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new d692df0358 feat: Optimize hash util for `MapArray` (#20179)
d692df0358 is described below
commit d692df03585f77bd8112d41fa80e2ae239785e9c
Author: Jonathan Chen <[email protected]>
AuthorDate: Wed Feb 18 08:42:02 2026 -0500
feat: Optimize hash util for `MapArray` (#20179)
## Which issue does this PR close?
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enhancements and this helps us generate change logs for our releases.
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- Closes #20151 .
## Rationale for this change
Reduce the irrelevant data being used to hash for `MapArray`
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in the issue then this section is not needed.
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## What changes are included in this PR?
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## Are these changes tested?
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---
datafusion/common/benches/with_hashes.rs | 252 ++++++++++++++++++++++++++++++-
datafusion/common/src/hash_utils.rs | 126 +++++++++++++++-
2 files changed, 363 insertions(+), 15 deletions(-)
diff --git a/datafusion/common/benches/with_hashes.rs
b/datafusion/common/benches/with_hashes.rs
index 64ce8e8dfe..9ee31d9c4b 100644
--- a/datafusion/common/benches/with_hashes.rs
+++ b/datafusion/common/benches/with_hashes.rs
@@ -19,13 +19,13 @@
use ahash::RandomState;
use arrow::array::{
- Array, ArrayRef, ArrowPrimitiveType, DictionaryArray, GenericStringArray,
- NullBufferBuilder, OffsetSizeTrait, PrimitiveArray, RunArray,
StringViewArray,
- StructArray, make_array,
+ Array, ArrayRef, ArrowPrimitiveType, DictionaryArray, GenericStringArray,
Int32Array,
+ Int64Array, ListArray, MapArray, NullBufferBuilder, OffsetSizeTrait,
PrimitiveArray,
+ RunArray, StringViewArray, StructArray, UnionArray, make_array,
};
-use arrow::buffer::NullBuffer;
+use arrow::buffer::{NullBuffer, OffsetBuffer, ScalarBuffer};
use arrow::datatypes::{
- ArrowDictionaryKeyType, DataType, Field, Fields, Int32Type, Int64Type,
+ ArrowDictionaryKeyType, DataType, Field, Fields, Int32Type, Int64Type,
UnionFields,
};
use criterion::{Bencher, Criterion, criterion_group, criterion_main};
use datafusion_common::hash_utils::with_hashes;
@@ -40,6 +40,7 @@ const BATCH_SIZE: usize = 8192;
struct BenchData {
name: &'static str,
array: ArrayRef,
+ /// Union arrays can't have null bitmasks added
supports_nulls: bool,
}
@@ -78,6 +79,26 @@ fn criterion_benchmark(c: &mut Criterion) {
array: pool.dictionary_array::<Int32Type>(BATCH_SIZE),
supports_nulls: true,
},
+ BenchData {
+ name: "list_array",
+ array: list_array(BATCH_SIZE),
+ supports_nulls: true,
+ },
+ BenchData {
+ name: "map_array",
+ array: map_array(BATCH_SIZE),
+ supports_nulls: true,
+ },
+ BenchData {
+ name: "sparse_union",
+ array: sparse_union_array(BATCH_SIZE),
+ supports_nulls: false,
+ },
+ BenchData {
+ name: "dense_union",
+ array: dense_union_array(BATCH_SIZE),
+ supports_nulls: false,
+ },
BenchData {
name: "struct_array",
array: create_struct_array(&pool, BATCH_SIZE),
@@ -103,10 +124,9 @@ fn criterion_benchmark(c: &mut Criterion) {
let arrays = vec![array.clone(), array.clone(), array.clone()];
do_hash_test(b, &arrays);
});
-
+ // Union arrays can't have null bitmasks
if supports_nulls {
let nullable_array = add_nulls(&array);
-
c.bench_function(&format!("{name}: single, nulls"), |b| {
do_hash_test(b, std::slice::from_ref(&nullable_array));
});
@@ -268,6 +288,222 @@ where
Arc::new(array)
}
+/// Benchmark sliced arrays to demonstrate the optimization for when an array
is
+/// sliced, the underlying buffer may be much larger than what's referenced by
+/// the slice. The optimization avoids hashing unreferenced elements.
