feat: add GroupColumn support for Decimal256 in multi-column GROUP BY

datafusion/physical-plan/benches/multi_group_by.rs

@@ -27,9 +27,9 @@
 //! covers a `(FixedSizeBinary, Int32)` key to exercise the
 //! `FixedSizeBinaryGroupValueBuilder`.
 
-use arrow::array::{ArrayRef, Int32Array, UInt32Array};
+use arrow::array::{ArrayRef, Decimal256Array, Int32Array, UInt32Array};
 use arrow::compute::take;
-use arrow::datatypes::{DataType, Field, Schema, SchemaRef};
+use arrow::datatypes::{DataType, Field, Schema, SchemaRef, i256};
 use arrow::util::bench_util::create_fsb_array;
 use criterion::{BenchmarkId, Criterion, criterion_group, criterion_main};
 use datafusion_physical_plan::aggregates::group_values::GroupValues;
@@ -444,6 +444,92 @@ fn bench_fixed_size_binary(c: &mut Criterion) {
     group.finish();
 }
 
+fn make_decimal256_schema() -> SchemaRef {
+    Arc::new(Schema::new(vec![
+        Field::new("dec", DataType::Decimal256(50, 0), false),
+        Field::new("id", DataType::Int32, false),
+    ]))
+}
+
+/// Generate `(Decimal256(50, 0), Int32)` batches with `num_distinct_groups`
+/// distinct keys. Each distinct value is `i256::from_i128(g)`; precision > 38
+/// keeps it a genuine `Decimal256`. The `Int32` column is keyed identically so
+/// the combined cardinality equals `num_distinct_groups`.
+fn generate_decimal256_batches(
+    num_distinct_groups: usize,
+    num_rows: usize,
+    batch_size: usize,
+) -> Vec<Vec<ArrayRef>> {
+    let num_full_batches = num_rows / batch_size;
+    let remainder = num_rows % batch_size;
+    let num_batches = num_full_batches + if remainder > 0 { 1 } else { 0 };
+
+    (0..num_batches)
+        .map(|batch_idx| {
+            let batch_start = batch_idx * batch_size;
+            let current_batch_size = if batch_idx == num_batches - 1 && remainder > 0 {
+                remainder
+            } else {
+                batch_size
+            };
+
+            let group_ids = (0..current_batch_size)
+                .map(|row| (batch_start + row) % num_distinct_groups);
+
+            let keys = Decimal256Array::from_iter_values(
+                group_ids.clone().map(|g| i256::from_i128(g as i128)),
+            )
+            .with_precision_and_scale(50, 0)
+            .unwrap();
+            let id: Int32Array = group_ids.map(|g| g as i32).collect();
+
+            vec![Arc::new(keys) as ArrayRef, Arc::new(id) as ArrayRef]
+        })
+        .collect()
+}
+
+/// Experiment 8: Group count sweep for a `(Decimal256, Int32)` key.
+///
+/// Exercises the primitive `GroupColumn` builder for `Decimal256` (32-byte
+/// `i256` native) on the multi-column path (previously such a schema fell back
+/// to `GroupValuesRows`).
+fn bench_decimal256(c: &mut Criterion) {
+    let mut group = c.benchmark_group("decimal256");
+    group.sample_size(15);
+
+    let schema = make_decimal256_schema();
+
+    for num_groups in [1_000, 1_000_000] {
+        let batches =
+            generate_decimal256_batches(num_groups, 1_000_000, DEFAULT_BATCH_SIZE);
+
+        for vectorized in [true, false] {
+            let label = if vectorized {
+                "vectorized"
+            } else {
+                "row_based"
+            };
+            group.bench_with_input(
+                BenchmarkId::new(label, format!("grp_{num_groups}")),
+                &batches,
+                |b, batches| {
+                    b.iter_batched_ref(
+                        || {
+                            (
+                                create_group_values(&schema, vectorized),
+                                Vec::<usize>::with_capacity(DEFAULT_BATCH_SIZE),
+                            )
+                        },
+                        |(gv, groups)| bench_intern(gv, batches, groups),
+                        criterion::BatchSize::LargeInput,
+                    );
+                },
+            );
+        }
+    }
+    group.finish();
+}
+
 criterion_group!(
     benches,
     bench_issue_17850_regression,
@@ -453,5 +539,6 @@ criterion_group!(
     bench_high_cardinality_scaling,
     bench_group_count_sweep,
     bench_fixed_size_binary,
+    bench_decimal256,
 );
 criterion_main!(benches);

