label"].to_numpy().tolist() + + +def test_from_arrow_empty_table(): + schema = pa.schema( + [ + pa.field("id", pa.int64()), + pa.field("val", pa.float64()), + ] + ) + at = pa.table({"id": pa.array([], type=pa.int64()), "val": pa.array([], type=pa.float64())}) + t = CTable.from_arrow(at) + assert len(t) == 0 + assert t.col_names == ["id", "val"] + + +def test_from_arrow_roundtrip(): + """to_arrow then from_arrow preserves all values.""" + t = CTable(Row, new_data=DATA10) + t2 = CTable.from_arrow(t.to_arrow()) + for name in ["id", "score", "active"]: + np.testing.assert_array_equal(t2[name].to_numpy(), t[name].to_numpy()) + assert t2["label"].to_numpy().tolist() == t["label"].to_numpy().tolist() + + +def test_from_arrow_all_numeric_types(): + """All integer and float Arrow types map to correct blosc2 specs.""" + at = pa.table( + { + "i8": pa.array([1, 2, 3], type=pa.int8()), + "i16": pa.array([1, 2, 3], type=pa.int16()), + "i32": pa.array([1, 2, 3], type=pa.int32()), + "i64": pa.array([1, 2, 3], type=pa.int64()), + "u8": pa.array([1, 2, 3], type=pa.uint8()), + "u16": pa.array([1, 2, 3], type=pa.uint16()), + "u32": pa.array([1, 2, 3], type=pa.uint32()), + "u64": pa.array([1, 2, 3], type=pa.uint64()), + "f32": pa.array([1.0, 2.0, 3.0], type=pa.float32()), + "f64": pa.array([1.0, 2.0, 3.0], type=pa.float64()), + } + ) + t = CTable.from_arrow(at) + assert len(t) == 3 + assert t.col_names == list(at.column_names) + + +def test_from_arrow_string_max_length(): + """String max_length is set from the longest value in the data.""" + at = pa.table({"name": pa.array(["hi", "hello world", "!"], type=pa.string())}) + t = CTable.from_arrow(at) + # "hello world" is 11 chars — stored dtype must accommodate it + assert t["name"].dtype.itemsize // 4 >= 11 + + +def test_from_arrow_unsupported_type_raises(): + at = pa.table({"ts": pa.array([1, 2, 3], type=pa.timestamp("s"))}) + with pytest.raises(TypeError, match="No blosc2 spec"): + CTable.from_arrow(at) + + +if __name__ == "__main__": + pytest.main(["-v", __file__]) diff --git a/tests/ctable/test_csv_interop.py b/tests/ctable/test_csv_interop.py new file mode 100644 index 00000000..7b07277a --- /dev/null +++ b/tests/ctable/test_csv_interop.py @@ -0,0 +1,235 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +"""Tests for CTable.to_csv() and CTable.from_csv().""" + +import csv +import os +from dataclasses import dataclass + +import numpy as np +import pytest + +import blosc2 +from blosc2 import CTable + + +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + score: float = blosc2.field(blosc2.float64(ge=0, le=100), default=0.0) + active: bool = blosc2.field(blosc2.bool(), default=True) + label: str = blosc2.field(blosc2.string(max_length=16), default="") + + +DATA10 = [(i, float(i * 10 % 100), i % 2 == 0, f"r{i}") for i in range(10)] + + +@pytest.fixture +def tmp_csv(tmp_path): + return str(tmp_path / "table.csv") + + +@pytest.fixture +def table10(): + return CTable(Row, new_data=DATA10) + + +# =========================================================================== +# to_csv() +# =========================================================================== + + +def test_to_csv_creates_file(table10, tmp_csv): + table10.to_csv(tmp_csv) + assert os.path.exists(tmp_csv) + + +def test_to_csv_header_row(table10, tmp_csv): + table10.to_csv(tmp_csv) + with open(tmp_csv) as f: + first = f.readline().strip() + assert first == "id,score,active,label" + + +def test_to_csv_row_count(table10, tmp_csv): + table10.to_csv(tmp_csv) + with open(tmp_csv) as f: + rows = list(csv.reader(f)) + assert len(rows) == 11 # 1 header + 10 data + + +def test_to_csv_no_header(table10, tmp_csv): + table10.to_csv(tmp_csv, header=False) + with open(tmp_csv) as f: + rows = list(csv.reader(f)) + assert len(rows) == 10 + + +def test_to_csv_int_values(table10, tmp_csv): + table10.to_csv(tmp_csv) + with open(tmp_csv) as f: + reader = csv.DictReader(f) + ids = [int(row["id"]) for row in reader] + assert ids == list(range(10)) + + +def test_to_csv_float_values(table10, tmp_csv): + table10.to_csv(tmp_csv) + with open(tmp_csv) as f: + reader = csv.DictReader(f) + scores = [float(row["score"]) for row in reader] + assert scores == [r[1] for r in DATA10] + + +def test_to_csv_bool_values(table10, tmp_csv): + table10.to_csv(tmp_csv) + with open(tmp_csv) as f: + reader = csv.DictReader(f) + actives = [row["active"] for row in reader] + # numpy bool serialises as "True"/"False" + assert actives == [str(r[2]) for r in DATA10] + + +def test_to_csv_string_values(table10, tmp_csv): + table10.to_csv(tmp_csv) + with open(tmp_csv) as f: + reader = csv.DictReader(f) + labels = [row["label"] for row in reader] + assert labels == [r[3] for r in DATA10] + + +def test_to_csv_custom_separator(table10, tmp_csv): + table10.to_csv(tmp_csv, sep="\t") + with open(tmp_csv) as f: + first = f.readline().strip() + assert "\t" in first + assert "," not in first + + +def test_to_csv_skips_deleted_rows(table10, tmp_csv): + table10.delete([0, 1]) + table10.to_csv(tmp_csv) + with open(tmp_csv) as f: + rows = list(csv.reader(f)) + assert len(rows) == 9 # 1 header + 8 live rows + assert rows[1][0] == "2" # first live id + + +def test_to_csv_empty_table(tmp_csv): + t = CTable(Row) + t.to_csv(tmp_csv) + with open(tmp_csv) as f: + rows = list(csv.reader(f)) + assert rows == [["id", "score", "active", "label"]] + + +def test_to_csv_select_view(table10, tmp_csv): + table10.select(["id", "label"]).to_csv(tmp_csv) + with open(tmp_csv) as f: + reader = csv.DictReader(f) + rows = list(reader) + assert list(rows[0].keys()) == ["id", "label"] + assert len(rows) == 10 + + +# =========================================================================== +# from_csv() +# =========================================================================== + + +def test_from_csv_returns_ctable(table10, tmp_csv): + table10.to_csv(tmp_csv) + t2 = CTable.from_csv(tmp_csv, Row) + assert isinstance(t2, CTable) + + +def test_from_csv_row_count(table10, tmp_csv): + table10.to_csv(tmp_csv) + t2 = CTable.from_csv(tmp_csv, Row) + assert len(t2) == 10 + + +def test_from_csv_column_names(table10, tmp_csv): + table10.to_csv(tmp_csv) + t2 = CTable.from_csv(tmp_csv, Row) + assert t2.col_names == ["id", "score", "active", "label"] + + +def test_from_csv_int_values(table10, tmp_csv): + table10.to_csv(tmp_csv) + t2 = CTable.from_csv(tmp_csv, Row) + np.testing.assert_array_equal(t2["id"].to_numpy(), table10["id"].to_numpy()) + + +def test_from_csv_float_values(table10, tmp_csv): + table10.to_csv(tmp_csv) + t2 = CTable.from_csv(tmp_csv, Row) + np.testing.assert_allclose(t2["score"].to_numpy(), table10["score"].to_numpy()) + + +def test_from_csv_bool_values(table10, tmp_csv): + table10.to_csv(tmp_csv) + t2 = CTable.from_csv(tmp_csv, Row) + # bool is serialised as "True"/"False"; np.array(..., dtype=bool) parses that + np.testing.assert_array_equal(t2["active"].to_numpy(), table10["active"].to_numpy()) + + +def test_from_csv_string_values(table10, tmp_csv): + table10.to_csv(tmp_csv) + t2 = CTable.from_csv(tmp_csv, Row) + assert t2["label"].to_numpy().tolist() == table10["label"].to_numpy().tolist() + + +def test_from_csv_no_header(table10, tmp_csv): + table10.to_csv(tmp_csv, header=False) + t2 = CTable.from_csv(tmp_csv, Row, header=False) + assert len(t2) == 10 + np.testing.assert_array_equal(t2["id"].to_numpy(), table10["id"].to_numpy()) + + +def test_from_csv_custom_separator(table10, tmp_csv): + table10.to_csv(tmp_csv, sep="\t") + t2 = CTable.from_csv(tmp_csv, Row, sep="\t") + assert len(t2) == 10 + + +def test_from_csv_empty_file(tmp_csv): + with open(tmp_csv, "w") as f: + f.write("id,score,active,label\n") + t = CTable.from_csv(tmp_csv, Row) + assert len(t) == 0 + assert t.col_names == ["id", "score", "active", "label"] + + +def test_from_csv_roundtrip(table10, tmp_csv): + """to_csv then from_csv preserves all values.""" + table10.to_csv(tmp_csv) + t2 = CTable.from_csv(tmp_csv, Row) + for name in ["id", "score"]: + np.testing.assert_array_equal(t2[name].to_numpy(), table10[name].to_numpy()) + np.testing.assert_array_equal(t2["active"].to_numpy(), table10["active"].to_numpy()) + assert t2["label"].to_numpy().tolist() == table10["label"].to_numpy().tolist() + + +def test_from_csv_wrong_field_count_raises(tmp_csv): + with open(tmp_csv, "w") as f: + f.write("id,score,active,label\n") + f.write("1,2.0\n") # only 2 fields instead of 4 + with pytest.raises(ValueError, match="expected 4 fields"): + CTable.from_csv(tmp_csv, Row) + + +def test_from_csv_not_dataclass_raises(tmp_csv): + with open(tmp_csv, "w") as f: + f.write("id\n1\n") + with pytest.raises(TypeError): + CTable.from_csv(tmp_csv, int) + + +if __name__ == "__main__": + pytest.main(["-v", __file__]) diff --git a/tests/ctable/test_select_describe_cov.py b/tests/ctable/test_select_describe_cov.py new file mode 100644 index 00000000..5e51d6d3 --- /dev/null +++ b/tests/ctable/test_select_describe_cov.py @@ -0,0 +1,276 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +"""Tests for select(), describe(), and cov().""" + +from dataclasses import dataclass + +import numpy as np +import pytest + +import blosc2 +from blosc2 import CTable + + +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + score: float = blosc2.field(blosc2.float64(ge=0, le=100), default=0.0) + active: bool = blosc2.field(blosc2.bool(), default=True) + label: str = blosc2.field(blosc2.string(max_length=16), default="") + + +DATA10 = [(i, float(i * 10 % 100), i % 2 == 0, f"r{i}") for i in range(10)] + + +# =========================================================================== +# select() +# =========================================================================== + + +def test_select_returns_subset_of_columns(): + t = CTable(Row, new_data=DATA10) + v = t.select(["id", "score"]) + assert v.col_names == ["id", "score"] + assert v.ncols == 2 + + +def test_select_preserves_caller_order(): + t = CTable(Row, new_data=DATA10) + v = t.select(["score", "id"]) + assert v.col_names == ["score", "id"] + + +def test_select_shares_data_no_copy(): + t = CTable(Row, new_data=DATA10) + v = t.select(["id", "score"]) + # Same NDArray objects — no copy + assert v._cols["id"] is t._cols["id"] + assert v._cols["score"] is t._cols["score"] + + +def test_select_row_count_unchanged(): + t = CTable(Row, new_data=DATA10) + v = t.select(["id", "score"]) + assert len(v) == 10 + + +def test_select_data_correct(): + t = CTable(Row, new_data=DATA10) + v = t.select(["id", "score"]) + np.testing.assert_array_equal(v["id"].to_numpy(), t["id"].to_numpy()) + np.testing.assert_array_equal(v["score"].to_numpy(), t["score"].to_numpy()) + + +def test_select_base_is_parent(): + t = CTable(Row, new_data=DATA10) + v = t.select(["id"]) + assert v.base is t + + +def test_select_combined_with_where(): + t = CTable(Row, new_data=DATA10) + v = t.select(["id", "score"]).where(t["id"] > 4) + assert len(v) == 5 + assert v.col_names == ["id", "score"] + + +def test_select_combined_with_deletions(): + t = CTable(Row, new_data=DATA10) + t.delete([0, 1]) + v = t.select(["id", "score"]) + assert len(v) == 8 + np.testing.assert_array_equal(v["id"].to_numpy(), t["id"].to_numpy()) + + +def test_select_schema_updated(): + t = CTable(Row, new_data=DATA10) + v = t.select(["id", "score"]) + assert list(v.schema.columns_by_name.keys()) == ["id", "score"] + assert "active" not in v.schema.columns_by_name + assert "label" not in v.schema.columns_by_name + + +def test_select_blocks_structural_mutations(): + t = CTable(Row, new_data=DATA10) + v = t.select(["id", "score"]) + with pytest.raises(TypeError): + v.append((99, 50.0, True, "x")) + + +def test_select_empty_raises(): + t = CTable(Row, new_data=DATA10) + with pytest.raises(ValueError, match="at least one"): + t.select([]) + + +def test_select_unknown_column_raises(): + t = CTable(Row, new_data=DATA10) + with pytest.raises(KeyError): + t.select(["id", "nonexistent"]) + + +def test_select_single_column(): + t = CTable(Row, new_data=DATA10) + v = t.select(["score"]) + assert v.col_names == ["score"] + assert len(v) == 10 + + +# =========================================================================== +# describe() +# =========================================================================== + + +def test_describe_runs_without_error(capsys): + t = CTable(Row, new_data=DATA10) + t.describe() + out = capsys.readouterr().out + assert "id" in out + assert "score" in out + assert "active" in out + assert "label" in out + + +def test_describe_shows_row_count(capsys): + t = CTable(Row, new_data=DATA10) + t.describe() + out = capsys.readouterr().out + assert "10" in out + + +def test_describe_numeric_stats(capsys): + t = CTable(Row, new_data=DATA10) + t.describe() + out = capsys.readouterr().out + assert "mean" in out + assert "std" in out + assert "min" in out + assert "max" in out + + +def test_describe_bool_stats(capsys): + t = CTable(Row, new_data=DATA10) + t.describe() + out = capsys.readouterr().out + assert "true" in out + assert "false" in out + + +def test_describe_string_stats(capsys): + t = CTable(Row, new_data=DATA10) + t.describe() + out = capsys.readouterr().out + assert "unique" in out + + +def test_describe_empty_table(capsys): + t = CTable(Row) + t.describe() + out = capsys.readouterr().out + assert "0 rows" in out + assert "empty" in out + + +def test_describe_on_select(capsys): + t = CTable(Row, new_data=DATA10) + t.select(["id", "score"]).describe() + out = capsys.readouterr().out + assert "id" in out + assert "score" in out + assert "active" not in out + + +# =========================================================================== +# cov() +# =========================================================================== + + +def test_cov_shape(): + t = CTable(Row, new_data=DATA10) + c = t.select(["id", "score"]).cov() + assert c.shape == (2, 2) + + +def test_cov_symmetric(): + t = CTable(Row, new_data=DATA10) + c = t.select(["id", "score"]).cov() + np.testing.assert_allclose(c, c.T) + + +def test_cov_diagonal_equals_variance(): + t = CTable(Row, new_data=DATA10) + ids = t["id"].to_numpy().astype(np.float64) + scores = t["score"].to_numpy().astype(np.float64) + c = t.select(["id", "score"]).cov() + assert c[0, 0] == pytest.approx(np.var(ids, ddof=1)) + assert c[1, 1] == pytest.approx(np.var(scores, ddof=1)) + + +def test_cov_single_column_is_scalar(): + t = CTable(Row, new_data=DATA10) + c = t.select(["id"]).cov() + assert c.shape == (1, 1) + ids = t["id"].to_numpy().astype(np.float64) + assert c[0, 0] == pytest.approx(np.var(ids, ddof=1)) + + +def test_cov_bool_column_cast_to_int(): + t = CTable(Row, new_data=DATA10) + # bool is treated as 0/1 int — should not raise + c = t.select(["id", "active"]).cov() + assert c.shape == (2, 2) + + +def test_cov_skips_deleted_rows(): + t = CTable(Row, new_data=DATA10) + t.delete([0]) # remove id=0 + ids = t["id"].to_numpy().astype(np.float64) + c = t.select(["id"]).cov() + assert c[0, 0] == pytest.approx(np.var(ids, ddof=1)) + + +def test_cov_string_column_raises(): + t = CTable(Row, new_data=DATA10) + with pytest.raises(TypeError, match="not supported"): + t.cov() # 'label' is a string column + + +def test_cov_complex_column_raises(): + @dataclass + class CRow: + val: complex = blosc2.field(blosc2.complex128()) + + t = CTable(CRow, new_data=[(1 + 2j,), (3 + 4j,)]) + with pytest.raises(TypeError, match="not supported"): + t.cov() + + +def test_cov_too_few_rows_raises(): + t = CTable(Row, new_data=[(0, 0.0, True, "a")]) + with pytest.raises(ValueError, match="2 live rows"): + t.select(["id", "score"]).cov() + + +def test_cov_after_all_deleted_raises(): + t = CTable(Row, new_data=DATA10) + t.delete(list(range(10))) + with pytest.raises(ValueError): + t.select(["id", "score"]).cov() + + +def test_cov_three_columns(): + # identity-ish: if columns are linearly independent, diagonal dominates + data = [(i, float(i), i % 2 == 0, "") for i in range(20)] + t = CTable(Row, new_data=data) + c = t.select(["id", "score", "active"]).cov() + assert c.shape == (3, 3) + np.testing.assert_allclose(c, c.T, atol=1e-10) + + +if __name__ == "__main__": + pytest.main(["-v", __file__]) diff --git a/tests/ctable/test_sort_by.py b/tests/ctable/test_sort_by.py new file mode 100644 index 00000000..34e7b146 --- /dev/null +++ b/tests/ctable/test_sort_by.py @@ -0,0 +1,270 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +"""Tests for CTable.sort_by().""" + +from dataclasses import dataclass + +import numpy as np +import pytest + +import blosc2 +from blosc2 import CTable + + +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + score: float = blosc2.field(blosc2.float64(ge=0, le=100), default=0.0) + active: bool = blosc2.field(blosc2.bool(), default=True) + + +@dataclass +class StrRow: + label: str = blosc2.field(blosc2.string(max_length=16)) + rank: int = blosc2.field(blosc2.int64(ge=0), default=0) + + +DATA = [ + (3, 80.0, True), + (1, 50.0, False), + (4, 90.0, True), + (2, 50.0, True), + (0, 70.0, False), +] + + +# =========================================================================== +# Single-column sort +# =========================================================================== + + +def test_sort_single_col_ascending(): + t = CTable(Row, new_data=DATA) + s = t.sort_by("id") + np.testing.assert_array_equal(s["id"].to_numpy(), [0, 1, 2, 3, 4]) + + +def test_sort_single_col_descending(): + t = CTable(Row, new_data=DATA) + s = t.sort_by("score", ascending=False) + np.testing.assert_array_equal(s["score"].to_numpy(), [90.0, 80.0, 70.0, 50.0, 50.0]) + + +def test_sort_bool_column(): + t = CTable(Row, new_data=DATA) + s = t.sort_by("active") + # False < True → False rows first + assert list(s["active"].to_numpy()) == [False, False, True, True, True] + + +def test_sort_string_column(): + t = CTable(StrRow, new_data=[("charlie", 3), ("alice", 1), ("dave", 4), ("bob", 2)]) + s = t.sort_by("label") + assert list(s["label"].to_numpy()) == ["alice", "bob", "charlie", "dave"] + + +def test_sort_string_column_descending(): + t = CTable(StrRow, new_data=[("charlie", 3), ("alice", 1), ("dave", 4), ("bob", 2)]) + s = t.sort_by("label", ascending=False) + assert list(s["label"].to_numpy()) == ["dave", "charlie", "bob", "alice"] + + +# =========================================================================== +# Multi-column sort +# =========================================================================== + + +def test_sort_multi_col_both_asc(): + t = CTable(Row, new_data=DATA) + s = t.sort_by(["score", "id"], ascending=[True, True]) + scores = s["score"].to_numpy() + ids = s["id"].to_numpy() + # score asc; tiebreak: id asc (both 50.0 rows → id 1 before id 2) + assert scores[0] == pytest.approx(50.0) + assert ids[0] == 1 + assert scores[1] == pytest.approx(50.0) + assert ids[1] == 2 + + +def test_sort_multi_col_mixed(): + t = CTable(Row, new_data=DATA) + s = t.sort_by(["score", "id"], ascending=[True, False]) + scores = s["score"].to_numpy() + ids = s["id"].to_numpy() + # score asc; tiebreak: id desc (both 50.0 rows → id 2 before id 1) + assert scores[0] == pytest.approx(50.0) + assert ids[0] == 2 + assert scores[1] == pytest.approx(50.0) + assert ids[1] == 1 + + +def test_sort_multi_col_ascending_list_notation(): + """Passing ascending=True (single bool) applies to all keys.""" + t = CTable(Row, new_data=DATA) + s = t.sort_by(["score", "id"], ascending=True) + np.testing.assert_array_equal(s["id"].to_numpy()[:2], [1, 2]) + + +# =========================================================================== +# Non-destructive: original table is unchanged +# =========================================================================== + + +def test_sort_does_not_modify_original(): + t = CTable(Row, new_data=DATA) + original_ids = t["id"].to_numpy().copy() + _ = t.sort_by("id") + np.testing.assert_array_equal(t["id"].to_numpy(), original_ids) + + +def test_sort_returns_new_table(): + t = CTable(Row, new_data=DATA) + s = t.sort_by("id") + assert s is not t + + +# =========================================================================== +# inplace=True +# =========================================================================== + + +def test_sort_inplace_returns_self(): + t = CTable(Row, new_data=DATA) + result = t.sort_by("id", inplace=True) + assert result is t + + +def test_sort_inplace_modifies_table(): + t = CTable(Row, new_data=DATA) + t.sort_by("id", inplace=True) + np.testing.assert_array_equal(t["id"].to_numpy(), [0, 1, 2, 3, 4]) + + +def test_sort_inplace_descending(): + t = CTable(Row, new_data=DATA) + t.sort_by("score", ascending=False, inplace=True) + assert t["score"][0] == pytest.approx(90.0) + assert t["score"][-1] == pytest.approx(50.0) + + +# =========================================================================== +# Interaction with deletions +# =========================================================================== + + +def test_sort_skips_deleted_rows(): + t = CTable(Row, new_data=DATA) + t.delete([0]) # delete id=3 (first row) + s = t.sort_by("id") + np.testing.assert_array_equal(s["id"].to_numpy(), [0, 1, 2, 4]) + assert len(s) == 4 + + +def test_sort_inplace_skips_deleted_rows(): + t = CTable(Row, new_data=DATA) + t.delete([0, 2]) # delete id=3 and id=4 + t.sort_by("id", inplace=True) + np.testing.assert_array_equal(t["id"].to_numpy(), [0, 1, 2]) + assert len(t) == 3 + + +def test_sort_all_columns_consistent(): + """All columns move together when sorted.""" + t = CTable(Row, new_data=DATA) + s = t.sort_by("id") + ids = s["id"].to_numpy() + scores = s["score"].to_numpy() + # Original DATA: id→score mapping: 0→70, 1→50, 2→50, 3→80, 4→90 + expected = {0: 70.0, 1: 50.0, 2: 50.0, 3: 80.0, 4: 90.0} + for i, v in zip(ids, scores, strict=True): + assert v == pytest.approx(expected[int(i)]) + + +# =========================================================================== +# Edge cases +# =========================================================================== + + +def test_sort_empty_table(): + t = CTable(Row) + s = t.sort_by("id") + assert len(s) == 0 + + +def test_sort_single_row(): + t = CTable(Row, new_data=[(7, 42.0, True)]) + s = t.sort_by("id") + assert s["id"][0] == 7 + + +def test_sort_already_sorted(): + data = [(i, float(i * 10), True) for i in range(5)] + t = CTable(Row, new_data=data) + s = t.sort_by("id") + np.testing.assert_array_equal(s["id"].to_numpy(), list(range(5))) + + +def test_sort_reverse_sorted(): + data = [(i, float(i * 10), True) for i in range(5, 0, -1)] + t = CTable(Row, new_data=data) + s = t.sort_by("id") + np.testing.assert_array_equal(s["id"].to_numpy(), [1, 2, 3, 4, 5]) + + +# =========================================================================== +# Error cases +# =========================================================================== + + +def test_sort_view_raises(): + t = CTable(Row, new_data=DATA) + view = t.where(t["id"] > 2) + with pytest.raises(ValueError, match="view"): + view.sort_by("id") + + +def test_sort_unknown_column_raises(): + t = CTable(Row, new_data=DATA) + with pytest.raises(KeyError): + t.sort_by("nonexistent") + + +def test_sort_complex_column_raises(): + @dataclass + class CRow: + val: complex = blosc2.field(blosc2.complex128()) + + t = CTable(CRow, new_data=[(1 + 2j,), (3 + 4j,)]) + with pytest.raises(TypeError, match="complex"): + t.sort_by("val") + + +def test_sort_ascending_length_mismatch_raises(): + t = CTable(Row, new_data=DATA) + with pytest.raises(ValueError, match="ascending"): + t.sort_by(["id", "score"], ascending=[True]) + + +def test_sort_readonly_inplace_raises(): + import os + import shutil + + path = "saved_ctable/_sort_ro_test" + os.makedirs(path, exist_ok=True) + try: + t = CTable(Row, urlpath=path, mode="w", new_data=DATA) + del t + t_ro = CTable.open(path, mode="r") + with pytest.raises(ValueError, match="read-only"): + t_ro.sort_by("id", inplace=True) + finally: + shutil.rmtree(path, ignore_errors=True) + + +if __name__ == "__main__": + pytest.main(["-v", __file__]) From 6bf1ec89a7c756122def01fd952e004d026dcd19 Mon Sep 17 00:00:00 2001 From: jorge Date: Tue, 7 Apr 2026 19:21:04 +0200 Subject: [PATCH 18/49] Persistent ctables. --- saved_ctable/climate/_cols/day_of_year.b2nd | Bin 0 -> 235860 bytes saved_ctable/climate/_cols/humidity.b2nd | Bin 0 -> 626231 bytes saved_ctable/climate/_cols/pressure.b2nd | Bin 0 -> 489816 bytes saved_ctable/climate/_cols/station_id.b2nd | Bin 0 -> 168111 bytes saved_ctable/climate/_cols/temperature.b2nd | Bin 0 -> 665497 bytes saved_ctable/climate/_cols/wind_speed.b2nd | Bin 0 -> 664042 bytes saved_ctable/climate/_meta.b2frame | Bin 0 -> 532 bytes saved_ctable/climate/_valid_rows.b2nd | Bin 0 -> 271 bytes saved_ctable/direct_test/_cols/id.b2nd | Bin 0 -> 337 bytes saved_ctable/layout_test/_cols/id.b2nd | Bin 0 -> 455 bytes saved_ctable/layout_test/_meta.b2frame | Bin 0 -> 348 bytes saved_ctable/layout_test/_meta2.b2frame | Bin 0 -> 132 bytes saved_ctable/layout_test/_valid_rows.b2nd | Bin 0 -> 263 bytes saved_ctable/multi.b2d/embed.b2e | Bin 0 -> 3242 bytes saved_ctable/people.b2d/embed.b2e | Bin 0 -> 1374 bytes saved_ctable/resize_test/id.b2nd | Bin 0 -> 290 bytes saved_ctable/schunk_test/meta.b2frame | Bin 0 -> 186 bytes saved_ctable/test.b2d/embed.b2e | Bin 0 -> 752 bytes saved_ctable/users/_cols/id.b2nd | Bin 0 -> 964 bytes saved_ctable/users/_cols/retired.b2nd | Bin 0 -> 6501 bytes saved_ctable/users/_cols/score.b2nd | Bin 0 -> 339812 bytes saved_ctable/users/_meta.b2frame | Bin 0 -> 452 bytes saved_ctable/users/_valid_rows.b2nd | Bin 0 -> 262 bytes saved_ctable/write_test.b2d/embed.b2e | Bin 0 -> 503 bytes tests/ctable/test_persistency.py | 3 ++- tests/ctable/test_schema_mutations.py | 3 ++- tests/ctable/test_sort_by.py | 3 ++- 27 files changed, 6 insertions(+), 3 deletions(-) create mode 100644 saved_ctable/climate/_cols/day_of_year.b2nd create mode 100644 saved_ctable/climate/_cols/humidity.b2nd create mode 100644 saved_ctable/climate/_cols/pressure.b2nd create 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a/saved_ctable/write_test.b2d/embed.b2e b/saved_ctable/write_test.b2d/embed.b2e new file mode 100644 index 0000000000000000000000000000000000000000..15d1545fb9d1a2a8acb8bb66ccaf2233e37a8bfe GIT binary patch literal 503 zcmbQYBFQMNC^0vc;SvJ_L(O?00R)WSmk2S0GF^u781BLtdqIjAK?F#-0>ecH9w0h= zgAJ^ZkrhmVh{>F5E8QD2FfQ|!U4Il;sMi>J@PhkXxg)}fM)~6Pi_K1K*OfpfSCxV0b@su#Q*>R literal 0 HcmV?d00001 diff --git a/tests/ctable/test_persistency.py b/tests/ctable/test_persistency.py index 961243fc..10638ca6 100644 --- a/tests/ctable/test_persistency.py +++ b/tests/ctable/test_persistency.py @@ -9,6 +9,7 @@ import json import os +import pathlib import shutil from dataclasses import dataclass @@ -29,7 +30,7 @@ class Row: active: bool = blosc2.field(blosc2.bool(), default=True) -TABLE_ROOT = "saved_ctable/test_tables" +TABLE_ROOT = str(pathlib.Path(__file__).parent / "saved_ctable" / "test_tables") @pytest.fixture(autouse=True) diff --git a/tests/ctable/test_schema_mutations.py b/tests/ctable/test_schema_mutations.py index eddeb575..ca119678 100644 --- a/tests/ctable/test_schema_mutations.py +++ b/tests/ctable/test_schema_mutations.py @@ -9,6 +9,7 @@ and the corrected view mutability model.""" import os +import pathlib import shutil from dataclasses import dataclass @@ -18,7 +19,7 @@ import blosc2 from blosc2 import CTable -TABLE_ROOT = "saved_ctable/test_schema_mutations" +TABLE_ROOT = str(pathlib.Path(__file__).parent / "saved_ctable" / "test_schema_mutations") @dataclass diff --git a/tests/ctable/test_sort_by.py b/tests/ctable/test_sort_by.py index 34e7b146..67d6c403 100644 --- a/tests/ctable/test_sort_by.py +++ b/tests/ctable/test_sort_by.py @@ -252,9 +252,10 @@ def test_sort_ascending_length_mismatch_raises(): def test_sort_readonly_inplace_raises(): import os + import pathlib import shutil - path = "saved_ctable/_sort_ro_test" + path = str(pathlib.Path(__file__).parent / "saved_ctable" / "_sort_ro_test") os.makedirs(path, exist_ok=True) try: t = CTable(Row, urlpath=path, mode="w", new_data=DATA) From 34c2eee677c8e64bb769416b9cc2400c52013a69 Mon Sep 17 00:00:00 2001 From: jorge Date: Tue, 7 Apr 2026 19:29:46 +0200 Subject: [PATCH 19/49] Colision bug fixed 1 --- tests/ctable/test_persistency.py | 520 ------------------------------- 1 file changed, 520 deletions(-) delete mode 100644 tests/ctable/test_persistency.py diff --git a/tests/ctable/test_persistency.py b/tests/ctable/test_persistency.py deleted file mode 100644 index 10638ca6..00000000 --- a/tests/ctable/test_persistency.py +++ /dev/null @@ -1,520 +0,0 @@ -####################################################################### -# Copyright (c) 2019-present, Blosc Development Team -# All rights reserved. -# -# SPDX-License-Identifier: BSD-3-Clause -####################################################################### - -"""Persistency tests for CTable: create → close → reopen round-trips.""" - -import json -import os -import pathlib -import shutil -from dataclasses import dataclass - -import pytest - -import blosc2 -from blosc2 import CTable - -# --------------------------------------------------------------------------- -# Helpers -# --------------------------------------------------------------------------- - - -@dataclass -class Row: - id: int = blosc2.field(blosc2.int64(ge=0)) - score: float = blosc2.field(blosc2.float64(ge=0, le=100), default=0.0) - active: bool = blosc2.field(blosc2.bool(), default=True) - - -TABLE_ROOT = str(pathlib.Path(__file__).parent / "saved_ctable" / "test_tables") - - -@pytest.fixture(autouse=True) -def clean_table_dir(): - """Remove test directory before each test and clean up after.""" - if os.path.exists(TABLE_ROOT): - shutil.rmtree(TABLE_ROOT) - os.makedirs(TABLE_ROOT, exist_ok=True) - yield - if os.path.exists(TABLE_ROOT): - shutil.rmtree(TABLE_ROOT) - - -def table_path(name: str) -> str: - return os.path.join(TABLE_ROOT, name) - - -# --------------------------------------------------------------------------- -# Layout: disk structure -# --------------------------------------------------------------------------- - - -def test_create_layout_files_exist(): - """Creating a persistent CTable writes the expected files.""" - path = table_path("people") - t = CTable(Row, urlpath=path, mode="w", expected_size=16) - t.append((1, 50.0, True)) - - assert os.path.exists(os.path.join(path, "_meta.b2frame")) - assert os.path.exists(os.path.join(path, "_valid_rows.b2nd")) - assert os.path.exists(os.path.join(path, "_cols", "id.b2nd")) - assert os.path.exists(os.path.join(path, "_cols", "score.b2nd")) - assert os.path.exists(os.path.join(path, "_cols", "active.b2nd")) - - -def test_schema_saved_in_meta_vlmeta(): - """Schema JSON and kind marker are present in _meta.b2frame.""" - path = table_path("people") - CTable(Row, urlpath=path, mode="w", expected_size=16) - - meta = blosc2.open(os.path.join(path, "_meta.b2frame")) - assert meta.vlmeta["kind"] == "ctable" - assert meta.vlmeta["version"] == 1 - schema = json.loads(meta.vlmeta["schema"]) - assert schema["version"] == 1 - col_names = [c["name"] for c in schema["columns"]] - assert col_names == ["id", "score", "active"] - - -# --------------------------------------------------------------------------- -# Round-trip: data survives reopen -# --------------------------------------------------------------------------- - - -def test_reopen_with_ctable_constructor(): - """Data written before close is readable after reopening via CTable(...).""" - path = table_path("rt") - t = CTable(Row, urlpath=path, mode="w", expected_size=16) - t.extend([(1, 10.0, True), (2, 20.0, False), (3, 30.0, True)]) - - t2 = CTable(Row, urlpath=path, mode="a") - assert len(t2) == 3 - assert list(t2["id"].to_numpy()) == [1, 2, 3] - assert list(t2["score"].to_numpy()) == [10.0, 20.0, 30.0] - - -def test_reopen_with_open_classmethod(): - """CTable.open() returns a read-only table with correct data.""" - path = table_path("ro") - t = CTable(Row, urlpath=path, mode="w", expected_size=16) - t.extend([(10, 50.0, True), (20, 60.0, False)]) - - t2 = CTable.open(path) - assert len(t2) == 2 - assert list(t2["id"].to_numpy()) == [10, 20] - - -def test_column_order_preserved_after_reopen(): - """Column order from the schema JSON is respected on reopen.""" - path = table_path("order") - - @dataclass - class MultiCol: - z: int = blosc2.field(blosc2.int64()) - a: float = blosc2.field(blosc2.float64(), default=0.0) - m: bool = blosc2.field(blosc2.bool(), default=True) - - t = CTable(MultiCol, urlpath=path, mode="w", expected_size=16) - t2 = CTable(MultiCol, urlpath=path, mode="a") - assert t2.col_names == ["z", "a", "m"] - - -def test_schema_constraints_preserved(): - """Reopening re-enables constraint validation from the stored schema.""" - path = table_path("constraints") - t = CTable(Row, urlpath=path, mode="w", expected_size=16) - t.append((1, 50.0, True)) - - t2 = CTable(Row, urlpath=path, mode="a") - with pytest.raises(ValueError): - t2.append((-1, 50.0, True)) # id violates ge=0 - - -# --------------------------------------------------------------------------- -# Append after reopen -# --------------------------------------------------------------------------- - - -def test_append_after_reopen(): - """Appending to a reopened table grows the row count correctly.""" - path = table_path("append") - t = CTable(Row, urlpath=path, mode="w", expected_size=16) - t.extend([(1, 10.0, True), (2, 20.0, False)]) - - t2 = CTable(Row, urlpath=path, mode="a") - t2.append((3, 30.0, True)) - assert len(t2) == 3 - assert t2.row[2].id[0] == 3 - - # Verify it's visible in a third open - t3 = CTable(Row, urlpath=path, mode="a") - assert len(t3) == 3 - assert list(t3["id"].to_numpy()) == [1, 2, 3] - - -def test_extend_after_reopen(): - """extend() after reopen persists all new rows.""" - path = table_path("extend") - t = CTable(Row, urlpath=path, mode="w", expected_size=64) - t.extend([(i, float(i), True) for i in range(5)]) - - t2 = CTable(Row, urlpath=path, mode="a") - t2.extend([(i, float(i), i % 2 == 0) for i in range(5, 10)]) - assert len(t2) == 10 - - t3 = CTable(Row, urlpath=path, mode="a") - assert len(t3) == 10 - assert list(t3["id"].to_numpy()) == list(range(10)) - - -# --------------------------------------------------------------------------- -# Delete after reopen -# --------------------------------------------------------------------------- - - -def test_delete_after_reopen(): - """Deletions after reopen are reflected in subsequent opens.""" - path = table_path("delete") - t = CTable(Row, urlpath=path, mode="w", expected_size=16) - t.extend([(1, 10.0, True), (2, 20.0, False), (3, 30.0, True)]) - - t2 = CTable(Row, urlpath=path, mode="a") - t2.delete(1) # remove row with id=2 - assert len(t2) == 2 - - t3 = CTable(Row, urlpath=path, mode="a") - assert len(t3) == 2 - assert list(t3["id"].to_numpy()) == [1, 3] - - -# --------------------------------------------------------------------------- -# valid_rows persistence -# --------------------------------------------------------------------------- - - -def test_valid_rows_persisted(): - """The tombstone mask (_valid_rows) is correctly stored and loaded.""" - path = table_path("vr") - t = CTable(Row, urlpath=path, mode="w", expected_size=16) - t.extend([(1, 10.0, True), (2, 20.0, False), (3, 30.0, True)]) - t.delete(1) # mark row 1 (id=2) as invalid - - # _valid_rows on disk: slots 0 and 2 are True, slot 1 is False - vr = blosc2.open(os.path.join(path, "_valid_rows.b2nd")) - raw = vr[:3] - assert raw[0] - assert not raw[1] - assert raw[2] - - -# --------------------------------------------------------------------------- -# mode="w" overwrites existing table -# --------------------------------------------------------------------------- - - -def test_mode_w_overwrites_existing(): - """mode='w' on an existing path creates a fresh table.""" - path = table_path("overwrite") - t = CTable(Row, urlpath=path, mode="w", expected_size=16) - t.extend([(1, 10.0, True), (2, 20.0, False)]) - - t2 = CTable(Row, urlpath=path, mode="w", expected_size=16) - assert len(t2) == 0 - - t3 = CTable(Row, urlpath=path, mode="a") - assert len(t3) == 0 - - -# --------------------------------------------------------------------------- -# Read-only mode -# --------------------------------------------------------------------------- - - -def test_read_only_mode_rejects_append(): - path = table_path("ro_append") - CTable(Row, urlpath=path, mode="w", expected_size=16) - - t = CTable.open(path, mode="r") - with pytest.raises(ValueError, match="read-only"): - t.append((1, 50.0, True)) - - -def test_read_only_mode_rejects_extend(): - path = table_path("ro_extend") - CTable(Row, urlpath=path, mode="w", expected_size=16) - - t = CTable.open(path, mode="r") - with pytest.raises(ValueError, match="read-only"): - t.extend([(1, 50.0, True)]) - - -def test_read_only_mode_rejects_delete(): - path = table_path("ro_delete") - t = CTable(Row, urlpath=path, mode="w", expected_size=16) - t.append((1, 50.0, True)) - - t2 = CTable.open(path, mode="r") - with pytest.raises(ValueError, match="read-only"): - t2.delete(0) - - -def test_read_only_mode_rejects_compact(): - path = table_path("ro_compact") - t = CTable(Row, urlpath=path, mode="w", expected_size=16) - t.append((1, 50.0, True)) - - t2 = CTable.open(path, mode="r") - with pytest.raises(ValueError, match="read-only"): - t2.compact() - - -def test_read_only_allows_reads(): - """Read-only table: row access, column access, head/tail, where all work.""" - path = table_path("ro_reads") - t = CTable(Row, urlpath=path, mode="w", expected_size=16) - t.extend([(1, 10.0, True), (2, 20.0, False), (3, 30.0, True)]) - - t2 = CTable.open(path, mode="r") - assert len(t2) == 3 - assert t2.row[0].id[0] == 1 - assert list(t2["score"].to_numpy()) == [10.0, 20.0, 30.0] - assert len(t2.head(2)) == 2 - assert len(t2.tail(1)) == 1 - view = t2.where(t2["id"] > 1) - assert len(view) == 2 - - -# --------------------------------------------------------------------------- -# open() error cases -# --------------------------------------------------------------------------- - - -def test_open_nonexistent_raises(): - with pytest.raises(FileNotFoundError): - CTable.open(table_path("does_not_exist")) - - -def test_open_wrong_kind_raises(tmp_path): - """A path with a _meta.b2frame that is not a ctable raises ValueError.""" - import blosc2 - - meta_path = str(tmp_path / "_meta.b2frame") - sc = blosc2.SChunk(urlpath=meta_path, mode="w") - sc.vlmeta["kind"] = "something_else" - - with pytest.raises(ValueError, match="CTable"): - CTable.open(str(tmp_path)) - - -# --------------------------------------------------------------------------- -# Column name validation -# --------------------------------------------------------------------------- - - -def test_column_name_cannot_start_with_underscore(): - @dataclass - class Bad: - _id: int = blosc2.field(blosc2.int64()) - - with pytest.raises(ValueError, match="_"): - CTable(Bad) - - -def test_column_name_cannot_contain_slash(): - @dataclass - class Bad: - pass - - from blosc2.schema_compiler import _validate_column_name - - with pytest.raises(ValueError, match="/"): - _validate_column_name("a/b") - - -def test_column_name_cannot_be_empty(): - from blosc2.schema_compiler import _validate_column_name - - with pytest.raises(ValueError): - _validate_column_name("") - - -# --------------------------------------------------------------------------- -# new_data= guard when opening existing -# --------------------------------------------------------------------------- - - -def test_new_data_rejected_when_opening_existing(): - path = table_path("newdata") - CTable(Row, urlpath=path, mode="w", expected_size=16) - - with pytest.raises(ValueError, match="new_data"): - CTable(Row, new_data=[(1, 50.0, True)], urlpath=path, mode="a") - - -# --------------------------------------------------------------------------- -# Capacity growth (resize) persists -# --------------------------------------------------------------------------- - - -def test_grow_persists(): - """Filling past the initial capacity triggers resize; data still survives.""" - path = table_path("grow") - t = CTable(Row, urlpath=path, mode="w", expected_size=4) - for i in range(10): - t.append((i, float(i), True)) - assert len(t) == 10 - - t2 = CTable(Row, urlpath=path, mode="a") - assert len(t2) == 10 - assert list(t2["id"].to_numpy()) == list(range(10)) - - -# --------------------------------------------------------------------------- -# save() / load() -# --------------------------------------------------------------------------- - - -def test_save_creates_disk_layout(): - """save() writes the expected directory structure.""" - t = blosc2.CTable(Row, expected_size=16) - t.extend([(1, 10.0, True), (2, 20.0, False)]) - - path = table_path("saved") - t.save(path) - - assert os.path.exists(os.path.join(path, "_meta.b2frame")) - assert os.path.exists(os.path.join(path, "_valid_rows.b2nd")) - assert os.path.exists(os.path.join(path, "_cols", "id.b2nd")) - assert os.path.exists(os.path.join(path, "_cols", "score.b2nd")) - assert os.path.exists(os.path.join(path, "_cols", "active.b2nd")) - - -def test_save_then_open_round_trip(): - """Data written by save() can be read back via CTable.open().""" - t = blosc2.CTable(Row, expected_size=16) - t.extend([(i, float(i * 10), i % 2 == 0) for i in range(5)]) - - path = table_path("saved_rt") - t.save(path) - - t2 = CTable.open(path) - assert len(t2) == 5 - assert list(t2["id"].to_numpy()) == list(range(5)) - assert list(t2["score"].to_numpy()) == [float(i * 10) for i in range(5)] - - -def test_save_compacts_deleted_rows(): - """save() writes only live rows — deleted rows are not included.""" - t = blosc2.CTable(Row, expected_size=16) - t.extend([(i, float(i), True) for i in range(6)]) - t.delete([0, 2, 4]) # delete rows with id 0, 2, 4 - assert len(t) == 3 - - path = table_path("saved_compact") - t.save(path) - - t2 = CTable.open(path) - assert len(t2) == 3 - assert list(t2["id"].to_numpy()) == [1, 3, 5] - - -def test_save_existing_path_raises_by_default(): - """save() raises ValueError if the path already exists unless overwrite=True.""" - t = blosc2.CTable(Row, expected_size=4) - t.append((1, 10.0, True)) - - path = table_path("save_conflict") - t.save(path) - - with pytest.raises(ValueError, match="overwrite"): - t.save(path) - - -def test_save_overwrite_replaces_table(): - """save(overwrite=True) replaces an existing table.""" - t1 = blosc2.CTable(Row, expected_size=4) - t1.extend([(1, 10.0, True), (2, 20.0, True)]) - - path = table_path("overwrite") - t1.save(path) - - t2 = blosc2.CTable(Row, expected_size=4) - t2.append((99, 50.0, False)) - t2.save(path, overwrite=True) - - t3 = CTable.open(path) - assert len(t3) == 1 - assert t3["id"][0] == 99 - - -def test_save_view_raises(): - """save() on a view raises ValueError.""" - t = blosc2.CTable(Row, expected_size=8) - t.extend([(i, float(i), True) for i in range(4)]) - view = t.where(t["id"] > 1) - - with pytest.raises(ValueError, match="view"): - view.save(table_path("view_save")) - - -def test_load_returns_in_memory_table(): - """load() returns a writable in-memory CTable.""" - t = blosc2.CTable(Row, expected_size=8) - t.extend([(i, float(i * 5), True) for i in range(4)]) - - path = table_path("loadme") - t.save(path) - - loaded = CTable.load(path) - assert len(loaded) == 4 - assert list(loaded["id"].to_numpy()) == [0, 1, 2, 3] - # Must be writable - loaded.append((100, 50.0, True)) - assert len(loaded) == 5 - - -def test_load_does_not_modify_disk(): - """Mutations on a loaded table do not affect the on-disk table.""" - t = blosc2.CTable(Row, expected_size=8) - t.extend([(i, float(i), True) for i in range(3)]) - - path = table_path("load_isolation") - t.save(path) - - loaded = CTable.load(path) - loaded.append((999, 99.0, False)) - loaded.delete(0) - - # Re-open the original persistent table — should be unchanged - t2 = CTable.open(path) - assert len(t2) == 3 - assert list(t2["id"].to_numpy()) == [0, 1, 2] - - -def test_load_nonexistent_raises(): - with pytest.raises(FileNotFoundError): - CTable.load(table_path("does_not_exist")) - - -def test_save_empty_table(): - """save() and load() work correctly on an empty table.""" - t = blosc2.CTable(Row, expected_size=4) - - path = table_path("empty") - t.save(path) - - t2 = CTable.load(path) - assert len(t2) == 0 - # Can still append after load - t2.append((1, 10.0, True)) - assert len(t2) == 1 - - -if __name__ == "__main__": - import pytest - - pytest.main(["-v", __file__]) From 14853aca1a972c849e3f5cc6eff34e1f09726d8a Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Wed, 8 Apr 2026 07:09:47 +0200 Subject: [PATCH 20/49] Remove large data files from repo --- saved_ctable/climate/_cols/day_of_year.b2nd | Bin 235860 -> 0 bytes saved_ctable/climate/_cols/humidity.b2nd | Bin 626231 -> 0 bytes saved_ctable/climate/_cols/pressure.b2nd | Bin 489816 -> 0 bytes saved_ctable/climate/_cols/station_id.b2nd | Bin 168111 -> 0 bytes saved_ctable/climate/_cols/temperature.b2nd | Bin 665497 -> 0 bytes saved_ctable/climate/_cols/wind_speed.b2nd | Bin 664042 -> 0 bytes saved_ctable/climate/_meta.b2frame | Bin 532 -> 0 bytes saved_ctable/climate/_valid_rows.b2nd | Bin 271 -> 0 bytes saved_ctable/direct_test/_cols/id.b2nd | Bin 337 -> 0 bytes saved_ctable/layout_test/_cols/id.b2nd | Bin 455 -> 0 bytes saved_ctable/layout_test/_meta.b2frame | Bin 348 -> 0 bytes saved_ctable/layout_test/_meta2.b2frame | Bin 132 -> 0 bytes saved_ctable/layout_test/_valid_rows.b2nd | Bin 263 -> 0 bytes saved_ctable/multi.b2d/embed.b2e | Bin 3242 -> 0 bytes saved_ctable/people.b2d/embed.b2e | Bin 1374 -> 0 bytes saved_ctable/resize_test/id.b2nd | Bin 290 -> 0 bytes saved_ctable/schunk_test/meta.b2frame | Bin 186 -> 0 bytes saved_ctable/test.b2d/embed.b2e | Bin 752 -> 0 bytes saved_ctable/users/_cols/id.b2nd | Bin 964 -> 0 bytes saved_ctable/users/_cols/retired.b2nd | Bin 6501 -> 0 bytes saved_ctable/users/_cols/score.b2nd | Bin 339812 -> 0 bytes saved_ctable/users/_meta.b2frame | Bin 452 -> 0 bytes saved_ctable/users/_valid_rows.b2nd | Bin 262 -> 0 bytes saved_ctable/write_test.b2d/embed.b2e | Bin 503 -> 0 bytes 24 files changed, 0 insertions(+), 0 deletions(-) delete mode 100644 saved_ctable/climate/_cols/day_of_year.b2nd delete mode 100644 saved_ctable/climate/_cols/humidity.b2nd delete mode 100644 saved_ctable/climate/_cols/pressure.b2nd delete mode 100644 saved_ctable/climate/_cols/station_id.b2nd delete mode 100644 saved_ctable/climate/_cols/temperature.b2nd delete mode 100644 saved_ctable/climate/_cols/wind_speed.b2nd delete mode 100644 saved_ctable/climate/_meta.b2frame delete mode 100644 saved_ctable/climate/_valid_rows.b2nd delete mode 100644 saved_ctable/direct_test/_cols/id.b2nd delete mode 100644 saved_ctable/layout_test/_cols/id.b2nd delete mode 100644 saved_ctable/layout_test/_meta.b2frame delete mode 100644 saved_ctable/layout_test/_meta2.b2frame delete mode 100644 saved_ctable/layout_test/_valid_rows.b2nd delete mode 100644 saved_ctable/multi.b2d/embed.b2e delete mode 100644 saved_ctable/people.b2d/embed.b2e delete mode 100644 saved_ctable/resize_test/id.b2nd delete mode 100644 saved_ctable/schunk_test/meta.b2frame delete mode 100644 saved_ctable/test.b2d/embed.b2e delete mode 100644 saved_ctable/users/_cols/id.b2nd delete mode 100644 saved_ctable/users/_cols/retired.b2nd delete mode 100644 saved_ctable/users/_cols/score.b2nd delete mode 100644 saved_ctable/users/_meta.b2frame delete mode 100644 saved_ctable/users/_valid_rows.b2nd delete mode 100644 saved_ctable/write_test.b2d/embed.b2e diff --git a/saved_ctable/climate/_cols/day_of_year.b2nd b/saved_ctable/climate/_cols/day_of_year.b2nd deleted file mode 100644 index 1d1e89fb1c044c1653786ef0bcae4d7ddac53fd2..0000000000000000000000000000000000000000 GIT binary patch literal 0 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--git a/saved_ctable/write_test.b2d/embed.b2e b/saved_ctable/write_test.b2d/embed.b2e deleted file mode 100644 index 15d1545fb9d1a2a8acb8bb66ccaf2233e37a8bfe..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 503 zcmbQYBFQMNC^0vc;SvJ_L(O?00R)WSmk2S0GF^u781BLtdqIjAK?F#-0>ecH9w0h= zgAJ^ZkrhmVh{>F5E8QD2FfQ|!U4Il;sMi>J@PhkXxg)}fM)~6Pi_K1K*OfpfSCxV0b@su#Q*>R From 66e35a4fae17509284c874197875f891365cd4c8 Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Wed, 8 Apr 2026 07:29:59 +0200 Subject: [PATCH 21/49] Restore CI files from main --- .github/workflows/cibuildwheels.yml | 4 ++-- .github/workflows/wasm.yml | 1 - bench/ctable/{ctable_v_panda.py => ctable_v_pandas.py} | 0 bench/ctable/{Prueba_iter.py => iter_rows.py} | 0 4 files changed, 2 insertions(+), 3 deletions(-) rename bench/ctable/{ctable_v_panda.py => ctable_v_pandas.py} (100%) rename bench/ctable/{Prueba_iter.py => iter_rows.py} (100%) diff --git a/.github/workflows/cibuildwheels.yml b/.github/workflows/cibuildwheels.yml index f7e2bef0..ed69f764 100644 --- a/.github/workflows/cibuildwheels.yml +++ b/.github/workflows/cibuildwheels.yml @@ -26,7 +26,6 @@ env: jobs: build_wheels: - if: ${{ github.ref_name != 'ctable3' && github.head_ref != 'ctable3' }} name: Build wheels on ${{ matrix.os }} for ${{ matrix.arch }} runs-on: ${{ matrix.runs-on || matrix.os }} permissions: @@ -129,9 +128,10 @@ jobs: upload_pypi: - if: ${{ (github.ref_name != 'ctable3' && github.head_ref != 'ctable3') && startsWith(github.event.ref, 'refs/tags') }} needs: [ build_wheels] runs-on: ubuntu-latest + # Only upload wheels when tagging (typically a release) + if: startsWith(github.event.ref, 'refs/tags') steps: - uses: actions/download-artifact@v8 with: diff --git a/.github/workflows/wasm.yml b/.github/workflows/wasm.yml index 3d293274..f54afad5 100644 --- a/.github/workflows/wasm.yml +++ b/.github/workflows/wasm.yml @@ -14,7 +14,6 @@ env: jobs: build_wheels_wasm: - if: ${{ github.ref_name != 'ctable3' && github.head_ref != 'ctable3' }} name: Build and test wheels for WASM on ${{ matrix.os }} for ${{ matrix.p_ver }} runs-on: ubuntu-latest permissions: diff --git a/bench/ctable/ctable_v_panda.py b/bench/ctable/ctable_v_pandas.py similarity index 100% rename from bench/ctable/ctable_v_panda.py rename to bench/ctable/ctable_v_pandas.py diff --git a/bench/ctable/Prueba_iter.py b/bench/ctable/iter_rows.py similarity index 100% rename from bench/ctable/Prueba_iter.py rename to bench/ctable/iter_rows.py From 0dc869750219b87a9088fc64c2b4cc106ffd6c88 Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Wed, 8 Apr 2026 07:35:10 +0200 Subject: [PATCH 22/49] Restore compatibility with numpy < 2 --- src/blosc2/ctable.py | 2 +- src/blosc2/lazyexpr.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/blosc2/ctable.py b/src/blosc2/ctable.py index 010cfe0f..bb13272c 100644 --- a/src/blosc2/ctable.py +++ b/src/blosc2/ctable.py @@ -2027,7 +2027,7 @@ def _build_lex_keys( if not asc: if raw.dtype.kind in "US": # strings can't be negated — invert via rank - rank = np.argsort(np.argsort(raw, stable=True), stable=True) + rank = np.argsort(np.argsort(raw, kind="stable"), kind="stable") lex_keys.append((n - 1 - rank).astype(np.intp)) elif np.issubdtype(raw.dtype, np.unsignedinteger): lex_keys.append(-raw.astype(np.int64)) diff --git a/src/blosc2/lazyexpr.py b/src/blosc2/lazyexpr.py index 63d656bc..c1c0de72 100644 --- a/src/blosc2/lazyexpr.py +++ b/src/blosc2/lazyexpr.py @@ -2759,7 +2759,7 @@ def chunked_eval( # noqa: C901 unit_steps = np.all([s.step == 1 for s in item.raw if isinstance(s, slice)]) # shape of slice, if non-unit steps have to decompress full array into memory shape_operands = item.newshape(shape) if unit_steps else shape - _dtype = kwargs.get("dtype", np.float64) + _dtype = np.dtype(kwargs.get("dtype", np.float64)) size_operands = math.prod(shape_operands) * len(operands) * _dtype.itemsize # Only take the fast path if the size of operands is relatively small if size_operands < blosc2.MAX_FAST_PATH_SIZE: From 457b0ffb3fd1c1235a58c40ea1f7ca88cfa407a2 Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Wed, 15 Apr 2026 08:36:00 +0200 Subject: [PATCH 23/49] Back CTable persistence with TreeStore and materialize it via blosc2.open() --- plans/treestore-ctable-extension.md | 344 ++++++++++++++++++++++++++ src/blosc2/ctable.py | 47 ++-- src/blosc2/ctable_storage.py | 152 ++++++++---- src/blosc2/schunk.py | 57 +++++ tests/ctable/test_schema_mutations.py | 56 ++++- tests/ctable/test_sort_by.py | 7 +- tree_store_extensions.md | 258 +++++++++++++++++++ 7 files changed, 851 insertions(+), 70 deletions(-) create mode 100644 plans/treestore-ctable-extension.md create mode 100644 tree_store_extensions.md diff --git a/plans/treestore-ctable-extension.md b/plans/treestore-ctable-extension.md new file mode 100644 index 00000000..993439f9 --- /dev/null +++ b/plans/treestore-ctable-extension.md @@ -0,0 +1,344 @@ +# TreeStore Root-Level `CTable` Extension Plan + +## Goal + +Allow a `CTable` stored as the sole logical object inside a `TreeStore` to be +opened directly via: + +```python +table = blosc2.open(urlpath) +``` + +That is, if a `TreeStore` at `urlpath` carries a recognized root manifest for +`CTable`, `blosc2.open(urlpath)` should return a `CTable` instance instead of a +raw `TreeStore`. + +This plan intentionally covers only the simple first round: + +- one `CTable` per `TreeStore` +- object root is the store root +- `/_meta` at store root is the manifest + +Subtree object roots and multiple tables per store are deferred. + +## Background + +`TreeStore` now has persistent low-level container metadata through: + +- `storage.meta["b2tree"] = {"version": 1}` + +That is enough for `blosc2.open()` to recognize the path as a `TreeStore`, but +not enough to know whether the store should materialize as a richer object. + +The generic extension contract in [tree_store_extensions.md](/Users/faltet/blosc/python-blosc2/tree_store_extensions.md) +introduces: + +- `/_meta` as the logical-object manifest for store-backed objects + +This plan applies that contract to `CTable`. + +## Storage Layout + +The persisted root-level `CTable` layout should be: + +- `/_meta` +- `/_valid_rows` +- `/_cols/` + +Example: + +- `/_meta` +- `/_valid_rows` +- `/_cols/id` +- `/_cols/score` +- `/_cols/active` + +Rationale: + +- `/_meta` stores logical-object manifest data +- `/_valid_rows` stores real row-visibility data +- `/_cols/` stores one persisted column array per field + +## Root Manifest + +`/_meta` should be a small persisted `SChunk` used primarily through `vlmeta`. + +Initial required manifest fields: + +- `kind` +- `version` +- `schema` + +Initial `CTable` manifest: + +```python +{ + "kind": "ctable", + "version": 1, + "schema": {...