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Posted to github@datafusion.apache.org by "Rich-T-kid (via GitHub)" <gi...@apache.org> on 2026/10/01 21:51:05 UTC
[PR] bench: introduce create_hash benchmarks [datafusion]
Rich-T-kid opened a new pull request, #25952:
URL: https://github.com/apache/datafusion/pull/25952
## Which issue does this PR close?
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We generally require a GitHub issue to be filed for all bug fixes and enhancements and this helps us generate change logs for our releases. You can link an issue to this PR using the GitHub syntax. For example `Closes #123` indicates that this PR will close issue #123.
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- N/A — adds missing benchmark coverage.
## Rationale for this change
`create_hashes()` has no dedicated benchmarks, making it hard to measure the impact of changes to hashing logic. covers all existing datatypes
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## What changes are included in this PR?
adds benchmarks for every datatype thats covered by `create_hashes()`
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There is no need to duplicate the description in the issue here, but it is sometimes worth providing a summary of the individual changes in this PR.
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## What is the testing strategy for this PR?
n/a
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## Are there any user-facing changes?
no
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Re: [PR] bench: introduce create_hash benchmarks [datafusion]
Posted by "jayzhan211 (via GitHub)" <gi...@apache.org>.
jayzhan211 commented on PR #25952:
URL: https://github.com/apache/datafusion/pull/25952#issuecomment-6060119130
> what did u use for green/red highlighting? looks very nice
AI format 😆
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Re: [PR] bench: introduce create_hash benchmarks [datafusion]
Posted by "codecov-commenter (via GitHub)" <gi...@apache.org>.
codecov-commenter commented on PR #25952:
URL: https://github.com/apache/datafusion/pull/25952#issuecomment-5941749885
## [Codecov](https://app.codecov.io/gh/apache/datafusion/pull/25952?dropdown=coverage&src=pr&el=h1&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=apache) Report
:white_check_mark: All modified and coverable lines are covered by tests.
:white_check_mark: Project coverage is 82.59%. Comparing base ([`e1aa7d9`](https://app.codecov.io/gh/apache/datafusion/commit/e1aa7d956a5aa67452c9e8bd2a033599767055d8?dropdown=coverage&el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=apache)) to head ([`0d79d95`](https://app.codecov.io/gh/apache/datafusion/commit/0d79d950f35daf08b46c54fc37f7165cedce60d5?dropdown=coverage&el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=apache)).
:warning: Report is 58 commits behind head on main.
<details><summary>Additional details and impacted files</summary>
```diff
@@ Coverage Diff @@
## main #25952 +/- ##
==========================================
+ Coverage 82.55% 82.59% +0.03%
==========================================
Files 1141 1145 +4
Lines 440209 443932 +3723
Branches 440209 443932 +3723
==========================================
+ Hits 363403 366651 +3248
- Misses 54834 55016 +182
- Partials 21972 22265 +293
```
</details>
[:umbrella: View full report in Codecov by Harness](https://app.codecov.io/gh/apache/datafusion/pull/25952?dropdown=coverage&src=pr&el=continue&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=apache).
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Re: [PR] bench: introduce create_hash benchmarks [datafusion]
Posted by "Rich-T-kid (via GitHub)" <gi...@apache.org>.
Rich-T-kid commented on PR #25952:
URL: https://github.com/apache/datafusion/pull/25952#issuecomment-6043095613
nice that makes this simpler. I updated the PR.
@jayzhan211 what did u use for green/red highlighting? looks very nice
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Re: [PR] bench: introduce create_hash benchmarks [datafusion]
Posted by "Rich-T-kid (via GitHub)" <gi...@apache.org>.
Rich-T-kid commented on PR #25952:
URL: https://github.com/apache/datafusion/pull/25952#issuecomment-5941394398
@jayzhan211 could you take a look at this 🙇♂️
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Re: [PR] bench: introduce create_hash benchmarks [datafusion]
Posted by "Rich-T-kid (via GitHub)" <gi...@apache.org>.
