9.paramiko_threading_queue_multiprocessing
一.paramiko模块使用
1.paramiko ssh客户端
import paramiko #创建ssh对象 ssh = paramiko.SSHClient() #允许连接不在know_hosts文件中的主机 ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy()) #连接服务器 ssh.connect(hostname='test.abc.com',port=22,username='zhangsan',password='*****') #执行命令 stdin,stdout,stderr = ssh.exec_command('df') #标准输入和错误输出只有一个有结果 result = stdout.read() #关闭连接 ssh.close()
2.sftp使用
import paramiko #定义stftp连接socket transport = paramiko.Transport(('hostname',22)) #创建连接 transport.connect(username='zhangsan',password='123') sftp = paramiko.SFTPClient.from_transport(transport) #将location.py上传至服务器/tmp/test.py sftp.put('/tmp/location.py','/tmp/test.py') sftp.get('remove_path','local_path') transport.close()
二.进程、线程
一） 什么是线程
线程概念：线程是操作系统能够进行运算的最小单位。他被包含在进程之中，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).
二）进程和线程的区别
- 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.
三) 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，擦。。。，那这还叫什么多线程呀？莫如此早的下结结论，听我现场讲。
四)python threading 调用
线程有两种调用方式，如下：
1.直接式调用
import threading import time def sayhi(num): #定义每个线程要运行的函数 print("running on number:%s" %num) time.sleep(3) if __name__ == '__main__': t1 = threading.Thread(target=sayhi,args=(1,)) #生成一个线程实例 t2 = threading.Thread(target=sayhi,args=(2,)) #生成另一个线程实例 t1.start() #启动线程 t2.start() #启动另一个线程 print(t1.getName()) #获取线程名 print(t2.getName())
2.继承式调用
import threading import time class MyThread(threading.Thread): def __init__(self,num): super(MyThread,self).__init__(self) self.num = num def run(self): print ("running on number:%s" % self.num) time.sleep(3) if __name__ == '__main__': t1 = MyThread(1) t2 = MyThread(2) t1.start() t2.start()
3.多线程并行执行
import time import threading def run(n): print ('[%s]------running-----',n) time.sleep(2) print ("----done----") t_list=[] for i in range(50): t1 = threading.Thread(target=run,args=(i,)) t1.start() t_list.append(t1) if __name__ == '__main__': start_time = time.time() for i in range(50): t_list[i].join() print ('main over....time:%n',(time.time()-start_time))
五）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.
1.join用法，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A会在调用B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，如下：
a.不加join的情况,t start后，主线程并没有等线程t1运行结束再执行，而是先把5次循环打印执行完毕，然后sleep(10)后，线程t把传入的999打印出来。
import threading import time class MyThread(threading.Thread): def __init__(self,id): super(MyThread,self).__init__() self.id = id def run(self): x = 0 time.sleep(10) print (self.id) if __name__ == "__main__": t = MyThread(999) t.start() for i in range(5): print (i)
输出结果如下：
0 1 2 3 4 999 Process finished with exit code 0
b.加入join的情况，运行后，999之前有明显的停顿，线程t1开始后，主线程停在了join()方法处，等sleep(10)后，线程t1操作结束，被join接着主线程循环打印
import threading import time class MyThread(threading.Thread): def __init__(self, id): super(MyThread,self).__init__() self.id = id def run(self): x = 0 time.sleep(10) print (self.id) if __name__ == "__main__": t1 = MyThread(999) t1.start() t1.join() for i in range(5): print(i)
输出结果:
999
0
1
2
3
4
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，钥匙主线程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，还有个特别注意的:必须在start()方法调用之前设置，如果不设置为守护线程，程序会被无限挂起。
a.把主程序设置为守护进程，当主程序执行完后，不在执行子线程
import threading import time class MyThread(threading.Thread): def __init__(self,id): super(MyThread,self).__init__() def run(self): time.sleep(5) print ("This is",self.getName() ) if __name__ == '__main__': t1=MyThread(999) t1.setDaemon(True) t1.start() print ('I am the father thread.')
输出结果：
I am the father thread.
b.注释掉setDaemon()
import threading import time class MyThread(threading.Thread): def __init__(self,id): super(MyThread,self).__init__() def run(self): time.sleep(5) print ("This is",self.getName() ) if __name__ == '__main__': t1=MyThread(999) #t1.setDaemon(True) t1.start() print ('I am the father thread.')
输出结果：
I am the father thread.