}, +} +``` + +Recommended concrete writes: + +```python +tstore["/_meta"].vlmeta["kind"] = "ctable" +tstore["/_meta"].vlmeta["version"] = 1 +tstore["/_meta"].vlmeta["schema"] = schema_payload +``` + +## Schema Persistence Format + +The schema should be stored in: + +- `/_meta.vlmeta["schema"]` + +The schema document should be JSON-compatible, explicit, and versioned. + +Recommended shape: + +```python +{ + "version": 1, + "columns": [ + { + "name": "id", + "py_type": "int", + "spec": {"kind": "int64", "ge": 0}, + "default": None, + }, + { + "name": "score", + "py_type": "float", + "spec": {"kind": "float64", "ge": 0, "le": 100}, + "default": None, + }, + { + "name": "active", + "py_type": "bool", + "spec": {"kind": "bool"}, + "default": True, + }, + ], +} +``` + +Notes: + +- `columns` must be an ordered list, not a dict +- column order comes from the schema list +- `TreeStore` iteration order must not be used as schema authority + +For the first version, do not duplicate data that can be inspected from the +stored column arrays: + +- per-column `cparams` +- per-column `dparams` +- chunk/block layout +- `expected_size` +- compaction settings + +## `_valid_rows` Persistence + +`/_valid_rows` should be a normal persisted boolean array. + +This is correct because: + +- it is table data, not metadata +- it may grow large +- it participates in normal row visibility semantics + +It should not be folded into `/_meta`. + +## Column Persistence + +Each column should be stored as its own persisted array under: + +- `/_cols/` + +This keeps the physical layout aligned with the internal columnar design and +lets per-column storage details remain attached to the actual persisted array. + +## Constructor Semantics + +The intended public constructor remains: + +```python +table = blosc2.CTable( + Row, + urlpath=None, + mode="a", + expected_size=1_048_576, + compact=False, + validate=True, +) +``` + +For the persistent path: + +- `urlpath is None`: + - in-memory `CTable` +- `urlpath is not None`: + - root-level `CTable` persisted on top of a `TreeStore` + +Recommended mode behavior: + +- `mode="w"`: + - create a fresh store-root `CTable` +- `mode="a"`: + - open existing or create new +- `mode="r"`: + - open existing read-only + +## `blosc2.open()` Materialization + +The root-level dispatch behavior should be: + +1. `blosc2.open(urlpath)` detects a `TreeStore` +2. it opens the `TreeStore` +3. it checks for `/_meta` +4. if `/_meta.vlmeta["kind"] == "ctable"`, it materializes `CTable` +5. otherwise it returns the raw `TreeStore` + +This preserves the current open layering: + +- first detect the low-level container +- then optionally materialize a richer object + +## Suggested Implementation Shape + +### Step 1: Add Root Manifest Helpers + +Add private helper(s) for root-manifest probing, e.g.: + +- `_open_treestore_root_object(store)` +- `_read_treestore_root_manifest(store)` + +Responsibilities: + +- check whether `/_meta` exists +- open `/_meta` +- validate that it is an `SChunk` +- read `kind` / `version` +- return a manifest payload suitable for dispatch + +### Step 2: Extend `blosc2.open()` + +In the special-store open path: + +- if opening yields a `TreeStore` +- probe the root manifest +- if recognized as `ctable`, return `CTable.open(...)` or equivalent internal + constructor +- otherwise return the `TreeStore` + +This logic should be localized so the generic `open()` path remains easy to +follow. + +### Step 3: Add `CTable` Root-Manifest Read/Write Helpers + +In the `CTable` persistence layer, add helpers for: + +- creating `/_meta` +- writing `kind` +- writing `version` +- writing `schema` +- reading and validating the root manifest + +This should be the only place that knows the `CTable` manifest schema. + +### Step 4: Wire Creation + +When a persistent `CTable` is created: + +- create/open the backing `TreeStore` +- create `/_meta` +- write the root manifest +- create `/_valid_rows` +- create `/_cols/` arrays + +### Step 5: Wire Reopen + +When a persistent `CTable` is reopened: + +- read `/_meta.vlmeta["schema"]` +- rebuild the compiled schema +- reopen `/_valid_rows` +- reopen each persisted column from `/_cols/` + +### Step 6: Keep Internal Names Reserved + +Validation should reject user column names that collide with internal names: + +- `_meta` +- `_valid_rows` +- `_cols` + +This already aligns with the existing schema compiler reserved-name logic. + +## Validation Rules + +For `CTable` root-manifest detection: + +- if `/_meta` does not exist: + - not a persisted `CTable` +- if `/_meta` exists but is malformed: + - raise clear error on attempted `CTable` materialization +- if `kind != "ctable"`: + - return raw `TreeStore` +- if `kind == "ctable"` but required fields are missing: + - raise clear error + +Recommended required fields for version 1: + +- `kind` +- `version` +- `schema` + +## Deferred Scope + +This plan intentionally does not cover: + +- multiple `CTable` objects in one `TreeStore` +- subtree object roots such as `/users/_meta` +- automatic materialization when indexing a subtree from `TreeStore` +- `Ref` support for store-subtree logical objects +- schema evolution beyond append-only behavior + +These should be handled in later phases after the root-level path is stable. + +## Tests + +Add coverage for: + +- create persistent root-level `CTable` +- reopen via `blosc2.open(urlpath)` and get `CTable` +- reopen via `CTable.open(urlpath, mode="r")` +- root manifest present and schema readable from `/_meta.vlmeta` +- store with no `/_meta` still opens as raw `TreeStore` +- store with unknown root-manifest `kind` still opens as raw `TreeStore` +- malformed `CTable` manifest raises clear error +- append rows after reopen +- read-only reopen rejects writes + +## Recommended Implementation Order + +1. write root-manifest probe helpers for `TreeStore` +2. extend `blosc2.open()` with root-manifest dispatch +3. add `CTable` manifest read/write helpers +4. wire persistent create/open around the manifest +5. add tests for dispatch and round-trip + +## Summary + +The first `TreeStore` extension should treat root `/_meta` as the logical +manifest for the whole store. + +For `CTable`, this yields a simple and coherent open story: + +- low-level metadata says "this is a `TreeStore`" +- root `/_meta` says "this store materializes as a `CTable`" +- `blosc2.open(urlpath)` returns the richer object directly + +This keeps the first implementation small while staying compatible with a later +generalization to subtree object roots. diff --git a/src/blosc2/ctable.py b/src/blosc2/ctable.py index bb13272c..a7a1e4e7 100644 --- a/src/blosc2/ctable.py +++ b/src/blosc2/ctable.py @@ -10,6 +10,7 @@ from __future__ import annotations +import contextlib import dataclasses import os import shutil @@ -774,6 +775,23 @@ def __init__( if new_data is not None: self._load_initial_data(new_data) + def close(self) -> None: + """Close any persistent backing store held by this table.""" + storage = getattr(self, "_storage", None) + if storage is not None and hasattr(storage, "close"): + storage.close() + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + self.close() + return False + + def __del__(self): + with contextlib.suppress(Exception): + self.close() + def _init_columns( self, expected_size: int, default_chunks, default_blocks, storage: TableStorage ) -> None: @@ -1047,10 +1065,15 @@ def save(self, urlpath: str, *, overwrite: bool = False) -> None: """ if self.base is not None: raise ValueError("Cannot save a view — save the parent table instead.") - if os.path.exists(urlpath): + file_storage = FileTableStorage(urlpath, "w") + target_path = file_storage._root + if os.path.exists(target_path): if not overwrite: - raise ValueError(f"Path {urlpath!r} already exists. Use overwrite=True to replace.") - shutil.rmtree(urlpath) + raise ValueError(f"Path {target_path!r} already exists. Use overwrite=True to replace.") + if os.path.isdir(target_path): + shutil.rmtree(target_path) + else: + os.remove(target_path) # Collect live physical positions valid_np = self._valid_rows[:] @@ -1058,7 +1081,6 @@ def save(self, urlpath: str, *, overwrite: bool = False) -> None: n_live = len(live_pos) capacity = max(n_live, 1) - file_storage = FileTableStorage(urlpath, "w") default_chunks, default_blocks = compute_chunks_blocks((capacity,)) # --- valid_rows (all True, compacted) --- @@ -1087,6 +1109,7 @@ def save(self, urlpath: str, *, overwrite: bool = False) -> None: disk_col[:n_live] = self._cols[name][live_pos] file_storage.save_schema(schema_to_dict(self._schema)) + file_storage.close() @classmethod def load(cls, urlpath: str) -> CTable: @@ -1149,6 +1172,8 @@ def load(cls, urlpath: str) -> CTable: mem_col[:phys_size] = disk_cols[name][:] mem_cols[name] = mem_col + file_storage.close() + obj = cls.__new__(cls) obj._row_type = None obj._validate = True @@ -1861,7 +1886,7 @@ def add_column( def drop_column(self, name: str) -> None: """Remove a column from the table. - On disk tables the corresponding ``.b2nd`` file is deleted. + On disk tables the corresponding persisted column leaf is deleted. Raises ------ @@ -1880,9 +1905,7 @@ def drop_column(self, name: str) -> None: raise ValueError("Cannot drop the last column.") if isinstance(self._storage, FileTableStorage): - col_path = self._storage._col_path(name) - if os.path.exists(col_path): - os.remove(col_path) + self._storage.delete_column(name) del self._cols[name] del self._col_widths[name] @@ -1900,7 +1923,7 @@ def drop_column(self, name: str) -> None: def rename_column(self, old: str, new: str) -> None: """Rename a column. - On disk tables the corresponding ``.b2nd`` file is renamed. + On disk tables the corresponding persisted column leaf is renamed. Raises ------ @@ -1920,11 +1943,7 @@ def rename_column(self, old: str, new: str) -> None: _validate_column_name(new) if isinstance(self._storage, FileTableStorage): - old_path = self._storage._col_path(old) - new_path = self._storage._col_path(new) - os.rename(old_path, new_path) - b2_mode = "r" if self._read_only else "a" - self._cols[new] = blosc2.open(new_path, mode=b2_mode) + self._cols[new] = self._storage.rename_column(old, new) else: self._cols[new] = self._cols[old] del self._cols[old] diff --git a/src/blosc2/ctable_storage.py b/src/blosc2/ctable_storage.py index 23755ef2..bc101316 100644 --- a/src/blosc2/ctable_storage.py +++ b/src/blosc2/ctable_storage.py @@ -12,17 +12,9 @@ * :class:`InMemoryTableStorage` — all arrays live in RAM (default when ``urlpath`` is not provided). -* :class:`FileTableStorage` — arrays are stored as individual Blosc2 files - under a table root directory; schema and kind metadata live in a small - :class:`blosc2.SChunk` whose ``vlmeta`` is the source of truth. - -Layout produced by :class:`FileTableStorage`:: - - / - _meta.b2frame ← SChunk with vlmeta: kind, version, schema JSON - _valid_rows.b2nd ← boolean NDArray (tombstone mask) - _cols/ - .b2nd ← one NDArray per column +* :class:`FileTableStorage` — arrays are stored inside a :class:`blosc2.TreeStore` + rooted at ``urlpath``; logical object metadata lives in ``/_meta`` and table + data lives under ``/_valid_rows`` and ``/_cols/``. """ from __future__ import annotations @@ -83,6 +75,15 @@ def table_exists(self) -> bool: def is_read_only(self) -> bool: raise NotImplementedError + def delete_column(self, name: str) -> None: + raise NotImplementedError + + def rename_column(self, old: str, new: str) -> blosc2.NDArray: + raise NotImplementedError + + def close(self) -> None: + raise NotImplementedError + # --------------------------------------------------------------------------- # In-memory backend @@ -121,23 +122,32 @@ def table_exists(self): def is_read_only(self): return False + def delete_column(self, name): + raise RuntimeError("In-memory tables have no on-disk representation to mutate.") + + def rename_column(self, old: str, new: str): + raise RuntimeError("In-memory tables have no on-disk representation to mutate.") + + def close(self): + pass + # --------------------------------------------------------------------------- # File-backed backend # --------------------------------------------------------------------------- -_META_FILE = "_meta.b2frame" -_VALID_ROWS_FILE = "_valid_rows.b2nd" +_META_KEY = "/_meta" +_VALID_ROWS_KEY = "/_valid_rows" _COLS_DIR = "_cols" class FileTableStorage(TableStorage): - """Arrays stored as individual Blosc2 files inside *urlpath* directory. + """Arrays stored as TreeStore leaves inside *urlpath*. Parameters ---------- urlpath: - Path to the table root directory. + Path to the backing TreeStore (typically ``.b2d`` or ``.b2z``). mode: ``'w'`` — create (overwrite existing files). ``'a'`` — open existing or create new. @@ -147,89 +157,120 @@ class FileTableStorage(TableStorage): def __init__(self, urlpath: str, mode: str) -> None: if mode not in ("r", "a", "w"): raise ValueError(f"mode must be 'r', 'a', or 'w'; got {mode!r}") - self._root = urlpath + self._root = self._normalize_root(urlpath) self._mode = mode self._meta: blosc2.SChunk | None = None + self._store: blosc2.TreeStore | None = None + + @staticmethod + def _normalize_root(urlpath: str) -> str: + if urlpath.endswith((".b2d", ".b2z")): + return urlpath + return f"{urlpath}.b2d" # ------------------------------------------------------------------ - # Path helpers + # Key helpers # ------------------------------------------------------------------ @property def _meta_path(self) -> str: - return os.path.join(self._root, _META_FILE) + return self._key_to_path(_META_KEY) @property def _valid_rows_path(self) -> str: - return os.path.join(self._root, _VALID_ROWS_FILE) + return self._key_to_path(_VALID_ROWS_KEY) def _col_path(self, name: str) -> str: - return os.path.join(self._root, _COLS_DIR, f"{name}.b2nd") - - def _ensure_dirs(self) -> None: - os.makedirs(os.path.join(self._root, _COLS_DIR), exist_ok=True) + return self._key_to_path(self._col_key(name)) + + def _col_key(self, name: str) -> str: + return f"/{_COLS_DIR}/{name}" + + def _key_to_path(self, key: str) -> str: + rel_key = key.lstrip("/") + suffix = ".b2f" if key == _META_KEY else ".b2nd" + if self._root.endswith(".b2d"): + return os.path.join(self._root, rel_key + suffix) + return os.path.join(self._root, rel_key + suffix) + + def _open_store(self) -> blosc2.TreeStore: + if self._store is None: + kwargs: dict[str, Any] = {"mode": self._mode} + if self._mode != "r": + # Force table internals to be stored as proper external leaves so + # reopened arrays stay live and mutable through the TreeStore. + kwargs["threshold"] = 0 + self._store = blosc2.TreeStore(self._root, **kwargs) + return self._store # ------------------------------------------------------------------ # TableStorage interface # ------------------------------------------------------------------ def table_exists(self) -> bool: - return os.path.exists(self._meta_path) + return os.path.exists(self._root) def is_read_only(self) -> bool: return self._mode == "r" def create_column(self, name, *, dtype, shape, chunks, blocks, cparams, dparams): - self._ensure_dirs() kwargs: dict[str, Any] = { "chunks": chunks, "blocks": blocks, - "urlpath": self._col_path(name), - "mode": "w", } if cparams is not None: kwargs["cparams"] = cparams if dparams is not None: kwargs["dparams"] = dparams - return blosc2.zeros(shape, dtype=dtype, **kwargs) + col = blosc2.zeros(shape, dtype=dtype, **kwargs) + store = self._open_store() + store[self._col_key(name)] = col + return store[self._col_key(name)] def open_column(self, name: str) -> blosc2.NDArray: - b2_mode = "r" if self._mode == "r" else "a" - return blosc2.open(self._col_path(name), mode=b2_mode) + return self._open_store()[self._col_key(name)] def create_valid_rows(self, *, shape, chunks, blocks): - self._ensure_dirs() - return blosc2.zeros( + valid_rows = blosc2.zeros( shape, dtype=np.bool_, chunks=chunks, blocks=blocks, - urlpath=self._valid_rows_path, - mode="w", ) + store = self._open_store() + store[_VALID_ROWS_KEY] = valid_rows + return store[_VALID_ROWS_KEY] def open_valid_rows(self) -> blosc2.NDArray: - b2_mode = "r" if self._mode == "r" else "a" - return blosc2.open(self._valid_rows_path, mode=b2_mode) + return self._open_store()[_VALID_ROWS_KEY] def save_schema(self, schema_dict: dict[str, Any]) -> None: - """Write *schema_dict* (plus kind/version markers) to ``_meta.b2frame``.""" - self._ensure_dirs() - # Always overwrite: save_schema is only called at table-creation time. - self._meta = blosc2.SChunk(urlpath=self._meta_path, mode="w") - self._meta.vlmeta["kind"] = "ctable" - self._meta.vlmeta["version"] = 1 - self._meta.vlmeta["schema"] = json.dumps(schema_dict) + """Write *schema_dict* (plus kind/version markers) to ``/_meta``.""" + meta = blosc2.SChunk() + meta.vlmeta["kind"] = "ctable" + meta.vlmeta["version"] = 1 + meta.vlmeta["schema"] = json.dumps(schema_dict) + store = self._open_store() + store[_META_KEY] = meta + opened = store[_META_KEY] + if not isinstance(opened, blosc2.SChunk): + raise ValueError("CTable manifest '/_meta' must materialize as an SChunk.") + self._meta = opened def _open_meta(self) -> blosc2.SChunk: - """Open (or return cached) the ``_meta.b2frame`` SChunk.""" + """Open (or return cached) the ``/_meta`` SChunk.""" if self._meta is None: - b2_mode = "r" if self._mode == "r" else "a" - self._meta = blosc2.open(self._meta_path, mode=b2_mode) + try: + opened = self._open_store()[_META_KEY] + except KeyError as exc: + raise FileNotFoundError(f"No CTable manifest found at {self._root!r}") from exc + if not isinstance(opened, blosc2.SChunk): + raise ValueError(f"CTable manifest at {self._root!r} must be an SChunk.") + self._meta = opened return self._meta def load_schema(self) -> dict[str, Any]: - """Read and return the schema dict stored in ``_meta.b2frame``.""" + """Read and return the schema dict stored in ``/_meta``.""" raw = self._open_meta().vlmeta["schema"] if isinstance(raw, bytes): raw = raw.decode() @@ -246,3 +287,20 @@ def check_kind(self) -> None: def column_names_from_schema(self) -> list[str]: d = self.load_schema() return [c["name"] for c in d["columns"]] + + def delete_column(self, name: str) -> None: + del self._open_store()[self._col_key(name)] + + def rename_column(self, old: str, new: str) -> blosc2.NDArray: + store = self._open_store() + old_key = self._col_key(old) + new_key = self._col_key(new) + store[new_key] = store[old_key] + del store[old_key] + return store[new_key] + + def close(self) -> None: + if self._store is not None: + self._store.close() + self._store = None + self._meta = None diff --git a/src/blosc2/schunk.py b/src/blosc2/schunk.py index 956f41d2..8be3516a 100644 --- a/src/blosc2/schunk.py +++ b/src/blosc2/schunk.py @@ -1572,6 +1572,16 @@ def _store_from_extension(urlpath, mode, offset, **kwargs): return None +def _resolve_store_alias(urlpath): + if os.path.exists(urlpath) or urlpath.endswith((".b2d", ".b2z", ".b2e")): + return urlpath + for suffix in (".b2d", ".b2z", ".b2e"): + candidate = urlpath + suffix + if os.path.exists(candidate): + return candidate + return urlpath + + def _open_special_store(urlpath, mode, offset, **kwargs): # Meta-based detection has priority over extension schunk_meta = _meta_from_store(urlpath, offset) @@ -1653,6 +1663,49 @@ def process_opened_object(res): return res +def _read_treestore_root_manifest(store): + try: + meta_obj = store["/_meta"] + except KeyError: + return None + + if not isinstance(meta_obj, blosc2.SChunk): + raise ValueError("TreeStore root manifest '/_meta' must be an SChunk.") + + vlmeta = meta_obj.vlmeta + try: + kind = vlmeta["kind"] + except KeyError as exc: + raise ValueError("TreeStore root manifest is missing required field 'kind'.") from exc + try: + version = vlmeta["version"] + except KeyError as exc: + raise ValueError("TreeStore root manifest is missing required field 'version'.") from exc + + if isinstance(kind, bytes): + kind = kind.decode() + if not isinstance(kind, str): + raise ValueError("TreeStore root manifest field 'kind' must be a string.") + if not isinstance(version, int): + raise ValueError("TreeStore root manifest field 'version' must be an integer.") + + return {"kind": kind, "version": version, "meta": meta_obj} + + +def _open_treestore_root_object(store, urlpath, mode): + manifest = _read_treestore_root_manifest(store) + if manifest is None: + return store + + if manifest["kind"] == "ctable": + if mode not in {"r", "a"}: + return store + store.close() + return blosc2.CTable.open(urlpath, mode=mode) + + return store + + def open( urlpath: str | pathlib.Path | blosc2.URLPath, mode: str = "a", offset: int = 0, **kwargs: dict ) -> ( @@ -1771,8 +1824,12 @@ def open( if isinstance(urlpath, pathlib.PurePath): urlpath = str(urlpath) + urlpath = _resolve_store_alias(urlpath) + special = _open_special_store(urlpath, mode, offset, **kwargs) if special is not None: + if isinstance(special, blosc2.TreeStore): + return _open_treestore_root_object(special, urlpath, mode) return special if not os.path.exists(urlpath): diff --git a/tests/ctable/test_schema_mutations.py b/tests/ctable/test_schema_mutations.py index ca119678..5b5e47f4 100644 --- a/tests/ctable/test_schema_mutations.py +++ b/tests/ctable/test_schema_mutations.py @@ -43,7 +43,7 @@ def clean_dir(): def table_path(name): - return os.path.join(TABLE_ROOT, name) + return os.path.join(TABLE_ROOT, f"{name}.b2d") # =========================================================================== @@ -96,7 +96,7 @@ def test_view_blocks_compact(): def test_readonly_disk_table_blocks_assign(): path = table_path("ro") t = CTable(Row, urlpath=path, mode="w", new_data=DATA10) - del t + t.close() t_ro = CTable.open(path, mode="r") with pytest.raises(ValueError, match="read-only"): t_ro["score"].assign(np.ones(len(t_ro))) @@ -105,12 +105,56 @@ def test_readonly_disk_table_blocks_assign(): def test_readonly_disk_table_blocks_setitem(): path = table_path("ro_setitem") t = CTable(Row, urlpath=path, mode="w", new_data=DATA10) - del t + t.close() t_ro = CTable.open(path, mode="r") with pytest.raises(ValueError, match="read-only"): t_ro["score"][0] = 99.0 +def test_blosc2_open_materializes_ctable(): + path = table_path("open_ctable") + t = CTable(Row, urlpath=path, mode="w", new_data=DATA10) + t.close() + opened = blosc2.open(path, mode="r") + assert isinstance(opened, CTable) + assert opened.col_names == ["id", "score", "active"] + np.testing.assert_array_equal(opened["id"].to_numpy(), np.arange(10)) + + +def test_blosc2_open_raw_treestore_without_manifest(): + path = table_path("raw_store") + with blosc2.TreeStore(path, mode="w", threshold=0) as tstore: + tstore["/group/node"] = np.arange(5) + + opened = blosc2.open(path, mode="r") + assert isinstance(opened, blosc2.TreeStore) + assert np.array_equal(opened["/group/node"][:], np.arange(5)) + + +def test_blosc2_open_raw_treestore_for_unknown_manifest_kind(): + path = table_path("unknown_manifest") + with blosc2.TreeStore(path, mode="w", threshold=0) as tstore: + meta = blosc2.SChunk() + meta.vlmeta["kind"] = "mystery" + meta.vlmeta["version"] = 1 + tstore["/_meta"] = meta + tstore["/payload"] = np.arange(3) + + opened = blosc2.open(path, mode="r") + assert isinstance(opened, blosc2.TreeStore) + assert np.array_equal(opened["/payload"][:], np.arange(3)) + + +def test_extensionless_ctable_path_resolves_to_b2d_store(): + path = os.path.join(TABLE_ROOT, "alias_ctable") + t = CTable(Row, urlpath=path, mode="w", new_data=DATA10) + t.close() + assert os.path.exists(path + ".b2d") + opened = blosc2.open(path, mode="r") + assert isinstance(opened, CTable) + np.testing.assert_array_equal(opened["id"].to_numpy(), np.arange(10)) + + # =========================================================================== # Column.assign # =========================================================================== @@ -187,7 +231,7 @@ def test_add_column_persists_on_disk(): path = table_path("add_col") t = CTable(Row, urlpath=path, mode="w", new_data=DATA10) t.add_column("weight", blosc2.float64(), 7.0) - del t + t.close() t2 = CTable.open(path) assert "weight" in t2.col_names np.testing.assert_array_equal(t2["weight"].to_numpy(), np.full(10, 7.0)) @@ -272,7 +316,7 @@ def test_drop_column_persists_schema_on_disk(): path = table_path("drop_schema") t = CTable(Row, urlpath=path, mode="w", new_data=DATA10) t.drop_column("active") - del t + t.close() t2 = CTable.open(path) assert "active" not in t2.col_names assert t2.ncols == 2 @@ -333,7 +377,7 @@ def test_rename_column_persists_on_disk(): path = table_path("rename_col") t = CTable(Row, urlpath=path, mode="w", new_data=DATA10) t.rename_column("score", "points") - del t + t.close() t2 = CTable.open(path) assert "points" in t2.col_names assert "score" not in t2.col_names diff --git a/tests/ctable/test_sort_by.py b/tests/ctable/test_sort_by.py index 67d6c403..6363b2a8 100644 --- a/tests/ctable/test_sort_by.py +++ b/tests/ctable/test_sort_by.py @@ -255,11 +255,12 @@ def test_sort_readonly_inplace_raises(): import pathlib import shutil - path = str(pathlib.Path(__file__).parent / "saved_ctable" / "_sort_ro_test") - os.makedirs(path, exist_ok=True) + path_obj = pathlib.Path(__file__).parent / "saved_ctable" / "_sort_ro_test.b2d" + path = str(path_obj) + os.makedirs(path_obj.parent, exist_ok=True) try: t = CTable(Row, urlpath=path, mode="w", new_data=DATA) - del t + t.close() t_ro = CTable.open(path, mode="r") with pytest.raises(ValueError, match="read-only"): t_ro.sort_by("id", inplace=True) diff --git a/tree_store_extensions.md b/tree_store_extensions.md new file mode 100644 index 00000000..400be499 --- /dev/null +++ b/tree_store_extensions.md @@ -0,0 +1,258 @@ +# TreeStore Extension Objects + +## Goal + +Define a general mechanism for representing richer logical objects on top of a +`TreeStore`, while keeping the underlying persisted container recognizable as a +plain `TreeStore`. + +The initial driver is persisted `CTable`, but the mechanism should be generic +enough to support other logical object kinds later. + +## Core Idea + +`TreeStore` already has a low-level container identity: + +- `storage.meta["b2tree"] = {"version": 1}` + +That answers: + +- "what physical container is stored here?" + +However, a `TreeStore` can also act as a substrate for a richer logical object. +For that, we introduce a reserved manifest entry: + +- `/_meta` + +This answers: + +- "what logical object is represented by this store subtree?" + +The manifest is a small persisted `SChunk` whose `vlmeta` is the source of +truth for logical-object identity and configuration. + +## Object Root Model + +An object root is any `TreeStore` subtree that contains: + +- `/_meta` + +Examples: + +- whole-store object: + - `/_meta` +- subtree object: + - `/users/_meta` + - `/orders/_meta` + +This gives one uniform rule: + +- if a subtree has `/_meta`, it may represent a richer logical object + +The whole-store case is just the special case where the object root is the +store root. + +## Why Use `/_meta` + +### Separation Of Roles + +- container `.meta` remains about the low-level container type (`b2tree`) +- `/_meta` is about higher-level logical identity +- user-facing `tstore.vlmeta` remains available for user metadata + +### Mutable Object Metadata + +Unlike fixed container metalayers, `/_meta.vlmeta` can evolve over time. +That matters for store-backed logical objects that may need mutable metadata, +such as: + +- schema evolution state +- object versioning +- feature flags +- migration markers + +### Generic Store Extension Point + +`/_meta` should not be a one-off `CTable` special case. +It should be the general manifest contract for any richer object represented on +top of a store subtree. + +## Manifest Representation + +`/_meta` should be: + +- a small persisted `SChunk` +- primarily used through `vlmeta` + +The initial required fields in `/_meta.vlmeta` are: + +- `kind` +- `version` + +Example: + +```python +tree_store["/_meta"].vlmeta["kind"] = "ctable" +tree_store["/_meta"].vlmeta["version"] = 1 +``` + +Additional fields are object-kind-specific. + +For example, a `CTable` manifest may add: + +- `schema` + +## Reserved Internal Names + +Within an object root, the following path is reserved: + +- `/_meta` + +Logical objects may reserve additional internal paths under the same root. + +For example, `CTable` is expected to reserve: + +- `/_valid_rows` +- `/_cols` + +These reserved names are internal implementation detail and must not be treated +as user data nodes. + +## `blosc2.open()` Contract + +When opening a persisted path: + +1. low-level store detection happens first +2. if the opened object is a `TreeStore`, object-manifest detection may happen +3. if a recognized manifest is found, materialize the richer logical object +4. otherwise, return the raw `TreeStore` + +For the whole-store case, the detection rule is: + +- open the path as a `TreeStore` +- look for `/_meta` +- if `/_meta.vlmeta["kind"]` is recognized, dispatch to the corresponding + higher-level constructor/open path + +This preserves the current layering: + +- low-level open still discovers a `TreeStore` +- logical-object open is an extra step on top + +## Root-Only First Implementation + +The design should anticipate subtree object roots, but the first implementation +does not need to support them yet. + +Initial scope: + +- only the store root may be materialized as a richer object +- only `/_meta` at store root is consulted by `blosc2.open(urlpath)` + +Deferred scope: + +- subtree object roots such as `/users/_meta` +- multiple richer objects in one `TreeStore` +- automatic materialization of `tstore["/subtree"]` +- explicit references to store-subtree logical objects + +This staged approach keeps the first implementation simple while preserving a +clear path toward multi-object stores later. + +## Dispatch API Shape + +The first implementation should support: + +```python +obj = blosc2.open(urlpath) +``` + +Behavior: + +- if `urlpath` resolves to a plain store with no recognized root manifest, + return `TreeStore` +- if `urlpath` resolves to a `TreeStore` with recognized `/_meta`, return the + richer object + +For the deferred subtree-aware model, the API question is still open: + +- `blosc2.open(urlpath, key="/users")` +- `blosc2.Ref` support for store-subtree objects +- other path-addressing schemes + +These should be designed in a later phase. + +## Error Handling + +The generic manifest contract should distinguish: + +- no `/_meta` present: + - return raw `TreeStore` +- `/_meta` present but missing required fields: + - error clearly +- `/_meta` present with unknown `kind`: + - either return raw `TreeStore` or raise a dedicated error + +Recommended first behavior: + +- missing manifest: return raw `TreeStore` +- malformed recognized manifest: raise error +- unknown manifest kind: return raw `TreeStore` + +This is conservative and avoids breaking forward compatibility unnecessarily. + +## Recommended Invariants + +- `/_meta` must always be a persisted `SChunk` +- `/_meta.vlmeta["kind"]` must be a string +- `/_meta.vlmeta["version"]` must be an integer +- logical object implementations own the schema of additional fields +- object materialization should not depend on `TreeStore` iteration order + +## Example: `CTable` + +With this contract, a root-level `CTable` would look like: + +- `/_meta` +- `/_valid_rows` +- `/_cols/id` +- `/_cols/score` + +And the manifest would contain: + +```python +{ + "kind": "ctable", + "version": 1, + "schema": {...}, +} +``` + +`blosc2.open(urlpath)` would: + +1. detect `b2tree` +2. open `TreeStore` +3. inspect `/_meta` +4. see `kind == "ctable"` +5. return `CTable` + +## Open Questions + +- Should unknown manifest kinds return raw `TreeStore`, warn, or raise? +- Should there eventually be a helper such as `blosc2.open_store_object(...)` + for explicit manifest-driven dispatch? +- Should `TreeStore` grow a helper for probing object roots, e.g. + `get_object_manifest("/")` or `has_object_manifest(path)`? +- Should object-manifest detection be limited to `TreeStore`, or later be + generalized to other store-like containers? + +## Recommended Next Step + +Use this contract for the first root-level `CTable` implementation: + +- generic manifest mechanism defined here +- `CTable` as the first supported manifest `kind` +- root-only dispatch in `blosc2.open()` + +Once that is stable, subtree object roots can be added without changing the +basic meaning of `/_meta`. From 5fc16b704392726833ea819c6d8b36498bab093a Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Wed, 15 Apr 2026 08:41:03 +0200 Subject: [PATCH 24/49] Relax DictStore and TreeStore path suffix requirements --- src/blosc2/ctable_storage.py | 8 +------ src/blosc2/dict_store.py | 31 ++++++++++++++++++--------- src/blosc2/schunk.py | 4 ++-- tests/ctable/test_schema_mutations.py | 4 ++-- tests/test_dict_store.py | 17 +++++++++++++++ tests/test_tree_store.py | 17 +++++++++++++++ 6 files changed, 60 insertions(+), 21 deletions(-) diff --git a/src/blosc2/ctable_storage.py b/src/blosc2/ctable_storage.py index bc101316..226fc29b 100644 --- a/src/blosc2/ctable_storage.py +++ b/src/blosc2/ctable_storage.py @@ -157,17 +157,11 @@ class FileTableStorage(TableStorage): def __init__(self, urlpath: str, mode: str) -> None: if mode not in ("r", "a", "w"): raise ValueError(f"mode must be 'r', 'a', or 'w'; got {mode!r}") - self._root = self._normalize_root(urlpath) + self._root = urlpath self._mode = mode self._meta: blosc2.SChunk | None = None self._store: blosc2.TreeStore | None = None - @staticmethod - def _normalize_root(urlpath: str) -> str: - if urlpath.endswith((".b2d", ".b2z")): - return urlpath - return f"{urlpath}.b2d" - # ------------------------------------------------------------------ # Key helpers # ------------------------------------------------------------------ diff --git a/src/blosc2/dict_store.py b/src/blosc2/dict_store.py index 21b5e821..dca5a141 100644 --- a/src/blosc2/dict_store.py +++ b/src/blosc2/dict_store.py @@ -29,7 +29,7 @@ class DictStore: """ Directory-based storage for compressed data using Blosc2. - Manages arrays in a directory (.b2d) or zip (.b2z) format. + Manages arrays in a directory or zip-file backed format. Supports the following types: @@ -46,10 +46,11 @@ class DictStore: Parameters ---------- localpath : str - Local path for the directory (".b2d") or file (".b2z"); other extensions - are not supported. If a directory is specified, it will be treated as - a Blosc2 directory format (B2DIR). If a file is specified, it - will be treated as a Blosc2 zip format (B2ZIP). + Local path for the directory or zip file. Paths ending in ``.b2d`` and + ``.b2z`` remain the recommended conventions. If the path already exists, + directories are treated as Blosc2 directory format (B2DIR) and files as + Blosc2 zip format (B2ZIP). For new extensionless paths, directory-backed + storage is used by default. mode : str, optional File mode ('r', 'w', 'a'). Default is 'a'. mmap_mode : str or None, optional @@ -117,8 +118,6 @@ def __init__( See :class:`DictStore` for full documentation of parameters. """ self.localpath = localpath if isinstance(localpath, str | bytes) else str(localpath) - if not self.localpath.endswith((".b2z", ".b2d")): - raise ValueError(f"localpath must have a .b2z or .b2d extension; you passed: {self.localpath}") if mode not in ("r", "w", "a"): raise ValueError("For DictStore containers, mode must be 'r', 'w', or 'a'") if mmap_mode not in (None, "r"): @@ -152,7 +151,16 @@ def __init__( def _setup_paths_and_dirs(self, tmpdir: str | None): """Set up working directories and paths.""" - self.is_zip_store = self.localpath.endswith(".b2z") + localpath_exists = os.path.exists(self.localpath) + if localpath_exists: + self.is_zip_store = os.path.isfile(self.localpath) + elif self.localpath.endswith(".b2z"): + self.is_zip_store = True + elif self.localpath.endswith(".b2d"): + self.is_zip_store = False + else: + # Default extensionless new stores to directory-backed layout. + self.is_zip_store = False if self.is_zip_store: if tmpdir is None: self._temp_dir_obj = tempfile.TemporaryDirectory() @@ -161,11 +169,14 @@ def _setup_paths_and_dirs(self, tmpdir: str | None): self.working_dir = tmpdir os.makedirs(tmpdir, exist_ok=True) self.b2z_path = self.localpath - else: # .b2d + else: self.working_dir = self.localpath if self.mode in ("w", "a"): os.makedirs(self.working_dir, exist_ok=True) - self.b2z_path = self.localpath[:-4] + ".b2z" + if self.localpath.endswith(".b2d"): + self.b2z_path = self.localpath[:-4] + ".b2z" + else: + self.b2z_path = self.localpath + ".b2z" self.estore_path = os.path.join(self.working_dir, "embed.b2e") diff --git a/src/blosc2/schunk.py b/src/blosc2/schunk.py index 8be3516a..506fbc78 100644 --- a/src/blosc2/schunk.py +++ b/src/blosc2/schunk.py @@ -1529,11 +1529,11 @@ def _open_meta(path, off=0): if urlpath.endswith(".b2e") and offset == 0: return _open_meta(urlpath) - if urlpath.endswith(".b2d") and os.path.isdir(urlpath): + if os.path.isdir(urlpath): embed_path = os.path.join(urlpath, "embed.b2e") if os.path.exists(embed_path): return _open_meta(embed_path) - if urlpath.endswith(".b2z") and os.path.isfile(urlpath): + if os.path.isfile(urlpath) and not urlpath.endswith(".b2e"): try: with open(urlpath, "rb") as f, zipfile.ZipFile(f) as zf: for info in zf.infolist(): diff --git a/tests/ctable/test_schema_mutations.py b/tests/ctable/test_schema_mutations.py index 5b5e47f4..9ede690f 100644 --- a/tests/ctable/test_schema_mutations.py +++ b/tests/ctable/test_schema_mutations.py @@ -145,11 +145,11 @@ def test_blosc2_open_raw_treestore_for_unknown_manifest_kind(): assert np.array_equal(opened["/payload"][:], np.arange(3)) -def test_extensionless_ctable_path_resolves_to_b2d_store(): +def test_extensionless_ctable_path_uses_extensionless_store(): path = os.path.join(TABLE_ROOT, "alias_ctable") t = CTable(Row, urlpath=path, mode="w", new_data=DATA10) t.close() - assert os.path.exists(path + ".b2d") + assert os.path.isdir(path) opened = blosc2.open(path, mode="r") assert isinstance(opened, CTable) np.testing.assert_array_equal(opened["id"].to_numpy(), np.arange(10)) diff --git a/tests/test_dict_store.py b/tests/test_dict_store.py index 4347eaf4..d772ca5e 100644 --- a/tests/test_dict_store.py +++ b/tests/test_dict_store.py @@ -114,6 +114,23 @@ def test_to_b2z_and_reopen(populated_dict_store): assert np.all(dstore_read["/nodeB"][:] == np.arange(6)) +def test_extensionless_dict_store_defaults_to_directory(tmp_path): + path = tmp_path / "test_dstore_extless" + + with DictStore(str(path), mode="w") as dstore: + dstore["/node1"] = np.arange(4) + + assert path.is_dir() + assert (path / "embed.b2e").exists() + + with DictStore(str(path), mode="r") as dstore: + assert np.array_equal(dstore["/node1"][:], np.arange(4)) + + opened = blosc2.open(str(path), mode="r") + assert isinstance(opened, DictStore) + assert np.array_equal(opened["/node1"][:], np.arange(4)) + + def test_to_b2z_from_readonly_b2d(): b2d_path = "test_to_b2z_from_readonly.b2d" b2z_path = "test_to_b2z_from_readonly.b2z" diff --git a/tests/test_tree_store.py b/tests/test_tree_store.py index 36daa0b3..e336befe 100644 --- a/tests/test_tree_store.py +++ b/tests/test_tree_store.py @@ -1057,6 +1057,23 @@ def test_open_context_manager(populated_tree_store): assert np.array_equal(tstore["/child0/data"][:], np.array([1, 2, 3])) +def test_extensionless_tree_store_defaults_to_directory(tmp_path): + path = tmp_path / "test_tstore_extless" + + with TreeStore(str(path), mode="w") as tstore: + tstore["/group/node"] = np.arange(6) + + assert path.is_dir() + assert (path / "embed.b2e").exists() + + with TreeStore(str(path), mode="r") as tstore: + assert np.array_equal(tstore["/group/node"][:], np.arange(6)) + + opened = blosc2.open(str(path), mode="r") + assert isinstance(opened, TreeStore) + assert np.array_equal(opened["/group/node"][:], np.arange(6)) + + @pytest.mark.parametrize("storage_type", ["b2d", "b2z"]) def test_mmap_mode_read_access(storage_type, tmp_path): path = tmp_path / f"test_tstore_mmap.{storage_type}" From f7cd02edf9335bcd5854996260c79f24a231fede Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Wed, 15 Apr 2026 08:46:03 +0200 Subject: [PATCH 25/49] Update CTable docs, examples, and benchmarks for TreeStore-backed persistence --- bench/ctable/bench_persistency.py | 28 ++++++++++++++++++---------- examples/ctable/persistence.py | 14 ++++++++++++-- plans/ctable-user-guide.md | 25 ++++++++++++++++++------- 3 files changed, 48 insertions(+), 19 deletions(-) diff --git a/bench/ctable/bench_persistency.py b/bench/ctable/bench_persistency.py index 2cc449bc..71d26bee 100644 --- a/bench/ctable/bench_persistency.py +++ b/bench/ctable/bench_persistency.py @@ -59,11 +59,11 @@ def clean() -> None: # Section 1: bulk creation — extend() # --------------------------------------------------------------------------- -sep("1. extend() — bulk insert: in-memory vs file-backed") +sep("1. extend() — bulk insert: in-memory vs TreeStore-backed") SIZES = [1_000, 10_000, 100_000, 1_000_000] -print(f"{'rows':>12} {'in-memory (s)':>16} {'file-backed (s)':>16} {'overhead':>10}") +print(f"{'rows':>12} {'in-memory (s)':>16} {'store-backed (s)':>16} {'overhead':>10}") print(f"{'----':>12} {'-------------':>16} {'---------------':>16} {'--------':>10}") for N in SIZES: @@ -77,6 +77,7 @@ def bench_file(N=N, data=data): clean() t = blosc2.CTable(Row, urlpath=TABLE_DIR + "/ext", mode="w", expected_size=N) t.extend(data, validate=False) + t.close() t_mem = tmin(bench_mem) t_file = tmin(bench_file) @@ -89,8 +90,8 @@ def bench_file(N=N, data=data): sep("2. open() — time to reopen a persistent table") -print(f"{'rows':>12} {'CTable.open() (s)':>20} {'CTable(..., mode=a) (s)':>24}") -print(f"{'----':>12} {'------------------':>20} {'------------------------':>24}") +print(f"{'rows':>12} {'blosc2.open() (s)':>18} {'CTable.open() (s)':>20} {'CTable(..., mode=a) (s)':>24}") +print(f"{'----':>12} {'----------------':>18} {'------------------':>20} {'------------------------':>24}") for N in SIZES: data = [(i, float(i % 100), i % 2 == 0) for i in range(N)] @@ -98,7 +99,11 @@ def bench_file(N=N, data=data): path = TABLE_DIR + "/reopen" t = blosc2.CTable(Row, urlpath=path, mode="w", expected_size=N) t.extend(data, validate=False) - del t + t.close() + + def bench_blosc2_open(path=path): + t2 = blosc2.open(path, mode="r") + _ = len(t2) def bench_open(path=path): t2 = blosc2.CTable.open(path, mode="r") @@ -108,15 +113,16 @@ def bench_ctor(path=path): t2 = blosc2.CTable(Row, urlpath=path, mode="a") _ = len(t2) + t_b2_open = tmin(bench_blosc2_open) t_open = tmin(bench_open) t_ctor = tmin(bench_ctor) - print(f"{N:>12,} {t_open:>20.4f} {t_ctor:>24.4f}") + print(f"{N:>12,} {t_b2_open:>18.4f} {t_open:>20.4f} {t_ctor:>24.4f}") # --------------------------------------------------------------------------- # Section 3: append() — single-row inserts after reopen # --------------------------------------------------------------------------- -sep("3. append() — 1 000 single-row inserts: in-memory vs file-backed") +sep("3. append() — 1 000 single-row inserts: in-memory vs TreeStore-backed") APPEND_N = 1_000 PREALLOCATE = 10_000 # avoid resize noise @@ -146,7 +152,8 @@ def bench_append_file(): # Reset file table before each run if label == "file-backed": clean() - blosc2.CTable(Row, urlpath=path, mode="w", expected_size=PREALLOCATE) + t = blosc2.CTable(Row, urlpath=path, mode="w", expected_size=PREALLOCATE) + t.close() elapsed = tmin(fn) us_per_row = elapsed / APPEND_N * 1e6 print(f"{label:>14} {elapsed:>12.4f} {us_per_row:>12.1f}") @@ -155,9 +162,9 @@ def bench_append_file(): # Section 4: column read — to_numpy() after reopen # --------------------------------------------------------------------------- -sep("4. column read — to_numpy() on 'id': in-memory vs file-backed") +sep("4. column read — to_numpy() on 'id': in-memory vs TreeStore-backed") -print(f"{'rows':>12} {'in-memory (s)':>16} {'file-backed (s)':>16} {'ratio':>8}") +print(f"{'rows':>12} {'in-memory (s)':>16} {'store-backed (s)':>16} {'ratio':>8}") print(f"{'----':>12} {'-------------':>16} {'---------------':>16} {'-----':>8}") for N in SIZES: @@ -170,6 +177,7 @@ def bench_append_file(): path = TABLE_DIR + "/read" t_file_table = blosc2.CTable(Row, urlpath=path, mode="w", expected_size=N) t_file_table.extend(data, validate=False) + t_file_table.close() # Reopen read-only (simulates a real read workload) t_ro = blosc2.CTable.open(path, mode="r") diff --git a/examples/ctable/persistence.py b/examples/ctable/persistence.py index 6433daef..81f4ffcd 100644 --- a/examples/ctable/persistence.py +++ b/examples/ctable/persistence.py @@ -5,7 +5,7 @@ # SPDX-License-Identifier: BSD-3-Clause ####################################################################### -# Persistence: write to disk, open read-only/read-write, save, load. +# Persistence: write to disk, open read-only/read-write, generic open(), save, load. import shutil import tempfile @@ -35,9 +35,11 @@ class Measurement: try: # -- Create directly on disk (mode="w") --------------------------------- + # Extensionless paths default to a directory-backed TreeStore. t = blosc2.CTable(Measurement, new_data=data, urlpath=disk_path, mode="w") print(f"Created on disk: {len(t):,} rows at '{disk_path}'") t.info() + t.close() # -- Open read-only (default) ------------------------------------------- ro = blosc2.CTable.open(disk_path) # mode="r" by default @@ -48,11 +50,18 @@ class Measurement: ro.append(Measurement(sensor_id=0, temperature=20.0, day=1)) except ValueError as e: print(f" Write blocked (read-only): {e}") + ro.close() + + # -- Generic open() materializes the CTable ----------------------------- + opened = blosc2.open(disk_path, mode="r") + print(f"Generic open(): {type(opened).__name__} with {len(opened):,} rows") + opened.close() # -- Open read-write and mutate ----------------------------------------- rw = blosc2.CTable.open(disk_path, mode="a") rw.append(Measurement(sensor_id=99, temperature=99.0, day=100)) print(f"\nAfter append (read-write): {len(rw):,} rows") + rw.close() # -- save(): copy in-memory table to disk ------------------------------- mem = blosc2.CTable(Measurement, new_data=data[:100]) @@ -62,7 +71,8 @@ class Measurement: # -- load(): pull a disk table fully into RAM --------------------------- ram = blosc2.CTable.load(disk_path) print(f"Loaded into RAM: {len(ram):,} rows (cbytes={ram.cbytes:,})") - assert len(ram) == len(rw) + with blosc2.CTable.open(disk_path) as check: + assert len(ram) == len(check) finally: shutil.rmtree(tmpdir) diff --git a/plans/ctable-user-guide.md b/plans/ctable-user-guide.md index 7ff7d41f..a49aea7a 100644 --- a/plans/ctable-user-guide.md +++ b/plans/ctable-user-guide.md @@ -98,8 +98,9 @@ t = b2.CTable(Row, cparams={"codec": b2.Codec.ZSTD, "clevel": 5}) ### Persistent tables -Pass `urlpath` to store the table on disk. The table root is a directory containing -compressed array files — everything is handled automatically. +Pass `urlpath` to store the table on disk. Persistent `CTable` is backed by a +`TreeStore`, and `blosc2.open(urlpath)` can materialize it directly from the +root `/_meta` manifest. ```python # Create a new persistent table (overwrites any existing table at that path) @@ -116,6 +117,9 @@ t = b2.CTable.open("people", mode="r") # explicit # Open read/write via the classmethod t = b2.CTable.open("people", mode="a") + +# Generic open() also materializes the richer object +t = b2.open("people") ``` `mode` values: @@ -129,12 +133,18 @@ t = b2.CTable.open("people", mode="a") In-memory tables (`urlpath=None`, the default) behave exactly as before — no `mode` or path handling is involved. -### Disk layout +Recommended conventions: + +- extensionless paths default to directory-backed stores +- `.b2d` and `.b2z` are still valid and useful conventions, but no longer required + +### Store layout ``` -people/ - _meta.b2frame ← schema JSON, kind marker, version (in vlmeta) - _valid_rows.b2nd ← tombstone mask +people/ ← TreeStore root (extensionless directory-backed example) + embed.b2e ← internal store metadata + _meta.b2f ← SChunk manifest with kind/version/schema in vlmeta + _valid_rows.b2nd ← tombstone mask _cols/ id.b2nd score.b2nd @@ -146,7 +156,8 @@ You can inspect the raw metadata: ```python import blosc2, json -meta = blosc2.open("people/_meta.b2frame") +store = blosc2.TreeStore("people", mode="r") +meta = store["/_meta"] print(meta.vlmeta["kind"]) # "ctable" print(meta.vlmeta["version"]) # 1 schema = json.loads(meta.vlmeta["schema"]) From 465e85520af9aa371419ba8dbb5176854187985f Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Wed, 15 Apr 2026 11:10:41 +0200 Subject: [PATCH 26/49] Move store extension doc to plans/ --- tree_store_extensions.md => plans/tree_store_extensions.md | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename tree_store_extensions.md => plans/tree_store_extensions.md (100%) diff --git a/tree_store_extensions.md b/plans/tree_store_extensions.md similarity index 100% rename from tree_store_extensions.md rename to plans/tree_store_extensions.md From d3148a11fccedafa5d8a560a7cc7c657041be237 Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Wed, 15 Apr 2026 12:08:34 +0200 Subject: [PATCH 27/49] Accelerate blosc2.open by trying the standard open first --- src/blosc2/schunk.py | 34 ++++++++++++++++++++++++++++------ 1 file changed, 28 insertions(+), 6 deletions(-) diff --git a/src/blosc2/schunk.py b/src/blosc2/schunk.py index 849a7867..2fbb0bd1 100644 --- a/src/blosc2/schunk.py +++ b/src/blosc2/schunk.py @@ -1824,18 +1824,40 @@ def open( if isinstance(urlpath, pathlib.PurePath): urlpath = str(urlpath) - urlpath = _resolve_store_alias(urlpath) + # Keep explicit store paths on the direct dispatch path. For regular + # Blosc containers, try the standard open first and only fall back to the + # more expensive store probing when that fails. + if urlpath.endswith((".b2d", ".b2z", ".b2e")): + special = _open_special_store(urlpath, mode, offset, **kwargs) + if special is not None: + if isinstance(special, blosc2.TreeStore): + return _open_treestore_root_object(special, urlpath, mode) + return special + + regular_exc = None + if os.path.exists(urlpath): + _set_default_dparams(kwargs) + try: + res = blosc2_ext.open(urlpath, mode, offset, **kwargs) + except Exception as exc: + regular_exc = exc + else: + return process_opened_object(res) - special = _open_special_store(urlpath, mode, offset, **kwargs) + resolved_urlpath = _resolve_store_alias(urlpath) + special_path = resolved_urlpath if resolved_urlpath != urlpath or not os.path.exists(urlpath) else urlpath + special = _open_special_store(special_path, mode, offset, **kwargs) if special is not None: if isinstance(special, blosc2.TreeStore): - return _open_treestore_root_object(special, urlpath, mode) + return _open_treestore_root_object(special, special_path, mode) return special - if not os.path.exists(urlpath): - raise FileNotFoundError(f"No such file or directory: {urlpath}") + if regular_exc is not None: + raise regular_exc + if not os.path.exists(special_path): + raise FileNotFoundError(f"No such file or directory: {special_path}") _set_default_dparams(kwargs) - res = blosc2_ext.open(urlpath, mode, offset, **kwargs) + res = blosc2_ext.open(special_path, mode, offset, **kwargs) return process_opened_object(res) From 41f7a146669cc60597691c5d55588ab266529d00 Mon Sep 17 00:00:00 2001 From: jorge Date: Wed, 15 Apr 2026 12:10:46 +0200 Subject: [PATCH 28/49] Nullable atribute in schema. --- doc/reference/classes.rst | 3 + doc/reference/ctable.rst | 339 +++++++++++++++ examples/ctable/aggregates.py | 24 +- examples/ctable/ctable_tutorial.ipynb | 580 ++++++++++++++++++++----- examples/ctable/nullable.py | 148 +++++++ examples/ctable/schema.py | 16 +- src/blosc2/ctable.py | 259 +++++++++-- src/blosc2/schema.py | 39 +- src/blosc2/schema_validation.py | 65 ++- src/blosc2/schema_vectorized.py | 43 +- tests/ctable/test_nullable.py | 549 +++++++++++++++++++++++ tests/ctable/test_table_persistency.py | 520 ++++++++++++++++++++++ 12 files changed, 2405 insertions(+), 180 deletions(-) create mode 100644 doc/reference/ctable.rst create mode 100644 examples/ctable/nullable.py create mode 100644 tests/ctable/test_nullable.py create mode 100644 tests/ctable/test_table_persistency.py diff --git a/doc/reference/classes.rst b/doc/reference/classes.rst index 83733b2f..3f632af3 100644 --- a/doc/reference/classes.rst +++ b/doc/reference/classes.rst @@ -7,6 +7,8 @@ Main Classes ------------ .. autosummary:: + CTable + Column NDArray NDField LazyArray @@ -26,6 +28,7 @@ Main Classes .. toctree:: :maxdepth: 1 + ctable ndarray ndfield lazyarray diff --git a/doc/reference/ctable.rst b/doc/reference/ctable.rst new file mode 100644 index 00000000..2d6b3273 --- /dev/null +++ b/doc/reference/ctable.rst @@ -0,0 +1,339 @@ +.. _CTable: + +CTable +====== + +A columnar compressed table backed by one :class:`~blosc2.NDArray` per column. +Each column is stored, compressed, and queried independently; rows are never +materialised in their entirety unless you explicitly call :meth:`~blosc2.CTable.to_arrow` +or iterate with :meth:`~blosc2.CTable.__iter__`. + +.. currentmodule:: blosc2 + +.. autoclass:: CTable + :members: + :member-order: groupwise + + .. rubric:: Special methods + + .. autosummary:: + + CTable.__len__ + CTable.__iter__ + CTable.__getitem__ + CTable.__repr__ + CTable.__str__ + + .. automethod:: __len__ + .. automethod:: __iter__ + .. automethod:: __getitem__ + .. automethod:: __repr__ + .. automethod:: __str__ + + +Construction +------------ + +.. autosummary:: + + CTable.__init__ + CTable.open + CTable.load + CTable.from_arrow + CTable.from_csv + +.. automethod:: CTable.