Threading and the GIL: Concurrent Python

Learning Objectives: After this lesson, you'll understand threading in Python, the Global Interpreter Lock (GIL) and its implications, synchronization primitives, and when threading helps vs. hurts performance.

Understanding Concurrency vs Parallelism

Before diving into threading, let's clarify these often-confused concepts:

Basic Threading

Creating Threads

Daemon Threads

The Global Interpreter Lock (GIL)

The GIL is a mutex that protects access to Python objects, preventing multiple threads from executing Python bytecode simultaneously.

Understanding the GIL

When Threading DOES Help: I/O-Bound Tasks

Synchronization Primitives

When multiple threads access shared data, we need synchronization.

Locks (Mutex)

RLock (Reentrant Lock)

Semaphore - Limited Resource Access

Event - Thread Signaling

Condition - Complex Coordination

Thread-Safe Data Structures

Thread Pooling

Practice Exercises

Exercise 1: Thread-Safe Cache

Exercise 2: Worker Pool with Results

Key Takeaways

When to Use Threading

Next Steps

In the next lesson, we'll explore Multiprocessing—bypass the GIL with true parallelism, use process pools, manage shared state, and understand inter-process communication.

Ready for true parallelism? Multiprocessing awaits!