python_进程,线程

太过爱你忘了你带给我的痛 2022-01-30 07:09 276阅读 0赞

### **什么是进程(process)？** ###

An executing instance of a program is called a process.

Each process provides the resources needed to execute a program. A process has a virtual address space, executable code, open handles to system objects, a security context, a unique process identifier, environment variables, a priority class, minimum and maximum working set sizes, and at least one thread of execution. Each process is started with a single thread, often called the primary thread, but can create additional threads from any of its threads.

程序并不能单独运行，只有将程序装载到内存中，系统为它分配资源才能运行，而这种执行的程序就称之为进程。程序和进程的区别就在于：程序是指令的集合，它是进程运行的静态描述文本；进程是程序的一次执行活动，属于动态概念。

在多道编程中，我们允许多个程序同时加载到内存中，在操作系统的调度下，可以实现并发地执行。这是这样的设计，大大提高了CPU的利用率。进程的出现让每个用户感觉到自己独享CPU，因此，进程就是为了在CPU上实现多道编程而提出的。

### **有了进程为什么还要线程？** ###

进程有很多优点，它提供了多道编程，让我们感觉我们每个人都拥有自己的CPU和其他资源，可以提高计算机的利用率。很多人就不理解了，既然进程这么优秀，为什么还要线程呢？其实，仔细观察就会发现进程还是有很多缺陷的，主要体现在两点上：

*  进程只能在一个时间干一件事，如果想同时干两件事或多件事，进程就无能为力了。
 *  进程在执行的过程中如果阻塞，例如等待输入，整个进程就会挂起，即使进程中有些工作不依赖于输入的数据，也将无法执行。

再直白一点， 一个操作系统就像是一个工厂，工厂里面有很多个生产车间，不同的车间生产不同的产品，每个车间就相当于一个进程，且你的工厂又穷，供电不足，同一时间只能给一个车间供电，为了能让所有车间都能同时生产，你的工厂的电工只能给不同的车间分时供电，但是轮到你的qq车间时，发现只有一个干活的工人，结果生产效率极低，为了解决这个问题，应该怎么办呢？。。。。没错，你肯定想到了，就是多加几个工人，让几个人工人并行工作，这每个工人，就是线程！

### **什么是线程(thread)？** ###

线程是操作系统能够进行运算调度的最小单位。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

A thread is an execution context, which is all the information a CPU needs to execute a stream of instructions.

Suppose you're reading a book, and you want to take a break right now, but you want to be able to come back and resume reading from the exact point where you stopped. One way to achieve that is by jotting down the page number, line number, and word number. So your execution context for reading a book is these 3 numbers.

If you have a roommate, and she's using the same technique, she can take the book while you're not using it, and resume reading from where she stopped. Then you can take it back, and resume it from where you were.

Threads work in the same way. A CPU is giving you the illusion that it's doing multiple computations at the same time. It does that by spending a bit of time on each computation. It can do that because it has an execution context for each computation. Just like you can share a book with your friend, many tasks can share a CPU.

On a more technical level, an execution context (therefore a thread) consists of the values of the CPU's registers.

Last: threads are different from processes. A thread is a context of execution, while a process is a bunch of resources associated with a computation. A process can have one or many threads.

Clarification: the resources associated with a process include memory pages (all the threads in a process have the same view of the memory), file descriptors (e.g., open sockets), and security credentials (e.g., the ID of the user who started the process).

### 进程与线程的区别？ ###

1.  Threads share the address space of the process that created it; processes have their own address space.
2.  Threads have direct access to the data segment of its process; processes have their own copy of the data segment of the parent process.
3.  Threads can directly communicate with other threads of its process; processes must use interprocess communication to communicate with sibling processes.
4.  New threads are easily created; new processes require duplication of the parent process.
5.  Threads can exercise considerable control over threads of the same process; processes can only exercise control over child processes.
6.  Changes to the main thread (cancellation, priority change, etc.) may affect the behavior of the other threads of the process; changes to the parent process does not affect child processes.