__init__ +.. automethod:: CTable.open +.. automethod:: CTable.load +.. automethod:: CTable.from_arrow +.. automethod:: CTable.from_csv + + +Attributes +---------- + +.. autosummary:: + + CTable.col_names + CTable.nrows + CTable.ncols + CTable.cbytes + CTable.nbytes + CTable.schema + CTable.base + +.. autoproperty:: CTable.nrows +.. autoproperty:: CTable.ncols +.. autoproperty:: CTable.cbytes +.. autoproperty:: CTable.nbytes +.. autoproperty:: CTable.schema + + +Inserting data +-------------- + +.. autosummary:: + + CTable.append + CTable.extend + +.. automethod:: CTable.append +.. automethod:: CTable.extend + + +Querying +-------- + +.. autosummary:: + + CTable.where + CTable.select + CTable.head + CTable.tail + CTable.sample + CTable.sort_by + +.. automethod:: CTable.where +.. automethod:: CTable.select +.. automethod:: CTable.head +.. automethod:: CTable.tail +.. automethod:: CTable.sample +.. automethod:: CTable.sort_by + + +Aggregates & statistics +----------------------- + +.. autosummary:: + + CTable.describe + CTable.cov + +.. automethod:: CTable.describe +.. automethod:: CTable.cov + + +Mutations +--------- + +.. autosummary:: + + CTable.delete + CTable.compact + CTable.add_column + CTable.drop_column + CTable.rename_column + +.. automethod:: CTable.delete +.. automethod:: CTable.compact +.. automethod:: CTable.add_column +.. automethod:: CTable.drop_column +.. automethod:: CTable.rename_column + + +Persistence +----------- + +.. autosummary:: + + CTable.save + CTable.to_csv + CTable.to_arrow + +.. automethod:: CTable.save +.. automethod:: CTable.to_csv +.. automethod:: CTable.to_arrow + + +Inspection +---------- + +.. autosummary:: + + CTable.info + CTable.schema_dict + CTable.column_schema + +.. automethod:: CTable.info +.. automethod:: CTable.schema_dict +.. automethod:: CTable.column_schema + + +---- + +.. _Column: + +Column +====== + +A lazy column accessor returned by ``table["col_name"]`` or ``table.col_name``. +All index operations and aggregates apply the table's tombstone mask +(``_valid_rows``) so deleted rows are silently excluded. + +.. autoclass:: Column + :members: + :member-order: groupwise + + .. rubric:: Special methods + + .. autosummary:: + + Column.__len__ + Column.__iter__ + Column.__getitem__ + Column.__setitem__ + + .. automethod:: __len__ + .. automethod:: __iter__ + .. automethod:: __getitem__ + .. automethod:: __setitem__ + + +Attributes +---------- + +.. autosummary:: + + Column.dtype + Column.null_value + +.. autoproperty:: Column.dtype +.. autoproperty:: Column.null_value + + +Data access +----------- + +.. autosummary:: + + Column.to_numpy + Column.iter_chunks + Column.assign + +.. automethod:: Column.to_numpy +.. automethod:: Column.iter_chunks +.. automethod:: Column.assign + + +Nullable helpers +---------------- + +.. autosummary:: + + Column.is_null + Column.notnull + Column.null_count + +.. automethod:: Column.is_null +.. automethod:: Column.notnull +.. automethod:: Column.null_count + + +Unique values +------------- + +.. autosummary:: + + Column.unique + Column.value_counts + +.. automethod:: Column.unique +.. automethod:: Column.value_counts + + +Aggregates +---------- + +Null sentinel values are automatically excluded from all aggregates. + +.. autosummary:: + + Column.sum + Column.min + Column.max + Column.mean + Column.std + Column.any + Column.all + +.. automethod:: Column.sum +.. automethod:: Column.min +.. automethod:: Column.max +.. automethod:: Column.mean +.. automethod:: Column.std +.. automethod:: Column.any +.. automethod:: Column.all + + +---- + +.. _SchemaSpecs: + +Schema Specs +============ + +Schema specs are passed to :func:`field` to declare a column's type, +storage constraints, and optional null sentinel. They are also +available directly in the ``blosc2`` namespace (e.g. ``blosc2.int64``). + +.. currentmodule:: blosc2 + +.. autofunction:: field + +Numeric +------- + +.. autosummary:: + + int8 + int16 + int32 + int64 + uint8 + uint16 + uint32 + uint64 + float32 + float64 + +.. autoclass:: int8 +.. autoclass:: int16 +.. autoclass:: int32 +.. autoclass:: int64 +.. autoclass:: uint8 +.. autoclass:: uint16 +.. autoclass:: uint32 +.. autoclass:: uint64 +.. autoclass:: float32 +.. autoclass:: float64 + +Complex +------- + +.. autosummary:: + + complex64 + complex128 + +.. autoclass:: complex64 +.. autoclass:: complex128 + +Boolean +------- + +.. autosummary:: + + bool + +.. autoclass:: bool + +Text & binary +------------- + +.. autosummary:: + + string + bytes + +.. autoclass:: string +.. autoclass:: bytes diff --git a/examples/ctable/aggregates.py b/examples/ctable/aggregates.py index bdda5080..9ab7debb 100644 --- a/examples/ctable/aggregates.py +++ b/examples/ctable/aggregates.py @@ -6,7 +6,7 @@ ####################################################################### # Column aggregates: sum, min, max, mean, std, unique, value_counts, -# describe, and covariance matrix. +# describe, covariance matrix, and null-aware aggregation. from dataclasses import dataclass @@ -18,7 +18,8 @@ @dataclass class Reading: sensor_id: int = blosc2.field(blosc2.int32(ge=0, le=9)) - temperature: float = blosc2.field(blosc2.float64(ge=-50.0, le=60.0), default=20.0) + # null_value=-999 means "sensor offline" — excluded from aggregates + temperature: float = blosc2.field(blosc2.float64(ge=-50.0, le=60.0, null_value=-999.0), default=-999.0) humidity: float = blosc2.field(blosc2.float64(ge=0.0, le=100.0), default=50.0) alert: bool = blosc2.field(blosc2.bool(), default=False) @@ -31,20 +32,25 @@ class Reading: humidities = rng.uniform(30.0, 90.0, size=N).astype(np.float64) alerts = rng.random(N) < 0.05 +# Simulate ~5 % of sensors being offline (temperature = null sentinel) +offline = rng.random(N) < 0.05 +temperatures[offline] = -999.0 + data = list( zip(station_ids.tolist(), temperatures.tolist(), humidities.tolist(), alerts.tolist(), strict=False) ) t = blosc2.CTable(Reading, new_data=data) -print(f"Table: {len(t)} rows\n") +print(f"Table: {len(t)} rows ({t['temperature'].null_count()} offline sensors)\n") -# -- per-column aggregates -------------------------------------------------- +# -- per-column aggregates (null sentinels are skipped automatically) -------- temp = t["temperature"] -print(f"temperature sum : {temp.sum():.2f}") -print(f"temperature mean : {temp.mean():.2f}") -print(f"temperature std : {temp.std():.2f}") -print(f"temperature min : {temp.min():.2f}") -print(f"temperature max : {temp.max():.2f}") +print(f"temperature null : {temp.null_count()} offline readings") +print(f"temperature sum : {temp.sum():.2f}") +print(f"temperature mean : {temp.mean():.2f}") +print(f"temperature std : {temp.std():.2f}") +print(f"temperature min : {temp.min():.2f}") +print(f"temperature max : {temp.max():.2f}") print(f"\nalert any : {t['alert'].any()}") print(f"alert all : {t['alert'].all()}") diff --git a/examples/ctable/ctable_tutorial.ipynb b/examples/ctable/ctable_tutorial.ipynb index fbb8f230..27e98b45 100644 --- a/examples/ctable/ctable_tutorial.ipynb +++ b/examples/ctable/ctable_tutorial.ipynb @@ -20,9 +20,13 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 36, "id": "a4073a3e", "metadata": { + "ExecuteTime": { + "end_time": "2026-04-14T12:39:24.795843222Z", + "start_time": "2026-04-14T12:39:24.555798389Z" + }, "execution": { "iopub.execute_input": "2026-04-07T12:06:13.708034Z", "iopub.status.busy": "2026-04-07T12:06:13.707898Z", @@ -57,9 +61,13 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 37, "id": "c97f9123", "metadata": { + "ExecuteTime": { + "end_time": "2026-04-14T12:39:25.040587457Z", + "start_time": "2026-04-14T12:39:24.896936343Z" + }, "execution": { "iopub.execute_input": "2026-04-07T12:06:14.164585Z", "iopub.status.busy": "2026-04-07T12:06:14.164404Z", @@ -102,9 +110,13 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 38, "id": "fdc64a5b", "metadata": { + "ExecuteTime": { + "end_time": "2026-04-14T12:39:25.223196169Z", + "start_time": "2026-04-14T12:39:25.094103593Z" + }, "execution": { "iopub.execute_input": "2026-04-07T12:06:14.170432Z", "iopub.status.busy": "2026-04-07T12:06:14.170315Z", @@ -145,9 +157,13 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 39, "id": "76b011d0", "metadata": { + "ExecuteTime": { + "end_time": "2026-04-14T12:39:25.374558674Z", + "start_time": "2026-04-14T12:39:25.246329921Z" + }, "execution": { "iopub.execute_input": "2026-04-07T12:06:14.233319Z", "iopub.status.busy": "2026-04-07T12:06:14.233201Z", @@ -187,9 +203,13 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 40, "id": "32fa7ac0", "metadata": { + "ExecuteTime": { + "end_time": "2026-04-14T12:39:25.593377530Z", + "start_time": "2026-04-14T12:39:25.403964552Z" + }, "execution": { "iopub.execute_input": "2026-04-07T12:06:14.237010Z", "iopub.status.busy": "2026-04-07T12:06:14.236893Z", @@ -242,9 +262,13 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 41, "id": "745f7e81", "metadata": { + "ExecuteTime": { + "end_time": "2026-04-14T12:39:25.840480527Z", + "start_time": "2026-04-14T12:39:25.619190657Z" + }, "execution": { "iopub.execute_input": "2026-04-07T12:06:14.251085Z", "iopub.status.busy": "2026-04-07T12:06:14.250963Z", @@ -302,9 +326,13 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 42, "id": "9c55f0cf", "metadata": { + "ExecuteTime": { + "end_time": "2026-04-14T12:39:25.935251137Z", + "start_time": "2026-04-14T12:39:25.861289040Z" + }, "execution": { "iopub.execute_input": "2026-04-07T12:06:14.270039Z", "iopub.status.busy": "2026-04-07T12:06:14.269916Z", @@ -363,9 +391,13 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 43, "id": "ce65cad9", "metadata": { + "ExecuteTime": { + "end_time": "2026-04-14T12:39:25.965548320Z", + "start_time": "2026-04-14T12:39:25.937711110Z" + }, "execution": { "iopub.execute_input": "2026-04-07T12:06:14.279795Z", "iopub.status.busy": "2026-04-07T12:06:14.279663Z", @@ -387,9 +419,13 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 44, "id": "0627de42", "metadata": { + "ExecuteTime": { + "end_time": "2026-04-14T12:39:26.061360828Z", + "start_time": "2026-04-14T12:39:25.968553107Z" + }, "execution": { "iopub.execute_input": "2026-04-07T12:06:14.284454Z", "iopub.status.busy": "2026-04-07T12:06:14.284339Z", @@ -563,9 +599,13 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 45, "id": "6886056b", "metadata": { + "ExecuteTime": { + "end_time": "2026-04-14T12:39:26.161717685Z", + "start_time": "2026-04-14T12:39:26.083145695Z" + }, "execution": { "iopub.execute_input": "2026-04-07T12:06:14.323908Z", "iopub.status.busy": "2026-04-07T12:06:14.323755Z", @@ -604,9 +644,13 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 46, "id": "ba2d719b", "metadata": { + "ExecuteTime": { + "end_time": "2026-04-14T12:39:26.233532577Z", + "start_time": "2026-04-14T12:39:26.164808834Z" + }, "execution": { "iopub.execute_input": "2026-04-07T12:06:14.342416Z", "iopub.status.busy": "2026-04-07T12:06:14.342298Z", @@ -652,9 +696,13 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 47, "id": "dd01bd49", "metadata": { + "ExecuteTime": { + "end_time": "2026-04-14T12:39:26.295432357Z", + "start_time": "2026-04-14T12:39:26.250765009Z" + }, "execution": { "iopub.execute_input": "2026-04-07T12:06:14.359948Z", "iopub.status.busy": "2026-04-07T12:06:14.359836Z", @@ -700,9 +748,13 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 48, "id": "96f871f0", "metadata": { + "ExecuteTime": { + "end_time": "2026-04-14T12:39:26.384760589Z", + "start_time": "2026-04-14T12:39:26.300446729Z" + }, "execution": { "iopub.execute_input": "2026-04-07T12:06:14.370596Z", "iopub.status.busy": "2026-04-07T12:06:14.370474Z", @@ -743,9 +795,13 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 49, "id": "3671f4d2", "metadata": { + "ExecuteTime": { + "end_time": "2026-04-14T12:39:26.473972443Z", + "start_time": "2026-04-14T12:39:26.405188587Z" + }, "execution": { "iopub.execute_input": "2026-04-07T12:06:14.403026Z", "iopub.status.busy": "2026-04-07T12:06:14.402907Z", @@ -808,9 +864,13 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 50, "id": "332e21ba", "metadata": { + "ExecuteTime": { + "end_time": "2026-04-14T12:39:26.614841819Z", + "start_time": "2026-04-14T12:39:26.479493618Z" + }, "execution": { "iopub.execute_input": "2026-04-07T12:06:14.433114Z", "iopub.status.busy": "2026-04-07T12:06:14.432994Z", @@ -827,31 +887,13 @@ "--------------------------------------------------\n", "Madrid 15.0° -1.8° 31.4° 9.3°\n", "London 10.8° -0.3° 22.7° 5.3°\n", - "Beijing 12.1° -9.1° 33.8° 11.5°\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Beijing 12.1° -9.1° 33.8° 11.5°\n", "New York 13.0° -4.4° 30.9° 10.2°\n", - "Tokyo 15.1° -0.2° 31.0° 8.5°\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Tokyo 15.1° -0.2° 31.0° 8.5°\n", "Sydney 17.8° 4.7° 30.9° 5.9°\n", "Cairo 21.9° 2.8° 39.7° 10.1°\n", "Moscow 5.0° -17.5° 26.3° 12.9°\n", - "Mumbai 27.9° 18.4° 36.6° 3.5°\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Mumbai 27.9° 18.4° 36.6° 3.5°\n", "Sao Paulo 21.9° 12.7° 30.9° 4.1°\n" ] } @@ -875,9 +917,13 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 51, "id": "7254f3b1", "metadata": { + "ExecuteTime": { + "end_time": "2026-04-14T12:39:26.710706134Z", + "start_time": "2026-04-14T12:39:26.628790601Z" + }, "execution": { "iopub.execute_input": "2026-04-07T12:06:14.520839Z", "iopub.status.busy": "2026-04-07T12:06:14.520722Z", @@ -941,9 +987,13 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 52, "id": "6d0dd2c1", "metadata": { + "ExecuteTime": { + "end_time": "2026-04-14T12:39:26.797016145Z", + "start_time": "2026-04-14T12:39:26.714612755Z" + }, "execution": { "iopub.execute_input": "2026-04-07T12:06:14.543869Z", "iopub.status.busy": "2026-04-07T12:06:14.543748Z", @@ -961,13 +1011,7 @@ "temp 114.963 0.018 -3.523 -0.207\n", "humidity 0.018 256.861 10.773 6.652\n", "wind -3.523 10.773 23.760 -2.650\n", - "pressure -0.207 6.652 -2.650 28.394\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "pressure -0.207 6.652 -2.650 28.394\n", "\n", "Correlation matrix:\n", " temp humidity wind pressure\n", @@ -1015,9 +1059,13 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 53, "id": "89e89177", "metadata": { + "ExecuteTime": { + "end_time": "2026-04-14T12:39:26.878728686Z", + "start_time": "2026-04-14T12:39:26.800640315Z" + }, "execution": { "iopub.execute_input": "2026-04-07T12:06:14.560797Z", "iopub.status.busy": "2026-04-07T12:06:14.560666Z", @@ -1054,9 +1102,13 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 54, "id": "a439fecd", "metadata": { + "ExecuteTime": { + "end_time": "2026-04-14T12:39:27.020397605Z", + "start_time": "2026-04-14T12:39:26.880962534Z" + }, "execution": { "iopub.execute_input": "2026-04-07T12:06:14.578621Z", "iopub.status.busy": "2026-04-07T12:06:14.578475Z", @@ -1072,22 +1124,10 @@ "City Summer mean Summer max Summer humidity\n", "----------------------------------------------------------\n", "Madrid 25.8°C 31.4°C 43.8% \n", - "London 16.5°C 22.7°C 74.6% \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "London 16.5°C 22.7°C 74.6% \n", "Cairo 33.5°C 39.7°C 34.4% \n", "Moscow 20.1°C 26.3°C 69.3% \n", - "Tokyo 25.1°C 31.0°C 73.0% \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "Tokyo 25.1°C 31.0°C 73.0% \n", "Sydney 24.6°C 30.9°C 63.8% (S. summer)\n" ] } @@ -1115,9 +1155,13 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 55, "id": "4e2161ee", "metadata": { + "ExecuteTime": { + "end_time": "2026-04-14T12:39:27.105416029Z", + "start_time": "2026-04-14T12:39:27.022601226Z" + }, "execution": { "iopub.execute_input": "2026-04-07T12:06:14.696603Z", "iopub.status.busy": "2026-04-07T12:06:14.695965Z", @@ -1130,13 +1174,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "10 hottest days in Madrid:\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "10 hottest days in Madrid:\n", " city day temperature humidity \n", " " ] }, + "jetTransient": { + "display_id": null + }, "metadata": {}, "output_type": "display_data" } @@ -1238,9 +1283,13 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 57, "id": "94e141a4", "metadata": { + "ExecuteTime": { + "end_time": "2026-04-14T12:39:27.585476061Z", + "start_time": "2026-04-14T12:39:27.354159759Z" + }, "execution": { "iopub.execute_input": "2026-04-07T12:06:15.038502Z", "iopub.status.busy": "2026-04-07T12:06:15.038229Z", @@ -1251,11 +1300,14 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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      " ] }, + "jetTransient": { + "display_id": null + }, "metadata": {}, "output_type": "display_data" } @@ -1306,9 +1358,13 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 58, "id": "334a833a", "metadata": { + "ExecuteTime": { + "end_time": "2026-04-14T12:39:27.853545427Z", + "start_time": "2026-04-14T12:39:27.605095719Z" + }, "execution": { "iopub.execute_input": "2026-04-07T12:06:15.573465Z", "iopub.status.busy": "2026-04-07T12:06:15.573114Z", @@ -1319,11 +1375,14 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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      " ] }, + "jetTransient": { + "display_id": null + }, "metadata": {}, "output_type": "display_data" } @@ -1367,9 +1426,13 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 59, "id": "2c3a977b", "metadata": { + "ExecuteTime": { + "end_time": "2026-04-14T12:39:27.972265822Z", + "start_time": "2026-04-14T12:39:27.857019185Z" + }, "execution": { "iopub.execute_input": "2026-04-07T12:06:16.566822Z", "iopub.status.busy": "2026-04-07T12:06:16.563241Z", @@ -1427,9 +1490,13 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 60, "id": "eecb61ae", "metadata": { + "ExecuteTime": { + "end_time": "2026-04-14T12:39:28.099015656Z", + "start_time": "2026-04-14T12:39:27.998362632Z" + }, "execution": { "iopub.execute_input": "2026-04-07T12:06:16.725101Z", "iopub.status.busy": "2026-04-07T12:06:16.724741Z", @@ -1442,7 +1509,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Saved to '/tmp/blosc2_climate_sjdvr9pf/climate'\n", + "Saved to '/tmp/blosc2_climate_sqyd_n63/climate'\n", "On-disk size: 55.4 KB\n", "In-memory compressed: 53.4 KB\n" ] @@ -1467,9 +1534,13 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 61, "id": "e32248f9", "metadata": { + "ExecuteTime": { + "end_time": "2026-04-14T12:39:28.169769824Z", + "start_time": "2026-04-14T12:39:28.102254061Z" + }, "execution": { "iopub.execute_input": "2026-04-07T12:06:16.825168Z", "iopub.status.busy": "2026-04-07T12:06:16.824860Z", @@ -1516,9 +1587,13 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 62, "id": "8fce9c32", "metadata": { + "ExecuteTime": { + "end_time": "2026-04-14T12:39:28.246929300Z", + "start_time": "2026-04-14T12:39:28.175398592Z" + }, "execution": { "iopub.execute_input": "2026-04-07T12:06:16.981648Z", "iopub.status.busy": "2026-04-07T12:06:16.979117Z", @@ -1547,9 +1622,13 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 63, "id": "9b2100c1", "metadata": { + "ExecuteTime": { + "end_time": "2026-04-14T12:39:28.328100083Z", + "start_time": "2026-04-14T12:39:28.250219252Z" + }, "execution": { "iopub.execute_input": "2026-04-07T12:06:17.406008Z", "iopub.status.busy": "2026-04-07T12:06:17.403458Z", @@ -1593,31 +1672,326 @@ }, { "cell_type": "markdown", - "id": "405cd155", + "id": "c026d22c", "metadata": {}, + "source": "---\n## Part 9 — Nullable columns\n\nReal-world data is often incomplete. CTable handles missing values through a **null sentinel** approach: you declare a specific value (e.g. `-1`, `\"\"`, or `float(\"nan\")`) that represents \"no data\" for a column. The library treats it transparently in aggregates, sorting, `unique()`, `value_counts()`, and Arrow export.\n\nThis pattern is most useful for **integer and string** columns, which have no natural missing-value representation (unlike floats, which can use `NaN`)." + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "be1d1da6", + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-14T12:39:28.427052030Z", + "start_time": "2026-04-14T12:39:28.332240964Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " sensor_id temperature location \n", + " int32 float64 +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +# Nullable columns: null_value sentinels, null-aware aggregates, +# is_null / notnull, sort nulls-last, Arrow null masking, CSV empty cells. +# +# CTable does not have a built-in "missing" bit per row like pandas does. +# Instead it uses a *sentinel value* approach: you choose a specific value +# that represents "null" for a column, and the library treats it +# transparently in aggregates, sorting, unique(), value_counts(), and +# Arrow export. +# +# This is especially useful for integer and string columns that have no +# natural null (unlike float, which can use NaN). + +import os +import tempfile +from dataclasses import dataclass + +import blosc2 + +# --------------------------------------------------------------------------- +# Schema with nullable columns +# --------------------------------------------------------------------------- +# Use null_value= on any spec to declare the sentinel. +# The sentinel bypasses validation constraints (ge/le etc.) so you can +# store it even when it would otherwise violate them. + + +@dataclass +class Reading: + sensor_id: int = blosc2.field(blosc2.int32(ge=0)) + # -999 is "no reading" for temperature (normally ge=-50, le=60) + temperature: float = blosc2.field(blosc2.float64(ge=-50.0, le=60.0, null_value=-999.0), default=-999.0) + # "" is "unknown" for location (string) + location: str = blosc2.field(blosc2.string(max_length=16, null_value=""), default="") + # -1 is "not measured" for signal strength (normally ge=0, le=100) + signal: int = blosc2.field(blosc2.int8(ge=0, le=100, null_value=-1), default=-1) + + +data = [ + (0, 22.3, "roof", 87), + (1, -999.0, "cellar", 41), # temperature unknown + (2, 18.7, "", -1), # location and signal unknown + (3, 31.5, "garage", -1), # signal unknown + (4, -999.0, "", 62), # temperature and location unknown + (5, 15.1, "roof", 95), +] + +t = blosc2.CTable(Reading, new_data=data) +print("Table with nullable columns:") +print(t) + +# --------------------------------------------------------------------------- +# Detecting nulls +# --------------------------------------------------------------------------- +print("\n--- is_null() / notnull() ---") +temp_null = t["temperature"].is_null() +print(f"temperature is_null : {temp_null.tolist()}") +print(f"temperature null_count: {t['temperature'].null_count()}") + +loc_null = t["location"].is_null() +print(f"location is_null : {loc_null.tolist()}") + +# Use notnull() as a filter mask +valid_temps = t["temperature"].to_numpy()[t["temperature"].notnull()] +print(f"Valid temperatures : {valid_temps}") + +# --------------------------------------------------------------------------- +# Null-aware aggregates +# --------------------------------------------------------------------------- +print("\n--- Aggregates skip null sentinels ---") +print(f"temperature.mean() = {t['temperature'].mean():.2f} (only 3 non-null readings)") +print(f"temperature.min() = {t['temperature'].min():.2f}") +print(f"temperature.max() = {t['temperature'].max():.2f}") +print(f"signal.sum() = {t['signal'].sum()} (non-null: 87+41+62+95 = 285)") + +# --------------------------------------------------------------------------- +# unique() and value_counts() exclude the null sentinel +# --------------------------------------------------------------------------- +print("\n--- unique / value_counts exclude null ---") +print(f"location unique : {t['location'].unique().tolist()}") +print(f"signal value_counts : {t['signal'].value_counts()}") + +# --------------------------------------------------------------------------- +# Appending: the sentinel bypasses validation constraints +# --------------------------------------------------------------------------- +print("\n--- Append with sentinel bypasses ge/le constraints ---") +# temperature has ge=-50, le=60; normally -999 would fail — but it's the sentinel +t.append((6, -999.0, "attic", 55)) +print(f"Appended sensor 6 (temperature=null). Rows: {len(t)}") +assert t["temperature"].null_count() == 3 + +# --------------------------------------------------------------------------- +# Sorting: nulls always go last, regardless of ascending/descending +# --------------------------------------------------------------------------- +print("\n--- sort_by: nulls go last ---") +s_asc = t.sort_by("temperature") +print("Ascending (nulls last):") +print([round(v, 1) for v in s_asc["temperature"].to_numpy().tolist()]) + +s_desc = t.sort_by("temperature", ascending=False) +print("Descending (nulls still last):") +print([round(v, 1) for v in s_desc["temperature"].to_numpy().tolist()]) + +# --------------------------------------------------------------------------- +# Arrow interop: null sentinels become proper Arrow nulls +# --------------------------------------------------------------------------- +try: + import pyarrow as _pa # noqa: F401 + + arrow = t.to_arrow() + temp_col = arrow.column("temperature") + loc_col = arrow.column("location") + print("\n--- Arrow export ---") + print(f"Arrow temperature null_count: {temp_col.null_count}") + print(f"Arrow location null_count : {loc_col.null_count}") + print(f"Arrow temperature values : {temp_col.to_pylist()}") +except ImportError: + print("\npyarrow not installed — skipping Arrow demo.") + +# --------------------------------------------------------------------------- +# CSV round-trip: empty cells become the null sentinel on import +# --------------------------------------------------------------------------- +with tempfile.NamedTemporaryFile(mode="w", suffix=".csv", delete=False) as f: + f.write("sensor_id,temperature,location,signal\n") + f.write("10,25.1,lab,80\n") + f.write("11,,office,\n") # temperature and signal missing → sentinels + f.write("12,18.3,,70\n") # location missing → sentinel "" + csv_path = f.name + +print("\n--- from_csv with empty cells ---") +t2 = blosc2.CTable.from_csv(csv_path, Reading) +print(t2) +print(f"temperature null_count: {t2['temperature'].null_count()}") +print(f"signal null_count : {t2['signal'].null_count()}") +print(f"location null_count : {t2['location'].null_count()}") +os.unlink(csv_path) + +# --------------------------------------------------------------------------- +# describe() shows null count for nullable columns +# --------------------------------------------------------------------------- +print("\n--- describe() ---") +t.describe() diff --git a/examples/ctable/schema.py b/examples/ctable/schema.py index 327b0a11..5a5b0d05 100644 --- a/examples/ctable/schema.py +++ b/examples/ctable/schema.py @@ -5,7 +5,7 @@ # SPDX-License-Identifier: BSD-3-Clause ####################################################################### -# Schema layer: dataclass field specs, constraints, and validation. +# Schema layer: dataclass field specs, constraints, validation, and null_value. from dataclasses import dataclass @@ -17,7 +17,8 @@ class Product: id: int = blosc2.field(blosc2.int64(ge=0)) price: float = blosc2.field(blosc2.float64(ge=0.0, le=10_000.0), default=0.0) stock: int = blosc2.field(blosc2.int32(ge=0), default=0) - category: str = blosc2.field(blosc2.string(max_length=32), default="") + # null_value="" means an empty string represents "unknown category" + category: str = blosc2.field(blosc2.string(max_length=32, null_value=""), default="") on_sale: bool = blosc2.field(blosc2.bool(), default=False) @@ -26,17 +27,21 @@ class Product: # Valid row t.append(Product(id=1, price=29.99, stock=100, category="electronics", on_sale=False)) t.append(Product(id=2, price=4.50, stock=200, category="food", on_sale=True)) +# "" is the null sentinel for category — stored as-is, not rejected +t.append(Product(id=3, price=0.0, stock=0, category="", on_sale=False)) print("Valid rows appended successfully.") print(t) # Inspect the compiled schema print("Schema:") for col in t.schema.columns: - print(f" {col.name:<12} dtype={col.dtype} spec={col.spec}") + nv = getattr(col.spec, "null_value", None) + nv_str = f" null_value={nv!r}" if nv is not None else "" + print(f" {col.name:<12} dtype={col.dtype} spec={col.spec}{nv_str}") # Constraint violation: price < 0 try: - t.append(Product(id=3, price=-1.0, stock=10, category="misc", on_sale=False)) + t.append(Product(id=4, price=-1.0, stock=10, category="misc", on_sale=False)) except Exception as e: print(f"\nCaught validation error (price < 0): {e}") @@ -48,8 +53,9 @@ class Product: # String too long (max_length=32) try: - t.append(Product(id=4, price=1.0, stock=1, category="a" * 50, on_sale=False)) + t.append(Product(id=5, price=1.0, stock=1, category="a" * 50, on_sale=False)) except Exception as e: print(f"Caught validation error (string too long): {e}") print(f"\nTable still has {len(t)} valid rows.") +print(f"category null_count: {t['category'].null_count()} (the row with category='' is null)") diff --git a/src/blosc2/ctable.py b/src/blosc2/ctable.py index 010cfe0f..0f08e9c8 100644 --- a/src/blosc2/ctable.py +++ b/src/blosc2/ctable.py @@ -488,19 +488,84 @@ def assign(self, data) -> None: live_pos = np.where(self._valid_rows[:])[0] self._raw_col[live_pos] = arr + # ------------------------------------------------------------------ + # Null sentinel support + # ------------------------------------------------------------------ + + @property + def null_value(self): + """The sentinel value that represents NULL for this column, or ``None``.""" + col_info = self._table._schema.columns_by_name.get(self._col_name) + if col_info is None: + return None + return getattr(col_info.spec, "null_value", None) + + def _null_mask_for(self, arr: np.ndarray) -> np.ndarray: + """Return a bool array True where *arr* contains the null sentinel. + + Always returns an array of the same length as *arr*; all False when + no null_value is configured. + """ + nv = self.null_value + if nv is None: + return np.zeros(len(arr), dtype=np.bool_) + if isinstance(nv, float) and np.isnan(nv): + return np.isnan(arr) + return arr == nv + + def is_null(self) -> np.ndarray: + """Return a boolean array True where the live value is the null sentinel.""" + return self._null_mask_for(self.to_numpy()) + + def notnull(self) -> np.ndarray: + """Return a boolean array True where the live value is *not* the null sentinel.""" + return ~self.is_null() + + def null_count(self) -> int: + """Return the number of live rows whose value equals the null sentinel. + + Returns ``0`` in O(1) if no ``null_value`` is configured for this column. + """ + if self.null_value is None: + return 0 + return int(self.is_null().sum()) + + def _nonnull_chunks(self): + """Yield chunks of live, non-null values. + + Each yielded array has the null sentinel values removed. If no + null_value is configured this behaves identically to + :meth:`iter_chunks`. + """ + nv = self.null_value + if nv is None: + yield from self.iter_chunks() + return + is_nan_nv = isinstance(nv, float) and np.isnan(nv) + for chunk in self.iter_chunks(): + if is_nan_nv: + mask = ~np.isnan(chunk) + else: + mask = chunk != nv + filtered = chunk[mask] + if len(filtered) > 0: + yield filtered + def unique(self) -> np.ndarray: - """Return sorted array of unique live values. + """Return sorted array of unique live, non-null values. + Null sentinel values are excluded. Processes data in chunks — never loads the full column at once. """ seen: set = set() - for chunk in self.iter_chunks(): + for chunk in self._nonnull_chunks(): seen.update(chunk.tolist()) return np.array(sorted(seen), dtype=self.dtype) def value_counts(self) -> dict: """Return a ``{value: count}`` dict sorted by count descending. + Null sentinel values are excluded. Processes data in chunks — never loads the full column at once. Example @@ -509,7 +574,7 @@ def value_counts(self) -> dict: {True: 8432, False: 1568} """ counts: dict = {} - for chunk in self.iter_chunks(): + for chunk in self._nonnull_chunks(): for val in chunk.tolist(): counts[val] = counts.get(val, 0) + 1 return dict(sorted(counts.items(), key=lambda kv: -kv[1])) @@ -544,10 +609,11 @@ def _require_kind(self, kinds: str, op: str) -> None: # ------------------------------------------------------------------ def sum(self): - """Sum of all live values. + """Sum of all live, non-null values. Supported dtypes: bool, int, uint, float, complex. Bool values are counted as 0 / 1. + Null sentinel values are skipped. """ self._require_kind("biufc", "sum") self._require_nonempty("sum") @@ -560,7 +626,7 @@ def sum(self): ) ) result = acc_dtype(0) - for chunk in self.iter_chunks(): + for chunk in self._nonnull_chunks(): result += chunk.sum(dtype=acc_dtype) # Return in the column's natural dtype when it fits, else keep wide if self.dtype.kind in "biu": @@ -568,56 +634,65 @@ def sum(self): return result def min(self): - """Minimum live value. + """Minimum live, non-null value. Supported dtypes: bool, int, uint, float, string, bytes. Strings are compared lexicographically. + Null sentinel values are skipped. """ self._require_kind("biufUS", "min") self._require_nonempty("min") result = None is_str = self.dtype.kind in "US" - for chunk in self.iter_chunks(): + for chunk in self._nonnull_chunks(): # numpy .min()/.max() don't support string dtypes in recent NumPy; # fall back to Python's built-in min/max which work on any comparable type. chunk_min = min(chunk) if is_str else chunk.min() if result is None or chunk_min < result: result = chunk_min + if result is None: + raise ValueError("min() called on a column where all values are null.") return result def max(self): - """Maximum live value. + """Maximum live, non-null value. Supported dtypes: bool, int, uint, float, string, bytes. Strings are compared lexicographically. + Null sentinel values are skipped. """ self._require_kind("biufUS", "max") self._require_nonempty("max") result = None is_str = self.dtype.kind in "US" - for chunk in self.iter_chunks(): + for chunk in self._nonnull_chunks(): chunk_max = max(chunk) if is_str else chunk.max() if result is None or chunk_max > result: result = chunk_max + if result is None: + raise ValueError("max() called on a column where all values are null.") return result def mean(self) -> float: - """Arithmetic mean of all live values. + """Arithmetic mean of all live, non-null values. Supported dtypes: bool, int, uint, float. + Null sentinel values are skipped. Always returns a Python float. """ self._require_kind("biuf", "mean") self._require_nonempty("mean") total = np.float64(0) count = 0 - for chunk in self.iter_chunks(): + for chunk in self._nonnull_chunks(): total += chunk.sum(dtype=np.float64) count += len(chunk) + if count == 0: + return float("nan") return float(total / count) def std(self, ddof: int = 0) -> float: - """Standard deviation of all live values (single-pass, Welford's algorithm). + """Standard deviation of all live, non-null values (single-pass, Welford's algorithm). Parameters ---------- @@ -626,6 +701,7 @@ def std(self, ddof: int = 0) -> float: std; ``1`` gives the sample std (divides by N-1). Supported dtypes: bool, int, uint, float. + Null sentinel values are skipped. Always returns a Python float. """ self._require_kind("biuf", "std") @@ -637,7 +713,7 @@ def std(self, ddof: int = 0) -> float: mean_total = np.float64(0) M2_total = np.float64(0) - for chunk in self.iter_chunks(): + for chunk in self._nonnull_chunks(): chunk = chunk.astype(np.float64) n_b = np.int64(len(chunk)) mean_b = chunk.mean() @@ -658,22 +734,24 @@ def std(self, ddof: int = 0) -> float: return float(np.sqrt(M2_total / divisor)) def any(self) -> bool: - """Return True if at least one live value is True. + """Return True if at least one live, non-null value is True. Supported dtypes: bool. + Null sentinel values are skipped. Short-circuits on the first True found. """ self._require_kind("b", "any") - return any(chunk.any() for chunk in self.iter_chunks()) + return any(chunk.any() for chunk in self._nonnull_chunks()) def all(self) -> bool: - """Return True if every live value is True. + """Return True if every live, non-null value is True. Supported dtypes: bool. + Null sentinel values are skipped. Short-circuits on the first False found. """ self._require_kind("b", "all") - return all(chunk.all() for chunk in self.iter_chunks()) + return all(chunk.all() for chunk in self._nonnull_chunks()) # --------------------------------------------------------------------------- @@ -692,6 +770,9 @@ def _fmt_bytes(n: int) -> str: return f"{n / 1024**3:.2f} GB" +_EXPECTED_SIZE_DEFAULT = 1_048_576 + + class CTable(Generic[RowT]): def __init__( self, @@ -700,12 +781,20 @@ def __init__( *, urlpath: str | None = None, mode: str = "a", - expected_size: int = 1_048_576, + expected_size: int | None = None, compact: bool = False, validate: bool = True, cparams: dict[str, Any] | None = None, dparams: dict[str, Any] | None = None, ) -> None: + # Auto-size: if the caller didn't specify expected_size and new_data has a + # known length, pre-allocate just enough (×2 for headroom, min 64). + # Fall back to 1 M when new_data has no __len__ or is absent. + if expected_size is None: + if new_data is not None and hasattr(new_data, "__len__"): + expected_size = max(len(new_data) * 2, 64) + else: + expected_size = _EXPECTED_SIZE_DEFAULT self._row_type = row_type self._validate = validate self._table_cparams = cparams @@ -1361,33 +1450,51 @@ def describe(self) -> None: lines.append("") continue + nc = col.null_count() + n_nonnull = n - nc + if dtype.kind in "biufc" and dtype.kind != "c": # numeric + bool if dtype.kind == "b": arr = col.to_numpy() + # Exclude null sentinels from true/false counts + if col.null_value is not None: + arr = arr[col.notnull()] true_n = int(arr.sum()) lines.append(f" count : {n:,}") + if nc > 0: + lines.append(f" null : {nc:,} ({nc / n * 100:.1f} %)") lines.append(f" true : {true_n:,} ({true_n / n * 100:.1f} %)") - lines.append(f" false : {n - true_n:,} ({(n - true_n) / n * 100:.1f} %)") + lines.append(f" false : {n - true_n - nc:,} ({(n - true_n - nc) / n * 100:.1f} %)") else: - mn = col.min() - mx = col.max() - avg = col.mean() - sd = col.std() fmt = ".4g" lines.append(f" count : {n:,}") - lines.append(f" mean : {avg:{fmt}}") - lines.append(f" std : {sd:{fmt}}") - lines.append(f" min : {mn:{fmt}}") - lines.append(f" max : {mx:{fmt}}") + if nc > 0: + lines.append(f" null : {nc:,} ({nc / n * 100:.1f} %)") + if n_nonnull > 0: + mn = col.min() + mx = col.max() + avg = col.mean() + sd = col.std() + lines.append(f" mean : {avg:{fmt}}") + lines.append(f" std : {sd:{fmt}}") + lines.append(f" min : {mn:{fmt}}") + lines.append(f" max : {mx:{fmt}}") + else: + lines.append(" (all values are null)") elif dtype.kind in "US": - mn = col.min() - mx = col.max() nu = len(col.unique()) lines.append(f" count : {n:,}") + if nc > 0: + lines.append(f" null : {nc:,} ({nc / n * 100:.1f} %)") lines.append(f" unique : {nu:,}") - lines.append(f" min : {mn!r}") - lines.append(f" max : {mx!r}") + if n_nonnull > 0: + mn = col.min() + mx = col.max() + lines.append(f" min : {str(mn)!r}") + lines.append(f" max : {str(mx)!r}") + else: + lines.append(" (all values are null)") else: lines.append(f" count : {n:,}") lines.append(f" (stats not available for dtype {dtype})") @@ -1429,15 +1536,34 @@ def cov(self) -> np.ndarray: if self._n_rows < 2: raise ValueError(f"cov() requires at least 2 live rows, got {self._n_rows}.") - # Build (n_cols, n_rows) matrix — one row per column - arrays = [] + # Build (n_cols, n_rows) matrix — one row per column. + # Compute a combined null mask: any row that is null in *any* column + # is excluded from all columns (listwise deletion). + raw_arrays = [] + null_union = None for name in self.col_names: - arr = self[name].to_numpy() + col = self[name] + arr = col.to_numpy() + nm = col._null_mask_for(arr) + if nm.any(): + null_union = nm if null_union is None else (null_union | nm) + raw_arrays.append(arr) + + arrays = [] + for arr in raw_arrays: + if null_union is not None: + arr = arr[~null_union] if arr.dtype == np.bool_: arr = arr.astype(np.int8) arrays.append(arr.astype(np.float64)) - data = np.stack(arrays, axis=0) # shape (ncols, n_live) + n_valid = len(arrays[0]) if arrays else 0 + if n_valid < 2: + raise ValueError( + f"cov() requires at least 2 non-null rows, got {n_valid} after excluding nulls." + ) + + data = np.stack(arrays, axis=0) # shape (ncols, n_valid) return np.atleast_2d(np.cov(data)) # ------------------------------------------------------------------ @@ -1467,13 +1593,28 @@ def to_arrow(self): for name in self.col_names: col = self[name] arr = col.to_numpy() + # Only compute null mask when a sentinel is actually configured — + # avoids allocating a 1M-element zeros array for every non-nullable column. + nv = col.null_value + if nv is not None: + null_mask = col._null_mask_for(arr) + has_nulls = bool(null_mask.any()) + else: + null_mask = None + has_nulls = False kind = arr.dtype.kind if kind == "U": - pa_arr = pa.array(arr.tolist(), type=pa.string()) + values = arr.tolist() + if has_nulls: + values = [None if null_mask[i] else v for i, v in enumerate(values)] + pa_arr = pa.array(values, type=pa.string()) elif kind == "S": - pa_arr = pa.array(arr.tolist(), type=pa.large_binary()) + values = arr.tolist() + if has_nulls: + values = [None if null_mask[i] else v for i, v in enumerate(values)] + pa_arr = pa.array(values, type=pa.large_binary()) else: - pa_arr = pa.array(arr) + pa_arr = pa.array(arr, mask=null_mask if has_nulls else None) arrays[name] = pa_arr return pa.table(arrays) @@ -1758,15 +1899,26 @@ def from_csv( if n > 0: for i, col in enumerate(schema.columns): + nv = getattr(col.spec, "null_value", None) if col.dtype == np.bool_: # np.array(["False"], dtype=bool) treats any non-empty # string as True. Parse "True"/"False"/"1"/"0" explicitly. - arr = np.array( - [v.strip() in ("True", "true", "1") for v in col_data[i]], - dtype=np.bool_, - ) + # Empty cells → null_value (or False if no null_value). + def _parse_bool(v, _nv=nv): + stripped = v.strip() + if stripped == "" and _nv is not None: + return _nv + return stripped in ("True", "true", "1") + + arr = np.array([_parse_bool(v) for v in col_data[i]], dtype=np.bool_) else: - arr = np.array(col_data[i], dtype=col.dtype) + raw_strings = col_data[i] + if nv is not None: + # Replace empty cells with the null sentinel string representation, + # then cast. For numeric types, store nv directly. + nv_str = str(nv) + raw_strings = [nv_str if v.strip() == "" else v for v in raw_strings] + arr = np.array(raw_strings, dtype=col.dtype) new_cols[col.name][:n] = arr new_valid[:n] = True obj._n_rows = n @@ -2020,10 +2172,19 @@ def _build_lex_keys( live_pos: np.ndarray, n: int, ) -> list[np.ndarray]: - """Build the key list for np.lexsort (innermost = last = primary key).""" + """Build the key list for np.lexsort (innermost = last = primary key). + + For nullable columns a null-indicator key (0=non-null, 1=null) is + inserted immediately after the value key, making it more significant. + This ensures nulls sort last regardless of ascending/descending order. + """ lex_keys = [] for name, asc in zip(reversed(cols), reversed(ascending), strict=True): raw = self._cols[name][live_pos] + col_info = self._schema.columns_by_name.get(name) + nv = getattr(col_info.spec, "null_value", None) if col_info else None + + # Value key if not asc: if raw.dtype.kind in "US": # strings can't be negated — invert via rank @@ -2035,6 +2196,16 @@ def _build_lex_keys( lex_keys.append(-raw) else: lex_keys.append(raw) + + # Null indicator key — more significant than the value key above, + # so nulls always sort last (0 before 1 → non-null before null). + if nv is not None: + if isinstance(nv, float) and np.isnan(nv): + null_ind = np.isnan(raw).astype(np.intp) + else: + null_ind = (raw == nv).astype(np.intp) + lex_keys.append(null_ind) + return lex_keys def sort_by( diff --git a/src/blosc2/schema.py b/src/blosc2/schema.py index 98df998b..28e1cac9 100644 --- a/src/blosc2/schema.py +++ b/src/blosc2/schema.py @@ -34,11 +34,25 @@ class SchemaSpec: Subclasses carry the logical type, storage dtype, and optional validation constraints for one column. + + Numpy dtype attributes (``itemsize``, ``kind``, ``type``, ``str``, + ``name``) are mirrored at class level so that schema spec classes can + be used anywhere blosc2 internals expect a dtype-like object. """ dtype: np.dtype python_type: type + def __init_subclass__(cls, **kwargs): + super().__init_subclass__(**kwargs) + # Mirror numpy dtype attributes at class level for duck-typing. + _np_dtype = cls.__dict__.get("dtype") + if isinstance(_np_dtype, np.dtype): + cls.itemsize = _np_dtype.itemsize + cls.kind = _np_dtype.kind + cls.str = _np_dtype.str + cls.name = _np_dtype.name + def to_pydantic_kwargs(self) -> dict[str, Any]: """Return kwargs for building a Pydantic field annotation.""" raise NotImplementedError @@ -62,13 +76,15 @@ class _NumericSpec(SchemaSpec): _kind: str # set by each concrete subclass - def __init__(self, *, ge=None, gt=None, le=None, lt=None): + def __init__(self, *, ge=None, gt=None, le=None, lt=None, null_value=None): self.ge = ge self.gt = gt self.le = le self.lt = lt + self.null_value = null_value def to_pydantic_kwargs(self) -> dict[str, Any]: + # null_value is not a Pydantic constraint — exclude it from Pydantic kwargs. return { k: v for k, v in {"ge": self.ge, "gt": self.gt, "le": self.le, "lt": self.lt}.items() @@ -76,7 +92,10 @@ def to_pydantic_kwargs(self) -> dict[str, Any]: } def to_metadata_dict(self) -> dict[str, Any]: - return {"kind": self._kind, **self.to_pydantic_kwargs()} + d: dict[str, Any] = {"kind": self._kind, **self.to_pydantic_kwargs()} + if self.null_value is not None: + d["null_value"] = self.null_value + return d # ── Signed integers ────────────────────────────────────────────────────────── @@ -238,10 +257,11 @@ class string(SchemaSpec): python_type = str _DEFAULT_MAX_LENGTH = 32 - def __init__(self, *, min_length=None, max_length=None, pattern=None): + def __init__(self, *, min_length=None, max_length=None, pattern=None, null_value=None): self.min_length = min_length self.max_length = max_length if max_length is not None else self._DEFAULT_MAX_LENGTH self.pattern = pattern + self.null_value = null_value self.dtype = np.dtype(f"U{self.max_length}") def to_pydantic_kwargs(self) -> dict[str, Any]: @@ -255,7 +275,10 @@ def to_pydantic_kwargs(self) -> dict[str, Any]: return d def to_metadata_dict(self) -> dict[str, Any]: - return {"kind": "string", **self.to_pydantic_kwargs()} + d: dict[str, Any] = {"kind": "string", **self.to_pydantic_kwargs()} + if self.null_value is not None: + d["null_value"] = self.null_value + return d class bytes(SchemaSpec): @@ -273,9 +296,10 @@ class bytes(SchemaSpec): python_type = _builtin_bytes _DEFAULT_MAX_LENGTH = 32 - def __init__(self, *, min_length=None, max_length=None): + def __init__(self, *, min_length=None, max_length=None, null_value=None): self.min_length = min_length self.max_length = max_length if max_length is not None else self._DEFAULT_MAX_LENGTH + self.null_value = null_value self.dtype = np.dtype(f"S{self.max_length}") def to_pydantic_kwargs(self) -> dict[str, Any]: @@ -287,7 +311,10 @@ def to_pydantic_kwargs(self) -> dict[str, Any]: return d def to_metadata_dict(self) -> dict[str, Any]: - return {"kind": "bytes", **self.to_pydantic_kwargs()} + d: dict[str, Any] = {"kind": "bytes", **self.to_pydantic_kwargs()} + if self.null_value is not None: + d["null_value"] = self.null_value + return d # --------------------------------------------------------------------------- diff --git a/src/blosc2/schema_validation.py b/src/blosc2/schema_validation.py index dd750122..50f6c6ef 100644 --- a/src/blosc2/schema_validation.py +++ b/src/blosc2/schema_validation.py @@ -47,6 +47,57 @@ def build_validator_model(schema: CompiledSchema) -> type[BaseModel]: return model_cls +def _is_null_value(val, null_value) -> bool: + """Return True if *val* equals the null sentinel, handling NaN correctly.""" + import math + + if null_value is None: + return False + try: + if isinstance(null_value, float) and math.isnan(null_value): + return isinstance(val, float) and math.isnan(val) + except TypeError: + pass + return val == null_value + + +def _safe_placeholder(col) -> Any: + """Return a value that passes Pydantic validation for *col* (used for null bypass).""" + spec = col.spec + ge = getattr(spec, "ge", None) + gt = getattr(spec, "gt", None) + if ge is not None: + return ge + if gt is not None: + return gt + 1 + # For string/bytes, use an empty string + if col.dtype.kind in ("U", "S"): + return "" + # For bool + if col.dtype.kind == "b": + return False + return 0 + + +def _mask_nulls(schema: CompiledSchema, row: dict[str, Any]) -> tuple[dict[str, Any], dict[str, Any]]: + """Replace null sentinel values with safe placeholders. + + Returns (masked_row, null_positions) where null_positions maps + column name → original null value for columns that were masked. + """ + masked = dict(row) + nulled: dict[str, Any] = {} + for col in schema.columns: + nv = getattr(col.spec, "null_value", None) + if nv is None: + continue + val = row.get(col.name) + if val is not None and _is_null_value(val, nv): + nulled[col.name] = val + masked[col.name] = _safe_placeholder(col) + return masked, nulled + + def validate_row(schema: CompiledSchema, row: dict[str, Any]) -> dict[str, Any]: """Validate a single row dict and return the coerced values. @@ -69,12 +120,15 @@ def validate_row(schema: CompiledSchema, row: dict[str, Any]) -> dict[str, Any]: name and the violated constraint. """ model_cls = build_validator_model(schema) + masked_row, nulled = _mask_nulls(schema, row) try: - instance = model_cls(**row) + instance = model_cls(**masked_row) except ValidationError as exc: # Re-raise as a plain ValueError so callers don't need to import Pydantic. raise ValueError(str(exc)) from exc - return instance.model_dump() + result = instance.model_dump() + result.update(nulled) + return result def validate_rows_rowwise(schema: CompiledSchema, rows: list[dict[str, Any]]) -> list[dict[str, Any]]: @@ -96,9 +150,12 @@ def validate_rows_rowwise(schema: CompiledSchema, rows: list[dict[str, Any]]) -> model_cls = build_validator_model(schema) result = [] for i, row in enumerate(rows): + masked_row, nulled = _mask_nulls(schema, row) try: - instance = model_cls(**row) + instance = model_cls(**masked_row) except ValidationError as exc: raise ValueError(f"Row {i}: {exc}") from exc - result.append(instance.model_dump()) + validated = instance.model_dump() + validated.update(nulled) + result.append(validated) return result diff --git a/src/blosc2/schema_vectorized.py b/src/blosc2/schema_vectorized.py index 26a31874..8d0e6bba 100644 --- a/src/blosc2/schema_vectorized.py +++ b/src/blosc2/schema_vectorized.py @@ -44,6 +44,21 @@ def _validate_string_lengths(col: CompiledColumn, arr: Any) -> None: ) +def _null_mask_for_spec(arr: np.ndarray, spec) -> np.ndarray | None: + """Return a boolean mask True where values are the null sentinel, or None if no null_value.""" + null_value = getattr(spec, "null_value", None) + if null_value is None: + return None + try: + import math + + if isinstance(null_value, float) and math.isnan(null_value): + return np.isnan(arr) + except TypeError: + pass + return arr == null_value + + def validate_column_values(col: CompiledColumn, values: Any) -> None: """Check all constraint attributes of *col*'s spec against *values*. @@ -62,33 +77,43 @@ def validate_column_values(col: CompiledColumn, values: Any) -> None: spec = col.spec arr = np.asarray(values) + # Compute null mask so sentinels bypass constraint checks + null_mask = _null_mask_for_spec(arr, spec) + # non_null is a boolean array True for positions that must be validated + if null_mask is not None: + non_null = ~null_mask + check = arr[non_null] + else: + non_null = None + check = arr + # Numeric bounds if getattr(spec, "ge", None) is not None: - bad = arr < spec.ge + bad = check < spec.ge if np.any(bad): - first = arr[bad][0] + first = check[bad][0] raise ValueError(f"Column '{col.name}': value {first!r} violates constraint ge={spec.ge}") if getattr(spec, "gt", None) is not None: - bad = arr <= spec.gt + bad = check <= spec.gt if np.any(bad): - first = arr[bad][0] + first = check[bad][0] raise ValueError(f"Column '{col.name}': value {first!r} violates constraint gt={spec.gt}") if getattr(spec, "le", None) is not None: - bad = arr > spec.le + bad = check > spec.le if np.any(bad): - first = arr[bad][0] + first = check[bad][0] raise ValueError(f"Column '{col.name}': value {first!r} violates constraint le={spec.le}") if getattr(spec, "lt", None) is not None: - bad = arr >= spec.lt + bad = check >= spec.lt if np.any(bad): - first = arr[bad][0] + first = check[bad][0] raise ValueError(f"Column '{col.name}': value {first!r} violates constraint lt={spec.lt}") # String / bytes length bounds # np.char.str_len is a true C-level vectorized operation for 'U' and 'S' # dtypes. Fall back to np.vectorize(len) only for unexpected object arrays. if getattr(spec, "max_length", None) is not None or getattr(spec, "min_length", None) is not None: - _validate_string_lengths(col, arr) + _validate_string_lengths(col, check) def validate_column_batch(schema: CompiledSchema, columns: dict[str, Any]) -> None: diff --git a/tests/ctable/test_nullable.py b/tests/ctable/test_nullable.py new file mode 100644 index 00000000..1a13df62 --- /dev/null +++ b/tests/ctable/test_nullable.py @@ -0,0 +1,549 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +"""Tests for nullable column support (null_value sentinel).""" + +from __future__ import annotations + +import math +import os +import pathlib +import shutil +import tempfile +from dataclasses import dataclass + +import numpy as np +import pytest + +import blosc2 +from blosc2 import CTable + +# --------------------------------------------------------------------------- +# Schemas used across tests +# --------------------------------------------------------------------------- + + +@dataclass +class IntRow: + id: int = blosc2.field(blosc2.int64()) + score: int = blosc2.field(blosc2.int64(ge=0, le=1000, null_value=-1)) + + +@dataclass +class FloatRow: + name: str = blosc2.field(blosc2.string(max_length=16)) + value: float = blosc2.field(blosc2.float64(null_value=float("nan"))) + + +@dataclass +class StrRow: + label: str = blosc2.field(blosc2.string(max_length=16, null_value="")) + rank: int = blosc2.field(blosc2.int64()) + + +@dataclass +class BoolRow: + code: int = blosc2.field(blosc2.int64(null_value=-999)) + flag: bool = blosc2.field(blosc2.bool(), default=False) + + +TABLE_ROOT = pathlib.Path(__file__).parent / "saved_ctable" / "test_nullable" + + +@pytest.fixture(autouse=True) +def clean_dir(): + if TABLE_ROOT.exists(): + shutil.rmtree(TABLE_ROOT) + TABLE_ROOT.mkdir(parents=True, exist_ok=True) + yield + if TABLE_ROOT.exists(): + shutil.rmtree(TABLE_ROOT) + + +def table_path(name: str) -> str: + return str(TABLE_ROOT / name) + + +# =========================================================================== +# null_value property +# =========================================================================== + + +def test_null_value_property_set(): + t = CTable(IntRow, new_data=[(1, 10), (2, -1)]) + assert t["score"].null_value == -1 + + +def test_null_value_property_not_set(): + t = CTable(IntRow, new_data=[(1, 10)]) + assert t["id"].null_value is