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🧵 Threads Basics

A thread is an OS-level unit of execution inside a process. Multiple threads share the same memory, which makes communication easy—but also makes bugs (like races) easy.

Python threads are most useful for I/O-bound work:

  • waiting for network responses
  • reading/writing files
  • calling slow APIs

For CPU-bound work, Python’s GIL (Global Interpreter Lock) often prevents threads from running Python bytecode in parallel. In those cases, prefer processes.

âś… Creating and Joining Threads

import threading

def worker():
    print("Hello from a thread")

t = threading.Thread(target=worker)
t.start()
t.join()
  • start() begins running the thread.
  • join() waits for it to finish.

âś… Thread Pools

For many small tasks, use a thread pool:

  • concurrent.futures.ThreadPoolExecutor
  • executor.submit(...) / executor.map(...)

Thread pools manage thread creation and reuse for you.

🔍 Key Takeaways

  • Threads overlap work, especially when waiting on I/O.
  • Always join() (or use a pool) when you need results.
  • Be careful with shared state—synchronization comes next.

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