Parallelism in Python with Threads & Processes: 3 Key Concepts

Python is renowned for its simplicity and readability, but handling computationally intensive tasks can be a challenge. That’s where threads and processes come to the rescue. By understanding these powerful concepts and utilizing Python’s built-in libraries, you can unlock parallelism and supercharge your code’s performance.

1. Threads vs. Processes: Two Flavors of Parallelism

While both threads and processes enable concurrent execution, they differ in fundamental ways:

• Processes: Independent instances of a running program. Each process has its own memory space, making them isolated and robust. However, communication between processes is more complex.
• Threads: Lightweight units of execution within a process. Threads share the same memory space, making communication and data sharing easier. However, they are less isolated and may require careful synchronization.

Analogy: Think of a process as a house, and threads as the residents within that house. The house (process) has its own resources, while the residents (threads) share those resources.

2. When to Choose Threads or Processes in Python

The choice between threads and processes often depends on the nature of your task:

• CPU-Bound Tasks: If your program is computationally intensive and can benefit from utilizing multiple CPU cores, processes are generally preferred.
• I/O-Bound Tasks: If your program spends a lot of time waiting for input/output operations (like network requests or file reading), threads can be more efficient. They allow your program to overlap I/O operations, improving overall responsiveness.

3. Shared Memory vs. Separate Memory: The Key Difference

The primary distinction lies in memory management:

• Processes: Each process has its own independent memory space. Communication between processes typically involves serialization and deserialization of data, which can be slower.
• Threads: All threads within a process share the same memory. This allows for direct and efficient communication but requires careful synchronization to prevent race conditions.

Python’s Multithreading and Multiprocessing Modules

Python offers two powerful modules for working with threads and processes:

• threading: Provides tools for creating and managing threads within your Python program.
• multiprocessing: Enables you to create multiple processes and communicate between them.

Example: Threading for Web Scraping

import threading
import requests

def fetch_url(url):
    response = requests.get(url)
    # Process the response data

urls = ["https://www.example.com", "https://www.anothersite.com"]

threads = []
for url in urls:
    thread = threading.Thread(target=fetch_url, args=(url,))
    threads.append(thread)
    thread.start()

for thread in threads:
    thread.join()  # Wait for all threads to complete

Frequently Asked Questions (FAQ)