None + + +def test_null_value_nan(): + t = CTable(FloatRow, new_data=[("a", 1.0), ("b", float("nan"))]) + nv = t["value"].null_value + assert isinstance(nv, float) + assert math.isnan(nv) + + +def test_null_value_string(): + t = CTable(StrRow, new_data=[("hello", 1), ("", 2)]) + assert t["label"].null_value == "" + + +# =========================================================================== +# is_null / notnull / null_count +# =========================================================================== + + +def test_is_null_basic(): + t = CTable(IntRow, new_data=[(1, 10), (2, -1), (3, 20), (4, -1)]) + mask = t["score"].is_null() + assert list(mask) == [False, True, False, True] + + +def test_notnull_basic(): + t = CTable(IntRow, new_data=[(1, 10), (2, -1), (3, 20)]) + mask = t["score"].notnull() + assert list(mask) == [True, False, True] + + +def test_null_count(): + t = CTable(IntRow, new_data=[(1, 10), (2, -1), (3, -1), (4, 5)]) + assert t["score"].null_count() == 2 + + +def test_null_count_zero(): + t = CTable(IntRow, new_data=[(1, 10), (2, 20)]) + assert t["score"].null_count() == 0 + + +def test_is_null_nan(): + t = CTable(FloatRow, new_data=[("a", 1.0), ("b", float("nan")), ("c", 3.0)]) + mask = t["value"].is_null() + assert list(mask) == [False, True, False] + + +def test_is_null_string_sentinel(): + t = CTable(StrRow, new_data=[("hello", 1), ("", 2), ("world", 3)]) + mask = t["label"].is_null() + assert list(mask) == [False, True, False] + + +def test_is_null_no_null_value(): + t = CTable(IntRow, new_data=[(1, 10), (2, 20)]) + # id has no null_value — is_null always returns all False + mask = t["id"].is_null() + assert list(mask) == [False, False] + + +def test_null_count_after_delete(): + t = CTable(IntRow, new_data=[(1, 10), (2, -1), (3, -1), (4, 5)]) + t.delete([1]) # delete the row with score=-1 at physical index 1 + # Remaining: (1,10), (3,-1), (4,5) + assert t["score"].null_count() == 1 + + +# =========================================================================== +# Aggregates skip nulls +# =========================================================================== + + +def test_sum_skips_null(): + t = CTable(IntRow, new_data=[(1, 10), (2, -1), (3, 20), (4, -1)]) + assert t["score"].sum() == 30 + + +def test_mean_skips_null(): + t = CTable(IntRow, new_data=[(1, 10), (2, -1), (3, 30), (4, -1)]) + assert t["score"].mean() == pytest.approx(20.0) + + +def test_std_skips_null(): + t = CTable(IntRow, new_data=[(1, 10), (2, -1), (3, 10), (4, -1)]) + # population std of [10, 10] = 0 + assert t["score"].std() == pytest.approx(0.0) + + +def test_min_skips_null(): + t = CTable(IntRow, new_data=[(1, 10), (2, -1), (3, 5)]) + assert t["score"].min() == 5 + + +def test_max_skips_null(): + t = CTable(IntRow, new_data=[(1, 10), (2, -1), (3, 5)]) + assert t["score"].max() == 10 + + +def test_min_nan_skips(): + t = CTable(FloatRow, new_data=[("a", float("nan")), ("b", 3.0), ("c", 1.0)]) + assert t["value"].min() == pytest.approx(1.0) + + +def test_max_nan_skips(): + t = CTable(FloatRow, new_data=[("a", float("nan")), ("b", 3.0), ("c", 1.0)]) + assert t["value"].max() == pytest.approx(3.0) + + +def test_mean_nan_returns_nan_when_all_null(): + t = CTable(FloatRow, new_data=[("a", float("nan")), ("b", float("nan"))]) + result = t["value"].mean() + assert math.isnan(result) + + +def test_min_all_null_raises(): + t = CTable(IntRow, new_data=[(1, -1), (2, -1)]) + with pytest.raises(ValueError, match="null"): + t["score"].min() + + +def test_max_all_null_raises(): + t = CTable(IntRow, new_data=[(1, -1), (2, -1)]) + with pytest.raises(ValueError, match="null"): + t["score"].max() + + +def test_any_skips_null(): + """any() on bool column with null_value — null rows are skipped.""" + + @dataclass + class BoolNull: + flag: bool = blosc2.field(blosc2.bool()) + active: bool = blosc2.field(blosc2.bool()) + + # bool doesn't support null_value directly in this test — just verify _nonnull_chunks + # behaves like iter_chunks when no null_value is set (already covered by existing tests). + t = CTable(BoolNull, new_data=[(True, False), (False, True)]) + assert t["flag"].any() is True + assert t["active"].any() is True + + +# =========================================================================== +# unique / value_counts exclude nulls +# =========================================================================== + + +def test_unique_excludes_null(): + t = CTable(IntRow, new_data=[(1, 10), (2, -1), (3, 10), (4, -1), (5, 20)]) + u = t["score"].unique() + assert list(u) == [10, 20] + assert -1 not in u + + +def test_value_counts_excludes_null(): + t = CTable(IntRow, new_data=[(1, 10), (2, -1), (3, 10), (4, -1), (5, 20)]) + vc = t["score"].value_counts() + assert -1 not in vc + assert vc[10] == 2 + assert vc[20] == 1 + + +def test_unique_string_excludes_null(): + t = CTable(StrRow, new_data=[("hello", 1), ("", 2), ("hello", 3), ("world", 4)]) + u = t["label"].unique() + assert "" not in list(u) + assert "hello" in list(u) + assert "world" in list(u) + + +# =========================================================================== +# Append / extend with null sentinel bypass Pydantic +# =========================================================================== + + +def test_append_null_bypasses_constraint(): + """Appending a null sentinel that violates ge/le should succeed.""" + t = CTable(IntRow) + # score has ge=0, le=1000 but null_value=-1; appending -1 should not raise + t.append((1, -1)) + assert t["score"][0] == -1 + + +def test_append_normal_value_still_validated(): + t = CTable(IntRow) + with pytest.raises(ValueError): + t.append((1, 9999)) # violates le=1000 + + +def test_extend_null_bypasses_constraint(): + """extend() with null sentinel should not raise a constraint error.""" + t = CTable(IntRow) + t.extend([(1, 10), (2, -1), (3, 20)]) + scores = t["score"].to_numpy() + assert scores[1] == -1 + + +def test_extend_normal_value_still_validated(): + t = CTable(IntRow) + with pytest.raises(ValueError): + t.extend([(1, 10), (2, 9999)]) # 9999 violates le=1000 + + +# =========================================================================== +# sort_by: nulls last +# =========================================================================== + + +def test_sort_nulls_last_ascending(): + t = CTable(IntRow, new_data=[(1, 5), (2, -1), (3, 2), (4, -1), (5, 8)]) + s = t.sort_by("score") + scores = list(s["score"].to_numpy()) + # Non-null values sorted first, nulls (-1) at end + assert scores[:3] == [2, 5, 8] + assert scores[3] == -1 + assert scores[4] == -1 + + +def test_sort_nulls_last_descending(): + t = CTable(IntRow, new_data=[(1, 5), (2, -1), (3, 2), (4, -1), (5, 8)]) + s = t.sort_by("score", ascending=False) + scores = list(s["score"].to_numpy()) + # Non-null values sorted descending first, nulls last + assert scores[:3] == [8, 5, 2] + assert scores[3] == -1 + assert scores[4] == -1 + + +def test_sort_nulls_last_nan(): + t = CTable(FloatRow, new_data=[("a", 3.0), ("b", float("nan")), ("c", 1.0)]) + s = t.sort_by("value") + values = list(s["value"].to_numpy()) + assert values[0] == pytest.approx(1.0) + assert values[1] == pytest.approx(3.0) + assert math.isnan(values[2]) + + +def test_sort_multi_nulls_last(): + t = CTable(IntRow, new_data=[(1, -1), (2, 5), (3, -1), (4, 5)]) + s = t.sort_by(["score", "id"]) + scores = list(s["score"].to_numpy()) + ids = list(s["id"].to_numpy()) + # score 5 rows first (id 2, then id 4), then score -1 rows + assert scores[:2] == [5, 5] + assert ids[:2] == [2, 4] + assert scores[2] == -1 + assert scores[3] == -1 + + +def test_sort_no_nulls_unchanged(): + """Columns without null_value still sort normally.""" + t = CTable(IntRow, new_data=[(3, 30), (1, 10), (2, 20)]) + s = t.sort_by("id") + np.testing.assert_array_equal(s["id"].to_numpy(), [1, 2, 3]) + + +# =========================================================================== +# describe() shows null count +# =========================================================================== + + +def test_describe_shows_null_count(capsys): + t = CTable(IntRow, new_data=[(1, 10), (2, -1), (3, 20)]) + t.describe() + out = capsys.readouterr().out + assert "null" in out.lower() + assert "1" in out # 1 null + + +def test_describe_no_null_line_when_zero_nulls(capsys): + t = CTable(IntRow, new_data=[(1, 10), (2, 20)]) + t.describe() + out = capsys.readouterr().out + # No null line when null_count == 0 + # The word "null" should not appear in the score section + # (the column has null_value=-1 but no actual null values) + assert "null" not in out.lower() + + +# =========================================================================== +# to_arrow: null masking +# =========================================================================== + + +def test_to_arrow_null_masking(): + pytest.importorskip("pyarrow") + + t = CTable(IntRow, new_data=[(1, 10), (2, -1), (3, 20)]) + arrow = t.to_arrow() + score_col = arrow.column("score") + assert score_col[0].as_py() == 10 + assert score_col[1].as_py() is None # null sentinel → Arrow null + assert score_col[2].as_py() == 20 + + +def test_to_arrow_nan_masking(): + pytest.importorskip("pyarrow") + + t = CTable(FloatRow, new_data=[("a", 1.0), ("b", float("nan")), ("c", 3.0)]) + arrow = t.to_arrow() + val_col = arrow.column("value") + assert val_col[0].as_py() == pytest.approx(1.0) + assert val_col[1].as_py() is None # NaN sentinel → Arrow null + assert val_col[2].as_py() == pytest.approx(3.0) + + +def test_to_arrow_string_null_masking(): + pytest.importorskip("pyarrow") + + t = CTable(StrRow, new_data=[("hello", 1), ("", 2), ("world", 3)]) + arrow = t.to_arrow() + label_col = arrow.column("label") + assert label_col[0].as_py() == "hello" + assert label_col[1].as_py() is None # empty string → Arrow null + assert label_col[2].as_py() == "world" + + +def test_to_arrow_no_null_value_no_masking(): + pytest.importorskip("pyarrow") + + t = CTable(IntRow, new_data=[(1, 10), (2, 20)]) + arrow = t.to_arrow() + # id column has no null_value → all values present + id_col = arrow.column("id") + assert id_col.null_count == 0 + + +# =========================================================================== +# from_csv: empty cells → sentinel +# =========================================================================== + + +def test_from_csv_empty_cell_to_null(): + with tempfile.NamedTemporaryFile(mode="w", suffix=".csv", delete=False) as f: + f.write("id,score\n") + f.write("1,10\n") + f.write("2,\n") # empty score → null sentinel + f.write("3,20\n") + fname = f.name + try: + t = CTable.from_csv(fname, IntRow) + scores = t["score"].to_numpy() + assert scores[0] == 10 + assert scores[1] == -1 # null sentinel + assert scores[2] == 20 + assert t["score"].null_count() == 1 + finally: + os.unlink(fname) + + +def test_from_csv_empty_string_cell_to_null(): + with tempfile.NamedTemporaryFile(mode="w", suffix=".csv", delete=False) as f: + f.write("label,rank\n") + f.write("hello,1\n") + f.write(",2\n") # empty label → null sentinel "" + f.write("world,3\n") + fname = f.name + try: + t = CTable.from_csv(fname, StrRow) + labels = t["label"].to_numpy() + assert labels[0] == "hello" + assert labels[1] == "" # null sentinel + assert labels[2] == "world" + assert t["label"].null_count() == 1 + finally: + os.unlink(fname) + + +def test_from_csv_no_null_value_non_empty_cells(): + """Without null_value, normal values are read and stored correctly.""" + + @dataclass + class SimpleRow: + x: int = blosc2.field(blosc2.int64()) + + with tempfile.NamedTemporaryFile(mode="w", suffix=".csv", delete=False) as f: + f.write("x\n") + f.write("5\n") + f.write("7\n") + f.write("10\n") + fname = f.name + try: + t = CTable.from_csv(fname, SimpleRow) + arr = t["x"].to_numpy() + assert list(arr) == [5, 7, 10] + assert t["x"].null_count() == 0 + finally: + os.unlink(fname) + + +# =========================================================================== +# Persistence: null_value round-trips through schema serialization +# =========================================================================== + + +def test_null_value_persists_to_disk(): + path = table_path("null_persist") + t = CTable(IntRow, urlpath=path, mode="w", new_data=[(1, 10), (2, -1), (3, 20)]) + del t + t2 = CTable.open(path) + assert t2["score"].null_value == -1 + assert t2["score"].null_count() == 1 + + +def test_null_value_nan_persists(): + path = table_path("null_nan_persist") + t = CTable(FloatRow, urlpath=path, mode="w", new_data=[("a", 1.0), ("b", float("nan"))]) + del t + t2 = CTable.open(path) + nv = t2["value"].null_value + assert isinstance(nv, float) + assert math.isnan(nv) + assert t2["value"].null_count() == 1 + + +def test_null_value_string_persists(): + path = table_path("null_str_persist") + t = CTable(StrRow, urlpath=path, mode="w", new_data=[("hello", 1), ("", 2)]) + del t + t2 = CTable.open(path) + assert t2["label"].null_value == "" + assert t2["label"].null_count() == 1 + + +# =========================================================================== +# Edge cases +# =========================================================================== + + +def test_all_nulls_unique_empty(): + t = CTable(IntRow, new_data=[(1, -1), (2, -1)]) + u = t["score"].unique() + assert len(u) == 0 + + +def test_all_nulls_value_counts_empty(): + t = CTable(IntRow, new_data=[(1, -1), (2, -1)]) + vc = t["score"].value_counts() + assert len(vc) == 0 + + +def test_null_value_does_not_affect_non_nullable_column(): + t = CTable(IntRow, new_data=[(1, 10), (2, 20)]) + # id column has no null_value — aggregates work normally + assert t["id"].sum() == 3 + assert t["id"].min() == 1 + assert t["id"].max() == 2 + + +def test_schema_null_value_in_metadata(): + """null_value appears in schema_to_dict output for persistence.""" + from blosc2.schema_compiler import schema_to_dict + + @dataclass + class SomeRow: + x: int = blosc2.field(blosc2.int64(null_value=-999)) + label: str = blosc2.field(blosc2.string(max_length=8, null_value="N/A")) + + t = CTable(SomeRow, new_data=[(1, "hello"), (-999, "N/A")]) + d = schema_to_dict(t._schema) + cols = {c["name"]: c for c in d["columns"]} + assert cols["x"]["null_value"] == -999 + assert cols["label"]["null_value"] == "N/A" + + +if __name__ == "__main__": + pytest.main(["-v", __file__]) diff --git a/tests/ctable/test_table_persistency.py b/tests/ctable/test_table_persistency.py new file mode 100644 index 00000000..10638ca6 --- /dev/null +++ b/tests/ctable/test_table_persistency.py @@ -0,0 +1,520 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +"""Persistency tests for CTable: create → close → reopen round-trips.""" + +import json +import os +import pathlib +import shutil +from dataclasses import dataclass + +import pytest + +import blosc2 +from blosc2 import CTable + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + + +@dataclass +class Row: + id: int = blosc2.field(blosc2.int64(ge=0)) + score: float = blosc2.field(blosc2.float64(ge=0, le=100), default=0.0) + active: bool = blosc2.field(blosc2.bool(), default=True) + + +TABLE_ROOT = str(pathlib.Path(__file__).parent / "saved_ctable" / "test_tables") + + +@pytest.fixture(autouse=True) +def clean_table_dir(): + """Remove test directory before each test and clean up after.""" + if os.path.exists(TABLE_ROOT): + shutil.rmtree(TABLE_ROOT) + os.makedirs(TABLE_ROOT, exist_ok=True) + yield + if os.path.exists(TABLE_ROOT): + shutil.rmtree(TABLE_ROOT) + + +def table_path(name: str) -> str: + return os.path.join(TABLE_ROOT, name) + + +# --------------------------------------------------------------------------- +# Layout: disk structure +# --------------------------------------------------------------------------- + + +def test_create_layout_files_exist(): + """Creating a persistent CTable writes the expected files.""" + path = table_path("people") + t = CTable(Row, urlpath=path, mode="w", expected_size=16) + t.append((1, 50.0, True)) + + assert os.path.exists(os.path.join(path, "_meta.b2frame")) + assert os.path.exists(os.path.join(path, "_valid_rows.b2nd")) + assert os.path.exists(os.path.join(path, "_cols", "id.b2nd")) + assert os.path.exists(os.path.join(path, "_cols", "score.b2nd")) + assert os.path.exists(os.path.join(path, "_cols", "active.b2nd")) + + +def test_schema_saved_in_meta_vlmeta(): + """Schema JSON and kind marker are present in _meta.b2frame.""" + path = table_path("people") + CTable(Row, urlpath=path, mode="w", expected_size=16) + + meta = blosc2.open(os.path.join(path, "_meta.b2frame")) + assert meta.vlmeta["kind"] == "ctable" + assert meta.vlmeta["version"] == 1 + schema = json.loads(meta.vlmeta["schema"]) + assert schema["version"] == 1 + col_names = [c["name"] for c in schema["columns"]] + assert col_names == ["id", "score", "active"] + + +# --------------------------------------------------------------------------- +# Round-trip: data survives reopen +# --------------------------------------------------------------------------- + + +def test_reopen_with_ctable_constructor(): + """Data written before close is readable after reopening via CTable(...).""" + path = table_path("rt") + t = CTable(Row, urlpath=path, mode="w", expected_size=16) + t.extend([(1, 10.0, True), (2, 20.0, False), (3, 30.0, True)]) + + t2 = CTable(Row, urlpath=path, mode="a") + assert len(t2) == 3 + assert list(t2["id"].to_numpy()) == [1, 2, 3] + assert list(t2["score"].to_numpy()) == [10.0, 20.0, 30.0] + + +def test_reopen_with_open_classmethod(): + """CTable.open() returns a read-only table with correct data.""" + path = table_path("ro") + t = CTable(Row, urlpath=path, mode="w", expected_size=16) + t.extend([(10, 50.0, True), (20, 60.0, False)]) + + t2 = CTable.open(path) + assert len(t2) == 2 + assert list(t2["id"].to_numpy()) == [10, 20] + + +def test_column_order_preserved_after_reopen(): + """Column order from the schema JSON is respected on reopen.""" + path = table_path("order") + + @dataclass + class MultiCol: + z: int = blosc2.field(blosc2.int64()) + a: float = blosc2.field(blosc2.float64(), default=0.0) + m: bool = blosc2.field(blosc2.bool(), default=True) + + t = CTable(MultiCol, urlpath=path, mode="w", expected_size=16) + t2 = CTable(MultiCol, urlpath=path, mode="a") + assert t2.col_names == ["z", "a", "m"] + + +def test_schema_constraints_preserved(): + """Reopening re-enables constraint validation from the stored schema.""" + path = table_path("constraints") + t = CTable(Row, urlpath=path, mode="w", expected_size=16) + t.append((1, 50.0, True)) + + t2 = CTable(Row, urlpath=path, mode="a") + with pytest.raises(ValueError): + t2.append((-1, 50.0, True)) # id violates ge=0 + + +# --------------------------------------------------------------------------- +# Append after reopen +# --------------------------------------------------------------------------- + + +def test_append_after_reopen(): + """Appending to a reopened table grows the row count correctly.""" + path = table_path("append") + t = CTable(Row, urlpath=path, mode="w", expected_size=16) + t.extend([(1, 10.0, True), (2, 20.0, False)]) + + t2 = CTable(Row, urlpath=path, mode="a") + t2.append((3, 30.0, True)) + assert len(t2) == 3 + assert t2.row[2].id[0] == 3 + + # Verify it's visible in a third open + t3 = CTable(Row, urlpath=path, mode="a") + assert len(t3) == 3 + assert list(t3["id"].to_numpy()) == [1, 2, 3] + + +def test_extend_after_reopen(): + """extend() after reopen persists all new rows.""" + path = table_path("extend") + t = CTable(Row, urlpath=path, mode="w", expected_size=64) + t.extend([(i, float(i), True) for i in range(5)]) + + t2 = CTable(Row, urlpath=path, mode="a") + t2.extend([(i, float(i), i % 2 == 0) for i in range(5, 10)]) + assert len(t2) == 10 + + t3 = CTable(Row, urlpath=path, mode="a") + assert len(t3) == 10 + assert list(t3["id"].to_numpy()) == list(range(10)) + + +# --------------------------------------------------------------------------- +# Delete after reopen +# --------------------------------------------------------------------------- + + +def test_delete_after_reopen(): + """Deletions after reopen are reflected in subsequent opens.""" + path = table_path("delete") + t = CTable(Row, urlpath=path, mode="w", expected_size=16) + t.extend([(1, 10.0, True), (2, 20.0, False), (3, 30.0, True)]) + + t2 = CTable(Row, urlpath=path, mode="a") + t2.delete(1) # remove row with id=2 + assert len(t2) == 2 + + t3 = CTable(Row, urlpath=path, mode="a") + assert len(t3) == 2 + assert list(t3["id"].to_numpy()) == [1, 3] + + +# --------------------------------------------------------------------------- +# valid_rows persistence +# --------------------------------------------------------------------------- + + +def test_valid_rows_persisted(): + """The tombstone mask (_valid_rows) is correctly stored and loaded.""" + path = table_path("vr") + t = CTable(Row, urlpath=path, mode="w", expected_size=16) + t.extend([(1, 10.0, True), (2, 20.0, False), (3, 30.0, True)]) + t.delete(1) # mark row 1 (id=2) as invalid + + # _valid_rows on disk: slots 0 and 2 are True, slot 1 is False + vr = blosc2.open(os.path.join(path, "_valid_rows.b2nd")) + raw = vr[:3] + assert raw[0] + assert not raw[1] + assert raw[2] + + +# --------------------------------------------------------------------------- +# mode="w" overwrites existing table +# --------------------------------------------------------------------------- + + +def test_mode_w_overwrites_existing(): + """mode='w' on an existing path creates a fresh table.""" + path = table_path("overwrite") + t = CTable(Row, urlpath=path, mode="w", expected_size=16) + t.extend([(1, 10.0, True), (2, 20.0, False)]) + + t2 = CTable(Row, urlpath=path, mode="w", expected_size=16) + assert len(t2) == 0 + + t3 = CTable(Row, urlpath=path, mode="a") + assert len(t3) == 0 + + +# --------------------------------------------------------------------------- +# Read-only mode +# --------------------------------------------------------------------------- + + +def test_read_only_mode_rejects_append(): + path = table_path("ro_append") + CTable(Row, urlpath=path, mode="w", expected_size=16) + + t = CTable.open(path, mode="r") + with pytest.raises(ValueError, match="read-only"): + t.append((1, 50.0, True)) + + +def test_read_only_mode_rejects_extend(): + path = table_path("ro_extend") + CTable(Row, urlpath=path, mode="w", expected_size=16) + + t = CTable.open(path, mode="r") + with pytest.raises(ValueError, match="read-only"): + t.extend([(1, 50.0, True)]) + + +def test_read_only_mode_rejects_delete(): + path = table_path("ro_delete") + t = CTable(Row, urlpath=path, mode="w", expected_size=16) + t.append((1, 50.0, True)) + + t2 = CTable.open(path, mode="r") + with pytest.raises(ValueError, match="read-only"): + t2.delete(0) + + +def test_read_only_mode_rejects_compact(): + path = table_path("ro_compact") + t = CTable(Row, urlpath=path, mode="w", expected_size=16) + t.append((1, 50.0, True)) + + t2 = CTable.open(path, mode="r") + with pytest.raises(ValueError, match="read-only"): + t2.compact() + + +def test_read_only_allows_reads(): + """Read-only table: row access, column access, head/tail, where all work.""" + path = table_path("ro_reads") + t = CTable(Row, urlpath=path, mode="w", expected_size=16) + t.extend([(1, 10.0, True), (2, 20.0, False), (3, 30.0, True)]) + + t2 = CTable.open(path, mode="r") + assert len(t2) == 3 + assert t2.row[0].id[0] == 1 + assert list(t2["score"].to_numpy()) == [10.0, 20.0, 30.0] + assert len(t2.head(2)) == 2 + assert len(t2.tail(1)) == 1 + view = t2.where(t2["id"] > 1) + assert len(view) == 2 + + +# --------------------------------------------------------------------------- +# open() error cases +# --------------------------------------------------------------------------- + + +def test_open_nonexistent_raises(): + with pytest.raises(FileNotFoundError): + CTable.open(table_path("does_not_exist")) + + +def test_open_wrong_kind_raises(tmp_path): + """A path with a _meta.b2frame that is not a ctable raises ValueError.""" + import blosc2 + + meta_path = str(tmp_path / "_meta.b2frame") + sc = blosc2.SChunk(urlpath=meta_path, mode="w") + sc.vlmeta["kind"] = "something_else" + + with pytest.raises(ValueError, match="CTable"): + CTable.open(str(tmp_path)) + + +# --------------------------------------------------------------------------- +# Column name validation +# --------------------------------------------------------------------------- + + +def test_column_name_cannot_start_with_underscore(): + @dataclass + class Bad: + _id: int = blosc2.field(blosc2.int64()) + + with pytest.raises(ValueError, match="_"): + CTable(Bad) + + +def test_column_name_cannot_contain_slash(): + @dataclass + class Bad: + pass + + from blosc2.schema_compiler import _validate_column_name + + with pytest.raises(ValueError, match="/"): + _validate_column_name("a/b") + + +def test_column_name_cannot_be_empty(): + from blosc2.schema_compiler import _validate_column_name + + with pytest.raises(ValueError): + _validate_column_name("") + + +# --------------------------------------------------------------------------- +# new_data= guard when opening existing +# --------------------------------------------------------------------------- + + +def test_new_data_rejected_when_opening_existing(): + path = table_path("newdata") + CTable(Row, urlpath=path, mode="w", expected_size=16) + + with pytest.raises(ValueError, match="new_data"): + CTable(Row, new_data=[(1, 50.0, True)], urlpath=path, mode="a") + + +# --------------------------------------------------------------------------- +# Capacity growth (resize) persists +# --------------------------------------------------------------------------- + + +def test_grow_persists(): + """Filling past the initial capacity triggers resize; data still survives.""" + path = table_path("grow") + t = CTable(Row, urlpath=path, mode="w", expected_size=4) + for i in range(10): + t.append((i, float(i), True)) + assert len(t) == 10 + + t2 = CTable(Row, urlpath=path, mode="a") + assert len(t2) == 10 + assert list(t2["id"].to_numpy()) == list(range(10)) + + +# --------------------------------------------------------------------------- +# save() / load() +# --------------------------------------------------------------------------- + + +def test_save_creates_disk_layout(): + """save() writes the expected directory structure.""" + t = blosc2.CTable(Row, expected_size=16) + t.extend([(1, 10.0, True), (2, 20.0, False)]) + + path = table_path("saved") + t.save(path) + + assert os.path.exists(os.path.join(path, "_meta.b2frame")) + assert os.path.exists(os.path.join(path, "_valid_rows.b2nd")) + assert os.path.exists(os.path.join(path, "_cols", "id.b2nd")) + assert os.path.exists(os.path.join(path, "_cols", "score.b2nd")) + assert os.path.exists(os.path.join(path, "_cols", "active.b2nd")) + + +def test_save_then_open_round_trip(): + """Data written by save() can be read back via CTable.open().""" + t = blosc2.CTable(Row, expected_size=16) + t.extend([(i, float(i * 10), i % 2 == 0) for i in range(5)]) + + path = table_path("saved_rt") + t.save(path) + + t2 = CTable.open(path) + assert len(t2) == 5 + assert list(t2["id"].to_numpy()) == list(range(5)) + assert list(t2["score"].to_numpy()) == [float(i * 10) for i in range(5)] + + +def test_save_compacts_deleted_rows(): + """save() writes only live rows — deleted rows are not included.""" + t = blosc2.CTable(Row, expected_size=16) + t.extend([(i, float(i), True) for i in range(6)]) + t.delete([0, 2, 4]) # delete rows with id 0, 2, 4 + assert len(t) == 3 + + path = table_path("saved_compact") + t.save(path) + + t2 = CTable.open(path) + assert len(t2) == 3 + assert list(t2["id"].to_numpy()) == [1, 3, 5] + + +def test_save_existing_path_raises_by_default(): + """save() raises ValueError if the path already exists unless overwrite=True.""" + t = blosc2.CTable(Row, expected_size=4) + t.append((1, 10.0, True)) + + path = table_path("save_conflict") + t.save(path) + + with pytest.raises(ValueError, match="overwrite"): + t.save(path) + + +def test_save_overwrite_replaces_table(): + """save(overwrite=True) replaces an existing table.""" + t1 = blosc2.CTable(Row, expected_size=4) + t1.extend([(1, 10.0, True), (2, 20.0, True)]) + + path = table_path("overwrite") + t1.save(path) + + t2 = blosc2.CTable(Row, expected_size=4) + t2.append((99, 50.0, False)) + t2.save(path, overwrite=True) + + t3 = CTable.open(path) + assert len(t3) == 1 + assert t3["id"][0] == 99 + + +def test_save_view_raises(): + """save() on a view raises ValueError.""" + t = blosc2.CTable(Row, expected_size=8) + t.extend([(i, float(i), True) for i in range(4)]) + view = t.where(t["id"] > 1) + + with pytest.raises(ValueError, match="view"): + view.save(table_path("view_save")) + + +def test_load_returns_in_memory_table(): + """load() returns a writable in-memory CTable.""" + t = blosc2.CTable(Row, expected_size=8) + t.extend([(i, float(i * 5), True) for i in range(4)]) + + path = table_path("loadme") + t.save(path) + + loaded = CTable.load(path) + assert len(loaded) == 4 + assert list(loaded["id"].to_numpy()) == [0, 1, 2, 3] + # Must be writable + loaded.append((100, 50.0, True)) + assert len(loaded) == 5 + + +def test_load_does_not_modify_disk(): + """Mutations on a loaded table do not affect the on-disk table.""" + t = blosc2.CTable(Row, expected_size=8) + t.extend([(i, float(i), True) for i in range(3)]) + + path = table_path("load_isolation") + t.save(path) + + loaded = CTable.load(path) + loaded.append((999, 99.0, False)) + loaded.delete(0) + + # Re-open the original persistent table — should be unchanged + t2 = CTable.open(path) + assert len(t2) == 3 + assert list(t2["id"].to_numpy()) == [0, 1, 2] + + +def test_load_nonexistent_raises(): + with pytest.raises(FileNotFoundError): + CTable.load(table_path("does_not_exist")) + + +def test_save_empty_table(): + """save() and load() work correctly on an empty table.""" + t = blosc2.CTable(Row, expected_size=4) + + path = table_path("empty") + t.save(path) + + t2 = CTable.load(path) + assert len(t2) == 0 + # Can still append after load + t2.append((1, 10.0, True)) + assert len(t2) == 1 + + +if __name__ == "__main__": + import pytest + + pytest.main(["-v", __file__]) From bc4d4ffd0a57e601cfc9acbbfec602941b0f531e Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Wed, 15 Apr 2026 12:26:14 +0200 Subject: [PATCH 29/49] Fix issues when array is a numpy array, not blosc2 --- src/blosc2/indexing.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/src/blosc2/indexing.py b/src/blosc2/indexing.py index 22f86ce6..a77a2da0 100644 --- a/src/blosc2/indexing.py +++ b/src/blosc2/indexing.py @@ -256,7 +256,7 @@ def _copy_descriptor_for_token(array: blosc2.NDArray, token: str) -> dict: def _is_persistent_array(array: blosc2.NDArray) -> bool: - return array.urlpath is not None + return getattr(array, "urlpath", None) is not None def _tmpdir_for_array(array: blosc2.NDArray) -> str | None: @@ -267,8 +267,9 @@ def _tmpdir_for_array(array: blosc2.NDArray) -> str | None: size limits on ``/tmp`` (commonly a tmpfs with only a few GB). For in-memory arrays we fall back to the system default (``None``). """ - if array.urlpath is not None: - return str(Path(array.urlpath).resolve().parent) + urlpath = getattr(array, "urlpath", None) + if urlpath is not None: + return str(Path(urlpath).resolve().parent) return None From fcb9efa2c7f531612b9b79460540691890bfce35 Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Wed, 15 Apr 2026 12:45:43 +0200 Subject: [PATCH 30/49] For large temp arange arrays, use blosc2.arange instead of np.arange --- src/blosc2/ctable.py | 20 ++++++++++++++++---- 1 file changed, 16 insertions(+), 4 deletions(-) diff --git a/src/blosc2/ctable.py b/src/blosc2/ctable.py index a7a1e4e7..3b1f3863 100644 --- a/src/blosc2/ctable.py +++ b/src/blosc2/ctable.py @@ -51,6 +51,18 @@ def wrapper(*args, **kwargs): # and legacy Pydantic models during the transition period. RowT = TypeVar("RowT") +# Arrays larger than this threshold use blosc2.arange instead of np.arange to +# avoid large transient allocations when mapping logical to physical row positions. +_BLOSC2_ARANGE_THRESHOLD = 1_000_000 + + +def _arange(start, stop=None, step=1) -> blosc2.NDArray | np.ndarray: + """Return a range array, using blosc2 for large n to save memory.""" + if stop is None: + start, stop = 0, start + n = len(range(start, stop, step)) + return blosc2.arange(start, stop, step) if n >= _BLOSC2_ARANGE_THRESHOLD else np.arange(start, stop, step) + # --------------------------------------------------------------------------- # Legacy Pydantic-compat helpers @@ -268,7 +280,7 @@ def __getitem__(self, key: int | slice | list | np.ndarray): return self._raw_col[int(pos_true)] elif isinstance(key, slice): - real_pos = blosc2.where(self._valid_rows, np.arange(len(self._valid_rows))).compute() + real_pos = blosc2.where(self._valid_rows, _arange(len(self._valid_rows))).compute() start, stop, step = key.indices(len(real_pos)) mask = blosc2.zeros(len(self._table._valid_rows), dtype=np.bool_) if step == 1: @@ -276,7 +288,7 @@ def __getitem__(self, key: int | slice | list | np.ndarray): phys_stop = real_pos[stop - 1] mask[phys_start : phys_stop + 1] = True else: - lindices = np.arange(start, stop, step) + lindices = _arange(start, stop, step) phys_indices = real_pos[lindices] mask[phys_indices[:]] = True return Column(self._table, self._col_name, mask=mask) @@ -294,7 +306,7 @@ def __getitem__(self, key: int | slice | list | np.ndarray): return self._raw_col[phys_indices] elif isinstance(key, (list, tuple, np.ndarray)): - real_pos = blosc2.where(self._valid_rows, np.arange(len(self._valid_rows))).compute() + real_pos = blosc2.where(self._valid_rows, _arange(len(self._valid_rows))).compute() phys_indices = np.array([real_pos[i] for i in key], dtype=np.int64) return self._raw_col[phys_indices] @@ -326,7 +338,7 @@ def __setitem__(self, key: int | slice | list | np.ndarray, value): self._raw_col[phys_indices] = value elif isinstance(key, (slice, list, tuple, np.ndarray)): - real_pos = blosc2.where(self._valid_rows, np.arange(len(self._valid_rows))).compute() + real_pos = blosc2.where(self._valid_rows, _arange(len(self._valid_rows))).compute() if isinstance(key, slice): lindices = range(*key.indices(len(real_pos))) phys_indices = np.array([real_pos[i] for i in lindices], dtype=np.int64) From eaccd53ec1d0c370ed353d45883608b2f968b7df Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Wed, 15 Apr 2026 12:54:14 +0200 Subject: [PATCH 31/49] Shaving test suite run time by a little bit --- tests/ndarray/test_concat.py | 4 ++-- tests/ndarray/test_full.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/ndarray/test_concat.py b/tests/ndarray/test_concat.py index c9e42067..0d18cfa2 100644 --- a/tests/ndarray/test_concat.py +++ b/tests/ndarray/test_concat.py @@ -31,7 +31,7 @@ ([21, 121, 101, 10], [2, 121, 101, 10], "f4", 0), ([121, 21, 101, 10], [121, 12, 101, 10], "i8", 1), ([121, 121, 10, 10], [121, 121, 1, 10], "i8", 2), - ([121, 121, 101, 2], [121, 121, 101, 10], "i8", -1), + ([121, 121, 101, 2], [121, 121, 101, 5], "i8", -1), ], ) def test_concat2(shape1, shape2, dtype, axis): @@ -56,7 +56,7 @@ def test_concat2(shape1, shape2, dtype, axis): ([21, 121, 101, 10], [2, 121, 101, 10], [1, 121, 101, 10], "f4", 0), ([121, 21, 101, 10], [121, 12, 101, 10], [121, 1, 101, 10], "i8", 1), ([121, 121, 10, 10], [121, 121, 1, 10], [121, 121, 3, 10], "i8", 2), - ([121, 121, 101, 2], [121, 121, 101, 10], [121, 121, 101, 1], "i8", -1), + ([121, 121, 101, 2], [121, 121, 101, 5], [121, 121, 101, 1], "i8", -1), ], ) def test_concat3(shape1, shape2, shape3, dtype, axis): diff --git a/tests/ndarray/test_full.py b/tests/ndarray/test_full.py index 8a52441c..2670835c 100644 --- a/tests/ndarray/test_full.py +++ b/tests/ndarray/test_full.py @@ -223,7 +223,7 @@ def test_complex_datatype(): ("f_022", " Date: Wed, 15 Apr 2026 14:37:02 +0200 Subject: [PATCH 32/49] Add persistent index support to CTable - New CTableIndex handle with col_name, kind, name, stale properties - create_index(), drop_index(), rebuild_index(), compact_index() methods - index() lookup and indexes property on CTable - _CTableIndexProxy duck-type shim routes sidecar files to /_indexes// for persistent tables - Index catalog stored in /_meta vlmeta; survives table close/reopen - where() automatically uses a fresh index; falls back to scan when stale - Epoch tracking: mutations (append, extend, setitem, assign, sort_by, compact) mark all indexes stale; delete() bumps visibility_epoch only - Views raise ValueError for all index management methods - Add _indexes to reserved column names in schema_compiler - 32 new tests in tests/ctable/test_ctable_indexing.py - New example examples/ctable/indexing.py - New tutorial doc/getting_started/tutorials/15.indexing-ctables.ipynb Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- .../tutorials/15.indexing-ctables.ipynb | 472 ++++++++++++ examples/ctable/indexing.py | 97 +++ plans/ctable-indexing.md | 713 ++++++++++++++++++ src/blosc2/ctable.py | 682 ++++++++++++++++- src/blosc2/ctable_storage.py | 96 +++ src/blosc2/schema_compiler.py | 2 +- tests/ctable/test_ctable_indexing.py | 369 +++++++++ 7 files changed, 2429 insertions(+), 2 deletions(-) create mode 100644 doc/getting_started/tutorials/15.indexing-ctables.ipynb create mode 100644 examples/ctable/indexing.py create mode 100644 plans/ctable-indexing.md create mode 100644 tests/ctable/test_ctable_indexing.py diff --git a/doc/getting_started/tutorials/15.indexing-ctables.ipynb b/doc/getting_started/tutorials/15.indexing-ctables.ipynb new file mode 100644 index 00000000..6c8ea4da --- /dev/null +++ b/doc/getting_started/tutorials/15.indexing-ctables.ipynb @@ -0,0 +1,472 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "44fdf4b9", + "metadata": {}, + "source": [ + "# Indexing CTables\n", + "\n", + "CTable supports **persistent, table-owned indexes** that speed up `where()` queries on numeric columns. \n", + "An index maps sorted-value ranges to the chunk positions that contain matching rows, allowing Blosc2 to skip large parts of the table without reading every row.\n", + "\n", + "This tutorial covers:\n", + "\n", + "1. Creating an index on a CTable column\n", + "2. Querying with an index (automatic)\n", + "3. Stale detection and automatic scan fallback\n", + "4. Rebuilding and dropping indexes\n", + "5. Persistent tables: indexes survive close/reopen\n", + "6. Views and indexes\n" + ] + }, + { + "cell_type": "markdown", + "id": "da26cc61", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "We will use a simple measurement table with three numeric columns.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b23746ca", + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-15T12:29:02.229246Z", + "start_time": "2026-04-15T12:29:00.966071Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Table: 500 rows\n" + ] + } + ], + "source": [ + "import dataclasses\n", + "\n", + "import numpy as np\n", + "\n", + "import blosc2\n", + "\n", + "\n", + "@dataclasses.dataclass\n", + "class Measurement:\n", + " sensor_id: int = blosc2.field(blosc2.int32())\n", + " temperature: float = blosc2.field(blosc2.float64())\n", + " region: int = blosc2.field(blosc2.int32())\n", + "\n", + "\n", + "N = 500\n", + "t = blosc2.CTable(Measurement)\n", + "rng = np.random.default_rng(42)\n", + "for i in range(N):\n", + " t.append([i, 15.0 + rng.random() * 25, int(rng.integers(0, 4))])\n", + "\n", + "print(f\"Table: {N} rows\")" + ] + }, + { + "cell_type": "markdown", + "id": "2be47ee8", + "metadata": {}, + "source": [ + "## Creating an index\n", + "\n", + "Call `create_index(col_name)` to build a bucket index on a column. \n", + "The returned `CTableIndex` handle shows the column name, kind, and whether the index is stale.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2ac1f281", + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-15T12:29:12.081628Z", + "start_time": "2026-04-15T12:29:12.033154Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "stale? False\n", + "all indexes: []\n" + ] + } + ], + "source": [ + "idx = t.create_index(\"sensor_id\")\n", + "print(idx)\n", + "print(\"stale?\", idx.stale)\n", + "print(\"all indexes:\", t.indexes)" + ] + }, + { + "cell_type": "markdown", + "id": "792416cc", + "metadata": {}, + "source": [ + "## Querying with an index\n", + "\n", + "`where()` automatically uses an available (non-stale) index when the filter expression matches the indexed column. \n", + "The result is identical to a full scan.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "dcc2dc87", + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-15T12:29:18.333378Z", + "start_time": "2026-04-15T12:29:18.283229Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rows sensor_id > 450: 49\n", + "sensor_ids: [451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499]\n" + ] + } + ], + "source": [ + "result = t.where(t[\"sensor_id\"] > 450)\n", + "print(\"Rows sensor_id > 450:\", len(result))\n", + "print(\"sensor_ids:\", sorted(int(v) for v in result[\"sensor_id\"].to_numpy()))" + ] + }, + { + "cell_type": "markdown", + "id": "8b3b9725", + "metadata": {}, + "source": [ + "## Stale detection\n", + "\n", + "Any mutation — `append`, `extend`, `Column.__setitem__`, `Column.assign`, `sort_by`, `compact` —\n", + "marks all indexes **stale**. \n", + "When an index is stale, `where()` falls back to a full scan automatically so results are always correct.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b0132381", + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-15T12:29:24.936335Z", + "start_time": "2026-04-15T12:29:24.884590Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "stale after append? True\n", + "Found row: 1\n" + ] + } + ], + "source": [ + "t.append([9999, 30.0, 1]) # any mutation marks indexes stale\n", + "\n", + "idx = t.index(\"sensor_id\")\n", + "print(\"stale after append?\", idx.stale)\n", + "\n", + "# Query still works — scan fallback\n", + "result_stale = t.where(t[\"sensor_id\"] == 9999)\n", + "print(\"Found row:\", len(result_stale))" + ] + }, + { + "cell_type": "markdown", + "id": "110f792f", + "metadata": {}, + "source": [ + "Note: `delete()` only bumps the *visibility epoch* (it does not change column values) so it does **not** mark indexes stale.\n", + "\n", + "## Rebuilding an index\n", + "\n", + "`rebuild_index(col_name)` drops the old index and builds a fresh one from the current table state.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "dc4d2897", + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-15T12:29:31.023914Z", + "start_time": "2026-04-15T12:29:30.970979Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "stale after rebuild? False\n", + "Found row via rebuilt index: 1\n" + ] + } + ], + "source": [ + "idx = t.rebuild_index(\"sensor_id\")\n", + "print(\"stale after rebuild?\", idx.stale)\n", + "\n", + "result_rebuilt = t.where(t[\"sensor_id\"] == 9999)\n", + "print(\"Found row via rebuilt index:\", len(result_rebuilt))" + ] + }, + { + "cell_type": "markdown", + "id": "38363aa3", + "metadata": {}, + "source": [ + "## Dropping an index\n", + "\n", + "`drop_index(col_name)` removes the index from the catalog and deletes any sidecar files (for persistent tables).\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e1583b4f", + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-15T12:29:34.666155Z", + "start_time": "2026-04-15T12:29:34.628535Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Indexes after drop: []\n" + ] + } + ], + "source": [ + "t.drop_index(\"sensor_id\")\n", + "print(\"Indexes after drop:\", t.indexes)" + ] + }, + { + "cell_type": "markdown", + "id": "aab1e6ec", + "metadata": {}, + "source": [ + "## Persistent tables\n", + "\n", + "Indexes on persistent tables (tables with a `urlpath`) survive close and reopen because the catalog is stored inside the table's own `/_meta` sidecar and the index data lives under `/_indexes//`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "85d42133", + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-15T12:29:41.237153Z", + "start_time": "2026-04-15T12:29:39.916230Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Created: \n", + "Sidecar files: 7\n", + "Rows > 280 (before close): 19\n" + ] + } + ], + "source": [ + "import shutil\n", + "import tempfile\n", + "from pathlib import Path\n", + "\n", + "tmpdir = Path(tempfile.mkdtemp())\n", + "path = str(tmpdir / \"sensors.b2d\")\n", + "\n", + "# Create a persistent table and build an index\n", + "pt = blosc2.CTable(Measurement, urlpath=path, mode=\"w\")\n", + "rng2 = np.random.default_rng(0)\n", + "for i in range(300):\n", + " pt.append([i, 15.0 + rng2.random() * 25, int(rng2.integers(0, 4))])\n", + "\n", + "pidx = pt.create_index(\"sensor_id\")\n", + "print(\"Created:\", pidx)\n", + "\n", + "# Sidecar files\n", + "index_dir = Path(path) / \"_indexes\" / \"sensor_id\"\n", + "print(\"Sidecar files:\", len(list(index_dir.glob(\"**/*.b2nd\"))))\n", + "\n", + "# Query before close\n", + "r1 = pt.where(pt[\"sensor_id\"] > 280)\n", + "print(\"Rows > 280 (before close):\", len(r1))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "149ddba5", + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-15T12:29:45.139325Z", + "start_time": "2026-04-15T12:29:45.095849Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Indexes after reopen: []\n", + "Rows > 280 (after reopen): 19\n", + "Results match ✓\n" + ] + } + ], + "source": [ + "# Close and reopen — catalog is preserved\n", + "del pt\n", + "pt2 = blosc2.open(path)\n", + "\n", + "print(\"Indexes after reopen:\", pt2.indexes)\n", + "\n", + "r2 = pt2.where(pt2[\"sensor_id\"] > 280)\n", + "print(\"Rows > 280 (after reopen):\", len(r2))\n", + "\n", + "ids1 = sorted(int(v) for v in r1[\"sensor_id\"].to_numpy())\n", + "ids2 = sorted(int(v) for v in r2[\"sensor_id\"].to_numpy())\n", + "assert ids1 == ids2, \"Results differ!\"\n", + "print(\"Results match ✓\")\n", + "\n", + "shutil.rmtree(tmpdir, ignore_errors=True)" + ] + }, + { + "cell_type": "markdown", + "id": "2743e784", + "metadata": {}, + "source": [ + "## Views and indexes\n", + "\n", + "A *view* (the result of `where()`) is a filtered window into the underlying table. \n", + "Index management methods (`create_index`, `drop_index`, `rebuild_index`, `compact_index`) are **not** available on views — they raise `ValueError`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "83db418b", + "metadata": { + "ExecuteTime": { + "end_time": "2026-04-15T12:29:51.038611Z", + "start_time": "2026-04-15T12:29:50.906410Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "View type: CTable\n", + "create_index on view: Cannot create an index on a view.\n", + "drop_index on view: Cannot drop an index from a view.\n" + ] + } + ], + "source": [ + "t2 = blosc2.CTable(Measurement)\n", + "for i in range(50):\n", + " t2.append([i, 20.0, i % 3])\n", + "t2.create_index(\"sensor_id\")\n", + "\n", + "view = t2.where(t2[\"sensor_id\"] > 10)\n", + "print(\"View type:\", type(view).__name__)\n", + "\n", + "try:\n", + " view.create_index(\"sensor_id\")\n", + "except ValueError as e:\n", + " print(\"create_index on view:\", e)\n", + "\n", + "try:\n", + " view.drop_index(\"sensor_id\")\n", + "except ValueError as e:\n", + " print(\"drop_index on view:\", e)" + ] + }, + { + "cell_type": "markdown", + "id": "f5e87579", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "| Operation | Method |\n", + "|---|---|\n", + "| Build index | `t.create_index(col)` |\n", + "| Query (auto) | `t.where(expr)` — uses index when fresh |\n", + "| Check if stale | `t.index(col).stale` |\n", + "| Rebuild | `t.rebuild_index(col)` |\n", + "| Drop | `t.drop_index(col)` |\n", + "| Compact (full indexes) | `t.compact_index(col)` |\n", + "| List all | `t.indexes` |\n", + "\n", + "Key behaviours:\n", + "\n", + "- **Mutations** (`append`, `extend`, `setitem`, `assign`, `sort_by`, `compact`) mark indexes stale.\n", + "- **Stale indexes** trigger automatic scan fallback — no user intervention needed.\n", + "- **Persistent indexes** survive table close and reopen.\n", + "- **Views** cannot own indexes; only root tables can.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "363827fec805190a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/ctable/indexing.py b/examples/ctable/indexing.py new file mode 100644 index 00000000..ebf9245f --- /dev/null +++ b/examples/ctable/indexing.py @@ -0,0 +1,97 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +# CTable indexing: create, query, rebuild, and drop indexes on columns. + +import shutil +import tempfile +from dataclasses import dataclass +from pathlib import Path + +import blosc2 + + +@dataclass +class Measurement: + sensor_id: int = blosc2.field(blosc2.int32()) + temperature: float = blosc2.field(blosc2.float64()) + region: int = blosc2.field(blosc2.int32()) + + +# ------------------------------------------------------------------------- +# In-memory table +# ------------------------------------------------------------------------- + +t = blosc2.CTable(Measurement) +for i in range(200): + t.append([i, 15.0 + (i % 30) * 0.5, i % 4]) + +# Create a bucket index on 'sensor_id' (default kind). +idx = t.create_index("sensor_id") +print("Index created:", idx) +print("Stale?", idx.stale) + +# The where() call automatically uses the index when possible. +result = t.where(t["sensor_id"] > 180) +print("Rows with sensor_id > 180 (via index):", len(result)) +print("sensor_ids:", sorted(int(v) for v in result["sensor_id"].to_numpy())) + +# List all indexes on the table. +print("\nAll indexes:", t.indexes) + +# After mutating the table the index is marked stale and where() falls +# back to a full scan automatically — results are still correct. +t.append([999, 42.0, 2]) +idx = t.index("sensor_id") +print("\nAfter append, stale?", idx.stale) + +result_stale = t.where(t["sensor_id"] == 999) +print("Row with sensor_id=999 (scan fallback):", len(result_stale)) + +# Rebuild the index to make it current again. +idx = t.rebuild_index("sensor_id") +print("\nAfter rebuild, stale?", idx.stale) + +result_rebuilt = t.where(t["sensor_id"] == 999) +print("Row with sensor_id=999 (via rebuilt index):", len(result_rebuilt)) + +# Drop the index. +t.drop_index("sensor_id") +print("\nIndexes after drop:", t.indexes) + +# ------------------------------------------------------------------------- +# Persistent table +# ------------------------------------------------------------------------- + +tmpdir = Path(tempfile.mkdtemp()) +path = str(tmpdir / "measurements.b2d") + +try: + pt = blosc2.CTable(Measurement, urlpath=path, mode="w") + for i in range(200): + pt.append([i, 15.0 + (i % 30) * 0.5, i % 4]) + + pidx = pt.create_index("sensor_id") + print("\nPersistent index created:", pidx) + + # Sidecar files are stored under /_indexes/sensor_id/ + index_dir = Path(path) / "_indexes" / "sensor_id" + sidecars = list(index_dir.glob("**/*.b2nd")) + print(f"Sidecar files: {len(sidecars)}") + + result_p = pt.where(pt["sensor_id"] > 190) + print("Rows sensor_id > 190:", len(result_p)) + + # Close and reopen — the index catalog is persisted. + del pt + pt2 = blosc2.open(path) + print("\nAfter reopen, indexes:", pt2.indexes) + result_p2 = pt2.where(pt2["sensor_id"] > 190) + print("Rows sensor_id > 190 (after reopen):", len(result_p2)) + +finally: + shutil.rmtree(tmpdir, ignore_errors=True) diff --git a/plans/ctable-indexing.md b/plans/ctable-indexing.md new file mode 100644 index 00000000..077db1cc --- /dev/null +++ b/plans/ctable-indexing.md @@ -0,0 +1,713 @@ +# CTable Indexing Integration Plan + +## Goal + +Add persistent, table-owned indexing to `CTable` so that: + +- indexes can be created on `CTable` columns +- persistent indexes live inside the `TreeStore` that backs the table +- `CTable.where(...)` can reuse the existing index machinery as directly as possible +- index management feels aligned with the current `NDArray` indexing API + +This plan is for design and implementation guidance only. It does not assume +that all pieces must land in one patch. + +## Current Situation + +### What already exists + +`CTable` already supports persistent storage on top of `TreeStore`: + +- `/_meta` +- `/_valid_rows` +- `/_cols/` + +This is implemented in [src/blosc2/ctable_storage.py](/Users/faltet/blosc/python-blosc2/src/blosc2/ctable_storage.py) +and used by [src/blosc2/ctable.py](/Users/faltet/blosc/python-blosc2/src/blosc2/ctable.py). + +The generic indexing engine already exists for 1-D `NDArray` targets: + +- summary / bucket / partial / full indexes +- persistent descriptors in `array.schunk.vlmeta` +- sidecar arrays stored next to the indexed array +- query planning via `plan_query(...)` +- ordered reuse via `plan_ordered_query(...)` + +This lives in [src/blosc2/indexing.py](/Users/faltet/blosc/python-blosc2/src/blosc2/indexing.py) +and is exposed through `NDArray.create_index()`, `drop_index()`, `rebuild_index()`, +`compact_index()`, `index()`, and `indexes`. + +### What is missing + +`CTable` cannot currently reuse that machinery cleanly because: + +1. `CTable.where(...)` eagerly computes a boolean filter and never gives the + planner a table-aware lazy query shape. +2. the current index engine assumes that one index belongs to one `NDArray` + and stores its descriptor in that array's `vlmeta`. +3. persistent sidecar path derivation is based on `array.urlpath`, which places + index files next to the array file rather than inside a table-owned subtree. +4. `CTable` has row visibility semantics through `_valid_rows`, which means + "row still exists" and "row currently matches" are distinct concerns. + +## Design Principles + +The implementation should follow these rules: + +- indexes are table-managed, not column-autonomous +- column indexes are still built from and logically targeted at individual column arrays +- persistent index artifacts must be part of the table store layout +- the public API should mirror existing `NDArray` indexing names where possible +- delete visibility should not force index rebuilds when it can be handled by + post-filtering with `_valid_rows` +- planner and evaluator logic should be reused, not reimplemented from scratch +- unsupported queries must keep a correct scan fallback + +## Proposed Storage Layout + +Extend the persistent `CTable` layout with a reserved index subtree: + +- `/_meta` +- `/_valid_rows` +- `/_cols/` +- `/_indexes//...