python--再看并行之协程线程进程

1、gevent协程适合I/O密集，不适合CPU密集。

3、gevent协程无法发挥多核优势，事实上，协程只是以单线程的方式在运行。

3、子程序就是协程的一种特例

项目实际应用

from gevent import monkey
from gevent.monkey import patch_all
from gevent.pool import Pool
import time

def lr_classify():
    print('lr_classify')

def rf_classify():
    print('rf_classify')

def svm_classify():
    print('svm_classify')

def decisionTree_classify():
    print('decisionTree_classify')

def gbt_classify():
    print('gbt_classify')

def naive_bayes_classify():
    print('naive_bayes_classify')

model_dict = {'logistic':lr_classify
              ,'random_forest':rf_classify
              ,'linear_svm':svm_classify
              ,'decision_tree':decisionTree_classify
              ,'gbt':gbt_classify
              ,'naive_bayes':naive_bayes_classify}

def get_task(names):
    return [task for name,task in model_dict.items() if name in names]
#     return model_dict.get(name,'no such model')

start = time.time()

model_list = ['logistic','gbt', 'naive_bayes', 'linear_svm', 'decision_tree', 'random_forest']

names = model_dict.keys()&(set(model_list))

tasks = get_task(names)
p = Pool(6)

# jobs =  [i for i in range(10)]
for task in tasks:
    p.spawn(task)
# for task in tasks:
#     p.apply_async(task,(None,))
#     p.spawn
p.join()
end = time.time()
print('cost {} seconds in total...'.format((end-start)))

#输出结果如下：
lr_classify
svm_classify
gbt_classify
naive_bayes_classify
decisionTree_classify
rf_classify
cost 0.0018892288208007812 seconds in total...

线程进程协程比较

# 多线程
import threading
import time

def loop_5(interval):
    for i in range(5):
        print('loop_5: ',i)
        time.sleep(interval)

def loop_10(interval):
    for i in range(10):
        print('loop_10: ',i)
        time.sleep(interval)

if __name__ == '__main__':
    print('start...:')
    start = time.time()
    threads = []
    tasks = [loop_5,loop_10]
    for task in tasks:
        t = threading.Thread(target=task,args=(1,))
        threads.append(t)
        t.start()
    for t in threads:
        t.join()
    end = time.time()
    print('end...and cost {} seconds in total...'.format((end-start)))

# 输出如下
start...:
loop_5: loop_10:   00

loop_5: loop_10:  1
 1
loop_10:  2
loop_5:  2
loop_10:  3
loop_5:  3
loop_10:  4
loop_5:  4
loop_10:  5
loop_10:  6
loop_10:  7
loop_10:  8
loop_10:  9
end...and cost 10.02077603340149 seconds in total...

#多进程

from multiprocessing  import Pool
import time

def loop_5(interval):
    for i in range(5):
        print('loop_5: ',i)
        time.sleep(interval)

def loop_10(interval):
    for i in range(10):
        print('loop_10: ',i)
        time.sleep(interval)

if __name__ == '__main__':
    print('start ...')
    start = time.time()
    p = Pool(2)
    tasks = [loop_5,loop_10]
    for task in tasks:
        p.apply_async(task,args=(1,))
    p.close()
    p.join()
    end = time.time()
    print('end...cost {} seconds in total...'.format((end-start)))

# 然而，发现多进程仍然耗费 10 秒左右,不难理解啊，因为loop_5虽然跟10交替跑，但是还是要等待10跑完才会主进程结束啊。。。
start ...
loop_5:  0
loop_10:  0
loop_5:  1
loop_10:  1
loop_5:  2
loop_10:  2
loop_5:  3
loop_10:  3
loop_5:  4
loop_10:  4
loop_10:  5
loop_10:  6
loop_10:  7
loop_10:  8
loop_10:  9
end...cost 10.231749534606934 seconds in total...

