Rework agent to better handle multithreaded checks
The custom thread_pool could not restart in certain cases due to limitations with python threads. This moves to using multiprocessing.dummy for the thread pool and gevent to allow thread timeouts. Also cleans up some of the asynchronous logic. Pools have constant numbers of threads, so thread count is checked at pool creation No longer tracks job start time, as this is handled by timeouts. Move resultsq instantiation so pool restart does not drop data. Mark checks as done at end of run rather than when results are processed. Closes-Bug: 1446757 Change-Id: I28bdd8d8404087097bff9f93ff60f5cc86a72fbb
This commit is contained in:
Ryan Bak
parent
076cca54ba
commit
a0259e6b14
@ -1,824 +0,0 @@
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# {{{ http://code.activestate.com/recipes/576519/ (r9)
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# Author: David Decotigny, Oct 1 2008
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# (C) Copyright 2015 Hewlett Packard Enterprise Development Company LP
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# @brief Pool of threads similar to multiprocessing.Pool
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# See http://docs.python.org/dev/library/multiprocessing.html
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# Differences: added imap_async and imap_unordered_async, and terminate()
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# has to be called explicitly (it's not registered by atexit).
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#
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# The general idea is that we submit works to a workqueue, either as
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# single Jobs (one function to call), or JobSequences (batch of
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# Jobs). Each Job is associated with an ApplyResult object which has 2
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# states: waiting for the Job to complete, or Ready. Instead of
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# waiting for the jobs to finish, we wait for their ApplyResult object
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# to become ready: an event mechanism is used for that.
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# When we apply a function to several arguments in "parallel", we need
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# a way to wait for all/part of the Jobs to be processed: that's what
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# "collectors" are for; they group and wait for a set of ApplyResult
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# objects. Once a collector is ready to be used, we can use a
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# CollectorIterator to iterate over the result values it's collecting.
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#
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# The methods of a Pool object use all these concepts and expose
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# them to their caller in a very simple way.
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import Queue
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import sys
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import threading
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import traceback
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# Item pushed on the work queue to tell the worker threads to terminate
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SENTINEL = "QUIT"
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def is_sentinel(obj):
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"""Predicate to determine whether an item from the queue is the
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signal to stop
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"""
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return isinstance(obj, str) and obj == SENTINEL
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class TimeoutError(Exception):
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"""Raised when a result is not available within the given timeout.
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"""
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pass
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class PoolWorker(threading.Thread):
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"""Thread that consumes WorkUnits from a queue to process them.
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"""
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def __init__(self, workq, *args, **kwds):
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"""\param workq: Queue object to consume the work units from.
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"""
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threading.Thread.__init__(self, *args, **kwds)
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self._workq = workq
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self.running = False
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def run(self):
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"""Process the work unit, or wait for sentinel to exit.
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"""
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while True:
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self.running = True
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workunit = self._workq.get()
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if is_sentinel(workunit):
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# Got sentinel
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break
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# Run the job / sequence
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workunit.process()
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self.running = False
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class Pool(object):
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"""The Pool class represents a pool of worker threads.
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It has methods which allows tasks to be offloaded to the
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worker processes in a few different ways.
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"""
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def __init__(self, nworkers, name="Pool"):
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"""\param nworkers (integer) number of worker threads to start
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\param name (string) prefix for the worker threads' name
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"""
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self._workq = Queue.Queue()
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self._closed = False
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self._workers = []
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for idx in xrange(nworkers):
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thr = PoolWorker(self._workq, name="Worker-%s-%d" % (name, idx))
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try:
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thr.start()
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except Exception:
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# If one thread has a problem, undo everything
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self.terminate()
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raise
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else:
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self._workers.append(thr)
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def get_nworkers(self):
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return len([w for w in self._workers if w.running])
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def apply(self, func, args=(), kwds=None):
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"""Equivalent of the apply() builtin function.
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It blocks till the result is ready.
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"""
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if not kwds:
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kwds = dict()
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return self.apply_async(func, args, kwds).get()
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def map(self, func, iterable, chunksize=None):
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"""A parallel equivalent of the map() builtin function.
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It blocks till the result is ready.
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This method chops the iterable into a number of chunks which
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it submits to the process pool as separate tasks. The
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(approximate) size of these chunks can be specified by setting
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chunksize to a positive integer.
