Thread management best practices

When working with threads or thread pools in concurrent code, follow these practices to improve reliability and debuggability:

1. Always name your threads and thread pools using the thread_name_prefix parameter. This makes debugging and monitoring much easier when issues arise.

# Good
executor = ThreadPoolExecutor(max_workers=10, thread_name_prefix="data-processor")

# Good
thread = threading.Thread(name="result-sender", target=process_results)

# Avoid - unnamed threads make debugging difficult
executor = ThreadPoolExecutor(max_workers=10)

1. Set appropriate timeouts for blocking operations like thread joins and future results to prevent indefinitely blocking the main process:

# Good - explicit timeout prevents hanging
thread.join(timeout=1.0)
future.result(timeout=5.0)

# Avoid - may block indefinitely
thread.join()

1. Use daemon threads when the thread should terminate when the main process exits and shouldn’t block application shutdown:

# Good for service threads that can terminate with the program
thread = threading.Thread(target=background_task, daemon=True)

1. Be careful with timeouts in concurrent contexts - understand how your concurrency primitives handle timeouts. For example, when using as_completed, the timeout is already handled and doesn’t need to be repeated in the future.result() call.

Following these practices will help avoid common threading pitfalls like application hang during shutdown, thread leaks, and difficult-to-debug concurrency issues.