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fix: apply single_distinct_to_groupby to aliased distinct aggregates #23486

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fix: apply single_distinct_to_groupby to aliased distinct aggregates #23486

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f30dd7c
fix: apply single_distinct_to_groupby to aliased distinct aggregates
gmhelmold Jul 11, 2026
aa7f9e5
Merge branch 'main' into fix/single-distinct-aliased-agg
gmhelmold Jul 12, 2026
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231 changes: 184 additions & 47 deletions datafusion/optimizer/src/single_distinct_to_groupby.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -29,7 +29,7 @@ use datafusion_expr::builder::project;
use datafusion_expr::expr::AggregateFunctionParams;
use datafusion_expr::{
Expr, col,
expr::AggregateFunction,
expr::{AggregateFunction, Alias},
logical_plan::{Aggregate, LogicalPlan},
};

Expand Down Expand Up @@ -62,10 +62,21 @@ impl SingleDistinctToGroupBy {
}

/// Check whether all aggregate exprs are distinct on a single field.
///
/// Aggregate exprs may arrive wrapped in an [`Expr::Alias`] (e.g. the
/// DataFrame API's `count(col("b")).distinct().alias("n")` produces
/// `Alias(AggregateFunction)`, whereas the SQL planner hoists the alias into
/// an outer `Projection` and leaves a bare `AggregateFunction` here). Unwrap
/// one alias layer, mirroring the `unalias()` idiom used elsewhere in the
/// optimizer (see `decorrelate.rs`), so both shapes are recognized alike.
fn is_single_distinct_agg(aggr_expr: &[Expr]) -> Result<bool> {
let mut fields_set = HashSet::new();
let mut aggregate_count = 0;
for expr in aggr_expr {
let expr = match expr {
Expr::Alias(Alias { expr, .. }) => expr.as_ref(),
_ => expr,
};
if let Expr::AggregateFunction(AggregateFunction {
func,
params:
Expand Down Expand Up @@ -179,8 +190,39 @@ impl OptimizerRule for SingleDistinctToGroupBy {
let mut inner_aggr_exprs = vec![];
let outer_aggr_exprs = aggr_expr
.into_iter()
.map(|aggr_expr| match aggr_expr {
Expr::AggregateFunction(AggregateFunction {
.map(|aggr_expr| {
// Peel off a single Alias layer (DataFrame API shape,
// see `is_single_distinct_agg` above) so the rewrite
// below sees the same bare AggregateFunction it would
// for the SQL-planner shape. The user-facing alias
// doesn't need to be re-applied here: the final
// `alias_expr` projection built below already
// restores each output's original name (alias or
// not) from `schema.qualified_field(idx)`, the same
// mechanism that preserves auto-generated display
// names like `count(DISTINCT test.b)` for the
// unaliased case.
let agg_fn = match aggr_expr {
Expr::AggregateFunction(agg_fn) => agg_fn,
Expr::Alias(Alias {
expr,
relation,
name,
metadata,
}) => match *expr {
Expr::AggregateFunction(agg_fn) => agg_fn,
other => {
return Ok(Expr::Alias(Alias {
expr: Box::new(other),
relation,
name,
metadata,
}));
}
},
other => return Ok(other),
};
let AggregateFunction {
func,
params:
AggregateFunctionParams {
Expand All @@ -190,54 +232,50 @@ impl OptimizerRule for SingleDistinctToGroupBy {
order_by,
null_treatment,
},
}) => {
if distinct {
assert_eq_or_internal_err!(
args.len(),
1,
"DISTINCT aggregate should have exactly one argument"
);
let arg = args.swap_remove(0);

if group_fields_set.insert(arg.schema_name().to_string())
{
inner_group_exprs
.push(arg.alias(SINGLE_DISTINCT_ALIAS));
}
Ok(Expr::AggregateFunction(AggregateFunction::new_udf(
func,
vec![col(SINGLE_DISTINCT_ALIAS)],
false, // intentional to remove distinct here
} = agg_fn;
if distinct {
assert_eq_or_internal_err!(
args.len(),
1,
"DISTINCT aggregate should have exactly one argument"
);
let arg = args.swap_remove(0);

if group_fields_set.insert(arg.schema_name().to_string()) {
inner_group_exprs.push(arg.alias(SINGLE_DISTINCT_ALIAS));
}
Ok(Expr::AggregateFunction(AggregateFunction::new_udf(
func,
vec![col(SINGLE_DISTINCT_ALIAS)],
false, // intentional to remove distinct here
filter,
order_by,
null_treatment,
)))
// if the aggregate function is not distinct, we need to rewrite it like two phase aggregation
} else {
index += 1;
let alias_str = format!("alias{index}");
inner_aggr_exprs.push(
Expr::AggregateFunction(AggregateFunction::new_udf(
Arc::clone(&func),
args,
false,
filter,
order_by,
null_treatment,
)))
// if the aggregate function is not distinct, we need to rewrite it like two phase aggregation
} else {
index += 1;
let alias_str = format!("alias{index}");
inner_aggr_exprs.push(
Expr::AggregateFunction(AggregateFunction::new_udf(
Arc::clone(&func),
args,
false,
filter,
order_by,
null_treatment,
))
.alias(&alias_str),
);
Ok(Expr::AggregateFunction(AggregateFunction::new_udf(
func,
vec![col(&alias_str)],
false,
None,
vec![],
None,
)))
}
))
.alias(&alias_str),
);
Ok(Expr::AggregateFunction(AggregateFunction::new_udf(
func,
vec![col(&alias_str)],
false,
None,
vec![],
None,
)))
}
_ => Ok(aggr_expr),
})
.collect::<Result<Vec<_>>>()?;