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"""
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return self.map_async(func, iterable, chunksize).get()
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def imap(self, func, iterable, chunksize=1):
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"""An equivalent of itertools.imap().
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The chunksize argument is the same as the one used by the
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map() method. For very long iterables using a large value for
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chunksize can make make the job complete much faster than
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using the default value of 1.
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Also if chunksize is 1 then the next() method of the iterator
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returned by the imap() method has an optional timeout
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parameter: next(timeout) will raise processing.TimeoutError if
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the result cannot be returned within timeout seconds.
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"""
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collector = OrderedResultCollector(as_iterator=True)
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self._create_sequences(func, iterable, chunksize, collector)
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return iter(collector)
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def imap_unordered(self, func, iterable, chunksize=1):
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"""The same as imap() except that the ordering of the results
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from the returned iterator should be considered arbitrary.
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(Only when there is only one worker process is the order
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guaranteed to be "correct".)
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"""
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collector = UnorderedResultCollector()
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self._create_sequences(func, iterable, chunksize, collector)
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return iter(collector)
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def apply_async(self, func, args=(), kwds=None, callback=None):
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"""A variant of the apply() method which returns an ApplyResult object.
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If callback is specified then it should be a callable which
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accepts a single argument. When the result becomes ready,
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callback is applied to it (unless the call failed). callback
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should complete immediately since otherwise the thread which
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handles the results will get blocked.
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"""
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if not kwds:
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kwds = dict()
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assert not self._closed # No lock here. We assume it's atomic...
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apply_result = ApplyResult(callback=callback)
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job = Job(func, args, kwds, apply_result)
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self._workq.put(job)
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return apply_result
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def map_async(self, func, iterable, chunksize=None, callback=None):
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"""A variant of the map() method which returns a ApplyResult object.
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If callback is specified then it should be a callable which
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accepts a single argument. When the result becomes ready
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callback is applied to it (unless the call failed). callback
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should complete immediately since otherwise the thread which
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handles the results will get blocked.
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"""
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apply_result = ApplyResult(callback=callback)
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collector = OrderedResultCollector(apply_result, as_iterator=False)
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self._create_sequences(func, iterable, chunksize, collector)
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return apply_result
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def imap_async(self, func, iterable, chunksize=None, callback=None):
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"""A variant of the imap() method which returns an ApplyResult
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object that provides an iterator (next method(timeout)
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available).
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If callback is specified then it should be a callable which
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accepts a single argument. When the resulting iterator becomes
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ready, callback is applied to it (unless the call
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failed). callback should complete immediately since otherwise
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the thread which handles the results will get blocked.
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"""
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apply_result = ApplyResult(callback=callback)
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collector = OrderedResultCollector(apply_result, as_iterator=True)
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self._create_sequences(func, iterable, chunksize, collector)
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return apply_result
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def imap_unordered_async(self, func, iterable, chunksize=None,
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callback=None):
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"""A variant of the imap_unordered() method which returns an
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ApplyResult object that provides an iterator (next method(timeout)
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available).
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If callback is specified then it should be a callable which
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accepts a single argument. When the resulting iterator becomes
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ready, callback is applied to it (unless the call
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failed). callback should complete immediately since otherwise
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the thread which handles the results will get blocked.
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"""
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apply_result = ApplyResult(callback=callback)
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collector = UnorderedResultCollector(apply_result)
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self._create_sequences(func, iterable, chunksize, collector)
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return apply_result
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def close(self):
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"""Prevents any more tasks from being submitted to the pool.
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Once all the tasks have been completed the worker
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processes will exit.
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"""
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# No lock here. We assume it's sufficiently atomic...
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self._closed = True
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def terminate(self):
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"""Stops the worker processes immediately without completing outstanding work.
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When the pool object is garbage collected terminate() will be called immediately.
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"""
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self.close()
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# Clearing the job queue
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try:
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while True:
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self._workq.get_nowait()
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except Queue.Empty:
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pass
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# Send one sentinel for each worker thread: each thread will die
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# eventually, leaving the next sentinel for the next thread
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for thr in self._workers:
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self._workq.put(SENTINEL)
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def join(self):
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"""Wait for the worker processes to exit.
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One must call close() or terminate() before using join().
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"""
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for thr in self._workers:
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thr.join()
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def _create_sequences(self, func, iterable, chunksize, collector=None):
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"""Create the WorkUnit objects to process and pushes them on the work queue.