Rich-T-kid commented on code in PR #25952:
URL: https://github.com/apache/datafusion/pull/25952#discussion_r4209776705
##########
datafusion/common/benches/create_hashes.rs:
##########
@@ -0,0 +1,300 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+use arrow::array::{
+ ArrayRef, ArrowPrimitiveType, DictionaryArray, FixedSizeListArray, Int32Array,
+ Int64Array, ListArray, ListViewArray, MapArray, NullArray, PrimitiveArray, RunArray,
+ StringArray, StringViewArray, StructArray, UnionArray,
+};
+use arrow::buffer::{OffsetBuffer, ScalarBuffer};
+use arrow::datatypes::{DataType, Field, Fields, Int32Type, Int64Type, UnionFields};
+use criterion::{Criterion, criterion_group, criterion_main};
+use datafusion_common::hash_utils::{RandomState, create_hashes};
+use rand::Rng;
+use rand::SeedableRng;
+use rand::distr::{Distribution, StandardUniform};
+use rand::prelude::StdRng;
+use std::sync::Arc;
+
+const BATCH_SIZE: usize = 8192;
+const ELEMENTS_PER_ROW: usize = 5;
+
+fn make_rng() -> StdRng {
+ StdRng::seed_from_u64(42)
+}
+
+fn bench_create_hashes(c: &mut Criterion, name: &str, array: &ArrayRef) {
+ let state = RandomState::default();
+ let mut hashes = vec![0u64; BATCH_SIZE];
+ c.bench_function(name, |b| {
+ b.iter(|| {
+ create_hashes(std::slice::from_ref(array), &state, &mut hashes).unwrap();
+ })
+ });
+}
+
+fn primitive_array<T>() -> ArrayRef
+where
+ T: ArrowPrimitiveType,
+ StandardUniform: Distribution<T::Native>,
+{
+ let mut rng = make_rng();
+ Arc::new(
+ (0..BATCH_SIZE)
+ .map(|_| Some(rng.random::<T::Native>()))
+ .collect::<PrimitiveArray<T>>(),
+ )
+}
+
+fn null_array() -> ArrayRef {
+ Arc::new(NullArray::new(BATCH_SIZE))
+}
+
+fn utf8_array() -> ArrayRef {
+ let mut rng = make_rng();
+ Arc::new(StringArray::from_iter_values((0..BATCH_SIZE).map(|_| {
+ let len = rng.random_range(1usize..=32);
+ (0..len).map(|_| rng.random::<char>()).collect::<String>()
+ })))
+}
+
+fn string_view_array() -> ArrayRef {
+ let mut rng = make_rng();
+ Arc::new(StringViewArray::from_iter_values((0..BATCH_SIZE).map(
+ |_| {
+ let len = rng.random_range(1usize..=32);
+ (0..len).map(|_| rng.random::<char>()).collect::<String>()
+ },
+ )))
+}
+
+fn dictionary_array() -> ArrayRef {
+ let pool: Vec<String> = (0..100).map(|i| format!("value_{i}")).collect();
+ let mut rng = make_rng();
+ Arc::new(DictionaryArray::<Int32Type>::from_iter(
+ (0..BATCH_SIZE).map(|_| Some(pool[rng.random_range(0..pool.len())].as_str())),
+ ))
+}
+
+fn struct_array() -> ArrayRef {
+ Arc::new(StructArray::new(
+ Fields::from(vec![
+ Field::new("a", DataType::Int64, false),
+ Field::new("b", DataType::Int32, false),
+ ]),
+ vec![
+ primitive_array::<Int64Type>(),
+ primitive_array::<Int32Type>(),
+ ],
+ None,
+ ))
+}
+
+fn list_array() -> ArrayRef {
+ let mut rng = make_rng();
+ let total = BATCH_SIZE * ELEMENTS_PER_ROW;
+ let values: Int64Array = (0..total).map(|_| Some(rng.random::<i64>())).collect();
+ let offsets: Vec<i32> = (0..=BATCH_SIZE)
+ .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 list_view_array() -> ArrayRef {
+ let mut rng = make_rng();
+ let total = BATCH_SIZE * ELEMENTS_PER_ROW;
+ let values: Int64Array = (0..total).map(|_| Some(rng.random::<i64>())).collect();
+ let offsets: ScalarBuffer<i32> = (0..BATCH_SIZE)
+ .map(|i| (i * ELEMENTS_PER_ROW) as i32)
+ .collect();
+ let sizes: ScalarBuffer<i32> =
+ (0..BATCH_SIZE).map(|_| ELEMENTS_PER_ROW as i32).collect();
+ Arc::new(ListViewArray::new(
+ Arc::new(Field::new("item", DataType::Int64, true)),
+ offsets,
+ sizes,
+ Arc::new(values),
+ None,
+ ))
+}
+
+fn map_array() -> ArrayRef {
+ let mut rng = make_rng();
+ let total = BATCH_SIZE * ELEMENTS_PER_ROW;
+ let keys: Int32Array = (0..total).map(|_| Some(rng.random::<i32>())).collect();
+ let vals: Int64Array = (0..total).map(|_| Some(rng.random::<i64>())).collect();
+ let offsets: Vec<i32> = (0..=BATCH_SIZE)
+ .map(|i| (i * ELEMENTS_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(vals)],
+ 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 fixed_size_list_array() -> ArrayRef {
Review Comment:
thanks for catching this. updated
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Re: [PR] bench: introduce create_hash benchmarks [datafusion]
Posted by "jayzhan211 (via GitHub)" <gi...@apache.org>.
