《Effective Python》读书笔记08: 并发与并行

作者: andylin02
学习章节: 第7章 – 并发与并行
关键词： 线程, 子进程, 协程, GIL, asyncio, concurrent.futures, Queue, 死锁, 数据竞争

第7章思维导图：8条建议全景

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第7章 并发与并行 8条建议

并发模型

第52条: 线程 vs 子进程 vs 协程

第53条: threading与GIL

第54条: multiprocessing

第55条: asyncio协程

协作与安全

第56条: Queue协调线程

第57条: 使用concurrent.futures

第58条: 避免数据竞争

第59条: 避免死锁

第7章详细笔记：逐条精讲与源代码

本章直面 Python 并发编程的挑战，帮你理清三种并发模型（线程、进程、协程）的适用场景与最佳实践，并教你如何安全地协调并发任务，避免常见陷阱。

第52条：明白线程、子进程与协程的区别

核心：Python 提供三种主要的并发方式，各有适用场景：

# 示意：不同场景选择不同模型
# I/O 密集：线程或协程
# CPU 密集：多进程
# 高并发网络：协程

算法选择：GIL（全局解释器锁）导致 Python 多线程无法同时运行 Python 字节码，只允许一个线程执行，所以纯 Python 计算无法利用多核。

第53条：使用 threading 处理阻塞式 I/O，但避开 GIL 限制

核心：当任务是 I/O 密集型（如读写文件、网络请求），GIL 会在 I/O 阻塞时释放，多线程能提高吞吐量。但对于 CPU 密集型，GIL 会让多线程比单线程还慢（上下文切换开销）。

import threading
import time

def io_task(seconds):
"""模拟 I/O 阻塞"""
time.sleep(seconds)
print(f"{threading.current_thread().name} 完成")

# I/O 密集型：多线程有效加速
start = time.time()
threads = []
for i in range(5):
t = threading.Thread(target=io_task, args=(0.5,))
threads.append(t)
t.start()
for t in threads:
t.join()
print(f"多线程 I/O 耗时: {time.time() – start:.2f}s") # 约 0.5s，说明并行

GIL 图解：

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线程1

获得GIL

线程2

等待GIL

执行一段字节码

释放GIL

线程2获得GIL

执行字节码

释放GIL

关键收获：多线程处理 I/O 密集任务效果好；CPU 密集任务应使用多进程或 C 扩展。

第54条：使用 multiprocessing 绕过 GIL，实现真正并行

核心：multiprocessing 模块启动独立的 Python 解释器进程，每个进程拥有自己的 GIL，因此可以同时利用多核 CPU。代价是进程间通信（IPC）比线程间共享内存更重。

import multiprocessing
import time

def cpu_intensive(n):
total = 0
for i in range(10**7):
total += i
return total

start = time.time()
with multiprocessing.Pool(processes=4) as pool:
results = pool.map(cpu_intensive, range(4))
print(f"多进程耗时: {time.time() – start:.2f}s")

数据共享：子进程间数据不共享，需使用 multiprocessing.Queue、Pipe 或 Value/Array（共享内存）。

关键收获：CPU 密集计算必须用多进程才能利用多核；务必在 if __name__ == '__main__': 下启动进程，以避免递归创建。

第55条：使用 asyncio 编写高并发 I/O 程序

核心：协程（async def/await）在单线程内通过事件循环实现并发，可以轻松管理成千上万个网络连接，没有线程切换开销，且没有 GIL 竞争。但所有 I/O 操作必须使用异步库（如 aiohttp）。

import asyncio

async def fetch_data(url):
print(f"开始请求 {url}")
await asyncio.sleep(1) # 模拟异步 I/O
print(f"完成 {url}")
return f"数据来自 {url}"

async def main():
urls = ['url1', 'url2', 'url3']
tasks = [fetch_data(url) for url in urls]
results = await asyncio.gather(*tasks)
print(results)

asyncio.run(main())

协程规则：

• 定义：async def
• 调用异步函数：await
• 运行入口：asyncio.run()
• 并发运行多个协程：asyncio.gather() 或 asyncio.create_task()