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_parse

线程共享创建它的进程的地址空间；进程有自己的地址空间。

线程可以直接访问其进程的数据段；进程具有父进程的数据段的自身副本。

线程可以直接与其进程的其他线程通信；进程必须使用进程间通信与兄弟进程通信。

新线程很容易创建；新进程需要父进程的重复。

线程可以对同一进程的线程进行相当大的控制；进程只能对子进程进行控制。

对主线程的更改（取消、优先级更改等）可能会影响进程的其他线程的行为；对父进程的更改不会影响子进程。

## Python GIL(Global Interpreter Lock)　　 ##

> In CPython, the global interpreter lock, or GIL, is a mutex that prevents multiple native threads from executing Python bytecodes at once. This lock is necessary mainly because CPython’s memory management is not thread-safe. (However, since the GIL exists, other features have grown to depend on the guarantees that it enforces.)
> 
> 上面的核心意思就是，**无论你启多少个线程，你有多少个cpu, Python在执行的时候会淡定的在同一时刻只允许一个线程运行**

首先需要明确的一点是`GIL`并不是Python的特性，它是在实现Python解析器(CPython)时所引入的一个概念。就好比C++是一套语言（语法）标准，但是可以用不同的编译器来编译成可执行代码。有名的编译器例如GCC，INTEL C++，Visual C++等。Python也一样，同样一段代码可以通过CPython，PyPy，Psyco等不同的Python执行环境来执行。像其中的JPython就没有GIL。然而因为CPython是大部分环境下默认的Python执行环境。所以在很多人的概念里CPython就是Python，也就想当然的把`GIL`归结为Python语言的缺陷。所以这里要先明确一点：GIL并不是Python的特性，Python完全可以不依赖于GIL

这篇文章透彻的剖析了GIL对python多线程的影响，强烈推荐看一下：[http://www.dabeaz.com/python/UnderstandingGIL.pdf][http_www.dabeaz.com_python_UnderstandingGIL.pdf]

--------------------

## Python threading模块 ##

**直接调用**

_data_ = '2019/5/17 9:46'
    _author = 'XiaoYang'
    
    import threading
    
    
    
    def run(name):
        print("six six,,,,%s" %name)
    
    
    t1  = threading.Thread(target=run,args=("lisi"))
    t1.start()

**继承式调用**

import threading
    
    
    class  ImgServer(threading.Thread):
        def __init__(self, num):
            threading.Thread.__init__(self)
            self.num = num
    
        def run(self):
            print("running on number:%s" % self.num)
      
    t1 = ImgServer(1)
    t1.start()

**Join & Daemon**

Some threads do background tasks, like sending keepalive packets, or performing periodic garbage collection, or whatever. These are only useful when the main program is running, and it's okay to kill them off once the other, non-daemon, threads have exited.

Without daemon threads, you'd have to keep track of them, and tell them to exit, before your program can completely quit. By setting them as daemon threads, you can let them run and forget about them, and when your program quits, any daemon threads are killed automatically.

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_parse

一些线程执行后台任务，比如发送保持连接的数据包，或者执行定期的垃圾收集，等等。这些只在主程序运行时有用，一旦其他非守护进程的线程退出，就可以将它们杀死。

如果没有守护进程线程，在程序完全退出之前，您必须跟踪它们，并告诉它们退出。通过将它们设置为守护进程线程，可以让它们运行并忽略它们，当程序退出时，任何守护进程线程都会自动终止。

import time
    import threading
    
    
    def run(n):
        print('[%s]------running----\n' % 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----")

console  print  result

[0]------running----
    
    starting thread Thread-2
    [1]------running----
    
    ---main thread done----

join所完成的工作就是线程同步，即主线程任务结束之后，进入阻塞状态，一直等待其他的子线程执行结束之后，主线程在终止，

**线程锁(互斥锁Mutex)**

一个进程下可以启动多个线程，多个线程共享父进程的内存空间，也就意味着每个线程可以访问同一份数据，此时，如果2个线程同时要修改同一份数据，会出现什么状况？

import time
    import threading
     
    def addNum():
        global num #在每个线程中都获取这个全局变量
        print('--get num:',num )
        time.sleep(1)
        num  -=1 #对此公共变量进行-1操作
     
    num = 100  #设定一个共享变量
    thread_list = []
    for i in range(100):
        t = threading.Thread(target=addNum)
        t.start()
        thread_list.append(t)
     
    for t in thread_list: #等待所有线程执行完毕
        t.join()
     
     
    print('final num:', num )

