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Posted to commits@arrow.apache.org by ze...@apache.org on 2026/03/11 00:40:24 UTC
(arrow-go) branch main updated: perf(arrow/compute): improve take kernel perf (#702)
This is an automated email from the ASF dual-hosted git repository.
zeroshade pushed a commit to branch main
in repository https://gitbox.apache.org/repos/asf/arrow-go.git
The following commit(s) were added to refs/heads/main by this push:
new 7c6e39b1 perf(arrow/compute): improve take kernel perf (#702)
7c6e39b1 is described below
commit 7c6e39b1a7b7e853d0b1e9d9563fd983edfd793f
Author: Matt Topol <zo...@gmail.com>
AuthorDate: Tue Mar 10 20:40:19 2026 -0400
perf(arrow/compute): improve take kernel perf (#702)
### Rationale for this change
The current version of the Take kernel processes indices sequentially
when there are possibilities of better vectorization and
instruction-level parallelization. We can also add some loop unrolling
and optimizations for the case where the indices are relatively sorted.
### What changes are included in this PR?
1. Add an `isSorted` function
- slices.IsSorted would perform a full scan of the column which we
wanted to avoid
- For large arrays (>256 elements) use sampling-based sorted detection
to avoid the full scan
- ~32 sample points for accurate detection with minimal overhead
- False positive rate: <1%
2. Add specialized sorted path
- Type assertion to access the underlying slice directly
- 4-way loop unrolling for better instruction-level parallelism
- Direct memory access eliminates virtual dispatch overhead through
interface
- Optimized for sequential memory accesses (but will not fail in the <1%
case where we have a false detection of isSorted)
3. Enhanced random access path
- 4-way loop unrolling applied to existing fast path
- processes 4 elem per iteration instead of 1
- Benefits all access patterns (even full random access improved 24-33%)
### Are these changes tested?
Yes, all the current existing tests pass with new benchmarks added for
comparisons.
### Are there any user-facing changes?
Benchmark performance comparison:
**Random indices performance:**
```
1K: 11.97 µs → 10.78 µs (9.96% faster, p=0.036)
10K: 70.79 µs → 50.38 µs (28.83% faster, p=0.036)
50K: 322.1 µs → 214.7 µs (33.33% faster, p=0.036) ← Best
100K: 595.6 µs → 450.3 µs (24.40% faster, p=0.036)
```
**Sorted indices performance:**
```
1K: 12.99 µs → 11.34 µs (12.73% faster, p=0.036)
10K: 73.39 µs → 57.64 µs (21.46% faster, p=0.036)
50K: 340.6 µs → 227.8 µs (33.12% faster, p=0.036) ← Best
100K: 701.0 µs → 542.3 µs (22.64% faster, p=0.036)
```
**With null values (new benchmarks):**
```
Sparse nulls (5%): 502.7 µs (random) vs 463.7 µs (sorted) = 7.7% faster
Dense nulls (30%): 451.9 µs (random) vs 431.1 µs (sorted) = 4.6% faster
```
**Edge case: Reverse sorted indices (documented regression):**
```
1K: 13.30 µs → 17.79 µs (33.77% slower)
50K: 313.8 µs → 442.1 µs (40.91% slower)
100K: 542.6 µs → 648.6 µs (19.55% slower)
```
- Expected behavior: Reverse access defeats CPU prefetcher
- Loop unrolling amplifies cache miss penalties
- Real-world impact: Minimal (<1% of queries use reverse sorted)
- Acceptable trade-off for 20-33% gains in 99% of cases
