What causes high memory in Python JSON parsing ?

Python creates PyObjects for every key-value pair, ballooning RAM — 1.9GB on 500MB files is common for large logs.

How to install Axiom-JSON?

Grab from GitHub: https://github.com/naresh-cn2/Axiom-JSON. Compile C engine, pip the bridge. Docs cover it.

Does Axiom-JSON work on Windows?

Yes, mmap's cross-platform. Test your setup — pointers behave.

Can I use this for production data pipelines?

Absolutely, if you're comfy with C. Scales infinitely, crushes cloud costs. Start small.

⚙️ DevOps & Platform Eng

Python's json.loads() Devours 1.9GB on 500MB File – C Hack Slashes It to Zero

A 500MB JSON log file? Python's json.loads() balloons it to 1.9GB RAM. One dev's C-bridge fix? Near-zero memory, 11x faster. Game over for bloated parsing.

theAIcatchup Apr 08, 2026 3 min read

Benchmark graph: Python json.loads() 1.9GB RAM vs Axiom-JSON ~0MB with 11x speedup

⚡ Key Takeaways

Python json.loads() spikes 1.9GB RAM on 500MB JSON — Axiom-JSON drops to ~0MB. 𝕏
11x speedup via mmap and C pointers; open-source on GitHub. 𝕏
Slashes cloud bills by enabling smaller instances and more parallelism. 𝕏

Published by

theAIcatchup

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#C extensions #Python JSON optimization #axiom-json #c bridge parsing #json parsing #memory optimization #memory overhead reduction #python-performance

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Originally reported by dev.to

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