Python全局解释器锁（GIL）：提高多线程性能的最佳实践

# CPU密集型任务示例
def compute(n):
    for i in range(n):
        i * i

# 单线程版本
def single_thread():
    compute(10**7)
    compute(10**7)

# 多线程版本
def multi_thread():
    import threading
    t1 = threading.Thread(target=compute, args=(10**7,))
    t2 = threading.Thread(target=compute, args=(10**7,))
    t1.start()
    t2.start()
    t1.join()
    t2.join()

import requests
import time

def fetch(url):
    response = requests.get(url)
    return len(response.text)

# I/O密集型任务对比...

# URL列表: ['http://example.com'...]
def single_thread(urls):
    for url in urls:
        fetch(url)

def multi_thread(urls):
    import concurrent.futures
    with concurrent.futures.ThreadPoolExecutor() as executor:
        executor.map(fetch, urls)

from multiprocessing import Pool

def parallel_compute(n):
    with Pool(4) as p:
        p.map(compute, [n]*4) 

# example.pyx (Cython)
cpdef void compute(int n):
    cdef int i
    for i in range(n): 
        i * i

import aiohttp 
import asyncio 

async def async_fetch(session, url):
    async with session.get(url) as response:
        return len(await response.text())

async def main(urls):
    async with aiohttp.ClientSession() as session:
        tasks = [async_fetch(session, url) for url in urls]
        return await asyncio.gather(*tasks)

urls = [...] # list of URLs 

start = time.time() 
results = [fetch(url) for url in urls] 
print(f"同步耗时: {time.time()-start:.2f}s") 

from concurrent.futures import ThreadPoolExecutor 

with ThreadPoolExecutor(max_workers=20) as executor:
     results = list(executor.map(fetch, urls)) 

from concurrent.futures import ProcessPoolExecutor 

with ProcessPoolExecutor() as executor: 
     results = list(executor.map(fetch, urls))