Parallel Workloads with `multiprocessing`

Standard Library

multiprocessing Module

Bypass the GIL with separate processes. Learn pools, shared state, and communication primitives.

Use Process or PoolAvoid deadlocks

Patterns

from multiprocessing import Pool

def analyze(order_id: str) -> dict:
    ...

with Pool(processes=4) as pool:
    results = pool.map(analyze, order_ids)

• Process gives manual control.
• Pool or ProcessPoolExecutor handle workers automatically.

Communication

Tool	Use
Queue	Send messages between processes
Pipe	Lightweight bidirectional communication
Manager	Share proxy objects (list, dict)
Value / Array	Shared memory primitives

Tips

• Guard entry point with if __name__ == '__main__': on Windows.
• Processes are heavier than threads—batch work to amortize overhead.
• Avoid sharing large objects; pass IDs and fetch data lazily.

Next up in your learning path

threading

Use threads for I/O-bound workloads.

Companion

functools.lru_cache

Cache expensive CPU work before parallelizing.

Optimization

Future concurrency cluster

We’ll expand into asyncio once fundamentals are done.

Preview