关键收获：协程是处理大量并发 I/O 的最佳选择，但需要整条调用链都是异步的，否则阻塞会拖慢整个事件循环。

第56条：使用 Queue 协调线程之间的工作

核心：多线程之间共享数据时，必须防止数据竞争。使用 queue.Queue 可以安全地在线程间传递数据，它内部已经实现了所有必要的锁。

import threading
from queue import Queue

def producer(q, data):
for item in data:
q.put(item)
q.put(None) # 结束信号

def consumer(q):
while True:
item = q.get()
if item is None:
break
print(f"处理 {item}")

q = Queue()
t1 = threading.Thread(target=producer, args=(q, [1,2,3]))
t2 = threading.Thread(target=consumer, args=(q,))
t1.start(); t2.start()
t1.join(); t2.join()

流水线架构：Queue 可以串联多个工作阶段，形成生产者-消费者流水线。

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put

get

get

put

put

get

生产者线程

Queue

消费者线程1

消费者线程2

下一个Queue

后续处理线程

关键收获：用 Queue 解耦线程，避免显式锁，大大降低并发编程复杂度。

第57条：使用 concurrent.futures 简化并发执行

核心：concurrent.futures 提供了 ThreadPoolExecutor 和 ProcessPoolExecutor，统一了线程和进程的并发接口。使用 submit 或 map 可以轻松提交任务并获取结果。

from concurrent.futures import ThreadPoolExecutor, as_completed

def download(url):
# 模拟下载
import time, random
time.sleep(random.random())
return f"{url} 内容"

urls = ['a.com', 'b.com', 'c.com']
with ThreadPoolExecutor(max_workers=3) as executor:
# 方式1: 使用 map，保持顺序
results = executor.map(download, urls)
print(list(results))

# 方式2: 使用 submit + as_completed，先完成的先处理
future_to_url = {executor.submit(download, url): url for url in urls}
for future in as_completed(future_to_url):
url = future_to_url[future]
print(f"{url} 完成: {future.result()}")

关键收获：concurrent.futures 屏蔽了底层并发模型的差异，让你轻松从线程切换到进程，同时提供灵活的结果处理。

第58条：避免竞态条件

核心：多线程/多进程同时读写共享数据时，由于执行顺序不确定，会导致不可重现的错误，即数据竞争。必须使用锁（Lock）或原子操作来保护临界区。

import threading

# ❌ 没有锁：结果不确定
counter = 0
def increment_bad():
global counter
for _ in range(100000):
counter += 1

# ✅ 使用 Lock 保护
lock = threading.Lock()
def increment_good():
global counter
for _ in range(100000):
with lock:
counter += 1

threads = [threading.Thread(target=increment_good) for _ in range(5)]
for t in threads: t.start()
for t in threads: t.join()
print(counter) # 正确输出 500000

