1. 多线程 (threading 模块)
   基本使用

import threading
import time

def print_numbers():
    """打印数字的线程函数"""
    for i in range(5):
        print(f"数字: {i}")
        time.sleep(1)

def print_letters():
    """打印字母的线程函数"""
    for letter in 'ABCDE':
        print(f"字母: {letter}")
        time.sleep(1)

# 创建线程
t1 = threading.Thread(target=print_numbers)
t2 = threading.Thread(target=print_letters)

# 启动线程
t1.start()
t2.start()

# 等待线程结束
t1.join()
t2.join()

print("所有线程执行完毕!")

带参数的线程

def worker(thread_id, count):
    for i in range(count):
        print(f"线程 {thread_id} 正在工作: {i}")
        time.sleep(0.5)

# 创建多个带参数的线程
threads = []
for i in range(3):
    t = threading.Thread(target=worker, args=(i, 3))
    threads.append(t)
    t.start()

# 等待所有线程完成
for t in threads:
    t.join()

线程同步 - 锁机制

import threading

class Counter:
    def __init__(self):
        self.value = 0
        self.lock = threading.Lock()
    
    def increment(self):
        with self.lock:  # 自动获取和释放锁
            temp = self.value
            time.sleep(0.001)  # 模拟复杂操作
            self.value = temp + 1

counter = Counter()

def increment_counter():
    for _ in range(100):
        counter.increment()

# 创建多个线程同时修改计数器
threads = []
for _ in range(5):
    t = threading.Thread(target=increment_counter)
    threads.append(t)
    t.start()

for t in threads:
    t.join()

print(f"最终计数值: {counter.value}")  # 应该是 500

线程池 (concurrent.futures)

from concurrent.futures import ThreadPoolExecutor
import requests

def download_url(url):
    """模拟下载任务"""
    print(f"开始下载: {url}")
    time.sleep(2)  # 模拟网络延迟
    print(f"完成下载: {url}")
    return f"{url} 的内容"

# 使用线程池
urls = [
    "http://example.com/1",
    "http://example.com/2", 
    "http://example.com/3",
    "http://example.com/4"
]

with ThreadPoolExecutor(max_workers=3) as executor:
    # 提交所有任务
    futures = [executor.submit(download_url, url) for url in urls]
    
    # 获取结果
    for future in futures:
        result = future.result()
        print(f"得到结果: {result}")