golang-benchmark

To measure Go code performance, write benchmarks using `b.Loop()` and execute them with multiple iterations like `-count=10` to ensure statistical significance. This approach provides reliable data to analyze execution speed and identify bottlenecks.

Go profiling with pprof works by capturing CPU, memory, and goroutine activity during benchmark execution. Interpreting these profiles allows you to locate performance hotspots and understand where the code spends time or allocates memory.

The best way to compare Go benchmark results is to run benchmarks with a high count like `-count=20` before and after code changes, then analyze the output using benchstat. This validates that performance improvements are statistically measurable.

You need benchstat to validate Go performance optimizations because it provides statistical comparison of benchmark results. It ensures that observed execution speed improvements are reliable and not random variations between test runs.

You need to run multiple iterations for statistical significance in Go benchmarks when you modify a critical function and want to confirm performance gains. Running multiple counts ensures your execution speed measurements are reliable and measurable.