Expand Down Expand Up @@ -362,6 +400,38 @@ mod tests {
)
}

// https://github.com/apache/datafusion/issues/23401
// DataFrame API shape: `count(col("b")).distinct().alias("n")` places the
// alias directly on the aggregate (`Expr::Alias(AggregateFunction)`),
// unlike the SQL planner which hoists it into an outer Projection and
// leaves a bare `Expr::AggregateFunction` in `aggr_expr`. This must still
// be rewritten, and the user-chosen alias must survive in the output.
#[test]
fn single_distinct_aliased() -> Result<()> {
let table_scan = test_table_scan()?;

let expr = count_udaf()
.call(vec![col("b")])
.distinct()
.build()?
.alias("n");
let plan = LogicalPlanBuilder::from(table_scan)
.aggregate(Vec::<Expr>::new(), vec![expr])?
.build()?;

// Should work, and the output column stays named `n` (not the
// auto-generated `count(DISTINCT test.b)` display name).
assert_optimized_plan_equal!(
plan,
@r"
Projection: count(alias1) AS n [n:Int64]
Aggregate: groupBy=[[]], aggr=[[count(alias1)]] [count(alias1):Int64]
Aggregate: groupBy=[[test.b AS alias1]], aggr=[[]] [alias1:UInt32]
TableScan: test [a:UInt32, b:UInt32, c:UInt32]
"
)
}

// Currently this optimization is disabled for CUBE/ROLLUP/GROUPING SET
#[test]
fn single_distinct_and_grouping_set() -> Result<()> {
Expand Down Expand Up @@ -468,6 +538,47 @@ mod tests {
)
}

#[test]
fn single_distinct_aliased_and_groupby() -> Result<()> {
let table_scan = test_table_scan()?;

let plan = LogicalPlanBuilder::from(table_scan)
.aggregate(vec![col("a")], vec![count_distinct(col("b")).alias("n")])?
.build()?;

// Should work: alias preserved alongside a real group-by column.
assert_optimized_plan_equal!(
plan,
@r"
Projection: test.a, count(alias1) AS n [a:UInt32, n:Int64]
Aggregate: groupBy=[[test.a]], aggr=[[count(alias1)]] [a:UInt32, count(alias1):Int64]
Aggregate: groupBy=[[test.a, test.b AS alias1]], aggr=[[]] [a:UInt32, alias1:UInt32]
TableScan: test [a:UInt32, b:UInt32, c:UInt32]
"
)
}

// Negative case: an aliased NON-distinct aggregate alone must not be
// rewritten (no distinct field to collapse on) — same as the unaliased
// shape covered by `not_exist_distinct`.
#[test]
fn non_distinct_aliased() -> Result<()> {
let table_scan = test_table_scan()?;

let plan = LogicalPlanBuilder::from(table_scan)
.aggregate(vec![col("a")], vec![sum(col("c")).alias("s")])?
.build()?;

// Do nothing
assert_optimized_plan_equal!(
plan,
@r"
Aggregate: groupBy=[[test.a]], aggr=[[sum(test.c) AS s]] [a:UInt32, s:UInt64;N]
TableScan: test [a:UInt32, b:UInt32, c:UInt32]
"
)
}

#[test]
fn two_distinct_and_groupby() -> Result<()> {
let table_scan = test_table_scan()?;
Expand All @@ -489,6 +600,32 @@ mod tests {
)
}

// Negative case: two aliased distinct aggregates on different fields must
// not be rewritten (mirrors `two_distinct_and_groupby` but with aliases).
#[test]
fn two_distinct_aliased_and_groupby() -> Result<()> {
let table_scan = test_table_scan()?;

let plan = LogicalPlanBuilder::from(table_scan)
.aggregate(
vec![col("a")],
vec![
count_distinct(col("b")).alias("cb"),
count_distinct(col("c")).alias("cc"),
],
)?
.build()?;

// Do nothing
assert_optimized_plan_equal!(
plan,
@r"
Aggregate: groupBy=[[test.a]], aggr=[[count(DISTINCT test.b) AS cb, count(DISTINCT test.c) AS cc]] [a:UInt32, cb:Int64, cc:Int64]
TableScan: test [a:UInt32, b:UInt32, c:UInt32]
"
)
}

#[test]
fn one_field_two_distinct_and_groupby() -> Result<()> {
let table_scan = test_table_scan()?;
Expand Down