import multiprocessing
import time

def worker(task_name, duration):
    print(f"進(jìn)程 {task_name} 開(kāi)始執(zhí)行，預(yù)計(jì)耗時(shí) {duration} 秒")
    time.sleep(duration)
    print(f"進(jìn)程 {task_name} 執(zhí)行完成")
    return f"{task_name} 處理結(jié)果"

if __name__ == "__main__":
    processes = []
    tasks = [("任務(wù)A", 2), ("任務(wù)B", 3), ("任務(wù)C", 1)]
    
    # 創(chuàng)建并啟動(dòng)多個(gè)進(jìn)程
    for name, duration in tasks:
        p = multiprocessing.Process(target=worker, args=(name, duration))
        processes.append(p)
        p.start()
        print(f"已啟動(dòng)進(jìn)程: {name}")
    
    # 等待所有進(jìn)程完成
    for p in processes:
        p.join()
    
    print("所有進(jìn)程執(zhí)行完畢")

import multiprocessing
import os

def worker(x):
    print(f"進(jìn)程 {os.getpid()} 正在處理數(shù)據(jù): {x}")
    result = x * x  # 模擬計(jì)算任務(wù)
    return result

if __name__ == "__main__":
    # 創(chuàng)建進(jìn)程池，進(jìn)程數(shù)通常設(shè)為CPU核心數(shù)
    with multiprocessing.Pool(processes=multiprocessing.cpu_count()) as pool:
        data = [1, 2, 3, 4, 5, 6, 7, 8]
        
        # 方法1: map - 阻塞式，按順序返回結(jié)果
        results = pool.map(worker, data)
        print(f"map 結(jié)果: {results}")
        
        # 方法2: map_async - 非阻塞式
        async_result = pool.map_async(worker, data)
        results_async = async_result.get()  # 獲取結(jié)果時(shí)會(huì)阻塞
        print(f"map_async 結(jié)果: {results_async}")

import multiprocessing
import time
import random

def worker(task_id):
    sleep_time = random.uniform(0.5, 2.0)
    time.sleep(sleep_time)
    return f"任務(wù){(diào)task_id} 完成，耗時(shí){sleep_time:.2f}秒"

if __name__ == "__main__":
    with multiprocessing.Pool(4) as pool:
        # 提交多個(gè)異步任務(wù)
        async_results = []
        for i in range(6):
            result = pool.apply_async(worker, args=(i,))
            async_results.append(result)
        
        print("所有任務(wù)已提交，主進(jìn)程可繼續(xù)執(zhí)行其他工作...")
        
        # 按完成順序獲取結(jié)果（無(wú)序）
        for result in async_results:
            print(result.get())  # 阻塞直到有結(jié)果可用

def worker(queue):
    data = queue.get()
    processed = f"處理后的: {data}"
    queue.put(processed)

def error_callback(error):
    print(f"任務(wù)執(zhí)行出錯(cuò): {error}")

pool.apply_async(worker, args=(data,), error_callback=error_callback)

# 根據(jù)任務(wù)類(lèi)型調(diào)整參數(shù)
results = pool.map(worker, data, chunksize=len(data)//multiprocessing.cpu_count())

import multiprocessing
import requests

def download_url(url):
    try:
        response = requests.get(url, timeout=10)
        return f"{url}: 成功, 大小 {len(response.content)} bytes"
    except Exception as e:
        return f"{url}: 失敗, 錯(cuò)誤 {str(e)}"

if __name__ == "__main__":
    urls = [
        "https://httpbin.org/delay/1",
        "https://httpbin.org/delay/2", 
        "https://httpbin.org/delay/1"
    ]
    
    with multiprocessing.Pool(3) as pool:
        results = pool.map(download_url, urls)
    
    for result in results:
        print(result)