正常来讲，这个num结果应该是0， 但在python 2.7上多运行几次，会发现，最后打印出来的num结果不总是0，为什么每次运行的结果不一样呢？ 哈，很简单，假设你有A,B两个线程，此时都 要对num 进行减1操作， 由于2个线程是并发同时运行的，所以2个线程很有可能同时拿走了num=100这个初始变量交给cpu去运算，当A线程去处完的结果是99，但此时B线程运算完的结果也是99，两个线程同时CPU运算的结果再赋值给num变量后，结果就都是99。那怎么办呢？ 很简单，每个线程在要修改公共数据时，为了避免自己在还没改完的时候别人也来修改此数据，可以给这个数据加一把锁， 这样其它线程想修改此数据时就必须等待你修改完毕并把锁释放掉后才能再访问此数据。

\*注：不要在3.x上运行，不知为什么，3.x上的结果总是正确的，可能是自动加了锁

加锁版本

import time
    import threading
     
    def addNum():
        global num #在每个线程中都获取这个全局变量
        print('--get num:',num )
        time.sleep(1)
        lock.acquire() #修改数据前加锁
        num  -=1 #对此公共变量进行-1操作
        lock.release() #修改后释放
     
    num = 100  #设定一个共享变量
    thread_list = []
    lock = threading.Lock() #生成全局锁
    for i in range(100):
        t = threading.Thread(target=addNum)
        t.start()
        thread_list.append(t)
     
    for t in thread_list: #等待所有线程执行完毕
        t.join()
     
    print('final num:', num )

**RLock（递归锁）**

说白了就是在一个大锁中还要再包含子锁

import threading,time
     
    def run1():
        print("grab the first part data")
        lock.acquire()
        global num
        num +=1
        lock.release()
        return num
    def run2():
        print("grab the second part data")
        lock.acquire()
        global  num2
        num2+=1
        lock.release()
        return num2
    def run3():
        lock.acquire()
        res = run1()
        print('--------between run1 and run2-----')
        res2 = run2()
        lock.release()
        print(res,res2)
     
     
    if __name__ == '__main__':
     
        num,num2 = 0,0
        lock = threading.RLock()
        for i in range(10):
            t = threading.Thread(target=run3)
            t.start()
     
    while threading.active_count() != 1:
        print(threading.active_count())
    else:
        print('----all threads done---')
        print(num,num2)

信号量

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" %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()
     
    while threading.active_count() != 1:
        pass #print threading.active_count()
    else:
        print('----all threads done---')
        print(num)

**Events**

An event is a simple synchronization object;

the event represents an internal flag, and threads  
can wait for the flag to be set, or set or clear the flag themselves.

event = threading.Event()  
  
\# a client thread can wait for the flag to be set  
event.wait()  
  
\# a server thread can set or reset it  
event.set()  
event.clear()  
If the flag is set, the wait method doesn’t do anything.  
If the flag is cleared, wait will block until it becomes set again.  
Any number of threads may wait for the same event.

