
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。
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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,您就可以并行操作并节省宝贵的时间。
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