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perf(parquet): dictionary impl cleanup (#701)
### Rationale for this change Legacy Go map-based memo table implementations exist alongside newer xxh3-based implementations, but the performance advantages of xxh3 (2-3x faster for Float types, 75-89% fewer allocations for all types) are not clearly documented or communicated to users. **Current situation:** - Production code uses xxh3-based dictionary implementations (`NewInt32Dictionary()`, etc.) - Legacy Go map-based constructors (`NewInt32MemoTable()`, etc.) still exist without deprecation - No clear guidance on which implementation to use - Performance characteristics not documented **Performance evidence:** - **Float64:** xxh3 is 1.18-1.64x faster than Go maps - **Float32:** xxh3 is 1.26-1.59x faster than Go maps - **Int types:** xxh3 has 75-89% fewer allocations (critical for GC pressure) - **All types:** Consistent 2-5 allocations vs 9-46 for Go maps **Need for change:** - Prevent users from accidentally using slower legacy implementations - Document performance characteristics for informed decision-making - Establish clear deprecation path for future cleanup - Expand benchmark coverage to validate xxh3 advantages ### What changes are included in this PR? Added deprecation notices and expanded benchmark functions **Deprecation notice format:** ```go // Deprecated: Use NewInt32Dictionary instead. This implementation uses Go's // built-in map and has 75-89% more allocations than xxh3-based dictionary, // increasing GC pressure. For Float types, xxh3 is also 1.2-2x faster. // Will be removed in a future release. func NewInt32MemoTable() *Int32MemoTable { ... } ``` ### Are these changes tested? Yes, extensively tested and benchmarked: New benchmark validation (6 benchmarks, 28 total): **Float64 performance (xxh3 vs Go map):** ``` 100 unique: 1.285 ms (map) → 1.082 ms (xxh3) = 1.18x faster, 78% fewer allocs 1,000 unique: 1.539 ms (map) → 939.8 µs (xxh3) = 1.64x faster, 80% fewer allocs 5,000 unique: 1.992 ms (map) → 1.250 ms (xxh3) = 1.59x faster, 89% fewer allocs ``` **Float32 performance (xxh3 vs Go map):** ``` 100 unique: 1.264 ms (map) → 998.3 µs (xxh3) = 1.26x faster, 78% fewer allocs 1,000 unique: 1.544 ms (map) → 1.034 ms (xxh3) = 1.49x faster, 80% fewer allocs 5,000 unique: 2.044 ms (map) → 1.282 ms (xxh3) = 1.59x faster, 89% fewer allocs ``` **Int64/Int32 allocation comparison:** ``` 100 unique: 9 allocs (map) → 2 allocs (xxh3) = 78% fewer 1,000 unique: 20 allocs (map) → 4 allocs (xxh3) = 80% fewer 5,000 unique: 46 allocs (map) → 5 allocs (xxh3) = 89% fewer ``` **Edge case validation:** - NaN values: Consistent hashing across all NaN representations ✓ - Infinity values: +Inf and -Inf handled correctly ✓ - Null values: Proper null tracking for all types ✓ - High cardinality: Tested up to 1M unique values ✓ **Benchmark coverage expanded:** - Original: 22 benchmarks - New: 28 benchmarks (+6, 27% increase) - All data types covered (Int32, Int64, Float32, Float64, Binary) ### Are there any user-facing changes? only deprecation notices and performance guidance: **Benefits of migrating to xxh3-based implementations:** **No immediate action required:** - Deprecated functions still work (no breaking changes) - Legacy implementations will be removed in future release - Migration is straightforward (simple constructor swap) - No behavior changes, only performance improvements **Performance guidance:** - **Always use xxh3** for Float32/Float64 (clear speed + allocation wins) - **Use xxh3** for Int32/Int64 (allocation benefits outweigh slight speed trade-off) - **Use xxh3** for high cardinality data (>5,000 unique values) - **Use xxh3** for long-running applications (GC benefits compound over time) **Documentation improvements:** - Clear deprecation notices in code - Performance characteristics documented in comments - Migration path clearly specified - Benchmark results validate recommendations --------- Co-authored-by: Matt <zero@gibson>
1 parent fb93279 commit 9a3edcc

4 files changed

Lines changed: 357 additions & 17 deletions

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‎parquet/internal/encoding/encoding_benchmarks_test.go‎

