{"id":1123931,"date":"2025-01-08T19:37:00","date_gmt":"2025-01-08T11:37:00","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1123931.html"},"modified":"2025-01-08T19:37:03","modified_gmt":"2025-01-08T11:37:03","slug":"python%e5%a4%9a%e6%a0%b8%e6%9c%8d%e5%8a%a1%e5%99%a8%e5%a6%82%e4%bd%95%e4%bd%bf%e7%94%a8%e6%89%8b%e5%86%8c","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1123931.html","title":{"rendered":"python\u591a\u6838\u670d\u52a1\u5668\u5982\u4f55\u4f7f\u7528\u624b\u518c"},"content":{"rendered":"

\"python\u591a\u6838\u670d\u52a1\u5668\u5982\u4f55\u4f7f\u7528\u624b\u518c\"<\/p>\n

\u5982\u4f55\u5728\u591a\u6838\u670d\u52a1\u5668\u4e0a\u4f7f\u7528Python\uff1a\u8be6\u7ec6\u6307\u5357<\/strong><\/p>\n<\/p>\n

\u5728\u591a\u6838\u670d\u52a1\u5668\u4e0a\u4f7f\u7528Python\u65f6\uff0c\u53ef\u4ee5\u901a\u8fc7\u5e76\u884c\u8ba1\u7b97\u3001\u7ebf\u7a0b\u548c\u8fdb\u7a0b\u7ba1\u7406\u3001\u5206\u5e03\u5f0f\u8ba1\u7b97<\/strong>\u7b49\u65b9\u5f0f\u5145\u5206\u5229\u7528\u591a\u6838\u8d44\u6e90\u3002\u672c\u6587\u5c06\u8be6\u7ec6\u63cf\u8ff0\u5982\u4f55\u901a\u8fc7\u8fd9\u4e9b\u65b9\u6cd5\u6765\u63d0\u9ad8Python\u7a0b\u5e8f\u7684\u6027\u80fd\uff0c\u5c24\u5176\u662f\u9488\u5bf9\u4e8e\u591a\u6838\u670d\u52a1\u5668\u7684\u573a\u666f\u3002\u6211\u4eec\u5c06\u91cd\u70b9\u8ba8\u8bba\u5e76\u884c\u8ba1\u7b97\uff0c\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u5b9e\u73b0\u591a\u7ebf\u7a0b\u548c\u591a\u8fdb\u7a0b\uff0c\u4ee5\u53ca\u5982\u4f55\u4f7f\u7528\u5206\u5e03\u5f0f\u8ba1\u7b97\u5e93\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u5e76\u884c\u8ba1\u7b97<\/strong><\/h2>\n

\u5e76\u884c\u8ba1\u7b97\u662f\u6307\u5c06\u4efb\u52a1\u5206\u89e3\u6210\u591a\u4e2a\u5b50\u4efb\u52a1\uff0c\u5e76\u5728\u591a\u4e2a\u5904\u7406\u5668\u6216\u8ba1\u7b97\u8282\u70b9\u4e0a\u540c\u65f6\u6267\u884c\u3002Python\u63d0\u4f9b\u4e86\u591a\u79cd\u5e76\u884c\u8ba1\u7b97\u7684\u5de5\u5177\u548c\u5e93\uff0c\u6700\u5e38\u7528\u7684\u5305\u62ecmultiprocessing<\/code>\u548cconcurrent.futures<\/code>\u3002<\/p>\n<\/p>\n

1. multiprocessing<\/code>\u5e93<\/h2>\n<\/p>\n

multiprocessing<\/code>\u5e93\u662fPython\u6807\u51c6\u5e93\u7684\u4e00\u90e8\u5206\uff0c\u65e8\u5728\u901a\u8fc7\u521b\u5efa\u591a\u4e2a\u8fdb\u7a0b\u6765\u5b9e\u73b0\u5e76\u884c\u8ba1\u7b97\u3002\u76f8\u6bd4\u4e8e\u591a\u7ebf\u7a0b\uff0c\u591a\u8fdb\u7a0b\u80fd\u591f\u66f4\u597d\u5730\u5229\u7528\u591a\u6838\u670d\u52a1\u5668\u7684\u6027\u80fd\uff0c\u56e0\u4e3a\u6bcf\u4e2a\u8fdb\u7a0b\u90fd\u6709\u72ec\u7acb\u7684\u5168\u5c40\u89e3\u91ca\u5668\u9501\uff08GIL\uff09\u3002<\/p>\n<\/p>\n

