multiprocessing 多进程使用

apply、apply_async、map、map_async四种方法的对比：

            Multi-args(多参数)   Concurrence（并发）    Blocking（阻塞）     Ordered-results（返回结果顺序）
map               no              yes                  yes                yes
apply            yes               no                  yes                yes
map_async         no              yes                   no                 no
apply_async      yes              yes                   no                 no

注以下例子皆为多参数下的使用情况及耗时情况：

apply使用：

import time
from multiprocessing import Pool, Process

def worker(x ,y):
    result = x * y
    time.sleep(1.5)
    return result

if __name__ == '__main__':
    start = time.time()
    result_list = []
    pool = Pool(processes=4)
    for i in range(10):
        result = pool.apply(worker, args= (i, i))
        result_list.append(result)
    pool.close()
    pool.join()
    end = time.time()
    print(end-start)

结果：

[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
15.509403467178345

apply_async使用：

import time
from multiprocessing import Pool, Process

def worker(x ,y):
    result = x * y
    time.sleep(1.5)
    return result

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

if __name__ == '__main__':
    start = time.time()
    result_list = []
    pool = Pool(processes=4)
    for i in range(10):
        result = pool.apply_async(worker, args= (i, i), callback=collect_result)
    pool.close()
    pool.join()
    end = time.time()
    print(end-start)

结果：

[1, 0, 4, 9, 25, 16, 36, 49, 81, 64]
4.955698490142822

map使用：

import time
from multiprocessing import Pool, Process

def worker(z):
    result = z[0] * z[1]
    time.sleep(1.5)
    return result


if __name__ == '__main__':
    start = time.time()
    result_list = []
    pool = Pool(processes=4)
    for i in range(10):
        result = pool.map(worker, ([(i, i)]))
        result_list.append(result)
    pool.close()
    pool.join()
    print(result_list)
    end = time.time()
    print(end-start)

结果：

[[0], [1], [4], [9], [16], [25], [36], [49], [64], [81]]
15.50440001487732

map_async使用：

import time
from multiprocessing import Pool, Process

def worker(z):
    result = z[0] * z[1]
    time.sleep(1.5)
    return result

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

if __name__ == '__main__':
    start = time.time()
    result_list = []
    pool = Pool(processes=4)
    for i in range(10):
        result = pool.map_async(worker, ([(i, i)]), callback=collect_result)
    pool.close()
    pool.join()
    print(result_list)
    end = time.time()
    print(end-start)

结果：

[[0], [9], [1], [4], [16], [36], [49], [25], [64], [81]]
4.948090314865112