` + +Recommended concrete shape: + +- `/_indexes//_meta` +- `/_indexes//summary.chunk` +- `/_indexes//summary.block` +- `/_indexes//bucket.values` +- `/_indexes//bucket.bucket_positions` +- `/_indexes//bucket.offsets` +- `/_indexes//bucket_nav.l1` +- `/_indexes//bucket_nav.l2` +- `/_indexes//partial.values` +- `/_indexes//partial.positions` +- `/_indexes//partial.offsets` +- `/_indexes//partial_nav.l1` +- `/_indexes//partial_nav.l2` +- `/_indexes//full.values` +- `/_indexes//full.positions` +- `/_indexes//full_nav.l1` +- `/_indexes//full_nav.l2` +- `/_indexes//full_run..values` +- `/_indexes//full_run..positions` + +Notes: + +- `token` should match the current indexing token model: + - field token for column indexes + - normalized expression token for expression indexes +- all index payloads should stay under `/_indexes//` +- query-cache payloads, if reused for `CTable`, should also be table-owned and + not emitted as sibling files outside the table root + +## Metadata Placement + +The top-level table manifest in `/_meta.vlmeta` should gain index catalog +entries and epoch counters. + +Recommended fields: + +``` +{ + "kind": "ctable", + "version": 1, + "schema": {...}, + "index_catalog_version": 1, + "value_epoch": 0, + "visibility_epoch": 0, + "indexes": { + "id": { + "name": "id", + "token": "id", + "target": {"source": "column", "column": "id"}, + "kind": "full", + "version": 1, + "persistent": True, + "stale": False, + "built_value_epoch": 3, + ... + } + } +} +``` + +Notes: + +- do not keep a historical list of epochs +- overwrite descriptor metadata on rebuild +- descriptors remain small; large payloads stay in `/_indexes/...` +- index catalog ownership remains at the table level, not per-column + +## Public API + +The `CTable` surface should mirror `NDArray` as closely as possible: + +```python +table.create_index("id", kind=blosc2.IndexKind.FULL) +table.drop_index("id") +table.rebuild_index("id") +table.compact_index("id") +table.index("id") +table.indexes +``` + +### Initial target support + +Phase 1 should support column indexes only: + +- `table.create_index("id", kind=...)` +- `table.create_index(field="id", kind=...)` + +Phase 2 can add expression indexes: + +- `table.create_index(expression="abs(score - baseline)", operands=...)` + +but only when all operands resolve to columns from the same `CTable`. + +### Descriptor identity + +Use one active index per target, matching current `NDArray` behavior: + +- one index per column token +- one index per normalized expression token +- optional `name=` remains a label, not identity + +## Query Integration Model + +### Current `CTable` behavior + +Today, `CTable` column comparisons produce `NDArray` or `LazyExpr` results over +physical rows, and `CTable.where(...)` then: + +1. computes the filter +2. pads or trims it +3. intersects it with `_valid_rows` +4. returns a view + +This is correct but fully scan-based. + +### Proposed behavior + +Teach `CTable.where(...)` to detect when the incoming predicate is a `LazyExpr` +that can be interpreted as a table query over table-owned columns. + +For such predicates: + +1. normalize the expression into a table-query descriptor +2. ask a new `CTable` planner for candidate physical row positions +3. intersect candidates with `_valid_rows` +4. evaluate any residual predicate only on surviving candidates +5. produce the final boolean mask or direct row-position set +6. return the usual `CTable` view + +If any step is unsupported, fall back to the current eager full-filter path. + +## Planner Strategy + +Do not build a second independent indexing engine for `CTable`. + +Instead, refactor the current engine into: + +- reusable target normalization +- reusable index build logic +- reusable query plan primitives +- storage backends: + - `NDArrayIndexStorage` + - `CTableIndexStorage` + +### Reusable concepts from current `indexing.py` + +The following should be kept conceptually unchanged: + +- index kinds: summary / bucket / partial / full +- descriptor structure where practical +- target token resolution +- exact and segment planning +- ordered full-index reuse +- full-index compaction model + +### New `CTable` planner layer + +Add a thin planner layer that: + +- maps expression operands back to `CTable` columns +- resolves which indexed columns can participate +- requests index plans from the underlying column index implementation +- intersects or combines candidate physical positions +- reports a reason when indexed planning is not possible + +For v1: + +- single-column predicates should be first-class +- multi-column conjunctions should be supported when each term can be planned independently +- disjunctions can initially fall back to scan if they complicate correctness + +## Row Visibility Semantics + +`CTable` indexes should be defined over physical row positions, not over the +current live-row numbering. + +That means: + +- index payloads refer to physical positions in the backing arrays +- `_valid_rows` remains the source of truth for row visibility +- deleted rows are filtered at query execution time + +This is important because deletes in `CTable` do not rewrite columns; they only +flip visibility bits. + +## Epoch Model + +The epoch model is intentionally small. + +### Table-level counters + +Store only: + +- `value_epoch` +- `visibility_epoch` + +Both are monotonically increasing integers in top-level table metadata. + +### Per-index metadata + +Each descriptor stores: + +- `built_value_epoch` + +Optionally later: + +- `built_visibility_epoch` + +but this is not required in the first implementation. + +### Why this is enough + +- if indexed values or row order change, the index may be invalid: + bump `value_epoch` +- if only `_valid_rows` changes, the index still points to correct physical + rows; execution can intersect with current visibility: + bump `visibility_epoch` + +No epoch history is retained. There is no cleanup problem because only current +scalar values are stored. + +## Mutation Rules + +### Mutations that should bump `value_epoch` + +- `append(...)` +- `extend(...)` +- column writes through `Column.__setitem__` +- `Column.assign(...)` +- `compact()` +- `sort_by(inplace=True)` +- any future row rewrite / reorder operation +- add / drop / rename column for affected targets + +### Mutations that should bump `visibility_epoch` only + +- `delete(...)` + +### Initial stale policy + +For a first implementation, keep rebuild behavior conservative: + +- if a mutation changes indexed values or row positions: + - set affected indexes stale + - bump `value_epoch` +- if only visibility changes: + - do not set indexes stale + - bump `visibility_epoch` + +This is simpler than trying to preserve append-compatible incremental +maintenance on day one. + +## Incremental Maintenance Policy + +The current `NDArray` engine supports limited append maintenance for some index +types. `CTable` does not need to replicate all of that immediately. + +Recommended rollout: + +### Phase 1 + +- create / drop / rebuild / compact indexes +- mark value-changing mutations stale +- keep deletes valid via `_valid_rows` + +### Phase 2 + +- optimize append / extend maintenance for column indexes +- reuse full-index append-run logic where practical +- decide whether summary / bucket / partial can be refreshed incrementally for + appended ranges without rebuilding everything + +The plan should prefer correctness and clear ownership before maintenance +optimizations. + +## Ordered Queries + +The smoothest integration with current `CTable` querying is: + +- filtering remains `table.where(predicate)` +- ordered access is added later in a table-appropriate way + +Possible later APIs: + +- `table.where(expr).sort_by("id")` with index reuse +- `table.where(expr).argsort(order="id")` on a row-index result abstraction +- dedicated row-position helpers for internal use + +For the first version, the main target should be indexed filtering, not full +ordered traversal. + +However, the storage format should not block future ordered reuse, so `full` +indexes should still store enough information to support: + +- ordered filtered row positions +- stable tie handling +- secondary refinement + +## Refactoring Needed in `indexing.py` + +The current implementation mixes three concerns: + +1. planner / evaluator logic +2. metadata ownership +3. sidecar path naming and opening + +To support `CTable`, split these concerns. + +### Step A: storage abstraction + +Introduce an internal storage protocol with responsibilities like: + +- load/save index catalog +- derive payload location for a component +- open/store/remove sidecar arrays +- load/save query-cache catalog and payloads + +Concrete implementations: + +- `NDArrayIndexStorage` +- `CTableIndexStorage` + +### Step B: generic target abstraction + +Introduce an internal target wrapper that represents: + +- base length +- dtype +- chunks / blocks +- slice access for the indexed value stream +- optional block-read helpers +- identity for query cache keys + +For `CTable`, the target for a column index is the column `NDArray`, but +descriptor ownership and sidecar storage are table-owned. + +### Step C: planner entry points + +Keep the existing `NDArray` public entry points intact, but allow internal +planner functions to accept the new abstractions rather than hard-coded raw +`NDArray` ownership assumptions. + +## `CTable` Internal Changes + +### New helpers on `CTable` + +Add private helpers for: + +- resolving the root table from a view +- checking whether a `LazyExpr` is table-plannable +- mapping operands back to column names +- building a physical-position result into a boolean mask +- reading and writing index metadata via storage + +### New helpers on `FileTableStorage` + +Add persistent helpers for: + +- `index_root(token)` +- `index_component_key(token, component_name)` +- create/open/delete index sidecars under `/_indexes/...` +- load/save index catalog in `/_meta` +- load/save table epoch counters + +### View behavior + +Views should not own indexes. + +Rules: + +- creating or dropping indexes on a view should raise +- querying a view may reuse root-table indexes +- planner must always combine indexed matches with the view's current mask + +## Expression Index Scope + +Expression indexes are valuable but should not be part of the first patch +unless the column-index path is already stable. + +Recommended sequence: + +1. column indexes only +2. exact-match multi-column filtering using multiple column indexes +3. expression indexes over same-table columns +4. ordered reuse + +When expression indexes are added, require: + +- all operands belong to the same base `CTable` +- expression normalization produces a stable token +- dependencies are stored by column name, not transient operand aliases + +## Query Cache Scope + +The existing query cache in `indexing.py` is array-owned. + +For `CTable`, if reused, it should be table-owned as well: + +- cache identity should include the table root plus query descriptor +- cache invalidation should happen on `value_epoch` changes +- visibility-only changes can either: + - invalidate conservatively in v1, or + - be ignored if cached results are always post-filtered through current `_valid_rows` + +To keep the first version smaller, query-cache reuse can be deferred entirely. + +## Validation and Reserved Names + +Extend reserved internal names for persistent `CTable` layout: + +- `_meta` +- `_valid_rows` +- `_cols` +- `_indexes` + +If the schema compiler already blocks these, document it. If not, extend the +reserved-name validation explicitly. + +## Error Handling + +Recommended behavior: + +- creating an index on a view: `ValueError` +- creating an index on a missing column: `KeyError` +- creating an unsupported index target: `TypeError` or `ValueError` +- querying with a non-plannable expression: silent scan fallback +- querying with malformed index metadata: clear error on open/use +- compacting a non-`full` index: same semantics as current engine + +## Testing Plan + +### Storage and metadata + +Add tests for: + +- create persistent `CTable` column index +- reopen table and see the index catalog +- verify index payloads are stored under `/_indexes/...` +- verify no sidecar siblings are emitted outside the table root layout +- drop index removes `/_indexes//...` + +### Query correctness + +Add tests for: + +- equality and range predicates on indexed columns +- same queries on reopened persistent tables +- results match scan-based filtering +- deleted rows are excluded without rebuilding the index +- appending after index creation follows the chosen stale policy + +### View semantics + +Add tests for: + +- view queries can reuse parent indexes +- creating indexes on views is rejected +- view mask and `_valid_rows` are both respected + +### Mutation semantics + +Add tests for: + +- delete bumps visibility only and keeps index query correctness +- overwrite of indexed column marks index stale +- compact marks index stale +- inplace sort marks index stale +- rebuild refreshes `built_value_epoch` + +### Multi-column planning + +Add tests for: + +- one indexed term + one unindexed residual term +- two indexed conjunctive terms +- unsupported disjunction falls back correctly + +## Documentation Plan + +The feature should not land with code and tests only. It needs user-facing +documentation from the start. + +### Examples + +Add runnable examples under `examples/ctable` covering at least: + +- creating a `CTable` index on one column +- querying a `CTable` with an indexed predicate +- reopening a persistent table and reusing the index +- basic index management such as `indexes`, `index(...)`, `drop_index(...)`, + and `rebuild_index(...)` + +### Tutorial + +Add a dedicated tutorial notebook at: + +- `doc/getting_started/tutorials/15.indexing-ctables.ipynb` + +The tutorial should explain: + +- what a `CTable` index is +- how indexes relate to columns and to the table as a whole +- how persistence works for indexed tables +- what kinds of queries benefit from indexes +- what happens after deletes and other mutations +- how to inspect and maintain indexes + +### API docstrings and Sphinx integration + +Do not treat docstrings as optional follow-up work. + +For every new public `CTable` indexing API entry point, add fully descriptive +docstrings with small examples, following the style already used elsewhere in +the codebase. + +This includes, as applicable: + +- `CTable.create_index(...)` +- `CTable.drop_index(...)` +- `CTable.rebuild_index(...)` +- `CTable.compact_index(...)` +- `CTable.index(...)` +- `CTable.indexes` + +The docstrings should cover: + +- parameters +- return values +- persistence behavior +- mutation / stale behavior where relevant +- short examples that show the intended usage + +These APIs should also be integrated into the Sphinx docs so they are reachable +from the generated documentation, not only from source docstrings. + +## Recommended Implementation Order + +### Phase 1: storage foundations + +1. add `/_indexes` reserved subtree conventions +2. extend `FileTableStorage` with index catalog and sidecar helpers +3. add table-level epoch metadata + +### Phase 2: API skeleton + +4. add `CTable.create_index`, `drop_index`, `rebuild_index`, `compact_index`, + `index`, and `indexes` +5. implement build/drop/rebuild against column targets only +6. keep query path unchanged initially + +### Phase 3: planner integration + +7. refactor `indexing.py` storage ownership assumptions +8. add `CTable` query planner shim +9. teach `CTable.where(...)` to use indexed planning when possible +10. keep scan fallback for everything else + +### Phase 4: mutation policy + +11. wire `value_epoch` / `visibility_epoch` +12. mark affected indexes stale on value-changing mutations +13. keep delete visibility index-safe without rebuild + +### Phase 5: follow-up optimizations + +14. consider append-aware maintenance +15. consider expression indexes +16. consider ordered reuse for table queries +17. consider query-cache reuse + +### Phase 6: documentation + +18. add `examples/ctable` indexing examples +19. add `doc/getting_started/tutorials/15.indexing-ctables.ipynb` +20. add full public docstrings with examples for the `CTable` indexing API +21. integrate the new API and tutorial into Sphinx documentation + +## Non-Goals for the First Implementation + +Do not include these in the first patch unless they come almost for free: + +- full expression-index support +- ordered query reuse for `CTable` +- disjunction planning across multiple indexes +- aggressive incremental maintenance for all index kinds +- index-aware query caching +- cross-table expression operands + +## Future Work + +One possible future storage evolution would be to make each persisted column a +subtree root instead of a single leaf object. + +That would allow a layout more like: + +- `/_cols/id/data` +- `/_cols/id/indexes/...` +- `/_cols/id/missing/...` +- `/_cols/id/sidecars/...` +- `/_cols/score/data` +- `/_cols/score/indexes/...` + +Potential benefits: + +- stronger locality between a column and its derived artifacts +- easier `rename_column()` and `drop_column()` handling +- a natural home for future per-column sidecars beyond indexes +- room for explicit missing-value bitmaps, nullability metadata, sketches, or + other derived column structures + +Potential costs: + +- this would be a real `CTable` storage-schema change, not just an indexing feature +- current persisted tables would need migration or dual-layout support +- `FileTableStorage` and open/materialization logic would become more complex +- the benefit is broader than indexing, so it is better considered as part of a + larger storage-layout revision + +For that reason, this plan does not assume that redesign. It keeps the current +column-leaf layout and places indexes in a table-owned `/_indexes` subtree. + +## Summary + +The right model is: + +- indexes are table-managed, not column-autonomous +- column indexes are still built from and logically targeted at individual + column arrays +- persistent index artifacts live under `/_indexes` +- existing `indexing.py` logic is reused through refactoring, not duplicated +- deletes remain cheap by treating indexes as physical-row structures and + applying `_valid_rows` at execution time +- epoch tracking stays minimal: a small number of table-level counters, not a + growing history + +This keeps the user model coherent with current `CTable` persistence and as +close as possible to the existing `NDArray` indexing API. diff --git a/src/blosc2/ctable.py b/src/blosc2/ctable.py index 3b1f3863..006e62c4 100644 --- a/src/blosc2/ctable.py +++ b/src/blosc2/ctable.py @@ -47,6 +47,108 @@ def wrapper(*args, **kwargs): compute_display_width, ) +# --------------------------------------------------------------------------- +# Index proxy and CTableIndex +# --------------------------------------------------------------------------- + + +class _FakeVlMeta: + """Minimal vlmeta stand-in that accepts writes without touching a real SChunk.""" + + def __init__(self): + self._data: dict = {} + + def __getitem__(self, key): + return self._data[key] + + def __setitem__(self, key, value): + self._data[key] = value + + def get(self, key, default=None): + return self._data.get(key, default) + + +class _FakeSchunk: + """Minimal SChunk stand-in whose vlmeta stores in memory.""" + + def __init__(self): + self.vlmeta = _FakeVlMeta() + + +class _CTableIndexProxy: + """Minimal shim that lets the ``indexing`` module build sidecars for a + CTable column without touching the column's own ``schunk.vlmeta``. + + Attributes mirror those required by the internal build functions: + ``urlpath``, ``schunk``, ``shape``, ``ndim``, ``dtype``, ``chunks``, + ``blocks``, and item access via ``__getitem__``. + """ + + def __init__(self, col_array: blosc2.NDArray, anchor_urlpath: str | None) -> None: + self._col_array = col_array + self.urlpath = anchor_urlpath # controls sidecar placement + self.schunk = _FakeSchunk() + self.shape = col_array.shape + self.ndim = col_array.ndim + self.dtype = col_array.dtype + self.chunks = col_array.chunks + self.blocks = col_array.blocks + + def __getitem__(self, key): + return self._col_array[key] + + +class CTableIndex: + """A handle on an index attached to a :class:`CTable` column. + + Returned by :meth:`CTable.index` and items of :attr:`CTable.indexes`. + Provides :meth:`drop`, :meth:`rebuild`, and :meth:`compact` convenience + methods that delegate back to the owning table. + """ + + def __init__(self, table: CTable, col_name: str, descriptor: dict) -> None: + self._table = table + self._col_name = col_name + self._descriptor = descriptor + + @property + def col_name(self) -> str: + """Column name this index targets.""" + return self._col_name + + @property + def kind(self) -> str: + """Index kind string (``'bucket'``, ``'partial'``, or ``'full'``).""" + return self._descriptor.get("kind", "") + + @property + def stale(self) -> bool: + """True if the index is stale and needs rebuilding.""" + return bool(self._descriptor.get("stale", False)) + + @property + def name(self) -> str | None: + """Optional human-readable name assigned at creation time.""" + return self._descriptor.get("name") or None + + def drop(self) -> None: + """Drop this index from the owning table.""" + self._table.drop_index(self._col_name) + + def rebuild(self) -> CTableIndex: + """Rebuild this index and return the updated handle.""" + return self._table.rebuild_index(self._col_name) + + def compact(self) -> CTableIndex: + """Compact this index (merge incremental runs) and return the updated handle.""" + return self._table.compact_index(self._col_name) + + def __repr__(self) -> str: + stale_str = " (stale)" if self.stale else "" + name_str = f" name={self.name!r}" if self.name else "" + return f"" + + # RowT is intentionally left unbound so CTable works with both dataclasses # and legacy Pydantic models during the transition period. RowT = TypeVar("RowT") @@ -61,7 +163,9 @@ def _arange(start, stop=None, step=1) -> blosc2.NDArray | np.ndarray: if stop is None: start, stop = 0, start n = len(range(start, stop, step)) - return blosc2.arange(start, stop, step) if n >= _BLOSC2_ARANGE_THRESHOLD else np.arange(start, stop, step) + return ( + blosc2.arange(start, stop, step) if n >= _BLOSC2_ARANGE_THRESHOLD else np.arange(start, stop, step) + ) # --------------------------------------------------------------------------- @@ -351,6 +455,7 @@ def __setitem__(self, key: int | slice | list | np.ndarray, value): else: raise TypeError(f"Invalid index type: {type(key)}") + self._table._root_table._mark_all_indexes_stale() def __iter__(self): arr = self._valid_rows @@ -500,6 +605,7 @@ def assign(self, data) -> None: raise TypeError(f"Cannot coerce data to column dtype {self.dtype!r}: {exc}") from exc live_pos = np.where(self._valid_rows[:])[0] self._raw_col[live_pos] = arr + self._table._root_table._mark_all_indexes_stale() def unique(self) -> np.ndarray: """Return sorted array of unique live values. @@ -2019,6 +2125,7 @@ def compact(self): self._valid_rows[: self._n_rows] = True self._valid_rows[self._n_rows :] = False self._last_pos = self._n_rows # next write goes right after live rows + self._mark_all_indexes_stale() def _normalise_sort_keys( self, @@ -2130,6 +2237,7 @@ def sort_by( self._valid_rows[n:] = False self._n_rows = n self._last_pos = n + self._mark_all_indexes_stale() return self else: # Build a new in-memory table with the sorted rows @@ -2231,6 +2339,564 @@ def schema_dict(self) -> dict[str, Any]: """Return a JSON-compatible dict describing this table's schema.""" return schema_to_dict(self._schema) + # ------------------------------------------------------------------ + # Index management + # ------------------------------------------------------------------ + + @property + def _root_table(self) -> CTable: + """Return the root (non-view) table; *self* if not a view.""" + t = self + while t.base is not None: + t = t.base + return t + + def _mark_all_indexes_stale(self) -> None: + """Bump value_epoch and mark every catalog entry stale on the root table.""" + root = self._root_table + root._storage.bump_value_epoch() + catalog = root._storage.load_index_catalog() + if not catalog: + return + changed = False + for desc in catalog.values(): + if not desc.get("stale", False): + desc["stale"] = True + changed = True + if changed: + root._storage.save_index_catalog(catalog) + + def _build_index_persistent( + self, + col_name: str, + col_arr: blosc2.NDArray, + *, + kind: str, + optlevel: int, + name_hint: str | None, + build: str, + tmpdir: str | None, + cparams_obj, + ) -> dict: + """Build index sidecar files for a persistent-table column; return the descriptor.""" + import tempfile + from pathlib import Path + + from blosc2.indexing import ( + _PERSISTENT_INDEXES, + _array_key, + _build_bucket_descriptor, + _build_bucket_descriptor_ooc, + _build_descriptor, + _build_full_descriptor, + _build_full_descriptor_ooc, + _build_levels_descriptor, + _build_levels_descriptor_ooc, + _build_partial_descriptor, + _build_partial_descriptor_ooc, + _copy_descriptor, + _field_target_descriptor, + _resolve_full_index_tmpdir, + _resolve_ooc_mode, + _target_token, + _values_for_target, + ) + + anchor = self._storage.index_anchor_path(col_name) + os.makedirs(os.path.dirname(anchor), exist_ok=True) + proxy = _CTableIndexProxy(col_arr, anchor) + proxy_key = _array_key(proxy) + _PERSISTENT_INDEXES.pop(proxy_key, None) # clear any stale cache entry + + target = _field_target_descriptor(None) + token = _target_token(target) + persistent = True + dtype = col_arr.dtype + use_ooc = _resolve_ooc_mode(kind, build) + + if use_ooc: + resolved_tmpdir = _resolve_full_index_tmpdir(proxy, tmpdir) + levels = _build_levels_descriptor_ooc(proxy, target, token, kind, dtype, persistent, cparams_obj) + bucket = ( + _build_bucket_descriptor_ooc( + proxy, target, token, kind, dtype, optlevel, persistent, cparams_obj + ) + if kind == "bucket" + else None + ) + partial = ( + _build_partial_descriptor_ooc( + proxy, target, token, kind, dtype, optlevel, persistent, cparams_obj + ) + if kind == "partial" + else None + ) + full = None + if kind == "full": + with tempfile.TemporaryDirectory(prefix="blosc2-index-ooc-", dir=resolved_tmpdir) as td: + full = _build_full_descriptor_ooc( + proxy, target, token, kind, dtype, persistent, Path(td), cparams_obj + ) + descriptor = _build_descriptor( + proxy, + target, + token, + kind, + optlevel, + persistent, + True, + name_hint, + dtype, + levels, + bucket, + partial, + full, + cparams_obj, + ) + else: + values = _values_for_target(proxy, target) + levels = _build_levels_descriptor( + proxy, target, token, kind, dtype, values, persistent, cparams_obj + ) + bucket = ( + _build_bucket_descriptor(proxy, token, kind, values, optlevel, persistent, cparams_obj) + if kind == "bucket" + else None + ) + partial = ( + _build_partial_descriptor(proxy, token, kind, values, optlevel, persistent, cparams_obj) + if kind == "partial" + else None + ) + full = ( + _build_full_descriptor(proxy, token, kind, values, persistent, cparams_obj) + if kind == "full" + else None + ) + descriptor = _build_descriptor( + proxy, + target, + token, + kind, + optlevel, + persistent, + False, + name_hint, + dtype, + levels, + bucket, + partial, + full, + cparams_obj, + ) + + result = _copy_descriptor(descriptor) + _PERSISTENT_INDEXES.pop(proxy_key, None) # evict proxy to avoid memory leak + return result + + def create_index( + self, + col_name: str, + *, + kind: blosc2.IndexKind = blosc2.IndexKind.BUCKET, + optlevel: int = 5, + name: str | None = None, + build: str = "auto", + tmpdir: str | None = None, + **kwargs, + ) -> CTableIndex: + """Build and register an index for a column. + + Parameters + ---------- + col_name: + Name of the column to index. + kind: + Index kind. One of :attr:`blosc2.IndexKind.BUCKET` (default), + :attr:`blosc2.IndexKind.PARTIAL`, or :attr:`blosc2.IndexKind.FULL`. + optlevel: + Optimisation level (1–9). Higher values give more precise pruning + at the cost of larger index files. Default is 5. + name: + Optional human-readable label for the index. + build: + Build strategy: ``'auto'``, ``'memory'``, or ``'ooc'`` (out-of-core). + tmpdir: + Temporary directory for out-of-core builds. ``None`` means use the + column's own directory (persistent tables) or the system temporary + directory (in-memory tables). + **kwargs: + Pass ``cparams=`` to customise index compression. + + Returns + ------- + CTableIndex + A handle on the newly created index. + + Raises + ------ + ValueError + If called on a view. + KeyError + If *col_name* is not a column of this table. + """ + if self.base is not None: + raise ValueError("Cannot create an index on a view.") + if col_name not in self._cols: + raise KeyError(f"No column named {col_name!r}. Available: {self.col_names}") + catalog = self._storage.load_index_catalog() + if col_name in catalog: + raise ValueError( + f"Index already exists for column {col_name!r}. " + "Call rebuild_index() to replace it or drop_index() first." + ) + + from blosc2.indexing import ( + _IN_MEMORY_INDEXES, + _copy_descriptor, + _normalize_build_mode, + _normalize_index_cparams, + _normalize_index_kind, + ) + from blosc2.indexing import ( + create_index as _ix_create_index, + ) + + cparams_obj = _normalize_index_cparams(kwargs.pop("cparams", None)) + if kwargs: + raise TypeError(f"unexpected keyword argument(s): {', '.join(sorted(kwargs))}") + + kind_str = _normalize_index_kind(kind) + build_str = _normalize_build_mode(build) + col_arr = self._cols[col_name] + is_persistent = self._storage.index_anchor_path(col_name) is not None + + if is_persistent: + descriptor = self._build_index_persistent( + col_name, + col_arr, + kind=kind_str, + optlevel=optlevel, + name_hint=name, + build=build_str, + tmpdir=tmpdir, + cparams_obj=cparams_obj, + ) + else: + _ix_create_index( + col_arr, + field=None, + kind=blosc2.IndexKind(kind_str), + optlevel=optlevel, + name=name, + build=build, + tmpdir=tmpdir, + cparams=cparams_obj, + ) + store = _IN_MEMORY_INDEXES[id(col_arr)] + descriptor = _copy_descriptor(store["indexes"]["__self__"]) + + value_epoch, _ = self._storage.get_epoch_counters() + descriptor["built_value_epoch"] = value_epoch + + catalog = self._storage.load_index_catalog() + catalog[col_name] = descriptor + self._storage.save_index_catalog(catalog) + return CTableIndex(self, col_name, descriptor) + + def drop_index(self, col_name: str) -> None: + """Remove the index for *col_name* and delete any sidecar files. + + Parameters + ---------- + col_name: + Column whose index should be dropped. + + Raises + ------ + ValueError + If called on a view. + KeyError + If no index exists for *col_name*. + """ + if self.base is not None: + raise ValueError("Cannot drop an index from a view.") + + from pathlib import Path + + from blosc2.indexing import ( + _IN_MEMORY_INDEXES, + _PERSISTENT_INDEXES, + _array_key, + _clear_cached_data, + _drop_descriptor_sidecars, + _is_persistent_array, + ) + from blosc2.indexing import ( + drop_index as _ix_drop_index, + ) + + catalog = self._storage.load_index_catalog() + if col_name not in catalog: + raise KeyError(f"No index found for column {col_name!r}.") + + descriptor = catalog.pop(col_name) + token = descriptor["token"] + col_arr = self._cols[col_name] + + if _is_persistent_array(col_arr): + _drop_descriptor_sidecars(descriptor) + _clear_cached_data(col_arr, token) + arr_key = _array_key(col_arr) + store = _PERSISTENT_INDEXES.get(arr_key) + if store is not None: + store["indexes"].pop(token, None) + anchor = self._storage.index_anchor_path(col_name) + proxy_key = ("persistent", str(Path(anchor).resolve())) + _PERSISTENT_INDEXES.pop(proxy_key, None) + index_dir = os.path.dirname(anchor) + with contextlib.suppress(OSError): + os.rmdir(index_dir) + else: + with contextlib.suppress(Exception): + _ix_drop_index(col_arr, field=None) + store = _IN_MEMORY_INDEXES.get(id(col_arr)) + if store is not None: + store["indexes"].pop(token, None) + + self._storage.save_index_catalog(catalog) + + def rebuild_index(self, col_name: str) -> CTableIndex: + """Drop and recreate the index for *col_name* with the same parameters. + + Parameters + ---------- + col_name: + Column whose index should be rebuilt. + + Returns + ------- + CTableIndex + A handle on the newly built index. + + Raises + ------ + ValueError + If called on a view. + KeyError + If no index exists for *col_name*. + """ + if self.base is not None: + raise ValueError("Cannot rebuild an index on a view.") + + catalog = self._storage.load_index_catalog() + if col_name not in catalog: + raise KeyError(f"No index found for column {col_name!r}.") + + old_desc = catalog[col_name] + old_kind = old_desc["kind"] + old_optlevel = int(old_desc.get("optlevel", 5)) + old_name = old_desc.get("name") or None + old_ooc = bool(old_desc.get("ooc", False)) + old_cparams = old_desc.get("cparams") + build_str = "ooc" if old_ooc else "memory" + + self.drop_index(col_name) + return self.create_index( + col_name, + kind=old_kind, + optlevel=old_optlevel, + name=old_name, + build=build_str, + cparams=old_cparams, + ) + + def compact_index(self, col_name: str) -> CTableIndex: + """Compact the index for *col_name*, merging any incremental append runs. + + Only meaningful for ``kind='full'`` indexes. For other kinds the call + is a no-op and returns the current handle. + + Parameters + ---------- + col_name: + Column whose index should be compacted. + + Returns + ------- + CTableIndex + A handle reflecting the (possibly updated) index descriptor. + + Raises + ------ + ValueError + If called on a view. + KeyError + If no index exists for *col_name*. + """ + if self.base is not None: + raise ValueError("Cannot compact an index on a view.") + + from blosc2.indexing import ( + _IN_MEMORY_INDEXES, + _PERSISTENT_INDEXES, + _array_key, + _copy_descriptor, + _default_index_store, + _is_persistent_array, + ) + from blosc2.indexing import ( + compact_index as _ix_compact_index, + ) + + catalog = self._storage.load_index_catalog() + if col_name not in catalog: + raise KeyError(f"No index found for column {col_name!r}.") + + col_arr = self._cols[col_name] + descriptor = catalog[col_name] + + if _is_persistent_array(col_arr): + anchor = self._storage.index_anchor_path(col_name) + proxy = _CTableIndexProxy(col_arr, anchor) + proxy_key = _array_key(proxy) + store = _default_index_store() + store["indexes"][descriptor["token"]] = descriptor + _PERSISTENT_INDEXES[proxy_key] = store + try: + _ix_compact_index(proxy) + updated_store = _PERSISTENT_INDEXES.get(proxy_key) or store + updated_desc = _copy_descriptor(updated_store["indexes"][descriptor["token"]]) + finally: + _PERSISTENT_INDEXES.pop(proxy_key, None) + updated_desc["built_value_epoch"] = descriptor.get("built_value_epoch", 0) + catalog[col_name] = updated_desc + self._storage.save_index_catalog(catalog) + return CTableIndex(self, col_name, updated_desc) + else: + _ix_compact_index(col_arr) + store = _IN_MEMORY_INDEXES.get(id(col_arr)) + if store: + token = descriptor["token"] + updated_desc = _copy_descriptor(store["indexes"].get(token, descriptor)) + updated_desc["built_value_epoch"] = descriptor.get("built_value_epoch", 0) + catalog[col_name] = updated_desc + self._storage.save_index_catalog(catalog) + return CTableIndex(self, col_name, updated_desc) + return CTableIndex(self, col_name, descriptor) + + def index(self, col_name: str) -> CTableIndex: + """Return the index handle for *col_name*. + + Parameters + ---------- + col_name: + Column name to look up. + + Returns + ------- + CTableIndex + + Raises + ------ + KeyError + If no index exists for *col_name*. + """ + catalog = self._root_table._storage.load_index_catalog() + if col_name not in catalog: + raise KeyError(f"No index found for column {col_name!r}.") + return CTableIndex(self, col_name, catalog[col_name]) + + @property + def indexes(self) -> list[CTableIndex]: + """Return a list of :class:`CTableIndex` handles for all active indexes.""" + catalog = self._root_table._storage.load_index_catalog() + return [CTableIndex(self, col_name, desc) for col_name, desc in catalog.items()] + + @staticmethod + def _find_indexed_column(root_cols, catalog, operands): + """Return ``(col_name, col_arr)`` for the first operand NDArray that maps + to a root-table column with a live (non-stale) catalog entry, or ``(None, None)``.""" + for operand in operands.values(): + if not isinstance(operand, blosc2.NDArray): + continue + for col_name, col_arr in root_cols.items(): + if col_arr is operand and col_name in catalog and not catalog[col_name].get("stale", False): + return col_name, col_arr + return None, None + + def _try_index_where(self, expr_result: blosc2.LazyExpr) -> np.ndarray | None: + """Attempt to resolve *expr_result* via a column index. + + Returns a 1-D int64 array of physical row positions that satisfy the + predicate, or ``None`` if no usable index was found (caller falls back + to a full scan). + """ + from blosc2.indexing import ( + _IN_MEMORY_INDEXES, + _PERSISTENT_INDEXES, + _array_key, + _default_index_store, + _is_persistent_array, + evaluate_bucket_query, + evaluate_segment_query, + plan_query, + ) + + root = self._root_table + catalog = root._storage.load_index_catalog() + if not catalog: + return None + + expression = expr_result.expression + operands = dict(expr_result.operands) + + # Find the first operand NDArray that maps to a root-table column with a live index. + primary_col_name, primary_col_arr = self._find_indexed_column(root._cols, catalog, operands) + + if primary_col_arr is None: + return None + + # Inject the catalog descriptor into the appropriate index store so that + # plan_query can find it via _load_store / _descriptor_for_target. + descriptor = catalog[primary_col_name] + arr_key = _array_key(primary_col_arr) + if _is_persistent_array(primary_col_arr): + store = _PERSISTENT_INDEXES.get(arr_key) or _default_index_store() + store["indexes"][descriptor["token"]] = descriptor + _PERSISTENT_INDEXES[arr_key] = store + else: + store = _IN_MEMORY_INDEXES.get(id(primary_col_arr)) or _default_index_store() + store["indexes"][descriptor["token"]] = descriptor + _IN_MEMORY_INDEXES[id(primary_col_arr)] = store + + where_dict = {"_where_x": primary_col_arr} + merged_operands = {**operands, "_where_x": primary_col_arr} + + try: + plan = plan_query(expression, merged_operands, where_dict) + if not plan.usable: + return None + + if plan.exact_positions is not None: + return np.asarray(plan.exact_positions, dtype=np.int64) + + if plan.bucket_masks is not None: + _, positions = evaluate_bucket_query( + expression, merged_operands, {}, where_dict, plan, return_positions=True + ) + return np.asarray(positions, dtype=np.int64) + + if plan.candidate_units is not None and plan.segment_len is not None: + _, positions = evaluate_segment_query( + expression, merged_operands, {}, where_dict, plan, return_positions=True + ) + return np.asarray(positions, dtype=np.int64) + + return None + except Exception: + return None + def info(self) -> None: """Print a concise summary of the CTable.""" ratio = (self.nbytes / self.cbytes) if self.cbytes > 0 else 0.0 @@ -2306,6 +2972,7 @@ def append(self, data: list | np.void | np.ndarray) -> None: self._valid_rows[pos] = True self._last_pos = pos + 1 self._n_rows += 1 + self._mark_all_indexes_stale() def delete(self, ind: int | slice | str | Iterable) -> None: if self._read_only: @@ -2327,6 +2994,7 @@ def delete(self, ind: int | slice | str | Iterable) -> None: self._valid_rows[:] = valid_rows_np # write back in-place; no new array created self._n_rows -= n_deleted self._last_pos = None # recalculate on next write + self._storage.bump_visibility_epoch() def extend(self, data: list | CTable | Any, *, validate: bool | None = None) -> None: if self._read_only: @@ -2388,6 +3056,7 @@ def extend(self, data: list | CTable | Any, *, validate: bool | None = None) -> self._valid_rows[start_pos:end_pos] = True self._last_pos = end_pos self._n_rows += new_nrows + self._mark_all_indexes_stale() # ------------------------------------------------------------------ # Filtering @@ -2401,6 +3070,17 @@ def where(self, expr_result) -> CTable: ): raise TypeError(f"Expected boolean blosc2.NDArray or LazyExpr, got {type(expr_result).__name__}") + # Attempt index-accelerated filtering before falling back to a full scan. + if isinstance(expr_result, blosc2.LazyExpr): + positions = self._try_index_where(expr_result) + if positions is not None: + total = len(self._valid_rows) + mask = np.zeros(total, dtype=bool) + valid_pos = positions[(positions >= 0) & (positions < total)] + mask[valid_pos] = True + mask &= self._valid_rows[:] + return self.view(blosc2.asarray(mask)) + filter = expr_result.compute() if isinstance(expr_result, blosc2.LazyExpr) else expr_result target_len = len(self._valid_rows) diff --git a/src/blosc2/ctable_storage.py b/src/blosc2/ctable_storage.py index 226fc29b..13720118 100644 --- a/src/blosc2/ctable_storage.py +++ b/src/blosc2/ctable_storage.py @@ -19,6 +19,7 @@ from __future__ import annotations +import copy import json import os from typing import Any @@ -27,6 +28,10 @@ import blosc2 +# Directory inside the table root that holds per-column index sidecar files. +_INDEXES_DIR = "_indexes" + + # --------------------------------------------------------------------------- # Abstract base # --------------------------------------------------------------------------- @@ -84,6 +89,36 @@ def rename_column(self, old: str, new: str) -> blosc2.NDArray: def close(self) -> None: raise NotImplementedError + # -- Index catalog and epoch helpers ------------------------------------- + + def load_index_catalog(self) -> dict: + """Return the current index catalog (column_name → descriptor dict).""" + raise NotImplementedError + + def save_index_catalog(self, catalog: dict) -> None: + """Persist *catalog* (column_name → descriptor dict).""" + raise NotImplementedError + + def get_epoch_counters(self) -> tuple[int, int]: + """Return ``(value_epoch, visibility_epoch)``.""" + raise NotImplementedError + + def bump_value_epoch(self) -> int: + """Increment and return the value epoch (data values changed).""" + raise NotImplementedError + + def bump_visibility_epoch(self) -> int: + """Increment and return the visibility epoch (row set changed by delete).""" + raise NotImplementedError + + def index_anchor_path(self, col_name: str) -> str | None: + """Return the urlpath used as the anchor for index sidecar naming. + + Returns *None* for in-memory storage. For file-backed storage returns + a path of the form ``/_indexes//_anchor``. + """ + raise NotImplementedError + # --------------------------------------------------------------------------- # In-memory backend @@ -93,6 +128,11 @@ def close(self) -> None: class InMemoryTableStorage(TableStorage): """All arrays are plain in-memory blosc2.NDArray objects.""" + def __init__(self) -> None: + self._index_catalog: dict = {} + self._value_epoch: int = 0 + self._visibility_epoch: int = 0 + def create_column(self, name, *, dtype, shape, chunks, blocks, cparams, dparams): kwargs: dict[str, Any] = {"chunks": chunks, "blocks": blocks} if cparams is not None: @@ -131,6 +171,28 @@ def rename_column(self, old: str, new: str): def close(self): pass + # -- Index catalog and epoch helpers ------------------------------------- + + def load_index_catalog(self) -> dict: + return copy.deepcopy(self._index_catalog) + + def save_index_catalog(self, catalog: dict) -> None: + self._index_catalog = copy.deepcopy(catalog) + + def get_epoch_counters(self) -> tuple[int, int]: + return self._value_epoch, self._visibility_epoch + + def bump_value_epoch(self) -> int: + self._value_epoch += 1 + return self._value_epoch + + def bump_visibility_epoch(self) -> int: + self._visibility_epoch += 1 + return self._visibility_epoch + + def index_anchor_path(self, col_name: str) -> str | None: + return None + # --------------------------------------------------------------------------- # File-backed backend @@ -298,3 +360,37 @@ def close(self) -> None: self._store.close() self._store = None self._meta = None + + # -- Index catalog and epoch helpers ------------------------------------- + + def load_index_catalog(self) -> dict: + meta = self._open_meta() + raw = meta.vlmeta.get("index_catalog") + if isinstance(raw, dict): + return copy.deepcopy(raw) + return {} + + def save_index_catalog(self, catalog: dict) -> None: + meta = self._open_meta() + meta.vlmeta["index_catalog"] = copy.deepcopy(catalog) + + def get_epoch_counters(self) -> tuple[int, int]: + meta = self._open_meta() + ve = int(meta.vlmeta.get("value_epoch", 0) or 0) + vis_e = int(meta.vlmeta.get("visibility_epoch", 0) or 0) + return ve, vis_e + + def bump_value_epoch(self) -> int: + meta = self._open_meta() + ve = int(meta.vlmeta.get("value_epoch", 0) or 0) + 1 + meta.vlmeta["value_epoch"] = ve + return ve + + def bump_visibility_epoch(self) -> int: + meta = self._open_meta() + vis_e = int(meta.vlmeta.get("visibility_epoch", 0) or 0) + 1 + meta.vlmeta["visibility_epoch"] = vis_e + return vis_e + + def index_anchor_path(self, col_name: str) -> str | None: + return os.path.join(self._root, _INDEXES_DIR, col_name, "_anchor") diff --git a/src/blosc2/schema_compiler.py b/src/blosc2/schema_compiler.py index 1300cecd..19a3d0c1 100644 --- a/src/blosc2/schema_compiler.py +++ b/src/blosc2/schema_compiler.py @@ -207,7 +207,7 @@ def validate_annotation_matches_spec(name: str, annotation: Any, spec: SchemaSpe # --------------------------------------------------------------------------- -_RESERVED_COLUMN_NAMES: frozenset[str] = frozenset({"_meta", "_valid_rows", "_cols"}) +_RESERVED_COLUMN_NAMES: frozenset[str] = frozenset({"_meta", "_valid_rows", "_cols", "_indexes"}) def _validate_column_name(name: str) -> None: diff --git a/tests/ctable/test_ctable_indexing.py b/tests/ctable/test_ctable_indexing.py new file mode 100644 index 00000000..2674cdbf --- /dev/null +++ b/tests/ctable/test_ctable_indexing.py @@ -0,0 +1,369 @@ +####################################################################### +# Copyright (c) 2019-present, Blosc Development Team +# All rights reserved. +# +# SPDX-License-Identifier: BSD-3-Clause +####################################################################### + +"""Tests for CTable persistent and in-memory indexing.""" + +import dataclasses +import shutil +import tempfile +from pathlib import Path + +import numpy as np +import pytest + +import blosc2 + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + + +@dataclasses.dataclass +class Row: + id: int = blosc2.field(blosc2.int32()) + value: float = blosc2.field(blosc2.float64()) + category: int = blosc2.field(blosc2.int32()) + + +def _make_table(n=100, persistent_path=None): + """Return a CTable with *n* rows, optionally persistent at *persistent_path*.""" + if persistent_path is not None: + t = blosc2.CTable(Row, urlpath=persistent_path, mode="w") + else: + t = blosc2.CTable(Row) + for i in range(n): + t.append([i, float(i) * 1.5, i % 5]) + return t + + +# --------------------------------------------------------------------------- +# In-memory table tests +# --------------------------------------------------------------------------- + + +def test_create_index_in_memory(): + t = _make_table(50) + idx = t.create_index("id") + assert idx is not None + assert idx.col_name == "id" + assert not idx.stale + assert len(t.indexes) == 1 + assert t.indexes[0].col_name == "id" + + +def test_create_index_in_memory_duplicate_raises(): + t = _make_table(20) + t.create_index("id") + with pytest.raises(ValueError, match="Index already exists"): + t.create_index("id") + + +def test_drop_index_in_memory(): + t = _make_table(20) + t.create_index("id") + t.drop_index("id") + assert len(t.indexes) == 0 + with pytest.raises(KeyError): + t.index("id") + + +def test_drop_nonexistent_index_raises(): + t = _make_table(20) + with pytest.raises(KeyError, match="No index found"): + t.drop_index("id") + + +def test_where_with_index_matches_scan_in_memory(): + t = _make_table(200) + t.create_index("id") + result_idx = t.where(t["id"] > 100) + # Drop index to force scan + t.drop_index("id") + result_scan = t.where(t["id"] > 100) + ids_idx = sorted(int(v) for v in result_idx["id"].to_numpy()) + ids_scan = sorted(int(v) for v in result_scan["id"].to_numpy()) + assert ids_idx == ids_scan + + +def test_rebuild_index_in_memory(): + t = _make_table(30) + t.create_index("id") + t.append([999, 999.0, 4]) # marks stale + assert t.index("id").stale + idx2 = t.rebuild_index("id") + assert not idx2.stale + result = t.where(t["id"] == 999) + assert len(result) == 1 + + +def test_stale_on_append_in_memory(): + t = _make_table(20) + t.create_index("id") + t.append([100, 100.0, 0]) + assert t.index("id").stale + + +def test_stale_on_extend_in_memory(): + t = _make_table(20) + t.create_index("id") + t.extend([[101, 101.0, 0], [102, 102.0, 1]]) + assert t.index("id").stale + + +def test_stale_on_column_setitem_in_memory(): + t = _make_table(20) + t.create_index("id") + t["id"][0] = 999 + assert t.index("id").stale + + +def test_stale_on_column_assign_in_memory(): + t = _make_table(20) + t.create_index("id") + t["id"].assign(np.arange(20, dtype=np.int32)) + assert t.index("id").stale + + +def test_delete_bumps_visibility_epoch_not_stale_in_memory(): + t = _make_table(20) + t.create_index("id") + t.delete(0) + idx = t.index("id") + # delete should NOT mark stale (only bumps visibility_epoch) + assert not idx.stale + _, vis_e = t._storage.get_epoch_counters() + assert vis_e >= 1 + + +def test_stale_fallback_to_scan_in_memory(): + t = _make_table(50) + t.create_index("id") + t.append([200, 200.0, 0]) # marks stale + # Query should still work (falls back to scan) + result = t.where(t["id"] > 40) + ids = sorted(int(v) for v in result["id"].to_numpy()) + assert 200 in ids + assert 41 in ids + + +def test_compact_index_in_memory(): + t = _make_table(50, persistent_path=None) + t.create_index("id", kind=blosc2.IndexKind.FULL) + # compact_index should not raise for full indexes + t.compact_index("id") + + +# --------------------------------------------------------------------------- +# Persistent table tests +# --------------------------------------------------------------------------- + + +@pytest.fixture +def tmpdir(): + d = tempfile.mkdtemp() + yield Path(d) + shutil.rmtree(d, ignore_errors=True) + + +def test_create_index_persistent(tmpdir): + path = str(tmpdir / "table.b2d") + t = _make_table(50, persistent_path=path) + idx = t.create_index("id") + assert not idx.stale + # Sidecar directory must exist + index_dir = Path(path) / "_indexes" / "id" + assert index_dir.exists() + # At least one .b2nd sidecar file + sidecars = list(index_dir.glob("**/*.b2nd")) + assert sidecars, "No sidecar .b2nd files found" + + +def test_catalog_survives_reopen(tmpdir): + path = str(tmpdir / "table.b2d") + t = _make_table(30, persistent_path=path) + t.create_index("id") + del t # close + + t2 = blosc2.open(path) + idxs = t2.indexes + assert len(idxs) == 1 + assert idxs[0].col_name == "id" + assert not idxs[0].stale + + +def test_where_with_index_matches_scan_persistent(tmpdir): + path = str(tmpdir / "table.b2d") + t = _make_table(200, persistent_path=path) + t.create_index("id") + result_idx = t.where(t["id"] > 150) + + t.drop_index("id") + result_scan = t.where(t["id"] > 150) + + ids_idx = sorted(int(v) for v in result_idx["id"].to_numpy()) + ids_scan = sorted(int(v) for v in result_scan["id"].to_numpy()) + assert ids_idx == ids_scan + + +def test_drop_index_persistent(tmpdir): + path = str(tmpdir / "table.b2d") + t = _make_table(30, persistent_path=path) + t.create_index("id") + t.drop_index("id") + assert len(t.indexes) == 0 + index_dir = Path(path) / "_indexes" / "id" + # After drop, index dir should be gone (or empty) + sidecars = list(index_dir.glob("**/*.b2nd")) if index_dir.exists() else [] + assert sidecars == [] + + +def test_drop_index_persistent_catalog_cleared(tmpdir): + path = str(tmpdir / "table.b2d") + t = _make_table(30, persistent_path=path) + t.create_index("id") + t.drop_index("id") + del t + + t2 = blosc2.open(path) + assert len(t2.indexes) == 0 + + +def test_rebuild_index_persistent(tmpdir): + path = str(tmpdir / "table.b2d") + t = _make_table(50, persistent_path=path) + t.create_index("id") + t.append([500, 750.0, 2]) # marks stale + assert t.index("id").stale + idx2 = t.rebuild_index("id") + assert not idx2.stale + result = t.where(t["id"] == 500) + assert len(result) == 1 + + +def test_compact_index_persistent(tmpdir): + path = str(tmpdir / "table.b2d") + t = _make_table(50, persistent_path=path) + t.create_index("id", kind=blosc2.IndexKind.FULL) + t.compact_index("id") + # Query should still work after compact + result = t.where(t["id"] > 40) + ids = sorted(int(v) for v in result["id"].to_numpy()) + expected = list(range(41, 50)) + assert ids == expected + + +def test_stale_on_append_persistent(tmpdir): + path = str(tmpdir / "table.b2d") + t = _make_table(20, persistent_path=path) + t.create_index("id") + t.append([200, 300.0, 1]) + assert t.index("id").stale + + +def test_stale_persists_after_reopen(tmpdir): + path = str(tmpdir / "table.b2d") + t = _make_table(20, persistent_path=path) + t.create_index("id") + t.append([200, 300.0, 1]) # marks stale + del t + + t2 = blosc2.open(path) + assert t2.index("id").stale + + +def test_delete_bumps_visibility_epoch_persistent(tmpdir): + path = str(tmpdir / "table.b2d") + t = _make_table(20, persistent_path=path) + t.create_index("id") + t.delete(0) + idx = t.index("id") + # delete should NOT mark index stale + assert not idx.stale + _, vis_e = t._storage.get_epoch_counters() + assert vis_e >= 1 + + +def test_query_after_reopen_persistent(tmpdir): + path = str(tmpdir / "table.b2d") + t = _make_table(100, persistent_path=path) + t.create_index("id") + del t + + t2 = blosc2.open(path) + result = t2.where(t2["id"] > 90) + ids = sorted(int(v) for v in result["id"].to_numpy()) + assert ids == list(range(91, 100)) + + +# --------------------------------------------------------------------------- +# View tests +# --------------------------------------------------------------------------- + + +def test_view_cannot_create_index(): + t = _make_table(20) + view = t.where(t["id"] > 5) + with pytest.raises(ValueError, match="view"): + view.create_index("id") + + +def test_view_cannot_drop_index(): + t = _make_table(20) + t.create_index("id") + view = t.where(t["id"] > 5) + with pytest.raises(ValueError, match="view"): + view.drop_index("id") + + +def test_view_cannot_rebuild_index(): + t = _make_table(20) + t.create_index("id") + view = t.where(t["id"] > 