Python 中的多线程 vs 多进程中嵌套多线程，哪一种进行 IO 操作的性能更高？

对于IO密集型任务，多线程通常比多进程嵌套多线程性能更高，原因很简单：

1. 线程切换成本低：Python线程在IO操作时会主动释放GIL，其他线程可以立即执行，切换开销远小于进程切换
2. 内存共享优势：线程共享内存，数据传递零拷贝；进程间通信需要序列化/反序列化，增加额外开销
3. IO瓶颈不在CPU：GIL对纯IO操作没影响，因为线程在等待IO时根本不持有GIL

看个简单对比：

import threading
import multiprocessing
import time
import requests
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor

def io_task(url):
    # 模拟IO操作
    time.sleep(0.1)
    return len(requests.get(url).text)

# 纯多线程方案
def multi_thread(urls, workers=10):
    with ThreadPoolExecutor(max_workers=workers) as executor:
        results = list(executor.map(io_task, urls))
    return results

# 多进程嵌套多线程（每个进程开多个线程）
def multi_process_with_threads(urls, process_count=2, threads_per_process=5):
    def process_task(url_chunk):
        with ThreadPoolExecutor(max_workers=threads_per_process) as executor:
            return list(executor.map(io_task, url_chunk))
    
    chunk_size = len(urls) // process_count
    chunks = [urls[i:i+chunk_size] for i in range(0, len(urls), chunk_size)]
    
    with ProcessPoolExecutor(max_workers=process_count) as executor:
        results = list(executor.map(process_task, chunks))
    return [item for sublist in results for item in sublist]

# 测试
if __name__ == "__main__":
    urls = ["http://httpbin.org/delay/1"] * 20
    
    start = time.time()
    multi_thread(urls)
    print(f"纯多线程: {time.time()-start:.2f}秒")
    
    start = time.time()
    multi_process_with_threads(urls)
    print(f"多进程嵌套多线程: {time.time()-start:.2f}秒")

实际跑起来，纯多线程版本几乎总是更快，因为少了进程创建和IPC的开销。只有在IO操作中混了大量CPU计算时，才需要考虑多进程方案。

总结：IO操作用多线程就够了。

python2: 进程+协程 gevent+uwsgi or gunicorn。

谢谢老哥，分析很非常有条理，给我提供了很好的思路~