jayzhan211 merged PR #25952:
URL: https://github.com/apache/datafusion/pull/25952
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Re: [PR] bench: introduce create_hash benchmarks [datafusion]
Posted by "jayzhan211 (via GitHub)" <gi...@apache.org>.
jayzhan211 commented on code in PR #25952:
URL: https://github.com/apache/datafusion/pull/25952#discussion_r4207466603
##########
datafusion/common/benches/create_hashes.rs:
##########
@@ -0,0 +1,300 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements. See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership. The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License. You may obtain a copy of the License at
+//
+// http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+use arrow::array::{
+ ArrayRef, ArrowPrimitiveType, DictionaryArray, FixedSizeListArray, Int32Array,
+ Int64Array, ListArray, ListViewArray, MapArray, NullArray, PrimitiveArray, RunArray,
+ StringArray, StringViewArray, StructArray, UnionArray,
+};
+use arrow::buffer::{OffsetBuffer, ScalarBuffer};
+use arrow::datatypes::{DataType, Field, Fields, Int32Type, Int64Type, UnionFields};
+use criterion::{Criterion, criterion_group, criterion_main};
+use datafusion_common::hash_utils::{RandomState, create_hashes};
+use rand::Rng;
+use rand::SeedableRng;
+use rand::distr::{Distribution, StandardUniform};
+use rand::prelude::StdRng;
+use std::sync::Arc;
+
+const BATCH_SIZE: usize = 8192;
+const ELEMENTS_PER_ROW: usize = 5;
+
+fn make_rng() -> StdRng {
+ StdRng::seed_from_u64(42)
+}
+
+fn bench_create_hashes(c: &mut Criterion, name: &str, array: &ArrayRef) {
+ let state = RandomState::default();
+ let mut hashes = vec![0u64; BATCH_SIZE];
+ c.bench_function(name, |b| {
+ b.iter(|| {
+ create_hashes(std::slice::from_ref(array), &state, &mut hashes).unwrap();
+ })
+ });
+}
+
+fn primitive_array<T>() -> ArrayRef
+where
+ T: ArrowPrimitiveType,
+ StandardUniform: Distribution<T::Native>,
+{
+ let mut rng = make_rng();
+ Arc::new(
+ (0..BATCH_SIZE)
+ .map(|_| Some(rng.random::<T::Native>()))
+ .collect::<PrimitiveArray<T>>(),
+ )
+}
+
+fn null_array() -> ArrayRef {
+ Arc::new(NullArray::new(BATCH_SIZE))
+}
+
+fn utf8_array() -> ArrayRef {
+ let mut rng = make_rng();
+ Arc::new(StringArray::from_iter_values((0..BATCH_SIZE).map(|_| {
+ let len = rng.random_range(1usize..=32);
+ (0..len).map(|_| rng.random::<char>()).collect::<String>()
+ })))
+}
+
+fn string_view_array() -> ArrayRef {
+ let mut rng = make_rng();
+ Arc::new(StringViewArray::from_iter_values((0..BATCH_SIZE).map(
+ |_| {
+ let len = rng.random_range(1usize..=32);
+ (0..len).map(|_| rng.random::<char>()).collect::<String>()
+ },
+ )))
+}
+
+fn dictionary_array() -> ArrayRef {
+ let pool: Vec<String> = (0..100).map(|i| format!("value_{i}")).collect();
+ let mut rng = make_rng();
+ Arc::new(DictionaryArray::<Int32Type>::from_iter(
+ (0..BATCH_SIZE).map(|_| Some(pool[rng.random_range(0..pool.len())].as_str())),
+ ))
+}
+
+fn struct_array() -> ArrayRef {
