# dgryski/go-perfbook

Thoughts on Go performance optimization

Repository: https://github.com/dgryski/go-perfbook
Canonical: https://ross.abutalabs.com/products/go-perfbook
License Family: other
Topics: optimization, performance, performance-optimization, performance-analysis
Last push: 2022-01-05T14:13:12+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3755, "days_push": 1701, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 10896, forks 598 (observed 2026-08-28T04:10:44.615575+00:00)

## What it is
An open-source book by Damian Gryski collecting best practices for writing high-performance Go code, covering general optimization methodology plus Go-specific techniques like garbage collection, profiling, and assembly. It is maintained as a multi-language markdown document with community contributions.

## Use cases
- learn how to optimize Go code performance
- find Go profiling and benchmarking best practices
- understand Go garbage collection tuning
- speed up a slow Go service
- learn common Go standard library performance gotchas
- study an optimization workflow for backend code

## When to avoid
- you need performance guidance for languages other than Go
- you want a runnable tool or library rather than a written guide
- you need up-to-date coverage of the newest Go compiler features

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: documentation, benchmarking, developer-tools
- domain: developer-tools, performance, tutorials
- platform: go, cross-platform
- tags: go, performance-optimization, profiling, book, best-practices

## Member repositories
- dgryski/go-perfbook (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:44.615575+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:17:26.207135+00:00, confidence not recorded.
  - readme: https://github.com/dgryski/go-perfbook (fetched 2026-08-28T04:10:44.615575+00:00, sha 21df2afa976f)
- Data as of 2026-08-30T08:39:29.467469+00:00.