学习PYTHON之路， DAY 10 进程、线程、协程篇

线程

线程是应用程序中工作的最小单元。它被包含在进程之中，是进程中的实际运作单位。一条线程指的是进程中一个单一顺序的控制流，一个进程中可以并发多个线程，每条线程并行执行不同的任务。

直接调用

import threading
import time
  
def show(arg):
    time.sleep(1)
    print 'thread'+str(arg)
  
for i in range(10):
    t = threading.Thread(target=show, args=(i,))
    t.start()
  
print 'main thread stop'

继承式调用

import threading
import time
 
 
class MyThread(threading.Thread):
    def __init__(self,num):
        threading.Thread.__init__(self)
        self.num = num
 
    def run(self):#定义每个线程要运行的函数
 
        print("running on number:%s" %self.num)
 
        time.sleep(3)
 
if __name__ == '__main__':
 
    t1 = MyThread(1)
    t2 = MyThread(2)
    t1.start()
    t2.start()

更多方法：

• start            线程准备就绪，等待CPU调度
• setName      为线程设置名称
• getName      获取线程名称
• setDaemon   设置为后台线程或前台线程（默认）
                     如果是后台线程，主线程执行过程中，后台线程也在进行，主线程执行完毕后，后台线程不论成功与否，均停止
                      如果是前台线程，主线程执行过程中，前台线程也在进行，主线程执行完毕后，等待前台线程也执行完成后，程序停止
• join              逐个执行每个线程，执行完毕后继续往下执行，该方法使得多线程变得无意义
• run              线程被cpu调度后自动执行线程对象的run方法

import time
import threading
 
 
def run(n):
 
    print('[%s]------running----
' % n)
    time.sleep(2)
    print('--done--')
 
def main():
    for i in range(5):
        t = threading.Thread(target=run,args=[i,])
        t.start()
        t.join(1)
        print('starting thread', t.getName())
 
 
m = threading.Thread(target=main,args=[])
m.setDaemon(True) #将main线程设置为Daemon线程,它做为程序主线程的守护线程,当主线程退出时,m线程也会退出,由m启动的其它子线程会同时退出,不管是否执行完任务
m.start()
m.join(timeout=2)
print("---main thread done----")

线程锁（Lock、RLock）

由于线程之间是进行随机调度，并且每个线程可能只执行n条执行之后，当多个线程同时修改同一条数据时可能会出现脏数据，所以，出现了线程锁 - 同一时刻允许一个线程执行操作。

import threading
import time
   
gl_num = 0
   
lock = threading.RLock()
   
def Func():
    lock.acquire()
    global gl_num
    gl_num +=1
    time.sleep(1)
    print gl_num
    lock.release()
       
for i in range(10):
    t = threading.Thread(target=Func)
    t.start()

信号量（Semaphore）

互斥锁 同时只允许一个线程更改数据，而Semaphore是同时允许一定数量的线程更改数据 ，比如厕所有3个坑，那最多只允许3个人上厕所，后面的人只能等里面有人出来了才能再进去。

import threading,time
 
def run(n):
    semaphore.acquire()
    time.sleep(1)
    print("run the thread: %s" %n)
    semaphore.release()
 
if __name__ == '__main__':
 
    num= 0
    semaphore  = threading.BoundedSemaphore(5) #最多允许5个线程同时运行
    for i in range(20):
        t = threading.Thread(target=run,args=(i,))
        t.start()

事件（event）

python线程的事件用于主线程控制其他线程的执行，事件主要提供了三个方法 set、wait、clear。

事件处理的机制：全局定义了一个“Flag”，如果“Flag”值为 False，那么当程序执行 event.wait 方法时就会阻塞，如果“Flag”值为True，那么event.wait 方法时便不再阻塞。