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Each work unit is meant to process a slice of iterable of size chunksize.
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If collector is specified, then the ApplyResult objects associated with
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the jobs will notify collector when their result becomes ready.
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\return the list of WorkUnit objects (basically: JobSequences)
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pushed onto the work queue
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"""
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assert not self._closed # No lock here. We assume it's atomic...
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sequences = []
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results = []
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it_ = iter(iterable)
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exit_loop = False
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while not exit_loop:
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seq = []
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for i in xrange(chunksize or 1):
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try:
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arg = it_.next()
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except StopIteration:
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exit_loop = True
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break
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apply_result = ApplyResult(collector)
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job = Job(func, (arg,), {}, apply_result)
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seq.append(job)
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results.append(apply_result)
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sequences.append(JobSequence(seq))
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for seq in sequences:
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self._workq.put(seq)
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return sequences
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class WorkUnit(object):
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"""ABC for a unit of work submitted to the worker threads.
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It's basically just an object equipped with a process() method
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"""
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def process(self):
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"""Do the work. Shouldn't raise any exception"""
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raise NotImplementedError("Children must override Process")
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class Job(WorkUnit):
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"""A work unit that corresponds to the execution of a single function.
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"""
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def __init__(self, func, args, kwds, apply_result):
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"""\param func/args/kwds used to call the function
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\param apply_result ApplyResult object that holds the result
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of the function call
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"""
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WorkUnit.__init__(self)
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self._func = func
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self._args = args
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self._kwds = kwds
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self._result = apply_result
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def process(self):
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"""Call the function with the args/kwds and tell the ApplyResult
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that its result is ready. Correctly handles the exceptions
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happening during the execution of the function.
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"""
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try:
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result = self._func(*self._args, **self._kwds)
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except Exception:
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self._result._set_exception()
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else:
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self._result._set_value(result)
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class JobSequence(WorkUnit):
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"""A work unit that corresponds to the processing of a continuous
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sequence of Job objects
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"""
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def __init__(self, jobs):
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WorkUnit.__init__(self)
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self._jobs = jobs
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def process(self):
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"""Call process() on all the Job objects that have been specified.
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"""
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for job in self._jobs:
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job.process()
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class ApplyResult(object):
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"""An object associated with a Job object that holds its result:
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it's available during the whole life the Job and after, even when
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the Job didn't process yet. It's possible to use this object to
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wait for the result/exception of the job to be available.
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The result objects returns by the Pool::*_async() methods are of
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this type
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"""
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def __init__(self, collector=None, callback=None):
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"""\param collector when not None, the notify_ready() method of
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the collector will be called when the result from the Job is
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ready
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\param callback when not None, function to call when the
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result becomes available (this is the paramater passed to the
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Pool::*_async() methods.
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"""
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self._success = False
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self._event = threading.Event()
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self._data = None
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self._collector = None
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self._callback = callback
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if collector is not None:
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collector.register_result(self)
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self._collector = collector
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def get(self, timeout=None):
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"""Returns the result when it arrives.
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If timeout is not None and the result does not arrive within timeout
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seconds then TimeoutError is raised. If the remote call raised an
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exception then that exception will be re-raised by get().
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"""
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if not self.wait(timeout):
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raise TimeoutError("Result not available within %fs" % timeout)
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if self._success:
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return self._data
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raise self._data[0], self._data[1], self._data[2]
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def wait(self, timeout=None):
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"""Waits until the result is available or until timeout seconds pass.
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"""
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self._event.wait(timeout)
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return self._event.isSet()
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def ready(self):
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"""Returns whether the call has completed.
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"""
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return self._event.isSet()
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def successful(self):
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"""Returns whether the call completed without raising an exception.
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Will raise AssertionError if the result is not ready.
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"""
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assert self.ready()
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return self._success
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def _set_value(self, value):
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"""Called by a Job object to tell the result is ready, and
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provides the value of this result. The object will become
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ready and successful. The collector's notify_ready() method
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will be called, and the callback method too.