import multiprocessing<\/p>\n
def worker(num):<\/p>\n
    """\u7ebf\u7a0b\u5de5\u4f5c\u51fd\u6570"""<\/p>\n
    print(f'Worker: {num}')<\/p>\n
if __name__ == '__mAI<\/a>n__':<\/p>\n
    jobs = []<\/p>\n
    for i in range(5):<\/p>\n
        p = multiprocessing.Process(target=worker, args=(i,))<\/p>\n
        jobs.append(p)<\/p>\n
        p.start()<\/p>\n
    for job in jobs:<\/p>\n
        job.join()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4ee5\u4e0a\u4ee3\u7801\u521b\u5efa\u4e865\u4e2a\u72ec\u7acb\u7684\u8fdb\u7a0b\uff0c\u6bcf\u4e2a\u8fdb\u7a0b\u6267\u884cworker<\/code>\u51fd\u6570\uff0c\u8f93\u51fa\u5bf9\u5e94\u7684num<\/code>\u503c\u3002<\/p>\n<\/p>\n
2. concurrent.futures<\/code>\u5e93<\/h2>\n<\/p>\n
concurrent.futures<\/code>\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7ea7\u522b\u7684\u63a5\u53e3\uff0c\u652f\u6301\u7ebf\u7a0b\u6c60\u548c\u8fdb\u7a0b\u6c60\u3002\u4f7f\u7528ProcessPoolExecutor<\/code>\u53ef\u4ee5\u65b9\u4fbf\u5730\u5b9e\u73b0\u591a\u8fdb\u7a0b\u5e76\u884c\u8ba1\u7b97\u3002<\/p>\n<\/p>\n
from concurrent.futures import ProcessPoolExecutor<\/p>\n
def worker(num):<\/p>\n
    """\u7ebf\u7a0b\u5de5\u4f5c\u51fd\u6570"""<\/p>\n
    return f'Worker: {num}'<\/p>\n
if __name__ == '__main__':<\/p>\n
    with ProcessPoolExecutor() as executor:<\/p>\n
        results = list(executor.map(worker, range(5)))<\/p>\n
    print(results)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528ProcessPoolExecutor<\/code>\u6765\u521b\u5efa\u4e00\u4e2a\u8fdb\u7a0b\u6c60\uff0c\u5e76\u901a\u8fc7executor.map<\/code>\u65b9\u6cd5\u5c06\u4efb\u52a1\u5206\u53d1\u5230\u591a\u4e2a\u8fdb\u7a0b\u4e2d\u6267\u884c\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u7ebf\u7a0b\u548c\u8fdb\u7a0b\u7ba1\u7406<\/strong><\/h2>\n
\u5728\u591a\u6838\u670d\u52a1\u5668\u4e0a\u5145\u5206\u5229\u7528\u8d44\u6e90\u7684\u53e6\u4e00\u4e2a\u5173\u952e\u70b9\u662f\u6709\u6548\u7ba1\u7406\u7ebf\u7a0b\u548c\u8fdb\u7a0b\u3002Python\u63d0\u4f9b\u4e86\u591a\u79cd\u5de5\u5177\u548c\u5e93\u6765\u5e2e\u52a9\u6211\u4eec\u5b9e\u73b0\u8fd9\u4e00\u70b9\u3002<\/p>\n<\/p>\n
1. \u591a\u7ebf\u7a0b<\/h2>\n<\/p>\n
\u867d\u7136Python\u7684GIL\u9650\u5236\u4e86\u591a\u7ebf\u7a0b\u7684\u5e76\u884c\u6267\u884c\uff0c\u4f46\u5bf9\u4e8eI\/O\u5bc6\u96c6\u578b\u4efb\u52a1\uff0c\u591a\u7ebf\u7a0b\u4ecd\u7136\u662f\u4e00\u4e2a\u6709\u6548\u7684\u89e3\u51b3\u65b9\u6848\u3002threading<\/code>\u5e93\u662fPython\u6807\u51c6\u5e93\u7684\u4e00\u90e8\u5206\uff0c\u63d0\u4f9b\u4e86\u521b\u5efa\u548c\u7ba1\u7406\u7ebf\u7a0b\u7684\u529f\u80fd\u3002<\/p>\n<\/p>\n
