Python并发示例脚本

1、使用theading模块实现

# ！/usr/bin/env python3
# -*- coding:utf-8 -*-
# __author__ =

import subprocess, json, os
import threading, queue

kubectl_cmd = '/usr/local/bin/kubectl'
node_roles = 'worker'
max_conn = 5


def exec_kubectl(node_roles):
    kubectlcommand = f"sudo {kubectl_cmd} get node | grep {node_roles}"
    # 使用awk和sed把shell命令执行结果转换为json格式
    awkcommand = "awk '{ printf \"\\\"%s\\\", \", $1 } END { print \"\" }' | sed 's/, $//; s/^/[/; s/$/]/'"
    command = f"{kubectlcommand}|{awkcommand}"
    result = subprocess.run(command, shell=True, capture_output=True, text=True)
    return json.loads(result.stdout)


def exec_nci(dev_q):
    while not dev_q.empty():
        res = dev_q.get()
        os.system(f"echo {res}")


if __name__ == '__main__':
    devs_q = queue.Queue()
    ts = []
    for i in exec_kubectl(node_roles):
        devs_q.put(i)
    for i in range(max_conn):
        t = threading.Thread(target=exec_nci, args=(devs_q,))
        t.start()
        ts.append(t)
    for t in ts:
        t.join()

2、使用ThreadPoolExecutor模块实现

# ！/usr/bin/env python3
# -*- coding:utf-8 -*-
# __author__ =

from concurrent.futures import ThreadPoolExecutor, as_completed
import time  

# 计算平方的函数
def square(x):  
    time.sleep(1)  # 模拟延迟  
    return x * x  

# 回调函数  
def callback(future):  
    result = future.result()  # 获取结果
    print(f'Result: {result}')  

def main():  
    numbers = [1, 2, 3, 4, 5]

    # 创建一个线程池  
    with ThreadPoolExecutor(max_workers=4) as executor:
        # 提交任务并设置回调函数
        futures = {executor.submit(square, num): num for num in numbers}

        # 使用as_completed可以确保无论任务提交的顺序如何，回调函数都会在每个任务完成后立即执行
        for future in as_completed(futures):
            future.add_done_callback(callback)

if __name__ == "__main__":  
    main()

3、使用multiprocessing模块实现

# ！/usr/bin/env python3
# -*- coding:utf-8 -*-
# __author__ =

import multiprocessing  
import time  

# 计算平方的函数  
def square(x):  
    time.sleep(1)  # 模拟延迟  
    return x * x  

# 回调函数  
def callback(result):  
    print(f'Result: {result}')  

def main():  
    # 创建一个进程池  
    with multiprocessing.Pool(processes=4) as pool:  # 可以根据系统的CPU核心数设置进程数量  
        numbers = [1, 2, 3, 4, 5]  

        # 使用 apply_async 方法提交任务
        for number in numbers:  
            pool.apply_async(square, args=(number,), callback=callback)  

        # 关闭池并等待所有任务完成
        pool.close()  
        pool.join()  # 等待所有进程结束

if __name__ == "__main__":  
    main()