Comprehensive walkthrough of Python's multiprocessing module covering Process, Pool, Queue, Pipe, Lock, Value, Array, and Manager primitives. Explains why multiprocessing bypasses the GIL for CPU-bound work, compares start methods (spawn, fork, forkserver) across platforms, demonstrates producer-consumer patterns with sentinel values, shows safe shared-state patterns with locks, and benchmarks pool sizes to find the performance sweet spot. Also covers common pitfalls like pickling errors, deadlocks from improper join/queue ordering, and zombie processes.
Table of contents
IntroductionKey TakeawaysWhat Is Python Multiprocessing and When Should You Use ItPython Multiprocessing Process Class ExamplePython Multiprocessing Pool ExamplePython Multiprocessing Queue ExamplePython Multiprocessing Lock ExampleShared State Between ProcessesProcess Start Methods: spawn, fork, and forkserverPerformance Benchmarks: Serial vs. MultiprocessingPython Multiprocessing vs. Threading vs. asyncioCommon Errors and How to Fix ThemFrequently Asked QuestionsConclusionSort: