Pool的四种方法比较：Python multiprocessing.Pool: Difference between map, apply, map_async, apply_async

最新推荐文章于 2025-06-24 14:59:15 发布

hellocsz

最新推荐文章于 2025-06-24 14:59:15 发布

阅读量1.8k

点赞数

分类专栏：个人

个人专栏收录该内容

41 篇文章

订阅专栏

本文介绍了Python中使用multiprocessing模块进行并行处理的方法。对比了Pool.map、apply、map_async及apply_async四种方式的特点，并提供了具体的代码示例。map适用于单参数任务且结果有序返回；apply适合多参数任务但不并发；map_async支持异步处理；apply_async则实现真正的并行执行。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

multiprocessing.Pool is cool to do parallel jobs in Python. But some tutorials only take Pool.map for example, in which they used special cases of function accepting single argument.

There are four choices to mapping jobs to process. Here are the differences:

             Multi-args   Concurrence    Blocking     Ordered-results
map          no           yes            yes          yes
apply        yes          no             yes          no
map_async    no           yes            no           yes
apply_async  yes          yes            no           no

In Python 3, a new function starmap can accept multiple arguments.

Note that map and map_async are called for a list of jobs in one time, but apply and apply_async can only called for one job. However,apply_async execute a job in background therefore in parallel. See examples:

# map
results = pool.map(worker, [1, 2, 3])

# apply
for x, y in [[1, 1], [2, 2]]:
    results.append(pool.apply(worker, (x, y)))

def collect_result(result):
    results.append(result)

# map_async
pool.map_async(worker, jobs, callback=collect_result)

# apply_async
for x, y in [[1, 1], [2, 2]]:
    pool.apply_async(worker, (x, y), callback=collect_result)