• clear：将“Flag”设置为False
• set：将“Flag”设置为True

import threading
 
 
def do(event):
    print 'start'
    event.wait()
    print 'execute'
 
 
event_obj = threading.Event()
for i in range(10):
    t = threading.Thread(target=do, args=(event_obj,))
    t.start()
 
event_obj.clear()
inp = raw_input('input:')
if inp == 'true':
    event_obj.set()

条件（Condition）

使得线程等待，只有满足某条件时，才释放n个线程

def condition_func():

    ret = False
    inp = input('>>>')
    if inp == '1':
        ret = True

    return ret


def run(n):
    con.acquire()
    con.wait_for(condition_func)
    print("run the thread: %s" %n)
    con.release()

if __name__ == '__main__':

    con = threading.Condition()
    for i in range(10):
        t = threading.Thread(target=run, args=(i,))
        t.start()

队列

class queue.Queue(maxsize=0) #先入先出

class queue.LifoQueue(maxsize=0) #last in fisrt out 

class queue.PriorityQueue(maxsize=0) #存储数据时可设置优先级的队列

Queue.qsize()

 

Queue.empty() #return True if empty  

 

Queue.full() # return True if full 

 

Queue.put(item, block=True, timeout=None)Queue.put_nowait(item)Equivalent to put(item, False).

 

Queue.get(block=True, timeout=None)Queue.get_nowait()

Equivalent to get(False)

Queue.task_done()

import time,random
import queue,threading
q = queue.Queue()
def Producer(name):
  count = 0
  while count <20:
    time.sleep(random.randrange(3))
    q.put(count)
    print('Producer %s has produced %s baozi..' %(name, count))
    count +=1
def Consumer(name):
  count = 0
  while count <20:
    time.sleep(random.randrange(4))
    if not q.empty():
        data = q.get()
        print(data)
        print('33[32;1mConsumer %s has eat %s baozi...33[0m' %(name, data))
    else:
        print("-----no baozi anymore----")
    count +=1
p1 = threading.Thread(target=Producer, args=('A',))
c1 = threading.Thread(target=Consumer, args=('B',))
p1.start()
c1.start()

线程池

import queue
import threading
import time


class ThreadPool:
    def __init__(self, maxsize=5):
        self.maxsize = maxsize
        self._q = queue.Queue(maxsize)
        for i in range(maxsize):
            self._q.put(threading.Thread)
        # 【threading.Thread，threading.Thread，threading.Thread，threading.Thread，threading.Thread】
    def get_thread(self):
        return self._q.get()

    def add_thread(self):
        self._q.put(threading.Thread)

pool = ThreadPool(5)

def task(arg,p):
    print(arg)
    time.sleep(1)
    p.add_thread()

for i in range(100):
    # threading.Thread类
    t = pool.get_thread()
    obj = t(target=task,args=(i,pool,))
    obj.start()

#!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author:Alex Li
import queue
import threading
import contextlib
import time

StopEvent = object()

class ThreadPool(object):

    def __init__(self, max_num, max_task_num = None):
        if max_task_num:
            self.q = queue.Queue(max_task_num)
        else:
            self.q = queue.Queue()
        self.max_num = max_num
        self.cancel = False
        self.terminal = False
        self.generate_list = []
        self.free_list = []

    def run(self, func, args, callback=None):
        """
        线程池执行一个任务
        :param func: 任务函数
        :param args: 任务函数所需参数
        :param callback: 任务执行失败或成功后执行的回调函数，回调函数有两个参数1、任务函数执行状态；2、任务函数返回值（默认为None，即：不执行回调函数）
        :return: 如果线程池已经终止，则返回True否则None
        """
        if self.cancel:
            return
        if len(self.free_list) == 0 and len(self.generate_list) < self.max_num:
            self.generate_thread()
        w = (func, args, callback,)
        self.q.put(w)

    def generate_thread(self):
        """
        创建一个线程
        """
        t = threading.Thread(target=self.call)
        t.start()

    def call(self):
        """
        循环去获取任务函数并执行任务函数
        """
        current_thread = threading.currentThread
        self.generate_list.append(current_thread)

