skills/go-benchmark-testing/SKILL.md
Automates benchmark test creation for Go projects using the standard testing package with consistent software testing patterns. Use when creating performance benchmarks, profiling tests, or when the user mentions benchmarking, performance testing, or optimization.
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Instructions for AI coding agents on automating benchmark test creation using consistent software testing patterns in this Go project.
Performance Measurement
Benchmark tests measure the execution time and memory allocation of functions, providing quantifiable metrics for performance analysis.
Regression Detection
Continuous benchmarking helps identify performance regressions early in the development cycle before they reach production.
Optimization Guidance
Benchmark results guide optimization efforts by identifying bottlenecks and quantifying the impact of performance improvements.
Comparative Analysis
Benchmarks enable comparison of different implementations or algorithms to make informed decisions about performance trade-offs.
Resource Profiling
Memory allocation tracking helps identify unnecessary allocations and optimize memory usage patterns.
The FIRST principles for benchmark testing focus on creating reliable and meaningful measurements.
Fast
Benchmark setup and teardown should be minimal and excluded from timing to ensure accurate measurement of the function under test.
Independent
Each benchmark should be self-contained and not depend on shared state or results from other benchmarks to ensure isolated performance measurements.
Repeatable
Benchmarks should produce consistent, comparable results across runs and environments by controlling inputs and avoiding non-deterministic operations.
Self-Validating
Benchmarks should optionally validate results to prevent the compiler from optimizing away the code under measurement.
Timely
Benchmarks should be established before optimization work begins to provide a performance baseline and measure the impact of changes.
Microbenchmarking is a software testing technique that measures the performance of small, isolated code units to identify performance characteristics and bottlenecks.
Comparative Benchmarking is a testing approach that compares the performance of different implementations or algorithms side-by-side using consistent workloads.
Memory Profiling is the process of measuring memory allocations and usage patterns during benchmark execution using -benchmem flag.
Statistical Benchmarking uses multiple iterations to calculate statistical measures (mean, variance) to ensure reliable and reproducible results.
Sub-benchmarks organize related benchmark cases using b.Run() to group variations of the same function with different input scenarios.
Table-Driven Testing is a software testing technique in which benchmark cases are organized in a tabular format to systematically cover different input scenarios.
Identify
Identify performance-critical functions in pkg/ or internal/ that benefit from performance tracking (e.g., pkg/<package>/<file>.go).
Add/Create
Create benchmark tests in the same package (e.g., pkg/<package>/<file>_test.go).
Benchmark Test Coverage Requirements
Focus on functions that:
Apply Templates
Structure all benchmark tests using the template pattern.
Baseline Measurements
Establish performance baselines by running benchmarks on stable code before making changes.
| Command | Description |
| --------------------------------------------------------------- | -------------------------------------------------- |
| make go-test-bench | Execute all benchmarks with memory statistics |
| go test -bench=BenchmarkPercent -benchmem ./pkg/percent | Execute a specific benchmark function |
| go test -bench=. -benchmem -cpuprofile=cpu.prof ./pkg/percent | Generate CPU profile for performance analysis |
| go test -bench=. -benchmem -memprofile=mem.prof ./pkg/percent | Generate memory profile for allocation analysis |
| go test -bench=. -benchtime=10s ./pkg/percent | Run benchmarks for a specific duration |
| benchstat old.txt new.txt | Compare benchmark results before and after changes |
Test Framework
Use the standard Go
testingpackage withtesting.Bfor benchmark tests.
Include Imports
Include
testingand any packages needed for the function under test.
Benchmark Function Naming
Name benchmark functions with the
Benchmarkprefix followed by the function name (e.g.,BenchmarkPercentfor testingPercent()).
Benchmark Loop
Use
b.Loop()to control the number of iterations. The testing framework automatically adjusts the loop iterations to get reliable timing measurements.b.Loop()is preferred overb.Nas it provides better integration with the testing framework and more accurate measurements. Unlikeb.N-style benchmarks,b.Loop()integrates timer management, it automatically handlesb.ResetTimer()at the loop's start andb.StopTimer()at its end, eliminating the need to manually manage the benchmark timer for setup and cleanup code.
Timer Control
When using
b.Loop(), timer management is automatic and no manualb.ResetTimer(),b.StopTimer(), orb.StartTimer()calls are needed for typical benchmarks. For advanced scenarios not usingb.Loop(), useb.ResetTimer()to exclude setup time from measurements andb.StopTimer()/b.StartTimer()to exclude specific operations.
Sub-benchmarks
Use
b.Run()to organize related benchmark cases with different input scenarios. Each sub-benchmark runs independently with its ownb.Niterations.
Memory Reporting
Use
b.ReportAllocs()to report memory allocations per operation when not using-benchmemflag.
Result Validation
Optionally validate results in benchmarks to prevent compiler optimizations from eliminating dead code.
Use this template for new benchmark test functions. Replace placeholders with actual values and adjust as needed for the use case.
For benchmarking multiple scenarios or input variations, use sub-benchmarks with table-driven approach.
func Benchmark<FunctionName>(b *testing.B) {
// Define benchmark cases with different scenarios
benchmarks := []struct {
name string
param1 <type>
param2 <type>
// Add more parameters as needed
}{
{
name: "scenario description 1",
param1: <value1>,
param2: <value2>,
},
{
name: "scenario description 2",
param1: <value1>,
param2: <value2>,
},
// Add more benchmark cases
}
for _, bm := range benchmarks {
b.Run(bm.name, func(b *testing.B) {
// Arrange
// Setup code here (automatically excluded from timing by b.Loop)
// Act
for b.Loop() {
_, _ = <Function>(bm.param1, bm.param2)
}
})
}
}
For benchmarking a single scenario, use a simple loop without sub-benchmarks.
func Benchmark<FunctionName>(b *testing.B) {
// Arrange
// Setup code here (automatically excluded from timing by b.Loop)
param1 := <value1>
param2 := <value2>
// Act
for b.Loop() {
_, _ = <Function>(param1, param2)
}
}
For benchmarks that need to prevent compiler optimizations, store results in package-level variables.
var (
benchResult <type>
benchError error
)
func Benchmark<FunctionName>(b *testing.B) {
// Arrange
// Setup code here (automatically excluded from timing by b.Loop)
param1 := <value1>
param2 := <value2>
// Act
for b.Loop() {
benchResult, benchError = <Function>(param1, param2)
}
}
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