Use partial aggregation schema for spilling to avoid column mismatch in GroupedHashAggregateStream

datafusion/core/src/dataframe/mod.rs

@@ -20,11 +20,6 @@
 #[cfg(feature = "parquet")]
 mod parquet;
 
-use std::any::Any;
-use std::borrow::Cow;
-use std::collections::HashMap;
-use std::sync::Arc;
-
 use crate::arrow::record_batch::RecordBatch;
 use crate::arrow::util::pretty;
 use crate::datasource::file_format::csv::CsvFormatFactory;
@@ -43,6 +38,10 @@ use crate::physical_plan::{
     ExecutionPlan, SendableRecordBatchStream,
 };
 use crate::prelude::SessionContext;
+use std::any::Any;
+use std::borrow::Cow;
+use std::collections::HashMap;
+use std::sync::Arc;
 
 use arrow::array::{Array, ArrayRef, Int64Array, StringArray};
 use arrow::compute::{cast, concat};

datafusion/physical-plan/src/aggregates/mod.rs

@@ -1317,6 +1317,7 @@ mod tests {
     use crate::execution_plan::Boundedness;
     use crate::expressions::col;
     use crate::memory::MemoryExec;
+    use crate::metrics::MetricValue;
     use crate::test::assert_is_pending;
     use crate::test::exec::{assert_strong_count_converges_to_zero, BlockingExec};
     use crate::RecordBatchStream;
@@ -2783,4 +2784,137 @@ mod tests {
         assert_eq!(aggr_schema, expected_schema);
         Ok(())
     }
+
+    // test for https://github.com/apache/datafusion/issues/13949
+    async fn run_test_with_spill_pool_if_necessary(
+        pool_size: usize,
+        expect_spill: bool,
+    ) -> Result<()> {
+        fn create_record_batch(
+            schema: &Arc<Schema>,
+            data: (Vec<u32>, Vec<f64>),
+        ) -> Result<RecordBatch> {
+            Ok(RecordBatch::try_new(
+                Arc::clone(schema),
+                vec![
+                    Arc::new(UInt32Array::from(data.0)),
+                    Arc::new(Float64Array::from(data.1)),
+                ],
+            )?)
+        }
+
+        let schema = Arc::new(Schema::new(vec![
+            Field::new("a", DataType::UInt32, false),
+            Field::new("b", DataType::Float64, false),
+        ]));
+
+        let batches = vec![
+            create_record_batch(&schema, (vec![2, 3, 4, 4], vec![1.0, 2.0, 3.0, 4.0]))?,
+            create_record_batch(&schema, (vec![2, 3, 4, 4], vec![1.0, 2.0, 3.0, 4.0]))?,
+        ];
+        let plan: Arc<dyn ExecutionPlan> =
+            Arc::new(MemoryExec::try_new(&[batches], Arc::clone(&schema), None)?);
+
+        let grouping_set = PhysicalGroupBy::new(
+            vec![(col("a", &schema)?, "a".to_string())],
+            vec![],
+            vec![vec![false]],
+        );
+
+        // Test with MIN for simple intermediate state (min) and AVG for multiple intermediate states (partial sum, partial count).
+        let aggregates: Vec<Arc<AggregateFunctionExpr>> = vec![
+            Arc::new(
+                AggregateExprBuilder::new(
+                    datafusion_functions_aggregate::min_max::min_udaf(),
+                    vec![col("b", &schema)?],
+                )
+                .schema(Arc::clone(&schema))
+                .alias("MIN(b)")
+                .build()?,
+            ),
+            Arc::new(
+                AggregateExprBuilder::new(avg_udaf(), vec![col("b", &schema)?])
+                    .schema(Arc::clone(&schema))
+                    .alias("AVG(b)")
+                    .build()?,
+            ),
+        ];
+
+        let single_aggregate = Arc::new(AggregateExec::try_new(
+            AggregateMode::Single,
+            grouping_set,
+            aggregates,
+            vec![None, None],
+            plan,
+            Arc::clone(&schema),
+        )?);
+
+        let batch_size = 2;
+        let memory_pool = Arc::new(FairSpillPool::new(pool_size));
+        let task_ctx = Arc::new(
+            TaskContext::default()
+                .with_session_config(SessionConfig::new().with_batch_size(batch_size))
+                .with_runtime(Arc::new(
+                    RuntimeEnvBuilder::new()
+                        .with_memory_pool(memory_pool)
+                        .build()?,
+                )),
+        );
+
+        let result = collect(single_aggregate.execute(0, Arc::clone(&task_ctx))?).await?;
+
+        assert_spill_count_metric(expect_spill, single_aggregate);
+
+        #[rustfmt::skip]
+        assert_batches_sorted_eq!(
+            [
+                "+---+--------+--------+",
+                "| a | MIN(b) | AVG(b) |",
+                "+---+--------+--------+",
+                "| 2 | 1.0    | 1.0    |",
+                "| 3 | 2.0    | 2.0    |",
+                "| 4 | 3.0    | 3.5    |",
+                "+---+--------+--------+",
+            ],
+            &result
+        );
+
+        Ok(())
+    }
+
+    fn assert_spill_count_metric(
+        expect_spill: bool,
+        single_aggregate: Arc<AggregateExec>,
+    ) {
+        if let Some(metrics_set) = single_aggregate.metrics() {
+            let mut spill_count = 0;
+
+            // Inspect metrics for SpillCount
+            for metric in metrics_set.iter() {
+                if let MetricValue::SpillCount(count) = metric.value() {
+                    spill_count = count.value();
+                    break;
+                }
+            }
+
+            if expect_spill && spill_count == 0 {
+                panic!(
+                    "Expected spill but SpillCount metric not found or SpillCount was 0."
+                );
+            } else if !expect_spill && spill_count > 0 {
+                panic!("Expected no spill but found SpillCount metric with value greater than 0.");
+            }
+        } else {
+            panic!("No metrics returned from the operator; cannot verify spilling.");
+        }
+    }
+
+    #[tokio::test]
+    async fn test_aggregate_with_spill_if_necessary() -> Result<()> {
+        // test with spill
+        run_test_with_spill_pool_if_necessary(2_000, true).await?;
+        // test without spill
+        run_test_with_spill_pool_if_necessary(20_000, false).await?;
+        Ok(())
+    }
 }