### 不使用进程池

from multiprocessing  import Process
import time

def loop_5(interval):
    for i in range(5):
        print('loop_5: ',i)
        time.sleep(interval)

def loop_10(interval):
    for i in range(10):
        print('loop_10: ',i)
        time.sleep(interval)

if __name__ == '__main__':
    print('start ...')
    start = time.time()
    tasks = [loop_5,loop_10]
    processes = []
    for task in tasks:
        p = Process(target=task,args=(1,))
        processes.append(p)
        p.start()
    for p in processes:
        p.join()
    end = time.time()
    print('end...cost {} seconds in total...'.format((end-start)))

#输出结果如下：
start ...
loop_5:  0
loop_10:  0
loop_5:  1
loop_10:  1
loop_5:  2
loop_10:  2
loop_5:  3
loop_10:  3
loop_5:  4
loop_10:  4
loop_10:  5
loop_10:  6
loop_10:  7
loop_10:  8
loop_10:  9
end...cost 10.139782667160034 seconds in total...

协程进行文件复制

from gevent.pool import Pool
import time

def copy_file(src,target):
    with open(src,'r') as fr:
        with open(target,'w') as fw:
            for line in fr:
                fw.write(line)

if __name__ == '__main__':
    print('start...')
    start = time.time()
    p = Pool(6)

    args = [('./test.py','./test2.py'),('./test.py','./test3.py')]

    for arg in args:
        p.spawn(copy_file,*arg)
    p.join()
    end = time.time()
    print('cost {} seconds in total...'.format((end-start)))

多线程进行文件复制

import threading
import asyncio
import time
def copy(src,tar):
    print('{} start...'.format(threading.current_thread().name))
    with open(src,'rb') as binFileInputStream:
        with open(tar,'wb') as binFileOutputStream:
            binFileOutputStream.write(binFileInputStream.read())
    time.sleep(10)
    print('{} end...'.format(threading.current_thread().name))

threads = []
args = [('./test.py','./hella.py'),('./diabetes.csv','./diabetes.py')]
for i,arg in enumerate(args):
    t = threading.Thread(target=copy,args=arg,name='thread-{}'.format(i))
    threads.append(t)
for thread in threads:
    thread.start()

for thread in threads:
    thread.join()

多进程

from multiprocessing import Pool
import time
import random
import os

def copy_file_multiprocess(src,target):
    with open(src,'rb') as fr:
        with open(target,'wb') as fw:
            print('{} start to copy file...'.format(os.getpid()))
            fw.write(fr.read())
            time.sleep(random.random()*3)
            print('pid {} finished...'.format(os.getpid()))

if __name__ == '__main__':
    p = Pool(3)
    args = [('./config.txt','./babao/config.txt'),('./config.txt','./babao/config2.txt'),('./config.txt','./babao/config3.txt')]
    for i in range(3):
        p.apply_async(copy_file_multiprocess,args[i])
    print('Waiting for all subprocesses done...')
    p.close()
    p.join()
    print('All suprocesses finished!')

扩展知识

Python多线程编程时经常会用到join()和setDaemon()方法，基本用法如下：

join([time]): 等待至线程中止。这阻塞调用线程直至线程的join() 方法被调用中止-正常退出或者抛出未处理的异常-或者是可选的超时发生。
setDaemon，将该线程标记为守护线程或用户线程

1、join ()方法：主线程A中，创建了子线程B，并且在主线程A中调用了B.join()，那么，主线程A会在调用的地方等待，直到子线程B完成操作后，才可以接着往下执行，那么在调用这个线程时可以使用被调用线程的join方法。
原型：join([timeout])，里面的参数时可选的，代表线程运行的最大时间，即如果超过这个时间，不管这个此线程有没有执行完毕都会被回收，然后主线程或函数都会接着执行的。

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import threading
import time

class MyThread(threading.Thread):
    def __init__(self, id):
        threading.Thread.__init__(self)
        self.id = id

    def run(self):
        x = 0
        time.sleep(10)
        print(self.id)
        print('线程结束：'+str(time.time()))

if __name__ == "__main__":
    t1 = MyThread(999)
    print('线程开始：'+str(time.time()))
    t1.start()
    print('主线程打印开始：'+str(time.time()))
    for i in range(5):
        print(i)
    time.sleep(2)
    print('主线程打印结束：' + str(time.time()))
线程开始：1497534590.2784667
主线程打印开始：1497534590.2794669
0
1
2
3
4
主线程打印结束：1497534592.279581
999
线程结束：1497534600.2800388

从打印结果可知，线程t1 start后，主线程并没有等线程t1运行结束后再执行，而是在线程执行的同时，执行了后面的语句。

现在，把join()方法加到启动线程后面(其他代码不变)

