This article mainly introduces the implementation of python thread pool threadpool in detail. It has certain reference value. Interested friends can refer to it.
This article shares with you all the threadpool thread pool. The operation is for your reference. The specific content is as follows
First introduce the nouns you use:
Worker thread (worker): When creating a thread pool, create a job according to the specified number of threads. Threads, waiting to get tasks from the task queue;
Tasks (requests): That is, tasks processed by worker threads. There may be thousands of tasks, but there are only a few worker threads. Tasks create
task queue (request_queue) through makeRequests: a queue that stores tasks, implemented using queue. The working thread gets the task from the task queue for processing;
Task processing function (callable): After the working thread gets the task, it specifically processes the task by calling the task processing function of the task (request.callable_), and Return the processing result;
Task result queue (result_queue): After the task processing is completed, the returned processing result will be put into the task result queue (including exceptions);
Task exception handling function Or callback (exc_callback): get the result from the task result queue. If an exception is set, you need to call the exception callback to handle the exception;
Task result callback (callback): get the result from the task result queue, for result For further processing;
The previous section introduced the installation and use of thread pool threadpool. This section will mainly introduce the main process of thread pool work:
(1) Thread pool Creation
(2) Starting of the worker thread
(3) Creation of the task
(4) Pushing the task to the thread pool
(5) Thread processing task
(6) Task end processing
(7) Exit of worker thread
The following is the definition of threadpool:
class ThreadPool:
"""A thread pool, distributing work requests and collecting results.
See the module docstring for more information.
"""
def __init__(self, num_workers, q_size=0, resq_size=0, poll_timeout=5):
pass
def createWorkers(self, num_workers, poll_timeout=5):
pass
def dismissWorkers(self, num_workers, do_join=False):
pass
def joinAllDismissedWorkers(self):
pass
def putRequest(self, request, block=True, timeout=None):
pass
def poll(self, block=False):
pass
def wait(self):
pass1. Creation of thread pool (ThreadPool(args))
task_pool=threadpool.ThreadPool(num_works)
task_pool=threadpool.ThreadPool(num_works)
def __init__(self, num_workers, q_size=0, resq_size=0, poll_timeout=5):
"""Set up the thread pool and start num_workers worker threads.
``num_workers`` is the number of worker threads to start initially.
If ``q_size > 0`` the size of the work *request queue* is limited and
the thread pool blocks when the queue is full and it tries to put
more work requests in it (see ``putRequest`` method), unless you also
use a positive ``timeout`` value for ``putRequest``.
If ``resq_size > 0`` the size of the *results queue* is limited and the
worker threads will block when the queue is full and they try to put
new results in it.
.. warning:
If you set both ``q_size`` and ``resq_size`` to ``!= 0`` there is
the possibilty of a deadlock, when the results queue is not pulled
regularly and too many jobs are put in the work requests queue.
To prevent this, always set ``timeout > 0`` when calling
``ThreadPool.putRequest()`` and catch ``Queue.Full`` exceptions.
"""
self._requests_queue = Queue.Queue(q_size)#任务队列，通过threadpool.makeReuests(args)创建的任务都会放到此队列中
self._results_queue = Queue.Queue(resq_size)#字典，任务对应的任务执行结果</span>
self.workers = []#工作线程list，通过self.createWorkers()函数内创建的工作线程会放到此工作线程list中
self.dismissedWorkers = []#被设置线程事件并且没有被join的工作线程
self.workRequests = {}#字典，记录任务被分配到哪个工作线程中</span>
self.createWorkers(num_workers, poll_timeout)Among them, the initialization parameters are:
num_works: The number of threads in the thread pool
q_size: The length limit of the task queue. If the length of the queue is limited, then when putRequest() is called to add a task, the After limiting the length, putRequest will continue to try to add tasks unless a timeout or blocking is set in putRequest();
esq_size: The length of the task result queue;
pool_timeout: If the worker thread reads from the request queue, If the request is not received, pool_timeout will be blocked. If there is still no request, it will return directly;
Among them, member variables:
self._requests_queue: Task queue, created through threadpool.makeReuests(args) Tasks will be placed in this queue;
self._results_queue: Dictionary, task execution corresponding to the task
self.workers: Worker thread list, worker threads created through the self.createWorkers() function will be placed in this work In the thread list;
self.dismisssedWorkers: The thread event is set and the worker thread has not been joined
self.workRequests: Dictionary, recording the tasks pushed to the thread pool, the structure is requestID:request. The requestID is the unique identifier of the task, which will be introduced later.