通过Event来实现两个或多个线程间的交互，下面是一个红绿灯的例子，即起动一个线程做交通指挥灯，生成几个线程做车辆，车辆行驶按红灯停，绿灯行的规则。

import threading,time
    import random
    def light():
        if not event.isSet():
            event.set() #wait就不阻塞 #绿灯状态
        count = 0
        while True:
            if count < 10:
                print('\033[42;1m--green light on---\033[0m')
            elif count <13:
                print('\033[43;1m--yellow light on---\033[0m')
            elif count <20:
                if event.isSet():
                    event.clear()
                print('\033[41;1m--red light on---\033[0m')
            else:
                count = 0
                event.set() #打开绿灯
            time.sleep(1)
            count +=1
    def car(n):
        while 1:
            time.sleep(random.randrange(10))
            if  event.isSet(): #绿灯
                print("car [%s] is running.." % n)
            else:
                print("car [%s] is waiting for the red light.." %n)
    if __name__ == '__main__':
        event = threading.Event()
        Light = threading.Thread(target=light)
        Light.start()
        for i in range(3):
            t = threading.Thread(target=car,args=(i,))
            t.start()

### queue队列  ###

queue is especially useful in threaded programming when information must be exchanged safely between multiple threads.

***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**

Constructor for a priority queue. *maxsize* is an integer that sets the upperbound limit on the number of items that can be placed in the queue. Insertion will block once this size has been reached, until queue items are consumed. If *maxsize* is less than or equal to zero, the queue size is infinite.

The lowest valued entries are retrieved first (the lowest valued entry is the one returned by `sorted(list(entries))[0]`). A typical pattern for entries is a tuple in the form: `(priority_number, data)`.

exception queue.Empty

Exception raised when non-blocking get() (or get\_nowait()) is called on a Queue object which is empty.

exception queue.Full

Exception raised when non-blocking put() (or put\_nowait()) is called on a Queue object which is full.

`Queue.``qsize`()

`Queue.``empty`() \#return True if empty

`Queue.``full`() \# return True if full

`Queue.``put`(*item*, *block=True*, *timeout=None*)

Put *item* into the queue. If optional args *block* is true and *timeout* is None (the default), block if necessary until a free slot is available. If *timeout* is a positive number, it blocks at most *timeout* seconds and raises the [`Full`][Full] exception if no free slot was available within that time. Otherwise (*block* is false), put an item on the queue if a free slot is immediately available, else raise the [`Full`][Full] exception (*timeout* is ignored in that case).

`Queue.``put_nowait`(*item*)

Equivalent to `put(item, False)`.

`Queue.``get`(*block=True*, *timeout=None*)

Remove and return an item from the queue. If optional args *block* is true and *timeout* is None (the default), block if necessary until an item is available. If *timeout* is a positive number, it blocks at most *timeout* seconds and raises the [`Empty`][Empty] exception if no item was available within that time. Otherwise (*block* is false), return an item if one is immediately available, else raise the [`Empty`][Empty] exception (*timeout* is ignored in that case).

`Queue.``get_nowait`()

Equivalent to `get(False)`.

Two methods are offered to support tracking whether enqueued tasks have been fully processed by daemon consumer threads.

`Queue.``task_done`()

Indicate that a formerly enqueued task is complete. Used by queue consumer threads. For each [`get()`][get] used to fetch a task, a subsequent call to [`task_done()`][task_done] tells the queue that the processing on the task is complete.

If a [`join()`][join] is currently blocking, it will resume when all items have been processed (meaning that a [`task_done()`][task_done] call was received for every item that had been [`put()`][put] into the queue).

Raises a [`ValueError`][ValueError] if called more times than there were items placed in the queue.

`Queue.``join`() block直到queue被消费完毕

### 生产者消费者模型 ###

在并发编程中使用生产者和消费者模式能够解决绝大多数并发问题。该模式通过平衡生产线程和消费线程的工作能力来提高程序的整体处理数据的速度。

**为什么要使用生产者和消费者模式**

在线程世界里，生产者就是生产数据的线程，消费者就是消费数据的线程。在多线程开发当中，如果生产者处理速度很快，而消费者处理速度很慢，那么生产者就必须等待消费者处理完，才能继续生产数据。同样的道理，如果消费者的处理能力大于生产者，那么消费者就必须等待生产者。为了解决这个问题于是引入了生产者和消费者模式。