This is Thread-1
六）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 )
七）GIL VS Lock
机智的同学可能会问到这个问题，就是既然你之前说过了，Python已经有一个GIL来保证同一时间只能有一个线程来执行了，为什么这里还需要lock? 注意啦，这里的lock是用户级的lock,跟那个GIL没关系 ，具体我们通过下图来看一下+配合我现场讲给大家，就明白了。
那你又问了， 既然用户程序已经自己有锁了，那为什么C python还需要GIL呢？加入GIL主要的原因是为了降低程序的开发的复杂度，比如现在的你写python不需要关心内存回收的问题，因为Python解释器帮你自动定期进行内存回收，你可以理解为python解释器里有一个独立的线程，每过一段时间它起wake up做一次全局轮询看看哪些内存数据是可以被清空的，此时你自己的程序 里的线程和 py解释器自己的线程是并发运行的，假设你的线程删除了一个变量，py解释器的垃圾回收线程在清空这个变量的过程中的clearing时刻，可能一个其它线程正好又重新给这个还没来及得清空的内存空间赋值了，结果就有可能新赋值的数据被删除了，为了解决类似的问题，python解释器简单粗暴的加了锁，即当一个线程运行时，其它人都不能动，这样就解决了上述的问题， 这可以说是Python早期版本的遗留问题。
八)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)
九）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。
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)
十)Timer
This class represents an action that should be run only after a certain amount of time has passed
Timers are started, as with threads, by calling their start() method. The timer can be stopped (before its action has begun) by calling thecancel() method. The interval the timer will wait before executing its action may not be exactly the same as the interval specified by the user.
def hello(): print("hello, world") t = Timer(30.0, hello) t.start() # after 30 seconds, "hello, world" will be printed
十一)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()
这里还有一个event使用的例子，员工进公司门要刷卡， 我们这里设置一个线程是“门”， 再设置几个线程为“员工”，员工看到门没打开，就刷卡，刷完卡，门开了，员工就可以通过。
#_*_coding:utf-8_*_ __author__ = 'Alex Li' import threading import time import random def door(): door_open_time_counter = 0 while True: if door_swiping_event.is_set(): print("\033[32;1mdoor opening....\033[0m") door_open_time_counter +=1 else: print("\033[31;1mdoor closed...., swipe to open.\033[0m") door_open_time_counter = 0 #清空计时器 door_swiping_event.wait() if door_open_time_counter > 3:#门开了已经3s了,该关了 door_swiping_event.clear() time.sleep(0.5) def staff(n): print("staff [%s] is comming..." % n ) while True: if door_swiping_event.is_set(): print("\033[34;1mdoor is opened, passing.....\033[0m") break else: print("staff [%s] sees door got closed, swipping the card....." % n) print(door_swiping_event.set()) door_swiping_event.set() print("after set ",door_swiping_event.set()) time.sleep(0.5) door_swiping_event = threading.Event() #设置事件 door_thread = threading.Thread(target=door) door_thread.start() for i in range(5): p = threading.Thread(target=staff,args=(i,)) time.sleep(random.randrange(3)) p.start()
十二)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()(orget_nowait()) is called on aQueueobject which is empty.
- exception
queue.Full -
Exception raised when non-blocking
put()(orput_nowait()) is called on aQueueobject 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
Fullexception 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 theFullexception (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
Emptyexception if no item was available within that time. Otherwise (block is false), return an item if one is immediately available, else raise theEmptyexception (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 totask_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 atask_done()call was received for every item that had beenput()into the queue).Raises a
ValueErrorif called more times than there were items placed in the queue.
Queue.join() block直到queue被消费完毕
十三)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。
下面来学习一个最基本的生产者消费者模型的例子
import threading import queue def producer(): for i in range(10): q.put("骨头 %s" % i ) print("开始等待所有的骨头被取走...") q.join() print("所有的骨头被取完了...") def consumer(n): while q.qsize() >0: print("%s 取到" %n , q.get()) q.task_done() #告知这个任务执行完了 q = queue.Queue() p = threading.Thread(target=producer,) p.start() c1 = consumer("李闯")
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('\033[32;1mConsumer %s has eat %s baozi...\033[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()
十四）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.
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:
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()
十五)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() 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() represent the two ends of the pipe. Each connection object has send() and 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, dict, Namespace, Lock, RLock, Semaphore, BoundedSemaphore, Condition, Event, Barrier, Queue, Value and 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)
进程同步
Without using the lock output from the different processes is liable to get all mixed up.
from multiprocessing import Process, Lock def f(l, i): l.acquire() try: print('hello world', i) finally: l.release() if __name__ == '__main__': lock = Lock() for num in range(10): Process(target=f, args=(lock, num)).start()
十六）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：
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- apply
- apply_async
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View Codefrom 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。
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