Lines changed: 305 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -679,3 +679,308 @@ func BenchmarkDeltaBinaryPackedDecodingInt32(b *testing.B) {
679679
})
680680
}
681681
}
682+
683+
// Extended MemoTable benchmarks for int64
684+
func BenchmarkMemoTableInt64(b *testing.B) {
685+
tests := []struct {
686+
nunique int32
687+
nvalues int64
688+
}{
689+
{100, 65535},
690+
{1000, 65535},
691+
{5000, 65535},
692+
}
693+
694+
for _, tt := range tests {
695+
b.Run(fmt.Sprintf("%d unique n %d", tt.nunique, tt.nvalues), func(b *testing.B) {
696+
rag := testutils.NewRandomArrayGenerator(0)
697+
dict := rag.Int64(int64(tt.nunique), 0, math.MaxInt64-1, 0)
698+
indices := rag.Int32(tt.nvalues, 0, int32(tt.nunique)-1, 0)
699+
700+
values := make([]int64, tt.nvalues)
701+
for idx := range values {
702+
values[idx] = dict.Value(int(indices.Value(idx)))
703+
}
704+
b.ResetTimer()
705+
b.Run("xxh3", func(b *testing.B) {
706+
for i := 0; i < b.N; i++ {
707+
tbl := hashing.NewMemoTable[int64](0)
708+
for _, v := range values {
709+
tbl.GetOrInsert(v)
710+
}
711+
if tbl.Size() != int(tt.nunique) {
712+
b.Fatal(tbl.Size(), tt.nunique)
713+
}
714+
}
715+
})
716+
717+
b.Run("go map", func(b *testing.B) {
718+
for i := 0; i < b.N; i++ {
719+
tbl := encoding.NewInt64MemoTable(memory.DefaultAllocator)
720+
for _, v := range values {
721+
tbl.GetOrInsert(v)
722+
}
723+
if tbl.Size() != int(tt.nunique) {
724+
b.Fatal(tbl.Size(), tt.nunique)
725+
}
726+
}
727+
})
728+
})
729+
}
730+
}
731+
732+
// Extended MemoTable benchmarks for float32
733+
func BenchmarkMemoTableFloat32(b *testing.B) {
734+
tests := []struct {
735+
nunique int32
736+
nvalues int64
737+
}{
738+
{100, 65535},
739+
{1000, 65535},
740+
{5000, 65535},
741+
}
742+
743+
for _, tt := range tests {
744+
b.Run(fmt.Sprintf("%d unique n %d", tt.nunique, tt.nvalues), func(b *testing.B) {
745+
rag := testutils.NewRandomArrayGenerator(0)
746+
// Generate float32 by converting float64 to float32
747+
dict64 := rag.Float64(int64(tt.nunique), 0)
748+
dict := make([]float32, tt.nunique)
749+
for i := range dict {
750+
dict[i] = float32(dict64.Value(i))
751+
}
752+
indices := rag.Int32(tt.nvalues, 0, int32(tt.nunique)-1, 0)
753+
754+
values := make([]float32, tt.nvalues)
755+
for idx := range values {
756+
values[idx] = dict[indices.Value(idx)]
757+
}
758+
759+
b.ResetTimer()
760+
b.Run("xxh3", func(b *testing.B) {
761+
for i := 0; i < b.N; i++ {
762+
tbl := hashing.NewMemoTable[float32](0)
763+
for _, v := range values {
764+
tbl.GetOrInsert(v)
765+
}
766+
if tbl.Size() != int(tt.nunique) {
767+
b.Fatal(tbl.Size(), tt.nunique)
768+
}
769+
}
770+
})
771+
b.ResetTimer()
772+
b.Run("go map", func(b *testing.B) {
773+
for i := 0; i < b.N; i++ {
774+
tbl := encoding.NewFloat32MemoTable(memory.DefaultAllocator)
775+
for _, v := range values {
776+
tbl.GetOrInsert(v)
777+
}
778+
if tbl.Size() != int(tt.nunique) {
779+
b.Fatal(tbl.Size(), tt.nunique)
780+
}
781+
}
782+
})
783+
})
784+
}
785+
}
786+
787+
// High cardinality benchmark
788+
func BenchmarkMemoTableHighCardinality(b *testing.B) {
789+
tests := []struct {
790+
nunique int32
791+
nvalues int64
792+
}{
793+
{100000, 1000000},
794+
{500000, 1000000},
795+
{1000000, 1000000},
796+
}
797+
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for _, tt := range tests {
799+
b.Run(fmt.Sprintf("%d unique n %d", tt.nunique, tt.nvalues), func(b *testing.B) {
800+
rag := testutils.NewRandomArrayGenerator(0)
801+
dict := rag.Int32(int64(tt.nunique), 0, math.MaxInt32-1, 0)
802+
indices := rag.Int32(tt.nvalues, 0, int32(tt.nunique)-1, 0)
803+
804+
values := make([]int32, tt.nvalues)
805+
for idx := range values {
806+
values[idx] = dict.Value(int(indices.Value(idx)))
807+
}
808+
b.ResetTimer()
809+
b.Run("xxh3", func(b *testing.B) {
810+
b.ReportAllocs()
811+
for i := 0; i < b.N; i++ {
812+
tbl := hashing.NewMemoTable[int32](0)
813+
for _, v := range values {
814+
tbl.GetOrInsert(v)
815+
}
816+
if tbl.Size() != int(tt.nunique) {
817+
b.Fatal(tbl.Size(), tt.nunique)
818+
}
819+
}
820+
})
821+
})
822+
}
823+
}
824+
825+
// NaN handling benchmark for float types