临界区示意图：

#mermaid-svg-PDNG3DHsUTd6LNUt{font-family:\”trebuchet ms\”,verdana,arial,sans-serif;font-size:16px;fill:#333;}@keyframes edge-animation-frame{from{stroke-dashoffset:0;}}@keyframes dash{to{stroke-dashoffset:0;}}#mermaid-svg-PDNG3DHsUTd6LNUt .edge-animation-slow{stroke-dasharray:9,5!important;stroke-dashoffset:900;animation:dash 50s linear infinite;stroke-linecap:round;}#mermaid-svg-PDNG3DHsUTd6LNUt .edge-animation-fast{stroke-dasharray:9,5!important;stroke-dashoffset:900;animation:dash 20s linear infinite;stroke-linecap:round;}#mermaid-svg-PDNG3DHsUTd6LNUt .error-icon{fill:#552222;}#mermaid-svg-PDNG3DHsUTd6LNUt .error-text{fill:#552222;stroke:#552222;}#mermaid-svg-PDNG3DHsUTd6LNUt .edge-thickness-normal{stroke-width:1px;}#mermaid-svg-PDNG3DHsUTd6LNUt .edge-thickness-thick{stroke-width:3.5px;}#mermaid-svg-PDNG3DHsUTd6LNUt .edge-pattern-solid{stroke-dasharray:0;}#mermaid-svg-PDNG3DHsUTd6LNUt .edge-thickness-invisible{stroke-width:0;fill:none;}#mermaid-svg-PDNG3DHsUTd6LNUt .edge-pattern-dashed{stroke-dasharray:3;}#mermaid-svg-PDNG3DHsUTd6LNUt .edge-pattern-dotted{stroke-dasharray:2;}#mermaid-svg-PDNG3DHsUTd6LNUt .marker{fill:#333333;stroke:#333333;}#mermaid-svg-PDNG3DHsUTd6LNUt .marker.cross{stroke:#333333;}#mermaid-svg-PDNG3DHsUTd6LNUt svg{font-family:\”trebuchet ms\”,verdana,arial,sans-serif;font-size:16px;}#mermaid-svg-PDNG3DHsUTd6LNUt p{margin:0;}#mermaid-svg-PDNG3DHsUTd6LNUt .label{font-family:\”trebuchet ms\”,verdana,arial,sans-serif;color:#333;}#mermaid-svg-PDNG3DHsUTd6LNUt .cluster-label text{fill:#333;}#mermaid-svg-PDNG3DHsUTd6LNUt .cluster-label span{color:#333;}#mermaid-svg-PDNG3DHsUTd6LNUt .cluster-label span p{background-color:transparent;}#mermaid-svg-PDNG3DHsUTd6LNUt .label text,#mermaid-svg-PDNG3DHsUTd6LNUt span{fill:#333;color:#333;}#mermaid-svg-PDNG3DHsUTd6LNUt .node rect,#mermaid-svg-PDNG3DHsUTd6LNUt .node circle,#mermaid-svg-PDNG3DHsUTd6LNUt .node ellipse,#mermaid-svg-PDNG3DHsUTd6LNUt .node polygon,#mermaid-svg-PDNG3DHsUTd6LNUt .node path{fill:#ECECFF;stroke:#9370DB;stroke-width:1px;}#mermaid-svg-PDNG3DHsUTd6LNUt .rough-node .label text,#mermaid-svg-PDNG3DHsUTd6LNUt .node .label text,#mermaid-svg-PDNG3DHsUTd6LNUt .image-shape .label,#mermaid-svg-PDNG3DHsUTd6LNUt .icon-shape .label{text-anchor:middle;}#mermaid-svg-PDNG3DHsUTd6LNUt .node .katex path{fill:#000;stroke:#000;stroke-width:1px;}#mermaid-svg-PDNG3DHsUTd6LNUt .rough-node .label,#mermaid-svg-PDNG3DHsUTd6LNUt .node .label,#mermaid-svg-PDNG3DHsUTd6LNUt .image-shape .label,#mermaid-svg-PDNG3DHsUTd6LNUt .icon-shape .label{text-align:center;}#mermaid-svg-PDNG3DHsUTd6LNUt .node.clickable{cursor:pointer;}#mermaid-svg-PDNG3DHsUTd6LNUt .root .anchor path{fill:#333333!important;stroke-width:0;stroke:#333333;}#mermaid-svg-PDNG3DHsUTd6LNUt .arrowheadPath{fill:#333333;}#mermaid-svg-PDNG3DHsUTd6LNUt .edgePath .path{stroke:#333333;stroke-width:2.0px;}#mermaid-svg-PDNG3DHsUTd6LNUt .flowchart-link{stroke:#333333;fill:none;}#mermaid-svg-PDNG3DHsUTd6LNUt .edgeLabel{background-color:rgba(232,232,232, 0.8);text-align:center;}#mermaid-svg-PDNG3DHsUTd6LNUt .edgeLabel p{background-color:rgba(232,232,232, 0.8);}#mermaid-svg-PDNG3DHsUTd6LNUt .edgeLabel rect{opacity:0.5;background-color:rgba(232,232,232, 0.8);fill:rgba(232,232,232, 0.8);}#mermaid-svg-PDNG3DHsUTd6LNUt .labelBkg{background-color:rgba(232, 232, 232, 0.5);}#mermaid-svg-PDNG3DHsUTd6LNUt .cluster rect{fill:#ffffde;stroke:#aaaa33;stroke-width:1px;}#mermaid-svg-PDNG3DHsUTd6LNUt .cluster text{fill:#333;}#mermaid-svg-PDNG3DHsUTd6LNUt .cluster span{color:#333;}#mermaid-svg-PDNG3DHsUTd6LNUt div.mermaidTooltip{position:absolute;text-align:center;max-width:200px;padding:2px;font-family:\”trebuchet ms\”,verdana,arial,sans-serif;font-size:12px;background:hsl(80, 100%, 96.2745098039%);border:1px solid #aaaa33;border-radius:2px;pointer-events:none;z-index:100;}#mermaid-svg-PDNG3DHsUTd6LNUt .flowchartTitleText{text-anchor:middle;font-size:18px;fill:#333;}#mermaid-svg-PDNG3DHsUTd6LNUt rect.text{fill:none;stroke-width:0;}#mermaid-svg-PDNG3DHsUTd6LNUt .icon-shape,#mermaid-svg-PDNG3DHsUTd6LNUt .image-shape{background-color:rgba(232,232,232, 0.8);text-align:center;}#mermaid-svg-PDNG3DHsUTd6LNUt .icon-shape p,#mermaid-svg-PDNG3DHsUTd6LNUt .image-shape p{background-color:rgba(232,232,232, 0.8);padding:2px;}#mermaid-svg-PDNG3DHsUTd6LNUt .icon-shape .label rect,#mermaid-svg-PDNG3DHsUTd6LNUt .image-shape .label rect{opacity:0.5;background-color:rgba(232,232,232, 0.8);fill:rgba(232,232,232, 0.8);}#mermaid-svg-PDNG3DHsUTd6LNUt .label-icon{display:inline-block;height:1em;overflow:visible;vertical-align:-0.125em;}#mermaid-svg-PDNG3DHsUTd6LNUt .node .label-icon path{fill:currentColor;stroke:revert;stroke-width:revert;}#mermaid-svg-PDNG3DHsUTd6LNUt :root{–mermaid-font-family:\”trebuchet ms\”,verdana,arial,sans-serif;}

