How to Choose the Fastest JSON Library for Python: A Practical Benchmarking Guide

Using JSON extensively can expose bottlenecks in encoding or decoding, and while Python's built‑in library is adequate, several faster alternatives exist; choosing the right one requires a clear evaluation process.

The author outlines four steps: (1) verify the need for a new JSON library by measuring its impact on your workload, (2) define benchmarks that reflect your specific message sizes and patterns, (3) filter candidates based on criteria such as safety, custom encoding support, cross‑platform compatibility, and maintenance activity, and (4) benchmark the remaining libraries.

In the author's case, the focus was on encoding small log messages generated by the Eliot logging library, where JSON accounted for roughly 25% of CPU time; this motivated testing libraries like orjson, rapidjson, ujson, and hyperjson.

After filtering out ujson (due to stability issues) and hyperjson (macOS‑only and immature), the final comparison between rapidjson and orjson showed that orjson was the fastest even when additional Unicode decoding was required.

Despite orjson's superior performance, the author notes trade‑offs such as a smaller user base, lack of Conda packages, and the need to build it for Conda‑forge, emphasizing that the best choice depends on your own performance requirements and other constraints.

The key takeaway is to define your specific needs, run relevant benchmarks, and select the JSON library that best balances speed, safety, customizability, and ecosystem support for your project.