The "Abstraction Tax" is Real We love Python for its simplicity, but when we hit massive datasets, we pay a price. Standard libraries like Pandas are incredible, but they often struggle with memory overhead and the Global Interpreter Lock (GIL) when pushing the physical limits of hardware.I built HydraCore to prove that you don't always need a bigger AWS instance—sometimes you just need a closer relationship with the metal.🏗️ The Architecture: How it WorksTo achieve these speeds, I moved the ingestion logic out of the Python interpreter and into a native C-extension. The system relies on three architectural pillars:1. Zero-Copy Memory (mmap)Instead of reading a file into a buffer and then copying it into a Python object, I use mmap to map the file directly into the process's address space. This allows the OS to handle paging and gives us direct access to the raw bytes.2. The Hydra (Multi-threading)By using POSIX threads (pthreads) in C, I can bypass the GIL entirely.…