Concurrency#
Python provides multiple approaches for concurrent execution to handle CPU-bound
and I/O-bound tasks efficiently. The threading module enables lightweight
concurrent execution within a single process, while multiprocessing bypasses
the Global Interpreter Lock (GIL) by using separate processes for true parallelism.
The concurrent.futures module offers a high-level interface that abstracts
the differences between threads and processes behind a unified API.
Understanding when to use each approach is crucial: threads excel at I/O-bound tasks (network requests, file operations) where the GIL is released during waiting, while processes are better for CPU-bound tasks (computation, data processing) where true parallel execution is needed.