Concurrency in Python allows you to handle multiple tasks efficiently, improving application performance. Here's a breakdown of key concepts and tools:

1. Concurrency vs. Parallelism

  • Concurrency: Managing tasks in a non-blocking way (e.g., using threads or async/await).
  • Parallelism: Executing tasks simultaneously (e.g., multi-core processing).
Concurrency vs Parallelism

2. Python Concurrency Tools

  • Threads: Use threading module for I/O-bound tasks.
  • AsyncIO: Asynchronous programming with async/await keywords.
  • Multiprocessing: For CPU-bound tasks using multiprocessing.
  • Concurrent.futures: High-level interfaces for threads and processes.
AsyncIO Basics

3. Best Practices

  • Avoid global variables in threads for Thread_Safety.
  • Use asyncio for non-blocking I/O operations.
  • Leverage concurrent.futures.ThreadPoolExecutor for task parallelism.
Python Concurrency

For deeper insights, check our Python Concurrency Introduction. 🚀