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Go performance profiling patterns using pprof, benchmarks, and optimization strategies for identifying and fixing performance bottlenecks.
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| name | profiling |
| description | Go performance profiling patterns using pprof, benchmarks, and optimization strategies for identifying and fixing performance bottlenecks. |
| origin | fraudctl |
Patterns for identifying and fixing performance bottlenecks in Go applications.
# Run benchmarks with CPU profile
go test -bench=BenchmarkKNN_Predict \
-benchtime=5s \
-cpuprofile=cpu.prof \
-memprofile=mem.prof \
./internal/knn/
# Analyze CPU profile
go tool pprof --text cpu.prof | head -30
flat flat% sum% cum cum%
19.04s 66.25% 66.25% 19.05s 66.28% euclideanDistanceSquared (inline)
7.80s 27.14% 93.39% 28.17s 98.02% (*Dataset).Predict
| Column | Meaning |
|---|---|
| flat | Time in this function only |
| flat% | Percentage of total time |
| sum% | Cumulative percentage |
| cum | Time in this function + children |
| cum% | Cumulative percentage with children |
Rule: Optimize the function consuming the most time first.
In fraudctl:
euclideanDistanceSquared = 66% (inline, already optimal)Predict = 98% totalfunc BenchmarkPredict(b *testing.B) {
ds := NewDataset(100000)
query := make([]float64, 14)
b.ResetTimer()
for i := 0; i < b.N; i++ {
ds.Predict(query, 5)
}
}
func BenchmarkPredict(b *testing.B) {
ds := NewDataset(100000)
query := make([]float64, 14)
b.ReportAllocs()
b.ResetTimer()
for i := 0; i < b.N; i++ {
ds.Predict(query, 5)
}
}
func BenchmarkScalar(b *testing.B) {
// ... baseline implementation
}
func BenchmarkOptimized(b *testing.B) {
// ... optimized implementation
}
Run with benchstat:
go test -bench=Benchmark -benchtime=3s ./... > old.txt
# make changes
go test -bench=Benchmark -benchtime=3s ./... > new.txt
benchstat old.txt new.txt
| Pattern | When to Use | Expected Gain |
|---|---|---|
| Inline functions | Hot path, small functions | 10-30% |
| sync.Pool | Repeated allocations | 20-50% |
| Preallocate slices | Known size | 10-30% |
| Avoid interface{} | Hot paths | 5-15% |
| Contiguous memory | Cache-sensitive | 10-20% |
| Pattern | Why Low Impact |
|---|---|
| JSON parsing (jsoniter) | < 1% of hot path |
| SIMD (small vectors) | ~1% for 14D vectors |
| Compiler flags | Go already optimal |
# Profile with rate = 1 (every allocation)
go test -memprofilerate=1 -memprofile=mem.prof -bench=.
go tool pprof --text mem.prof
flat flat% sum% cum cum%
28912kB 27.37% 86.66% 28948kB 27.41% (*vectorPool).put
Focus on functions with high cum values — they're allocating or retaining memory.
| Task | Command |
|---|---|
| CPU profile | go test -cpuprofile=cpu.prof -bench=. |
| Memory profile | go test -memprofile=mem.prof -bench=. |
| Analyze text | go tool pprof --text profile.prof |
| Analyze web UI | go tool pprof -http=:8080 profile.prof |
| Compare benchmarks | benchstat old.txt new.txt |
Remember: Profile first, optimize what the profile shows. Don't guess — let the data guide you.
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