go-performance-best-practices

Identify hot paths in latency-critical Go services by running production-like benchmarks and applying pprof profiling. Optimize allocation reduction and GC tuning using sync.Pool, preallocation, and GOMEMLIMIT to improve latency and throughput.

Profile Go concurrency and mutex contention using pprof CPU and blocking profiling to identify hot paths. Apply sync.Pool, context cancellation, and proper goroutine management to reduce lock contention and prevent goroutine leaks.

Yes, you can use Profile-Guided Optimization (PGO) to improve Go application performance. PGO uses runtime profiling data to guide compiler optimizations, reducing latency and improving throughput across server and CLI Go applications.

Tune GOMEMLIMIT and garbage collection for Go services by setting memory limits to match container quotas and adjusting GC parameters. This reduces GC pressure and prevents out-of-memory errors in memory-constrained environments.

Yes, this supports optimizing buffered I/O and HTTP client tuning in Go. It provides patterns for buffered I/O operations and HTTP client configurations to reduce latency and improve throughput in server and CLI applications.

Use sync.Pool and preallocation for Go performance optimization to reduce memory allocations and GC pressure. sync.Pool reuses objects, while preallocation avoids slice and map resizing, minimizing hot path latency in high-throughput services.