+fn sliced_array_benchmark(c: &mut Criterion) {
+ // Test with different slice ratios: slice_size / total_size
+ // Smaller ratio = more potential savings from the optimization
+ let slice_ratios = [10, 5, 2]; // 1/10, 1/5, 1/2 of total
+
+ for ratio in slice_ratios {
+ let total_rows = BATCH_SIZE * ratio;
+ let slice_offset = BATCH_SIZE * (ratio / 2); // Take from middle
+ let slice_len = BATCH_SIZE;
+
+ // Sliced ListArray
+ {
+ let full_array = list_array(total_rows);
+ let sliced: ArrayRef = Arc::new(
+ full_array
+ .as_any()
+ .downcast_ref::<ListArray>()
+ .unwrap()
+ .slice(slice_offset, slice_len),
+ );
+ c.bench_function(
+ &format!("list_array_sliced: 1/{ratio} of {total_rows} rows"),
+ |b| {
+ do_hash_test_with_len(b, std::slice::from_ref(&sliced),
slice_len);
+ },
+ );
+ }
+
+ // Sliced MapArray
+ {
+ let full_array = map_array(total_rows);
+ let sliced: ArrayRef = Arc::new(
+ full_array
+ .as_any()
+ .downcast_ref::<MapArray>()
+ .unwrap()
+ .slice(slice_offset, slice_len),
+ );
+ c.bench_function(
+ &format!("map_array_sliced: 1/{ratio} of {total_rows} rows"),
+ |b| {
+ do_hash_test_with_len(b, std::slice::from_ref(&sliced),
slice_len);
+ },
+ );
+ }
+
+ // Sliced Sparse UnionArray
+ {
+ let full_array = sparse_union_array(total_rows);
+ let sliced: ArrayRef = Arc::new(
+ full_array
+ .as_any()
+ .downcast_ref::<UnionArray>()
+ .unwrap()
+ .slice(slice_offset, slice_len),
+ );
+ c.bench_function(
+ &format!("sparse_union_sliced: 1/{ratio} of {total_rows}
rows"),
+ |b| {
+ do_hash_test_with_len(b, std::slice::from_ref(&sliced),
slice_len);
+ },
+ );
+ }
+ }
+}
+
+fn do_hash_test_with_len(b: &mut Bencher, arrays: &[ArrayRef], expected_len:
usize) {
+ let state = RandomState::new();
+ b.iter(|| {
+ with_hashes(arrays, &state, |hashes| {
+ assert_eq!(hashes.len(), expected_len);
+ Ok(())
+ })
+ .unwrap();
+ });
+}
+
+fn list_array(num_rows: usize) -> ArrayRef {
+ let mut rng = make_rng();
+ let elements_per_row = 5;
+ let total_elements = num_rows * elements_per_row;
+
+ let values: Int64Array = (0..total_elements)
+ .map(|_| Some(rng.random::<i64>()))
+ .collect();
+ let offsets: Vec<i32> = (0..=num_rows)
+ .map(|i| (i * elements_per_row) as i32)
+ .collect();
+
+ Arc::new(ListArray::new(
+ Arc::new(Field::new("item", DataType::Int64, true)),
+ OffsetBuffer::new(ScalarBuffer::from(offsets)),
+ Arc::new(values),
+ None,
+ ))
+}
+
+fn map_array(num_rows: usize) -> ArrayRef {
+ let mut rng = make_rng();
+ let entries_per_row = 5;
+ let total_entries = num_rows * entries_per_row;
+
+ let keys: Int32Array = (0..total_entries)
+ .map(|_| Some(rng.random::<i32>()))
+ .collect();
+ let values: Int64Array = (0..total_entries)
+ .map(|_| Some(rng.random::<i64>()))
+ .collect();
+ let offsets: Vec<i32> = (0..=num_rows)
+ .map(|i| (i * entries_per_row) as i32)
+ .collect();
+
+ let entries = StructArray::try_new(
+ Fields::from(vec![
+ Field::new("keys", DataType::Int32, false),
+ Field::new("values", DataType::Int64, true),
+ ]),
+ vec![Arc::new(keys), Arc::new(values)],
+ None,
+ )
+ .unwrap();
+
+ Arc::new(MapArray::new(
+ Arc::new(Field::new(
+ "entries",
+ DataType::Struct(Fields::from(vec![
+ Field::new("keys", DataType::Int32, false),
+ Field::new("values", DataType::Int64, true),
+ ])),
+ false,
+ )),
+ OffsetBuffer::new(ScalarBuffer::from(offsets)),
+ entries,
+ None,
+ false,
+ ))
+}
+
+fn sparse_union_array(num_rows: usize) -> ArrayRef {
+ let mut rng = make_rng();
+ let num_types = 5;
+
+ let type_ids: Vec<i8> = (0..num_rows)
+ .map(|_| rng.random_range(0..num_types) as i8)
+ .collect();
+ let (fields, children): (Vec<_>, Vec<_>) = (0..num_types)
+ .map(|i| {
+ (
+ (
+ i as i8,
+ Arc::new(Field::new(format!("f{i}"), DataType::Int64,
true)),
+ ),
+ primitive_array::<Int64Type>(num_rows),
+ )
+ })
+ .unzip();
+
+ Arc::new(
+ UnionArray::try_new(
+ UnionFields::from_iter(fields),
+ ScalarBuffer::from(type_ids),
+ None,
+ children,
+ )
+ .unwrap(),
+ )
+}
+
+fn dense_union_array(num_rows: usize) -> ArrayRef {
