Python Concurrency: from beginner to pro
summary
Covers concrete claims, tools and timings: explains OS/process/thread internals, uses _thread, threading, subprocess, multiprocessing and concurrent.futures, and demonstrates a Flask crypto web server repo (https://github.com/santiagobasulto/pycon-concurrency-tutorial-2020) that is run with artificial sleep to compare approaches. Shows sequential HTTP checks ~6.8s, threads ~2.3s for IO work, prime tests where threads hurt (≈4.2s) and processes win (≈0.76s), and emphasizes GIL blocks CPU‑bound parallelism and the producer–consumer queue/pool patterns that avoid manual locking.
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chapters (7)
- 0:00Start
- 2:03What this tutorial is NOT about
- 4:33Martelli Model of Scalability
- 11:31The OS is the one protecting hardware
- 17:36The OS Scheduler
- 23:54We can run several instances of the same program (at the same time)
- 25:50A multithreaded example
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