python进阶08并发之四map, apply, map_async, apply_async差异

差异矩阵

python封装了4种常用方法，用于实现并发
其差异如下

	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

需要注意：map 和 map_async 入参为迭代器类型，可以批量调用。而apply和apply_async只能一个个调用。

# 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)

apply和apply_async

Pool.apply_async：调用立即返回而不是等待结果。AsyncResult返回一个对象。你调用其get()方法以检索函数调用的结果。该get()方法将阻塞直到功能完成。
因此，pool.apply(func, args, kwargs)等效于pool.apply_async(func, args, kwargs).get()。
相比Pool.apply，该Pool.apply_async方法还具有一个回调，则在函数完成时调用该回调。可以使用它来代替get()。

import multiprocessing as mp
import time

def foo_pool(x):
    time.sleep(2)
    return x*x

result_list = []
def log_result(result):
    # This is called whenever foo_pool(i) returns a result.
    # result_list is modified only by the main process, not the pool workers.
    result_list.append(result)

def apply_async_with_callback():
    pool = mp.Pool()
    for i in range(10):
        pool.apply_async(foo_pool, args = (i, ), callback = log_result)
    pool.close()
    pool.join()
    print(result_list)

if __name__ == '__main__':
    apply_async_with_callback()
可能会产生如下结果

[1, 0, 4, 9, 25, 16, 49, 36, 81, 64]