Use Scan API

bench-orchestrator/bench_orchestrator/comparison/analyzer.py

@@ -271,8 +271,11 @@ def compare_runs(
     # Pivot to get (query, engine, format) as rows, runs as columns
     pivot = combined.pivot_table(index=["query", "engine", "format"], columns="run", values="value", aggfunc="mean")
 
+    # Deduplicate labels while preserving order (two runs can share a label).
+    unique_labels = list(dict.fromkeys(labels))
+
     # Reorder columns to match input order
-    pivot = pivot[[label for label in labels if label in pivot.columns]]
+    pivot = pivot[[label for label in unique_labels if label in pivot.columns]]
 
     # Compute ratios relative to baseline
     if baseline_label in pivot.columns:
@@ -283,4 +286,4 @@ def compare_runs(
     else:
         result = pivot
 
-    return PivotComparison(df=result.reset_index(), baseline=baseline_label, columns=labels)
+    return PivotComparison(df=result.reset_index(), baseline=baseline_label, columns=unique_labels)

bench-orchestrator/bench_orchestrator/storage/store.py

@@ -96,9 +96,13 @@ def write_result(self, result: QueryResult) -> None:
             self._result_count += 1
 
     def write_raw_json(self, json_line: str) -> None:
-        """Write a raw JSON line directly (from benchmark binary output)."""
-        if self._results_file:
-            self._results_file.write(json_line.strip() + "\n")
+        """Write a raw JSON line directly (from benchmark binary output).
+
+        Non-JSON lines (e.g. DuckDB ASCII table output) are silently skipped.
+        """
+        line = json_line.strip()
+        if self._results_file and line.startswith("{"):
+            self._results_file.write(line + "\n")
             self._results_file.flush()
             self._result_count += 1
 

benchmarks/datafusion-bench/Cargo.toml

@@ -17,6 +17,7 @@ publish = false
 [dependencies]
 anyhow = { workspace = true }
 arrow-ipc.workspace = true
+async-trait = { workspace = true }
 clap = { workspace = true, features = ["derive"] }
 custom-labels = { workspace = true }
 datafusion = { workspace = true, features = [
@@ -36,6 +37,7 @@ opentelemetry_sdk.workspace = true
 parking_lot = { workspace = true }
 tokio = { workspace = true, features = ["full"] }
 url = { workspace = true }
+vortex = { workspace = true, features = ["object_store", "files", "tokio"] }
 vortex-bench = { workspace = true }
 vortex-cuda = { workspace = true, optional = true }
 vortex-datafusion = { workspace = true }

benchmarks/datafusion-bench/src/main.rs

@@ -26,13 +26,15 @@ use datafusion_physical_plan::collect;
 use futures::StreamExt;
 use parking_lot::Mutex;
 use tokio::fs::File;
+use vortex::scan::api::DataSourceRef;
 use vortex_bench::Benchmark;
 use vortex_bench::BenchmarkArg;
 use vortex_bench::CompactionStrategy;
 use vortex_bench::Engine;
 use vortex_bench::Format;
 use vortex_bench::Opt;
 use vortex_bench::Opts;
+use vortex_bench::SESSION;
 use vortex_bench::conversions::convert_parquet_directory_to_vortex;
 use vortex_bench::create_benchmark;
 use vortex_bench::create_output_writer;
@@ -220,13 +222,20 @@ async fn main() -> anyhow::Result<()> {
     Ok(())
 }
 
+fn use_scan_api() -> bool {
+    std::env::var("VORTEX_USE_SCAN_API").is_ok_and(|v| v == "1")
+}
+
 async fn register_benchmark_tables<B: Benchmark + ?Sized>(
     session: &SessionContext,
     benchmark: &B,
     format: Format,
 ) -> anyhow::Result<()> {
     match format {
         Format::Arrow => register_arrow_tables(session, benchmark).await,
+        _ if use_scan_api() && matches!(format, Format::OnDiskVortex | Format::VortexCompact) => {
+            register_v2_tables(session, benchmark, format).await
+        }
         _ => {
             let benchmark_base = benchmark.data_url().join(&format!("{}/", format.name()))?;
             let file_format = format_to_df_format(format);
@@ -265,6 +274,54 @@ async fn register_benchmark_tables<B: Benchmark + ?Sized>(
     }
 }
 
