How I cut Python JSON memory overhead from 1.9GB to ~0MB (11x Speedup)

The Problem: The "PyObject" TaxWe all love Python for its developer velocity, but for high-scale data engineering, the interpreter's overhead is a silent killer.I was recently benchmarking standard json.loads() on a 500MB JSON log file.The Result:⏱️ 3.20 seconds of execution time.📈 1,904 MB RAM spike.Why?Python's standard library creates a full-blown PyObject for every single key and value. When you are dealing with millions of log entries, your RAM becomes a graveyard of overhead. For a 500MB file, Python is essentially managing nearly 2GB in memory just to represent the data structures. For cloud infrastructure, this isn't just "slow"—it's an expensive AWS bill and a system crash waiting to happen.The Solution: Axiom-JSON (The C-Bridge)I decided to bypass the Python memory manager entirely for the heavy lifting. I built a bridge using:Memory Mapping ($mmap$): Instead of "loading" the file into a RAM buffer, I mapped the file's address space. The OS handles the paging, keeping the RAM