+ Arc::new(StructArray::new(
+ Fields::from(vec![
+ Field::new("a", DataType::Int64, false),
+ Field::new("b", DataType::Int32, false),
+ ]),
+ vec![
+ primitive_array::<Int64Type>(),
+ primitive_array::<Int32Type>(),
+ ],
+ None,
+ ))
+}
+
+fn list_array() -> ArrayRef {
+ let mut rng = make_rng();
+ let total = BATCH_SIZE * ELEMENTS_PER_ROW;
+ let values: Int64Array = (0..total).map(|_| Some(rng.random::<i64>())).collect();
+ let offsets: Vec<i32> = (0..=BATCH_SIZE)
+ .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 list_view_array() -> ArrayRef {
+ let mut rng = make_rng();
+ let total = BATCH_SIZE * ELEMENTS_PER_ROW;
+ let values: Int64Array = (0..total).map(|_| Some(rng.random::<i64>())).collect();
+ let offsets: ScalarBuffer<i32> = (0..BATCH_SIZE)
+ .map(|i| (i * ELEMENTS_PER_ROW) as i32)
+ .collect();
+ let sizes: ScalarBuffer<i32> =
+ (0..BATCH_SIZE).map(|_| ELEMENTS_PER_ROW as i32).collect();
+ Arc::new(ListViewArray::new(
+ Arc::new(Field::new("item", DataType::Int64, true)),
+ offsets,
+ sizes,
+ Arc::new(values),
+ None,
+ ))
+}
+
+fn map_array() -> ArrayRef {
+ let mut rng = make_rng();
+ let total = BATCH_SIZE * ELEMENTS_PER_ROW;
+ let keys: Int32Array = (0..total).map(|_| Some(rng.random::<i32>())).collect();
+ let vals: Int64Array = (0..total).map(|_| Some(rng.random::<i64>())).collect();
+ let offsets: Vec<i32> = (0..=BATCH_SIZE)
+ .map(|i| (i * ELEMENTS_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(vals)],
+ 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 fixed_size_list_array() -> ArrayRef {
Review Comment:
`primitive_array()` returns 8192 values, and with `list_size = 4` that is a 2048-row `FixedSizeListArray` hashed into an 8192-slot buffer. This case therefore measures a quarter of the rows of every other case. It only runs because `hash_fixed_list_array` skips the buffer-length check that `hash_array_primitive` has (`hash_utils.rs:316`). Running the same shape through `with_hashes`'s length check fails with `left: 2048, right: 8192`.
```diff
fn fixed_size_list_array() -> ArrayRef {
- let list_size = 4i32;
+ let list_size = 4;
+ let mut rng = make_rng();
+ let values: Int64Array = (0..BATCH_SIZE * list_size)
+ .map(|_| Some(rng.random::<i64>()))
+ .collect();
Arc::new(FixedSizeListArray::new(
Arc::new(Field::new("item", DataType::Int64, true)),
- list_size,
- primitive_array::<Int64Type>(),
+ list_size as i32,
+ Arc::new(values),
None,
))
}
```
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Re: [PR] bench: introduce create_hash benchmarks [datafusion]
Posted by "Rich-T-kid (via GitHub)" <gi...@apache.org>.
Rich-T-kid commented on PR #25952:
URL: https://github.com/apache/datafusion/pull/25952#issuecomment-5995814759
> @jayzhan211 could you take a look at this 🙇♂️
@jayzhan211 sorry for the double ping, PR is relatively small could you take a peak 🙏 . this is related to some optimization for repartionExec
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