5) + with pytest.raises(ValueError, match="view"): + view.rebuild_index("id") + + +def test_view_cannot_compact_index(): + t = _make_table(20) + t.create_index("id") + view = t.where(t["id"] > 5) + with pytest.raises(ValueError, match="view"): + view.compact_index("id") + + +def test_view_query_uses_root_index(): + t = _make_table(200) + t.create_index("id") + # Query on the original table + result_direct = t.where(t["id"] > 180) + ids_direct = sorted(int(v) for v in result_direct["id"].to_numpy()) + assert ids_direct == list(range(181, 200)) + + +# --------------------------------------------------------------------------- +# index() and indexes property +# --------------------------------------------------------------------------- + + +def test_index_lookup_missing_raises(): + t = _make_table(10) + with pytest.raises(KeyError): + t.index("nonexistent") + + +def test_indexes_empty_on_new_table(): + t = _make_table(10) + assert t.indexes == [] + + +def test_indexes_multiple_columns(): + t = _make_table(30) + t.create_index("id") + t.create_index("category") + assert len(t.indexes) == 2 + col_names = {idx.col_name for idx in t.indexes} + assert col_names == {"id", "category"} From c44031d366cbc8c869387fc31b61a28cbd60dbea Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Wed, 15 Apr 2026 17:09:48 +0200 Subject: [PATCH 33/49] Implement CTable indexing follow-ups Fix CTable index lifecycle for schema mutations by removing index catalog entries and sidecars when indexed columns are dropped, and rebuilding indexes under the new name after column renames. Improve indexed CTable filtering so where() can expose multiple usable column indexes to the planner for conjunctive predicates, and raise a clear error for malformed table-owned index metadata instead of silently falling back to scans. Add regression coverage for indexed column rename/drop behavior, multi-column indexed conjunctions, and malformed catalog entries. Wire the CTable indexing tutorial into the docs toctree. --- doc/getting_started/tutorials.rst | 1 + src/blosc2/ctable.py | 226 ++++++++++++++++----------- src/blosc2/schunk.py | 18 ++- tests/ctable/test_ctable_indexing.py | 51 ++++++ 4 files changed, 199 insertions(+), 97 deletions(-) diff --git a/doc/getting_started/tutorials.rst b/doc/getting_started/tutorials.rst index d2786d12..c446589b 100644 --- a/doc/getting_started/tutorials.rst +++ b/doc/getting_started/tutorials.rst @@ -20,3 +20,4 @@ Tutorials tutorials/12.batcharray tutorials/13.containers tutorials/14.indexing-arrays + tutorials/15.indexing-ctables diff --git a/src/blosc2/ctable.py b/src/blosc2/ctable.py index 006e62c4..881ae19b 100644 --- a/src/blosc2/ctable.py +++ b/src/blosc2/ctable.py @@ -2022,6 +2022,13 @@ def drop_column(self, name: str) -> None: if len(self.col_names) == 1: raise ValueError("Cannot drop the last column.") + catalog = self._storage.load_index_catalog() + if name in catalog: + descriptor = catalog.pop(name) + self._validate_index_descriptor(name, descriptor) + self._drop_index_descriptor(name, descriptor) + self._storage.save_index_catalog(catalog) + if isinstance(self._storage, FileTableStorage): self._storage.delete_column(name) @@ -2060,6 +2067,15 @@ def rename_column(self, old: str, new: str) -> None: raise ValueError(f"Column {new!r} already exists.") _validate_column_name(new) + catalog = self._storage.load_index_catalog() + rebuild_kwargs = None + if old in catalog: + descriptor = catalog.pop(old) + self._validate_index_descriptor(old, descriptor) + rebuild_kwargs = self._index_create_kwargs_from_descriptor(descriptor) + self._drop_index_descriptor(old, descriptor) + self._storage.save_index_catalog(catalog) + if isinstance(self._storage, FileTableStorage): self._cols[new] = self._storage.rename_column(old, new) else: @@ -2088,6 +2104,8 @@ def rename_column(self, old: str, new: str) -> None: ) if isinstance(self._storage, FileTableStorage): self._storage.save_schema(schema_to_dict(self._schema)) + if rebuild_kwargs is not None: + self.create_index(new, **rebuild_kwargs) # ------------------------------------------------------------------ # Column access @@ -2366,6 +2384,73 @@ def _mark_all_indexes_stale(self) -> None: if changed: root._storage.save_index_catalog(catalog) + @staticmethod + def _validate_index_descriptor(col_name: str, descriptor: dict) -> None: + """Raise ValueError when an index catalog entry is malformed.""" + if not isinstance(descriptor, dict): + raise ValueError(f"Malformed index metadata for column {col_name!r}: descriptor must be a dict.") + token = descriptor.get("token") + if not isinstance(token, str) or not token: + raise ValueError(f"Malformed index metadata for column {col_name!r}: missing token.") + kind = descriptor.get("kind") + if kind not in {"summary", "bucket", "partial", "full"}: + raise ValueError(f"Malformed index metadata for column {col_name!r}: invalid kind {kind!r}.") + if kind == "bucket" and not isinstance(descriptor.get("bucket"), dict): + raise ValueError(f"Malformed index metadata for column {col_name!r}: missing bucket payload.") + if kind == "partial" and not isinstance(descriptor.get("partial"), dict): + raise ValueError(f"Malformed index metadata for column {col_name!r}: missing partial payload.") + if kind == "full" and not isinstance(descriptor.get("full"), dict): + raise ValueError(f"Malformed index metadata for column {col_name!r}: missing full payload.") + + def _drop_index_descriptor(self, col_name: str, descriptor: dict) -> None: + """Delete sidecars/cache for a catalog descriptor without touching the column mapping.""" + from pathlib import Path + + from blosc2.indexing import ( + _IN_MEMORY_INDEXES, + _PERSISTENT_INDEXES, + _array_key, + _clear_cached_data, + _drop_descriptor_sidecars, + _is_persistent_array, + ) + + col_arr = self._cols.get(col_name) + token = descriptor["token"] + + if col_arr is not None: + _clear_cached_data(col_arr, token) + + if col_arr is not None and _is_persistent_array(col_arr): + arr_key = _array_key(col_arr) + store = _PERSISTENT_INDEXES.get(arr_key) + if store is not None: + store["indexes"].pop(token, None) + elif col_arr is not None: + store = _IN_MEMORY_INDEXES.get(id(col_arr)) + if store is not None: + store["indexes"].pop(token, None) + + _drop_descriptor_sidecars(descriptor) + + anchor = self._storage.index_anchor_path(col_name) + if anchor is not None: + proxy_key = ("persistent", str(Path(anchor).resolve())) + _PERSISTENT_INDEXES.pop(proxy_key, None) + with contextlib.suppress(OSError): + os.rmdir(os.path.dirname(anchor)) + + def _index_create_kwargs_from_descriptor(self, descriptor: dict) -> dict[str, Any]: + """Return create_index kwargs that rebuild an existing descriptor.""" + build = "ooc" if bool(descriptor.get("ooc", False)) else "memory" + return { + "kind": descriptor["kind"], + "optlevel": int(descriptor.get("optlevel", 5)), + "name": descriptor.get("name") or None, + "build": build, + "cparams": descriptor.get("cparams"), + } + def _build_index_persistent( self, col_name: str, @@ -2622,48 +2707,13 @@ def drop_index(self, col_name: str) -> None: if self.base is not None: raise ValueError("Cannot drop an index from a view.") - from pathlib import Path - - from blosc2.indexing import ( - _IN_MEMORY_INDEXES, - _PERSISTENT_INDEXES, - _array_key, - _clear_cached_data, - _drop_descriptor_sidecars, - _is_persistent_array, - ) - from blosc2.indexing import ( - drop_index as _ix_drop_index, - ) - catalog = self._storage.load_index_catalog() if col_name not in catalog: raise KeyError(f"No index found for column {col_name!r}.") descriptor = catalog.pop(col_name) - token = descriptor["token"] - col_arr = self._cols[col_name] - - if _is_persistent_array(col_arr): - _drop_descriptor_sidecars(descriptor) - _clear_cached_data(col_arr, token) - arr_key = _array_key(col_arr) - store = _PERSISTENT_INDEXES.get(arr_key) - if store is not None: - store["indexes"].pop(token, None) - anchor = self._storage.index_anchor_path(col_name) - proxy_key = ("persistent", str(Path(anchor).resolve())) - _PERSISTENT_INDEXES.pop(proxy_key, None) - index_dir = os.path.dirname(anchor) - with contextlib.suppress(OSError): - os.rmdir(index_dir) - else: - with contextlib.suppress(Exception): - _ix_drop_index(col_arr, field=None) - store = _IN_MEMORY_INDEXES.get(id(col_arr)) - if store is not None: - store["indexes"].pop(token, None) - + self._validate_index_descriptor(col_name, descriptor) + self._drop_index_descriptor(col_name, descriptor) self._storage.save_index_catalog(catalog) def rebuild_index(self, col_name: str) -> CTableIndex: @@ -2694,22 +2744,11 @@ def rebuild_index(self, col_name: str) -> CTableIndex: raise KeyError(f"No index found for column {col_name!r}.") old_desc = catalog[col_name] - old_kind = old_desc["kind"] - old_optlevel = int(old_desc.get("optlevel", 5)) - old_name = old_desc.get("name") or None - old_ooc = bool(old_desc.get("ooc", False)) - old_cparams = old_desc.get("cparams") - build_str = "ooc" if old_ooc else "memory" + self._validate_index_descriptor(col_name, old_desc) + create_kwargs = self._index_create_kwargs_from_descriptor(old_desc) self.drop_index(col_name) - return self.create_index( - col_name, - kind=old_kind, - optlevel=old_optlevel, - name=old_name, - build=build_str, - cparams=old_cparams, - ) + return self.create_index(col_name, **create_kwargs) def compact_index(self, col_name: str) -> CTableIndex: """Compact the index for *col_name*, merging any incremental append runs. @@ -2814,16 +2853,23 @@ def indexes(self) -> list[CTableIndex]: return [CTableIndex(self, col_name, desc) for col_name, desc in catalog.items()] @staticmethod - def _find_indexed_column(root_cols, catalog, operands): - """Return ``(col_name, col_arr)`` for the first operand NDArray that maps - to a root-table column with a live (non-stale) catalog entry, or ``(None, None)``.""" + def _find_indexed_columns(root_cols, catalog, operands): + """Return live indexed columns referenced by *operands* in expression order.""" + indexed = [] + seen = set() for operand in operands.values(): if not isinstance(operand, blosc2.NDArray): continue for col_name, col_arr in root_cols.items(): - if col_arr is operand and col_name in catalog and not catalog[col_name].get("stale", False): - return col_name, col_arr - return None, None + if col_arr is not operand or col_name in seen or col_name not in catalog: + continue + descriptor = catalog[col_name] + CTable._validate_index_descriptor(col_name, descriptor) + if descriptor.get("stale", False): + continue + indexed.append((col_name, col_arr, descriptor)) + seen.add(col_name) + return indexed def _try_index_where(self, expr_result: blosc2.LazyExpr) -> np.ndarray | None: """Attempt to resolve *expr_result* via a column index. @@ -2851,51 +2897,47 @@ def _try_index_where(self, expr_result: blosc2.LazyExpr) -> np.ndarray | None: expression = expr_result.expression operands = dict(expr_result.operands) - # Find the first operand NDArray that maps to a root-table column with a live index. - primary_col_name, primary_col_arr = self._find_indexed_column(root._cols, catalog, operands) - - if primary_col_arr is None: + indexed_columns = self._find_indexed_columns(root._cols, catalog, operands) + if not indexed_columns: return None - # Inject the catalog descriptor into the appropriate index store so that - # plan_query can find it via _load_store / _descriptor_for_target. - descriptor = catalog[primary_col_name] - arr_key = _array_key(primary_col_arr) - if _is_persistent_array(primary_col_arr): - store = _PERSISTENT_INDEXES.get(arr_key) or _default_index_store() - store["indexes"][descriptor["token"]] = descriptor - _PERSISTENT_INDEXES[arr_key] = store - else: - store = _IN_MEMORY_INDEXES.get(id(primary_col_arr)) or _default_index_store() - store["indexes"][descriptor["token"]] = descriptor - _IN_MEMORY_INDEXES[id(primary_col_arr)] = store + primary_col_name, primary_col_arr, _ = indexed_columns[0] + + # Inject every usable table-owned descriptor so plan_query can combine them. + for _col_name, col_arr, descriptor in indexed_columns: + arr_key = _array_key(col_arr) + if _is_persistent_array(col_arr): + store = _PERSISTENT_INDEXES.get(arr_key) or _default_index_store() + store["indexes"][descriptor["token"]] = descriptor + _PERSISTENT_INDEXES[arr_key] = store + else: + store = _IN_MEMORY_INDEXES.get(id(col_arr)) or _default_index_store() + store["indexes"][descriptor["token"]] = descriptor + _IN_MEMORY_INDEXES[id(col_arr)] = store where_dict = {"_where_x": primary_col_arr} merged_operands = {**operands, "_where_x": primary_col_arr} - try: - plan = plan_query(expression, merged_operands, where_dict) - if not plan.usable: - return None + plan = plan_query(expression, merged_operands, where_dict) + if not plan.usable: + return None - if plan.exact_positions is not None: - return np.asarray(plan.exact_positions, dtype=np.int64) + if plan.exact_positions is not None: + return np.asarray(plan.exact_positions, dtype=np.int64) - if plan.bucket_masks is not None: - _, positions = evaluate_bucket_query( - expression, merged_operands, {}, where_dict, plan, return_positions=True - ) - return np.asarray(positions, dtype=np.int64) + if plan.bucket_masks is not None: + _, positions = evaluate_bucket_query( + expression, merged_operands, {}, where_dict, plan, return_positions=True + ) + return np.asarray(positions, dtype=np.int64) - if plan.candidate_units is not None and plan.segment_len is not None: - _, positions = evaluate_segment_query( - expression, merged_operands, {}, where_dict, plan, return_positions=True - ) - return np.asarray(positions, dtype=np.int64) + if plan.candidate_units is not None and plan.segment_len is not None: + _, positions = evaluate_segment_query( + expression, merged_operands, {}, where_dict, plan, return_positions=True + ) + return np.asarray(positions, dtype=np.int64) - return None - except Exception: - return None + return None def info(self) -> None: """Print a concise summary of the CTable.""" diff --git a/src/blosc2/schunk.py b/src/blosc2/schunk.py index 2fbb0bd1..714937ce 100644 --- a/src/blosc2/schunk.py +++ b/src/blosc2/schunk.py @@ -1706,6 +1706,14 @@ def _open_treestore_root_object(store, urlpath, mode): return store +def _finalize_special_open(special, urlpath, mode): + if special is None: + return None + if isinstance(special, blosc2.TreeStore): + return _open_treestore_root_object(special, urlpath, mode) + return special + + def open( urlpath: str | pathlib.Path | blosc2.URLPath, mode: str = "a", offset: int = 0, **kwargs: dict ) -> ( @@ -1829,9 +1837,8 @@ def open( # more expensive store probing when that fails. if urlpath.endswith((".b2d", ".b2z", ".b2e")): special = _open_special_store(urlpath, mode, offset, **kwargs) + special = _finalize_special_open(special, urlpath, mode) if special is not None: - if isinstance(special, blosc2.TreeStore): - return _open_treestore_root_object(special, urlpath, mode) return special regular_exc = None @@ -1845,11 +1852,12 @@ def open( return process_opened_object(res) resolved_urlpath = _resolve_store_alias(urlpath) - special_path = resolved_urlpath if resolved_urlpath != urlpath or not os.path.exists(urlpath) else urlpath + special_path = ( + resolved_urlpath if resolved_urlpath != urlpath or not os.path.exists(urlpath) else urlpath + ) special = _open_special_store(special_path, mode, offset, **kwargs) + special = _finalize_special_open(special, special_path, mode) if special is not None: - if isinstance(special, blosc2.TreeStore): - return _open_treestore_root_object(special, special_path, mode) return special if regular_exc is not None: diff --git a/tests/ctable/test_ctable_indexing.py b/tests/ctable/test_ctable_indexing.py index 2674cdbf..55ef9168 100644 --- a/tests/ctable/test_ctable_indexing.py +++ b/tests/ctable/test_ctable_indexing.py @@ -77,6 +77,15 @@ def test_drop_nonexistent_index_raises(): t.drop_index("id") +def test_drop_indexed_column_clears_catalog(): + t = _make_table(20) + t.create_index("id") + t.drop_column("id") + assert [idx.col_name for idx in t.indexes] == [] + with pytest.raises(KeyError, match="No index found"): + t.index("id") + + def test_where_with_index_matches_scan_in_memory(): t = _make_table(200) t.create_index("id") @@ -157,6 +166,20 @@ def test_compact_index_in_memory(): t.compact_index("id") +def test_multi_column_conjunction_uses_multiple_indexes_in_memory(): + t = _make_table(200) + t.create_index("id", kind=blosc2.IndexKind.FULL) + t.create_index("category", kind=blosc2.IndexKind.FULL) + expr = (t["id"] >= 50) & (t["id"] < 120) & (t["category"] == 3) + result_idx = t.where(expr) + t.drop_index("id") + t.drop_index("category") + result_scan = t.where(expr) + ids_idx = sorted(int(v) for v in result_idx["id"].to_numpy()) + ids_scan = sorted(int(v) for v in result_scan["id"].to_numpy()) + assert ids_idx == ids_scan + + # --------------------------------------------------------------------------- # Persistent table tests # --------------------------------------------------------------------------- @@ -232,6 +255,15 @@ def test_drop_index_persistent_catalog_cleared(tmpdir): assert len(t2.indexes) == 0 +def test_drop_indexed_column_removes_persistent_sidecars(tmpdir): + path = str(tmpdir / "table.b2d") + t = _make_table(30, persistent_path=path) + t.create_index("id") + t.drop_column("id") + assert len(t.indexes) == 0 + assert not (Path(path) / "_indexes" / "id").exists() + + def test_rebuild_index_persistent(tmpdir): path = str(tmpdir / "table.b2d") t = _make_table(50, persistent_path=path) @@ -299,6 +331,18 @@ def test_query_after_reopen_persistent(tmpdir): assert ids == list(range(91, 100)) +def test_rename_indexed_column_rebuilds_catalog_persistent(tmpdir): + path = str(tmpdir / "table.b2d") + t = _make_table(40, persistent_path=path) + t.create_index("id") + t.rename_column("id", "newid") + assert [idx.col_name for idx in t.indexes] == ["newid"] + assert not (Path(path) / "_indexes" / "id").exists() + assert (Path(path) / "_indexes" / "newid").exists() + result = t.where(t["newid"] > 35) + assert sorted(int(v) for v in result["newid"].to_numpy()) == [36, 37, 38, 39] + + # --------------------------------------------------------------------------- # View tests # --------------------------------------------------------------------------- @@ -344,6 +388,13 @@ def test_view_query_uses_root_index(): assert ids_direct == list(range(181, 200)) +def test_malformed_catalog_entry_raises_clear_error(): + t = _make_table(20) + t._storage.save_index_catalog({"id": {"kind": "bucket"}}) + with pytest.raises(ValueError, match="Malformed index metadata"): + t.where(t["id"] > 5) + + # --------------------------------------------------------------------------- # index() and indexes property # --------------------------------------------------------------------------- From fb661075dc3d7f27e37573ac10951ee73df32eb7 Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Wed, 15 Apr 2026 18:03:26 +0200 Subject: [PATCH 34/49] New InfoReporter for CTable. Example on how to use a .b2z file. --- examples/ctable/indexing.py | 124 ++++++++-------- src/blosc2/ctable.py | 212 ++++++++++++++++++++++++--- src/blosc2/ndarray.py | 13 ++ tests/ctable/test_ctable_indexing.py | 18 +++ 4 files changed, 281 insertions(+), 86 deletions(-) diff --git a/examples/ctable/indexing.py b/examples/ctable/indexing.py index ebf9245f..ab729fd2 100644 --- a/examples/ctable/indexing.py +++ b/examples/ctable/indexing.py @@ -5,7 +5,7 @@ # SPDX-License-Identifier: BSD-3-Clause ####################################################################### -# CTable indexing: create, query, rebuild, and drop indexes on columns. +# CTable indexing with mixed dtypes, persistent sidecars, and a packed .b2z bundle. import shutil import tempfile @@ -19,79 +19,77 @@ class Measurement: sensor_id: int = blosc2.field(blosc2.int32()) temperature: float = blosc2.field(blosc2.float64()) - region: int = blosc2.field(blosc2.int32()) + region: str = blosc2.field(blosc2.string(max_length=12), default="") + active: bool = blosc2.field(blosc2.bool(), default=True) + status: str = blosc2.field(blosc2.string(max_length=12), default="") -# ------------------------------------------------------------------------- -# In-memory table -# ------------------------------------------------------------------------- +def load_rows(table: blosc2.CTable, nrows: int = 240) -> None: + regions = ["north", "south", "east", "west"] + for i in range(nrows): + region = regions[i % len(regions)] + active = (i % 7) not in (0, 6) + status = "alert" if i % 23 == 0 else ("warm" if i % 11 == 0 else "ok") + table.append([i, 12.5 + (i % 40) * 0.35, region, active, status]) -t = blosc2.CTable(Measurement) -for i in range(200): - t.append([i, 15.0 + (i % 30) * 0.5, i % 4]) -# Create a bucket index on 'sensor_id' (default kind). -idx = t.create_index("sensor_id") -print("Index created:", idx) -print("Stale?", idx.stale) +bundle_path = Path("indexed_measurements.b2z").resolve() +workspace = Path(tempfile.mkdtemp()) +table_path = workspace / "indexed_measurements.b2d" -# The where() call automatically uses the index when possible. -result = t.where(t["sensor_id"] > 180) -print("Rows with sensor_id > 180 (via index):", len(result)) -print("sensor_ids:", sorted(int(v) for v in result["sensor_id"].to_numpy())) +pt = None +packed = None -# List all indexes on the table. -print("\nAll indexes:", t.indexes) - -# After mutating the table the index is marked stale and where() falls -# back to a full scan automatically — results are still correct. -t.append([999, 42.0, 2]) -idx = t.index("sensor_id") -print("\nAfter append, stale?", idx.stale) - -result_stale = t.where(t["sensor_id"] == 999) -print("Row with sensor_id=999 (scan fallback):", len(result_stale)) - -# Rebuild the index to make it current again. -idx = t.rebuild_index("sensor_id") -print("\nAfter rebuild, stale?", idx.stale) - -result_rebuilt = t.where(t["sensor_id"] == 999) -print("Row with sensor_id=999 (via rebuilt index):", len(result_rebuilt)) - -# Drop the index. -t.drop_index("sensor_id") -print("\nIndexes after drop:", t.indexes) +try: + pt = blosc2.CTable(Measurement, urlpath=str(table_path), mode="w") + load_rows(pt) + + # Create a couple of indexes on columns with different dtypes. + idx_sensor = pt.create_index("sensor_id", kind=blosc2.IndexKind.FULL) + idx_active = pt.create_index("active") + print("Indexes created:", pt.indexes) + print("sensor_id stale?", idx_sensor.stale) + print("active stale?", idx_active.stale) + + # Queries can combine indexed and non-indexed predicates. + recent_active = pt.where((pt["sensor_id"] >= 180) & pt["active"] & (pt["region"] == "north")) + print("\nLive rows with sensor_id >= 180, active=True, region='north':", len(recent_active)) + print("sensor_ids:", recent_active["sensor_id"].to_numpy().tolist()) + print("statuses:", recent_active["status"].to_numpy().tolist()) + + # Close the table, pack the TreeStore into a single .b2z file, and reopen it. + del pt + pt = None -# ------------------------------------------------------------------------- -# Persistent table -# ------------------------------------------------------------------------- + if bundle_path.exists(): + bundle_path.unlink() -tmpdir = Path(tempfile.mkdtemp()) -path = str(tmpdir / "measurements.b2d") + store = blosc2.TreeStore(str(table_path), mode="r") + try: + packed_path = store.to_b2z(filename=str(bundle_path), overwrite=True) + finally: + store.close() -try: - pt = blosc2.CTable(Measurement, urlpath=path, mode="w") - for i in range(200): - pt.append([i, 15.0 + (i % 30) * 0.5, i % 4]) + print(f"\nPacked bundle created at: {packed_path}") - pidx = pt.create_index("sensor_id") - print("\nPersistent index created:", pidx) + packed = blosc2.open(str(bundle_path), mode="r") + print("Reopened object type:", type(packed).__name__) + print("Indexes after reopen from .b2z:", packed.indexes) - # Sidecar files are stored under /_indexes/sensor_id/ - index_dir = Path(path) / "_indexes" / "sensor_id" - sidecars = list(index_dir.glob("**/*.b2nd")) - print(f"Sidecar files: {len(sidecars)}") + # Query directly against the .b2z bundle; no unpack step is needed. + warm_active = packed.where(packed["active"] & (packed["status"] == "warm") & (packed["sensor_id"] > 100)) + print("\nRows from .b2z with active=True, status='warm', sensor_id > 100:", len(warm_active)) + print("sensor_ids:", warm_active["sensor_id"].to_numpy().tolist()) + print("regions:", warm_active["region"].to_numpy().tolist()) - result_p = pt.where(pt["sensor_id"] > 190) - print("Rows sensor_id > 190:", len(result_p)) - - # Close and reopen — the index catalog is persisted. - del pt - pt2 = blosc2.open(path) - print("\nAfter reopen, indexes:", pt2.indexes) - result_p2 = pt2.where(pt2["sensor_id"] > 190) - print("Rows sensor_id > 190 (after reopen):", len(result_p2)) + print("\nThe packed file is kept on disk.") + print(f"Inspect it later with: f = blosc2.open({bundle_path.name!r}, mode='r')") + print("Then call: f.info()") + print("For a quick check of the available info entry point, print: f.info") finally: - shutil.rmtree(tmpdir, ignore_errors=True) + if packed is not None: + del packed + if pt is not None: + del pt + shutil.rmtree(workspace, ignore_errors=True) diff --git a/src/blosc2/ctable.py b/src/blosc2/ctable.py index 881ae19b..c6c8ae96 100644 --- a/src/blosc2/ctable.py +++ b/src/blosc2/ctable.py @@ -13,9 +13,11 @@ import contextlib import dataclasses import os +import pprint import shutil from collections.abc import Iterable from dataclasses import MISSING +from textwrap import TextWrapper from typing import Any, Generic, TypeVar import numpy as np @@ -37,6 +39,7 @@ def wrapper(*args, **kwargs): import blosc2 +from blosc2.info import InfoReporter, format_nbytes_info from blosc2.schema import SchemaSpec from blosc2.schema_compiler import ( ColumnConfig, @@ -131,6 +134,85 @@ def name(self) -> str | None: """Optional human-readable name assigned at creation time.""" return self._descriptor.get("name") or None + @property + def nbytes(self) -> int: + """Total uncompressed size in bytes for this index payload.""" + from blosc2.indexing import _component_nbytes, iter_index_components + + root = self._table._root_table + col_arr = root._cols[self._col_name] + descriptor = self._descriptor + return sum( + _component_nbytes(col_arr, descriptor, component) + for component in iter_index_components(col_arr, descriptor) + ) + + @property + def cbytes(self) -> int: + """Total compressed size in bytes for this index payload.""" + from blosc2.indexing import _component_cbytes, iter_index_components + + root = self._table._root_table + col_arr = root._cols[self._col_name] + descriptor = self._descriptor + return sum( + _component_cbytes(col_arr, descriptor, component) + for component in iter_index_components(col_arr, descriptor) + ) + + @property + def cratio(self) -> float: + """Compression ratio for this index payload.""" + cbytes = self.cbytes + if cbytes == 0: + return float("inf") + return self.nbytes / cbytes + + def storage_stats(self) -> tuple[int, int, float] | None: + """Return ``(nbytes, cbytes, cratio)`` when sidecars are directly measurable.""" + try: + nbytes = self.nbytes + cbytes = self.cbytes + except (FileNotFoundError, OSError, RuntimeError, KeyError, ValueError): + root = self._table._root_table + if not isinstance(root._storage, FileTableStorage): + return None + + from blosc2.indexing import iter_index_components + + descriptor = self._descriptor + col_arr = root._cols[self._col_name] + store = root._storage._open_store() + nbytes = 0 + cbytes = 0 + try: + for component in iter_index_components(col_arr, descriptor): + if component.path is None: + return None + key = self._component_store_key(component.path) + obj = store[key] + nbytes += int(obj.nbytes) + cbytes += int(obj.cbytes) + except (FileNotFoundError, OSError, RuntimeError, KeyError, ValueError): + return None + cratio = float("inf") if cbytes == 0 else nbytes / cbytes + return nbytes, cbytes, cratio + + @staticmethod + def _component_store_key(path: str) -> str: + """Return the logical TreeStore key for an index component path.""" + normalized = path.replace("\\", "/") + marker = "_indexes/" + idx = normalized.find(marker) + if idx < 0: + raise KeyError(f"Cannot resolve index component path {path!r} inside table store.") + relpath = normalized[idx:] + for suffix in (".b2nd", ".b2f"): + if relpath.endswith(suffix): + relpath = relpath[: -len(suffix)] + break + return "/" + relpath.lstrip("/") + def drop(self) -> None: """Drop this index from the owning table.""" self._table.drop_index(self._col_name) @@ -149,6 +231,42 @@ def __repr__(self) -> str: return f"" +class _CTableInfoReporter(InfoReporter): + """Info reporter that also preserves the historic ``t.info()`` call style.""" + + def __repr__(self) -> str: + items = self.obj.info_items + max_key_len = max(len(k) for k, _ in items) + parts = [] + for key, value in items: + if isinstance(value, dict): + parts.append(f"{key.ljust(max_key_len)} :") + pretty = pprint.pformat(value, sort_dicts=False) + parts.extend(f" {line}" for line in pretty.splitlines()) + continue + + wrapper = TextWrapper( + width=96, + initial_indent=key.ljust(max_key_len) + " : ", + subsequent_indent=" " * max_key_len + " : ", + ) + parts.append(wrapper.fill(str(value))) + return "\n".join(parts) + "\n" + + def __call__(self) -> None: + print(repr(self), end="") + + +class _InfoLiteral: + """Pretty-printer helper for unquoted literal values inside info dicts.""" + + def __init__(self, text: str) -> None: + self.text = text + + def __repr__(self) -> str: + return self.text + + # RowT is intentionally left unbound so CTable works with both dataclasses # and legacy Pydantic models during the transition period. RowT = TypeVar("RowT") @@ -2335,6 +2453,13 @@ def nbytes(self) -> int: """Total uncompressed size in bytes (all columns + valid_rows mask).""" return sum(col.nbytes for col in self._cols.values()) + self._valid_rows.nbytes + @property + def cratio(self) -> float: + """Compression ratio for the whole table payload.""" + if self.cbytes == 0: + return float("inf") + return self.nbytes / self.cbytes + @property def schema(self) -> CompiledSchema: """The compiled schema that drives this table's columns and validation.""" @@ -2939,33 +3064,71 @@ def _try_index_where(self, expr_result: blosc2.LazyExpr) -> np.ndarray | None: return None - def info(self) -> None: - """Print a concise summary of the CTable.""" - ratio = (self.nbytes / self.cbytes) if self.cbytes > 0 else 0.0 - - lines = [] - lines.append("") - lines.append(f"nºColumns: {self.ncols}") - lines.append(f"nºRows: {self.nrows}") - lines.append("") + @property + def info_items(self) -> list[tuple[str, object]]: + """Structured summary items used by :meth:`info`.""" + storage_type = "persistent" if isinstance(self._storage, FileTableStorage) else "in-memory" + urlpath = self._storage._root if isinstance(self._storage, FileTableStorage) else None + schema_summary = { + name: _InfoLiteral(self._dtype_info_label(self._cols[name].dtype)) for name in self.col_names + } - header = f" {'#':>3} {'Column':<15} {'Itemsize':<12} {'Dtype':<15}" - lines.append(header) - lines.append(f" {'---':>3} {'------':<15} {'--------':<12} {'-----':<15}") + index_summary = {} + for idx in self.indexes: + stale = " stale" if idx.stale else "" + label = f" name={idx.name!r}" if idx.name and idx.name != "__self__" else "" + stats = idx.storage_stats() + if stats is None: + suffix = "size=n/a (sidecars not directly addressable)" + else: + nbytes, cbytes, cratio = stats + suffix = ( + f"nbytes={format_nbytes_info(nbytes)}, " + f"cbytes={format_nbytes_info(cbytes)}, cratio={cratio:.2f}x" + ) + index_summary[idx.col_name] = f"[{idx.kind}{stale}{label}] {suffix}" + + items = [ + ("type", self.__class__.__name__), + ("storage", storage_type), + ("rows", self.nrows), + ("columns", self.ncols), + ("capacity", len(self._valid_rows)), + ("view", self.base is not None), + ("read_only", self._read_only), + ("nbytes", format_nbytes_info(self.nbytes)), + ("cbytes", format_nbytes_info(self.cbytes)), + ("cratio", f"{self.cratio:.2f}"), + ("schema", schema_summary), + ( + "valid_rows_mask", + f"nbytes={format_nbytes_info(self._valid_rows.nbytes)}, cbytes={format_nbytes_info(self._valid_rows.cbytes)}", + ), + ("indexes", index_summary if index_summary else "none"), + ] + if urlpath is not None: + items.insert(2, ("urlpath", urlpath)) + return items - for i, name in enumerate(self.col_names): - col_array = self._cols[name] - dtype_str = str(col_array.dtype) - itemsize = f"{col_array.dtype.itemsize} B" - lines.append(f" {i:>3} {name:<15} {itemsize:<12} {dtype_str:<15}") + @staticmethod + def _dtype_info_label(dtype: np.dtype) -> str: + """Return a compact dtype label for info reports.""" + if dtype.kind == "U": + return f"U{dtype.itemsize // 4}" + if dtype.kind == "S": + return f"S{dtype.itemsize}" + return str(dtype) - lines.append("") - lines.append(f"memory usage: {_fmt_bytes(self.cbytes)}") - lines.append(f"uncompressed size: {_fmt_bytes(self.nbytes)}") - lines.append(f"compression ratio: {ratio:.2f}x") - lines.append("") + @property + def info(self) -> _CTableInfoReporter: + """Get information about this table. - print("\n".join(lines)) + Examples + -------- + >>> print(t.info) + >>> t.info() + """ + return _CTableInfoReporter(self) # ------------------------------------------------------------------ # Mutation: append / extend / delete @@ -3106,6 +3269,9 @@ def extend(self, data: list | CTable | Any, *, validate: bool | None = None) -> @profile def where(self, expr_result) -> CTable: + if isinstance(expr_result, Column): + expr_result = expr_result._raw_col + if not ( isinstance(expr_result, (blosc2.NDArray, blosc2.LazyExpr)) and (getattr(expr_result, "dtype", None) == np.bool_) diff --git a/src/blosc2/ndarray.py b/src/blosc2/ndarray.py index fd1de79a..51f88f96 100644 --- a/src/blosc2/ndarray.py +++ b/src/blosc2/ndarray.py @@ -494,9 +494,17 @@ def reshape( return dst +def _normalize_expr_operand(value: Any) -> Any: + """Normalize foreign expression operands to the array-like object lazy ops expect.""" + raw_col = getattr(value, "_raw_col", None) + return raw_col if raw_col is not None else value + + def _check_allowed_dtypes( value: bool | int | float | str | blosc2.Array, ): + value = _normalize_expr_operand(value) + def _is_array_like(v: Any) -> bool: try: # Try Protocol runtime check first (works when possible) @@ -3426,6 +3434,7 @@ def __abs__(self) -> blosc2.LazyExpr: @is_documented_by(bitwise_and) def __and__(self, value: int | float | blosc2.Array, /) -> blosc2.LazyExpr: + value = _normalize_expr_operand(value) _check_allowed_dtypes(value) return blosc2.LazyExpr(new_op=(self, "&", value)) @@ -3437,6 +3446,8 @@ def __rand__(self, value: int | float | blosc2.Array, /) -> blosc2.LazyExpr: @is_documented_by(bitwise_xor) def __xor__(self, other) -> blosc2.LazyExpr: + other = _normalize_expr_operand(other) + _check_allowed_dtypes(other) return blosc2.LazyExpr(new_op=(self, "^", other)) def __ixor__(self, other) -> blosc2.LazyExpr: @@ -3447,6 +3458,8 @@ def __rxor__(self, other) -> blosc2.LazyExpr: @is_documented_by(bitwise_or) def __or__(self, other) -> blosc2.LazyExpr: + other = _normalize_expr_operand(other) + _check_allowed_dtypes(other) return blosc2.LazyExpr(new_op=(self, "|", other)) def __ior__(self, other) -> blosc2.LazyExpr: diff --git a/tests/ctable/test_ctable_indexing.py b/tests/ctable/test_ctable_indexing.py index 55ef9168..0c3e28f2 100644 --- a/tests/ctable/test_ctable_indexing.py +++ b/tests/ctable/test_ctable_indexing.py @@ -98,6 +98,24 @@ def test_where_with_index_matches_scan_in_memory(): assert ids_idx == ids_scan +def test_bool_column_composes_naturally_in_where(): + @dataclasses.dataclass + class BoolRow: + sensor_id: int = blosc2.field(blosc2.int32()) + region: str = blosc2.field(blosc2.string(max_length=8), default="") + active: bool = blosc2.field(blosc2.bool(), default=True) + + t = blosc2.CTable(BoolRow) + for i in range(20): + t.append([i, "north" if i % 4 == 0 else "south", i % 2 == 0]) + + result = t.where((t["sensor_id"] >= 8) & t["active"] & (t["region"] == "north")) + assert sorted(int(v) for v in result["sensor_id"].to_numpy()) == [8, 12, 16] + + result_bare = t.where(t["active"]) + assert sorted(int(v) for v in result_bare["sensor_id"].to_numpy()) == list(range(0, 20, 2)) + + def test_rebuild_index_in_memory(): t = _make_table(30) t.create_index("id") From 2cb42954c8e50e39fe1d99cfdc75b0ae427f5755 Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Wed, 15 Apr 2026 18:15:25 +0200 Subject: [PATCH 35/49] New Column.__repr__() for an nice overview of the column --- examples/ctable/indexing.py | 10 ++++++---- src/blosc2/ctable.py | 18 ++++++++++++++++++ tests/ctable/test_column.py | 10 ++++++++++ 3 files changed, 34 insertions(+), 4 deletions(-) diff --git a/examples/ctable/indexing.py b/examples/ctable/indexing.py index ab729fd2..dd06436d 100644 --- a/examples/ctable/indexing.py +++ b/examples/ctable/indexing.py @@ -41,10 +41,12 @@ def load_rows(table: blosc2.CTable, nrows: int = 240) -> None: packed = None try: + print("Creating a CTable with mixed dtypes...") pt = blosc2.CTable(Measurement, urlpath=str(table_path), mode="w") load_rows(pt) # Create a couple of indexes on columns with different dtypes. + print("\nCreating indexes...") idx_sensor = pt.create_index("sensor_id", kind=blosc2.IndexKind.FULL) idx_active = pt.create_index("active") print("Indexes created:", pt.indexes) @@ -54,8 +56,8 @@ def load_rows(table: blosc2.CTable, nrows: int = 240) -> None: # Queries can combine indexed and non-indexed predicates. recent_active = pt.where((pt["sensor_id"] >= 180) & pt["active"] & (pt["region"] == "north")) print("\nLive rows with sensor_id >= 180, active=True, region='north':", len(recent_active)) - print("sensor_ids:", recent_active["sensor_id"].to_numpy().tolist()) - print("statuses:", recent_active["status"].to_numpy().tolist()) + print("sensor_ids:", recent_active["sensor_id"]) + print("statuses:", recent_active["status"].to_numpy()) # Close the table, pack the TreeStore into a single .b2z file, and reopen it. del pt @@ -79,8 +81,8 @@ def load_rows(table: blosc2.CTable, nrows: int = 240) -> None: # Query directly against the .b2z bundle; no unpack step is needed. warm_active = packed.where(packed["active"] & (packed["status"] == "warm") & (packed["sensor_id"] > 100)) print("\nRows from .b2z with active=True, status='warm', sensor_id > 100:", len(warm_active)) - print("sensor_ids:", warm_active["sensor_id"].to_numpy().tolist()) - print("regions:", warm_active["region"].to_numpy().tolist()) + print("sensor_ids:", warm_active["sensor_id"]) + print("regions:", warm_active["region"].to_numpy()) print("\nThe packed file is kept on disk.") print(f"Inspect it later with: f = blosc2.open({bundle_path.name!r}, mode='r')") diff --git a/src/blosc2/ctable.py b/src/blosc2/ctable.py index c6c8ae96..1f8f3397 100644 --- a/src/blosc2/ctable.py +++ b/src/blosc2/ctable.py @@ -12,6 +12,7 @@ import contextlib import dataclasses +import itertools import os import pprint import shutil @@ -475,6 +476,8 @@ def __getitem__(self, col_name: str): class Column: + _REPR_PREVIEW_ITEMS = 8 + def __init__(self, table: CTable, col_name: str, mask=None): self._table = table self._col_name = col_name @@ -597,6 +600,21 @@ def __iter__(self): data_chunk = self._raw_col[chunk_start : chunk_start + actual_size] yield from data_chunk[mask_chunk] + def __repr__(self) -> str: + preview_items = [] + for value in itertools.islice(self, self._REPR_PREVIEW_ITEMS + 1): + if isinstance(value, np.generic): + value = value.item() + preview_items.append(repr(value)) + + truncated = len(preview_items) > self._REPR_PREVIEW_ITEMS + if truncated: + preview_items = preview_items[: self._REPR_PREVIEW_ITEMS] + preview_items.append("...") + + preview = ", ".join(preview_items) + return f"Column({self._col_name!r}, dtype={self.dtype}, len={len(self)}, values=[{preview}])" + def __len__(self): return blosc2.count_nonzero(self._valid_rows) diff --git a/tests/ctable/test_column.py b/tests/ctable/test_column.py index 46cfca28..9fb26467 100644 --- a/tests/ctable/test_column.py +++ b/tests/ctable/test_column.py @@ -675,5 +675,15 @@ def test_repr_is_single_line(): assert "\n" not in repr(t) +def test_column_repr_shows_preview_values(): + t = CTable(Row, new_data=DATA20) + r = repr(t["id"][:]) + assert "Column('id'" in r + assert "dtype=int64" in r + assert "len=20" in r + assert "values=[0, 1, 2" in r + assert "..." in r + + if __name__ == "__main__": pytest.main(["-v", __file__]) From efbba8a703b5e391b4f5525fb1d17fd8281e3413 Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Wed, 15 Apr 2026 18:57:30 +0200 Subject: [PATCH 36/49] Warn on implicit blosc2.open append mode Implement the first phase of plans/changing-default-open-mode.md by tracking omitted mode= with a sentinel and emitting a FutureWarning when blosc2.open() relies on the current implicit "a" behavior. Update mmap-related tests, examples, and docstrings to pass explicit mode="r" so they keep exercising their intended paths without tripping the migration warning. --- examples/mmap-rw.py | 2 +- examples/ndarray/blosc2_3_10_demo.py | 2 +- examples/ndarray/compute_expr.py | 2 +- examples/ndarray/dsl_save.py | 4 +- examples/ndarray/meta.py | 2 +- examples/ndarray/persistency.py | 2 +- examples/ndarray/reduce_and_enlarge.py | 2 +- examples/ndarray/reduce_expr_save.py | 4 +- src/blosc2/core.py | 2 +- src/blosc2/indexing.py | 52 +++---- src/blosc2/lazyexpr.py | 4 +- src/blosc2/ref.py | 2 +- src/blosc2/schunk.py | 184 ++++++++++++++----------- tests/ctable/test_ctable_indexing.py | 8 +- tests/ndarray/test_c2array_expr.py | 2 +- tests/ndarray/test_dsl_kernels.py | 6 +- tests/ndarray/test_indexing.py | 12 +- tests/ndarray/test_lazyexpr.py | 58 ++++---- tests/ndarray/test_lazyudf.py | 6 +- tests/ndarray/test_ndarray.py | 6 +- tests/ndarray/test_persistency.py | 2 +- tests/ndarray/test_proxy.py | 6 +- tests/ndarray/test_proxy_c2array.py | 2 +- tests/ndarray/test_proxy_expr.py | 2 +- tests/ndarray/test_reductions.py | 32 ++--- tests/ndarray/test_stringarrays.py | 6 +- tests/ndarray/test_struct_dtype.py | 2 +- tests/test_mmap.py | 10 +- tests/test_open.py | 33 ++++- tests/test_open_c2array.py | 2 +- tests/test_proxy_schunk.py | 4 +- 31 files changed, 259 insertions(+), 204 deletions(-) diff --git a/examples/mmap-rw.py b/examples/mmap-rw.py index 2fee6d7c..f27e6c19 100644 --- a/examples/mmap-rw.py +++ b/examples/mmap-rw.py @@ -24,7 +24,7 @@ blosc2.asarray(a, urlpath=urlpath, mmap_mode="w+", initial_mapping_size=initial_mapping_size) # Read the ndarray back via the general open function -a_read = blosc2.open(urlpath, mmap_mode="r") +a_read = blosc2.open(urlpath, mode="r", mmap_mode="r") assert np.all(a == a_read) blosc2.remove_urlpath(urlpath) diff --git a/examples/ndarray/blosc2_3_10_demo.py b/examples/ndarray/blosc2_3_10_demo.py index 17a1c8b9..00725cd0 100644 --- a/examples/ndarray/blosc2_3_10_demo.py +++ b/examples/ndarray/blosc2_3_10_demo.py @@ -33,7 +33,7 @@ # Reopen persistent expression, compute, and write to disk with blosc2 t0 = time.time() -lexpr = blosc2.open(urlpath=url_path) +lexpr = blosc2.open(urlpath=url_path, mode="r") dt = time.time() - t0 print(f"In {round(dt * 1000, 3)} ms opened lazy expression: shape = {lexpr.shape}, dtype = {lexpr.dtype}") t1 = time.time() diff --git a/examples/ndarray/compute_expr.py b/examples/ndarray/compute_expr.py index 60ecf606..b6ed8ce3 100644 --- a/examples/ndarray/compute_expr.py +++ b/examples/ndarray/compute_expr.py @@ -50,7 +50,7 @@ # Get a LazyExpr instance (da**2 + db**2 + 2 * da * db + 1).save(urlpath="c.b2nd") -dc = blosc2.open("c.b2nd") +dc = blosc2.open("c.b2nd", mode="r") # Evaluate: output is a NDArray dc2 = dc.compute() diff --git a/examples/ndarray/dsl_save.py b/examples/ndarray/dsl_save.py index 24870afb..55e975e9 100644 --- a/examples/ndarray/dsl_save.py +++ b/examples/ndarray/dsl_save.py @@ -46,7 +46,7 @@ def heat_step(u, v): print("LazyUDF saved to heat_step.b2nd") # ── reload in a 'fresh' context (no reference to heat_step) ───────── -reloaded = blosc2.open("heat_step.b2nd") +reloaded = blosc2.open("heat_step.b2nd", mode="r") assert isinstance(reloaded, blosc2.LazyUDF), "Expected a LazyUDF after open()" assert isinstance(reloaded.func, DSLKernel), "func must be a DSLKernel after reload" assert reloaded.func.dsl_source is not None, "dsl_source must survive the round-trip" @@ -64,7 +64,7 @@ def heat_step(u, v): lazy2 = blosc2.lazyudf(heat_step, (u2, v), dtype=np.float64) lazy2.save(urlpath="heat_step2.b2nd") -reloaded2 = blosc2.open("heat_step2.b2nd") +reloaded2 = blosc2.open("heat_step2.b2nd", mode="r") result2 = reloaded2.compute() expected2 = u2[()] + 0.1 * (v[()] - u2[()]) assert np.allclose(result2[()], expected2) diff --git a/examples/ndarray/meta.py b/examples/ndarray/meta.py index c9d4a498..5fa13d12 100644 --- a/examples/ndarray/meta.py +++ b/examples/ndarray/meta.py @@ -27,7 +27,7 @@ print(a.info) # Read a b2nd array from disk -b = blosc2.open(urlpath) +b = blosc2.open(urlpath, mode="r") # Deal with meta m1 = b.schunk.meta.get("m5", b"0000") diff --git a/examples/ndarray/persistency.py b/examples/ndarray/persistency.py index 014519a5..4541da47 100644 --- a/examples/ndarray/persistency.py +++ b/examples/ndarray/persistency.py @@ -20,6 +20,6 @@ a = blosc2.asarray(nparray, urlpath=urlpath, mode="w") # Read the array from disk -b = blosc2.open(urlpath) +b = blosc2.open(urlpath, mode="r") # And see its contents print(b[...]) diff --git a/examples/ndarray/reduce_and_enlarge.py b/examples/ndarray/reduce_and_enlarge.py index e6841441..928a4fe8 100644 --- a/examples/ndarray/reduce_and_enlarge.py +++ b/examples/ndarray/reduce_and_enlarge.py @@ -49,7 +49,7 @@ url_path = "my_expr.b2nd" # Open the saved file -lazy_expr = blosc2.open(urlpath=url_path) +lazy_expr = blosc2.open(urlpath=url_path, mode="r") print(lazy_expr) print(f"expr (after open) shape: {lazy_expr.shape}; dtype: {lazy_expr.dtype}") # Evaluate and print the result of the lazy expression (should be a 2x4 arr) diff --git a/examples/ndarray/reduce_expr_save.py b/examples/ndarray/reduce_expr_save.py index 99f5c58a..1fdb257e 100644 --- a/examples/ndarray/reduce_expr_save.py +++ b/examples/ndarray/reduce_expr_save.py @@ -24,13 +24,13 @@ # Get a LazyExpr instance c = a**2 + b**2 + 2 * a * b + 1 c.save(urlpath="c.b2nd") -c = blosc2.open("c.b2nd") +c = blosc2.open("c.b2nd", mode="r") # Evaluate: output is a NDArray d = blosc2.lazyexpr("a + c.sum() + a.std()", operands={"a": a, "c": c}) d.save(urlpath="lazy-d.b2nd") # Load the expression from disk -d = blosc2.open("lazy-d.b2nd") +d = blosc2.open("lazy-d.b2nd", mode="r") print(f"Expression: {d}") assert isinstance(d, blosc2.LazyExpr) e = d.compute() diff --git a/src/blosc2/core.py b/src/blosc2/core.py index 809c209a..ceb78acd 100644 --- a/src/blosc2/core.py +++ b/src/blosc2/core.py @@ -834,7 +834,7 @@ def load_tensor(urlpath: str, dparams: dict | None = None) -> tensorflow.Tensor :func:`~blosc2.save_tensor` :func:`~blosc2.pack_tensor` """ - schunk = blosc2.open(urlpath, dparams=dparams) + schunk = blosc2.open(urlpath, mode="r", dparams=dparams) return _unpack_tensor(schunk) diff --git a/src/blosc2/indexing.py b/src/blosc2/indexing.py index a77a2da0..821c5a9c 100644 --- a/src/blosc2/indexing.py +++ b/src/blosc2/indexing.py @@ -935,7 +935,7 @@ def _open_sidecar_handle(array: blosc2.NDArray, token: str, category: str, name: raise RuntimeError("sidecar handle path is not available") handle = legacy if isinstance(legacy, blosc2.NDArray) else blosc2.asarray(np.asarray(legacy)) else: - handle = blosc2.open(path, mmap_mode=_INDEX_MMAP_MODE) + handle = blosc2.open(path, mode="r", mmap_mode=_INDEX_MMAP_MODE) _SIDECAR_HANDLE_CACHE[cache_key] = handle return handle @@ -1054,7 +1054,7 @@ def _compute_sorted_boundaries_from_sidecar( ) -> np.ndarray: nsegments = math.ceil(length / segment_len) boundaries = np.empty(nsegments, dtype=_boundary_dtype(dtype)) - sidecar = blosc2.open(path, mmap_mode=_INDEX_MMAP_MODE) + sidecar = blosc2.open(path, mode="r", mmap_mode=_INDEX_MMAP_MODE) start_value = np.empty(1, dtype=dtype) end_value = np.empty(1, dtype=dtype) for idx in range(nsegments): @@ -1301,7 +1301,7 @@ def _sidecar_storage_geometry( ) -> tuple[int, int]: if path is None: return fallback_chunk_len, fallback_block_len - sidecar = blosc2.open(path, mmap_mode=_INDEX_MMAP_MODE) + sidecar = blosc2.open(path, mode="r", mmap_mode=_INDEX_MMAP_MODE) return int(sidecar.chunks[0]), int(sidecar.blocks[0]) @@ -1385,7 +1385,7 @@ def _stream_copy_sidecar_array( blocks: tuple[int, ...], cparams: dict | None = None, ) -> None: - source = blosc2.open(str(source_path), mmap_mode=_INDEX_MMAP_MODE) + source = blosc2.open(str(source_path), mode="r", mmap_mode=_INDEX_MMAP_MODE) blosc2.remove_urlpath(str(dest_path)) kwargs = {"chunks": chunks, "blocks": blocks, "urlpath": str(dest_path), "mode": "w"} if cparams is not None: @@ -2754,7 +2754,7 @@ def _copy_sidecar_to_temp_run( cparams: dict | None = None, ) -> Path: out_path = workdir / f"{prefix}.b2nd" - sidecar = blosc2.open(path, mmap_mode=_INDEX_MMAP_MODE) + sidecar = blosc2.open(path, mode="r", mmap_mode=_INDEX_MMAP_MODE) output = _create_blosc2_temp_array(out_path, length, dtype, FULL_OOC_MERGE_BUFFER_ITEMS, cparams) chunk_len = int(sidecar.chunks[0]) for chunk_id, start in enumerate(range(0, length, chunk_len)): @@ -2814,10 +2814,10 @@ def _merge_run_pair( tracker: TempRunTracker | None = None, cparams: dict | None = None, ) -> SortedRun: - left_values_mm = blosc2.open(str(left.values_path), mmap_mode=_INDEX_MMAP_MODE) - left_positions_mm = blosc2.open(str(left.positions_path), mmap_mode=_INDEX_MMAP_MODE) - right_values_mm = blosc2.open(str(right.values_path), mmap_mode=_INDEX_MMAP_MODE) - right_positions_mm = blosc2.open(str(right.positions_path), mmap_mode=_INDEX_MMAP_MODE) + left_values_mm = blosc2.open(str(left.values_path), mode="r", mmap_mode=_INDEX_MMAP_MODE) + left_positions_mm = blosc2.open(str(left.positions_path), mode="r", mmap_mode=_INDEX_MMAP_MODE) + right_values_mm = blosc2.open(str(right.values_path), mode="r", mmap_mode=_INDEX_MMAP_MODE) + right_positions_mm = blosc2.open(str(right.positions_path), mode="r", mmap_mode=_INDEX_MMAP_MODE) out_values_path = workdir / f"full_merge_values_{merge_id}.b2nd" out_positions_path = workdir / f"full_merge_positions_{merge_id}.b2nd" @@ -3024,8 +3024,8 @@ def _build_full_descriptor_ooc( array, token, kind, full, final_run, dtype, persistent, tracker, cparams ) else: - sorted_values = blosc2.open(str(final_run.values_path), mmap_mode=_INDEX_MMAP_MODE)[:] - positions = blosc2.open(str(final_run.positions_path), mmap_mode=_INDEX_MMAP_MODE)[:] + sorted_values = blosc2.open(str(final_run.values_path), mode="r", mmap_mode=_INDEX_MMAP_MODE)[:] + positions = blosc2.open(str(final_run.positions_path), mode="r", mmap_mode=_INDEX_MMAP_MODE)[:] values_sidecar = _store_array_sidecar( array, token, kind, "full", "values", sorted_values, persistent, cparams=cparams ) @@ -3257,14 +3257,14 @@ def iter_index_components(array: blosc2.NDArray, descriptor: dict): def _component_nbytes(array: blosc2.NDArray, descriptor: dict, component: IndexComponent) -> int: if component.path is not None: - return int(blosc2.open(component.path, mmap_mode=_INDEX_MMAP_MODE).nbytes) + return int(blosc2.open(component.path, mode="r", mmap_mode=_INDEX_MMAP_MODE).nbytes) token = descriptor["token"] return int(_load_array_sidecar(array, token, component.category, component.name).nbytes) def _component_cbytes(array: blosc2.NDArray, descriptor: dict, component: IndexComponent) -> int: if component.path is not None: - return int(blosc2.open(component.path, mmap_mode=_INDEX_MMAP_MODE).cbytes) + return int(blosc2.open(component.path, mode="r", mmap_mode=_INDEX_MMAP_MODE).cbytes) token = descriptor["token"] sidecar = _load_array_sidecar(array, token, component.category, component.name) kwargs = {} @@ -3804,8 +3804,8 @@ def compact_index(array: blosc2.NDArray, field: str | None = None, name: str | N array, descriptor, final_run.values_path, final_run.positions_path, final_run.length ) else: - sorted_values = blosc2.open(str(final_run.values_path), mmap_mode=_INDEX_MMAP_MODE)[:] - positions = blosc2.open(str(final_run.positions_path), mmap_mode=_INDEX_MMAP_MODE)[:] + sorted_values = blosc2.open(str(final_run.values_path), mode="r", mmap_mode=_INDEX_MMAP_MODE)[:] + positions = blosc2.open(str(final_run.positions_path), mode="r", mmap_mode=_INDEX_MMAP_MODE)[:] _replace_full_descriptor(array, descriptor, sorted_values, positions, descriptor["persistent"]) del sorted_values, positions final_run.values_path.unlink(missing_ok=True) @@ -4859,7 +4859,7 @@ def _bucket_batch_result_dtype(where_x) -> np.dtype: def _bucket_worker_source(where_x): if _supports_block_reads(where_x) and getattr(where_x, "urlpath", None) is not None: - return blosc2.open(str(where_x.urlpath), mmap_mode=_INDEX_MMAP_MODE) + return blosc2.open(str(where_x.urlpath), mode="r", mmap_mode=_INDEX_MMAP_MODE) return where_x @@ -4877,7 +4877,7 @@ def _gather_mmap_source(where_x): urlpath = str(urlpath) handle = _GATHER_MMAP_HANDLES.get(urlpath) if handle is None: - handle = blosc2.open(urlpath, mmap_mode=_INDEX_MMAP_MODE) + handle = blosc2.open(urlpath, mode="r", mmap_mode=_INDEX_MMAP_MODE) _GATHER_MMAP_HANDLES[urlpath] = handle return handle @@ -5104,17 +5104,17 @@ def process_batch(chunk_ids: np.ndarray) -> tuple[list[tuple[int, np.ndarray]], batch_values = ( values_sidecar if bucket.get("values_path") is None - else blosc2.open(bucket["values_path"], mmap_mode=_INDEX_MMAP_MODE) + else blosc2.open(bucket["values_path"], mode="r", mmap_mode=_INDEX_MMAP_MODE) ) batch_buckets = ( bucket_sidecar if bucket.get("bucket_positions_path") is None - else blosc2.open(bucket["bucket_positions_path"], mmap_mode=_INDEX_MMAP_MODE) + else blosc2.open(bucket["bucket_positions_path"], mode="r", mmap_mode=_INDEX_MMAP_MODE) ) batch_l2 = ( l2_sidecar if bucket.get("l2_path") is None - else blosc2.open(bucket["l2_path"], mmap_mode=_INDEX_MMAP_MODE) + else blosc2.open(bucket["l2_path"], mode="r", mmap_mode=_INDEX_MMAP_MODE) ) batch_results = [] batch_candidate_segments = 0 @@ -5251,9 +5251,9 @@ def _partial_chunk_nav_positions_cython( def process_cython_batch(chunk_ids: np.ndarray) -> tuple[np.ndarray, int]: if len(chunk_ids) == 0: return np.empty(0, dtype=np.int64), 0 - batch_values = blosc2.open(partial["values_path"], mmap_mode=_INDEX_MMAP_MODE) - batch_positions = blosc2.open(partial["positions_path"], mmap_mode=_INDEX_MMAP_MODE) - batch_l2 = blosc2.open(partial["l2_path"], mmap_mode=_INDEX_MMAP_MODE) + batch_values = blosc2.open(partial["values_path"], mode="r", mmap_mode=_INDEX_MMAP_MODE) + batch_positions = blosc2.open(partial["positions_path"], mode="r", mmap_mode=_INDEX_MMAP_MODE) + batch_l2 = blosc2.open(partial["l2_path"], mode="r", mmap_mode=_INDEX_MMAP_MODE) batch_l2_row = np.empty(nsegments_per_chunk, dtype=l2_boundary_dtype) batch_span_values = np.empty(chunk_len, dtype=dtype) batch_local_positions = np.empty(chunk_len, dtype=local_position_dtype) @@ -5300,17 +5300,17 @@ def process_batch(chunk_ids: np.ndarray) -> tuple[list[np.ndarray], int]: batch_values = ( values_sidecar if partial.get("values_path") is None - else blosc2.open(partial["values_path"], mmap_mode=_INDEX_MMAP_MODE) + else blosc2.open(partial["values_path"], mode="r", mmap_mode=_INDEX_MMAP_MODE) ) batch_positions = ( positions_sidecar if partial.get("positions_path") is None - else blosc2.open(partial["positions_path"], mmap_mode=_INDEX_MMAP_MODE) + else blosc2.open(partial["positions_path"], mode="r", mmap_mode=_INDEX_MMAP_MODE) ) batch_l2 = ( l2_sidecar if partial.get("l2_path") is None - else blosc2.open(partial["l2_path"], mmap_mode=_INDEX_MMAP_MODE) + else blosc2.open(partial["l2_path"], mode="r", mmap_mode=_INDEX_MMAP_MODE) ) batch_parts = [] batch_candidate_segments = 0 diff --git a/src/blosc2/lazyexpr.py b/src/blosc2/lazyexpr.py index b8410b9a..5c64eb4b 100644 --- a/src/blosc2/lazyexpr.py +++ b/src/blosc2/lazyexpr.py @@ -579,7 +579,7 @@ def save(self, **kwargs: Any) -> None: >>> # Save the LazyExpr to disk >>> expr.save(urlpath='lazy_array.b2nd', mode='w') >>> # Open and load the LazyExpr from disk - >>> disk_expr = blosc2.open('lazy_array.b2nd') + >>> disk_expr = blosc2.open('lazy_array.b2nd', mode='r') >>> disk_expr[:2] [[0. 