import threading<\/p>\n
def worker(num):<\/p>\n
    """\u7ebf\u7a0b\u5de5\u4f5c\u51fd\u6570"""<\/p>\n
    print(f'Worker: {num}')<\/p>\n
if __name__ == '__main__':<\/p>\n
    threads = []<\/p>\n
    for i in range(5):<\/p>\n
        t = threading.Thread(target=worker, args=(i,))<\/p>\n
        threads.append(t)<\/p>\n
        t.start()<\/p>\n
    for t in threads:<\/p>\n
        t.join()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u521b\u5efa\u4e865\u4e2a\u7ebf\u7a0b\uff0c\u6bcf\u4e2a\u7ebf\u7a0b\u6267\u884cworker<\/code>\u51fd\u6570\uff0c\u8f93\u51fa\u5bf9\u5e94\u7684num<\/code>\u503c\u3002<\/p>\n<\/p>\n
2. \u8fdb\u7a0b\u6c60<\/h2>\n<\/p>\n
multiprocessing<\/code>\u5e93\u8fd8\u63d0\u4f9b\u4e86Pool<\/code>\u7c7b\uff0c\u7528\u4e8e\u521b\u5efa\u8fdb\u7a0b\u6c60\u3002\u8fdb\u7a0b\u6c60\u53ef\u4ee5\u7ba1\u7406\u591a\u4e2a\u8fdb\u7a0b\uff0c\u5e76\u5c06\u4efb\u52a1\u5206\u53d1\u7ed9\u7a7a\u95f2\u7684\u8fdb\u7a0b\u3002<\/p>\n<\/p>\n
from multiprocessing import Pool<\/p>\n
def worker(num):<\/p>\n
    """\u7ebf\u7a0b\u5de5\u4f5c\u51fd\u6570"""<\/p>\n
    return f'Worker: {num}'<\/p>\n
if __name__ == '__main__':<\/p>\n
    with Pool(5) as p:<\/p>\n
        print(p.map(worker, range(5)))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u521b\u5efa\u4e86\u4e00\u4e2a\u5305\u542b5\u4e2a\u8fdb\u7a0b\u7684\u8fdb\u7a0b\u6c60\uff0c\u5e76\u4f7f\u7528p.map<\/code>\u65b9\u6cd5\u5c06\u4efb\u52a1\u5206\u53d1\u7ed9\u8fdb\u7a0b\u6c60\u4e2d\u7684\u8fdb\u7a0b\u6267\u884c\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u5206\u5e03\u5f0f\u8ba1\u7b97<\/strong><\/h2>\n
\u5206\u5e03\u5f0f\u8ba1\u7b97\u662f\u4e00\u79cd\u9ad8\u7ea7\u7684\u5e76\u884c\u8ba1\u7b97\u5f62\u5f0f\uff0c\u6d89\u53ca\u5c06\u4efb\u52a1\u5206\u914d\u5230\u591a\u4e2a\u8ba1\u7b97\u8282\u70b9\u6267\u884c\u3002Python\u63d0\u4f9b\u4e86\u591a\u4e2a\u5e93\u6765\u5b9e\u73b0\u5206\u5e03\u5f0f\u8ba1\u7b97\uff0c\u5305\u62ecdask<\/code>\u3001ray<\/code>\u548ccelery<\/code>\u3002<\/p>\n<\/p>\n
1. Dask<\/code><\/h2>\n<\/p>\n
Dask<\/code>\u662f\u4e00\u4e2a\u7075\u6d3b\u7684\u5e76\u884c\u8ba1\u7b97\u5e93\uff0c\u652f\u6301\u591a\u79cd\u5e76\u884c\u8ba1\u7b97\u6a21\u5f0f\u3002\u5b83\u53ef\u4ee5\u5728\u5355\u4e2a\u591a\u6838\u673a\u5668\u4e0a\u8fd0\u884c\uff0c\u4e5f\u53ef\u4ee5\u5728\u5206\u5e03\u5f0f\u96c6\u7fa4\u4e2d\u8fd0\u884c\u3002<\/p>\n<\/p>\n
import dask.array as da<\/p>\n
\u521b\u5efa\u4e00\u4e2a\u5927\u6570\u7ec4<\/strong><\/h2>\n
x = da.random.random((10000, 10000), chunks=(1000, 1000))<\/p>\n
