The Go App Optimization Series is a collection of technical articles aimed at helping developers write faster, more efficient Go applications. Whether you're building high-throughput APIs, microservices, or distributed systems, this series offers practical patterns, real-world use cases, and low-level performance insights to guide your optimization efforts.

While Go doesn’t expose as many knobs for performance tuning as languages like C++ or Rust, it still provides plenty of opportunities to make your applications significantly faster. From memory reuse and allocation control to efficient networking and concurrency patterns, Go offers a pragmatic set of tools for writing high-performance code.

We focus on concrete techniques with mesurable impact you can apply immediately—covering everything from core language features to advanced networking strategies.

What’s Covered So Far

Common Go Patterns for Performance

In this first article, we explore a curated set of high-impact performance patterns every Go developer should know:

• Using sync.Pool effectively
• Avoiding unnecessary allocations
• Struct layout and memory alignment
• Efficient error handling
• Zero-cost abstractions with interfaces
• In-place sorting and slices reuse

Each pattern is grounded in practical use cases, with benchmarks and examples you can copy into your own codebase.

What’s Coming Next

High-Performance Networking in Go

In our upcoming deep dive into networking, we'll focus on building high-throughput network services with Go’s standard library and beyond. This includes:

• Efficient use of net/http and net.Conn
• Managing large volumes of concurrent connections
• Performance tuning with epoll/kqueue and GOMAXPROCS
• Load testing techniques and bottleneck diagnostics
• TBD...

We'll also explore when to drop down to lower-level libraries like fasthttp, and how to balance performance with maintainability.

Who This Is For

This series is ideal for:

• Backend engineers optimizing Go services in production
• Developers working on latency-sensitive systems
• Teams migrating to Go and building performance-critical paths
• Anyone curious about Go’s performance model and trade-offs

Stay tuned—more articles, code samples, and tools are on the way. You can bookmark this page to follow the series as it evolves.