+/// Register tables using the V2 `VortexTable` + `MultiFileDataSource` path.
+async fn register_v2_tables<B: Benchmark + ?Sized>(
+    session: &SessionContext,
+    benchmark: &B,
+    format: Format,
+) -> anyhow::Result<()> {
+    use vortex::file::multi::MultiFileDataSource;
+    use vortex::io::object_store::ObjectStoreFileSystem;
+    use vortex::io::session::RuntimeSessionExt;
+    use vortex::scan::api::DataSource as _;
+    use vortex_datafusion::v2::VortexTable;
+
+    let benchmark_base = benchmark.data_url().join(&format!("{}/", format.name()))?;
+
+    for table in benchmark.table_specs().iter() {
+        let pattern = benchmark.pattern(table.name, format);
+        let table_url = ListingTableUrl::try_new(benchmark_base.clone(), pattern.clone())?;
+        let store = session
+            .state()
+            .runtime_env()
+            .object_store(table_url.object_store())?;
+
+        let fs: vortex::io::filesystem::FileSystemRef =
+            Arc::new(ObjectStoreFileSystem::new(store.clone(), SESSION.handle()));
+        let base_prefix = benchmark_base.path().trim_start_matches('/').to_string();
+        let fs = fs.with_prefix(base_prefix);
+
+        let glob_pattern = match &pattern {
+            Some(p) => p.as_str().to_string(),
+            None => format!("*.{}", format.ext()),
+        };
+
+        let multi_ds = MultiFileDataSource::new(SESSION.clone())
+            .with_filesystem(fs)
+            .with_glob(glob_pattern)
+            .build()
+            .await?;
+
+        let arrow_schema = Arc::new(multi_ds.dtype().to_arrow_schema()?);
+        let data_source: DataSourceRef = Arc::new(multi_ds);
+
+        let table_provider = Arc::new(VortexTable::new(data_source, SESSION.clone(), arrow_schema));
+        session.register_table(table.name, table_provider)?;
+    }
+
+    Ok(())
+}
+
 /// Load Arrow IPC files into in-memory DataFusion tables.
 async fn register_arrow_tables<B: Benchmark + ?Sized>(
     session: &SessionContext,

benchmarks/duckdb-bench/src/main.rs

@@ -3,6 +3,7 @@
 
 mod validation;
 
+use std::ops::Deref;
 use std::path::PathBuf;
 
 use clap::Parser;
@@ -68,6 +69,10 @@ struct Args {
 
     #[arg(long = "opt", value_delimiter = ',', value_parser = value_parser!(Opt))]
     options: Vec<Opt>,
+
+    /// Print EXPLAIN output for each query instead of running benchmarks.
+    #[arg(long, default_value_t = false)]
+    explain: bool,
 }
 
 fn main() -> anyhow::Result<()> {
@@ -126,6 +131,33 @@ fn main() -> anyhow::Result<()> {
         })?;
     }
 
+    if args.explain {
+        for format in &args.formats {
+            let ctx = DuckClient::new(
+                &*benchmark,
+                *format,
+                args.delete_duckdb_database,
+                args.threads,
+            )?;
+            ctx.register_tables(&*benchmark, *format)?;
+
+            for (query_idx, query) in &filtered_queries {
+                println!("=== Q{query_idx} [{format}] ===");
+                println!("{query}");
+                println!();
+                let result = ctx.connection.query(&format!("EXPLAIN {query}"))?;
+                for chunk in result {
+                    let chunk_str =
+                        String::try_from(chunk.deref()).unwrap_or_else(|_| "<error>".to_string());
+                    println!("{chunk_str}");
+                }
+                println!();
+            }
+        }
+
+        return Ok(());
+    }
+
     let mut runner = SqlBenchmarkRunner::new(
         &*benchmark,
         Engine::DuckDB,

docs/developer-guide/integrations/duckdb.md

@@ -17,10 +17,9 @@ concurrently during the global initialization phase, with a concurrency limit pr
 the number of DuckDB worker threads to keep the I/O pipeline saturated without overwhelming
 the system.
 