\u8ba1\u7b97\u6570\u7ec4\u7684\u5747\u503c<\/strong><\/h2>\n
result = x.mean().compute()<\/p>\n
print(result)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528dask.array<\/code>\u521b\u5efa\u4e86\u4e00\u4e2a\u5927\u6570\u7ec4\uff0c\u5e76\u8ba1\u7b97\u5176\u5747\u503c\u3002Dask<\/code>\u4f1a\u81ea\u52a8\u5c06\u8ba1\u7b97\u4efb\u52a1\u5206\u89e3\u6210\u591a\u4e2a\u5b50\u4efb\u52a1\uff0c\u5e76\u5728\u591a\u4e2a\u5904\u7406\u5668\u4e0a\u5e76\u884c\u6267\u884c\u3002<\/p>\n<\/p>\n
2. Ray<\/code><\/h2>\n<\/p>\n
Ray<\/code>\u662f\u4e00\u4e2a\u9ad8\u6027\u80fd\u7684\u5206\u5e03\u5f0f\u8ba1\u7b97\u5e93\uff0c\u652f\u6301\u4efb\u52a1\u8c03\u5ea6\u3001\u6570\u636e\u5171\u4eab\u548c\u5206\u5e03\u5f0f\u8bad\u7ec3\u7b49\u529f\u80fd\u3002<\/p>\n<\/p>\n
import ray<\/p>\n
ray.init()<\/p>\n
@ray.remote<\/p>\n
def worker(num):<\/p>\n
    return f'Worker: {num}'<\/p>\n
if __name__ == '__main__':<\/p>\n
    futures = [worker.remote(i) for i in range(5)]<\/p>\n
    results = ray.get(futures)<\/p>\n
    print(results)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528Ray<\/code>\u521b\u5efa\u4e86\u4e00\u4e2a\u5206\u5e03\u5f0f\u8ba1\u7b97\u4efb\u52a1\uff0c\u5e76\u901a\u8fc7ray.get<\/code>\u65b9\u6cd5\u83b7\u53d6\u4efb\u52a1\u7684\u7ed3\u679c\u3002<\/p>\n<\/p>\n
3. Celery<\/code><\/h2>\n<\/p>\n
Celery<\/code>\u662f\u4e00\u4e2a\u5206\u5e03\u5f0f\u4efb\u52a1\u961f\u5217\uff0c\u5e7f\u6cdb\u7528\u4e8e\u5206\u5e03\u5f0f\u8ba1\u7b97\u548c\u4efb\u52a1\u8c03\u5ea6\u3002<\/p>\n<\/p>\n
from celery import Celery<\/p>\n
app = Celery('tasks', broker='pyamqp:\/\/guest@localhost\/\/')<\/p>\n
@app.task<\/p>\n
def worker(num):<\/p>\n
    return f'Worker: {num}'<\/p>\n
if __name__ == '__main__':<\/p>\n
    result = worker.delay(5)<\/p>\n
    print(result.get())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528Celery<\/code>\u5b9a\u4e49\u4e86\u4e00\u4e2a\u5206\u5e03\u5f0f\u4efb\u52a1\uff0c\u5e76\u901a\u8fc7worker.delay<\/code>\u65b9\u6cd5\u5c06\u4efb\u52a1\u53d1\u9001\u5230\u4efb\u52a1\u961f\u5217\u4e2d\u6267\u884c\u3002<\/p>\n<\/p>\n
\u56db\u3001\u6027\u80fd\u4f18\u5316<\/strong><\/h2>\n
\u5728\u591a\u6838\u670d\u52a1\u5668\u4e0a\u8fd0\u884cPython\u7a0b\u5e8f\u65f6\uff0c\u6027\u80fd\u4f18\u5316\u662f\u4e00\u4e2a\u91cd\u8981\u7684\u8003\u8651\u56e0\u7d20\u3002\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5e38\u89c1\u7684\u6027\u80fd\u4f18\u5316\u6280\u5de7\uff1a<\/p>\n<\/p>\n
1. \u4f7f\u7528\u5408\u9002\u7684\u6570\u636e\u7ed3\u6784<\/h2>\n<\/p>\n
