Python Multithreading

The threading Module

Python's built-in threading module lets you create and manage threads, which are lightweight units of execution that share the same memory space within a single program.

import threading
import time

def download(name):
    print(f"Starting {name}")
    time.sleep(2)
    print(f"Finished {name}")

t1 = threading.Thread(target=download, args=("file1",))
t2 = threading.Thread(target=download, args=("file2",))

t1.start()
t2.start()

t1.join()
t2.join()
print("All downloads complete")

The Global Interpreter Lock (GIL)

CPython (the standard Python interpreter) has a Global Interpreter Lock, or GIL, which allows only one thread to execute Python bytecode at a time. This means threads don't give you true parallel CPU computation for pure Python code.

Why Threads Still Help for I/O-Bound Tasks

The GIL is released while a thread waits on I/O operations, such as reading a file, calling a web API, or querying a database. So even with the GIL, threads make I/O-bound programs much faster by overlapping their waiting time.

import threading
import time

def fetch(url):
    print(f"Fetching {url}")
    time.sleep(1)  # simulates waiting for a network response
    print(f"Done: {url}")

urls = ["site1.com", "site2.com", "site3.com"]
threads = [threading.Thread(target=fetch, args=(u,)) for u in urls]

for t in threads:
    t.start()
for t in threads:
    t.join()

Thread and join()

You create a Thread object with a target function, start it with .start(), and use .join() to make the main program wait until that thread finishes before continuing.