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"""
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assert not self.ready()
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self._data = value
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self._success = True
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self._event.set()
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if self._collector is not None:
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self._collector.notify_ready(self)
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if self._callback is not None:
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try:
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self._callback(value)
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except Exception:
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traceback.print_exc()
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def _set_exception(self):
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"""Called by a Job object to tell that an exception occurred
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during the processing of the function. The object will become
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ready but not successful. The collector's notify_ready()
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method will be called, but NOT the callback method
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"""
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assert not self.ready()
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self._data = sys.exc_info()
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self._success = False
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self._event.set()
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if self._collector is not None:
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self._collector.notify_ready(self)
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class AbstractResultCollector(object):
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"""ABC to define the interface of a ResultCollector object.
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It is basically an object which knows whuich results it's waiting for,
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and which is able to get notify when they get available. It is
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also able to provide an iterator over the results when they are
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available.
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"""
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def __init__(self, to_notify):
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"""\param to_notify ApplyResult object to notify when all the
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results we're waiting for become available. Can be None.
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"""
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self._to_notify = to_notify
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def register_result(self, apply_result):
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"""Used to identify which results we're waiting for.
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Will always be called BEFORE the Jobs get submitted to the work
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queue, and BEFORE the __iter__ and _get_result() methods can
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be called
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\param apply_result ApplyResult object to add in our collection
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||||
"""
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raise NotImplementedError("Children classes must implement it")
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def notify_ready(self, apply_result):
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"""Called by the ApplyResult object (already registered via
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||||
|
||||
register_result()) that it is now ready (ie. the Job's result
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is available or an exception has been raised).
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\param apply_result ApplyResult object telling us that the job
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has been processed
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||||
"""
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||||
raise NotImplementedError("Children classes must implement it")
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||||
def _get_result(self, idx, timeout=None):
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"""Called by the CollectorIterator object to retrieve the
|
||||
|
||||
result's values one after another (order defined by the
|
||||
implementation)
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||||
\param idx The index of the result we want, wrt collector's order
|
||||
\param timeout integer telling how long to wait (in seconds)
|
||||
for the result at index idx to be available, or None (wait
|
||||
forever)
|
||||
"""
|
||||
raise NotImplementedError("Children classes must implement it")
|
||||
|
||||
def __iter__(self):
|
||||
"""Return a new CollectorIterator object for this collector.
|
||||
"""
|
||||
return CollectorIterator(self)
|
||||
|
||||
|
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class CollectorIterator(object):
|
||||
|
||||
"""An iterator that allows to iterate over the result values
|
||||
|
||||
available in the given collector object. Equipped with an extended
|
||||
next() method accepting a timeout argument. Created by the
|
||||
AbstractResultCollector::__iter__() method
|
||||
"""
|
||||
|
||||
def __init__(self, collector):
|
||||
"""\param AbstractResultCollector instance.
|
||||
"""
|
||||
self._collector = collector
|
||||
self._idx = 0
|
||||
|
||||
def __iter__(self):
|
||||
return self
|
||||
|
||||
def next(self, timeout=None):
|
||||
"""Return the next result value in the sequence.
|
||||
|
||||
Raise StopIteration at the end. Can raise the exception raised by
|
||||
the Job.
|
||||
"""
|
||||
try:
|
||||
apply_result = self._collector._get_result(self._idx, timeout)
|
||||
except IndexError:
|
||||
# Reset for next time
|
||||
self._idx = 0
|
||||
raise StopIteration
|
||||
except Exception:
|
||||
self._idx = 0
|
||||
raise
|
||||
self._idx += 1
|
||||
assert apply_result.ready()
|
||||
return apply_result.get(0)
|
||||
|
||||
|
||||
class UnorderedResultCollector(AbstractResultCollector):
|
||||
|
||||
"""An AbstractResultCollector implementation that collects the
|
||||
|
||||
values of the ApplyResult objects in the order they become ready. The
|
||||
CollectorIterator object returned by __iter__() will iterate over
|
||||
them in the order they become ready.
|
||||
"""
|
||||
|
||||
def __init__(self, to_notify=None):
|
||||
"""\param to_notify ApplyResult object to notify when all the
|
||||
|
||||
results we're waiting for become available. Can be None.
|
||||
"""
|
||||
AbstractResultCollector.__init__(self, to_notify)
|
||||
self._cond = threading.Condition()
|
||||
self._collection = []
|
||||
self._expected = 0
|
||||
|
||||
def register_result(self, apply_result):
|
||||
"""Used to identify which results we're waiting for.