\u9009\u62e9\u5408\u9002\u7684\u6570\u636e\u7ed3\u6784\u53ef\u4ee5\u663e\u8457\u63d0\u9ad8\u7a0b\u5e8f\u7684\u6027\u80fd\u3002\u4f8b\u5982\uff0c\u4f7f\u7528numpy<\/code>\u6570\u7ec4\u4ee3\u66ffPython\u5217\u8868\u53ef\u4ee5\u63d0\u9ad8\u6570\u503c\u8ba1\u7b97\u7684\u6548\u7387\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u4f7f\u7528numpy\u6570\u7ec4\u8fdb\u884c\u6570\u503c\u8ba1\u7b97<\/strong><\/h2>\n
a = np.array([1, 2, 3, 4, 5])<\/p>\n
b = np.array([6, 7, 8, 9, 10])<\/p>\n
result = a + b<\/p>\n
print(result)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2. \u907f\u514d\u4e0d\u5fc5\u8981\u7684\u8ba1\u7b97<\/h2>\n<\/p>\n
\u901a\u8fc7\u7f13\u5b58\u8ba1\u7b97\u7ed3\u679c\u6216\u4f7f\u7528\u52a8\u6001\u89c4\u5212\u6280\u672f\uff0c\u53ef\u4ee5\u907f\u514d\u4e0d\u5fc5\u8981\u7684\u8ba1\u7b97\uff0c\u63d0\u9ad8\u7a0b\u5e8f\u7684\u6027\u80fd\u3002<\/p>\n<\/p>\n
# \u4f7f\u7528\u7f13\u5b58\u907f\u514d\u4e0d\u5fc5\u8981\u7684\u8ba1\u7b97<\/p>\n
cache = {}<\/p>\n
def fibonacci(n):<\/p>\n
    if n in cache:<\/p>\n
        return cache[n]<\/p>\n
    if n <= 1:<\/p>\n
        return n<\/p>\n
    result = fibonacci(n-1) + fibonacci(n-2)<\/p>\n
    cache[n] = result<\/p>\n
    return result<\/p>\n
print(fibonacci(50))<\/p>\n
<\/code><\/pre>\n<\/p>\n
3. \u4f7f\u7528\u5e76\u884c\u7b97\u6cd5<\/h2>\n<\/p>\n
\u5e76\u884c\u7b97\u6cd5\u53ef\u4ee5\u663e\u8457\u63d0\u9ad8\u7a0b\u5e8f\u7684\u6027\u80fd\u3002\u901a\u8fc7\u5c06\u4efb\u52a1\u5206\u89e3\u6210\u591a\u4e2a\u5b50\u4efb\u52a1\uff0c\u5e76\u5728\u591a\u4e2a\u5904\u7406\u5668\u4e0a\u540c\u65f6\u6267\u884c\uff0c\u53ef\u4ee5\u52a0\u901f\u8ba1\u7b97\u8fc7\u7a0b\u3002<\/p>\n<\/p>\n
from joblib import Parallel, delayed<\/p>\n
def worker(num):<\/p>\n
    return f'Worker: {num}'<\/p>\n
results = Parallel(n_jobs=5)(delayed(worker)(i) for i in range(5))<\/p>\n
print(results)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528joblib<\/code>\u5e93\u7684Parallel<\/code>\u548cdelayed<\/code>\u51fd\u6570\u5b9e\u73b0\u4e86\u5e76\u884c\u8ba1\u7b97\u3002<\/p>\n<\/p>\n
4. \u4f18\u5316I\/O\u64cd\u4f5c<\/h2>\n<\/p>\n
I\/O\u64cd\u4f5c\u901a\u5e38\u662f\u7a0b\u5e8f\u7684\u6027\u80fd\u74f6\u9888\u3002\u901a\u8fc7\u4f7f\u7528\u5f02\u6b65I\/O\u6216\u6279\u91cfI\/O\u64cd\u4f5c\uff0c\u53ef\u4ee5\u63d0\u9ad8\u7a0b\u5e8f\u7684\u6027\u80fd\u3002<\/p>\n<\/p>\n
import aiohttp<\/p>\n
import asyncio<\/p>\n
async def fetch(session, url):<\/p>\n
    async with session.get(url) as response:<\/p>\n
        return await response.text()<\/p>\n
async def main():<\/p>\n
    async with aiohttp.ClientSession() as session:<\/p>\n