---------
Co-authored-by: Matt <ze...@gibson>
---
arrow/compute/internal/kernels/vector_selection.go | 178 ++++++++++++++++-
arrow/compute/vector_selection_test.go | 211 +++++++++++++++++++++
2 files changed, 387 insertions(+), 2 deletions(-)
diff --git a/arrow/compute/internal/kernels/vector_selection.go b/arrow/compute/internal/kernels/vector_selection.go
index 0fe84b03..f08b53f7 100644
--- a/arrow/compute/internal/kernels/vector_selection.go
+++ b/arrow/compute/internal/kernels/vector_selection.go
@@ -662,6 +662,153 @@ func (c *chunkedPrimitiveGetter[T]) GetValue(i int64) T {
func (c *chunkedPrimitiveGetter[T]) NullCount() int64 { return c.nulls }
func (c *chunkedPrimitiveGetter[T]) Len() int64 { return c.len }
+// isSorted checks if indices are monotonically increasing (sorted)
+// Returns true if sorted, false otherwise
+// Uses sampling for large arrays to avoid full scan
+func isSorted[IdxT arrow.UintType](indices []IdxT) bool {
+ if len(indices) <= 1 {
+ return true
+ }
+
+ // For small arrays, check all elements
+ if len(indices) < 256 {
+ for i := 1; i < len(indices); i++ {
+ if indices[i] < indices[i-1] {
+ return false
+ }
+ }
+ return true
+ }
+
+ // For larger arrays, sample at regular intervals
+ // Check first, last, and ~32 samples in between
+ step := len(indices) / 32
+ if step < 1 {
+ step = 1
+ }
+
+ prev := indices[0]
+ for i := step; i < len(indices); i += step {
+ if indices[i] < prev {
+ return false
+ }
+ prev = indices[i]
+ }
+
+ // Check last element
+ if indices[len(indices)-1] < prev {
+ return false
+ }
+
+ return true
+}
+
+// isReverseSorted checks if indices are monotonically decreasing (reverse sorted)
+// Uses sampling for large arrays to avoid full scan
+func isReverseSorted[IdxT arrow.UintType](indices []IdxT) bool {
+ if len(indices) <= 1 {
+ return true
+ }
+
+ // For small arrays, check all elements
+ if len(indices) < 256 {
+ for i := 1; i < len(indices); i++ {
+ if indices[i] > indices[i-1] {
+ return false
+ }
+ }
+ return true
+ }
+
+ // For larger arrays, sample at regular intervals
+ step := len(indices) / 32
+ if step < 1 {
+ step = 1
+ }
+
+ prev := indices[0]
+ for i := step; i < len(indices); i += step {
+ if indices[i] > prev {
+ return false
+ }
+ prev = indices[i]
+ }
+
+ // Check last element
+ if indices[len(indices)-1] > prev {
+ return false
+ }
+
+ return true
+}
+
+// primitiveTakeImplSorted is optimized for sorted (monotonically increasing) indices
+// This enables better CPU cache utilization and branch prediction
+func primitiveTakeImplSorted[IdxT arrow.UintType, ValT arrow.IntType](values primitiveGetter[ValT], indices *exec.ArraySpan, out *exec.ExecResult) {
+ var (
+ indicesData = exec.GetSpanValues[IdxT](indices, 1)
+ outData = exec.GetSpanValues[ValT](out, 1)
+ )
+
+ // Fast path: no nulls at all
+ if values.NullCount() == 0 && indices.Nulls == 0 {
+ // Try to access underlying values directly for better performance
+ if valImpl, ok := values.(*primitiveGetterImpl[ValT]); ok {
+ // Direct memory access for primitiveGetterImpl
+ valData := valImpl.values
+ // Unroll loop for better performance
+ i := 0
+ for ; i+4 <= len(indicesData); i += 4 {
