kexibq-official / cafs-lib

What is cafs-lib?

cafs-lib is a header-only C++20 library that sorts integer arrays when you have far fewer distinct values than total elements. Think sorting a million log entries where only 500 severity levels exist, or organizing survey responses across 50 age brackets. It uses AVX2 SIMD instructions to count occurrences in a single pass, then emits the sorted result. The library ships with Python, Rust, and Go bindings so you can use it from non-C++ codebases without rewriting your data pipeline.

Why is it gaining traction?

The benchmark numbers are eye-catching: 3 to 17 times faster than standard library sort on low-cardinality data. It automatically detects when input falls outside its sweet spot and falls back to pdqsort, so you get fast paths without manual case handling. The API is dead simple: one function call on a vector. No configuration, no tuning, no deciding which algorithm to invoke. For data engineering teams processing categorical columns or quantized features, this is a drop-in speedup with zero cognitive overhead.

Who should use this?

Database engineers building columnar storage or query engines will see the most benefit. Data scientists preprocessing low-cardinality features for machine learning pipelines can cut preprocessing time significantly. Anyone running GROUP BY or ORDER BY on enum-style columns in analytics workloads. If your integers have millions of distinct values, look elsewhere.

Verdict

At 14 stars, this is early-stage software with a credible technical foundation but limited community validation. The documentation is thorough and the benchmark methodology is rigorous, but you should test against your actual data before production deployment. Worth evaluating now, especially if low-cardinality sorting is a known bottleneck in your stack.