python 之并发编程更新版进程池与进程池比较与回调函数

一.更新版进程池与进程池比较

from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor

import os, time

def func(i):

    print('Process', i, os.getpid())

    time.sleep(0.1)

    print("Process..end")

    return 88899

# (1)ProcessPoolExcutor 进程池的基本使用(改良版)

相对于旧版的进程池,

一定会等待子进程全部执行完毕之后,再终止程序,相当于过去的Process流程

shutdown 相当于Process里面的join

if __name__ == "__main__":

    # (1)ProcessPoolExecutor() <==> Pool()

    p = ProcessPoolExecutor(5)

    # (2)submit() <==> apply_async()

    res = p.submit(func, 55)

    # (3)result() <==> get()

    res = res.result()

    print(res) #

    # (4)shutdown <==> close + join

    #p.shutdown()

    print("主进程执行结束...")

# (2)线程池

from threading import current_thread as ct

def func(i):

    print("thread",i,ct().ident)

    time.sleep(0.1)

    print("thread %s end" % (i))

#可以在参数中指定并发的线程数

tp = ThreadPoolExecutor(10)

for i in range(20):

    tp.submit(func,i)

#tp.shutdown()

print("主线程执行结束...")

# (3)线程池的返回值

from threading import current_thread as cthread

def func(i):

    print("thread", i, cthread().ident)

    # 加延迟防止个别线程因为执行速度过快,又接收任务,阻碍新线程的创建

    # time.sleep(0.1)

    print("threading %s end" % (i))

    # return "*" * i

    return cthread().ident

tp = ThreadPoolExecutor()

lst = []

setvar = set()

for i in range(10):

    res = tp.submit(func,i)

    lst.append(res)

for i in lst:

    # print(i.result())

    setvar.add(i.result())

print(setvar,len(setvar))

print("主线程执行结束...")

# (4)map 返回迭代器

from threading import current_thread as cthread

def func(i):

    print("threading",i,cthread().ident)

    time.sleep(0.1)

    print("thread %s end" % (i))

    return "*" * i

tp = ThreadPoolExecutor(5)

it = tp.map(func,range(20)) # map

from collections import Iterable,Iterator

print(isinstance(it,Iterator))

for i in it:

    print(i)

tp.shutdown()

print("主线程执行结束..")

二.回调函数

回调函数:

把函数当成参数传递的另外一个函数

函数先执行,最后在执行当参数传递的这个函数,整个过程是回调,这个参数是回调函数

# (1) 线程池的回调函数是由子线程完成

from concurrent.futures import  ThreadPoolExecutor

from threading import current_thread as cthread

import time

def func(i):

    print("thread",i,cthread().ident)

    time.sleep(0.1)

    print("thread %s end" % (i))

    return "*" * i

# 定义成回调函数

def call_back(args):

    print("call back:",cthread().ident)

    print(args.result())

tp = ThreadPoolExecutor(5)

for i in range(1,11):

    # submit(函数,参数).add_done_callback(要添加的回调函数)

    tp.submit(func,i).add_done_callback(call_back)

tp.shutdown()

print("主线程:",cthread().ident)

# (2) 进程池的回调函数是由主进程完成

from concurrent.futures import ProcessPoolExecutor

import os,time

def func(i):

    print("Process",i,os.getpid())

    time.sleep(0.1)

    print("Process %s end" % (i))

if __name__ == "__main__":

    p = ProcessPoolExecutor(5)

    p.submit(func,11)

    p.shutdown()

    print("主进程:",os.getpid())

例2：

from concurrent.futures import ProcessPoolExecutor

import os,time

def func(i):

    print("Process",i,os.getpid())

    time.sleep(0.1)

    print("Process %s end" % (i))

    return i * "*"

# 回调函数

def call_back(args):

    print("call back:",os.getpid())

    # print(args)

    print(args.result())

if __name__ == "__main__":

    # 同一时间最多允许5个进程并发

    tp = ProcessPoolExecutor(5)

    for i in range(1,11):

        tp.submit(func,i).add_done_callback(call_back)

    tp.shutdown()

    print("主进程id:",os.getpid())

巴特西

python 之并发编程更新版进程池与进程池比较与回调函数

一.更新版进程池与进程池比较

二.回调函数

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