Multiprocessing: True Parallelism in Python

Learning Objectives: After this lesson, you'll understand how to bypass the GIL with multiprocessing, use process pools efficiently, manage shared state safely, and implement inter-process communication patterns.

Why Multiprocessing?

Multiprocessing creates separate Python processes, each with its own GIL, enabling true parallelism.

Multiprocessing vs Threading

Basic Process Creation

Process Pools

Process pools manage a fixed number of worker processes for efficient task distribution.

Using Pool

Pool Methods

Shared State and Communication

Processes don't share memory by default. Here's how to share data.

Shared Memory Values

Manager for Complex Shared Objects

Queues for Communication

Pipes for Two-Way Communication

Practical Patterns

Map-Reduce Pattern

Parallel Pipeline

Worker Pool with Callbacks

Best Practices

Key Takeaways

Multiprocessing vs Alternatives

Next Steps

In the next lesson, we'll explore Async/Await Fundamentals—understand event loops, write coroutines, use asyncio for concurrent I/O, and build async patterns for production code.

Ready for non-blocking Python? Async/await awaits!