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import threading
import time

class MyThread(threading.Thread):
    def __init__(self, id):
        threading.Thread.__init__(self)
        self.id = id

    def run(self):
        x = 0
        time.sleep(10)
        print(self.id)
        print('线程结束：'+str(time.time()))

if __name__ == "__main__":
    t1 = MyThread(999)
    print('线程开始：'+str(time.time()))
    t1.start()
    t1.join()
    print('主线程打印开始：'+str(time.time()))
    for i in range(5):
        print(i)
    time.sleep(2)
    print('主线程打印结束：' + str(time.time()))
线程开始：1497535176.5019968
999
线程结束：1497535186.5025687
主线程打印开始：1497535186.5025687
0
1
2
3
4
主线程打印结束：1497535188.5026832

线程t1 start后，主线程停在了join()方法处，等子线程t1结束后，主线程继续执行join后面的语句。 

2、setDaemon()方法。主线程A中，创建了子线程B，并且在主线程A中调用了B.setDaemon()，这个的意思是，把主线程A设置为守护线程，这时候，要是主线程A执行结束了，就不管子线程B是否完成，一并和主线程A退出.这就是setDaemon方法的含义，这基本和join是相反的。此外，还有个要特别注意的：必须在start() 方法调用之前设置。

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import threading
import time

class MyThread(threading.Thread):
    def __init__(self, id):
        threading.Thread.__init__(self)
        self.id = id

    def run(self):
        x = 0
        time.sleep(10)
        print(self.id)
        print("This is：" + self.getName()) # 获取线程名称
        print('线程结束：' + str(time.time()))

if __name__ == "__main__":
    t1 = MyThread(999)
    print('线程开始：'+str(time.time()))
    t1.setDaemon(True)
    t1.start()
    print('主线程打印开始：'+str(time.time()))
    for i in range(5):
        print(i)
    time.sleep(2)
    print('主线程打印结束：' + str(time.time()))
线程开始：1497536678.8509264
主线程打印开始：1497536678.8509264
0
1
2
3
4
主线程打印结束：1497536680.8510408

t1.setDaemon(True)的操作，将子线程设置为了守护线程。根据setDaemon()方法的含义，父线程打印内容后便结束了，不管子线程是否执行完毕了。

如果在线程启动前没有加t1.setDaemon(True)，输出结果为：

线程开始：1497536865.3215919
主线程打印开始：1497536865.3215919
0
1
2
3
4
主线程打印结束：1497536867.3217063
999
This is：Thread-1
线程结束：1497536875.3221638

程序运行中，执行一个主线程，如果主线程又创建一个子线程，主线程和子线程就分兵两路，分别运行，那么当主线程完成想退出时，会检验子线程是否完成，如果子线程未完成，则主线程会等待子线程完成后再退出；

有时我们需要的是，子线程运行完，才继续运行主线程，这时就可以用join方法（在线程启动后面）；

但是有时候我们需要的是，只要主线程完成了，不管子线程是否完成，都要和主线程一起退出，这时就可以用setDaemon方法（在线程启动前面）。

这里一个坑 (pool.map 中不能使用匿名函数)， 就是 lambda 无法被 pickle，可见匿名函数并不是到处通用 ，正确的 下面再贴出

# -*- coding: utf-8 -*-
__author__ = 'Frank Li'
from functools import wraps,reduce
import threading as td
import multiprocessing as mp
from multiprocessing import Queue
from multiprocessing import Pool
import time

def time_count(func):
    @wraps(func)
    def inner(*args,**kw):
        start = time.time()
        result = func(*args,**kw)
        end = time.time()
        num = kw.get('num','default')
        print('func: {}-{} cost {:.2f}s\n'.format(func.__name__,num,end-start))# 这里可以插入日志
        return result
    return inner

@time_count
def multicore(data,q,**kw):
    q.put(reduce(lambda x,y:x+y,data))

@time_count
def multi_thread(data,q,**kw):
    q.put(reduce(lambda x, y: x + y, data))

@time_count
def main_process():
    q = Queue()
    datas = [range(10**5),range(10**6),range(10**7),range(10**8)]
    processes = []
    for i,data in enumerate(datas):
        p = mp.Process(target=multicore,name='process-{}'.format(i),args=(data,q),kwargs={'num':i})
        p.start()
        processes.append(p)
    for p in processes:
        p.join()

    sum = 0
    for n in range(len(datas)):
        sum += q.get()
    print('main_process sum result: {}\n'.format(sum))