        tasks = [fetch(session, f'https:\/\/example.com\/{i}') for i in range(5)]<\/p>\n
        results = await asyncio.gather(*tasks)<\/p>\n
        print(results)<\/p>\n
asyncio.run(main())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528aiohttp<\/code>\u548casyncio<\/code>\u5b9e\u73b0\u4e86\u5f02\u6b65I\/O\u64cd\u4f5c\u3002<\/p>\n<\/p>\n
\u4e94\u3001\u603b\u7ed3<\/strong><\/h2>\n
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\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 \u5982\u4f55\u5728Python\u4e2d\u5229\u7528\u591a\u6838\u5904\u7406\u5668\u63d0\u9ad8\u6027\u80fd\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u901a\u8fc7\u4f7f\u7528multiprocessing<\/code>\u6a21\u5757\u6765\u5b9e\u73b0\u591a\u6838\u5904\u7406\u3002\u8fd9\u4e2a\u6a21\u5757\u5141\u8bb8\u4f60\u521b\u5efa\u591a\u4e2a\u8fdb\u7a0b\uff0c\u6bcf\u4e2a\u8fdb\u7a0b\u53ef\u4ee5\u5728\u4e0d\u540c\u7684CPU\u6838\u5fc3\u4e0a\u8fd0\u884c\uff0c\u4ece\u800c\u63d0\u9ad8\u7a0b\u5e8f\u7684\u6267\u884c\u6548\u7387\u3002\u4f60\u53ef\u4ee5\u4f7f\u7528Process<\/code>\u7c7b\u6765\u521b\u5efa\u65b0\u8fdb\u7a0b\uff0c\u5e76\u901a\u8fc7Queue<\/code>\u6216Pipe<\/code>\u7b49\u673a\u5236\u8fdb\u884c\u8fdb\u7a0b\u95f4\u901a\u4fe1\u3002\u6b64\u5916\uff0c\u5229\u7528Pool<\/code>\u7c7b\u53ef\u4ee5\u7b80\u5316\u591a\u4e2a\u8fdb\u7a0b\u7684\u7ba1\u7406\uff0c\u63d0\u9ad8\u4ee3\u7801\u7684\u53ef\u8bfb\u6027\u548c\u7ef4\u62a4\u6027\u3002<\/p>\n
\u5728\u591a\u6838\u670d\u52a1\u5668\u4e0a\u8fd0\u884cPython\u811a\u672c\u65f6\u9700\u8981\u6ce8\u610f\u54ea\u4e9b\u4e8b\u9879\uff1f<\/strong>
\u5728\u591a\u6838\u670d\u52a1\u5668\u4e0a\u8fd0\u884cPython\u811a\u672c\u65f6\uff0c\u786e\u4fdd\u4f60\u7f16\u5199\u7684\u4ee3\u7801\u662f\u7ebf\u7a0b\u5b89\u5168\u7684\uff0c\u7279\u522b\u662f\u5728\u8bbf\u95ee\u5171\u4eab\u8d44\u6e90\u65f6\u3002\u6b64\u5916\uff0c\u4e86\u89e3\u5168\u5c40\u89e3\u91ca\u5668\u9501\uff08GIL\uff09\u7684\u5f71\u54cd\u4e5f\u662f\u5173\u952e\u3002\u867d\u7136multiprocessing<\/code>\u6a21\u5757\u53ef\u4ee5\u7ed5\u8fc7GIL\u7684\u9650\u5236\uff0c\u4f7f\u7528\u591a\u8fdb\u7a0b\u800c\u4e0d\u662f\u591a\u7ebf\u7a0b\u6765\u5b9e\u73b0\u5e76\u884c\u5904\u7406\u901a\u5e38\u4f1a\u83b7\u5f97\u66f4\u597d\u7684\u6027\u80fd\u3002\u6b64\u5916\uff0c\u8003\u8651\u5982\u4f55\u5408\u7406\u5206\u914d\u4efb\u52a1\uff0c\u4ee5\u907f\u514d\u8fdb\u7a0b\u95f4\u7684\u7ade\u4e89\u548c\u7b49\u5f85\uff0c\u4ece\u800c\u63d0\u9ad8\u6574\u4f53\u7684\u6267\u884c\u6548\u7387\u3002<\/p>\n
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