2. Startup of worker thread (self.createWorks(args))
Function definition:
def createWorkers(self, num_workers, poll_timeout=5):
"""Add num_workers worker threads to the pool.
``poll_timout`` sets the interval in seconds (int or float) for how
ofte threads should check whether they are dismissed, while waiting for
requests.
"""
for i in range(num_workers):
self.workers.append(WorkerThread(self._requests_queue,
self._results_queue, poll_timeout=poll_timeout))WorkerThread() inherits from thread, which is Python's built-in thread class, and puts the created WorkerThread object into the self.workers queue. Let’s take a look at the definition of the WorkerThread class:
It can be seen from self.__init__(args):
class WorkerThread(threading.Thread):
"""Background thread connected to the requests/results queues.
A worker thread sits in the background and picks up work requests from
one queue and puts the results in another until it is dismissed.
"""
def __init__(self, requests_queue, results_queue, poll_timeout=5, **kwds):
"""Set up thread in daemonic mode and start it immediatedly.
``requests_queue`` and ``results_queue`` are instances of
``Queue.Queue`` passed by the ``ThreadPool`` class when it creates a
new worker thread.
"""
threading.Thread.__init__(self, **kwds)
self.setDaemon(1)#
self._requests_queue = requests_queue#任务队列
self._results_queue = results_queue#任务结果队列
self._poll_timeout = poll_timeout#run函数中从任务队列中get任务时的超时时间，如果超时则继续while(true)；
self._dismissed = threading.Event()#线程事件，如果set线程事件则run会执行break,直接退出工作线程；
self.start()
def run(self):
"""Repeatedly process the job queue until told to exit."""
while True:
if self._dismissed.isSet():#如果设置了self._dismissed则退出工作线程
# we are dismissed, break out of loop
break
# get next work request. If we don't get a new request from the
# queue after self._poll_timout seconds, we jump to the start of
# the while loop again, to give the thread a chance to exit.
try:
request = self._requests_queue.get(True, self._poll_timeout)
except Queue.Empty:#尝从任务 队列self._requests_queue 中get任务，如果队列为空，则continue
continue
else:
if self._dismissed.isSet():#检测此工作线程事件是否被set,如果被设置，意味着要结束此工作线程，那么就需要将取到的任务返回到任务队列中，并且退出线程
# we are dismissed, put back request in queue and exit loop
self._requests_queue.put(request)
break
try:<span style="color:#如果线程事件没有被设置，那么执行任务处理函数request.callable，并将返回的result，压入到任务结果队列中
result = request.callable(*request.args, **request.kwds)
self._results_queue.put((request, result))
except:
request.exception = True
self._results_queue.put((request, sys.exc_info()))#如果任务处理函数出现异常，则将异常压入到队列中
def dismiss(self):</span>
"""Sets a flag to tell the thread to exit when done with current job.