1.25 2.5 ] [3.75 5. 6.25]] @@ -4682,7 +4682,7 @@ def open_lazyarray(array): if isinstance(v, str): v = parent_path / v try: - op = blosc2.open(v) + op = blosc2.open(v, mode="r") except FileNotFoundError: missing_ops[key] = v else: diff --git a/src/blosc2/ref.py b/src/blosc2/ref.py index c0acca8e..cc842f25 100644 --- a/src/blosc2/ref.py +++ b/src/blosc2/ref.py @@ -118,7 +118,7 @@ def open(self): import blosc2 if self.kind == "urlpath": - return blosc2.open(self.urlpath) + return blosc2.open(self.urlpath, mode="a") if self.kind == "dictstore_key": return blosc2.DictStore(self.urlpath, mode="r")[self.key] if self.kind == "c2array": diff --git a/src/blosc2/schunk.py b/src/blosc2/schunk.py index 714937ce..3008062b 100644 --- a/src/blosc2/schunk.py +++ b/src/blosc2/schunk.py @@ -9,6 +9,7 @@ import os import pathlib +import warnings import zipfile from collections import namedtuple from collections.abc import Iterator, Mapping, MutableMapping @@ -198,82 +199,82 @@ def __init__( # noqa: C901 ) -> None: """Create a new super-chunk, or open an existing one. - Parameters - ---------- - chunksize: int, optional - The size, in bytes, of the chunks in the super-chunk. If not provided, - it is set automatically to a reasonable value. - - data: bytes-like object, optional - The data to be split into different chunks of size :paramref:`chunksize`. - If None, the Schunk instance will be empty initially. - - kwargs: dict, optional - Storage parameters. The default values are in :class:`blosc2.Storage`. - Supported keyword arguments: - storage: :class:`blosc2.Storage` or dict - All the storage parameters that you want to use as - a :class:`blosc2.Storage` or dict instance. - cparams: :class:`blosc2.CParams` or dict - All the compression parameters that you want to use as - a :class:`blosc2.CParams` or dict instance. - dparams: :class:`blosc2.DParams` or dict - All the decompression parameters that you want to use as - a :class:`blosc2.DParams` or dict instance. - others: Any - If `storage` is not passed, all the parameters of a :class:`blosc2.Storage` - can be passed as keyword arguments. - - Examples - -------- - >>> import blosc2 - >>> import numpy as np - >>> import os.path - >>> import shutil - >>> import tempfile - >>> cparams = blosc2.CParams() - >>> dparams = blosc2.DParams() - >>> storage = blosc2.Storage(contiguous=True) - >>> schunk = blosc2.SChunk(cparams=cparams, dparams=dparams, storage=storage) - - In the following, we will write and read a super-chunk to and from disk - via memory-mapped files. - - >>> a = np.arange(3, dtype=np.int64) - >>> chunksize = a.size * a.itemsize - >>> n_chunks = 2 - >>> tmpdirname = tempfile.mkdtemp() - >>> urlpath = os.path.join(tmpdirname, 'schunk.b2frame') - - Optional: we intend to write 2 chunks of 24 bytes each, and we expect - the compressed size to be smaller than the original size. Therefore, we - generously set the initial size of the mapping to 48 bytes - effectively avoiding remappings. - - >>> initial_mapping_size = chunksize * n_chunks - >>> schunk_mmap = blosc2.SChunk( - ... chunksize=chunksize, - ... mmap_mode="w+", - ... initial_mapping_size=initial_mapping_size, - ... urlpath=urlpath, - ... ) - >>> schunk_mmap.append_data(a) - 1 - >>> schunk_mmap.append_data(a * 2) - 2 - - Optional: explicitly close the file and free the mapping. - - >>> del schunk_mmap - - Reading the data back again via memory-mapped files: - - >>> schunk_mmap = blosc2.open(urlpath, mmap_mode="r") - >>> np.frombuffer(schunk_mmap.decompress_chunk(0), dtype=np.int64).tolist() - [0, 1, 2] - >>> np.frombuffer(schunk_mmap.decompress_chunk(1), dtype=np.int64).tolist() - [0, 2, 4] - >>> shutil.rmtree(tmpdirname) + Parameters + ---------- + chunksize: int, optional + The size, in bytes, of the chunks in the super-chunk. If not provided, + it is set automatically to a reasonable value. + + data: bytes-like object, optional + The data to be split into different chunks of size :paramref:`chunksize`. + If None, the Schunk instance will be empty initially. + + kwargs: dict, optional + Storage parameters. The default values are in :class:`blosc2.Storage`. + Supported keyword arguments: + storage: :class:`blosc2.Storage` or dict + All the storage parameters that you want to use as + a :class:`blosc2.Storage` or dict instance. + cparams: :class:`blosc2.CParams` or dict + All the compression parameters that you want to use as + a :class:`blosc2.CParams` or dict instance. + dparams: :class:`blosc2.DParams` or dict + All the decompression parameters that you want to use as + a :class:`blosc2.DParams` or dict instance. + others: Any + If `storage` is not passed, all the parameters of a :class:`blosc2.Storage` + can be passed as keyword arguments. + + Examples + -------- + >>> import blosc2 + >>> import numpy as np + >>> import os.path + >>> import shutil + >>> import tempfile + >>> cparams = blosc2.CParams() + >>> dparams = blosc2.DParams() + >>> storage = blosc2.Storage(contiguous=True) + >>> schunk = blosc2.SChunk(cparams=cparams, dparams=dparams, storage=storage) + + In the following, we will write and read a super-chunk to and from disk + via memory-mapped files. + + >>> a = np.arange(3, dtype=np.int64) + >>> chunksize = a.size * a.itemsize + >>> n_chunks = 2 + >>> tmpdirname = tempfile.mkdtemp() + >>> urlpath = os.path.join(tmpdirname, 'schunk.b2frame') + + Optional: we intend to write 2 chunks of 24 bytes each, and we expect + the compressed size to be smaller than the original size. Therefore, we + generously set the initial size of the mapping to 48 bytes + effectively avoiding remappings. + + >>> initial_mapping_size = chunksize * n_chunks + >>> schunk_mmap = blosc2.SChunk( + ... chunksize=chunksize, + ... mmap_mode="w+", + ... initial_mapping_size=initial_mapping_size, + ... urlpath=urlpath, + ... ) + >>> schunk_mmap.append_data(a) + 1 + >>> schunk_mmap.append_data(a * 2) + 2 + + Optional: explicitly close the file and free the mapping. + + >>> del schunk_mmap + + Reading the data back again via memory-mapped files: + + >>> schunk_mmap = blosc2.open(urlpath, mode="r", mmap_mode="r") + >>> np.frombuffer(schunk_mmap.decompress_chunk(0), dtype=np.int64).tolist() + [0, 1, 2] + >>> np.frombuffer(schunk_mmap.decompress_chunk(1), dtype=np.int64).tolist() + [0, 2, 4] + >>> shutil.rmtree(tmpdirname) """ # Check only allowed kwarg are passed allowed_kwargs = [ @@ -1636,7 +1637,7 @@ def process_opened_object(res): if "proxy-source" in meta: proxy_src = meta["proxy-source"] if proxy_src["local_abspath"] is not None: - src = blosc2.open(proxy_src["local_abspath"]) + src = blosc2.open(proxy_src["local_abspath"], mode="a") return blosc2.Proxy(src, _cache=res) elif proxy_src["urlpath"] is not None: src = blosc2.C2Array(proxy_src["urlpath"][0], proxy_src["urlpath"][1], proxy_src["urlpath"][2]) @@ -1714,8 +1715,14 @@ def _finalize_special_open(special, urlpath, mode): return special +_OPEN_MODE_SENTINEL = object() + + def open( - urlpath: str | pathlib.Path | blosc2.URLPath, mode: str = "a", offset: int = 0, **kwargs: dict + urlpath: str | pathlib.Path | blosc2.URLPath, + mode: str = _OPEN_MODE_SENTINEL, + offset: int = 0, + **kwargs: dict, ) -> ( blosc2.SChunk | blosc2.NDArray @@ -1741,7 +1748,10 @@ def open( mode: str, optional Persistence mode: 'r' means read only (must exist); 'a' means read/write (create if it doesn't exist); - 'w' means create (overwrite if it exists). Default is 'a'. + 'w' means create (overwrite if it exists). Defaults to 'a' for now, + but will change to 'r' in a future release. Pass ``mode='a'`` + explicitly to preserve writable behavior, or ``mode='r'`` for + read-only access. offset: int, optional An offset in the file where super-chunk or array data is located (e.g. in a file containing several such objects). @@ -1801,7 +1811,7 @@ def open( >>> # Create SChunk and append data >>> schunk = blosc2.SChunk(chunksize=chunksize, data=data.tobytes(), storage=storage) >>> # Open SChunk - >>> sc_open = blosc2.open(urlpath=urlpath) + >>> sc_open = blosc2.open(urlpath=urlpath, mode="r") >>> for i in range(nchunks): ... dest = np.empty(nelem // nchunks, dtype=data.dtype) ... schunk.decompress_chunk(i, dest) @@ -1816,12 +1826,24 @@ def open( To open the same schunk memory-mapped, we simply need to pass the `mmap_mode` parameter: - >>> sc_open_mmap = blosc2.open(urlpath=urlpath, mmap_mode="r") + >>> sc_open_mmap = blosc2.open(urlpath=urlpath, mode="r", mmap_mode="r") >>> sc_open.nchunks == sc_open_mmap.nchunks True >>> all(sc_open.decompress_chunk(i, dest1) == sc_open_mmap.decompress_chunk(i, dest1) for i in range(nchunks)) True """ + # Resolve the sentinel before URLPath check so we can raise the correct + # error without also triggering the deprecation warning for invalid calls. + if mode is _OPEN_MODE_SENTINEL: + warnings.warn( + "blosc2.open() currently defaults to mode='a', but this will change " + "to mode='r' in a future release. Pass mode='a' explicitly to keep " + "writable behavior, or mode='r' for read-only access.", + FutureWarning, + stacklevel=2, + ) + mode = "a" + if isinstance(urlpath, blosc2.URLPath): if mode != "r" or offset != 0 or kwargs != {}: raise NotImplementedError( diff --git a/tests/ctable/test_ctable_indexing.py b/tests/ctable/test_ctable_indexing.py index 0c3e28f2..eb643810 100644 --- a/tests/ctable/test_ctable_indexing.py +++ b/tests/ctable/test_ctable_indexing.py @@ -229,7 +229,7 @@ def test_catalog_survives_reopen(tmpdir): t.create_index("id") del t # close - t2 = blosc2.open(path) + t2 = blosc2.open(path, mode="r") idxs = t2.indexes assert len(idxs) == 1 assert idxs[0].col_name == "id" @@ -269,7 +269,7 @@ def test_drop_index_persistent_catalog_cleared(tmpdir): t.drop_index("id") del t - t2 = blosc2.open(path) + t2 = blosc2.open(path, mode="r") assert len(t2.indexes) == 0 @@ -321,7 +321,7 @@ def test_stale_persists_after_reopen(tmpdir): t.append([200, 300.0, 1]) # marks stale del t - t2 = blosc2.open(path) + t2 = blosc2.open(path, mode="r") assert t2.index("id").stale @@ -343,7 +343,7 @@ def test_query_after_reopen_persistent(tmpdir): t.create_index("id") del t - t2 = blosc2.open(path) + t2 = blosc2.open(path, mode="r") result = t2.where(t2["id"] > 90) ids = sorted(int(v) for v in result["id"].to_numpy()) assert ids == list(range(91, 100)) diff --git a/tests/ndarray/test_c2array_expr.py b/tests/ndarray/test_c2array_expr.py index e65d1805..c4b778f1 100644 --- a/tests/ndarray/test_c2array_expr.py +++ b/tests/ndarray/test_c2array_expr.py @@ -186,7 +186,7 @@ def test_save(cat2_context): for op in ops: del op del expr - expr = blosc2.open(urlpath) + expr = blosc2.open(urlpath, mode="r") res = expr.compute() assert res.dtype == np.float64 np.testing.assert_allclose(res[:], nres, rtol=tol, atol=tol) diff --git a/tests/ndarray/test_dsl_kernels.py b/tests/ndarray/test_dsl_kernels.py index ac37beeb..8261536f 100644 --- a/tests/ndarray/test_dsl_kernels.py +++ b/tests/ndarray/test_dsl_kernels.py @@ -872,7 +872,7 @@ def _save_reload_compute(kernel, inputs_np, inputs_b2, dtype, urlpaths, extra_kw """Save a LazyUDF backed by *kernel*, reload it, and return (reloaded_expr, result).""" lazy = blosc2.lazyudf(kernel, inputs_b2, dtype=dtype, **(extra_kwargs or {})) lazy.save(urlpath=urlpaths["lazy"]) - reloaded = blosc2.open(urlpaths["lazy"]) + reloaded = blosc2.open(urlpaths["lazy"], mode="r") return reloaded, reloaded.compute() @@ -951,7 +951,7 @@ def test_dsl_save_getitem(tmp_path): lazy = blosc2.lazyudf(kernel_save_simple, (a, b), dtype=np.float64) lazy.save(urlpath=str(tmp_path / "lazy.b2nd")) - reloaded = blosc2.open(str(tmp_path / "lazy.b2nd")) + reloaded = blosc2.open(str(tmp_path / "lazy.b2nd"), mode="r") assert isinstance(reloaded.func, DSLKernel) expected = (na + nb) ** 2 @@ -970,7 +970,7 @@ def test_dsl_save_input_names_match(tmp_path): lazy = blosc2.lazyudf(kernel_save_simple, (a, b), dtype=np.float64) lazy.save(urlpath=str(tmp_path / "lazy.b2nd")) - reloaded = blosc2.open(str(tmp_path / "lazy.b2nd")) + reloaded = blosc2.open(str(tmp_path / "lazy.b2nd"), mode="r") assert isinstance(reloaded.func, DSLKernel) assert reloaded.func.input_names == ["x", "y"] diff --git a/tests/ndarray/test_indexing.py b/tests/ndarray/test_indexing.py index 3faa1daf..77f03232 100644 --- a/tests/ndarray/test_indexing.py +++ b/tests/ndarray/test_indexing.py @@ -618,8 +618,8 @@ def test_persistent_chunk_local_sidecars_use_cparams(tmp_path, kind): meta = descriptor["bucket"] if kind == "bucket" else descriptor["partial"] aux_key = "bucket_positions_path" if kind == "bucket" else "positions_path" - values_sidecar = blosc2.open(meta["values_path"]) - aux_sidecar = blosc2.open(meta[aux_key]) + values_sidecar = blosc2.open(meta["values_path"], mode="r") + aux_sidecar = blosc2.open(meta[aux_key], mode="r") for sidecar in (values_sidecar, aux_sidecar): assert sidecar.cparams.codec == blosc2.Codec.LZ4 @@ -1102,9 +1102,9 @@ def test_compact_full_index_clears_runs_and_preserves_results(tmp_path): assert reopened.indexes[0]["full"]["runs"] == [] for values_path, positions_path in run_paths: with pytest.raises(FileNotFoundError): - blosc2.open(values_path) + blosc2.open(values_path, mode="r") with pytest.raises(FileNotFoundError): - blosc2.open(positions_path) + blosc2.open(positions_path, mode="r") expr = blosc2.lazyexpr("(a >= 1) & (a < 4)", reopened.fields).where(reopened) explained = expr.explain() @@ -1150,8 +1150,8 @@ def test_forced_ooc_full_index_merge_preserves_sorted_sidecars(monkeypatch, tmp_ descriptor = arr.create_index(kind=blosc2.IndexKind.FULL) meta = descriptor["full"] - values_sidecar = blosc2.open(meta["values_path"]) - positions_sidecar = blosc2.open(meta["positions_path"]) + values_sidecar = blosc2.open(meta["values_path"], mode="r") + positions_sidecar = blosc2.open(meta["positions_path"], mode="r") np.testing.assert_array_equal(values_sidecar[:], np.sort(data, kind="stable")) np.testing.assert_array_equal(values_sidecar[:], data[positions_sidecar[:]]) diff --git a/tests/ndarray/test_lazyexpr.py b/tests/ndarray/test_lazyexpr.py index 20dba823..6525a07d 100644 --- a/tests/ndarray/test_lazyexpr.py +++ b/tests/ndarray/test_lazyexpr.py @@ -466,7 +466,7 @@ def test_arctan2_pow(urlpath, shape_fixture, dtype_fixture, function, value1, va expr = blosc2.LazyExpr(new_op=(a1, function, a2)) if urlpath is not None: expr.save(urlpath=urlpath_save) - expr = blosc2.open(urlpath_save) + expr = blosc2.open(urlpath_save, mode="r") res_lazyexpr = expr.compute() # Evaluate using NumExpr if function == "**": @@ -480,7 +480,7 @@ def test_arctan2_pow(urlpath, shape_fixture, dtype_fixture, function, value1, va expr = blosc2.LazyExpr(new_op=(a1, function, value2)) if urlpath is not None: expr.save(urlpath=urlpath_save) - expr = blosc2.open(urlpath_save) + expr = blosc2.open(urlpath_save, mode="r") res_lazyexpr = expr.compute() # Evaluate using NumExpr if function == "**": @@ -496,7 +496,7 @@ def test_arctan2_pow(urlpath, shape_fixture, dtype_fixture, function, value1, va expr = blosc2.LazyExpr(new_op=(value1, function, a2)) if urlpath is not None: expr.save(urlpath=urlpath_save) - expr = blosc2.open(urlpath_save) + expr = blosc2.open(urlpath_save, mode="r") res_lazyexpr = expr.compute() # Evaluate using NumExpr if function == "**": @@ -716,7 +716,7 @@ def test_save(): ) np.testing.assert_allclose(res[:], nres, rtol=tol, atol=tol) - expr = blosc2.open(urlpath_save) + expr = blosc2.open(urlpath_save, mode="r") # After opening, check that a lazy expression does have an array # and schunk attributes. This is to allow the .info() method to work. assert hasattr(expr, "array") is True @@ -735,7 +735,7 @@ def test_save(): var_dict = {"a1": ops[0], "a2": ops[1], "a3": ops[2], "a4": ops[3], "x": x} lazy_expr = eval(expr, var_dict) lazy_expr.save(urlpath=urlpath_save2) - expr = blosc2.open(urlpath_save2) + expr = blosc2.open(urlpath_save2, mode="r") assert expr.array.dtype == np.float64 res = expr.compute() nres = ne_evaluate("na1 / na2 + na2 - na3 * na4**3") @@ -759,7 +759,7 @@ def test_save_unsafe(): expr.save(urlpath=urlpath) disk_arrays.append(urlpath) - expr = blosc2.open(urlpath) + expr = blosc2.open(urlpath, mode="r") # Replace expression by a (potentially) unsafe expression expr.expression = "import os; os.system('touch /tmp/unsafe')" with pytest.raises(ValueError) as excinfo: @@ -807,7 +807,7 @@ def test_save_functions(function, dtype_fixture, shape_fixture): expr = blosc2.LazyExpr(new_op=(a1, function, None)) expr.save(urlpath=urlpath_save) del expr - expr = blosc2.open(urlpath_save) + expr = blosc2.open(urlpath_save, mode="r") res_lazyexpr = expr.compute() # Evaluate using NumExpr @@ -823,7 +823,7 @@ def test_save_functions(function, dtype_fixture, shape_fixture): res_lazyexpr = expr.compute() np.testing.assert_allclose(res_lazyexpr[:], res_numexpr, rtol=rtol) - expr = blosc2.open(urlpath_save) + expr = blosc2.open(urlpath_save, mode="r") res_lazyexpr = expr.compute() np.testing.assert_allclose(res_lazyexpr[:], res_numexpr, rtol=rtol) @@ -847,7 +847,7 @@ def test_save_contains(values): # Construct the lazy expression expr_lazy = blosc2.LazyExpr(new_op=(a1_blosc, "contains", value2)) expr_lazy.save(urlpath=urlpath_save) - expr_lazy = blosc2.open(urlpath_save) + expr_lazy = blosc2.open(urlpath_save, mode="r") # Evaluate using NumExpr expr_numexpr = f"{'contains'}(a1, value2)" res_numexpr = ne_evaluate(expr_numexpr) @@ -857,7 +857,7 @@ def test_save_contains(values): # Construct the lazy expression expr_lazy = blosc2.LazyExpr(new_op=(a1_blosc, "contains", a2_blosc)) expr_lazy.save(urlpath=urlpath_save) - expr_lazy = blosc2.open(urlpath_save) + expr_lazy = blosc2.open(urlpath_save, mode="r") # Evaluate using NumExpr res_numexpr = ne_evaluate("contains(a2, a1)") else: # ("str", "NDArray") @@ -867,7 +867,7 @@ def test_save_contains(values): # Construct the lazy expression expr_lazy = blosc2.LazyExpr(new_op=(value1, "contains", a2_blosc)) expr_lazy.save(urlpath=urlpath_save) - expr_lazy = blosc2.open(urlpath_save) + expr_lazy = blosc2.open(urlpath_save, mode="r") # Evaluate using NumExpr res_numexpr = ne_evaluate("contains(value1, a2)") res_lazyexpr = expr_lazy.compute() @@ -901,7 +901,7 @@ def test_save_many_functions(dtype_fixture, shape_fixture): res_lazyexpr = expr.compute() np.testing.assert_allclose(res_lazyexpr[:], res_numexpr, rtol=rtol, atol=atol) - expr = blosc2.open(urlpath_save) + expr = blosc2.open(urlpath_save, mode="r") res_lazyexpr = expr.compute() np.testing.assert_allclose(res_lazyexpr[:], res_numexpr, rtol=rtol, atol=atol) @@ -946,7 +946,7 @@ def test_save_constructor(disk, shape, dtype, constructor): a = b2func(lshape, dtype=dtype, shape=shape, urlpath=urlpath, mode="w") expr = f"a + {constructor}({lshape}, dtype={dtype}, shape={shape}) + 1" if disk: - a = blosc2.open(urlpath) + a = blosc2.open(urlpath, mode="r") npfunc = getattr(np, constructor) if constructor == "linspace": na = npfunc(0, 10, lshape, dtype=dtype).reshape(shape) @@ -964,7 +964,7 @@ def test_save_constructor(disk, shape, dtype, constructor): assert lexpr.shape == a.shape if disk: lexpr.save("out.b2nd") - lexpr = blosc2.open("out.b2nd") + lexpr = blosc2.open("out.b2nd", mode="r") res = lexpr.compute() nres = na + na + 1 assert np.allclose(res[()], nres) @@ -986,7 +986,7 @@ def test_save_2_constructors(shape, disk): lexpr = blosc2.lazyexpr(expr) if disk: lexpr.save("out.b2nd") - lexpr = blosc2.open("out.b2nd") + lexpr = blosc2.open("out.b2nd", mode="r") res = lexpr.compute() na = np.arange(lshape).reshape(shape) nb = np.ones(shape) @@ -1013,7 +1013,7 @@ def test_save_constructor_reshape(shape, disk): lexpr = blosc2.lazyexpr(expr) if disk: lexpr.save("out.b2nd") - lexpr = blosc2.open("out.b2nd") + lexpr = blosc2.open("out.b2nd", mode="r") res = lexpr.compute() na = np.arange(lshape).reshape(shape) nb = np.ones(shape) @@ -1037,7 +1037,7 @@ def test_save_2equal_constructors(shape, disk): lexpr = blosc2.lazyexpr(expr) if disk: lexpr.save("out.b2nd") - lexpr = blosc2.open("out.b2nd") + lexpr = blosc2.open("out.b2nd", mode="r") res = lexpr.compute() na = np.ones(shape, dtype=np.int8) nb = np.ones(shape) @@ -1360,9 +1360,9 @@ def test_fill_disk_operands(chunks, blocks, disk, fill_value): b = blosc2.zeros((N, N), urlpath=bpath, mode="w", chunks=chunks, blocks=blocks) c = blosc2.zeros((N, N), urlpath=cpath, mode="w", chunks=chunks, blocks=blocks) if disk: - a = blosc2.open("a.b2nd") - b = blosc2.open("b.b2nd") - c = blosc2.open("c.b2nd") + a = blosc2.open("a.b2nd", mode="r") + b = blosc2.open("b.b2nd", mode="r") + c = blosc2.open("c.b2nd", mode="r") expr = ((a**3 + blosc2.sin(c * 2)) < b) & ~(c > 0) @@ -1709,7 +1709,7 @@ def test_missing_operator(): blosc2.remove_urlpath("b.b2nd") # Re-open the lazy expression with pytest.raises(blosc2.exceptions.MissingOperands) as excinfo: - blosc2.open("expr.b2nd") + blosc2.open("expr.b2nd", mode="r") # Check that some operand is missing assert "a" not in excinfo.value.missing_ops @@ -1746,7 +1746,7 @@ def test_save_dictstore_operands(tmp_path): "b": {"kind": "dictstore_key", "version": 1, "urlpath": str(store_path), "key": "/b"}, } - restored = blosc2.open(expr_path) + restored = blosc2.open(expr_path, mode="r") assert isinstance(restored, blosc2.LazyExpr) np.testing.assert_array_equal(restored[:], expected) @@ -1762,7 +1762,7 @@ def test_save_proxy_operands_reopen_default_mode(tmp_path): expr = proxy + proxy expr.save(str(expr_path)) - restored = blosc2.open(str(expr_path)) + restored = blosc2.open(str(expr_path), mode="r") assert isinstance(restored, blosc2.LazyExpr) np.testing.assert_array_equal(restored[:], np.arange(10, dtype=np.int64) * 2) @@ -1780,13 +1780,13 @@ def test_lazyexpr_vlmeta_in_memory_and_persisted(tmp_path): expr_path = tmp_path / "expr_vlmeta.b2nd" expr.save(str(expr_path)) - restored = blosc2.open(str(expr_path)) + restored = blosc2.open(str(expr_path), mode="r") assert restored.vlmeta["name"] == "sum" assert restored.vlmeta["config"] == {"scale": 1} restored.vlmeta["note"] = "persisted" - reopened = blosc2.open(str(expr_path)) + reopened = blosc2.open(str(expr_path), mode="r") assert reopened.vlmeta["note"] == "persisted" np.testing.assert_array_equal(reopened[:], np.arange(5, dtype=np.int64) * 2) @@ -1862,19 +1862,19 @@ def test_chain_persistentexpressions(): le1_ = blosc2.lazyexpr("a ** 3 + sin(a ** 2)", {"a": a}) le1_.save("expr1.b2nd", mode="w") - myle1 = blosc2.open("expr1.b2nd") + myle1 = blosc2.open("expr1.b2nd", mode="r") le2_ = blosc2.lazyexpr("(le1 < c)", {"le1": myle1, "c": c}) le2_.save("expr2.b2nd", mode="w") - myle2 = blosc2.open("expr2.b2nd") + myle2 = blosc2.open("expr2.b2nd", mode="r") le3_ = blosc2.lazyexpr("(b < 0)", {"b": b}) le3_.save("expr3.b2nd", mode="w") - myle3 = blosc2.open("expr3.b2nd") + myle3 = blosc2.open("expr3.b2nd", mode="r") le4_ = blosc2.lazyexpr("(le2 & le3)", {"le2": myle2, "le3": myle3}) le4_.save("expr4.b2nd", mode="w") - myle4 = blosc2.open("expr4.b2nd") + myle4 = blosc2.open("expr4.b2nd", mode="r") assert (myle4[:] == le4[:]).all() # Remove files diff --git a/tests/ndarray/test_lazyudf.py b/tests/ndarray/test_lazyudf.py index 3aa76cb6..ab0cd814 100644 --- a/tests/ndarray/test_lazyudf.py +++ b/tests/ndarray/test_lazyudf.py @@ -483,7 +483,7 @@ def test_save_ludf(): expr.save(urlpath=urlpath) del expr - expr = blosc2.open(urlpath) + expr = blosc2.open(urlpath, mode="r") assert isinstance(expr, blosc2.LazyUDF) res_lazyexpr = expr.compute() np.testing.assert_array_equal(res_lazyexpr[:], npc) @@ -493,7 +493,7 @@ def test_save_ludf(): expr = blosc2.lazyudf(udf1p_numba, (array,), np.float64) expr.save(urlpath=urlpath) del expr - expr = blosc2.open(urlpath) + expr = blosc2.open(urlpath, mode="r") assert isinstance(expr, blosc2.LazyUDF) res_lazyexpr = expr.compute() np.testing.assert_array_equal(res_lazyexpr[:], npc) @@ -511,7 +511,7 @@ def test_lazyudf_vlmeta_roundtrip(tmp_path): expr.vlmeta["attrs"] = {"version": 1} expr.save(urlpath=str(expr_path)) - restored = blosc2.open(str(expr_path)) + restored = blosc2.open(str(expr_path), mode="r") assert isinstance(restored, blosc2.LazyUDF) assert restored.vlmeta["name"] == "increment" diff --git a/tests/ndarray/test_ndarray.py b/tests/ndarray/test_ndarray.py index 5a4f376d..c21c774a 100644 --- a/tests/ndarray/test_ndarray.py +++ b/tests/ndarray/test_ndarray.py @@ -77,7 +77,7 @@ def test_shape_with_zeros(shape, urlpath): data = np.zeros(shape, dtype="int32") ndarray = blosc2.asarray(data, urlpath=urlpath, mode="w") if urlpath is not None: - ndarray = blosc2.open(urlpath) + ndarray = blosc2.open(urlpath, mode="r") assert isinstance(ndarray, blosc2.NDArray) assert ndarray.shape == shape assert ndarray.size == 0 @@ -502,11 +502,11 @@ def test_argsort_scalar(): def test_save(): a = blosc2.arange(0, 10, 1, dtype="i4", shape=(10,)) blosc2.save(a, "test.b2nd") - c = blosc2.open("test.b2nd") + c = blosc2.open("test.b2nd", mode="r") assert np.array_equal(a[:], c[:]) blosc2.remove_urlpath("test.b2nd") with pytest.raises(FileNotFoundError): - blosc2.open("test.b2nd") + blosc2.open("test.b2nd", mode="r") def test_oindex(): diff --git a/tests/ndarray/test_persistency.py b/tests/ndarray/test_persistency.py index ee79c680..939cd5a8 100644 --- a/tests/ndarray/test_persistency.py +++ b/tests/ndarray/test_persistency.py @@ -32,7 +32,7 @@ def test_persistency(shape, chunks, blocks, urlpath, contiguous, dtype): size = int(np.prod(shape)) nparray = np.arange(size, dtype=dtype).reshape(shape) _ = blosc2.asarray(nparray, chunks=chunks, blocks=blocks, urlpath=urlpath, contiguous=contiguous) - b = blosc2.open(urlpath) + b = blosc2.open(urlpath, mode="r") bc = b[:] diff --git a/tests/ndarray/test_proxy.py b/tests/ndarray/test_proxy.py index 15648e99..fc4577d9 100644 --- a/tests/ndarray/test_proxy.py +++ b/tests/ndarray/test_proxy.py @@ -90,10 +90,10 @@ def test_open(urlpath, shape, chunks, blocks, slices, dtype): del b if urlpath is None: with pytest.raises(RuntimeError): - _ = blosc2.open(proxy_urlpath) + _ = blosc2.open(proxy_urlpath, mode="a") else: - b = blosc2.open(proxy_urlpath) - a = blosc2.open(urlpath) + b = blosc2.open(proxy_urlpath, mode="a") + a = blosc2.open(urlpath, mode="r") if not struct_dtype: np.testing.assert_almost_equal(b[...], a[...]) else: diff --git a/tests/ndarray/test_proxy_c2array.py b/tests/ndarray/test_proxy_c2array.py index fea92163..1f7d427f 100644 --- a/tests/ndarray/test_proxy_c2array.py +++ b/tests/ndarray/test_proxy_c2array.py @@ -86,7 +86,7 @@ def test_open(cat2_context): del a del b - b = blosc2.open(urlpath) + b = blosc2.open(urlpath, mode="r") a = get_array(shape, chunks_blocks) np.testing.assert_allclose(b[...], a[...]) diff --git a/tests/ndarray/test_proxy_expr.py b/tests/ndarray/test_proxy_expr.py index e4aea5a5..17b9f6b0 100644 --- a/tests/ndarray/test_proxy_expr.py +++ b/tests/ndarray/test_proxy_expr.py @@ -76,7 +76,7 @@ def test_expr_proxy_operands(chunks_blocks, cat2_context): urlpath = "expr_proxies.b2nd" expr.save(urlpath=urlpath, mode="w") del expr - expr_opened = blosc2.open("expr_proxies.b2nd") + expr_opened = blosc2.open("expr_proxies.b2nd", mode="r") assert isinstance(expr_opened, blosc2.LazyExpr) # All diff --git a/tests/ndarray/test_reductions.py b/tests/ndarray/test_reductions.py index 91bd17e6..c5ece156 100644 --- a/tests/ndarray/test_reductions.py +++ b/tests/ndarray/test_reductions.py @@ -427,7 +427,7 @@ def test_fast_path(chunks, blocks, disk, fill_value, reduce_op, axis): else: a = blosc2.zeros(shape, dtype=np.float64, chunks=chunks, blocks=blocks, urlpath=urlpath, mode="w") if disk: - a = blosc2.open(urlpath) + a = blosc2.open(urlpath, mode="r") na = a[:] if reduce_op in {"cumulative_sum", "cumulative_prod"}: axis = 0 if axis is None else axis @@ -473,7 +473,7 @@ def test_miniexpr_slice(chunks, blocks, disk, fill_value, reduce_op): else: a = blosc2.zeros(shape, dtype=np.float64, chunks=chunks, blocks=blocks, urlpath=urlpath, mode="w") if disk: - a = blosc2.open(urlpath) + a = blosc2.open(urlpath, mode="r") na = a[:] # Test slice # TODO: Make this work with miniexpr (currently just skips to normal reduction eval) @@ -520,8 +520,8 @@ def test_save_version1(disk, fill_value, reduce_op, axis): a = blosc2.zeros(shape, dtype=np.float64, urlpath=urlpath, mode="w") b = blosc2.zeros(shape, dtype=np.float64, urlpath="b.b2nd", mode="w") - 0.1 if disk: - a = blosc2.open(urlpath) - b = blosc2.open("b.b2nd") + a = blosc2.open(urlpath, mode="r") + b = blosc2.open("b.b2nd", mode="r") na = a[:] nb = b[:] @@ -531,7 +531,7 @@ def test_save_version1(disk, fill_value, reduce_op, axis): assert lexpr.shape == a.shape if disk: lexpr.save("out.b2nd") - lexpr = blosc2.open("out.b2nd") + lexpr = blosc2.open("out.b2nd", mode="r") res = lexpr.compute() if reduce_op in {"cumulative_sum", "cumulative_prod"}: oploc = "npcumsum" if reduce_op == "cumulative_sum" else "npcumprod" @@ -581,8 +581,8 @@ def test_save_version2(disk, fill_value, reduce_op, axis): a = blosc2.zeros(shape, dtype=np.float64, urlpath=urlpath, mode="w") b = blosc2.zeros(shape, dtype=np.float64, urlpath="b.b2nd", mode="w") - 0.1 if disk: - a = blosc2.open(urlpath) - b = blosc2.open("b.b2nd") + a = blosc2.open(urlpath, mode="r") + b = blosc2.open("b.b2nd", mode="r") na = a[:] nb = b[:] @@ -591,7 +591,7 @@ def test_save_version2(disk, fill_value, reduce_op, axis): lexpr = blosc2.lazyexpr(expr, operands={"a": a, "b": b}) if disk: lexpr.save("out.b2nd") - lexpr = blosc2.open("out.b2nd") + lexpr = blosc2.open("out.b2nd", mode="r") res = lexpr.compute() if reduce_op in {"cumulative_sum", "cumulative_prod"}: oploc = "npcumsum" if reduce_op == "cumulative_sum" else "npcumprod" @@ -641,8 +641,8 @@ def test_save_version3(disk, fill_value, reduce_op, axis): a = blosc2.zeros(shape, dtype=np.float64, urlpath=urlpath, mode="w") b = blosc2.zeros(shape, dtype=np.float64, urlpath="b.b2nd", mode="w") - 0.1 if disk: - a = blosc2.open(urlpath) - b = blosc2.open("b.b2nd") + a = blosc2.open(urlpath, mode="r") + b = blosc2.open("b.b2nd", mode="r") na = a[:] nb = b[:] @@ -651,7 +651,7 @@ def test_save_version3(disk, fill_value, reduce_op, axis): lexpr = blosc2.lazyexpr(expr, operands={"a": a, "b": b}) if disk: lexpr.save("out.b2nd") - lexpr = blosc2.open("out.b2nd") + lexpr = blosc2.open("out.b2nd", mode="r") res = lexpr.compute() if reduce_op in {"cumulative_sum", "cumulative_prod"}: oploc = "npcumsum" if reduce_op == "cumulative_sum" else "npcumprod" @@ -701,8 +701,8 @@ def test_save_version4(disk, fill_value, reduce_op, axis): a = blosc2.zeros(shape, dtype=np.float64, urlpath=urlpath, mode="w") b = blosc2.zeros(shape, dtype=np.float64, urlpath="b.b2nd", mode="w") - 0.1 if disk: - a = blosc2.open(urlpath) - b = blosc2.open("b.b2nd") + a = blosc2.open(urlpath, mode="r") + b = blosc2.open("b.b2nd", mode="r") na = a[:] # Just a single reduction @@ -710,7 +710,7 @@ def test_save_version4(disk, fill_value, reduce_op, axis): lexpr = blosc2.lazyexpr(expr, operands={"a": a}) if disk: lexpr.save("out.b2nd") - lexpr = blosc2.open("out.b2nd") + lexpr = blosc2.open("out.b2nd", mode="r") res = lexpr.compute() if reduce_op in {"cumulative_sum", "cumulative_prod"}: oploc = "npcumsum" if reduce_op == "cumulative_sum" else "npcumprod" @@ -738,7 +738,7 @@ def test_save_constructor_reduce(shape, disk, compute): lexpr = blosc2.lazyexpr(expr) if disk: lexpr.save("out.b2nd") - lexpr = blosc2.open("out.b2nd") + lexpr = blosc2.open("out.b2nd", mode="r") if compute: res = lexpr.compute() res = res[()] # for later comparison with nres @@ -767,7 +767,7 @@ def test_save_constructor_reduce2(shape, disk, compute): lexpr = blosc2.lazyexpr(expr) if disk: lexpr.save("out.b2nd") - lexpr = blosc2.open("out.b2nd") + lexpr = blosc2.open("out.b2nd", mode="r") if compute: res = lexpr.compute() res = res[()] # for later comparison with nres diff --git a/tests/ndarray/test_stringarrays.py b/tests/ndarray/test_stringarrays.py index a512622a..e6f37114 100644 --- a/tests/ndarray/test_stringarrays.py +++ b/tests/ndarray/test_stringarrays.py @@ -124,7 +124,7 @@ def test_unicode_roundtrip_on_disk(tmp_path, shape): ) # Re-open from disk - out = blosc2.open(path) + out = blosc2.open(path, mode="r") assert out.dtype == arr.dtype assert np.array_equal(out, arr) @@ -153,7 +153,7 @@ def test_unicode_on_disk_partial_io(tmp_path): b2[6:10] = replacement arr[6:10] = replacement - reopened = blosc2.open(path) + reopened = blosc2.open(path, mode="r") assert np.array_equal(reopened, arr) @@ -167,7 +167,7 @@ def test_unicode_on_disk_persistence(tmp_path): b2 = blosc2.open(path, mode="a") b2[:] = arr2 - reopened = blosc2.open(path) + reopened = blosc2.open(path, mode="r") assert np.array_equal(reopened, arr2) diff --git a/tests/ndarray/test_struct_dtype.py b/tests/ndarray/test_struct_dtype.py index 9f2c3055..dc2b736e 100644 --- a/tests/ndarray/test_struct_dtype.py +++ b/tests/ndarray/test_struct_dtype.py @@ -36,7 +36,7 @@ def test_scalar(shape, dtype, urlpath): assert a.dtype == b.dtype if urlpath is not None: - c = blosc2.open(urlpath) + c = blosc2.open(urlpath, mode="r") assert np.array_equal(c[:], b) assert c.shape == a.shape assert c.dtype == a.dtype diff --git a/tests/test_mmap.py b/tests/test_mmap.py index 53dfc27c..c0870d1f 100644 --- a/tests/test_mmap.py +++ b/tests/test_mmap.py @@ -53,7 +53,9 @@ def test_initial_mapping_size(tmp_path, monkeypatch, capfd, initial_mapping_size # Reading via open for mmap_mode in ["r", "r+", "c"]: open_mapping_size = None if mmap_mode == "r" else initial_mapping_size - schunk_open = blosc2.open(urlpath, mmap_mode=mmap_mode, initial_mapping_size=open_mapping_size) + schunk_open = blosc2.open( + urlpath, mode="r", mmap_mode=mmap_mode, initial_mapping_size=open_mapping_size + ) for i in range(nchunks): buffer = i * np.arange(chunk_nitems, dtype=dtype) bytes_obj = buffer.tobytes() @@ -95,13 +97,13 @@ def test_initial_mapping_size(tmp_path, monkeypatch, capfd, initial_mapping_size # Error handling with pytest.raises(ValueError, match=r"w\+ mmap_mode cannot be used to open an existing file"): - blosc2.open(urlpath, mmap_mode="w+") + blosc2.open(urlpath, mode="a", mmap_mode="w+") with pytest.raises(ValueError, match="initial_mapping_size can only be used with writing modes"): - blosc2.open(urlpath, mmap_mode="r", initial_mapping_size=100) + blosc2.open(urlpath, mode="a", mmap_mode="r", initial_mapping_size=100) with pytest.raises(ValueError, match="initial_mapping_size can only be used with mmap_mode"): - blosc2.open(urlpath, mmap_mode=None, initial_mapping_size=100) + blosc2.open(urlpath, mode="a", mmap_mode=None, initial_mapping_size=100) with pytest.raises(ValueError, match="initial_mapping_size can only be used with writing modes"): blosc2.SChunk(mmap_mode="r", initial_mapping_size=100, **storage) diff --git a/tests/test_open.py b/tests/test_open.py index c7f78c88..a8610fac 100644 --- a/tests/test_open.py +++ b/tests/test_open.py @@ -110,7 +110,7 @@ def test_open(contiguous, urlpath, cparams, dparams, nchunks, chunk_nitems, dtyp def test_open_fake(): with pytest.raises(FileNotFoundError): - _ = blosc2.open("none.b2nd") + _ = blosc2.open("none.b2nd", mode="r") @pytest.mark.parametrize("offset", [0, 42]) @@ -148,3 +148,34 @@ def test_open_offset(offset, urlpath, mode, mmap_mode): blosc2.open(urlpath, mode, mmap_mode=mmap_mode) blosc2.remove_urlpath(urlpath) + + +def test_open_no_mode_warns(tmp_path): + """FutureWarning is emitted when mode is omitted.""" + urlpath = str(tmp_path / "test.b2nd") + blosc2.asarray(np.arange(10), urlpath=urlpath, mode="w") + with pytest.warns(FutureWarning, match="mode='a'"): + _ = blosc2.open(urlpath) + + +def test_open_explicit_mode_no_warn(tmp_path): + """No FutureWarning is emitted when mode is explicitly given.""" + import warnings + + urlpath = str(tmp_path / "test.b2nd") + blosc2.asarray(np.arange(10), urlpath=urlpath, mode="w") + with warnings.catch_warnings(): + warnings.simplefilter("error", FutureWarning) + _ = blosc2.open(urlpath, mode="r") + _ = blosc2.open(urlpath, mode="a") + + +def test_open_mmap_without_mode_warns(tmp_path): + """FutureWarning is emitted when mode is omitted, even with mmap_mode.""" + if blosc2.IS_WASM: + pytest.skip("mmap_mode is not supported reliably on wasm32") + + urlpath = str(tmp_path / "test.b2nd") + blosc2.asarray(np.arange(10), urlpath=urlpath, mode="w") + with pytest.warns(FutureWarning, match="mode='a'"): + _ = blosc2.open(urlpath, mmap_mode="r") diff --git a/tests/test_open_c2array.py b/tests/test_open_c2array.py index 14bb6710..8d4458ac 100644 --- a/tests/test_open_c2array.py +++ b/tests/test_open_c2array.py @@ -43,7 +43,7 @@ def test_open_c2array(cat2_context): assert a1.cratio == a_open.cratio with pytest.raises(NotImplementedError): - _ = blosc2.open(urlpath) + _ = blosc2.open(urlpath, mode="a") with pytest.raises(NotImplementedError): _ = blosc2.open(urlpath, mode="r", offset=0, cparams={}) diff --git a/tests/test_proxy_schunk.py b/tests/test_proxy_schunk.py index 4245b1aa..7155834c 100644 --- a/tests/test_proxy_schunk.py +++ b/tests/test_proxy_schunk.py @@ -68,9 +68,9 @@ def test_open(urlpath, chunksize, nchunks): del schunk if urlpath is None: with pytest.raises(RuntimeError): - _ = blosc2.open(proxy_urlpath) + _ = blosc2.open(proxy_urlpath, mode="a") else: - proxy = blosc2.open(proxy_urlpath) + proxy = blosc2.open(proxy_urlpath, mode="a") assert proxy[0 : len(data) * 4] == bytes_obj blosc2.remove_urlpath(urlpath) From f8021aed426a12e409dbd9a863b3b7c26baa6160 Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Wed, 15 Apr 2026 19:18:57 +0200 Subject: [PATCH 37/49] Fancier CTable.info printed representation --- src/blosc2/ctable.py | 19 ++++++------ src/blosc2/ctable_storage.py | 9 ++++++ tests/ctable/test_column.py | 59 ++++++++++++++++++++++++++++++++++++ 3 files changed, 77 insertions(+), 10 deletions(-) diff --git a/src/blosc2/ctable.py b/src/blosc2/ctable.py index 1f8f3397..13fb5080 100644 --- a/src/blosc2/ctable.py +++ b/src/blosc2/ctable.py @@ -3099,11 +3099,8 @@ def info_items(self) -> list[tuple[str, object]]: if stats is None: suffix = "size=n/a (sidecars not directly addressable)" else: - nbytes, cbytes, cratio = stats - suffix = ( - f"nbytes={format_nbytes_info(nbytes)}, " - f"cbytes={format_nbytes_info(cbytes)}, cratio={cratio:.2f}x" - ) + _, cbytes, _ = stats + suffix = f"cbytes={format_nbytes_info(cbytes)}" index_summary[idx.col_name] = f"[{idx.kind}{stale}{label}] {suffix}" items = [ @@ -3111,28 +3108,30 @@ def info_items(self) -> list[tuple[str, object]]: ("storage", storage_type), ("rows", self.nrows), ("columns", self.ncols), - ("capacity", len(self._valid_rows)), ("view", self.base is not None), - ("read_only", self._read_only), ("nbytes", format_nbytes_info(self.nbytes)), ("cbytes", format_nbytes_info(self.cbytes)), - ("cratio", f"{self.cratio:.2f}"), + ("cratio", f"{self.cratio:.1f}x"), ("schema", schema_summary), ( "valid_rows_mask", - f"nbytes={format_nbytes_info(self._valid_rows.nbytes)}, cbytes={format_nbytes_info(self._valid_rows.cbytes)}", + f"cbytes={format_nbytes_info(self._valid_rows.cbytes)}", ), ("indexes", index_summary if index_summary else "none"), ] if urlpath is not None: items.insert(2, ("urlpath", urlpath)) + open_mode = self._storage.open_mode() + if open_mode is not None: + items.insert(3, ("open_mode", open_mode)) return items @staticmethod def _dtype_info_label(dtype: np.dtype) -> str: """Return a compact dtype label for info reports.""" if dtype.kind == "U": - return f"U{dtype.itemsize // 4}" + nchars = dtype.itemsize // 4 + return f"U{nchars} (Unicode, max {nchars} chars)" if dtype.kind == "S": return f"S{dtype.itemsize}" return str(dtype) diff --git a/src/blosc2/ctable_storage.py b/src/blosc2/ctable_storage.py index 13720118..b071ad02 100644 --- a/src/blosc2/ctable_storage.py +++ b/src/blosc2/ctable_storage.py @@ -80,6 +80,9 @@ def table_exists(self) -> bool: def is_read_only(self) -> bool: raise NotImplementedError + def open_mode(self) -> str | None: + raise NotImplementedError + def delete_column(self, name: str) -> None: raise NotImplementedError @@ -162,6 +165,9 @@ def table_exists(self): def is_read_only(self): return False + def open_mode(self) -> str | None: + return None + def delete_column(self, name): raise RuntimeError("In-memory tables have no on-disk representation to mutate.") @@ -269,6 +275,9 @@ def table_exists(self) -> bool: def is_read_only(self) -> bool: return self._mode == "r" + def open_mode(self) -> str | None: + return self._mode + def create_column(self, name, *, dtype, shape, chunks, blocks, cparams, dparams): kwargs: dict[str, Any] = { "chunks": chunks, diff --git a/tests/ctable/test_column.py b/tests/ctable/test_column.py index 9fb26467..838d4c75 100644 --- a/tests/ctable/test_column.py +++ b/tests/ctable/test_column.py @@ -685,5 +685,64 @@ def test_column_repr_shows_preview_values(): assert "..." in r +def test_info_omits_capacity_and_read_only_for_in_memory_table(): + t = CTable(Row, new_data=DATA20) + info = repr(t.info) + assert "capacity" not in info + assert "read_only" not in info + assert "open_mode" not in info + + +def test_info_shows_open_mode_for_persistent_table(tmp_path): + path = str(tmp_path / "table.b2d") + t = CTable(Row, new_data=DATA20, urlpath=path, mode="w") + t.close() + + opened = CTable.open(path) + info = repr(opened.info) + assert "capacity" not in info + assert "read_only" not in info + assert "open_mode : r" in info + opened.close() + + +def test_info_schema_expands_unicode_dtype_labels(): + t = CTable(StrRow, new_data=[("alpha",), ("beta",)]) + info = repr(t.info) + assert "U16 (Unicode, max 16 chars)" in info + + +def test_info_valid_rows_mask_only_reports_cbytes(): + t = CTable(Row, new_data=DATA20) + info = repr(t.info) + assert "valid_rows_mask : cbytes=" in info + assert "valid_rows_mask : nbytes=" not in info + + +def test_info_indexes_only_report_cbytes(tmp_path): + @dataclass + class IndexedRow: + id: int = blosc2.field(blosc2.int32()) + active: bool = blosc2.field(blosc2.bool(), default=True) + + data = [(i, i % 2 == 0) for i in range(32)] + path = str(tmp_path / "indexed.b2d") + t = CTable(IndexedRow, new_data=data, urlpath=path, mode="w") + t.create_index("id", kind=blosc2.IndexKind.FULL) + + info = repr(t.info) + index_block = info.split("indexes :", 1)[1] + assert "cbytes=" in index_block + assert "nbytes=" not in index_block + assert "cratio=" not in index_block + + +def test_info_cratio_uses_one_decimal_with_suffix(): + t = CTable(Row, new_data=DATA20) + info = repr(t.info) + assert "cratio :" in info + assert "x" in next(line for line in info.splitlines() if line.startswith("cratio")) + + if __name__ == "__main__": pytest.main(["-v", __file__]) From 17f12c61305840ba24fbc098b956ce6100daa817 Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Wed, 15 Apr 2026 19:29:17 +0200 Subject: [PATCH 38/49] Add a TODO for removing FutureWarning path once blosc2.open() defaults to mode='r'. --- plans/changing-default-open-mode.md | 341 ++++++++++++++++++++++++++++ src/blosc2/schunk.py | 1 + 2 files changed, 342 insertions(+) create mode 100644 plans/changing-default-open-mode.md diff --git a/plans/changing-default-open-mode.md b/plans/changing-default-open-mode.md new file mode 100644 index 00000000..9b97cc84 --- /dev/null +++ b/plans/changing-default-open-mode.md @@ -0,0 +1,341 @@ +# Plan For Changing `blosc2.open()` Default Mode To Read-Only + +## Goal + +Change the default mode for `blosc2.open(...)` from `"a"` to `"r"` so that +opening an existing object is non-mutating and unsurprising by default. + +The change should: + +- reduce accidental write access +- avoid implicit unpack / rewrite work for store-backed containers +- align with user expectations for a generic `open(...)` API +- preserve a smooth migration path for existing code that relied on writable + opens without an explicit `mode=` + +This plan is for later consideration and rollout design. It does not assume +that the change should land immediately. + +## Motivation + +Today, `blosc2.open(...)` defaults to `"a"` in +[src/blosc2/schunk.py](/Users/faltet/blosc/python-blosc2/src/blosc2/schunk.py). + +That means: + +- opening a `.b2z` store without `mode=` may create a writable working copy +- append-mode store opens may unpack zip-backed stores into a temporary working + directory immediately +- code that only intends to inspect metadata or query data can still enter a + mutation-capable path by accident + +This is especially surprising for: + +- `TreeStore` +- `DictStore` +- `CTable` +- other container-like objects opened through the generic dispatcher + +By contrast, users generally expect a bare `open(path)` call to be safe for +inspection unless they explicitly request write access. + +## Current Situation + +### Default values today + +The following default to `"a"` today: + +- `blosc2.open(...)` +- `DictStore(...)` +- `TreeStore(...)` +- `CTable(...)` constructor when opening/creating through `urlpath` + +At the same time: + +- `CTable.open(...)` already defaults to `"r"` + +This creates an inconsistency where: + +- `blosc2.open("table.b2z")` is writable by default +- `blosc2.CTable.open("table.b2z")` is read-only by default + +### Concrete user surprise + +For a `.b2z` store, append mode currently does extra work: + +1. create a working directory (usually temporary) +2. extract the archive into that working directory +3. serve reads/writes from the extracted layout +4. repack on close + +This is implemented in +[src/blosc2/dict_store.py](/Users/faltet/blosc/python-blosc2/src/blosc2/dict_store.py). + +That behavior is reasonable when the caller explicitly asked for `"a"`, but +surprising when it is triggered only because `mode` was omitted. + +## Desired End State + +The target behavior is: + +```python +blosc2.open(path) +``` + +should behave as if the user had written: + +```python +blosc2.open(path, mode="r") +``` + +unless the object category does not support read-only opening for technical +reasons. In such cases, the exception should be explicit and documented. + +The user should need to opt into mutation with: + +- `mode="a"` +- `mode="w"` + +## Design Principles + +The migration should follow these rules: + +- do not silently change semantics without a warning phase +- make the warning text concrete and actionable +- update all docs and examples before flipping the default +- keep the opt-in writable paths unchanged +- avoid introducing ambiguity about whether a store may be mutated +- prefer explicit `mode=` in library docs even after the default changes + +## Recommended Rollout + +### Phase 0: prepare the codebase + +Before warning users: + +1. audit internal calls to `blosc2.open(...)` +2. make all internal call sites spell out `mode=` +3. update examples, docs, and tests to use explicit modes +4. document the difference between: + - `mode="r"`: inspect/query only + - `mode="a"`: may unpack and repack stores + - `mode="w"`: overwrite/create + +This phase reduces ambiguity and makes later warning noise much more useful. + +### Phase 1: deprecation warning + +Keep the runtime default as `"a"`, but emit a `FutureWarning` when: + +- `blosc2.open(...)` is called without an explicit `mode=` + +The warning should fire only when `mode` was omitted, not when the caller +explicitly requested `"a"`. + +Recommended warning text: + +```python +FutureWarning( + "blosc2.open() currently defaults to mode='a', but this will change " + "to mode='r' in a future release. Pass mode='a' explicitly to keep " + "writable behavior, or mode='r' for read-only access." +) +``` + +Notes: + +- the wording should mention both the current and future defaults +- the wording should explain how to preserve current behavior +- the wording should not be container-specific + +### Phase 2: flip the default + +In the next planned breaking-compatible release window: + +- change the default mode in `blosc2.open(...)` from `"a"` to `"r"` + +At that point: + +- calls with omitted `mode` become read-only +- code that needs writable behavior must use `mode="a"` explicitly + +### Phase 3: remove warning-specific scaffolding + +After the default flip has been out for one full release cycle: + +- remove temporary warning helpers and migration notes that are no longer + useful +- keep release notes and changelog entries for historical context + +## Implementation Notes + +### Tracking whether `mode` was omitted + +To emit a warning only when appropriate, `blosc2.open(...)` needs to +distinguish: + +- caller omitted `mode` +- caller passed `mode="a"` explicitly + +A practical implementation is: + +1. change the function signature internally to use a sentinel +2. resolve the effective mode inside the function +3. warn only when the sentinel path is used + +For example: + +```python +_MODE_SENTINEL = object() + + +def open(urlpath, mode=_MODE_SENTINEL, **kwargs): + mode_was_omitted = mode is _MODE_SENTINEL + if mode_was_omitted: + mode = "a" # Phase 1 + warnings.warn(...) +``` + +Later, in Phase 2: + +```python +if mode_was_omitted: + mode = "r" +``` + +This is better than relying on `mode="a"` in the signature because that +signature cannot tell whether the user explicitly passed `"a"`. + +### Scope of change + +This plan is specifically about `blosc2.open(...)`. + +It does **not** require changing the defaults of: + +- `DictStore(...)` +- `TreeStore(...)` +- `CTable(...)` + +at the same time. + +However, the docs should explain that: + +- constructor-style APIs may still default to `"a"` +- generic `blosc2.open(...)` becomes read-only by default + +This narrower scope reduces breakage and focuses on the highest-surprise entry +point first. + +## Compatibility Risks + +The main breakage risk is downstream code that relies on: + +```python +obj = blosc2.open(path) +obj[...] = ... +``` + +without ever spelling out `mode="a"`. + +After the default flip, that code may: + +- fail with a read-only error +- stop persisting modifications +- expose behavior differences only at runtime + +This is why the warning phase is important. + +### Secondary risk: tests that mutate after open + +Internal and downstream tests may open objects generically and then mutate +them. These need to be found and updated during Phase 0. + +### Secondary risk: docs and notebooks + +Tutorials that currently omit `mode=` may accidentally teach users the old +behavior. These should be updated before the warning phase begins. + +## Documentation Changes + +### API docs + +Update the docstring for `blosc2.open(...)` to: + +- describe the migration +- clearly document the meaning of each mode +- mention that read-only is the recommended mode for inspection/querying + +### Examples + +Update examples to use explicit modes consistently: + +- inspection/querying: `mode="r"` +- mutation of existing stores: `mode="a"` +- create/overwrite: `mode="w"` + +### User-facing migration note + +Add a short migration note to release notes: + +- “`blosc2.open()` now defaults to read-only; pass `mode='a'` explicitly if + you need writable behavior.” + +## Testing Plan + +### Phase 1 tests + +Add tests that verify: + +- omitted `mode` emits `FutureWarning` +- explicit `mode="a"` does not warn +- explicit `mode="r"` does not warn +- effective behavior remains writable during the warning phase + +### Phase 2 tests + +After the flip, add/update tests that verify: + +- omitted `mode` is read-only +- writes after omitted-mode open fail clearly +- explicit `mode="a"` still allows mutation +- `.b2z` omitted-mode open does not enter append-style write setup + +### Documentation tests + +Where practical, examples should use explicit `mode=` so doctests remain clear +and stable across the transition. + +## Optional Compatibility Escape Hatch + +If downstream breakage risk is considered high, one temporary option is an +environment-variable override for one transition cycle, for example: + +- `BLOSC2_OPEN_DEFAULT_MODE=a` + +This should only be used if needed. It adds complexity and should not become a +permanent configuration surface unless there is a strong operational reason. + +## Related Follow-Up Worth Considering + +Even if the default changes to `"r"`, append mode for `.b2z` may still be more +eager than desirable. + +A separate improvement could make `.b2z` append behavior lazier: + +- open in `"a"` without extracting immediately +- extract only on first mutation +- keep read-only-style fast paths for pure reads + +That is orthogonal to the default-mode change and can be planned separately. + +## Summary + +The recommended path is: + +1. make internal/docs/example usage explicit +2. add a `FutureWarning` when `blosc2.open(...)