A collection of Go benchmarks comparing the performance of various standard library functions and common programming tasks.
Go Benchmarks is a repository that provides a comprehensive set of performance comparisons for different Go language constructs and standard library functions. It helps developers understand the performance implications of various coding approaches in Go, from string manipulation and concurrency patterns to hashing algorithms and data parsing.
Go developers who need to make informed decisions about performance-critical code, such as those working on high-throughput systems, data processing pipelines, or low-latency applications.
Developers choose this project because it offers empirically measured, reproducible benchmark data using Go's built-in benchmarking tools, allowing them to compare the speed and efficiency of different implementation approaches for common tasks without having to write their own tests.
Go(lang) benchmarks - (measure the speed of golang)
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The repository includes a wide range of benchmarks, from string operations and concurrency patterns to hashing algorithms and JSON processing, as detailed in the README sections like concat, concurrency_counter, and hash.
Uses Go's built-in `go test -bench` tools to provide measurable results with specific hardware (Apple M2 Max) and Go version (go1.26.1) details, ensuring reproducibility for comparisons.
Each benchmark comes with full Go code snippets, such as in the base64 or caseinsensitivecompare sections, allowing developers to inspect and adapt the implementations directly.
Covers varied domains like file operations (embed vs. disk reads), mathematical operations, and template rendering, helping in multi-faceted performance decisions.
Benchmarks are run only on an Apple M2 Max system, which may not reflect performance on other architectures (e.g., x86, ARM servers) or under different loads, reducing generalizability.
Focuses on isolated, synthetic tests that might not account for real-world factors like garbage collection, system contention, or integrated application behavior, potentially misleading for complex scenarios.
Some benchmarks, like in the hash section, depend on third-party packages (e.g., github.com/jzelinskie/whirlpool), which can introduce compatibility issues or require additional setup steps not covered in the README.
While raw performance numbers are provided, there's minimal explanation on how to interpret results in context, such as trade-offs between speed, memory usage, or security for hashing algorithms.