Python基础教程（五十三）多线程：Python多线程终极指南，突破GIL枷锁的实战秘籍

最新推荐文章于 2025-12-02 21:20:38 发布

原创最新推荐文章于 2025-12-02 21:20:38 发布 · 353 阅读

3 ·

CC 4.0 BY-SA版权

文章标签：

#java #开发语言

Python基础教程专栏收录该内容

99 篇文章

订阅专栏

1. GIL：Python多线程的双刃剑

Python的全局解释器锁（GIL）强制同一时刻仅一个线程执行字节码，导致CPU密集型任务无法通过多线程提速。但关键洞察在于：I/O操作期间GIL会被释放！这使得网络请求、文件读写等场景中，多线程能大幅减少等待时间。

2. 线程实战：I/O密集型任务加速

import threading
import time
import requests

def download(url):
    print(f"下载开始: {url}")
    response = requests.get(url)
    print(f"下载完成: {url}, 大小: {len(response.content)}字节")

urls = [
    "https://example.com",
    "https://python.org",
    "https://github.com"
]

# 单线程耗时测试
start = time.time()
for url in urls:
    download(url)
print(f"单线程耗时: {time.time()-start:.2f}秒")

# 多线程提速
start = time.time()
threads = []
for url in urls:
    t = threading.Thread(target=download, args=(url,))
    t.start()
    threads.append(t)
    
for t in threads:
    t.join()
print(f"多线程耗时: {time.time()-start:.2f}秒")

执行对比（结果因网络波动）：

单线程耗时: 1.82秒  
多线程耗时: 0.67秒  # 效率提升171%

3. 线程安全：Lock解决资源竞争

共享资源需用锁（Lock）避免竞争：

class BankAccount:
    def __init__(self):
        self.balance = 1000
        self.lock = threading.Lock()
    
    def deposit(self, amount):
        with self.lock:  # 自动获取和释放锁
            self.balance += amount

def transfer(account, amount, times):
    for _ in range(times):
        account.deposit(amount)

account = BankAccount()
threads = [
    threading.Thread(target=transfer, args=(account, 10, 100)),
    threading.Thread(target=transfer, args=(account, -10, 100))
]

for t in threads:
    t.start()
for t in threads:
    t.join()

print(f"最终余额: {account.balance}")  # 正确输出1000

4. 生产者-消费者模型：Queue线程通信

import queue

def producer(q, items):
    for item in items:
        q.put(item)
        print(f"生产: {item}")

def consumer(q):
    while True:
        item = q.get()
        if item is None:  # 终止信号
            break
        print(f"消费: {item}")
        q.task_done()

q = queue.Queue()
producer_thread = threading.Thread(
    target=producer, 
    args=(q, ["数据1", "数据2", "数据3"])
)
consumer_thread = threading.Thread(target=consumer, args=(q,))

producer_thread.start()
consumer_thread.start()

producer_thread.join()  # 等待生产完成
q.put(None)  # 发送结束信号
consumer_thread.join()