[Python 多线程] Concurrent (十五)

concurrent包只有一个模块：

concurrent.futures - 启动并行任务

异步并行任务编程模块，提供一个高级的异步可执行的便利接口。

futures模块提供了2个池执行器

ThreadPoolExecutor 异步调用的线程池的Executor

ProcessPoolExecutor 异步调用的进程池的Executor

ThreadPoolExecutor对象

首先需要定义一个池的执行器对象，Executor类子类对象。

ThreadPoolExecutor(max_worker=1)     池中至多创建max_workers个线程的池来同时异步执行，返回Executor实例

submit(fn,*args,**kwargs)    提交执行的函数即参数，返回Future实例

shutdown(wait=True)    清理池

Future类方法：

result()    可以查看调用的返回的结果

done()     如果调用被成功的取消或者执行完成，返回True

canceled()    如果调用被成功的取消，返回True

running()     如果正在运行且不能被取消，返回True

cancel()     尝试取消调用。如果已经执行且不能取消返回False，否则返回True

result(timeout=None)   取返回的结果，超时为None，一直等待返回；超时设置到期，抛出concurrent.futures.TimeoutError异常

execption(timeout=None)   取返回的异常，超时为None，一直等待返回；超时设置到期，抛出conncurrent.futures.TimeoutError异常

1) 线程池并行异步执行：

# 并行异步执行，线程 ThreadPoolExecutor
 
import threading
import logging
from concurrent import futures
import time
logging.basicConfig(level=logging.INFO,format="%(thread)s %(message)s")
 
def work(n): #工作函数
    logging.info('wokring-{}'.format(n))
    time.sleep(5)
    logging.info('end work-{}'.format(n))
 
executor = futures.ThreadPoolExecutor(3) #线程
 
fs = [] #线程池容器
 
for i in range(3):
    f = executor.submit(work,i) #提交执行的函数及参数
    fs.append(f)
 
for i in range(3,6):
    f = executor.submit(work,i)
    fs.append(f)
 
while True:
    time.sleep(2)
    logging.info(threading.enumerate())
 
    flag = True
 
    for f in fs:
        flag = flag and f.done() #调用是否被成功的取消或运行完成
 
    if flag:
        executor.shutdown() #清理池
        logging.info(threading.enumerate())
        break
 
#运行结果：
123145331777536 wokring-0
123145337032704 wokring-1
123145342287872 wokring-2
4320629568 [<_MainThread(MainThread, started 4320629568)>, <Thread(Thread-1, started daemon 123145331777536)>, <Thread(Thread-2, started daemon 123145337032704)>, <Thread(Thread-3, started daemon 123145342287872)>]
4320629568 [<_MainThread(MainThread, started 4320629568)>, <Thread(Thread-1, started daemon 123145331777536)>, <Thread(Thread-2, started daemon 123145337032704)>, <Thread(Thread-3, started daemon 123145342287872)>]
123145331777536 end work-0
123145331777536 wokring-3
123145337032704 end work-1
123145337032704 wokring-4
123145342287872 end work-2
123145342287872 wokring-5
4320629568 [<_MainThread(MainThread, started 4320629568)>, <Thread(Thread-1, started daemon 123145331777536)>, <Thread(Thread-2, started daemon 123145337032704)>, <Thread(Thread-3, started daemon 123145342287872)>]
4320629568 [<_MainThread(MainThread, started 4320629568)>, <Thread(Thread-1, started daemon 123145331777536)>, <Thread(Thread-2, started daemon 123145337032704)>, <Thread(Thread-3, started daemon 123145342287872)>]
123145331777536 end work-3
123145342287872 end work-5
123145337032704 end work-4
4320629568 [<_MainThread(MainThread, started 4320629568)>, <Thread(Thread-1, started daemon 123145331777536)>, <Thread(Thread-2, started daemon 123145337032704)>, <Thread(Thread-3, started daemon 123145342287872)>]
4320629568 [<_MainThread(MainThread, started 4320629568)>]

　　

2） 进程池并行异步执行：

import threading #ProcessPoolExecutor进程池
import logging
from concurrent import  futures
import time
logging.basicConfig(level=logging.INFO,format="%(thread)s %(message)s")
 
def work(n):
    logging.info('wokring-{}'.format(n))
    time.sleep(5)
    logging.info('end work-{}'.format(n))
 
if __name__ == "__main__":
    executor = futures.ProcessPoolExecutor(3) #进程
 
    fs = []
 
    for i in range(3):
        f = executor.submit(work,i)
        fs.append(f)
 
    for i in range(3,6):
        f = executor.submit(work,i)
        fs.append(f)
 
    while True:
        time.sleep(2)
        logging.info(threading.enumerate())
 
        flag = True
 
        for f in fs:
 
            flag = flag and f.done()
 
        if flag:
            executor.shutdown()
            logging.info(threading.enumerate())
            break
 
#运行结果：
4320629568 wokring-1
4320629568 wokring-2
4320629568 wokring-0
4320629568 [<_MainThread(MainThread, started 4320629568)>, <Thread(Thread-1, started daemon 123145319972864)>, <Thread(QueueFeederThread, started daemon 123145325228032)>]
4320629568 [<_MainThread(MainThread, started 4320629568)>, <Thread(Thread-1, started daemon 123145319972864)>, <Thread(QueueFeederThread, started daemon 123145325228032)>]
4320629568 end work-0
4320629568 end work-1
4320629568 end work-2
4320629568 wokring-3
4320629568 wokring-4
4320629568 wokring-5
4320629568 [<_MainThread(MainThread, started 4320629568)>, <Thread(Thread-1, started daemon 123145319972864)>, <Thread(QueueFeederThread, started daemon 123145325228032)>]
4320629568 [<_MainThread(MainThread, started 4320629568)>, <Thread(Thread-1, started daemon 123145319972864)>, <Thread(QueueFeederThread, started daemon 123145325228032)>]
4320629568 end work-3
4320629568 end work-4
4320629568 end work-5
4320629568 [<_MainThread(MainThread, started 4320629568)>, <Thread(Thread-1, started daemon 123145319972864)>, <Thread(QueueFeederThread, started daemon 123145325228032)>]
4320629568 [<_MainThread(MainThread, started 4320629568)>]

　　

支持上下文管理

concurrent.futures.ProcessPoolExecutor继承自conncurrent.futures.base.Executor，而父类有__enter__、__exit__方法，支持上下文管理。可以使用with语句

with ThreadPoolExecutor(max_workers=5) as executor:
	future = executor.submit(work,n)
	print(future.result())

　　

总结：

统一了线程池、进程池调用，简化了编程。

是Python简单的思想哲学的体现。

唯一的缺点：无法设置线程名称。