Multithreading (Python)

Definition

A technique for executing multiple parts of a program concurrently by creating separate threads of execution.

Why It Matters

Python’s Global Interpreter Lock (GIL) makes multithreading tricky. If you use threads for CPU-bound tasks in Python, your program might actually run slower. Understanding when to use threads versus processes is essential for building responsive, high-performance applications.

Core Concepts

• The “Concurrency” Model: Threads allow a program to perform tasks “at the same time” (e.g., downloading a file in the background while the UI remains responsive).
• threading.Thread: Create a thread with target=function_name and args=[arg1, arg2].
• start() vs. join():
  • .start(): Begins execution of the thread.
  • .join(): Blocks the main program until the thread finishes (synchronization).
• Shared State Danger: Multiple threads modifying the same variable can cause “Race Conditions.”

import threading

def print_numbers():
    for i in range(5):
        print(i)

thread = threading.Thread(target=print_numbers)
thread.start()
thread.join() # Wait for thread to finish

Connected Concepts

• Time Module (Python)
• Subprocess Module (Python)
• Bottlenecks