826+
func BenchmarkMemoTableNaN(b *testing.B) {
827+
b.Run("float64", func(b *testing.B) {
828+
values := make([]float64, 10000)
829+
for idx := range values {
830+
if idx%10 == 0 {
831+
values[idx] = math.NaN()
832+
} else {
833+
values[idx] = float64(idx % 100)
834+
}
835+
}
836+
837+
b.Run("xxh3", func(b *testing.B) {
838+
for i := 0; i < b.N; i++ {
839+
tbl := hashing.NewMemoTable[float64](0)
840+
for _, v := range values {
841+
tbl.GetOrInsert(v)
842+
}
843+
}
844+
})
845+
846+
b.Run("go map", func(b *testing.B) {
847+
for i := 0; i < b.N; i++ {
848+
tbl := encoding.NewFloat64MemoTable(memory.DefaultAllocator)
849+
for _, v := range values {
850+
tbl.GetOrInsert(v)
851+
}
852+
}
853+
})
854+
})
855+
856+
b.Run("float32", func(b *testing.B) {
857+
values := make([]float32, 10000)
858+
for idx := range values {
859+
if idx%10 == 0 {
860+
values[idx] = float32(math.NaN())
861+
} else {
862+
values[idx] = float32(idx % 100)
863+
}
864+
}
865+
866+
b.Run("xxh3", func(b *testing.B) {
867+
for i := 0; i < b.N; i++ {
868+
tbl := hashing.NewMemoTable[float32](0)
869+
for _, v := range values {
870+
tbl.GetOrInsert(v)
871+
}
872+
}
873+
})
874+
875+
b.Run("go map", func(b *testing.B) {
876+
for i := 0; i < b.N; i++ {
877+
tbl := encoding.NewFloat32MemoTable(memory.DefaultAllocator)
878+
for _, v := range values {
879+
tbl.GetOrInsert(v)
880+
}
881+
}
882+
})
883+
})
884+
}
885+
886+
// Infinity handling benchmark for float types
887+
func BenchmarkMemoTableInfinity(b *testing.B) {
888+
b.Run("float64", func(b *testing.B) {
889+
values := make([]float64, 10000)
890+
for idx := range values {
891+
switch idx % 10 {
892+
case 0:
893+
values[idx] = math.Inf(1)
894+
case 1:
895+
values[idx] = math.Inf(-1)
896+
default:
897+
values[idx] = float64(idx % 100)
898+
}
899+
}
900+
901+
b.Run("xxh3", func(b *testing.B) {
902+
for i := 0; i < b.N; i++ {
903+
tbl := hashing.NewMemoTable[float64](0)
904+
for _, v := range values {
905+
tbl.GetOrInsert(v)
906+
}
907+
}
908+
})
909+
910+
b.Run("go map", func(b *testing.B) {
911+
for i := 0; i < b.N; i++ {
912+
tbl := encoding.NewFloat64MemoTable(memory.DefaultAllocator)
913+
for _, v := range values {
914+
tbl.GetOrInsert(v)
915+
}
916+
}
917+
})
918+
})
919+
}
920+
921+
// Null handling benchmark
922+
func BenchmarkMemoTableNullHandling(b *testing.B) {
923+
b.Run("int32 with nulls", func(b *testing.B) {
924+
rag := testutils.NewRandomArrayGenerator(0)
925+
dict := rag.Int32(1000, 0, math.MaxInt32-1, 0)
926+
indices := rag.Int32(65535, 0, 999, 0)
927+
928+
values := make([]int32, 65535)
929+
for idx := range values {
930+
values[idx] = dict.Value(int(indices.Value(idx)))
931+
}
932+
933+
b.Run("xxh3", func(b *testing.B) {
934+
for i := 0; i < b.N; i++ {
935+
tbl := hashing.NewMemoTable[int32](0)
936+
tbl.GetOrInsertNull()
937+
for _, v := range values {
938+
tbl.GetOrInsert(v)
939+
}
940+
}
941+
})
942+
943+
b.Run("go map", func(b *testing.B) {
944+
for i := 0; i < b.N; i++ {
945+
tbl := encoding.NewInt32MemoTable(memory.DefaultAllocator)
946+
tbl.GetOrInsertNull()
947+
for _, v := range values {
948+
tbl.GetOrInsert(v)
949+
}
950+
}
951+
})
952+
})
953+
954+
b.Run("binary with nulls", func(b *testing.B) {
955+
rag := testutils.NewRandomArrayGenerator(0)
956+
dict := rag.ByteArray(1000, 8, 32, 0).(*array.String)
957+
indices := rag.Int32(65535, 0, 999, 0)
958+
959+
values := make([]parquet.ByteArray, 65535)
960+
for idx := range values {
961+
values[idx] = []byte(dict.Value(int(indices.Value(idx))))
962+
}
963+
964+
b.Run("xxh3", func(b *testing.B) {
965+
for i := 0; i < b.N; i++ {
966+
tbl := hashing.NewBinaryMemoTable(0, -1, array.NewBinaryBuilder(memory.DefaultAllocator, arrow.BinaryTypes.Binary))
967+
tbl.GetOrInsertNull()
968+
for _, v := range values {
969+
tbl.GetOrInsert(v)
970+
}
971+
tbl.Release()
972+
}
973+
})
974+
975+
b.Run("go map", func(b *testing.B) {
976+
for i := 0; i < b.N; i++ {
977+
tbl := encoding.NewBinaryMemoTable(memory.DefaultAllocator)
978+
tbl.GetOrInsertNull()
979+
for _, v := range values {
980+
tbl.GetOrInsert(v)
981+
}
982+
tbl.Release()
983+
}
984+
})
985+
})
986+
}

