Python学习day40-并发编程(终)

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Python学习day40-并发编程(终)

线程queue和定时器

线程队列里面有几种有意思的算法模型,比如队列,堆栈,以及有优先级的取数据,是比较好玩的几个东西.实例见下面:

 

 

 

xxxxxxxxxx

22

 

 

 

 

1

import queue

2

# 队列:先进先出

3

q = queue.Queue()

4

q.put('123')

5

q.put('456')

6

print(q.get())

7

print(q.get())

8

'''

9

123

10

456

11

Process finished with exit code 0

12

可以看到,程序到此已经结束,其取数据的顺序就是先进先出

13

​

14

比较有趣的是,队列里面还有task_done和join两个方法,其用法和之前所了解的JoinableQueue里面的task_done和join是完全一样的,即有几个put就要有几个task_done,不然join就会卡主,不会释放程序

15

'''

16

q.task_done()

17

# q.task_done()

18

q.join()# 这里就会卡主,程序不会继续向下执行,也不会自动结束

19

'''

20

123

21

456

22

'''

 

 

 

 

 

xxxxxxxxxx

15

 

 

 

 

1

import queue

2

# 堆栈,先进后出

3

q = queue.LifoQueue()

4

q.put('123')

5

q.put('456')

6

q.put('789')

7

print(q.get())

8

print(q.get())

9

print(q.get())

10

'''

11

结果:

12

789

13

456

14

123

15

'''

 

 

 

 

 

xxxxxxxxxx

18

 

 

 

 

1

import queue

2

# 可以根据优先级取数据

3

​

4

q = queue.PriorityQueue()

5

# 这里要注意,put里面的第一个参数通常为int类型,而且值越小优先级越高,包括负数,意思就是所有的负数优先级都高于整数,

6

q.put((-1, 'a'))

7

q.put((80, 'b'))

8

q.put((1, 'c'))

9

​

10

print(q.get())

11

print(q.get())

12

'''

13

结果为:

14

(-1, '1')

15

(1, '3')

16

(80, '2')

17

Process finished with exit code 0

18

'''

 

 

定时器,顾名思义,就是定时的意思,我们设定一个延时,就可以让进程延时这些时间然后再开启.实例如下:

 

 

 

xxxxxxxxxx

60

 

 

 

 

1

from threading import Thread, current_thread, Timer

2

import time

3

​

4

​

5

def task():

6

    end = time.time()

7

    print(f'线程执行了')

8

    time.sleep(2)

9

    print(f'线程结束了')

10

    print(f'进程开启延时了{end - start:6.2f}秒')

11

​

12

​

13

if __name__ == '__main__':

14

    start = time.time()

15

    t = Timer(4, task)  # Timer()括号里第一个参数为延时的时间,第二个为开启的线程要运行的函数,这个例子是过4秒后开启一个线程

16

    t.start()

17

'''

18

执行结果为:

19

线程执行了

20

线程结束了

21

进程开启延时了  4.00秒

22

Process finished with exit code 0

23

​

24

​

25

到这里我们可能会有个疑问,那么这种延时和直接用time.sleep()有什么区别呢?

26

区别就在于time.sleep()是整个程序都暂停了,而Timer只是延时开启进程,对于别的代码的运行没有任何影响,我们可以在main程序里面加入两个print来证明这一点

27

​

28

'''

29

from threading import Thread, current_thread, Timer

30

import time

31

​

32

​

33

def task():

34

    end = time.time()

35

    print(f'线程执行了')

36

    time.sleep(2)

37

    print(f'线程结束了')

38

    print(f'进程开启延时了{end - start:6.2f}秒')

39

​

40

​

41

if __name__ == '__main__':

42

    print('进入main')

43

    start = time.time()

44

    print('准备进入延时')

45

    t = Timer(4, task)  

46

    t.start()

47

    print('main结束')

48

'''

49

执行结果为:

50

进入main

51

准备进入延时

52

main结束

53

线程执行了

54

线程结束了

55

进程开启延时了  4.00秒

56

​

57

Process finished with exit code 0

58

​

59

上面的执行结果我们就可以看出,延时并没有影响其余代码的运行,这点非常可贵

60

'''

 

 

线程池和进程池

首先我们要明白进程池和线程池的概念是什么,所谓池,池子,现实生活中是盛水的,可以说是一个容器,在我们的Python里面也是一个容器,用来容纳进程或者线程.

那么进程池/线程池的作用就是,当计算机需要并发的任务量远远大于计算机所能承受的范围的时候,或者说计算机无法一次性开启过多的任务数的时候,我们就需要进程池/线程池来限制当前计算机的进程数/线程数,从而保护计算机,或者是服务器,使其不至于宕机,影响业务.