**什么是生产者消费者模式**

生产者消费者模式是通过一个容器来解决生产者和消费者的强耦合问题。生产者和消费者彼此之间不直接通讯，而通过阻塞队列来进行通讯，所以生产者生产完数据之后不用等待消费者处理，直接扔给阻塞队列，消费者不找生产者要数据，而是直接从阻塞队列里取，阻塞队列就相当于一个缓冲区，平衡了生产者和消费者的处理能力。

_data_ = '2019/5/18 19:51'
    _author = 'XiaoYang'
    
    import queue,threading
    import  time
    
    def comsumer(n):
        # 存在库存,消费面包
        while q.qsize() > 0:
            # 吃掉了面包
            print("%s吃掉了" %n,q.get())
            q.task_done()  # 告知这个任务执行完了
    
    
    def producer():
        for i in range(10):
            q.put("第%s个面包" %i)
        print("开始等待面包被取走")
        q.join()
        print("面包已经没有库存")
    
    q = queue.Queue()
    
    # 制作面包
    p1 = threading.Thread(target=producer,)
    p2 = threading.Thread(target=producer,)
    p1.start()
    p2.start()
    
    # 消费面包
    c = comsumer("xy")

## 多进程multiprocessing ##

[`multiprocessing`][multiprocessing] is a package that supports spawning processes using an API similar to the [`threading`][threading] module. The [`multiprocessing`][multiprocessing] package offers both local and remote concurrency, effectively side-stepping the [Global Interpreter Lock][] by using subprocesses instead of threads. Due to this, the [`multiprocessing`][multiprocessing] module allows the programmer to fully leverage multiple processors on a given machine. It runs on both Unix and Windows.

from multiprocessing import Process
    import time
    def f(name):
        time.sleep(2)
        print('hello', name)
     
    if __name__ == '__main__':
        p = Process(target=f, args=('bob',))
        p.start()
        p.join()

To show the individual process IDs involved, here is an expanded example:

_data_ = '2019/5/18 20:25'
    _author = 'XiaoYang'
    
    from multiprocessing import Process
    import os
    
    
    def info(title):
        print(title)
        print('module name:', __name__)
        print('parent process:', os.getppid())
        print('process id:', os.getpid())
        print("\n\n")
    
    
    def f(name):
        info('\033[31;1mfunction f\033[0m')
        print('hello', name)
    
    
    if __name__ == '__main__':
        info('\033[32;1mmain process line\033[0m')
        p = Process(target=f, args=('bob',))
        p.start()
        p.join()

### 进程间通讯　　 ###

不同进程间内存是不共享的，要想实现两个进程间的数据交换，可以用以下方法：

**Queues**

使用方法跟threading里的queue差不多

from multiprocessing import Process, Queue
     
    def f(q):
        q.put([42, None, 'hello'])
     
    if __name__ == '__main__':
        q = Queue()
        p = Process(target=f, args=(q,))
        p.start()
        print(q.get())    # prints "[42, None, 'hello']"
        p.join()

**Pipes**

The [`Pipe()`][Pipe] function returns a pair of connection objects connected by a pipe which by default is duplex (two-way). For example:

from multiprocessing import Process, Pipe
     
    def f(conn):
        conn.send([42, None, 'hello'])
        conn.close()
     
    if __name__ == '__main__':
        parent_conn, child_conn = Pipe()
        p = Process(target=f, args=(child_conn,))
        p.start()
        print(parent_conn.recv())   # prints "[42, None, 'hello']"
        p.join()

> The two connection objects returned by [`Pipe()`][Pipe] represent the two ends of the pipe. Each connection object has [`send()`][send] and [`recv()`][recv] methods (among others). Note that data in a pipe may become corrupted if two processes (or threads) try to read from or write to the *same* end of the pipe at the same time. Of course there is no risk of corruption from processes using different ends of the pipe at the same time.
> 
>

**Managers**

A manager object returned by `Manager()` controls a server process which holds Python objects and allows other processes to manipulate them using proxies.

