当涉及到在 Python 中同时运行多个任务时，concurrent.futures 模块是一个强大而简单的工具。在本文中，我们将探讨如何使用 ThreadPoolExecutor 并行执行任务，并结合实际示例。
在Python中，线程非常适合I/O操作占主导地位的任务，例如网络调用或文件读/写操作。使用 ThreadPoolExecutor，您可以：
让我们看一个简单的例子来理解这个概念。
from concurrent.futures import ThreadPoolExecutor
import time
# Function simulating a task
def task(n):
print(f"Task {n} started")
time.sleep(2) # Simulates a long-running task
print(f"Task {n} finished")
return f"Result of task {n}"
# Using ThreadPoolExecutor
def execute_tasks():
tasks = [1, 2, 3, 4, 5] # List of tasks
results = []
# Create a thread pool with 3 simultaneous threads
with ThreadPoolExecutor(max_workers=3) as executor:
# Execute tasks in parallel
results = executor.map(task, tasks)
return list(results)
if __name__ == "__main__":
results = execute_tasks()
print("All results:", results)
当您运行此代码时，您将看到类似这样的内容（以某种并行顺序）：
Task 1 started Task 2 started Task 3 started Task 1 finished Task 4 started Task 2 finished Task 5 started Task 3 finished Task 4 finished Task 5 finished All results: ['Result of task 1', 'Result of task 2', 'Result of task 3', 'Result of task 4', 'Result of task 5']
任务 1、2 和 3 同时启动，因为 max_workers=3。其他任务（4 和 5）等待线程可用。
限制线程数:
处理异常:
使用 ProcessPoolExecutor 执行 CPU 密集型任务:
这是一个真实的示例：并行获取多个 URL。
import requests
from concurrent.futures import ThreadPoolExecutor
# Function to fetch a URL
def fetch_url(url):
try:
response = requests.get(url)
return f"URL: {url}, Status: {response.status_code}"
except Exception as e:
return f"URL: {url}, Error: {e}"
# List of URLs to fetch
urls = [
"https://example.com",
"https://httpbin.org/get",
"https://jsonplaceholder.typicode.com/posts",
"https://invalid-url.com"
]
def fetch_all_urls(urls):
with ThreadPoolExecutor(max_workers=4) as executor:
results = executor.map(fetch_url, urls)
return list(results)
if __name__ == "__main__":
results = fetch_all_urls(urls)
for result in results:
print(result)
ThreadPoolExecutor 简化了 Python 中的线程管理，是加速 I/O 密集型任务的理想选择。只需几行代码，您就可以并行化操作并节省宝贵的时间。
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