Python Threads and Concurrency

Internal

• Python Language
• Concurrent (Parallel) Programming

TODO

• PROCESS PyOOP "Concurrency" + "Threads" + "The many problems with threads" + "Shared memory" + "The global interpreter lock" + "Thread overhead" + "Multiprocessing" + "Multiprocessing pools" + "Queues" + "The problems with multiprocessing" + "Futures" + "AsyncIO" + "AsyncIO in action" + "Reading an AsyncIO Future" + "AsyncIO for networking" + "Using executors to wrap blocking code" + "Streams" + "Executors" + "AsyncIO clients" + "Case Study"
• Develop Python Global Interpreter Lock (GIL)

Overview

Python is not the best choice for building highly concurrent, multi-threaded applications, particularly applications with many CPU-bound threads. The main reason for this is Python's Global Interpreter Lock (GIL), a mechanisms that prevents the interpreter from executing more than one instruction at a time, even in the presence of multiple cores. There are Python C extensions that use native multithreading in C or C++ to run code in parallel without being impacted by the GIL, as long as they do not need to regularly interact with Python objects.