A collection of Ruby idioms with performance benchmarks to help developers write faster Ruby code.
Fast Ruby is a community-driven collection of Ruby idioms and code patterns, each with benchmark data showing performance differences. It helps developers identify faster alternatives to common Ruby operations by providing empirical evidence from real-world benchmarks. The project documents performance characteristics across various Ruby versions and implementations.
Ruby developers who want to write more performant code, library maintainers optimizing their gems, and teams looking to improve application speed through micro-optimizations.
Developers choose Fast Ruby because it provides concrete, benchmark-backed evidence for performance improvements rather than anecdotal advice. It serves as a reference for making informed decisions about code patterns and helps avoid common performance pitfalls in Ruby.
:dash: Writing Fast Ruby :heart_eyes: -- Collect Common Ruby idioms.
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Each idiom includes real benchmark results using benchmark-ips, providing concrete evidence of performance differences rather than anecdotal advice, as shown in the detailed README examples.
Idioms are categorized by Ruby class (Array, Hash, String, etc.), making it easy to find relevant optimizations for specific code sections, as highlighted in the index.
Recommends and integrates with tools like the fasterer gem for static analysis, allowing automated checks based on the documented idioms, mentioned in the 'Analyze your code' section.
Includes cautions about when faster idioms might not be appropriate, such as the gsub vs tr example, promoting informed decision-making, as emphasized in the project philosophy.
Benchmarks are run on specific Ruby versions (e.g., Ruby 4.0.0), and performance characteristics can vary across implementations, making some advice less applicable for older or alternative Rubies.
The focus on micro-optimizations might lead developers to apply faster idioms blindly without considering side effects or readability, despite the project's warnings about context.
Primarily addresses method-level optimizations and does not cover broader performance issues like algorithm complexity or system architecture, which are often more impactful.