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

@@ -32,12 +32,12 @@ use crate::aggregates::group_values::multi_group_by::{
 use arrow::array::{Array, ArrayRef, BooleanBufferBuilder};
 use arrow::compute::cast;
 use arrow::datatypes::{
-    BinaryViewType, DataType, Date32Type, Date64Type, Decimal128Type, Field, Float32Type,
-    Float64Type, Int8Type, Int16Type, Int32Type, Int64Type, Schema, SchemaRef,
-    StringViewType, Time32MillisecondType, Time32SecondType, Time64MicrosecondType,
-    Time64NanosecondType, TimeUnit, TimestampMicrosecondType, TimestampMillisecondType,
-    TimestampNanosecondType, TimestampSecondType, UInt8Type, UInt16Type, UInt32Type,
-    UInt64Type,
+    BinaryViewType, DataType, Date32Type, Date64Type, Decimal128Type, Decimal256Type,
+    Field, Float32Type, Float64Type, Int8Type, Int16Type, Int32Type, Int64Type, Schema,
+    SchemaRef, StringViewType, Time32MillisecondType, Time32SecondType,
+    Time64MicrosecondType, Time64NanosecondType, TimeUnit, TimestampMicrosecondType,
+    TimestampMillisecondType, TimestampNanosecondType, TimestampSecondType, UInt8Type,
+    UInt16Type, UInt32Type, UInt64Type,
 };
 use datafusion_common::hash_utils::RandomState;
 use datafusion_common::hash_utils::create_hashes;
@@ -936,6 +936,7 @@ fn group_column_supported_type(data_type: &DataType) -> bool {
             | DataType::Float32
             | DataType::Float64
             | DataType::Decimal128(_, _)
+            | DataType::Decimal256(_, _)
             | DataType::Utf8
             | DataType::LargeUtf8
             | DataType::Binary
@@ -1034,6 +1035,9 @@ fn make_group_column(field: &Field) -> Result<Box<dyn GroupColumn>> {
         DataType::Decimal128(_, _) => {
             instantiate_primitive!(v, nullable, Decimal128Type, data_type)
         }
+        DataType::Decimal256(_, _) => {
+            instantiate_primitive!(v, nullable, Decimal256Type, data_type)
+        }
         DataType::Utf8 => {
             v.push(Box::new(ByteGroupValueBuilder::<i32>::new(
                 OutputType::Utf8,
@@ -1259,7 +1263,10 @@ enum Nulls {
 mod tests {
     use std::{collections::HashMap, sync::Arc};
 
-    use arrow::array::{ArrayRef, Int64Array, RecordBatch, StringArray, StringViewArray};
+    use arrow::array::{
+        Array, ArrayRef, Decimal256Array, Int64Array, RecordBatch, StringArray,
+        StringViewArray,
+    };
     use arrow::datatypes::{DataType, Field, Schema, SchemaRef};
     use arrow::{compute::concat_batches, util::pretty::pretty_format_batches};
     use datafusion_common::utils::proxy::HashTableAllocExt;
@@ -1295,6 +1302,7 @@ mod tests {
             DataType::Float32,
             DataType::Float64,
             DataType::Decimal128(38, 10),
+            DataType::Decimal256(76, 10),
             DataType::Utf8,
             DataType::LargeUtf8,
             DataType::Utf8View,
@@ -1326,7 +1334,6 @@ mod tests {
 
         let unsupported_cases: Vec<DataType> = vec![
             DataType::Float16,
-            DataType::Decimal256(76, 10),
             // Invalid Time-unit combinations: Time32 is defined only for
             // Second / Millisecond and Time64 only for Microsecond /
             // Nanosecond. The TimeUnit enum allows constructing the other
@@ -1352,6 +1359,51 @@ mod tests {
         }
     }
 
