Python Threads vs Processes: Which is Faster and When to Use Each

When writing Python programs that need to perform multiple tasks concurrently, developers often wonder if it's better to use threads or processes. The short answer is that processes are generally faster and more robust, but have higher overhead. Threads require less resources to create, but come with their own challenges.

What's the Difference Between a Thread and Process?

Fundamentally, a process has its own separate memory space and resources, while a thread shares memory with other threads in the same process.

When you create a new process in Python using the multiprocessing module, an entirely separate Python interpreter is spun up. This brings additional overhead, but ensures stability - if one process crashes, the others are unaffected.

Threads created with the threading module share the same interpreter and memory space. This makes them lightweight and fast to create, but also means they are not fully isolated. If one thread encounters an error, it can affect other threads and the entire process.

Benchmarking Threads vs Processes Performance

As a general rule, spawning new processes brings more overhead, while threads are quicker to create. But when it comes to executing CPU-bound tasks, processes tend to be faster since they can take advantage of multiple CPU cores, while threads may be limited by the Global Interpreter Lock (GIL).

Here is a simple benchmark you can run to compare threads and process performance with a CPU-bound task:

import threading
import multiprocessing
import time

# Test function
def calc_square(numbers):
    for n in numbers:
        time.sleep(0.01)
        result = n*n

if __name__ == "__main__":
    
    numbers = [1,2,3,4]

    # Threads test
    start = time.time()
    threads = []
    for _ in range(4):
        thread = threading.Thread(target=calc_square, args=(numbers,)) 
        threads.append(thread)
        thread.start()

    for thread in threads:
        thread.join()

    print("Threads time:", time.time() - start)

    # Processes test
    start = time.time()
    processes = []
    for _ in range(4):
        process = multiprocessing.Process(target=calc_square, args=(numbers,))
        processes.append(process)
        process.start()

    for process in processes:
        process.join()

    print("Processes time:", time.time() - start)

In this example, processes finish faster since they can utilize multiple CPU cores efficiently.

When Should I Use Threads or Processes?

As a rule of thumb, use processes when you need robustness and true parallelism. Use threads when you need a lot of concurrency but don't mind the limitations of sharing state between threads.

I hope this gives you a better understanding of the tradeoffs between Python threads and processes!