|
||||
|
||||
Will always be called BEFORE the Jobs get submitted to the work
|
||||
queue, and BEFORE the __iter__ and _get_result() methods can
|
||||
be called
|
||||
\param apply_result ApplyResult object to add in our collection
|
||||
"""
|
||||
self._expected += 1
|
||||
|
||||
def _get_result(self, idx, timeout=None):
|
||||
"""Called by the CollectorIterator object to retrieve the
|
||||
|
||||
result's values one after another, in the order the results have
|
||||
become available.
|
||||
\param idx The index of the result we want, wrt collector's order
|
||||
\param timeout integer telling how long to wait (in seconds)
|
||||
for the result at index idx to be available, or None (wait
|
||||
forever)
|
||||
"""
|
||||
self._cond.acquire()
|
||||
try:
|
||||
if idx >= self._expected:
|
||||
raise IndexError
|
||||
elif idx < len(self._collection):
|
||||
return self._collection[idx]
|
||||
elif idx != len(self._collection):
|
||||
# Violation of the sequence protocol
|
||||
raise IndexError()
|
||||
else:
|
||||
self._cond.wait(timeout=timeout)
|
||||
try:
|
||||
return self._collection[idx]
|
||||
except IndexError:
|
||||
# Still not added !
|
||||
raise TimeoutError("Timeout while waiting for results")
|
||||
finally:
|
||||
self._cond.release()
|
||||
|
||||
def notify_ready(self, apply_result):
|
||||
"""Called by the ApplyResult object (already registered via
|
||||
|
||||
register_result()) that it is now ready (ie. the Job's result
|
||||
is available or an exception has been raised).
|
||||
\param apply_result ApplyResult object telling us that the job
|
||||
has been processed
|
||||
"""
|
||||
first_item = False
|
||||
self._cond.acquire()
|
||||
try:
|
||||
self._collection.append(apply_result)
|
||||
first_item = (len(self._collection) == 1)
|
||||
|
||||
self._cond.notifyAll()
|
||||
finally:
|
||||
self._cond.release()
|
||||
|
||||
if first_item and self._to_notify is not None:
|
||||
self._to_notify._set_value(iter(self))
|
||||
|
||||
|
||||
class OrderedResultCollector(AbstractResultCollector):
|
||||
|
||||
"""An AbstractResultCollector implementation that collects the
|
||||
|
||||
values of the ApplyResult objects in the order they have been
|
||||
submitted. The CollectorIterator object returned by __iter__()
|
||||
will iterate over them in the order they have been submitted.
|
||||
"""
|
||||
|
||||
def __init__(self, to_notify=None, as_iterator=True):
|
||||
"""\param to_notify ApplyResult object to notify when all the
|
||||
|
||||
results we're waiting for become available. Can be None.
|
||||
\param as_iterator boolean telling whether the result value
|
||||
set on to_notify should be an iterator (available as soon as 1
|
||||
result arrived) or a list (available only after the last
|
||||
result arrived)
|
||||
"""
|
||||
AbstractResultCollector.__init__(self, to_notify)
|
||||
self._results = []
|
||||
self._lock = threading.Lock()
|
||||
self._remaining = 0
|
||||
self._as_iterator = as_iterator
|
||||
|
||||
def register_result(self, apply_result):
|
||||
"""Used to identify which results we're waiting for.
|
||||
|
||||
Will always be called BEFORE the Jobs get submitted to the work
|
||||
queue, and BEFORE the __iter__ and _get_result() methods can
|
||||
be called
|
||||
\param apply_result ApplyResult object to add in our collection
|
||||
"""
|
||||
self._results.append(apply_result)
|
||||
self._remaining += 1
|
||||
|
||||
def _get_result(self, idx, timeout=None):
|
||||
"""Called by the CollectorIterator object to retrieve the
|
||||
|
||||
result's values one after another (order defined by the
|
||||
implementation)
|
||||
\param idx The index of the result we want, wrt collector's order
|
||||
\param timeout integer telling how long to wait (in seconds)
|
||||
for the result at index idx to be available, or None (wait
|
||||
forever)
|
||||
"""
|
||||
res = self._results[idx]
|
||||
res.wait(timeout)
|
||||
return res
|
||||
|
||||
def notify_ready(self, apply_result):
|
||||
"""Called by the ApplyResult object (already registered via
|
||||
|
||||
register_result()) that it is now ready (ie. the Job's result
|
||||
is available or an exception has been raised).