@time_count
def main_thread():
    q = Queue()
    datas = [range(10 ** 5), range(10 ** 6), range(10 ** 7), range(10 ** 8)]
    threads = []
    for i,data in enumerate(datas):
        t = td.Thread(target=multi_thread,name='thread-{}'.format(i),args=(data,q),kwargs={'num':i})
        t.start()
        threads.append(t)

    for t in threads:
        t.join()

    sum = 0
    for n in range(len(datas)):
        sum += q.get()
    print('main_thread sum result: {}\n'.format(sum))

def another_multi_process_sub(data):
    return reduce(lambda x, y: x + y, data)

@time_count
def another_multi_process():
    pool = Pool(processes=2)
    datas = [range(10 ** 5), range(10 ** 6), range(10 ** 7), range(10 ** 8)]
    results = []
    for data in datas:
        res = pool.map(lambda data:lambda x,y:x+y,(data,))
        results.extend(res)
    sum = reduce(lambda x,y:x+y,results)
    print('another_multi_process sum result: {}'.format(sum))

@time_count
def multi_process_pool():
    pool = Pool(processes=2)
    datas = [range(10 ** 5), range(10 ** 6), range(10 ** 7), range(10 ** 8)]
    dataset= []
    sum = reduce(lambda x,y:x+y,[ pool.apply_async(another_multi_process_sub,(data,)).get() for data in datas])
    print('multi_process_pool sum result: {}'.format(sum))

def main():
    # main_process()
    # main_thread()
    another_multi_process()
    # multi_process_pool()

@time_count
def single_process():
    datas = [range(10 ** 5), range(10 ** 6), range(10 ** 7), range(10 ** 8)]
    sum = 0
    for data in datas:
        sum += reduce(lambda x,y:x+y,data)
    print('\n single process sum result: {}'.format(sum))

if __name__ == '__main__':
    main()
    single_process()

对比多进程 多线程 效率 ， 总结 优先使用顺序 (多进程+协程>多进程 >= 单进程多线程 >=单进程单线程)

# -*- coding: utf-8 -*-
__author__ = 'Frank Li'
from functools import wraps,reduce
import threading as td
import multiprocessing as mp
from multiprocessing import Queue
from multiprocessing import Pool
import time

def time_count(func):
    @wraps(func)
    def inner(*args,**kw):
        start = time.time()
        result = func(*args,**kw)
        end = time.time()
        num = kw.get('num','default')
        print('func: {}-{} cost {:.2f}s\n'.format(func.__name__,num,end-start))# 这里可以插入日志
        return result
    return inner

@time_count
def multicore(data,q,**kw):
    q.put(reduce(lambda x,y:x+y,data))

@time_count
def multi_thread(data,q,**kw):
    q.put(reduce(lambda x, y: x + y, data))

@time_count
def main_process():
    q = Queue()
    datas = [range(10**5),range(10**6),range(10**7),range(10**8)]
    processes = []
    for i,data in enumerate(datas):
        p = mp.Process(target=multicore,name='process-{}'.format(i),args=(data,q),kwargs={'num':i})
        p.start()
        processes.append(p)
    for p in processes:
        p.join()

    sum = 0
    for n in range(len(datas)):
        sum += q.get()
    print('main_process sum result: {}\n'.format(sum))

@time_count
def main_thread():
    q = Queue()
    datas = [range(10 ** 5), range(10 ** 6), range(10 ** 7), range(10 ** 8)]
    threads = []
    for i,data in enumerate(datas):
        t = td.Thread(target=multi_thread,name='thread-{}'.format(i),args=(data,q),kwargs={'num':i})
        t.start()
        threads.append(t)

    for t in threads:
        t.join()

    sum = 0
    for n in range(len(datas)):
        sum += q.get()
    print('main_thread sum result: {}\n'.format(sum))

def another_multi_process_sub(data):
    return reduce(lambda x, y: x + y, data)

@time_count
def another_multi_process():
    pool = Pool(processes=2)
    datas = [range(10 ** 5), range(10 ** 6), range(10 ** 7), range(10 ** 8)]
    results = []
    for data in datas:
        res = pool.map(another_multi_process_sub,(data,))
        results.extend(res)
    sum = reduce(lambda x,y:x+y,results)
    print('another_multi_process sum result: {}'.format(sum))

