如何在Python中使用多線程進行I/O綁定的任務？

对于I/O密集型任务，Python中的多线程能显著提升性能；使用concurrent.futures.ThreadPoolExecutor是推荐方法，它通过管理线程池简化并发操作；1. 使用ThreadPoolExecutor配合max_workers控制线程数，通常设为5–20；2. 用executor.map()并发执行I/O任务并按顺序获取结果，或用submit()与as_completed()处理结果；3. 需要更细控制时可用threading模块加queue.Queue实现持久化工作线程；4. 对高并发I/O，建议改用asyncio与aiohttp以降低开销；5. 避免过多线程、注意异常处理、使用线程安全的日志和共享数据机制；ThreadPoolExecutor适用于大多数场景，只有在需要更高性能或更精细控制时才选用底层方法或异步方案。

For I/O-bound tasks in Python—like making HTTP requests, reading files, or querying databases—using multi-threading can significantly improve performance by allowing your program to wait for multiple I/O operations concurrently, rather than one at a time.

Because of the Global Interpreter Lock (GIL) in CPython, multi-threading doesn’t speed up CPU-heavy tasks, but it works very well for I/O-bound work since threads can yield control while waiting for external resources.

Here’s how to use multi-threading effectively for I/O-bound tasks:

Use concurrent.futures.ThreadPoolExecutor

The easiest and most modern way is to use ThreadPoolExecutor from the concurrent.futures module. It manages a pool of threads and lets you submit tasks without dealing with low-level thread management.

import concurrent.futures
import requests

def fetch_url(url):
    response = requests.get(url)
    return len(response.content)

urls = [
    "https://httpbin.org/delay/1",
    "https://httpbin.org/delay/2",
    "https://httpbin.org/delay/1",
]

# Use ThreadPoolExecutor to run I/O tasks in parallel
with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
    results = list(executor.map(fetch_url, urls))

print(results)

• max_workers controls how many threads are created. A common choice is 5–20 for I/O tasks, depending on the system and task type.
• executor.map() runs the function on each URL concurrently and returns results in order.
• Alternatives: executor.submit() as_completed() if you want to process results as they finish.

Use threading with a queue for more control

If you need more fine-grained control (e.g., long-running worker threads), you can use the threading module with a queue.Queue.

import threading
import queue
import requests
import time

def worker(q):
    while True:
        url = q.get()
        if url is None:
            break
        try:
            response = requests.get(url)
            print(f"Fetched {url}: {response.status_code}")
        except Exception as e:
            print(f"Error fetching {url}: {e}")
        finally:
            q.task_done()

# Set up queue and threads
q = queue.Queue()
num_threads = 5

# Start worker threads
threads = []
for _ in range(num_threads):
    t = threading.Thread(target=worker, args=(q,))
    t.start()
    threads.append(t)

# Add URLs to the queue
urls = ["https://httpbin.org/get"] * 10
for url in urls:
    q.put(url)

# Wait for all tasks to complete
q.join()

# Stop workers
for _ in range(num_threads):
    q.put(None)
for t in threads:
    t.join()

This pattern is useful when you have a continuous stream of I/O tasks or want persistent workers.

Consider asyncio aiohttp for even better I/O performance

While threading works well, for high-volume I/O (e.g., hundreds of HTTP requests), an asynchronous approach using asyncio is often more efficient because it avoids thread overhead.

Example with aiohttp:

import asyncio
import aiohttp

async def fetch(session, url):
    async with session.get(url) as response:
        return await response.text()

async def main():
    urls = ["https://httpbin.org/get"] * 10
    async with aiohttp.ClientSession() as session:
        tasks = [fetch(session, url) for url in urls]
        responses = await asyncio.gather(*tasks)
    return responses

# Run it
results = asyncio.run(main())

• No threading, no GIL contention.
• Much lower memory and CPU overhead per operation.
• Steeper learning curve due to async/await syntax.

Key tips for multi-threading I/O tasks

• Don’t use too many threads: 10–50 is usually enough. Thousands can hurt performance due to overhead.
• Handle exceptions in threads: Uncaught exceptions in threads can fail silently.
• Use thread-safe logging: If logging from multiple threads, use logging safely (the logging module is thread-safe by default).
• Avoid shared mutable state: If threads must share data, use queue.Queue or locks (threading.Lock).
• Prefer ThreadPoolExecutor for simple cases—it’s clean and robust.

Basically, for I/O-bound tasks, ThreadPoolExecutor is the go-to for most Python developers. It’s simple, effective, and handles the complexity for you. Only go lower-level or switch to asyncio when you need finer control or higher throughput.

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