|
||||
\param apply_result ApplyResult object telling us that the job
|
||||
has been processed
|
||||
"""
|
||||
got_first = False
|
||||
got_last = False
|
||||
self._lock.acquire()
|
||||
try:
|
||||
assert self._remaining > 0
|
||||
got_first = (len(self._results) == self._remaining)
|
||||
self._remaining -= 1
|
||||
got_last = (self._remaining == 0)
|
||||
finally:
|
||||
self._lock.release()
|
||||
|
||||
if self._to_notify is not None:
|
||||
if self._as_iterator and got_first:
|
||||
self._to_notify._set_value(iter(self))
|
||||
elif not self._as_iterator and got_last:
|
||||
try:
|
||||
lst = [r.get(0) for r in self._results]
|
||||
except Exception:
|
||||
self._to_notify._set_exception()
|
||||
else:
|
||||
self._to_notify._set_value(lst)
|
||||
|
||||
|
||||
def _test():
|
||||
"""Some tests.
|
||||
"""
|
||||
import time
|
||||
|
||||
def f(x):
|
||||
return x * x
|
||||
|
||||
def work(seconds):
|
||||
print("[%d] Start to work for %fs..." % (threading.thread.get_ident(), seconds))
|
||||
time.sleep(seconds)
|
||||
print("[%d] Work done (%fs)." % (threading.thread.get_ident(), seconds))
|
||||
return "%d slept %fs" % (threading.thread.get_ident(), seconds)
|
||||
|
||||
# Test copy/pasted from multiprocessing
|
||||
pool = Pool(9) # start 4 worker threads
|
||||
|
||||
result = pool.apply_async(f, (10,)) # evaluate "f(10)" asynchronously
|
||||
print(result.get(timeout=1)) # prints "100" unless slow computer
|
||||
|
||||
print(pool.map(f, range(10))) # prints "[0, 1, 4,..., 81]"
|
||||
|
||||
it = pool.imap(f, range(10))
|
||||
print(it.next()) # prints "0"
|
||||
print(it.next()) # prints "1"
|
||||
print(it.next(timeout=1)) # prints "4" unless slow computer
|
||||
|
||||
# Test apply_sync exceptions
|
||||
result = pool.apply_async(time.sleep, (3,))
|
||||
try:
|
||||
print(result.get(timeout=1)) # raises `TimeoutError`
|
||||
except TimeoutError:
|
||||
print("Good. Got expected timeout exception.")
|
||||
else:
|
||||
assert False, "Expected exception !"
|
||||
print(result.get())
|
||||
|
||||
def cb(s):
|
||||
print("Result ready: %s" % s)
|
||||
|
||||
# Test imap()
|
||||
for res in pool.imap(work, xrange(10, 3, -1), chunksize=4):
|
||||
print("Item:", res)
|
||||
|
||||
# Test imap_unordered()
|
||||
for res in pool.imap_unordered(work, xrange(10, 3, -1)):
|
||||
print("Item:", res)
|
||||
|
||||
# Test map_async()
|
||||
result = pool.map_async(work, xrange(10), callback=cb)
|
||||
try:
|
||||
print(result.get(timeout=1)) # raises `TimeoutError`
|
||||
except TimeoutError:
|
||||
print("Good. Got expected timeout exception.")
|
||||
else:
|
||||
assert False, "Expected exception !"
|
||||
print(result.get())
|
||||
|
||||
# Test imap_async()
|
||||
result = pool.imap_async(work, xrange(3, 10), callback=cb)
|
||||
try:
|
||||
print(result.get(timeout=1)) # raises `TimeoutError`
|
||||
except TimeoutError:
|
||||
print("Good. Got expected timeout exception.")
|
||||
else:
|
||||
assert False, "Expected exception !"
|
||||
for i in result.get():
|
||||
print("Item:", i)
|
||||
print("### Loop again:")
|
||||
for i in result.get():
|
||||
print("Item2:", i)
|
||||
|
||||
# Test imap_unordered_async()
|
||||
result = pool.imap_unordered_async(work, xrange(10, 3, -1), callback=cb)
|
||||
try:
|
||||
print(result.get(timeout=1)) # raises `TimeoutError`
|
||||
except TimeoutError:
|
||||
print("Good. Got expected timeout exception.")