` is called without `mode=` +3. flip the default from `"a"` to `"r"` in the next suitable release window +4. keep writable behavior available via explicit `mode="a"` + +This delivers a safer and less surprising user experience while still giving +existing code a clear migration path. diff --git a/src/blosc2/schunk.py b/src/blosc2/schunk.py index 3008062b..6071cb74 100644 --- a/src/blosc2/schunk.py +++ b/src/blosc2/schunk.py @@ -1835,6 +1835,7 @@ def open( # Resolve the sentinel before URLPath check so we can raise the correct # error without also triggering the deprecation warning for invalid calls. if mode is _OPEN_MODE_SENTINEL: + # TODO: remove the sentinel/FutureWarning path once blosc2.open() defaults to mode="r". warnings.warn( "blosc2.open() currently defaults to mode='a', but this will change " "to mode='r' in a future release. Pass mode='a' explicitly to keep " From 8dddc8cda09401874a3a3d77f7526fba035e36ed Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Wed, 15 Apr 2026 19:35:45 +0200 Subject: [PATCH 39/49] Fix a regression when reopening a persisted Proxy with mode='r' --- src/blosc2/schunk.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/src/blosc2/schunk.py b/src/blosc2/schunk.py index 6071cb74..5d9ecadc 100644 --- a/src/blosc2/schunk.py +++ b/src/blosc2/schunk.py @@ -1635,13 +1635,17 @@ def _set_default_dparams(kwargs): def process_opened_object(res): meta = getattr(res, "schunk", res).meta if "proxy-source" in meta: + proxy_cache = res + cache_schunk = getattr(res, "schunk", res) + if getattr(cache_schunk, "urlpath", None) is not None and getattr(cache_schunk, "mode", None) == "r": + proxy_cache = blosc2_ext.open(cache_schunk.urlpath, "a", 0) proxy_src = meta["proxy-source"] if proxy_src["local_abspath"] is not None: src = blosc2.open(proxy_src["local_abspath"], mode="a") - return blosc2.Proxy(src, _cache=res) + return blosc2.Proxy(src, _cache=proxy_cache) elif proxy_src["urlpath"] is not None: src = blosc2.C2Array(proxy_src["urlpath"][0], proxy_src["urlpath"][1], proxy_src["urlpath"][2]) - return blosc2.Proxy(src, _cache=res) + return blosc2.Proxy(src, _cache=proxy_cache) elif not proxy_src["caterva2_env"]: raise RuntimeError("Could not find the source when opening a Proxy") From 034c0bfa99ca65c188fa1304349d49b6c7ed02af Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Thu, 16 Apr 2026 07:15:53 +0200 Subject: [PATCH 40/49] Fix .b2z double-open corruption caused by GC-triggered repacking MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Opening a .b2z store in append mode a second time (in a new process) was failing with "blosc2_schunk_open_offset returned NULL" because the first open had silently overwritten the archive with a near-empty ZIP file. Two root causes were identified and fixed: 1. Probe store in _open_treestore_root_object() called close() on a temporary TreeStore used only to read the manifest. This triggered to_b2z() and repacked the archive before the real open started. Fix: added DictStore.discard() (cleanup without repack) and switched the probe store to call discard() instead of close(). 2. CTable.__del__ (GC path) called storage.close() which chained into TreeStore.close() → to_b2z(). With nothing modified, the temp dir could be partially torn down, producing a corrupt archive. Fix: DictStore gains _closed and _modified flags; __del__ now calls discard() when _modified is False (no writes via __setitem__/ __delitem__), and close() otherwise. CTable.__del__ is changed to call storage.discard() directly. FileTableStorage.discard() and TableStorage.discard() added to complete the delegation chain. 3. TreeStore subtree views (created via __dict__.update from the parent) shared the parent's _temp_dir_obj. GC of a subtree could destroy the parent's temp dir. Fix: subtrees now set _closed=True immediately after copying the parent's __dict__. The contract is: - Explicit close() / with block → always repacks (user intent) - GC __del__ with no store-level writes → discard() (no repack, safe) - GC __del__ with __setitem__/__delitem__ writes → close() (repack) Add two regression tests: one at the DictStore layer and one for the full indexed-CTable-in-.b2z scenario that triggered the original report. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --- src/blosc2/ctable.py | 6 +++- src/blosc2/ctable_storage.py | 11 +++++++ src/blosc2/dict_store.py | 49 +++++++++++++++++++++++++++- src/blosc2/schunk.py | 6 +++- src/blosc2/tree_store.py | 6 +++- tests/ctable/test_ctable_indexing.py | 29 ++++++++++++++++ tests/test_dict_store.py | 22 +++++++++++++ 7 files changed, 125 insertions(+), 4 deletions(-) diff --git a/src/blosc2/ctable.py b/src/blosc2/ctable.py index 13fb5080..b1a14ab2 100644 --- a/src/blosc2/ctable.py +++ b/src/blosc2/ctable.py @@ -1044,7 +1044,11 @@ def __exit__(self, exc_type, exc_val, exc_tb): def __del__(self): with contextlib.suppress(Exception): - self.close() + storage = getattr(self, "_storage", None) + if storage is not None and hasattr(storage, "discard"): + storage.discard() + elif storage is not None and hasattr(storage, "close"): + storage.close() def _init_columns( self, expected_size: int, default_chunks, default_blocks, storage: TableStorage diff --git a/src/blosc2/ctable_storage.py b/src/blosc2/ctable_storage.py index b071ad02..54efa59b 100644 --- a/src/blosc2/ctable_storage.py +++ b/src/blosc2/ctable_storage.py @@ -92,6 +92,10 @@ def rename_column(self, old: str, new: str) -> blosc2.NDArray: def close(self) -> None: raise NotImplementedError + def discard(self) -> None: + """Clean up resources without persisting changes back to the archive.""" + self.close() + # -- Index catalog and epoch helpers ------------------------------------- def load_index_catalog(self) -> dict: @@ -370,6 +374,13 @@ def close(self) -> None: self._store = None self._meta = None + def discard(self) -> None: + """Clean up without repacking the .b2z archive.""" + if self._store is not None: + self._store.discard() + self._store = None + self._meta = None + # -- Index catalog and epoch helpers ------------------------------------- def load_index_catalog(self) -> dict: diff --git a/src/blosc2/dict_store.py b/src/blosc2/dict_store.py index 0e22839c..97a7f52b 100644 --- a/src/blosc2/dict_store.py +++ b/src/blosc2/dict_store.py @@ -141,6 +141,8 @@ def __init__( self.offsets = {} self.map_tree = {} self._temp_dir_obj = None + self._closed = False + self._modified = False self._setup_paths_and_dirs(tmpdir) @@ -315,6 +317,12 @@ def _init_write_append_mode( """Initialize store in write/append mode.""" if self.mode == "a" and os.path.exists(self.localpath): if self.is_zip_store: + # When using an explicit tmpdir the directory may already contain + # stale files from a previous open that was never closed. Clear + # it before extracting so we always start from a clean slate. + if self._temp_dir_obj is None: + shutil.rmtree(self.working_dir, ignore_errors=True) + os.makedirs(self.working_dir, exist_ok=True) with zipfile.ZipFile(self.localpath, "r") as zf: zf.extractall(self.working_dir) elif not os.path.isdir(self.working_dir): @@ -387,6 +395,7 @@ def __setitem__( self, key: str, value: blosc2.Array | SChunk | blosc2.VLArray | blosc2.BatchArray ) -> None: """Add a node to the DictStore.""" + self._modified = True if isinstance(value, np.ndarray): value = blosc2.asarray(value, cparams=self.cparams, dparams=self.dparams) # C2Array should always go to embed store; let estore handle it directly @@ -488,6 +497,7 @@ def get( def __delitem__(self, key: str) -> None: """Remove a node from the DictStore.""" + self._modified = True if key in self.map_tree: # Remove from map_tree and delete the external file filepath = self.map_tree[key] @@ -672,6 +682,10 @@ def _get_zip_offsets(self) -> dict[str, dict[str, int]]: def close(self) -> None: """Persist changes and cleanup.""" + if self._closed: + return + self._closed = True + # Repack estore # TODO: for some reason this is not working # if self.mode != "r": @@ -680,13 +694,46 @@ def close(self) -> None: # f.write(cframe) if self.is_zip_store and self.mode in ("w", "a"): - # Serialize to b2z file + # Serialize to b2z file. self.to_b2z(overwrite=True) # Clean up temporary directory if we created it if self._temp_dir_obj is not None: self._temp_dir_obj.cleanup() + def discard(self) -> None: + """Clean up resources *without* repacking the .b2z file. + + Use this instead of :meth:`close` when the store was opened only for + inspection and should be thrown away without persisting any changes + back to the archive. + """ + if self._closed: + return + self._closed = True + if self._temp_dir_obj is not None: + self._temp_dir_obj.cleanup() + + def __del__(self): + """Ensure the temporary directory is removed and, if writes were made + through this store's own API, the store is flushed back to the .b2z + file. + + When no Python-level writes went through ``__setitem__`` / ``__delitem__`` + (``_modified`` is False), we skip ``to_b2z()`` to avoid repacking a + potentially partial temp dir during garbage collection. Explicit + ``close()`` / ``__exit__`` always repacks regardless. + """ + try: + if not self._closed and self.is_zip_store and self.mode in ("w", "a") and not self._modified: + # Skip repacking — discard is safe and avoids corrupting the + # archive when the temp dir is torn down during GC. + self.discard() + else: + self.close() + except Exception: + pass + def __enter__(self): """Context manager enter.""" return self diff --git a/src/blosc2/schunk.py b/src/blosc2/schunk.py index 5d9ecadc..c38b4205 100644 --- a/src/blosc2/schunk.py +++ b/src/blosc2/schunk.py @@ -1705,7 +1705,11 @@ def _open_treestore_root_object(store, urlpath, mode): if manifest["kind"] == "ctable": if mode not in {"r", "a"}: return store - store.close() + # Discard the probe store without repacking — it was only opened + # to peek at the manifest. A full close() would trigger to_b2z() + # even though nothing was modified, and CTable.open() below will + # create its own store anyway. + store.discard() return blosc2.CTable.open(urlpath, mode=mode) return store diff --git a/src/blosc2/tree_store.py b/src/blosc2/tree_store.py index a378fa49..9a265e01 100644 --- a/src/blosc2/tree_store.py +++ b/src/blosc2/tree_store.py @@ -154,8 +154,12 @@ def __init__(self, *args, _from_parent_store=None, **kwargs): It supports the same arguments as :class:`blosc2.DictStore`. """ if _from_parent_store is not None: - # This is a subtree view, copy state from parent + # This is a subtree view, copy state from parent. + # Mark it as closed so DictStore.__del__ does not attempt to pack + # or clean up the shared backing store when this ephemeral view + # is garbage-collected. self.__dict__.update(_from_parent_store.__dict__) + self._closed = True else: # Call initialization and mark this storage as a b2tree object super().__init__(*args, **kwargs, _storage_meta={"b2tree": {"version": 1}}) diff --git a/tests/ctable/test_ctable_indexing.py b/tests/ctable/test_ctable_indexing.py index eb643810..a4a430ad 100644 --- a/tests/ctable/test_ctable_indexing.py +++ b/tests/ctable/test_ctable_indexing.py @@ -436,3 +436,32 @@ def test_indexes_multiple_columns(): assert len(t.indexes) == 2 col_names = {idx.col_name for idx in t.indexes} assert col_names == {"id", "category"} + + +def test_indexed_ctable_b2z_double_open_append_no_corruption(tmp_path): + """Opening an indexed CTable .b2z in append mode twice must not corrupt it. + + Regression test: GC of a CTable opened from .b2z was calling close() → + to_b2z() even when nothing was modified, overwriting the archive with a + near-empty ZIP that broke subsequent opens. + """ + path = str(tmp_path / "indexed.b2z") + b2d_path = str(tmp_path / "indexed.b2d") + + t = _make_table(50, persistent_path=b2d_path) + t.create_index("id") + t._storage._store.to_b2z(filename=path, overwrite=True) + t._storage._store.close() + shutil.rmtree(b2d_path) + + # First open without explicit close — GC must not corrupt the archive + t1 = blosc2.open(path, mode="a") + assert t1.nrows == 50 + assert len(t1.indexes) == 1 + del t1 # triggers __del__; must NOT repack/corrupt + + # Second open must succeed and see correct data + t2 = blosc2.open(path, mode="a") + assert t2.nrows == 50 + assert len(t2.indexes) == 1 + del t2 diff --git a/tests/test_dict_store.py b/tests/test_dict_store.py index d772ca5e..7c9c9dd8 100644 --- a/tests/test_dict_store.py +++ b/tests/test_dict_store.py @@ -693,3 +693,25 @@ def test_mmap_mode_validation(tmp_path): with pytest.raises(ValueError, match="mmap_mode='r' requires mode='r'"): DictStore(str(path), mode="a", mmap_mode="r") + + +def test_b2z_double_open_append_no_corruption(tmp_path): + """Opening a .b2z store twice in append mode must not corrupt the archive. + + Regression test: previously, GC of the first open's DictStore triggered + ``to_b2z()`` which overwrote the archive with a near-empty ZIP, causing the + second open to fail with ``blosc2_schunk_open_offset`` returning NULL. + """ + path = str(tmp_path / "double_open.b2z") + + with DictStore(path, mode="w") as ds: + ds["/arr"] = blosc2.arange(20) + + # First open — no explicit close (simulates the GC-triggered path) + ds1 = DictStore(path, mode="a") + assert np.array_equal(ds1["/arr"][:], np.arange(20)) + del ds1 # GC; must NOT corrupt the archive + + # Second open — must succeed and see correct data + with DictStore(path, mode="a") as ds2: + assert np.array_equal(ds2["/arr"][:], np.arange(20)) From e58b4c788636ebfeba2e51d5935ab7e76670e3e7 Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Thu, 16 Apr 2026 09:41:17 +0200 Subject: [PATCH 41/49] Temporarily unpacking a .b2z file defaults now to the same dir as the file --- src/blosc2/dict_store.py | 6 ++++-- src/blosc2/tree_store.py | 3 ++- 2 files changed, 6 insertions(+), 3 deletions(-) diff --git a/src/blosc2/dict_store.py b/src/blosc2/dict_store.py index 97a7f52b..3166bfeb 100644 --- a/src/blosc2/dict_store.py +++ b/src/blosc2/dict_store.py @@ -58,7 +58,8 @@ class DictStore: and only when ``mode="r"``. Default is None. tmpdir : str or None, optional Temporary directory to use when working with ".b2z" files. If None, - a system temporary directory will be managed. Default is None. + a temporary directory is created in the same directory as the ".b2z" + file, so that unpacked data stays on the same filesystem. Default is None. cparams : dict or None, optional Compression parameters for the internal embed store. If None, the default Blosc2 parameters are used. @@ -165,7 +166,8 @@ def _setup_paths_and_dirs(self, tmpdir: str | None): self.is_zip_store = False if self.is_zip_store: if tmpdir is None: - self._temp_dir_obj = tempfile.TemporaryDirectory() + b2z_parent = os.path.dirname(os.path.abspath(self.localpath)) + self._temp_dir_obj = tempfile.TemporaryDirectory(dir=b2z_parent) self.working_dir = self._temp_dir_obj.name else: self.working_dir = tmpdir diff --git a/src/blosc2/tree_store.py b/src/blosc2/tree_store.py index 9a265e01..6fd02e9a 100644 --- a/src/blosc2/tree_store.py +++ b/src/blosc2/tree_store.py @@ -105,7 +105,8 @@ class TreeStore(DictStore): File mode ('r', 'w', 'a'). Default is 'a'. tmpdir : str or None, optional Temporary directory to use when working with `.b2z` files. If None, - a system temporary directory will be managed. Default is None. + a temporary directory is created in the same directory as the `.b2z` + file, so that unpacked data stays on the same filesystem. Default is None. cparams : dict or None, optional Compression parameters for the internal embed store. If None, the default Blosc2 parameters are used. From 2b3eeff70ab2c9e93962a1ef7dca2218028935c0 Mon Sep 17 00:00:00 2001 From: jorge Date: Thu, 16 Apr 2026 11:33:50 +0200 Subject: [PATCH 42/49] Fix nullable validation, chunk sizing, print alignment, numpy mask support --- src/blosc2/ctable.py | 66 ++++++++++++++++++++------------- src/blosc2/schema.py | 1 + src/blosc2/schema_validation.py | 48 ++++++++++++------------ src/blosc2/schema_vectorized.py | 5 +-- 4 files changed, 65 insertions(+), 55 deletions(-) diff --git a/src/blosc2/ctable.py b/src/blosc2/ctable.py index 0f08e9c8..a9cb70e4 100644 --- a/src/blosc2/ctable.py +++ b/src/blosc2/ctable.py @@ -1010,8 +1010,14 @@ def __str__(self) -> str: sep = " ".join("─" * (w + 2) for w in widths.values()) + def fmt_cell(value, width: int) -> str: + s = str(value) + if len(s) > width: + s = s[: width - 1] + "…" + return f" {s:<{width}} " + def fmt_row(values: dict) -> str: - return " ".join(f" {values[n]!s:<{widths[n]}} " for n in self.col_names) + return " ".join(fmt_cell(values[n], widths[n]) for n in self.col_names) # -- batch-fetch values (one read per column, not one per cell) -- def rows_to_dicts(positions) -> list[dict]: @@ -1162,7 +1168,10 @@ def save(self, urlpath: str, *, overwrite: bool = False) -> None: # --- columns --- for col in self._schema.columns: name = col.name - col_storage = self._resolve_column_storage(col, default_chunks, default_blocks) + # Use dtype-aware defaults so large-itemsize columns (e.g. U4096) get + # sensible chunk/block sizes rather than the uint8-based defaults. + dtype_chunks, dtype_blocks = compute_chunks_blocks((capacity,), dtype=col.dtype) + col_storage = self._resolve_column_storage(col, dtype_chunks, dtype_blocks) disk_col = file_storage.create_column( name, dtype=col.dtype, @@ -1212,12 +1221,12 @@ def load(cls, urlpath: str) -> CTable: capacity = max(phys_size, 1) mem_storage = InMemoryTableStorage() - default_chunks, default_blocks = compute_chunks_blocks((capacity,)) + bool_chunks, bool_blocks = compute_chunks_blocks((capacity,), dtype=np.dtype(np.bool_)) mem_valid = mem_storage.create_valid_rows( shape=(capacity,), - chunks=default_chunks, - blocks=default_blocks, + chunks=bool_chunks, + blocks=bool_blocks, ) if phys_size > 0: mem_valid[:phys_size] = disk_valid[:] @@ -1225,12 +1234,13 @@ def load(cls, urlpath: str) -> CTable: mem_cols: dict[str, blosc2.NDArray] = {} for col in schema.columns: name = col.name + col_chunks, col_blocks = compute_chunks_blocks((capacity,), dtype=col.dtype) mem_col = mem_storage.create_column( name, dtype=col.dtype, shape=(capacity,), - chunks=default_chunks, - blocks=default_blocks, + chunks=col_chunks, + blocks=col_blocks, cparams=None, dparams=None, ) @@ -1284,6 +1294,8 @@ def _make_view(cls, parent: CTable, new_valid_rows: blosc2.NDArray) -> CTable: return obj def view(self, new_valid_rows): + if isinstance(new_valid_rows, np.ndarray) and new_valid_rows.dtype == np.bool_: + new_valid_rows = blosc2.asarray(new_valid_rows) if not ( isinstance(new_valid_rows, (blosc2.NDArray, blosc2.LazyExpr)) and (getattr(new_valid_rows, "dtype", None) == np.bool_) @@ -1798,6 +1810,24 @@ def to_csv(self, path: str, *, header: bool = True, sep: str = ",") -> None: for row in zip(*arrays, strict=True): writer.writerow(row) + @staticmethod + def _csv_col_to_array(raw: list[str], col, nv) -> np.ndarray: + """Convert a list of raw CSV strings to a numpy array for *col*.""" + if col.dtype == np.bool_: + + def _parse(v, _nv=nv): + stripped = v.strip() + if stripped == "" and _nv is not None: + return _nv + return stripped in ("True", "true", "1") + + return np.array([_parse(v) for v in raw], dtype=np.bool_) + if col.dtype.kind == "S": + prepared: list = [nv if (v.strip() == "" and nv is not None) else v.encode() for v in raw] + return np.array(prepared, dtype=col.dtype) + prepared2 = [nv if (v.strip() == "" and nv is not None) else v for v in raw] + return np.array(prepared2, dtype=col.dtype) + @classmethod def from_csv( cls, @@ -1900,25 +1930,7 @@ def from_csv( if n > 0: for i, col in enumerate(schema.columns): nv = getattr(col.spec, "null_value", None) - if col.dtype == np.bool_: - # np.array(["False"], dtype=bool) treats any non-empty - # string as True. Parse "True"/"False"/"1"/"0" explicitly. - # Empty cells → null_value (or False if no null_value). - def _parse_bool(v, _nv=nv): - stripped = v.strip() - if stripped == "" and _nv is not None: - return _nv - return stripped in ("True", "true", "1") - - arr = np.array([_parse_bool(v) for v in col_data[i]], dtype=np.bool_) - else: - raw_strings = col_data[i] - if nv is not None: - # Replace empty cells with the null sentinel string representation, - # then cast. For numeric types, store nv directly. - nv_str = str(nv) - raw_strings = [nv_str if v.strip() == "" else v for v in raw_strings] - arr = np.array(raw_strings, dtype=col.dtype) + arr = cls._csv_col_to_array(col_data[i], col, nv) new_cols[col.name][:n] = arr new_valid[:n] = True obj._n_rows = n @@ -2535,6 +2547,8 @@ def extend(self, data: list | CTable | Any, *, validate: bool | None = None) -> @profile def where(self, expr_result) -> CTable: + if isinstance(expr_result, np.ndarray) and expr_result.dtype == np.bool_: + expr_result = blosc2.asarray(expr_result) if not ( isinstance(expr_result, (blosc2.NDArray, blosc2.LazyExpr)) and (getattr(expr_result, "dtype", None) == np.bool_) diff --git a/src/blosc2/schema.py b/src/blosc2/schema.py index 28e1cac9..560d7117 100644 --- a/src/blosc2/schema.py +++ b/src/blosc2/schema.py @@ -50,6 +50,7 @@ def __init_subclass__(cls, **kwargs): if isinstance(_np_dtype, np.dtype): cls.itemsize = _np_dtype.itemsize cls.kind = _np_dtype.kind + cls.type = _np_dtype.type cls.str = _np_dtype.str cls.name = _np_dtype.name diff --git a/src/blosc2/schema_validation.py b/src/blosc2/schema_validation.py index 50f6c6ef..91f157f7 100644 --- a/src/blosc2/schema_validation.py +++ b/src/blosc2/schema_validation.py @@ -29,6 +29,11 @@ def build_validator_model(schema: CompiledSchema) -> type[BaseModel]: The model enforces all constraints declared in each column's :class:`~blosc2.schema.SchemaSpec` (``ge``, ``le``, ``gt``, ``lt``, ``max_length``, ``min_length``, ``pattern``). + + Nullable columns (those with a ``null_value``) are typed as + ``Optional[T]`` with ``default=None`` so that null sentinels can be + passed as ``None`` and bypass constraint validation entirely — no + placeholder guessing required. """ if schema.validator_model is not None: return schema.validator_model @@ -36,10 +41,17 @@ def build_validator_model(schema: CompiledSchema) -> type[BaseModel]: field_definitions: dict[str, Any] = {} for col in schema.columns: pydantic_kwargs = col.spec.to_pydantic_kwargs() + is_nullable = getattr(col.spec, "null_value", None) is not None + py_type = col.py_type | None if is_nullable else col.py_type + if col.default is MISSING: - field_definitions[col.name] = (col.py_type, Field(**pydantic_kwargs)) + default = None if is_nullable else MISSING + if default is MISSING: + field_definitions[col.name] = (py_type, Field(**pydantic_kwargs)) + else: + field_definitions[col.name] = (py_type, Field(default=default, **pydantic_kwargs)) else: - field_definitions[col.name] = (col.py_type, Field(default=col.default, **pydantic_kwargs)) + field_definitions[col.name] = (py_type, Field(default=col.default, **pydantic_kwargs)) cls_name = schema.row_cls.__name__ if schema.row_cls is not None else "Unknown" model_cls = create_model(f"_Validator_{cls_name}", **field_definitions) @@ -61,29 +73,15 @@ def _is_null_value(val, null_value) -> bool: return val == null_value -def _safe_placeholder(col) -> Any: - """Return a value that passes Pydantic validation for *col* (used for null bypass).""" - spec = col.spec - ge = getattr(spec, "ge", None) - gt = getattr(spec, "gt", None) - if ge is not None: - return ge - if gt is not None: - return gt + 1 - # For string/bytes, use an empty string - if col.dtype.kind in ("U", "S"): - return "" - # For bool - if col.dtype.kind == "b": - return False - return 0 - - def _mask_nulls(schema: CompiledSchema, row: dict[str, Any]) -> tuple[dict[str, Any], dict[str, Any]]: - """Replace null sentinel values with safe placeholders. + """Replace null sentinel values with ``None`` so Pydantic skips constraint checks. + + Nullable columns are declared as ``Optional[T]`` in the validator model, + so passing ``None`` is always valid regardless of ``ge``/``le``/``pattern`` + constraints. The original sentinel is stashed in *nulled* and restored + after validation. - Returns (masked_row, null_positions) where null_positions maps - column name → original null value for columns that were masked. + Returns (masked_row, nulled) where nulled maps column name → sentinel value. """ masked = dict(row) nulled: dict[str, Any] = {} @@ -92,9 +90,9 @@ def _mask_nulls(schema: CompiledSchema, row: dict[str, Any]) -> tuple[dict[str, if nv is None: continue val = row.get(col.name) - if val is not None and _is_null_value(val, nv): + if _is_null_value(val, nv): nulled[col.name] = val - masked[col.name] = _safe_placeholder(col) + masked[col.name] = None return masked, nulled diff --git a/src/blosc2/schema_vectorized.py b/src/blosc2/schema_vectorized.py index 8d0e6bba..c15f2dd2 100644 --- a/src/blosc2/schema_vectorized.py +++ b/src/blosc2/schema_vectorized.py @@ -79,12 +79,9 @@ def validate_column_values(col: CompiledColumn, values: Any) -> None: # Compute null mask so sentinels bypass constraint checks null_mask = _null_mask_for_spec(arr, spec) - # non_null is a boolean array True for positions that must be validated if null_mask is not None: - non_null = ~null_mask - check = arr[non_null] + check = arr[~null_mask] else: - non_null = None check = arr # Numeric bounds From 8d4603bd1536eaf8f7562e1a24eacc3e63ea3943 Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Thu, 16 Apr 2026 12:30:12 +0200 Subject: [PATCH 43/49] Fix some issues in tests --- src/blosc2/ctable.py | 5 +++++ tests/ctable/test_table_persistency.py | 23 ++++++++++++----------- 2 files changed, 17 insertions(+), 11 deletions(-) diff --git a/src/blosc2/ctable.py b/src/blosc2/ctable.py index a7a884eb..3cb4e55a 100644 --- a/src/blosc2/ctable.py +++ b/src/blosc2/ctable.py @@ -1063,6 +1063,11 @@ def __init__( # Choose storage backend if urlpath is not None: + if mode == "w" and os.path.exists(urlpath): + if os.path.isdir(urlpath): + shutil.rmtree(urlpath) + else: + os.remove(urlpath) storage: TableStorage = FileTableStorage(urlpath, mode) else: storage = InMemoryTableStorage() diff --git a/tests/ctable/test_table_persistency.py b/tests/ctable/test_table_persistency.py index 10638ca6..3e9045db 100644 --- a/tests/ctable/test_table_persistency.py +++ b/tests/ctable/test_table_persistency.py @@ -59,7 +59,7 @@ def test_create_layout_files_exist(): t = CTable(Row, urlpath=path, mode="w", expected_size=16) t.append((1, 50.0, True)) - assert os.path.exists(os.path.join(path, "_meta.b2frame")) + assert os.path.exists(os.path.join(path, "_meta.b2f")) assert os.path.exists(os.path.join(path, "_valid_rows.b2nd")) assert os.path.exists(os.path.join(path, "_cols", "id.b2nd")) assert os.path.exists(os.path.join(path, "_cols", "score.b2nd")) @@ -67,11 +67,11 @@ def test_create_layout_files_exist(): def test_schema_saved_in_meta_vlmeta(): - """Schema JSON and kind marker are present in _meta.b2frame.""" + """Schema JSON and kind marker are present in _meta.b2f.""" path = table_path("people") CTable(Row, urlpath=path, mode="w", expected_size=16) - meta = blosc2.open(os.path.join(path, "_meta.b2frame")) + meta = blosc2.open(os.path.join(path, "_meta.b2f"), mode="r") assert meta.vlmeta["kind"] == "ctable" assert meta.vlmeta["version"] == 1 schema = json.loads(meta.vlmeta["schema"]) @@ -204,7 +204,7 @@ def test_valid_rows_persisted(): t.delete(1) # mark row 1 (id=2) as invalid # _valid_rows on disk: slots 0 and 2 are True, slot 1 is False - vr = blosc2.open(os.path.join(path, "_valid_rows.b2nd")) + vr = blosc2.open(os.path.join(path, "_valid_rows.b2nd"), mode="r") raw = vr[:3] assert raw[0] assert not raw[1] @@ -299,15 +299,16 @@ def test_open_nonexistent_raises(): def test_open_wrong_kind_raises(tmp_path): - """A path with a _meta.b2frame that is not a ctable raises ValueError.""" - import blosc2 - - meta_path = str(tmp_path / "_meta.b2frame") - sc = blosc2.SChunk(urlpath=meta_path, mode="w") + """A path with a _meta.b2f that is not a ctable raises ValueError.""" + path = str(tmp_path / "fake_table") + store = blosc2.TreeStore(path, mode="w", threshold=0) + sc = blosc2.SChunk() sc.vlmeta["kind"] = "something_else" + store["/_meta"] = sc + store.close() with pytest.raises(ValueError, match="CTable"): - CTable.open(str(tmp_path)) + CTable.open(path) # --------------------------------------------------------------------------- @@ -386,7 +387,7 @@ def test_save_creates_disk_layout(): path = table_path("saved") t.save(path) - assert os.path.exists(os.path.join(path, "_meta.b2frame")) + assert os.path.exists(os.path.join(path, "_meta.b2f")) assert os.path.exists(os.path.join(path, "_valid_rows.b2nd")) assert os.path.exists(os.path.join(path, "_cols", "id.b2nd")) assert os.path.exists(os.path.join(path, "_cols", "score.b2nd")) From 3fafab14cc6ac3fbec7a954ff0384d53e5516cda Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Thu, 16 Apr 2026 20:31:26 +0200 Subject: [PATCH 44/49] Fix GC-induced thread hang on macOS with Python 3.14 MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Python 3.14 sets the gen-2 GC threshold to 0, so long-lived objects (like SChunk instances in pytest fixtures) never get auto-collected. Each SChunk carries 2×nthreads OS pthreads via its cctx/dctx. During pytest teardown, thousands of accumulated SChunks hit macOS's 6144 thread limit, causing an indefinite hang on gc.collect(). Three fixes applied: - Release the GIL during blosc2_schunk_free() in SChunk.__dealloc__ to prevent GIL deadlock when mass finalization triggers pthread_join - Add periodic gc.collect() every 50 tests in conftest.py to prevent thread accumulation past the macOS ceiling - Cap ThreadPoolExecutor in lazyexpr.py to os.cpu_count() workers (fixes #556) Closes #556 --- src/blosc2/blosc2_ext.pyx | 13 +++++++++++-- src/blosc2/lazyexpr.py | 2 +- tests/conftest.py | 19 +++++++++++++++++++ 3 files changed, 31 insertions(+), 3 deletions(-) diff --git a/src/blosc2/blosc2_ext.pyx b/src/blosc2/blosc2_ext.pyx index bd2677f3..8612b739 100644 --- a/src/blosc2/blosc2_ext.pyx +++ b/src/blosc2/blosc2_ext.pyx @@ -476,7 +476,7 @@ cdef extern from "blosc2.h": int64_t blosc2_schunk_to_buffer(blosc2_schunk* schunk, uint8_t** cframe, c_bool* needs_free) nogil void blosc2_schunk_avoid_cframe_free(blosc2_schunk *schunk, c_bool avoid_cframe_free) int64_t blosc2_schunk_to_file(blosc2_schunk* schunk, const char* urlpath) - int64_t blosc2_schunk_free(blosc2_schunk *schunk) + int64_t blosc2_schunk_free(blosc2_schunk *schunk) nogil int64_t blosc2_schunk_append_chunk(blosc2_schunk *schunk, uint8_t *chunk, c_bool copy) int64_t blosc2_schunk_update_chunk(blosc2_schunk *schunk, int64_t nchunk, uint8_t *chunk, c_bool copy) int64_t blosc2_schunk_insert_chunk(blosc2_schunk *schunk, int64_t nchunk, uint8_t *chunk, c_bool copy) @@ -2339,6 +2339,7 @@ cdef class SChunk: self.schunk.cctx = NULL def __dealloc__(self): + cdef blosc2_schunk *schunk_ptr if self.schunk != NULL and not self._is_view: # Free prefilters and postfilters params if self.schunk.storage.cparams.prefilter != NULL: @@ -2346,7 +2347,15 @@ cdef class SChunk: if self.schunk.storage.dparams.postfilter != NULL: self.remove_postfilter(func_name=None, _new_ctx=False) - blosc2_schunk_free(self.schunk) + # Release the GIL while freeing the C-Blosc2 super-chunk. + # blosc2_schunk_free -> blosc2_free_ctx -> release_threadpool + # joins worker pthreads; holding the GIL here can cause hangs + # when thousands of SChunks are finalized at once (e.g. during + # gc.collect() in Python 3.14+ where gen-2 threshold is 0). + schunk_ptr = self.schunk + self.schunk = NULL + with nogil: + blosc2_schunk_free(schunk_ptr) # postfilter diff --git a/src/blosc2/lazyexpr.py b/src/blosc2/lazyexpr.py index aad219ca..3b853aef 100644 --- a/src/blosc2/lazyexpr.py +++ b/src/blosc2/lazyexpr.py @@ -1182,7 +1182,7 @@ def get_chunk(arr, info, nchunk): async def async_read_chunks(arrs, info, queue): loop = asyncio.get_event_loop() shape, chunks_ = arrs[0].shape, arrs[0].chunks - with concurrent.futures.ThreadPoolExecutor() as executor: + with concurrent.futures.ThreadPoolExecutor(max_workers=os.cpu_count() or 1) as executor: my_chunk_iter = range(arrs[0].schunk.nchunks) if len(info) == 5: if info[-1] is not None: diff --git a/tests/conftest.py b/tests/conftest.py index 60a04aa9..35db4afe 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -5,6 +5,7 @@ # SPDX-License-Identifier: BSD-3-Clause ####################################################################### +import gc import os import sys @@ -14,6 +15,17 @@ import blosc2 +# Each SChunk allocates C-level thread pools (pthreads) for its compression +# and decompression contexts. Python 3.14 changed the GC gen-2 threshold +# to 0, so long-lived objects are never collected automatically; they +# accumulate until an explicit gc.collect() (e.g. pytest session cleanup). +# Joining thousands of idle pthreads at once can hit the macOS thread-count +# ceiling (6 144) and hang. Periodically forcing a full collection keeps +# the thread count bounded. +_GC_COLLECT_INTERVAL = 50 # collect every N tests +_test_counter = 0 + + def expected_nthreads(nthreads: int) -> int: return 1 if blosc2.IS_WASM else nthreads @@ -49,3 +61,10 @@ def pytest_runtest_call(item): item.runtest() except requests.exceptions.RequestException as exc: pytest.skip(f"Skipping network test due to request failure: {exc}") + + +def pytest_runtest_teardown(item, nextitem): + global _test_counter + _test_counter += 1 + if _test_counter % _GC_COLLECT_INTERVAL == 0: + gc.collect() From 7defba4eb76620e62a1ba0ced7001ee688b3c2c4 Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Thu, 16 Apr 2026 20:56:25 +0200 Subject: [PATCH 45/49] Break reference cycles and harden async chunk reader MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - FieldsAccessor: use weakref to parent NDArray and create NDField instances on access instead of eagerly. Breaks NDArray↔NDField reference cycles so gen-0/gen-1 GC can collect them, reducing thread accumulation on Python 3.14. - sync_read_chunks: wrap reader thread in try/finally with thread.join() to prevent thread leaks. Propagate exceptions from the async reader instead of silently swallowing them. Detect dead reader thread to avoid infinite wait on empty queue. - ThreadPoolExecutor: cap max_workers to min(len(arrs), nthreads) instead of os.cpu_count(), avoiding excess threads when there are few operands. --- src/blosc2/lazyexpr.py | 41 ++++++++++++++++++++++++++++++----------- src/blosc2/ndarray.py | 32 ++++++++++++++++++++------------ 2 files changed, 50 insertions(+), 23 deletions(-) diff --git a/src/blosc2/lazyexpr.py b/src/blosc2/lazyexpr.py index 3b853aef..74758a5f 100644 --- a/src/blosc2/lazyexpr.py +++ b/src/blosc2/lazyexpr.py @@ -1182,7 +1182,8 @@ def get_chunk(arr, info, nchunk): async def async_read_chunks(arrs, info, queue): loop = asyncio.get_event_loop() shape, chunks_ = arrs[0].shape, arrs[0].chunks - with concurrent.futures.ThreadPoolExecutor(max_workers=os.cpu_count() or 1) as executor: + max_workers = max(1, min(len(arrs), int(getattr(blosc2, "nthreads", 1) or 1))) + with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor: my_chunk_iter = range(arrs[0].schunk.nchunks) if len(info) == 5: if info[-1] is not None: @@ -1213,20 +1214,38 @@ def async_read_chunks_thread(arrs, info, queue): def sync_read_chunks(arrs, info): queue_size = 2 # maximum number of chunks in the queue queue = Queue(maxsize=queue_size) + worker_exc = None + + def _run_async_reader(): + nonlocal worker_exc + try: + async_read_chunks_thread(arrs, info, queue) + except BaseException as exc: + worker_exc = exc + queue.put(None) # Start the async file reading in a separate thread - thread = threading.Thread(target=async_read_chunks_thread, args=(arrs, info, queue)) + thread = threading.Thread(target=_run_async_reader) thread.start() - # Read the chunks synchronously from the queue - while True: - try: - chunks = queue.get(timeout=1) # Wait for the next chunk - if chunks is None: # End of chunks - break - yield chunks - except Empty: - continue + try: + # Read the chunks synchronously from the queue + while True: + try: + chunks = queue.get(timeout=1) # Wait for the next chunk + if chunks is None: # End of chunks + if worker_exc is not None: + raise worker_exc + break + yield chunks + except Empty: + if not thread.is_alive(): + if worker_exc is not None: + raise worker_exc + break + continue + finally: + thread.join() def read_nchunk(arrs, info): diff --git a/src/blosc2/ndarray.py b/src/blosc2/ndarray.py index b0284327..d336178c 100644 --- a/src/blosc2/ndarray.py +++ b/src/blosc2/ndarray.py @@ -10,6 +10,7 @@ import builtins import inspect import math +import weakref from abc import abstractmethod from collections import OrderedDict, namedtuple from collections.abc import Mapping @@ -146,23 +147,33 @@ def __getitem__(self, key: Any) -> Any: class FieldsAccessor(Mapping): """Read-only mapping of structured field views.""" - def __init__(self, field_views: dict[str, Any]): - self._field_views = field_views + def __init__(self, ndarr, field_names: Sequence[str]): + self._ndarr_ref = weakref.ref(ndarr) + self._field_names = tuple(field_names) + + def _ndarr(self): + ndarr = self._ndarr_ref() + if ndarr is None: + raise ReferenceError("owning NDArray has been released") + return ndarr def __getitem__(self, key: str) -> Any: - return self._field_views[key] + if key not in self._field_names: + raise KeyError(key) + return NDField(self._ndarr(), key) def __iter__(self) -> Iterator[str]: - return iter(self._field_views) + return iter(self._field_names) def __len__(self) -> int: - return len(self._field_views) + return len(self._field_names) def __setitem__(self, key: str, value: object) -> None: raise TypeError(f'assign through the field view, e.g. array.fields["{key}"][:] = values') def copy(self) -> dict[str, Any]: - return dict(self._field_views) + ndarr = self._ndarr() + return {field: NDField(ndarr, field) for field in self._field_names} def __or__(self, other: object) -> dict[str, Any]: if not isinstance(other, Mapping): @@ -175,7 +186,7 @@ def __ror__(self, other: object) -> dict[str, Any]: return dict(other) | self.copy() def __repr__(self) -> str: - return repr(self._field_views) + return repr(dict(self)) def is_documented_by(original): @@ -3743,11 +3754,8 @@ def __init__(self, **kwargs): base = kwargs.pop("_base", None) super().__init__(kwargs["_array"], base=base) # Accessor to fields - field_views = {} - if self.dtype.fields: - for field in self.dtype.fields: - field_views[field] = NDField(self, field) - self._fields = FieldsAccessor(field_views) + field_names = tuple(self.dtype.fields) if self.dtype.fields else () + self._fields = FieldsAccessor(self, field_names) @property def cparams(self) -> blosc2.CParams: From 115985e6c706ebeb55e794a4d568b6fc13ef1350 Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Thu, 16 Apr 2026 20:59:42 +0200 Subject: [PATCH 46/49] Break reference cycles and purge stale caches in ctable layer MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - _RowIndexer: use weakref to parent CTable to break reference cycles, allowing CTable instances to be collected without gen-2 GC. - indexing.py: stop caching writable sidecar handles in the process-wide _SIDECAR_HANDLE_CACHE after construction — they kept NDArray objects alive indefinitely across tests on macOS/Python 3.14. - indexing.py: add _purge_stale_persistent_caches() to evict index cache entries whose backing paths no longer exist. Call it in _load_store, _open_query_cache_store, _open_sidecar_handle, and _gather_mmap_source. - ref.py: open urlpath operands in mode='r' instead of the default mode='a'; persistent recipe operands only need read access. - test_ctable_indexing.py: add tests verifying that sidecar handles are not cached after index creation, that CTables release without explicit GC, and that stale persistent caches are purged correctly. --- src/blosc2/ctable.py | 8 +++- src/blosc2/indexing.py | 54 +++++++++++++++++++-- src/blosc2/ref.py | 4 +- tests/ctable/test_ctable_indexing.py | 70 ++++++++++++++++++++++++++++ 4 files changed, 130 insertions(+), 6 deletions(-) diff --git a/src/blosc2/ctable.py b/src/blosc2/ctable.py index 3cb4e55a..b4a2d331 100644 --- a/src/blosc2/ctable.py +++ b/src/blosc2/ctable.py @@ -16,6 +16,7 @@ import os import pprint import shutil +import weakref from collections.abc import Iterable from dataclasses import MISSING from textwrap import TextWrapper @@ -448,10 +449,13 @@ def _find_physical_index(arr: blosc2.NDArray, logical_key: int) -> int: class _RowIndexer: def __init__(self, table): - self._table = table + self._table_ref = weakref.ref(table) def __getitem__(self, item): - return self._table._run_row_logic(item) + table = self._table_ref() + if table is None: + raise ReferenceError("owning CTable has been released") + return table._run_row_logic(item) class _Row: diff --git a/src/blosc2/indexing.py b/src/blosc2/indexing.py index 821c5a9c..f640156c 100644 --- a/src/blosc2/indexing.py +++ b/src/blosc2/indexing.py @@ -106,6 +106,48 @@ def _cleanup_in_memory_store(key: int) -> None: _hot_cache_clear(scope=("memory", key)) +def _persistent_cache_path_exists(path: str | int) -> bool: + if not isinstance(path, str): + return False + path_obj = Path(path) + return path_obj.exists() or path_obj.parent.exists() + + +def _purge_stale_persistent_caches() -> None: + stale_scopes = { + key for key in _PERSISTENT_INDEXES if key[0] == "persistent" and not _persistent_cache_path_exists(key[1]) + } + for key in stale_scopes: + _PERSISTENT_INDEXES.pop(key, None) + + stale_data_keys = { + key for key in _DATA_CACHE if key[0][0] == "persistent" and not _persistent_cache_path_exists(key[0][1]) + } + stale_scopes.update(key[0] for key in stale_data_keys) + for key in stale_data_keys: + _DATA_CACHE.pop(key, None) + + stale_handle_keys = { + key + for key in _SIDECAR_HANDLE_CACHE + if key[0][0] == "persistent" and not _persistent_cache_path_exists(key[0][1]) + } + stale_scopes.update(key[0] for key in stale_handle_keys) + for key in stale_handle_keys: + _SIDECAR_HANDLE_CACHE.pop(key, None) + + stale_query_paths = [path for path in _QUERY_CACHE_STORE_HANDLES if not Path(path).exists()] + for path in stale_query_paths: + _QUERY_CACHE_STORE_HANDLES.pop(path, None) + + stale_gather_paths = [path for path in _GATHER_MMAP_HANDLES if not Path(path).exists()] + for path in stale_gather_paths: + _GATHER_MMAP_HANDLES.pop(path, None) + + for scope in stale_scopes: + _hot_cache_clear(scope=scope) + + @dataclass(slots=True) class IndexPlan: usable: bool @@ -286,6 +328,7 @@ def _resolve_full_index_tmpdir(array: blosc2.NDArray, tmpdir: str | None) -> str def _load_store(array: blosc2.NDArray) -> dict: + _purge_stale_persistent_caches() if _is_persistent_array(array): key = _array_key(array) cached = _PERSISTENT_INDEXES.get(key) @@ -405,6 +448,7 @@ def _open_query_cache_store(array: blosc2.NDArray, *, create: bool = False): Returns ``None`` if the array is not persistent. When *create* is True the store is created if it does not yet exist. """ + _purge_stale_persistent_caches() if not _is_persistent_array(array): return None path = _query_cache_payload_path(array) @@ -920,6 +964,7 @@ def _invalidate_sidecar_cache_entries(array: blosc2.NDArray, token: str, categor def _open_sidecar_handle(array: blosc2.NDArray, token: str, category: str, name: str, path: str | None): + _purge_stale_persistent_caches() cache_key = _sidecar_handle_cache_key(array, token, category, name) cached = _SIDECAR_HANDLE_CACHE.get(cache_key) if cached is not None: @@ -1108,8 +1153,11 @@ def _store_array_sidecar( kwargs["blocks"] = blocks if cparams is not None: kwargs["cparams"] = cparams + # Do not retain writable persistent handles in the process-wide cache. + # They keep native resources alive after index construction and can + # accumulate badly across tests on macOS/Python 3.14. handle = blosc2.asarray(data, **kwargs) - _SIDECAR_HANDLE_CACHE[handle_cache_key] = handle + del handle _DATA_CACHE.pop(cache_key, None) else: path = None @@ -1152,10 +1200,9 @@ def _create_persistent_sidecar_handle( kwargs["cparams"] = cparams if length == 0: handle = blosc2.asarray(np.empty(0, dtype=dtype), **kwargs) - _SIDECAR_HANDLE_CACHE[_sidecar_handle_cache_key(array, token, category, name)] = handle + del handle return None, {"path": path, "dtype": dtype.descr if dtype.fields else dtype.str} handle = blosc2.empty((length,), dtype=dtype, **kwargs) - _SIDECAR_HANDLE_CACHE[_sidecar_handle_cache_key(array, token, category, name)] = handle return handle, {"path": path, "dtype": dtype.descr if dtype.fields else dtype.str} @@ -4874,6 +4921,7 @@ def _gather_mmap_source(where_x): urlpath = getattr(where_x, "urlpath", None) if not _supports_block_reads(where_x) or urlpath is None: return where_x + _purge_stale_persistent_caches() urlpath = str(urlpath) handle = _GATHER_MMAP_HANDLES.get(urlpath) if handle is None: diff --git a/src/blosc2/ref.py b/src/blosc2/ref.py index cc842f25..b1eda6b1 100644 --- a/src/blosc2/ref.py +++ b/src/blosc2/ref.py @@ -118,7 +118,9 @@ def open(self): import blosc2 if self.kind == "urlpath": - return blosc2.open(self.urlpath, mode="a") + # Structured refs are used to reopen operands for persisted recipes. + # Read-only access avoids allocating unnecessary writable state. + return blosc2.open(self.urlpath, mode="r") if self.kind == "dictstore_key": return blosc2.DictStore(self.urlpath, mode="r")[self.key] if self.kind == "c2array": diff --git a/tests/ctable/test_ctable_indexing.py b/tests/ctable/test_ctable_indexing.py index a4a430ad..47e13b29 100644 --- a/tests/ctable/test_ctable_indexing.py +++ b/tests/ctable/test_ctable_indexing.py @@ -10,6 +10,7 @@ import dataclasses import shutil import tempfile +import weakref from pathlib import Path import numpy as np @@ -223,6 +224,33 @@ def test_create_index_persistent(tmpdir): assert sidecars, "No sidecar .b2nd files found" +def test_create_index_persistent_does_not_cache_sidecar_handles(tmpdir): + import blosc2.indexing as indexing + + path = str(tmpdir / "table.b2d") + t = _make_table(50, persistent_path=path) + t.create_index("id", kind=blosc2.IndexKind.FULL) + + cached = [ + key + for key in indexing._SIDECAR_HANDLE_CACHE + if key[0][0] == "persistent" and str(tmpdir) in key[0][1] + ] + assert cached == [] + + +def test_persistent_ctable_releases_immediately_without_gc(tmpdir): + path = str(tmpdir / "table.b2d") + + def build_table(): + t = _make_table(50, persistent_path=path) + t.create_index("id", kind=blosc2.IndexKind.FULL) + return weakref.ref(t) + + table_ref = build_table() + assert table_ref() is None + + def test_catalog_survives_reopen(tmpdir): path = str(tmpdir / "table.b2d") t = _make_table(30, persistent_path=path) @@ -250,6 +278,28 @@ def test_where_with_index_matches_scan_persistent(tmpdir): assert ids_idx == ids_scan +def test_persistent_index_drop_releases_sidecars_without_gc(tmpdir): + import gc + + def run_query_and_drop(): + path = str(tmpdir / "table.b2d") + t = _make_table(200, persistent_path=path) + t.create_index("id") + result = t.where(t["id"] > 150) + ids = sorted(int(v) for v in result["id"].to_numpy()) + assert ids == list(range(151, 200)) + t.drop_index("id") + + run_query_and_drop() + + sidecars = [ + obj + for obj in gc.get_objects() + if isinstance(obj, blosc2.NDArray) and obj.urlpath and str(tmpdir) in obj.urlpath and "__index__" in obj.urlpath + ] + assert sidecars == [] + + def test_drop_index_persistent(tmpdir): path = str(tmpdir / "table.b2d") t = _make_table(30, persistent_path=path) @@ -465,3 +515,23 @@ def test_indexed_ctable_b2z_double_open_append_no_corruption(tmp_path): assert t2.nrows == 50 assert len(t2.indexes) == 1 del t2 + + +def test_indexing_purges_stale_persistent_caches(): + import blosc2.indexing as indexing + + with tempfile.TemporaryDirectory() as tmpdir: + path = str(Path(tmpdir) / "table.b2d") + t = _make_table(50, persistent_path=path) + t.create_index("id") + _ = t.where(t["id"] > 10) + t.close() + + assert any(tmpdir in key[1] for key in indexing._PERSISTENT_INDEXES if key[0] == "persistent") + + indexing._purge_stale_persistent_caches() + + assert all(tmpdir not in key[1] for key in indexing._PERSISTENT_INDEXES if key[0] == "persistent") + assert all(tmpdir not in key[0][1] for key in indexing._SIDECAR_HANDLE_CACHE if key[0][0] == "persistent") + assert all(tmpdir not in path for path in indexing._QUERY_CACHE_STORE_HANDLES) + assert all(tmpdir not in path for path in indexing._GATHER_MMAP_HANDLES) From 468177b4cfdda30e893f779b9854724958b819d3 Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Fri, 17 Apr 2026 11:08:31 +0200 Subject: [PATCH 47/49] Fix for a re-entrant cache-cleanup bug in indexing code --- src/blosc2/indexing.py | 14 +++++++++----- tests/ctable/test_ctable_indexing.py | 25 +++++++++++++++++++++++++ 2 files changed, 34 insertions(+), 5 deletions(-) diff --git a/src/blosc2/indexing.py b/src/blosc2/indexing.py index f640156c..8beb2d59 100644 --- a/src/blosc2/indexing.py +++ b/src/blosc2/indexing.py @@ -115,13 +115,17 @@ def _persistent_cache_path_exists(path: str | int) -> bool: def _purge_stale_persistent_caches() -> None: stale_scopes = { - key for key in _PERSISTENT_INDEXES if key[0] == "persistent" and not _persistent_cache_path_exists(key[1]) + key + for key in tuple(_PERSISTENT_INDEXES) + if key[0] == "persistent" and not _persistent_cache_path_exists(key[1]) } for key in stale_scopes: _PERSISTENT_INDEXES.pop(key, None) stale_data_keys = { - key for key in _DATA_CACHE if key[0][0] == "persistent" and not _persistent_cache_path_exists(key[0][1]) + key + for key in tuple(_DATA_CACHE) + if key[0][0] == "persistent" and not _persistent_cache_path_exists(key[0][1]) } stale_scopes.update(key[0] for key in stale_data_keys) for key in stale_data_keys: @@ -129,18 +133,18 @@ def _purge_stale_persistent_caches() -> None: stale_handle_keys = { key - for key in _SIDECAR_HANDLE_CACHE + for key in tuple(_SIDECAR_HANDLE_CACHE) if key[0][0] == "persistent" and not _persistent_cache_path_exists(key[0][1]) } stale_scopes.update(key[0] for key in stale_handle_keys) for key in stale_handle_keys: _SIDECAR_HANDLE_CACHE.pop(key, None) - stale_query_paths = [path for path in _QUERY_CACHE_STORE_HANDLES if not Path(path).exists()] + stale_query_paths = [path for path in tuple(_QUERY_CACHE_STORE_HANDLES) if not Path(path).exists()] for path in stale_query_paths: _QUERY_CACHE_STORE_HANDLES.pop(path, None) - stale_gather_paths = [path for path in _GATHER_MMAP_HANDLES if not Path(path).exists()] + stale_gather_paths = [path for path in tuple(_GATHER_MMAP_HANDLES) if not Path(path).exists()] for path in stale_gather_paths: _GATHER_MMAP_HANDLES.pop(path, None) diff --git a/tests/ctable/test_ctable_indexing.py b/tests/ctable/test_ctable_indexing.py index 47e13b29..a293089b 100644 --- a/tests/ctable/test_ctable_indexing.py +++ b/tests/ctable/test_ctable_indexing.py @@ -535,3 +535,28 @@ def test_indexing_purges_stale_persistent_caches(): assert all(tmpdir not in key[0][1] for key in indexing._SIDECAR_HANDLE_CACHE if key[0][0] == "persistent") assert all(tmpdir not in path for path in indexing._QUERY_CACHE_STORE_HANDLES) assert all(tmpdir not in path for path in indexing._GATHER_MMAP_HANDLES) + + +def test_indexing_purge_tolerates_reentrant_sidecar_handle_cache_mutation(monkeypatch): + import blosc2.indexing as indexing + + stale_scope = ("persistent", "/tmp/stale-index.b2nd") + stale_key = (stale_scope, "token", "partial_handle", "offsets") + injected_key = (("memory", 12345), "token", "partial_handle", "offsets") + sentinel = object() + original_exists = indexing._persistent_cache_path_exists + + indexing._SIDECAR_HANDLE_CACHE[stale_key] = sentinel + + def mutating_exists(path): + indexing._SIDECAR_HANDLE_CACHE[injected_key] = sentinel + if path == stale_scope[1]: + return False + return original_exists(path) + + monkeypatch.setattr(indexing, "_persistent_cache_path_exists", mutating_exists) + + indexing._purge_stale_persistent_caches() + + assert stale_key not in indexing._SIDECAR_HANDLE_CACHE + indexing._SIDECAR_HANDLE_CACHE.pop(injected_key, None) From 2fbd41a1fba9994f0af06c66e79d83e538a5f6d0 Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Fri, 17 Apr 2026 11:40:38 +0200 Subject: [PATCH 48/49] =?UTF-8?q?Make=20test=20deterministic=20and=20close?= =?UTF-8?q?r=20to=20what=20it=E2=80=99s=20actually=20trying=20to=20verify?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- tests/ctable/test_ctable_indexing.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/tests/ctable/test_ctable_indexing.py b/tests/ctable/test_ctable_indexing.py index a293089b..d39fd6fd 100644 --- a/tests/ctable/test_ctable_indexing.py +++ b/tests/ctable/test_ctable_indexing.py @@ -527,11 +527,17 @@ def test_indexing_purges_stale_persistent_caches(): _ = t.where(t["id"] > 10) t.close() - assert any(tmpdir in key[1] for key in indexing._PERSISTENT_INDEXES if key[0] == "persistent") + persistent_scope = ("persistent", str(Path(path).resolve())) + indexing._PERSISTENT_INDEXES[persistent_scope] = {"version": 1, "indexes": {}} + indexing._DATA_CACHE[(persistent_scope, "token", "partial", "offsets")] = np.arange(3, dtype=np.int64) + indexing._SIDECAR_HANDLE_CACHE[(persistent_scope, "token", "partial_handle", "offsets")] = object() + indexing._QUERY_CACHE_STORE_HANDLES[str(Path(tmpdir) / "query-cache.b2frame")] = object() + indexing._GATHER_MMAP_HANDLES[str(Path(tmpdir) / "gather-cache.b2nd")] = object() indexing._purge_stale_persistent_caches() assert all(tmpdir not in key[1] for key in indexing._PERSISTENT_INDEXES if key[0] == "persistent") + assert all(tmpdir not in key[0][1] for key in indexing._DATA_CACHE if key[0][0] == "persistent") assert all(tmpdir not in key[0][1] for key in indexing._SIDECAR_HANDLE_CACHE if key[0][0] == "persistent") assert all(tmpdir not in path for path in indexing._QUERY_CACHE_STORE_HANDLES) assert all(tmpdir not in path for path in indexing._GATHER_MMAP_HANDLES) From 485ac501900cc460ffd9bcda39154ecc3c3a880d Mon Sep 17 00:00:00 2001 From: Francesc Alted Date: Fri, 17 Apr 2026 12:14:36 +0200 Subject: [PATCH 49/49] Fix ruff warnings --- src/blosc2/lazyexpr.py | 2 +- tests/conftest.py | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/src/blosc2/lazyexpr.py b/src/blosc2/lazyexpr.py index 74758a5f..e8772304 100644 --- a/src/blosc2/lazyexpr.py +++ b/src/blosc2/lazyexpr.py @@ -1241,7 +1241,7 @@ def _run_async_reader(): except Empty: if not thread.is_alive(): if worker_exc is not None: - raise worker_exc + raise worker_exc from None break continue finally: diff --git a/tests/conftest.py b/tests/conftest.py index 35db4afe..bfecf90f 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -14,7 +14,6 @@ import blosc2 - # Each SChunk allocates C-level thread pools (pthreads) for its compression # and decompression contexts. Python 3.14 changed the GC gen-2 threshold # to 0, so long-lived objects are never collected automatically; they