        event = self.q.get()
        while event != StopEvent:

            func, arguments, callback = event
            try:
                result = func(*arguments)
                success = True
            except Exception as e:
                success = False
                result = None

            if callback is not None:
                try:
                    callback(success, result)
                except Exception as e:
                    pass

            with self.worker_state(self.free_list, current_thread):
                if self.terminal:
                    event = StopEvent
                else:
                    event = self.q.get()
        else:

            self.generate_list.remove(current_thread)

    def close(self):
        """
        执行完所有的任务后，所有线程停止
        """
        self.cancel = True
        full_size = len(self.generate_list)
        while full_size:
            self.q.put(StopEvent)
            full_size -= 1

    def terminate(self):
        """
        无论是否还有任务，终止线程
        """
        self.terminal = True

        while self.generate_list:
            self.q.put(StopEvent)

        self.q.empty()

    @contextlib.contextmanager
    def worker_state(self, state_list, worker_thread):
        """
        用于记录线程中正在等待的线程数
        """
        state_list.append(worker_thread)
        try:
            yield
        finally:
            state_list.remove(worker_thread)


pool = ThreadPool(5)

def callback(status, result):
    # status, execute action status
    # result, execute action return value
    pass

def action(i):
    print(i)

for i in range(300):
    ret = pool.run(action, (i,), callback)

# time.sleep(5)
# print(len(pool.generate_list), len(pool.free_list))
# print(len(pool.generate_list), len(pool.free_list))

 上下文管理

import contextlib

@contextlib.contextmanager #加了这个装饰器，可以用with
def work(free_list, worker_thread):
    free_list.append(worker_thread)
    try:
        yield
    finally:
        free_list.remove(worker_thread)

free_list = []
worker_thread = '1'
with work(free_list, worker_thread):
    print(123)

运行顺序

多进程

from multiprocessing import Process
import time
def f(name):
    time.sleep(2)
    print('hello', name)
 
if __name__ == '__main__': #在windos进程只能做测试，一定要写这句
    p = Process(target=f, args=('bob',))
    p.start()
    p.join()

进程间通讯

Queues

使用方法跟threading里的queue差不多

from multiprocessing import Process, Queue

def f(i,q):
    print(i,q.get())

if __name__ == '__main__':
    q = Queue()

    q.put("h1")
    q.put("h2")
    q.put("h3")

    for i in range(10):
        p = Process(target=f, args=(i,q,))
        p.start()

 Managers

from multiprocessing import Process, Manager
 
def f(d, l):
    d[1] = '1'
    d['2'] = 2
    d[0.25] = None
    l.append(1)
    print(l)
 
if __name__ == '__main__':
    with Manager() as manager:
        d = manager.dict()
 
        l = manager.list(range(5))
        p_list = []
        for i in range(10):
            p = Process(target=f, args=(d, l))
            p.start()
            p_list.append(p)
        for res in p_list:
            res.join()
 
        print(d)
        print(l)

协程

协程一个标准定义：

1. 必须在只有一个单线程里实现并发
2. 修改共享数据不需加锁
3. 用户程序里自己保存多个控制流的上下文栈
4. 一个协程遇到IO操作自动切换到其它协程

greenlet

from greenlet import greenlet
 
 
def test1():
    print 12
    gr2.switch()
    print 34
    gr2.switch()
 
 
def test2():
    print 56
    gr1.switch()
    print 78
 
gr1 = greenlet(test1)
gr2 = greenlet(test2)
gr1.switch()

gevent

import gevent
 
def foo():
    print('Running in foo')
    gevent.sleep(0)
    print('Explicit context switch to foo again')
 
def bar():
    print('Explicit context to bar')
    gevent.sleep(0)
    print('Implicit context switch back to bar')
 
gevent.joinall([
    gevent.spawn(foo),
    gevent.spawn(bar),
])