Python的threading模塊適用於I/O密集型任務(wù)，因線程輕量且共享內(nèi)存，適合等待操作；multiprocessing模塊適用於CPU密集型任務(wù)，因進(jìn)程獨(dú)立運(yùn)行，繞過GIL限制，實(shí)現(xiàn)真正並行計(jì)算。 1. 主要區(qū)別在於線程共享內(nèi)存、輕量，而進(jìn)程獨(dú)立內(nèi)存、較重；2. I/O任務(wù)用線程（如網(wǎng)絡(luò)請(qǐng)求），CPU任務(wù)用進(jìn)程（如數(shù)值計(jì)算）；3. 線程通信簡單，進(jìn)程需用Queue或Pipe傳遞數(shù)據(jù)；4. 選擇依據(jù)是任務(wù)類型：等待多的用線程，計(jì)算重的用進(jìn)程，也可混合使用兩者優(yōu)勢(shì)。

Python's threading and multiprocessing modules both allow you to run tasks concurrently, but they work in very different ways and are suited for different kinds of tasks.

1. What's the Main Difference?

Threads are lightweight and share memory, while processes are heavier and have separate memory spaces.

• Threading runs multiple threads (small units of a process) inside a single process. Since they share memory, communication between threads is straightforward.
• Multiprocessing starts completely separate processes, each with its own Python interpreter and memory. This makes inter-process communication trickier, but avoids some issues like the Global Interpreter Lock (GIL).

This distinction matters a lot when deciding which one to use.

2. When to Use Threads vs. Processes

For I/O-bound Tasks — Use Threads

If your program spends time waiting for external input/output — like downloading files, reading from disk, or waiting on a network response — threads can be very effective.

Example:

 import threading, time

def wait_and_print():
    time.sleep(1)
    print("Done")

thread = threading.Thread(target=wait_and_print)
thread.start()

Since the thread is just waiting most of the time, it doesn't block other threads from running. And since I/O-bound tasks don't require much CPU, threading works fine here.

For CPU-bound Tasks — Use Processes

If your task uses heavy computation — like crunching numbers in a loop — threads won't speed things up in Python due to the GIL.

The GIL (Global Interpreter Lock) ensures only one thread executes Python bytecode at a time, even on multi-core systems. So no real parallelism for CPU-heavy code.

With multiprocessing, each process gets its own Python instance and memory space — so you truly get parallel execution across cores.

3. Communication Between Units

Sharing data between threads is simple because they live in the same memory space.

But with processes, you need special tools like Queue or Pipe to pass data around.

Example using multiprocessing queue:

 from multiprocessing import Process, Queue

def worker(q):
    q.put("Hello from process")

q = Queue()
p = Process(target=worker, args=(q,))
p.start()
print(q.get()) # Prints: Hello from process
p.join()

It's more setup than threads, but necessary to avoid conflicts when multiple processes try to modify shared data.

4. Which One Should You Pick?

There's no one-size-fits-all answer.

Use threads if:

• Your tasks spend time waiting (I/O-bound).
• You want simpler code structure.
• You don't need true parallelism.

Use processes if:

• Your tasks are CPU-heavy.
• You want to take advantage of multiple CPU cores.
• You're okay managing separate memory and using inter-process communication.

You can also mix them — run multiple processes, each with multiple threads — for complex applications.

Basically, pick based on what kind of work you're doing. Threads are easier and faster for waiting-based tasks, and processes are needed for serious number crunching.

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