Python's Global Interpreter Lock (GIL) restricts execution to a single CPU core for CPU-bound tasks. This makes running CPU-intensive operations inside multi-threaded Python scripts relatively slow. However, for I/O-bound operations (such as making parallel HTTP requests or reading sensor logs), threading is a lightweight solution.

Tuning background threads requires understanding where the GIL is released, and when to transition to multiprocessing.

1. Non-Blocking I/O Threading

In python, standard library functions that execute network calls or call system subprocesses release the GIL during execution. The following helper handles telemetry writes concurrently without blocking the main process loop:

# Running parallel threads for I/O operations
import threading

def process_and_send_logs(log_batch):
    # This thread task executes HTTP POST requests
    thread = threading.Thread(target=send_to_server, args=(log_batch,))
    thread.daemon = True
    thread.start()
    return thread

2. Shifting to Multiprocessing

For operations that parse large strings or perform heavy calculations, you must bypass the GIL by using multiprocessing to run tasks in separate OS processes, leveraging multi-core compute environments.

Choosing Between `asyncio`, `ThreadPoolExecutor`, and `ProcessPoolExecutor`

Use asyncio for high-concurrency network I/O (thousands of HTTP/WebSocket sockets), ThreadPoolExecutor for blocking C-extension or legacy SDK calls, and ProcessPoolExecutor (or Cython/Rust/WASM) for CPU-bound cryptography and matrix math.