-A `MultiScan` stream manages the set of active file scans. It prioritises completing in-progress
-scans before opening new files, ensuring that DuckDB's execution threads always have data to
-consume while background I/O proceeds. File footers are cached to avoid redundant parsing when
-the same file appears in multiple queries.
+Active file scans are driven concurrently via `try_flatten_unordered`, ensuring that DuckDB's
+execution threads always have data to consume while background I/O proceeds. File footers are
+cached to avoid redundant parsing when the same file appears in multiple queries.
 
 ## Threading Model
 

vortex-array/src/expr/stats/precision.rs

@@ -21,6 +21,8 @@ use crate::scalar::ScalarValue;
 /// This is statistic specific, for max this will be an upper bound. Meaning that the actual max
 /// in an array is guaranteed to be less than or equal to the inexact value, but equal to the exact
 /// value.
+///
+// TODO(ngates): should we model Unknown as a variant of Precision? Or have Option<Precision<T>>?
 #[derive(Debug, PartialEq, Eq, Clone, Copy)]
 pub enum Precision<T> {
     Exact(T),

vortex-datafusion/Cargo.toml

@@ -31,7 +31,6 @@ datafusion-physical-plan = { workspace = true }
 datafusion-pruning = { workspace = true }
 futures = { workspace = true }
 itertools = { workspace = true }
-moka = { workspace = true, features = ["future"] }
 object_store = { workspace = true }
 tokio = { workspace = true, features = ["rt-multi-thread", "fs"] }
 tokio-stream = { workspace = true }

vortex-datafusion/public-api.lock

@@ -18,12 +18,76 @@ pub type vortex_datafusion::metrics::VortexMetricsFinder::Error = core::convert:
 
 pub fn vortex_datafusion::metrics::VortexMetricsFinder::pre_visit(&mut self, plan: &dyn datafusion_physical_plan::execution_plan::ExecutionPlan) -> core::result::Result<bool, Self::Error>
 
