pythonMultiprocessing.Pool进程池模块详解

class multiprocessing.pool.Pool([processes[, initializer[, initargs[, maxtasksperchild[, context]]]]])

from multiprocessing import Pool
import time
def test(p):
       print(p)
       time.sleep(3)
if __name__=="__main__":
    pool = Pool(processes=10)
    for i  in range(500):
        '''
        ('\n'
         '    （1）遍历500个可迭代对象，往进程池放一个子进程\n'
         '    （2）执行这个子进程，等子进程执行完毕，再往进程池放一个子进程，再执行。（同时只执行一个子进程）\n'
         '     for循环执行完毕，再执行print函数。\n'
         '    ')
        '''
        pool.apply(test, args=(i,))   #维持执行的进程总数为10，当一个进程执行完后启动一个新进程.
    print('test')
    pool.close()
    pool.join()
'''
1
2
3
4
5
6
7
8
Process finished with exit code -1
'''

from multiprocessing import Pool
import time
def test(p):
       print(p)
       time.sleep(3)
if __name__=="__main__":
    pool = Pool(processes=2)
    for i  in range(500):
        '''
         （1）循环遍历，将500个子进程添加到进程池（相对父进程会阻塞）\n'
         （2）每次执行2个子进程，等一个子进程执行完后，立马启动新的子进程。（相对父进程不阻塞）\n'
        '''
        pool.apply_async(test, args=(i,))   #维持执行的进程总数为10，当一个进程执行完后启动一个新进程.
    print('test')
    pool.close()
    pool.join()
'''
test
0
1
2
3
4
5
6
7
Process finished with exit code -1
'''

from multiprocessing import Pool
def test(i):
    print(i)
if  __name__ == "__main__":
    lists = [1, 2, 3]
    pool = Pool(processes=2)       #定义最大的进程数
    pool.map(test, lists)          #p必须是一个可迭代变量。
    pool.close()
    pool.join()
'''
1
2
3
'''

from multiprocessing import Pool
import time
def test(p):
       print(p)
       time.sleep(3)
if __name__=="__main__":
    pool = Pool(processes=2)
    # for i  in range(500):
    #     '''
    #      （1）循环遍历，将500个子进程添加到进程池（相对父进程会阻塞）\n'
    #      （2）每次执行2个子进程，等一个子进程执行完后，立马启动新的子进程。（相对父进程不阻塞）\n'
    #     '''
    #     pool.apply_async(test, args=(i,))   #维持执行的进程总数为10，当一个进程执行完后启动一个新进程.
    pool.map_async(test, range(500))
    print('test')
    pool.close()
    pool.join()
'''
test
0
63
1
64
2
65
3
66
Process finished with exit code -1
'''

from multiprocessing import Pool
import time
def f(x):
    return x*x
if __name__ == '__main__':
    with Pool(processes=4) as pool:         # start 4 worker processes
        result = pool.apply_async(f, (10,)) # evaluate "f(10)" asynchronously in a single process
        print(result.get(timeout=1))        # prints "100" unless your computer is *very* slow
        print(pool.map(f, range(10)))       # prints "[0, 1, 4,..., 81]"
        it = pool.imap(f, range(10))
        print(next(it))                     # prints "0"
        print(next(it))                     # prints "1"
        print(it.next(timeout=1))           # prints "4" unless your computer is *very* slow
        result = pool.apply_async(time.sleep, (10,))
        print(result.get(timeout=1))        # raises multiprocessing.TimeoutError
'''
100
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
0
1
4
Traceback (most recent call last):
  File "C:/Users/BruceWong/Desktop/develop/multiprocessingpool.py", line 19, in <module>
    print(next(res))
TypeError: 'MapResult' object is not an iterator
Process finished with exit code 1