72b4b29ecd
Add comprehensive benchmarking suite for C++ optimization targets: - tests/benchmarks/dataset_hash_bench_test.go - dirOverallSHA256Hex profiling - tests/benchmarks/queue_bench_test.go - filesystem queue profiling - tests/benchmarks/artifact_and_snapshot_bench_test.go - scanArtifacts/extractTarGz profiling - tests/unit/worker/artifacts_test.go - moved from internal/ for clean separation Add parallel Go implementation as baseline for C++ comparison: - internal/worker/data_integrity.go: dirOverallSHA256HexParallel() with worker pool - Benchmarks show 2.1x speedup (3.97ms -> 1.90ms) vs sequential Exported wrappers for testing: - ScanArtifacts() - artifact scanning - ExtractTarGz() - tar.gz extraction - DirOverallSHA256HexParallel() - parallel hashing Profiling results (Apple M2 Ultra): - dirOverallSHA256Hex: 78% syscall overhead (target for mmap C++) - rebuildIndex: 96% syscall overhead (target for binary index C++) - scanArtifacts: 87% syscall overhead (target for fast traversal C++) - extractTarGz: 95% syscall overhead (target for parallel gzip C++) Related: C++ optimization strategy in memory 5d5f0bb6
105 lines
2.5 KiB
Go
105 lines
2.5 KiB
Go
package benchmarks
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import (
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"os"
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"path/filepath"
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"testing"
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"github.com/jfraeys/fetch_ml/internal/worker"
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)
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// BenchmarkDirOverallSHA256Hex profiles the directory hashing hot path.
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// This function walks directories, sorts files, and computes SHA256 hashes.
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// It's a Tier 1 candidate for C++ optimization via:
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// - Memory-mapped file reads
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// - Parallel hashing
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// - SIMD SHA256 (Intel SHA extensions or ARMv8 crypto)
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func BenchmarkDirOverallSHA256Hex(b *testing.B) {
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// Create a temp directory structure resembling a dataset
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tmpDir := b.TempDir()
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// Create nested structure with files of varying sizes
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sizes := []int{1024, 10240, 102400, 1024 * 1024} // 1KB to 1MB
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for i, size := range sizes {
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subdir := filepath.Join(tmpDir, "subdir", string(rune('a'+i)))
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if err := os.MkdirAll(subdir, 0750); err != nil {
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b.Fatal(err)
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}
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data := make([]byte, size)
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for j := range data {
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data[j] = byte(i + j%256)
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}
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if err := os.WriteFile(filepath.Join(subdir, "data.bin"), data, 0640); err != nil {
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b.Fatal(err)
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}
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}
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// Add some small metadata files
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metaDir := filepath.Join(tmpDir, "meta")
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if err := os.MkdirAll(metaDir, 0750); err != nil {
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b.Fatal(err)
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}
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for i := 0; i < 10; i++ {
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if err := os.WriteFile(
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filepath.Join(metaDir, "file"+string(rune('0'+i))+".json"),
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[]byte(`{"key": "value"}`),
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0640,
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); err != nil {
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b.Fatal(err)
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}
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}
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b.ResetTimer()
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b.ReportAllocs()
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for i := 0; i < b.N; i++ {
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_, err := worker.DirOverallSHA256Hex(tmpDir)
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if err != nil {
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b.Fatal(err)
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}
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}
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}
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// BenchmarkDirOverallSHA256HexLarge profiles with larger dataset simulation
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func BenchmarkDirOverallSHA256HexLarge(b *testing.B) {
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tmpDir := b.TempDir()
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// Create 50 files of 100KB each = ~5MB total
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for i := 0; i < 50; i++ {
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subdir := filepath.Join(tmpDir, "data", string(rune('a'+i%26)))
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if err := os.MkdirAll(subdir, 0750); err != nil {
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b.Fatal(err)
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}
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data := make([]byte, 100*1024)
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for j := range data {
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data[j] = byte(i + j%256)
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}
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if err := os.WriteFile(
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filepath.Join(subdir, "chunk"+string(rune('0'+i/26))+".bin"),
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data,
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0640,
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); err != nil {
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b.Fatal(err)
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}
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}
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b.Run("Sequential", func(b *testing.B) {
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b.ReportAllocs()
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for i := 0; i < b.N; i++ {
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_, err := worker.DirOverallSHA256Hex(tmpDir)
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if err != nil {
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b.Fatal(err)
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}
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}
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})
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b.Run("ParallelGo", func(b *testing.B) {
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b.ReportAllocs()
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for i := 0; i < b.N; i++ {
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_, err := worker.DirOverallSHA256HexParallel(tmpDir)
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if err != nil {
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b.Fatal(err)
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}
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}
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})
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}
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