Python之路,进程、线程、协程篇

本节内容

进程、与线程区别

cpu运行原理

python GIL全局解释器锁

线程
语法

join

线程锁之Lock\Rlock\信号量

将线程变为守护进程

Event事件　

queue队列

生产者消费者模型

Queue队列

开发一个线程池

进程
语法

进程间通讯

进程池　　　　

进程与线程

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).

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.

进程与线程的区别？

Threads share the address space of the process that created it; processes have their own address space.

Threads have direct access to the data segment of its process; processes have their own copy of the data segment of the parent process.

Threads can directly communicate with other threads of its process; processes must use interprocess communication to communicate with sibling processes.

New threads are easily created; new processes require duplication of the parent process.

Threads can exercise considerable control over threads of the same process; processes can only exercise control over child processes.

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.

Python GIL(Global Interpreter Lock)　　

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

Python threading模块

线程有2种调用方式，如下：

直接调用

继承式调用

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.

Note：Daemon threads are abruptly stopped at shutdown. Their resources (such as open files, database transactions, etc.) may not be released properly. If you want your threads to stop gracefully, make them non-daemonic and use a suitable signalling mechanism such as an [code]Event

.[/code]
　　

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？

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

　　

RLock（递归锁）

说白了就是在一个大锁中还要再包含子锁

　　

Semaphore(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。

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。

　　

　　

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

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

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

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

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()

used to fetch a task, a subsequent call to

task_done()

tells the queue that the processing on the task is complete.

If a

join()

is currently blocking, it will resume when all items have been processed (meaning that a

task_done()

call was received for every item that had been

put()

into the queue).

Raises a

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

　　

多进程multiprocessing

multiprocessing

is a package that supports spawning processes using an API similar to the

threading

module. The

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

module allows the programmer to fully leverage multiple processors on a given machine. It runs on both Unix and Windows.

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

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

Pipes

The

Pipe()

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



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

,

dict

,

Namespace

,

Lock

,

RLock

,

Semaphore

,

BoundedSemaphore

,

Condition

,

Event

,

Barrier

,

Queue

,

Value

and

Array

. For example,

　　

进程同步

Without using the lock output from the different processes is liable to get all mixed up.

　　

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