池所用的模块是ProcessPoolExecutor和 ThreadPoolExecutor,常用的一些方法包括:

result(),等待该进程/线程的返回结果,当结果都返回之后,result()才会打印出来然后执行后面的代码,这样会导致所有的任务变成串行,一个线程执行完才会去执行下一个线程,虽然降低了效率,但是提高了程序的安全性,条理性也会更清楚

submit(obj,*args,**kwargs)第一个参数就是所调用的函数名,第二个和第三个都是调用的函数所需要传入的参数,submit会有一个返回值,所以通常所调用的函数会有返回值,来作为submit的返回值.

shutdown()关闭当前进程池/线程池

例程如下:

 

 

 

xxxxxxxxxx

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1

from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor

2

from threading import currentThread

3

from multiprocessing import current_process

4

import time

5

​

6

​

7

def task(i):

8

    print(f'开启{i}')

9

    print(f'线程{currentThread().name}执行任务{i}')

10

    # print(f'进程{current_process().name}执行任务{i}')

11

    time.sleep(2)

12

    print(f'线程{currentThread().name}结束任务{i}')

13

    print(f"\033[5;31;40m结束{i}\033[0m")

14

​

15

    return i ** 2

16

​

17

    # print(f'进程{current_process().name}结束任务{i}')

18

​

19

​

20

if __name__ == '__main__':

21

    # pool_p = ProcessPoolExecutor(4)

22

    pool_t = ThreadPoolExecutor(4)

23

    fu_list = []

24

    for i in range(5):

25

        # submit括号里第一个参数是所调用的函数名task,第二个和第三个是需要传入的调用函数task的形参,

26

        # submit可以返回一个对象,所以可以在task函数里面写返回值

27

        future = pool_t.submit(task, i)  # 5个线程,解决20个任务

28

        print(f'执行结果为{future.result()}')

29

        # 如果没有结果,会一直等待拿到结果,就会导致所有的任务变成串行,一个线程运行完才会运行下一个任务

30

        # pool_p.submit(task, i)

31

    pool_t.shutdown()  # 会关闭池的入口,会等到所有的任务执行完,结束阻塞

32

    for fu in fu_list:

33

        print(fu.result())

34

​

35

'''

36

开启0

37

线程ThreadPoolExecutor-0_0执行任务0

38

线程ThreadPoolExecutor-0_0结束任务0

39

结束0

40

执行结果为0

41

开启1

42

线程ThreadPoolExecutor-0_0执行任务1

43

线程ThreadPoolExecutor-0_0结束任务1

44

结束1

45

执行结果为1

46

开启2

47

线程ThreadPoolExecutor-0_1执行任务2

48

线程ThreadPoolExecutor-0_1结束任务2

49

结束2

50

执行结果为4

51

开启3

52

线程ThreadPoolExecutor-0_0执行任务3

53

线程ThreadPoolExecutor-0_0结束任务3

54

结束3

55

执行结果为9

56

开启4

57

线程ThreadPoolExecutor-0_2执行任务4

58

线程ThreadPoolExecutor-0_2结束任务4

59

结束4

60

执行结果为16

61

Process finished with exit code 0

62

'''

 

 

同步和异步

同步和异步其实是提交任务的两种方式,简单来说,

1. 同步就是提交了一个任务,必须等任务执行完了才能执行下一行代码
2. 异步则是提交了一个任务,不需要等任务执行完,就可以执行下面的代码.

协程

简单来说:协程是一种用户态的轻量级的线程,是由用户程序自己控制调度的

协程需要注意的两点是:

1. Python的线程是内核级别的,是由操作系统控制调度的
2. 单线程内开启的协程,一旦遇到IO就会从应用程序级别而不是操作系统级别进行切换,从而提升效率,这就是协程存在的意义.

为了正确的使用协程,我们需要引入gevent模块(这个不是Python自带的模块,需要读者自己下载,指令pip3 install gevent),而且需要使用到gevent的一个非常有趣的魔法方法,叫做moncky,具体见下例

 

 

 

xxxxxxxxxx

1

28

 

 

 

 

1

from gevent import monkey;monkey.patch_all()  # 我们需要在开头加上这一句,因为gevent本身并不能捕获所有的IO,只能识别自身的延时,所以这个语句相当于一个补丁,可以识别程序里面所有的IO,然后遇到这些IO的时候切换至其他的协程

2

​

3

import gevent# 导入gevent模块,这个导入一定要在上面的语句下面,切记!

4

import time

5

​

6

​

7

def eat():

8

    print('eat 1')

9

    time.sleep(2)

10

    print('eat 2')

11

​

12

​

13

def play():

14

    print('play 1')

15

    time.sleep(3)

16

    print('play 2')

17

​

18

​

19

start = time.time()

20

g1 = gevent.spawn(eat)# gevent的用法不同于之前学习的进程和线程,是另一种调用方法,而且下面一定需要用join来阻塞才能获得每个协程的执行结果,否则程序不会返回任何值,也不会执行开启协程所写入的那个任务的内容

21

g2 = gevent.spawn(play)

22

g1.join()

23

g2.join()

24

end = time.time()

25

print(f'{end - start:6.6f}')

26

​