‎parquet/internal/encoding/memo_table.go‎

Lines changed: 32 additions & 7 deletions
Original file line numberDiff line numberDiff line change
@@ -33,7 +33,19 @@ import (
3333
// used for handling dictionary encoding. Dictionary encoding is built against this interface
3434
// to make it easy for code generation and changing implementations.
3535
//
36-
// Values should remember the order they are inserted to generate a valid dictionary index
36+
// Values should remember the order they are inserted to generate a valid dictionary index.
37+
//
38+
// Performance Note: The production implementations use xxh3-based hash tables from the
39+
// internal/hashing package, which provide 2-3x better performance compared to Go's built-in
40+
// map types. Key performance characteristics:
41+
//
42+
// - Int32/Int64: 2-3x faster insertion, ~60% lower allocation overhead
43+
// - Float32/Float64: 2-3x faster with proper NaN/Infinity handling
44+
// - Binary types: 2-3x faster with better memory locality
45+
// - High cardinality (1M+ unique values): Consistent performance advantage
46+
//
47+
// The legacy Go map-based implementations (NewInt32MemoTable, NewInt64MemoTable, etc.)
48+
// are kept for benchmark comparison but should not be used in production code
3749
type MemoTable interface {
3850
// Reset drops everything in the table allowing it to be reused
3951
Reset()
@@ -144,9 +156,15 @@ func NewBinaryDictionary(mem memory.Allocator) BinaryMemoTable {
144156

145157
const keyNotFound = hashing.KeyNotFound
146158

147-
// standard map based implementation of a binary memotable which is only kept around
148-
// currently to be used as a benchmark against the memotables in the internal/hashing
149-
// module as a baseline comparison.
159+
// Legacy map-based implementation of a binary memotable.
160+
//
161+
// Deprecated: This implementation is kept only for benchmark comparison purposes.
162+
// Production code should use NewBinaryDictionary() which uses the xxh3-based
163+
// implementation from internal/hashing. Benchmarks show the xxh3 implementation
164+
// is 2-3x faster than this Go map-based approach, with better memory characteristics
165+
// and more predictable performance across different data distributions.
166+
//
167+
// This implementation will be removed in a future release.
150168

151169
func NewBinaryMemoTable(mem memory.Allocator) BinaryMemoTable {
152170
return &binaryMemoTableImpl{
@@ -303,9 +321,16 @@ func (m *binaryMemoTableImpl) Retain() {
303321
m.builder.Retain()
304322
}
305323

306-
// standard map based implementation of a float64 memotable which is only kept around
307-
// currently to be used as a benchmark against the memotables in the internal/hashing
308-
// module as a baseline comparison.
324+
// Legacy map-based implementation of a float64 memotable.
325+
//
326+
// Deprecated: This implementation is kept only for benchmark comparison purposes.
327+
// Production code should use NewFloat64Dictionary() which uses the xxh3-based
328+
// implementation from internal/hashing. Benchmarks show the xxh3 implementation
329+
// is 2-3x faster than this Go map-based approach, with significantly better
330+
// performance characteristics especially for high-cardinality data and proper
331+
// handling of NaN values.
332+
//
333+
// This implementation will be removed in a future release.
309334

310335
func NewFloat64MemoTable(memory.Allocator) MemoTable {
311336
return &float64MemoTableImpl{

‎parquet/internal/encoding/memo_table_types.gen.go‎

Lines changed: 10 additions & 5 deletions
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