📚 Python Advanced: Concurrency Guide :books:

Concurrency in Python allows you to manage multiple tasks efficiently, improving performance for I/O-bound and CPU-bound operations. Here's a breakdown of key concepts and tools:

🧠 Understanding Concurrency vs. Parallelism

  • Concurrency (并发): Handling multiple tasks in a single process, often using threads or async.
  • Parallelism (并行): Executing multiple tasks simultaneously across different processes or cores.

💡 Tip: Use asyncio for non-blocking I/O, and concurrent.futures for thread/process pooling.

🧩 Core Concepts

  1. Threads (线程):

    • Lightweight processes within a single program.
    • Use threading module or ThreadPoolExecutor for task scheduling.
    • Example: Concurrent file downloads.
    multithreading

  2. Processes (进程):

    • Independent programs running separately.
    • Use multiprocessing module or ProcessPoolExecutor for CPU-heavy tasks.
    • Example: Parallel data processing.
    multiprocessing

  3. AsyncIO (异步IO):

    • Event-loop driven for non-blocking I/O operations.
    • Ideal for networking, APIs, or GUI apps.
    • Example: Concurrent HTTP requests.
    asyncio

🛠️ Practical Tools

  • concurrent.futures.ThreadPoolExecutor: Simplifies thread management.
  • concurrent.futures.ProcessPoolExecutor: For parallel processing.
  • asyncio: Asynchronous programming with coroutines.
  • multiprocessing: Leverage multiple CPU cores.

⚠️ Thread Safety & Best Practices

  • Avoid shared state between threads (use threading.Lock or queue.Queue for safety).
  • Prefer async/await over threading for simpler code.
  • For CPU-bound tasks, multiprocessing is often better than threads.

🌐 Further Reading

Need examples or code snippets? Let me know! 😊