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Concurrency: Threads and the GIL

Programming Python — Advanced Difficulty 3/5

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Concurrency: Threads and the GIL

Run overlapping work with threads, understand why the GIL limits them, and keep shared state correct with locks

Big Idea: A thread lets one Python process do several things that overlap in time. But CPython protects its internals with a single mutex called the Global Interpreter Lock (GIL): only one thread can execute Python bytecode at any instant. So threads give you real speedups for I/O-bound work (waiting on the network, disk or a database) but almost none for CPU-bound work. Whenever two threads touch the same mutable data you must serialise that access with a Lock, or you get race conditions.
By the end you will be able to:
  • distinguish concurrency from parallelism,
  • start and join threads with the threading module,
  • explain what the GIL guarantees and what it costs,
  • decide when threads help (I/O-bound) and when they do not (CPU-bound),
  • spot a race condition and fix it with a Lock or with lock:,
  • reach for ThreadPoolExecutor and queue.Queue instead of managing raw threads.

1. Concurrency is not the same as parallelism

Concurrency is a way of structuring a program so that several tasks are in progress at once — they can be interleaved on a single core, each making a little progress in turn. Parallelism is when tasks literally run at the same instant on multiple cores. Concurrency is about dealing with many things; parallelism is about doing many things simultaneously. Threads in CPython give you concurrency; because of the GIL they rarely give you parallelism for pure-Python CPU work.

This distinction is the key to the whole topic. Threads let a program wait on many things at once — twenty open sockets, ten files, five HTTP requests — so the total wall-clock time collapses toward the slowest single wait instead of the sum of all of them. What they cannot do is make one CPU-heavy Python loop finish faster.

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