"""
self._dismissed.set()Initializing variables:
self._request_queue: task queue;
self._resutls_queuqe,: task result queue;
self._pool_timeout: the timeout when getting the task from the task queue in the run function, if it times out, Continue while(true);
self._dismissed: Thread event, if set thread event, run will execute break and exit the working thread directly;
Finally call self.start() to start the thread, run function definition See above:
The execution steps of the run function while from above are as follows:
(1) If self._dismissed is set, exit the working thread, otherwise execute step 2
(2) Try Get the task from the task queue self._requests_queue. If the queue is empty, continue to execute the next while loop, otherwise execute step 3
(3) Check whether this worker thread event is set. If it is set, it means to To end this worker thread, you need to return the fetched task to the task queue and exit the thread. If the thread event is not set, the task processing function request.callable is executed and the returned result is pushed into the task result queue. If an exception occurs in the task processing function, the exception is pushed into the queue. Finally jump to step 4
(4) to continue looping and return to 1
到此工作线程创建完毕，根据设置的线程池线程数量，创建工作线程，工作线程从任务队列中get任务，进行任务处理，并将任务处理结果压入到任务结果队列中。
3、任务的创建（makeRequests）
任务的创建函数为threadpool.makeRequests(callable_,args_list,callback=None):
# utility functions
def makeRequests(callable_, args_list, callback=None,
exc_callback=_handle_thread_exception):
"""Create several work requests for same callable with different arguments.
Convenience function for creating several work requests for the same
callable where each invocation of the callable receives different values
for its arguments.
``args_list`` contains the parameters for each invocation of callable.
Each item in ``args_list`` should be either a 2-item tuple of the list of
positional arguments and a dictionary of keyword arguments or a single,
non-tuple argument.
See docstring for ``WorkRequest`` for info on ``callback`` and
``exc_callback``.
"""
requests = []
for item in args_list:
if isinstance(item, tuple):
requests.append(
WorkRequest(callable_, item[0], item[1], callback=callback,
exc_callback=exc_callback)
)
else:
requests.append(
WorkRequest(callable_, [item], None, callback=callback,
exc_callback=exc_callback)
)
return requests其中创建任务的函数参数具体意义为下：
callable_：注册的任务处理函数，当任务被放到任务队列后，工作线程中获取到该任务的线程，会执行此 callable_
args_list：首先args_list是列表，列表元素类型为元组，元组中有两个元素item[0]，item[1]，item[0]为位置参 数，item[1]为字典类型关键字参数。列表中元组的个数，代表启动的任务个数，在使用的时候一般都为单个元组，即一个makerequest()创建一个任务。
callback：回调函数，在poll函数中调用（后面讲解此函数），callable_调用结束后，会就任务结果放入到任务结果队列中（self._resutls_queue），在poll函数中，当从self._resutls_queue队列中get某个结果后，会执行此callback(request,result),其中result是request任务返回的结果。
exc_callback：异常回调函数，在poll函数中，如果某个request对应有执行异常，那么会调用此异常回调。
创建完成任务后，返回创建的任务。
外层记录此任务，放入到任务列表中。
上面是创建任务的函数，下面讲解任务对象的结构：
class WorkRequest:
"""A request to execute a callable for putting in the request queue later.
See the module function ``makeRequests`` for the common case
where you want to build several ``WorkRequest`` objects for the same
callable but with different arguments for each call.
"""
def __init__(self, callable_, args=None, kwds=None, requestID=None,
callback=None, exc_callback=_handle_thread_exception):
"""Create a work request for a callable and attach callbacks.
A work request consists of the a callable to be executed by a
worker thread, a list of positional arguments, a dictionary
of keyword arguments.
A ``callback`` function can be specified, that is called when the
results of the request are picked up from the result queue. It must
accept two anonymous arguments, the ``WorkRequest`` object and the
results of the callable, in that order. If you want to pass additional
information to the callback, just stick it on the request object.
You can also give custom callback for when an exception occurs with
the ``exc_callback`` keyword parameter. It should also accept two
anonymous arguments, the ``WorkRequest`` and a tuple with the exception
details as returned by ``sys.exc_info()``. The default implementation
of this callback just prints the exception info via
``traceback.print_exception``. If you want no exception handler
callback, just pass in ``None``.
``requestID``, if given, must be hashable since it is used by
``ThreadPool`` object to store the results of that work request in a
dictionary. It defaults to the return value of ``id(self)``.