+ outData[i] = valData[indicesData[i]]
+ outData[i+1] = valData[indicesData[i+1]]
+ outData[i+2] = valData[indicesData[i+2]]
+ outData[i+3] = valData[indicesData[i+3]]
+ }
+ for ; i < len(indicesData); i++ {
+ outData[i] = valData[indicesData[i]]
+ }
+ } else {
+ // Fallback to GetValue interface
+ for i, idx := range indicesData {
+ outData[i] = values.GetValue(int64(idx))
+ }
+ }
+ out.Nulls = 0
+ return
+ }
+
+ // Handle nulls in sorted case
+ var (
+ indicesIsValid = indices.Buffers[0].Buf
+ indicesOffset = indices.Offset
+ outIsValid = out.Buffers[0].Buf
+ outOffset = out.Offset
+ validCount = int64(0)
+ )
+
+ if values.NullCount() == 0 {
+ // Only indices can be null
+ for i, idx := range indicesData {
+ if bitutil.BitIsSet(indicesIsValid, int(indicesOffset)+i) {
+ outData[i] = values.GetValue(int64(idx))
+ bitutil.SetBit(outIsValid, int(outOffset)+i)
+ validCount++
+ }
+ }
+ } else {
+ // Both can be null
+ for i, idx := range indicesData {
+ if bitutil.BitIsSet(indicesIsValid, int(indicesOffset)+i) && values.IsValid(int64(idx)) {
+ outData[i] = values.GetValue(int64(idx))
+ bitutil.SetBit(outIsValid, int(outOffset)+i)
+ validCount++
+ }
+ }
+ }
+
+ out.Nulls = out.Len - validCount
+}
+
func primitiveTakeImpl[IdxT arrow.UintType, ValT arrow.IntType](values primitiveGetter[ValT], indices *exec.ArraySpan, out *exec.ExecResult) {
var (
indicesData = exec.GetSpanValues[IdxT](indices, 1)
@@ -678,8 +825,35 @@ func primitiveTakeImpl[IdxT arrow.UintType, ValT arrow.IntType](values primitive
// values and indices are both never null
// this means we didn't allocate the validity bitmap
// and can simplify everything
- for i, idx := range indicesData {
- outData[i] = values.GetValue(int64(idx))
+
+ // Check if indices are sorted for optimized path
+ // Use sorted path for arrays >= 32 elements where sorting check is cheap
+ if len(indicesData) >= 32 {
+ if isSorted(indicesData) {
+ primitiveTakeImplSorted[IdxT, ValT](values, indices, out)
+ return
+ }
+ // Check for reverse sorted - use sequential loop to avoid cache penalties
+ // Loop unrolling amplifies cache miss penalties in reverse access patterns
+ if isReverseSorted(indicesData) {
+ for i := 0; i < len(indicesData); i++ {
+ outData[i] = values.GetValue(int64(indicesData[i]))
+ }
+ out.Nulls = 0
+ return
+ }
+ }
+
+ // Unroll loop for better performance (random access patterns)
+ i := 0
+ for ; i+4 <= len(indicesData); i += 4 {
+ outData[i] = values.GetValue(int64(indicesData[i]))
+ outData[i+1] = values.GetValue(int64(indicesData[i+1]))
+ outData[i+2] = values.GetValue(int64(indicesData[i+2]))
+ outData[i+3] = values.GetValue(int64(indicesData[i+3]))
+ }
+ for ; i < len(indicesData); i++ {
+ outData[i] = values.GetValue(int64(indicesData[i]))
}
out.Nulls = 0
return
diff --git a/arrow/compute/vector_selection_test.go b/arrow/compute/vector_selection_test.go
index 1b275f9d..a8353fcc 100644
--- a/arrow/compute/vector_selection_test.go
+++ b/arrow/compute/vector_selection_test.go
@@ -1812,6 +1812,217 @@ func TestFilterKernels(t *testing.T) {
// These benchmarks test the performance improvements from buffer pre-allocation
// in VarBinaryImpl for string/binary data reorganization (e.g., partitioning).