+ let mut rng = make_rng();
+ let num_types = 5;
+ let type_ids: Vec<i8> = (0..num_rows)
+ .map(|_| rng.random_range(0..num_types) as i8)
+ .collect();
+
+ let mut type_counts = vec![0i32; num_types];
+ for &tid in &type_ids {
+ type_counts[tid as usize] += 1;
+ }
+
+ let mut current_offsets = vec![0i32; num_types];
+ let offsets: Vec<i32> = type_ids
+ .iter()
+ .map(|&tid| {
+ let offset = current_offsets[tid as usize];
+ current_offsets[tid as usize] += 1;
+ offset
+ })
+ .collect();
+
+ let (fields, children): (Vec<_>, Vec<_>) = (0..num_types)
+ .map(|i| {
+ (
+ (
+ i as i8,
+ Arc::new(Field::new(format!("f{i}"), DataType::Int64,
true)),
+ ),
+ primitive_array::<Int64Type>(type_counts[i] as usize),
+ )
+ })
+ .unzip();
+
+ Arc::new(
+ UnionArray::try_new(
+ UnionFields::from_iter(fields),
+ ScalarBuffer::from(type_ids),
+ Some(ScalarBuffer::from(offsets)),
+ children,
+ )
+ .unwrap(),
+ )
+}
+
fn boolean_array(array_len: usize) -> ArrayRef {
let mut rng = make_rng();
Arc::new(
@@ -329,5 +565,5 @@ where
)
}
-criterion_group!(benches, criterion_benchmark);
+criterion_group!(benches, criterion_benchmark, sliced_array_benchmark);
criterion_main!(benches);
diff --git a/datafusion/common/src/hash_utils.rs
b/datafusion/common/src/hash_utils.rs
index 1489f688c3..3be6118c55 100644
--- a/datafusion/common/src/hash_utils.rs
+++ b/datafusion/common/src/hash_utils.rs
@@ -20,10 +20,12 @@
use ahash::RandomState;
use arrow::array::types::{IntervalDayTime, IntervalMonthDayNano};
use arrow::array::*;
+use arrow::compute::take;
use arrow::datatypes::*;
#[cfg(not(feature = "force_hash_collisions"))]
use arrow::{downcast_dictionary_array, downcast_primitive_array};
use itertools::Itertools;
+use std::collections::HashMap;
#[cfg(not(feature = "force_hash_collisions"))]
use crate::cast::{
@@ -541,15 +543,29 @@ fn hash_map_array(
let offsets = array.offsets();
// Create hashes for each entry in each row
- let mut values_hashes = vec![0u64; array.entries().len()];
- create_hashes(array.entries().columns(), random_state, &mut
values_hashes)?;
+ let first_offset = offsets.first().copied().unwrap_or_default() as usize;
+ let last_offset = offsets.last().copied().unwrap_or_default() as usize;
+ let entries_len = last_offset - first_offset;
+
+ // Only hash the entries that are actually referenced
+ let mut values_hashes = vec![0u64; entries_len];
+ let entries = array.entries();
+ let sliced_columns: Vec<ArrayRef> = entries
+ .columns()
+ .iter()
+ .map(|col| col.slice(first_offset, entries_len))
+ .collect();
+ create_hashes(&sliced_columns, random_state, &mut values_hashes)?;
// Combine the hashes for entries on each row with each other and previous
hash for that row
+ // Adjust indices by first_offset since values_hashes is sliced starting
from first_offset
if let Some(nulls) = nulls {
for (i, (start, stop)) in
offsets.iter().zip(offsets.iter().skip(1)).enumerate() {
if nulls.is_valid(i) {
let hash = &mut hashes_buffer[i];
- for values_hash in
&values_hashes[start.as_usize()..stop.as_usize()] {
+ for values_hash in &values_hashes
+ [start.as_usize() - first_offset..stop.as_usize() -
first_offset]
+ {
*hash = combine_hashes(*hash, *values_hash);
}
}
@@ -557,7 +573,9 @@ fn hash_map_array(
} else {
for (i, (start, stop)) in
offsets.iter().zip(offsets.iter().skip(1)).enumerate() {
let hash = &mut hashes_buffer[i];
- for values_hash in
&values_hashes[start.as_usize()..stop.as_usize()] {
+ for values_hash in &values_hashes
+ [start.as_usize() - first_offset..stop.as_usize() -
first_offset]
+ {
*hash = combine_hashes(*hash, *values_hash);
}
}
@@ -662,14 +680,42 @@ fn hash_union_array(
random_state: &RandomState,
hashes_buffer: &mut [u64],
) -> Result<()> {
- use std::collections::HashMap;
-
let DataType::Union(union_fields, _mode) = array.data_type() else {
unreachable!()
};
- let mut child_hashes = HashMap::with_capacity(union_fields.len());
+ if array.is_dense() {
+ // Dense union: children only contain values of their type, so they're
already compact.