+    /// Decimal256 GROUP BY on the column-wise fast path (mirroring Decimal128):
+    /// dedups (incl. nulls) and preserves the Decimal256 precision/scale output
+    /// type. Uses precision > 38 so it's genuinely Decimal256, not Decimal128.
+    #[test]
+    fn test_group_values_column_decimal256() {
+        use arrow::datatypes::i256;
+
+        let schema = Arc::new(Schema::new(vec![Field::new(
+            "d",
+            DataType::Decimal256(50, 0),
+            true,
+        )]));
+        assert!(supported_schema(&schema));
+        let mut group_values =
+            GroupValuesColumn::<false>::try_new(Arc::clone(&schema)).unwrap();
+
+        // Two distinct values and a null with repeats: row 3 repeats row 0,
+        // row 4 repeats the null of row 1.
+        let a = i256::from_i128(100);
+        let b = i256::from_i128(200);
+        let input: ArrayRef = Arc::new(
+            Decimal256Array::from(vec![Some(a), None, Some(b), Some(a), None])
+                .with_precision_and_scale(50, 0)
+                .unwrap(),
+        );
+        let mut groups = Vec::new();
+        group_values.intern(&[input], &mut groups).unwrap();
+        assert_eq!(groups, vec![0, 1, 2, 0, 1]);
+
+        let emitted = group_values.emit(EmitTo::All).unwrap();
+        assert_eq!(emitted.len(), 1);
+        // The emitted key keeps its Decimal256 type (precision + scale), not
+        // the bare i256 native.
+        assert_eq!(emitted[0].data_type(), &DataType::Decimal256(50, 0));
+        let actual = emitted[0]
+            .as_any()
+            .downcast_ref::<Decimal256Array>()
+            .expect("emitted column should be a Decimal256Array");
+        // Three groups in first-seen order: 100, null, 200.
+        assert_eq!(actual.len(), 3);
+        assert_eq!(actual.value(0), a);
+        assert!(actual.is_null(1));
+        assert_eq!(actual.value(2), b);
+    }
+
     #[test]
     fn supported_schema_rejects_mix_of_supported_and_unsupported() {
         // One Float16 column among supported columns flips the whole

datafusion/sqllogictest/test_files/aggregate.slt

@@ -2327,6 +2327,39 @@ statement ok
 DROP TABLE approx_distinct_interval_test;
 
 
+# GROUP BY over Decimal256 group keys. Decimal256 columns route through the
+# GroupValuesColumn column-wise fast path (like Decimal128), so a Decimal256 key
+# no longer forces the row-encoded fallback. Equal decimals collapse and a
+# Decimal256 key groups alongside a primitive on the multi-column path. Queries
+# project only the INT counts / keys so the expected output does not depend on
+# decimal Display formatting.
+statement ok
+CREATE TABLE decimal256_group_test AS VALUES
+  (1, arrow_cast(100, 'Decimal256(20, 2)')),
+  (1, arrow_cast(100, 'Decimal256(20, 2)')),
+  (1, arrow_cast(250, 'Decimal256(20, 2)')),
+  (2, arrow_cast(100, 'Decimal256(20, 2)'));
+
+# Single Decimal256 group key: {100.00, 250.00}, with 100.00 appearing three times.
+query I
+SELECT count(*) FROM decimal256_group_test GROUP BY column2 ORDER BY count(*);
+----
+1
+3
+
+# Multi-column GROUP BY: a primitive key and a Decimal256 key on the same path.
+query II
+SELECT column1, count(*)
+FROM decimal256_group_test GROUP BY column1, column2 ORDER BY column1, count(*);
+----
+1 1
+1 2
+2 1
+
+statement ok
+DROP TABLE decimal256_group_test;
+
+
 
 ## This test executes the APPROX_PERCENTILE_CONT aggregation against the test
 ## data, asserting the estimated quantiles are ±5% their actual values.