@time_count
def multi_process_pool():
    pool = Pool(processes=2)
    datas = [range(10 ** 5), range(10 ** 6), range(10 ** 7), range(10 ** 8)]
    dataset= []
    sum = reduce(lambda x,y:x+y,[ pool.apply_async(another_multi_process_sub,(data,)).get() for data in datas])
    print('multi_process_pool sum result: {}'.format(sum))

def main():
    main_process()
    main_thread()
    another_multi_process()
    multi_process_pool()

@time_count
def single_process():
    datas = [range(10 ** 5), range(10 ** 6), range(10 ** 7), range(10 ** 8)]
    sum = 0
    for data in datas:
        sum += reduce(lambda x,y:x+y,data)
    print('\n single process sum result: {}'.format(sum))

if __name__ == '__main__':
    main()
    single_process()

下面结果 可能跟我电脑有其他进程没有关闭有关系，只是贴出一下，这个结果仁者见仁智者见智

# 结果如下 ：

func: multicore-0 cost 0.06s

func: multicore-1 cost 0.50s

func: multicore-2 cost 3.51s

func: multicore-3 cost 27.56s

main_process sum result: 5050504944450000

func: main_process-default cost 28.36s

func: multi_thread-0 cost 0.08s

func: multi_thread-1 cost 0.67s

func: multi_thread-2 cost 5.00s

func: multi_thread-3 cost 25.31s

main_thread sum result: 5050504944450000

func: main_thread-default cost 25.36s

another_multi_process sum result: 5050504944450000
func: another_multi_process-default cost 24.31s

multi_process_pool sum result: 5050504944450000
func: multi_process_pool-default cost 25.84s

single process sum result: 5050504944450000
func: single_process-default cost 25.58s

多核cpu 共享内存 ，进程通信安全问题， p1, p2 都有可能 抢到资源，一个抢到 一顿执行完事儿，第二个才能接着执行

# -*- coding: utf-8 -*-
__author__ = 'Frank Li'

import multiprocessing as mp
import time
'''
进程间共享内存，加锁
'''

def add_num(v,num,num_lock):
    with num_lock:
        for _ in range(10):
            v.value+=num
            time.sleep(0.1)
            print(v.value)

if __name__ == '__main__':
    v = mp.Value('i',0)  ### 还有一个 mp.Array
    num_lock = mp.Lock()
    p1 = mp.Process(target=add_num,args=(v,1,num_lock))
    p2 = mp.Process(target=add_num,args=(v,3,num_lock))
    p1.start()
    p2.start()
    p1.join()
    p2.join()

线程 操作共享变量 安全 加锁

# -*- coding: utf-8 -*-
__author__ = 'Frank Li'
import threading

def thread_job1():
    print('current threading... {}'.format(threading.current_thread()))
    global A,lock
    lock.acquire()
    for i in range(10):
        A+=1
        print('jb1 A value: {}'.format(A))
    lock.release()

def thread_job2():
    print('current threading... {}'.format(threading.current_thread()))
    global A,lock
    lock.acquire()
    for j in range(20):
        A+=5
        print('job2 value of A： {}'.format(A))
    lock.release()

def main():
    global A,lock
    A = 0
    lock = threading.Lock()

    t1 = threading.Thread(target=thread_job1,name='thread-1')
    t2 = threading.Thread(target=thread_job2,name='thread-2')
    t1.start()
    t2.start()
    t1.join()
    t2.join()

if __name__ == '__main__':
    main()

多线程

# -*- coding: utf-8 -*-
__author__ = 'Frank Li'
import threading
from threading import Thread
from queue import Queue

def thread_job(q,l=None):
    print(threading.current_thread())
    for i in range(len(l)):
        l[i] = l[i]**2
    q.put(l)

def multithreading(datas=None):
    q = Queue()
    threads = [] # 线程 列表
    # print(datas)
    for i,data in enumerate(datas):
        print(data)
        thread = Thread(target=thread_job,name='thread{}'.format(i),args=(q,data))
        thread.start()
        threads.append(thread)
    for thread in threads:
        thread.join()
    result = []

    for _ in range(len(datas)):
        result.append(q.get())
    print(result)

if __name__ == '__main__':
    datas = [[1,2],[3,4],[5,6],[7,8]]
    multithreading(datas)