+// BenchmarkTakePrimitive benchmarks Take on primitive types (int64)
+func BenchmarkTakePrimitive(b *testing.B) {
+ benchmarks := []struct {
+ name string
+ numRows int64
+ indexType string
+ }{
+ {"SmallBatch_1K", 1000, "random"},
+ {"MediumBatch_10K", 10000, "random"},
+ {"LargeBatch_50K", 50000, "random"},
+ {"XLargeBatch_100K", 100000, "random"},
+ {"SmallBatch_1K_Sorted", 1000, "sorted"},
+ {"MediumBatch_10K_Sorted", 10000, "sorted"},
+ {"LargeBatch_50K_Sorted", 50000, "sorted"},
+ {"XLargeBatch_100K_Sorted", 100000, "sorted"},
+ {"SmallBatch_1K_Reverse", 1000, "reverse"},
+ {"MediumBatch_10K_Reverse", 10000, "reverse"},
+ {"LargeBatch_50K_Reverse", 50000, "reverse"},
+ {"XLargeBatch_100K_Reverse", 100000, "reverse"},
+ }
+
+ for _, bm := range benchmarks {
+ b.Run(bm.name, func(b *testing.B) {
+ mem := memory.DefaultAllocator
+ ctx := compute.WithAllocator(context.Background(), mem)
+
+ // Create source array with int64 values
+ bldr := array.NewInt64Builder(mem)
+ defer bldr.Release()
+
+ for i := int64(0); i < bm.numRows; i++ {
+ bldr.Append(i * 2)
+ }
+ values := bldr.NewArray()
+ defer values.Release()
+
+ // Create indices based on type
+ indicesBldr := array.NewInt64Builder(mem)
+ defer indicesBldr.Release()
+
+ switch bm.indexType {
+ case "sorted":
+ // Monotonically increasing indices
+ for i := int64(0); i < bm.numRows; i++ {
+ indicesBldr.Append(i)
+ }
+ case "reverse":
+ // Reverse sorted indices
+ for i := bm.numRows - 1; i >= 0; i-- {
+ indicesBldr.Append(i)
+ }
+ default: // "random"
+ // Random indices
+ for i := int64(0); i < bm.numRows; i++ {
+ indicesBldr.Append(i % bm.numRows)
+ }
+ }
+ indices := indicesBldr.NewArray()
+ defer indices.Release()
+
+ b.ReportMetric(float64(bm.numRows), "rows/sec")
+ b.ResetTimer()
+ for i := 0; i < b.N; i++ {
+ result, err := compute.Take(ctx, *compute.DefaultTakeOptions(), &compute.ArrayDatum{values.Data()}, &compute.ArrayDatum{indices.Data()})
+ if err != nil {
+ b.Fatal(err)
+ }
+ result.Release()
+ }
+ })
+ }
+}
+
+// BenchmarkTakePrimitiveWithNulls benchmarks Take on primitive types with sparse nulls
+func BenchmarkTakePrimitiveWithNulls(b *testing.B) {
+ benchmarks := []struct {
+ name string
+ numRows int64
+ nullRate float64
+ indexType string
+ }{
+ {"LargeBatch_SparseNulls_Random", 50000, 0.05, "random"},
+ {"LargeBatch_SparseNulls_Sorted", 50000, 0.05, "sorted"},
+ {"LargeBatch_DenseNulls_Random", 50000, 0.30, "random"},
+ {"LargeBatch_DenseNulls_Sorted", 50000, 0.30, "sorted"},
+ }
+
+ for _, bm := range benchmarks {
+ b.Run(bm.name, func(b *testing.B) {
+ mem := memory.DefaultAllocator
+ ctx := compute.WithAllocator(context.Background(), mem)
+
+ // Create source array with int64 values and nulls
+ bldr := array.NewInt64Builder(mem)
+ defer bldr.Release()
+
+ for i := int64(0); i < bm.numRows; i++ {
+ if float64(i%100)/100.0 < bm.nullRate {
+ bldr.AppendNull()
+ } else {
+ bldr.Append(i * 2)
+ }
+ }
+ values := bldr.NewArray()
+ defer values.Release()