|
||||
else:
|
||||
assert False, "Expected exception !"
|
||||
for i in result.get():
|
||||
print("Item1:", i)
|
||||
for i in result.get():
|
||||
print("Item2:", i)
|
||||
r = result.get()
|
||||
for i in r:
|
||||
print("Item3:", i)
|
||||
for i in r:
|
||||
print("Item4:", i)
|
||||
for i in r:
|
||||
print("Item5:", i)
|
||||
|
||||
#
|
||||
# The case for the exceptions
|
||||
#
|
||||
|
||||
# Exceptions in imap_unordered_async()
|
||||
result = pool.imap_unordered_async(work, xrange(2, -10, -1), callback=cb)
|
||||
time.sleep(3)
|
||||
try:
|
||||
for i in result.get():
|
||||
print("Got item:", i)
|
||||
except IOError:
|
||||
print("Good. Got expected exception:")
|
||||
traceback.print_exc()
|
||||
|
||||
# Exceptions in imap_async()
|
||||
result = pool.imap_async(work, xrange(2, -10, -1), callback=cb)
|
||||
time.sleep(3)
|
||||
try:
|
||||
for i in result.get():
|
||||
print("Got item:", i)
|
||||
except IOError:
|
||||
print("Good. Got expected exception:")
|
||||
traceback.print_exc()
|
||||
|
||||
# Stop the test: need to stop the pool !!!
|
||||
pool.terminate()
|
||||
print("End of tests")
|
||||
|
||||
if __name__ == "__main__":
|
||||
_test()
|
||||
# end of http://code.activestate.com/recipes/576519/ }}}
|
||||
@ -5,8 +5,11 @@ import Queue
|
||||
import threading
|
||||
import time
|
||||
|
||||
from gevent import monkey
|
||||
from gevent import Timeout
|
||||
from multiprocessing.dummy import Pool as ThreadPool
|
||||
|
||||
import monasca_agent.collector.checks
|
||||
import monasca_agent.collector.checks.libs.thread_pool
|
||||
|
||||
|
||||
DEFAULT_TIMEOUT = 180
|
||||
@ -18,6 +21,7 @@ FAILURE = "FAILURE"
|
||||
up_down = collections.namedtuple('up_down', ['UP', 'DOWN'])
|
||||
Status = up_down('UP', 'DOWN')
|
||||
EventType = up_down("servicecheck.state_change.up", "servicecheck.state_change.down")
|
||||
monkey.patch_all()
|
||||
|
||||
|
||||
class ServicesCheck(monasca_agent.collector.checks.AgentCheck):
|
||||
@ -25,7 +29,7 @@ class ServicesCheck(monasca_agent.collector.checks.AgentCheck):
|
||||
|
||||
"""Services checks inherits from this class.
|
||||
|
||||
This class should never be directly instanciated.
|
||||
This class should never be directly instantiated.
|
||||
|
||||
Work flow:
|
||||
The main agent loop will call the check function for each instance for
|
||||
@ -47,82 +51,71 @@ class ServicesCheck(monasca_agent.collector.checks.AgentCheck):
|
||||
# A dictionary to keep track of service statuses
|
||||
self.statuses = {}
|
||||
self.notified = {}
|
||||
self.resultsq = Queue.Queue()
|
||||
self.nb_failures = 0
|
||||
self.pool_started = False
|
||||
self.pool = None
|
||||
|
||||
def stop(self):
|
||||
self.stop_pool()
|
||||
self.pool_started = False
|
||||
|
||||
def start_pool(self):
|
||||
# The pool size should be the minimum between the number of instances
|
||||
# and the DEFAULT_SIZE_POOL. It can also be overridden by the 'threads_count'
|
||||
# parameter in the init_config of the check
|
||||
self.log.info("Starting Thread Pool")
|
||||
default_size = min(self.instance_count(), DEFAULT_SIZE_POOL)
|
||||
self.pool_size = int(self.init_config.get('threads_count', default_size))
|
||||
self.timeout = int(self.agent_config.get('timeout', DEFAULT_TIMEOUT))
|
||||
|
||||
self.pool = monasca_agent.collector.checks.libs.thread_pool.Pool(self.pool_size)
|
||||
|
||||
self.resultsq = Queue.Queue()
|
||||
self.jobs_status = {}
|
||||
self.pool_started = True
|
||||
def start_pool(self):
|
||||
self.log.info("Starting Thread Pool")
|
||||