线程1

尝试获取锁

线程2

锁已被占用，等待

获得锁，进入临界区

修改共享数据

释放锁

获得锁

修改共享数据

释放锁

关键收获：任何共享状态都必须用锁保护；尽量使用 Queue 或 concurrent.futures 传递数据，避免直接共享状态。

第59条：避免死锁

核心：当多个线程/进程互相等待对方持有的资源时，程序会永久卡住，这就是死锁。要避免死锁，最重要的是按固定顺序获取锁，或使用超时机制。

import threading
import time

lock_a = threading.Lock()
lock_b = threading.Lock()

# ❌ 死锁示例：两个线程以相反顺序获取锁
def bad_worker1():
with lock_a:
time.sleep(0.1)
with lock_b:
print("Worker1 完成")

def bad_worker2():
with lock_b: # 注意：先获取 lock_b
time.sleep(0.1)
with lock_a:
print("Worker2 完成")

# ✅ 避免死锁：始终按 lock_a -> lock_b 顺序获取
def good_worker1():
with lock_a:
with lock_b:
print("Good Worker1 完成")

def good_worker2():
with lock_a: # 即使只用一个，也先获取 a
with lock_b:
print("Good Worker2 完成")

避免死锁原则：

• 锁的获取顺序全局一致
• 尽量使用上下文管理器（with lock:）
• 使用 Queue 等高级通信机制，避免手动加锁
• 为锁设置超时（lock.acquire(timeout=5)）

关键收获：死锁往往难以重现和调试，从设计层面就规划好锁的顺序，或者干脆不使用显式锁。

并发模型选择决策流程

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I/O 密集型

几百~几千并发

几十并发且已有同步代码

CPU 密集型

是

否

是

需要并发执行

任务是 I/O 密集型还是 CPU 密集型?

并发数量?

使用 asyncio 协程

使用 threading 线程

使用 multiprocessing 子进程

需要协调线程?

需要传递数据?

使用 Queue 或 concurrent.futures

直接使用 Thread

使用 asyncio.Queue

使用 ProcessPoolExecutor

第7章总结：并发编程的攻守之道

掌握这些，你将能根据场景游刃有余地选择并发策略，写出高性能且稳健的并发程序。

下一章预告：第8章 – 稳定性与性能

并发让程序跑得快，但稳定性与性能让它跑得远。下一章你将学到：

• 如何优雅地处理异常和管理资源（try/except/finally、with）
• 性能分析工具的使用，定位真正的瓶颈
• 内存优化的秘技：__slots__、弱引用等
• 如何在性能和代码清晰之间做出明智的权衡

第8章将教你写出不仅正确高效，而且健壮持久的生产级代码！

本文为个人学习笔记，仅用于知识分享。如有错误，欢迎指正。
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