"""
if requestID is None:
self.requestID = id(self)
else:
try:
self.requestID = hash(requestID)
except TypeError:
raise TypeError("requestID must be hashable.")
self.exception = False
self.callback = callback
self.exc_callback = exc_callback
self.callable = callable_
self.args = args or []
self.kwds = kwds or {}
def __str__(self):
return "<WorkRequest id=%s args=%r kwargs=%r exception=%s>" % \
(self.requestID, self.args, self.kwds, self.exception)上面self.callback 以及self.exc_callback，和self.callable_ ,args,dwds都已经讲解，就不在啰嗦了。
其中有一个任务的全局唯一标识，即self.requestID,通过获取自身内存首地址作为自己的唯一标识id(self)
self.exception 初始化为False,如果执行self.callable()过程中出现异常，那么此变量会标设置为True。
至此，任务创建完毕，调用makeRequests（）的上层记录有任务列表request_list.
4、任务的推送到线程池（putRequest）
上面小节中介绍了任务的创建，任务的个数可以成千上百，但是处理任务的线程数量只有我们在创建线程池的时候制定的线程数量来处理，指定的线程数量往往比任务的数量小得多，因此，每个线程必须处理多个任务。
本节介绍如何将创建的任务推送的线程池中，以让线程池由阻塞状态，获取任务，然后去处理任务。
任务的推送使用ThreadPool线程池类中的putRequest（self,request,block,timeout）来创建：
def putRequest(self, request, block=True, timeout=None): """Put work request into work queue and save its id for later.""" assert isinstance(request, WorkRequest) # don't reuse old work requests assert not getattr(request, 'exception', None) self._requests_queue.put(request, block, timeout) self.workRequests[request.requestID] = request
函数的主要作用就是将request任务，也就是上一小节中创建的任务，put到线程池的任务队列中（self._request_queue）。然后记录已经推送到线程池的任务，通过线程池的self.workReuests 字典来存储，结构为request.requestID:request。
至此，任务创建完成，并且已经将任务推送到线程池中。
5、线程处理任务
通过上一小节，任务已经推送到了线程中。在任务没有被推送到线程池中时，线程池中的线程都处在处在阻塞状态中，即在线程的self.run（）函数中，一直处于一下状态：
try: request = self._requests_queue.get(True, self._poll_timeout) except Queue.Empty:#尝从任务 队列self._requests_queue 中get任务，如果队列为空，则continue continue
现在任务已经推送到线程池中，那么get任务将会正常返回，会执行下面的步骤：
def run(self):
"""Repeatedly process the job queue until told to exit."""
while True:
if self._dismissed.isSet():#如果设置了self._dismissed则退出工作线程
# we are dismissed, break out of loop
break
# get next work request. If we don't get a new request from the
# queue after self._poll_timout seconds, we jump to the start of
# the while loop again, to give the thread a chance to exit.
try:
request = self._requests_queue.get(True, self._poll_timeout)
except Queue.Empty:#尝从任务 队列self._requests_queue 中get任务，如果队列为空，则continue
continue
else:
if self._dismissed.isSet():#检测此工作线程事件是否被set,如果被设置，意味着要结束此工作线程，那么就需要将取到的任务返回到任务队列中，并且退出线程
# we are dismissed, put back request in queue and exit loop
self._requests_queue.put(request)
break
try:#如果线程事件没有被设置，那么执行任务处理函数request.callable，并将返回的result，压入到任务结果队列中
result = request.callable(*request.args, **request.kwds)
self._results_queue.put((request, result))
except:
request.exception = True
self._results_queue.put((request, sys.exc_info()))#如果任务处理函数出现异常，则将异常压入到队列中获取任务--->调用任务的处理函数callable()处理任务--->将任务request以及任务返回的结果压入到self.results_queue队列中---->如果任务处理函数异常，那么将任务异常标识设置为True,并将任务request以及任务异常压入到self.results_queue队列中---->再次返回获取任务
如果，在while循环过程中，外部设置了线程事件，即self._dismissed.isSet为True,那么意味着此线程将会结束处理任务，那么会将get到的任务返回的任务队列中，并且退出线程。
6、任务结束处理
上面小节中，介绍了线程池不断的get任务，并且不断的处理任务。那么每个任务结束之后我们该怎么处理呢，线程池提供了wait（）以及poll（）函数。
当我们把任务提交个线程池之后，我们会调用wait（）来等待任务处理结束，结束后wait()将会返回，返回后我们可以进行下一步操作，例如重新创建任务，将任务继续推送到线程池中，或者结束线程池。结束线程池会在下一小节介绍，这一小节主要介绍wait()和poll（）操作。
先来看看wait()操作：
def wait(self):
"""Wait for results, blocking until all have arrived."""