+ // Use the default hashing approach which is efficient for dense
unions.
+ hash_union_array_default(array, union_fields, random_state,
hashes_buffer)
+ } else {
+ // Sparse union: each child has the same length as the union array.
+ // Optimization: only hash the elements that are actually referenced
by type_ids,
+ // instead of hashing all K*N elements (where K = num types, N = array
length).
+ hash_sparse_union_array(array, union_fields, random_state,
hashes_buffer)
+ }
+}
+
+/// Default hashing for union arrays - hashes all elements of each child array
fully.
+///
+/// This approach works for both dense and sparse union arrays:
+/// - Dense unions: children are compact (each child only contains values of
that type)
+/// - Sparse unions: children have the same length as the union array
+///
+/// For sparse unions with 3+ types, the optimized take/scatter approach in
+/// `hash_sparse_union_array` is more efficient, but for 1-2 types or dense
unions,
+/// this simpler approach is preferred.
+#[cfg(not(feature = "force_hash_collisions"))]
+fn hash_union_array_default(
+ array: &UnionArray,
+ union_fields: &UnionFields,
+ random_state: &RandomState,
+ hashes_buffer: &mut [u64],
+) -> Result<()> {
+ let mut child_hashes: HashMap<i8, Vec<u64>> =
+ HashMap::with_capacity(union_fields.len());
+ // Hash each child array fully
for (type_id, _field) in union_fields.iter() {
let child = array.child(type_id);
let mut child_hash_buffer = vec![0; child.len()];
@@ -678,6 +724,9 @@ fn hash_union_array(
child_hashes.insert(type_id, child_hash_buffer);
}
+ // Combine hashes for each row using the appropriate child offset
+ // For dense unions: value_offset points to the actual position in the
child
+ // For sparse unions: value_offset equals the row index
#[expect(clippy::needless_range_loop)]
for i in 0..array.len() {
let type_id = array.type_id(i);
@@ -690,6 +739,69 @@ fn hash_union_array(
Ok(())
}
+/// Hash a sparse union array.
+/// Sparse unions have child arrays with the same length as the union array.
+/// For 3+ types, we optimize by only hashing the N elements that are actually
used
+/// (via take/scatter), instead of hashing all K*N elements.
+///
+/// For 1-2 types, the overhead of take/scatter outweighs the benefit, so we
use
+/// the default approach of hashing all children (same as dense unions).
+#[cfg(not(feature = "force_hash_collisions"))]
+fn hash_sparse_union_array(
+ array: &UnionArray,
+ union_fields: &UnionFields,
+ random_state: &RandomState,
+ hashes_buffer: &mut [u64],
+) -> Result<()> {
+ use std::collections::HashMap;
+
+ // For 1-2 types, the take/scatter overhead isn't worth it.
+ // Fall back to the default approach (same as dense union).
+ if union_fields.len() <= 2 {
+ return hash_union_array_default(
+ array,
+ union_fields,
+ random_state,
+ hashes_buffer,
+ );
+ }
+
+ let type_ids = array.type_ids();
+
+ // Group indices by type_id
+ let mut indices_by_type: HashMap<i8, Vec<u32>> = HashMap::new();
+ for (i, &type_id) in type_ids.iter().enumerate() {
+ indices_by_type.entry(type_id).or_default().push(i as u32);
+ }
+
+ // For each type, extract only the needed elements, hash them, and scatter
back
+ for (type_id, _field) in union_fields.iter() {
+ if let Some(indices) = indices_by_type.get(&type_id) {
+ if indices.is_empty() {
+ continue;
+ }
+
+ let child = array.child(type_id);
+ let indices_array = UInt32Array::from(indices.clone());
+
+ // Extract only the elements we need using take()
+ let filtered = take(child.as_ref(), &indices_array, None)?;
+
+ // Hash the filtered array
+ let mut filtered_hashes = vec![0u64; filtered.len()];
+ create_hashes([&filtered], random_state, &mut filtered_hashes)?;
+
+ // Scatter hashes back to correct positions
+ for (hash, &idx) in filtered_hashes.iter().zip(indices.iter()) {
+ hashes_buffer[idx as usize] =
+ combine_hashes(hashes_buffer[idx as usize], *hash);
+ }
+ }
+ }
+
+ Ok(())
+}
+
#[cfg(not(feature = "force_hash_collisions"))]
fn hash_fixed_list_array(
array: &FixedSizeListArray,
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