self.pool = ThreadPool(self.pool_size)
|
||||
if threading.activeCount() > MAX_ALLOWED_THREADS:
|
||||
self.log.error("Thread count ({0}) exceeds maximum ({1})".format(threading.activeCount(),
|
||||
MAX_ALLOWED_THREADS))
|
||||
self.running_jobs = set()
|
||||
|
||||
def stop_pool(self):
|
||||
self.log.info("Stopping Thread Pool")
|
||||
if self.pool_started:
|
||||
self.pool.terminate()
|
||||
if self.pool:
|
||||
self.pool.close()
|
||||
self.pool.join()
|
||||
self.jobs_status.clear()
|
||||
assert self.pool.get_nworkers() == 0
|
||||
self.pool = None
|
||||
|
||||
def restart_pool(self):
|
||||
self.stop_pool()
|
||||
self.start_pool()
|
||||
|
||||
def check(self, instance):
|
||||
if not self.pool_started:
|
||||
if not self.pool:
|
||||
self.start_pool()
|
||||
if threading.activeCount() > MAX_ALLOWED_THREADS:
|
||||
exception = "Thread number ({0}) exceeds maximum ({1}). Skipping this check.".format(threading.activeCount(),
|
||||
MAX_ALLOWED_THREADS)
|
||||
if self.pool_size >= MAX_ALLOWED_THREADS:
|
||||
exception += " threads_count is set too high in the {0} plugin config.".format(self.name)
|
||||
else:
|
||||
exception += " Another plugin may have threads_count set too high."
|
||||
raise Exception(exception)
|
||||
self._process_results()
|
||||
self._clean()
|
||||
name = instance.get('name', None)
|
||||
if name is None:
|
||||
self.log.error('Each service check must have a name')
|
||||
return
|
||||
|
||||
if name not in self.jobs_status:
|
||||
if name not in self.running_jobs:
|
||||
# A given instance should be processed one at a time
|
||||
self.jobs_status[name] = time.time()
|
||||
self.running_jobs.add(name)
|
||||
self.pool.apply_async(self._process, args=(instance,))
|
||||
else:
|
||||
self.log.info("Instance: %s skipped because it's already running." % name)
|
||||
|
||||
def _process(self, instance):
|
||||
name = instance.get('name', None)
|
||||
|
||||
try:
|
||||
return_value = self._check(instance)
|
||||
with Timeout(self.timeout):
|
||||
return_value = self._check(instance)
|
||||
if not return_value:
|
||||
del self.jobs_status[name]
|
||||
return
|
||||
status, msg = return_value
|
||||
result = (status, msg, name, instance)
|
||||
# We put the results in the result queue
|
||||
self.resultsq.put(result)
|
||||
|
||||
except Timeout:
|
||||
self.log.error('ServiceCheck {0} timed out'.format(name))
|
||||
except Exception:
|
||||
self.log.exception('Failure in ServiceCheck {0}'.format(name))
|
||||
result = (FAILURE, FAILURE, FAILURE, FAILURE)
|
||||
self.resultsq.put(result)
|
||||
finally:
|
||||
self.running_jobs.remove(name)
|
||||
|
||||
def _process_results(self):
|
||||
for i in range(MAX_LOOP_ITERATIONS):
|
||||
@ -171,18 +164,8 @@ class ServicesCheck(monasca_agent.collector.checks.AgentCheck):
|
||||
if event is not None:
|
||||
self.events.append(event)
|
||||
|
||||
# The job is finished here, this instance can be re processed
|
||||
del self.jobs_status[name]
|
||||
|
||||
def _check(self, instance):
|
||||
"""This function should be implemented by inherited classes.
|
||||
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
def _clean(self):
|
||||
now = time.time()
|
||||
for name, start_time in self.jobs_status.items():
|
||||
if now - start_time > self.timeout:
|
||||
self.log.critical("Restarting Pool. One check is stuck.")
|
||||
self.restart_pool()
|
||||
|
||||
@ -10,6 +10,7 @@ oslo.utils
|
||||
oslo.vmware
|
||||
|
||||
PyYAML
|
||||
gevent
|
||||
gearman>=2.0.2,<2.1
|
||||
httplib2
|
||||
netaddr
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user