while 1:
try:
self.poll(True)
except NoResultsPending:
break等待任务处理结束，在所有任务处理完成之前一直处于block阶段，如果self.poll()返回异常NoResultsPending异常，然后wait返回，任务处理结束。
下面看看poll函数：
def poll(self, block=False):
"""Process any new results in the queue."""
while True:
# still results pending?
if not self.workRequests:
raise NoResultsPending
# are there still workers to process remaining requests?
elif block and not self.workers:
raise NoWorkersAvailable
try:
# get back next results
request, result = self._results_queue.get(block=block)
# has an exception occured?
if request.exception and request.exc_callback:
request.exc_callback(request, result)
# hand results to callback, if any
if request.callback and not \
(request.exception and request.exc_callback):
request.callback(request, result)
del self.workRequests[request.requestID]
except Queue.Empty:
break（1）首先，检测任务字典（{request.requestID:request}）是否为空，如果为空则抛出异常NoResultPending结束，否则到第2步；
（2）检测工作线程是否为空（如果某个线程的线程事件被设置，那么工作线程退出，并从self.workers中pop出），如果为空则抛出NoWorkerAvailable异常结束，否则进入第3步；
（3）从任务结果队列中get任务结果，如果抛出队列为空，那么break,返回，否则进入第4步；
（4）如果任务处理过程中出现异常，即设置了request.exception，并且设置了异常处理回调即request.exc_callback则执行异常回调，再回调中处理异常，返回后将任务从任务列表self.workRequests中移除，继续get任务，返回第1步。否则进入第5步；
（5）如果设置了任务结果回调即request.callback不为空，则执行任务结果回调即request.callbacl(request,result),并
将任务从任务列表self.workRequests中移除，继续get任务，返回第1步。
（6）重复进行上面的步骤直到抛出异常，或者任务队列为空，则poll返会；
至此抛出NoResultPending wait操作接受此异常后，至此wait()返回。
7、工作线程的退出
threadpool提供的工作线程退出的的操作有dismissWorkers()和joinAllDismissedWorker()操作：
def dismissWorkers(self, num_workers, do_join=False):
"""Tell num_workers worker threads to quit after their current task."""
dismiss_list = []
for i in range(min(num_workers, len(self.workers))):
worker = self.workers.pop()
worker.dismiss()
dismiss_list.append(worker)
if do_join:
for worker in dismiss_list:
worker.join()
else:
self.dismissedWorkers.extend(dismiss_list)
def joinAllDismissedWorkers(self):
"""Perform Thread.join() on all worker threads that have been dismissed.
"""
for worker in self.dismissedWorkers:
worker.join()
self.dismissedWorkers = []从dismissWorkers可看出，主要工作是从self.workers 工作线程中pop出指定的线程数量，并且设置此线程的线程事件，设置线程事件后，此线程self.run()函数，则会检测到此设置，并结束线程。
如果设置了在do_join，即设置了在此函数中join退出的线程，那么对退出的线程执行join操作。否则将pop出的线程放入到self.dismissedWorkers中，以等待joinAllDismissedWorkers操作去处理join线程。
8、总结
到此为止，threadpool线程池中所有的操作介绍完毕，其实现也做了具体的介绍。从上面可看出，线程池并没有那么复杂，只有几个简单的操作，主要是了解整个处理流程即可。
希望大家多多提出建议和意见。
相关推荐：
The above is the detailed content of Implementation of python thread pool threadpool. For more information, please follow other related articles on the PHP Chinese website!
![[Web front-end] Node.js quick start](https://img.php.cn/upload/course/000/000/067/662b5d34ba7c0227.png)
