Can I use this for production Go applications?

Yes, it includes guidance on setting up HTTP profiling endpoints and tuning runtime environment variables like GOMEMLIMIT for production stability.

What is the benefit of using sync.Pool in Go?

sync.Pool helps reduce garbage collection pressure and CPU overhead by reusing temporary objects instead of constantly allocating new memory.

How do I compare performance before and after optimization?

This skill demonstrates how to use Go's built-in benchmarking tools combined with benchstat to provide statistical evidence of performance changes.

How does this skill help with Go memory leaks?

It provides specific patterns for memory profiling and heap analysis using pprof to identify exactly where allocations are occurring and how they can be mitigated.

Go Performance Optimization

Name: Go Performance Optimization
Author: geoffjay

bygeoffjay

•

Analytics & Monitoring

Optimizes Go application performance through advanced profiling, memory management, and benchmarking techniques.

About

This skill empowers developers to build high-performance Go applications by providing expert patterns for CPU and memory profiling, heap analysis, and execution tracing. It offers actionable strategies for reducing allocations through escape analysis and sync.Pool, optimizing concurrency with sharded locks, and fine-tuning the Go runtime. Whether you are debugging a memory leak or squeezing extra throughput out of a hot path, this skill provides the necessary tools and benchmarks to measure and improve Go code efficiency scientifically.

Key Features

Comprehensive benchmarking patterns including parallel and sub-benchmarks
2 GitHub stars
Concurrency tuning using sharded locks and atomic operations
Memory optimization strategies like escape analysis and pre-allocation
Advanced CPU, memory, and HTTP profiling configurations
Garbage collection and runtime tuning guidance (GOGC, GOMEMLIMIT)

Use Cases

Reducing latency in high-throughput API endpoints
Identifying and fixing memory leaks in production Go services
Optimizing CPU-intensive data processing pipelines

About

Key Features

Comprehensive benchmarking patterns including parallel and sub-benchmarks
2 GitHub stars
Concurrency tuning using sharded locks and atomic operations
Memory optimization strategies like escape analysis and pre-allocation
Advanced CPU, memory, and HTTP profiling configurations
Garbage collection and runtime tuning guidance (GOGC, GOMEMLIMIT)

Use Cases

Reducing latency in high-throughput API endpoints
Identifying and fixing memory leaks in production Go services
Optimizing CPU-intensive data processing pipelines