Python Threading: Creation, Synchronization, Communication, Thread Pools, and the GIL

Python provides two threading modules: the low‑level thread module and the higher‑level threading module, which wraps thread and offers convenient run and start methods for creating and managing threads.

Threads can be created by passing the target function to Thread 's constructor or by subclassing threading.Thread and overriding its run method.

The join() method blocks the calling (usually main) thread until the target thread finishes or a timeout expires, while setDaemon(True) marks a thread as a daemon so it terminates automatically when the main thread ends; this must be set before start().

Python's standard library includes several synchronization primitives in threading.py: Lock , RLock , Semaphore , Event , Condition , and Barrier . These can be subclassed to build custom synchronization mechanisms.

For thread communication, the built‑in queue.Queue is the most common tool; it provides thread‑safe put() and get() operations, allowing multiple producer and consumer threads to exchange data safely.

Creating a thread for every short‑lived task can be inefficient because each thread incurs start‑up and teardown overhead. A thread pool mitigates this by pre‑creating a set of worker threads that repeatedly execute submitted tasks, reducing latency and improving stability.

Python's threadpool library demonstrates a ready‑made pool, and developers can also implement their own pool logic to suit specific requirements.

The Global Interpreter Lock (GIL) ensures that only one thread executes Python bytecode at a time in CPython, limiting multi‑core utilization for CPU‑bound workloads while having little impact on I/O‑bound programs; for CPU‑intensive tasks, multiprocessing or alternative interpreters without a GIL are recommended.