datafusion/physical-plan/src/aggregates/row_hash.rs

@@ -24,8 +24,8 @@ use std::vec;
 use crate::aggregates::group_values::{new_group_values, GroupValues};
 use crate::aggregates::order::GroupOrderingFull;
 use crate::aggregates::{
-    evaluate_group_by, evaluate_many, evaluate_optional, group_schema, AggregateMode,
-    PhysicalGroupBy,
+    create_schema, evaluate_group_by, evaluate_many, evaluate_optional, group_schema,
+    AggregateMode, PhysicalGroupBy,
 };
 use crate::metrics::{BaselineMetrics, MetricBuilder, RecordOutput};
 use crate::sorts::sort::sort_batch;
@@ -490,6 +490,31 @@ impl GroupedHashAggregateStream {
             .collect::<Result<_>>()?;
 
         let group_schema = group_schema(&agg.input().schema(), &agg_group_by)?;
+
+        // fix https://github.com/apache/datafusion/issues/13949
+        // Builds a **partial aggregation** schema by combining the group columns and
+        // the accumulator state columns produced by each aggregate expression.
+        //
+        // # Why Partial Aggregation Schema Is Needed
+        //
+        // In a multi-stage (partial/final) aggregation strategy, each partial-aggregate
+        // operator produces *intermediate* states (e.g., partial sums, counts) rather
+        // than final scalar values. These extra columns do **not** exist in the original
+        // input schema (which may be something like `[colA, colB, ...]`). Instead,
+        // each aggregator adds its own internal state columns (e.g., `[acc_state_1, acc_state_2, ...]`).
+        //
+        // Therefore, when we spill these intermediate states or pass them to another
+        // aggregation operator, we must use a schema that includes both the group
+        // columns **and** the partial-state columns.
+        let partial_agg_schema = create_schema(
+            &agg.input().schema(),
+            &agg_group_by,
+            &aggregate_exprs,
+            AggregateMode::Partial,
+        )?;
+
+        let partial_agg_schema = Arc::new(partial_agg_schema);
+
         let spill_expr = group_schema
             .fields
             .into_iter()
@@ -522,7 +547,7 @@ impl GroupedHashAggregateStream {
         let spill_state = SpillState {
             spills: vec![],
             spill_expr,
-            spill_schema: Arc::clone(&agg_schema),
+            spill_schema: partial_agg_schema,
             is_stream_merging: false,
             merging_aggregate_arguments,
             merging_group_by: PhysicalGroupBy::new_single(agg_group_by.expr.clone()),
@@ -964,9 +989,6 @@ impl GroupedHashAggregateStream {
             && self.update_memory_reservation().is_err()
         {
             assert_ne!(self.mode, AggregateMode::Partial);
-            // Use input batch (Partial mode) schema for spilling because
-            // the spilled data will be merged and re-evaluated later.
-            self.spill_state.spill_schema = batch.schema();
             self.spill()?;
             self.clear_shrink(batch);
         }