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Patterns and Techniques for Writing High-Performance Applications with Go

The Go App Optimization Guide is a series of in-depth, technical articles for developers who want to get more performance out of their Go code without relying on guesswork or cargo cult patterns. If you’re building services that need to handle real load—APIs, backend pipelines, or distributed systems—this guide focuses on the kind of low-level behavior and tuning opportunities that actually matter in production.

Go doesn’t give you the kind of fine-grained control you’d find in C++ or Rust, but it does give you just enough visibility to reason about performance—and just enough tooling to do something about it. From understanding allocation patterns and reducing GC overhead to building efficient network services and managing concurrency at scale, the series focuses on optimizations that are both practical and measurable.

The goal isn’t to write clever code—it’s to write fast, predictable code that holds up under pressure. Everything in this guide is backed by real use cases, stripped of theory, and aimed at what you can apply right now.

Common Go Patterns for Performance

This first article series covers a set of performance patterns that come up again and again when writing real-world Go code. It’s not an exhaustive list, but it hits the areas where small changes tend to make a noticeable difference:

  • Making proper use of sync.Pool
  • Cutting down on unnecessary allocations
  • Struct layout and memory alignment details that affect cache performance
  • Error handling that doesn’t drag down the fast path
  • Using interfaces without paying for them
  • Reusing slices and sorting in-place

Each pattern includes real code and numbers you can apply directly—no theory, no fluff.

High-Performance Networking in Go

This section takes a focused look at what it takes to build fast, reliable network services in Go. It covers not just how to use the standard library, but how to push it further when you’re dealing with real load.

Topics include:

  • Getting the most out of net/http and net.Conn
  • Handling large numbers of concurrent connections without falling over
  • Tuning for system-level performance with epoll, kqueue, and GOMAXPROCS
  • Running realistic load tests and tracking down bottlenecks

While the goal is to keep everything grounded in practical examples, this part leans more theoretical for now due to the exploratory nature of the networking topics being developed.

Who This Is For

This series is ideal for:

  • Engineers working on backend systems where Go’s performance actually matters
  • Developers building latency-sensitive services or handling high-throughput traffic
  • Teams moving critical paths to Go and needing to understand the trade-offs
  • Anyone who wants a clearer picture of how Go behaves under load

More content is coming soon—additional articles, practical code examples, and tooling insights. Bookmark this page to keep up as the series grows.