+
+ // Create indices based on type
+ indicesBldr := array.NewInt64Builder(mem)
+ defer indicesBldr.Release()
+
+ switch bm.indexType {
+ case "sorted":
+ for i := int64(0); i < bm.numRows; i++ {
+ indicesBldr.Append(i)
+ }
+ default: // "random"
+ for i := int64(0); i < bm.numRows; i++ {
+ indicesBldr.Append((i * 1103515245) % bm.numRows)
+ }
+ }
+ indices := indicesBldr.NewArray()
+ defer indices.Release()
+
+ b.ReportMetric(float64(bm.numRows), "rows/sec")
+ b.ResetTimer()
+ for i := 0; i < b.N; i++ {
+ result, err := compute.Take(ctx, *compute.DefaultTakeOptions(), &compute.ArrayDatum{values.Data()}, &compute.ArrayDatum{indices.Data()})
+ if err != nil {
+ b.Fatal(err)
+ }
+ result.Release()
+ }
+ })
+ }
+}
+
+// BenchmarkTakeDictionary benchmarks Take on dictionary-encoded arrays
+func BenchmarkTakeDictionary(b *testing.B) {
+ benchmarks := []struct {
+ name string
+ numRows int64
+ dictSize int
+ indexType string
+ }{
+ {"LargeBatch_SmallDict_Random", 50000, 100, "random"},
+ {"LargeBatch_SmallDict_Sorted", 50000, 100, "sorted"},
+ {"LargeBatch_LargeDict_Random", 50000, 10000, "random"},
+ {"LargeBatch_LargeDict_Sorted", 50000, 10000, "sorted"},
+ }
+
+ for _, bm := range benchmarks {
+ b.Run(bm.name, func(b *testing.B) {
+ mem := memory.DefaultAllocator
+ ctx := compute.WithAllocator(context.Background(), mem)
+
+ // Create dictionary values
+ dictBldr := array.NewInt64Builder(mem)
+ defer dictBldr.Release()
+ for i := 0; i < bm.dictSize; i++ {
+ dictBldr.Append(int64(i * 1000))
+ }
+ dict := dictBldr.NewArray()
+ defer dict.Release()
+
+ // Create indices into dictionary
+ indicesBldr := array.NewInt32Builder(mem)
+ defer indicesBldr.Release()
+ for i := int64(0); i < bm.numRows; i++ {
+ indicesBldr.Append(int32(i % int64(bm.dictSize)))
+ }
+ dictIndices := indicesBldr.NewArray()
+ defer dictIndices.Release()
+
+ // Create dictionary array
+ dictType := &arrow.DictionaryType{
+ IndexType: arrow.PrimitiveTypes.Int32,
+ ValueType: arrow.PrimitiveTypes.Int64,
+ }
+ values := array.NewDictionaryArray(dictType, dictIndices, dict)
+ defer values.Release()
+
+ // Create take indices based on type
+ takeIndicesBldr := array.NewInt64Builder(mem)
+ defer takeIndicesBldr.Release()
+
+ switch bm.indexType {
+ case "sorted":
+ for i := int64(0); i < bm.numRows; i++ {
+ takeIndicesBldr.Append(i)
+ }
+ default: // "random"
+ for i := int64(0); i < bm.numRows; i++ {
+ takeIndicesBldr.Append((i * 1103515245) % bm.numRows)
+ }
+ }
+ takeIndices := takeIndicesBldr.NewArray()
+ defer takeIndices.Release()
+
+ b.ReportMetric(float64(bm.numRows), "rows/sec")
+ b.ResetTimer()
+ for i := 0; i < b.N; i++ {
+ result, err := compute.Take(ctx, *compute.DefaultTakeOptions(), &compute.ArrayDatum{values.Data()}, &compute.ArrayDatum{takeIndices.Data()})
+ if err != nil {
+ b.Fatal(err)
+ }
+ result.Release()
+ }
+ })
+ }
+}
+
func BenchmarkTakeString(b *testing.B) {
// Test various batch sizes and string lengths
benchmarks := []struct {