+pub mod vortex_datafusion::v2
+
+pub struct vortex_datafusion::v2::VortexDataSource
+
+impl vortex_datafusion::v2::VortexDataSource
+
+pub fn vortex_datafusion::v2::VortexDataSource::builder(data_source: vortex_scan::api::DataSourceRef, session: vortex_session::VortexSession) -> vortex_datafusion::v2::source::VortexDataSourceBuilder
+
+impl core::clone::Clone for vortex_datafusion::v2::VortexDataSource
+
+pub fn vortex_datafusion::v2::VortexDataSource::clone(&self) -> vortex_datafusion::v2::VortexDataSource
+
+impl core::fmt::Debug for vortex_datafusion::v2::VortexDataSource
+
+pub fn vortex_datafusion::v2::VortexDataSource::fmt(&self, f: &mut core::fmt::Formatter<'_>) -> core::fmt::Result
+
+impl datafusion_datasource::source::DataSource for vortex_datafusion::v2::VortexDataSource
+
+pub fn vortex_datafusion::v2::VortexDataSource::as_any(&self) -> &dyn core::any::Any
+
+pub fn vortex_datafusion::v2::VortexDataSource::eq_properties(&self) -> datafusion_physical_expr::equivalence::properties::EquivalenceProperties
+
+pub fn vortex_datafusion::v2::VortexDataSource::fetch(&self) -> core::option::Option<usize>
+
+pub fn vortex_datafusion::v2::VortexDataSource::fmt_as(&self, _t: datafusion_physical_plan::display::DisplayFormatType, f: &mut core::fmt::Formatter<'_>) -> core::fmt::Result
+
+pub fn vortex_datafusion::v2::VortexDataSource::open(&self, partition: usize, _context: alloc::sync::Arc<datafusion_execution::task::TaskContext>) -> datafusion_common::error::Result<datafusion_execution::stream::SendableRecordBatchStream>
+
+pub fn vortex_datafusion::v2::VortexDataSource::output_partitioning(&self) -> datafusion_physical_expr::partitioning::Partitioning
+
+pub fn vortex_datafusion::v2::VortexDataSource::partition_statistics(&self, _partition: core::option::Option<usize>) -> datafusion_common::error::Result<datafusion_common::stats::Statistics>
+
+pub fn vortex_datafusion::v2::VortexDataSource::repartitioned(&self, target_partitions: usize, _repartition_file_min_size: usize, output_ordering: core::option::Option<datafusion_physical_expr_common::sort_expr::LexOrdering>) -> datafusion_common::error::Result<core::option::Option<alloc::sync::Arc<dyn datafusion_datasource::source::DataSource>>>
+
+pub fn vortex_datafusion::v2::VortexDataSource::try_pushdown_filters(&self, filters: alloc::vec::Vec<alloc::sync::Arc<dyn datafusion_physical_expr_common::physical_expr::PhysicalExpr>>, _config: &datafusion_common::config::ConfigOptions) -> datafusion_common::error::Result<datafusion_physical_plan::filter_pushdown::FilterPushdownPropagation<alloc::sync::Arc<dyn datafusion_datasource::source::DataSource>>>
+
+pub fn vortex_datafusion::v2::VortexDataSource::try_swapping_with_projection(&self, projection: &datafusion_physical_expr::projection::ProjectionExprs) -> datafusion_common::error::Result<core::option::Option<alloc::sync::Arc<dyn datafusion_datasource::source::DataSource>>>
+
+pub fn vortex_datafusion::v2::VortexDataSource::with_fetch(&self, limit: core::option::Option<usize>) -> core::option::Option<alloc::sync::Arc<dyn datafusion_datasource::source::DataSource>>
+
+pub struct vortex_datafusion::v2::VortexTable
+
+impl vortex_datafusion::v2::VortexTable
+
+pub fn vortex_datafusion::v2::VortexTable::new(data_source: vortex_scan::api::DataSourceRef, session: vortex_session::VortexSession, arrow_schema: arrow_schema::schema::SchemaRef) -> Self
+
+impl core::fmt::Debug for vortex_datafusion::v2::VortexTable
+
+pub fn vortex_datafusion::v2::VortexTable::fmt(&self, f: &mut core::fmt::Formatter<'_>) -> core::fmt::Result
+
+impl datafusion_catalog::table::TableProvider for vortex_datafusion::v2::VortexTable
+
+pub fn vortex_datafusion::v2::VortexTable::as_any(&self) -> &dyn core::any::Any
+
+pub fn vortex_datafusion::v2::VortexTable::scan<'life0, 'life1, 'life2, 'life3, 'async_trait>(&'life0 self, _state: &'life1 dyn datafusion_session::session::Session, projection: core::option::Option<&'life2 alloc::vec::Vec<usize>>, _filters: &'life3 [datafusion_expr::expr::Expr], _limit: core::option::Option<usize>) -> core::pin::Pin<alloc::boxed::Box<(dyn core::future::future::Future<Output = datafusion_common::error::Result<alloc::sync::Arc<dyn datafusion_physical_plan::execution_plan::ExecutionPlan>>> + core::marker::Send + 'async_trait)>> where Self: 'async_trait, 'life0: 'async_trait, 'life1: 'async_trait, 'life2: 'async_trait, 'life3: 'async_trait
+
+pub fn vortex_datafusion::v2::VortexTable::schema(&self) -> arrow_schema::schema::SchemaRef
+
+pub fn vortex_datafusion::v2::VortexTable::statistics(&self) -> core::option::Option<datafusion_common::stats::Statistics>
+
+pub fn vortex_datafusion::v2::VortexTable::table_type(&self) -> datafusion_expr::table_source::TableType
+
 pub struct vortex_datafusion::VortexAccessPlan
 
 impl vortex_datafusion::VortexAccessPlan
 
 pub fn vortex_datafusion::VortexAccessPlan::apply_to_builder<A>(&self, scan_builder: vortex_scan::scan_builder::ScanBuilder<A>) -> vortex_scan::scan_builder::ScanBuilder<A> where A: 'static + core::marker::Send
 
+pub fn vortex_datafusion::VortexAccessPlan::selection(&self) -> core::option::Option<&vortex_scan::selection::Selection>
+
 impl vortex_datafusion::VortexAccessPlan
 
 pub fn vortex_datafusion::VortexAccessPlan::with_selection(self, selection: vortex_scan::selection::Selection) -> Self

vortex-datafusion/src/convert/mod.rs

@@ -6,6 +6,7 @@ use vortex::error::VortexResult;
 pub(crate) mod exprs;
 mod scalars;
 pub(crate) mod schema;
+pub(crate) mod stats;
 
 /// First-party trait for implementing conversion from DataFusion types to Vortex types.
 pub(crate) trait FromDataFusion<D: ?Sized>: Sized {