A manager returned by `Manager()` will support types [`list`][list], [`dict`][dict], [`Namespace`][Namespace], [`Lock`][Lock], [`RLock`][RLock], [`Semaphore`][Semaphore], [`BoundedSemaphore`][BoundedSemaphore], [`Condition`][Condition], [`Event`][Event], [`Barrier`][Barrier], [`Queue`][Queue], [`Value`][Value] and [`Array`][Array]. For example,

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)

### 进程池　 ###

进程池内部维护一个进程序列，当使用时，则去进程池中获取一个进程，如果进程池序列中没有可供使用的进进程，那么程序就会等待，直到进程池中有可用进程为止。

进程池中有两个方法：

*  apply
 *  apply\_async

from  multiprocessing import Process,Pool
    import time
     
    def Foo(i):
        time.sleep(2)
        return i+100
     
    def Bar(arg):
        print('-->exec done:',arg)
     
    pool = Pool(5)
     
    for i in range(10):
        pool.apply_async(func=Foo, args=(i,),callback=Bar)
        #pool.apply(func=Foo, args=(i,))
     
    print('end')
    pool.close()
    pool.join()#进程池中进程执行完毕后再关闭，如果注释，那么程序直接关闭。

[http_www.dabeaz.com_python_UnderstandingGIL.pdf]: http://www.dabeaz.com/python/UnderstandingGIL.pdf
[Full]: https://docs.python.org/3.5/library/queue.html#queue.Full
[Empty]: https://docs.python.org/3.5/library/queue.html#queue.Empty
[get]: https://docs.python.org/3.5/library/queue.html#queue.Queue.get
[task_done]: https://docs.python.org/3.5/library/queue.html#queue.Queue.task_done
[join]: https://docs.python.org/3.5/library/queue.html#queue.Queue.join
[put]: https://docs.python.org/3.5/library/queue.html#queue.Queue.put
[ValueError]: https://docs.python.org/3.5/library/exceptions.html#ValueError
[multiprocessing]: https://docs.python.org/3.5/library/multiprocessing.html#module-multiprocessing
[threading]: https://docs.python.org/3.5/library/threading.html#module-threading
[Global Interpreter Lock]: https://docs.python.org/3.5/glossary.html#term-global-interpreter-lock
[Pipe]: https://docs.python.org/3.5/library/multiprocessing.html#multiprocessing.Pipe
[send]: https://docs.python.org/3.5/library/multiprocessing.html#multiprocessing.Connection.send
[recv]: https://docs.python.org/3.5/library/multiprocessing.html#multiprocessing.Connection.recv
[list]: https://docs.python.org/3.5/library/stdtypes.html#list
[dict]: https://docs.python.org/3.5/library/stdtypes.html#dict
[Namespace]: https://docs.python.org/3.5/library/multiprocessing.html#multiprocessing.managers.Namespace
[Lock]: https://docs.python.org/3.5/library/multiprocessing.html#multiprocessing.Lock
[RLock]: https://docs.python.org/3.5/library/multiprocessing.html#multiprocessing.RLock
[Semaphore]: https://docs.python.org/3.5/library/multiprocessing.html#multiprocessing.Semaphore
[BoundedSemaphore]: https://docs.python.org/3.5/library/multiprocessing.html#multiprocessing.BoundedSemaphore
[Condition]: https://docs.python.org/3.5/library/multiprocessing.html#multiprocessing.Condition
[Event]: https://docs.python.org/3.5/library/multiprocessing.html#multiprocessing.Event
[Barrier]: https://docs.python.org/3.5/library/multiprocessing.html#multiprocessing.Barrier
[Queue]: https://docs.python.org/3.5/library/multiprocessing.html#multiprocessing.Queue
[Value]: https://docs.python.org/3.5/library/multiprocessing.html#multiprocessing.Value
[Array]: https://docs.python.org/3.5/library/multiprocessing.html#multiprocessing.Array