puzpuzpuz

puzpuzpuz / go-concurrent-map-bench

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Benchmarks for concurrent hash map implementations in Go

60
0
100% credibility
Found Feb 23, 2026 at 38 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Go
AI Summary

A benchmark suite that compares the performance of multiple Go libraries for concurrent hash maps across various workloads, data sizes, key types, and contention scenarios, complete with plots and analysis.

How It Works

1
🔍 Discover speed comparison

You find this collection of speed tests for different ways to store and share data safely when multiple program parts access it at the same time.

2
📖 Explore the options

You read simple descriptions of each storage method and the real-world situations they test, like mostly reading or mixing reads with changes.

3
⚡ Run your own tests

You easily start the performance checks on your computer to measure how fast each one works in different scenarios.

4
📊 View the colorful charts

You look at graphs and tables showing speeds for various data sizes, key types, and busy conditions.

5
🔍 Study the summaries

You check strengths and weaknesses of each option to match your needs.

🏆 Pick the winner

You choose the fastest storage tool for your project, making everything run smoother and quicker.

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Star Growth

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AI-Generated Review

What is go-concurrent-map-bench?

This Go repo delivers benchmarks for concurrent hash map implementations, pitting stdlib sync.Map against third-party options like xsync.Map, cornelk/hashmap, haxmap, and orcaman/concurrent-map. It tests real-world workloads—read-heavy mixes from 75-100%, range scans under contention, string/int keys, map sizes from 100 to 1M entries, warm-up vs cold starts—reporting ops/s scaled by GOMAXPROCS and allocs/op. Run `go test -bench .` locally to generate plots and pick the fastest map for your app.

Why is it gaining traction?

Unlike scattered benchmarks concurrentiels or niche llm benchmarks github, it covers broad scenarios with visual plots and allocation tables, making tradeoffs obvious at a glance. Neutral design (author discloses bias), simple extension via Go test flags, and hardware-specific results stand out for devs chasing throughput in high-parallelism Go code.

Who should use this?

Go backend engineers building caches, session stores, or pub/sub systems with heavy goroutine contention. Teams evaluating hash map upgrades beyond sync.Map, especially for read-scaling services hitting L3/RAM sizes.

Verdict

Solid starter for concurrent map benchmarks in Go—run it before committing to a lib. 20 stars and 1.0% credibility score reflect niche maturity, but strong docs and easy runs make it worth forking over raw perf tests.

(198 words)

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