Summary

In this chapter, we discussed different options of concurrent programming in Python using the standard libraries. We started with multithreading with an introduction to the core concepts of concurrent programming. We introduced the challenges with multithreading, such as the GIL, which allows only one thread at a time to access Python objects. The concepts of locking and synchronization were explored with practical examples of Python code. We also discussed the types of task that multithreaded programming is more effective for using a case study.

We studied how to achieve concurrency using multiple processes in Python. With multiprocessing programming, we learned how to share data between processes using shared memory and the server process, and also how to exchange objects safely between processes using the Queue object and the Pipe object. In the end, we built the same case study as we did for the multithreading example, but using processes instead. Then, we introduced...