openstack/monasca-agent
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Merge "Rework agent to better handle multithreaded checks"

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Jenkins 2016-05-25 17:48:15 +00:00 committed by Gerrit Code Review
commit fa4529cac5
3 changed files with 28 additions and 868 deletions
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monasca_agent/collector/checks/libs/thread_pool.py
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@ -1,824 +0,0 @@
# {{{ http://code.activestate.com/recipes/576519/ (r9)
# Author: David Decotigny, Oct 1 2008
# (C) Copyright 2015 Hewlett Packard Enterprise Development Company LP
# @brief Pool of threads similar to multiprocessing.Pool
# See http://docs.python.org/dev/library/multiprocessing.html
# Differences: added imap_async and imap_unordered_async, and terminate()
# has to be called explicitly (it's not registered by atexit).
#
# The general idea is that we submit works to a workqueue, either as
# single Jobs (one function to call), or JobSequences (batch of
# Jobs). Each Job is associated with an ApplyResult object which has 2
# states: waiting for the Job to complete, or Ready. Instead of
# waiting for the jobs to finish, we wait for their ApplyResult object
# to become ready: an event mechanism is used for that.
# When we apply a function to several arguments in "parallel", we need
# a way to wait for all/part of the Jobs to be processed: that's what
# "collectors" are for; they group and wait for a set of ApplyResult
# objects. Once a collector is ready to be used, we can use a
# CollectorIterator to iterate over the result values it's collecting.
#
# The methods of a Pool object use all these concepts and expose
# them to their caller in a very simple way.
import Queue
import sys
import threading
import traceback
# Item pushed on the work queue to tell the worker threads to terminate
SENTINEL = "QUIT"
def is_sentinel(obj):
"""Predicate to determine whether an item from the queue is the
signal to stop
"""
return isinstance(obj, str) and obj == SENTINEL
class TimeoutError(Exception):
"""Raised when a result is not available within the given timeout.
"""
pass
class PoolWorker(threading.Thread):
"""Thread that consumes WorkUnits from a queue to process them.
"""
def __init__(self, workq, *args, **kwds):
"""\param workq: Queue object to consume the work units from.
"""
threading.Thread.__init__(self, *args, **kwds)
self._workq = workq
self.running = False
def run(self):
"""Process the work unit, or wait for sentinel to exit.
"""
while True:
self.running = True
workunit = self._workq.get()
if is_sentinel(workunit):
# Got sentinel
break
# Run the job / sequence
workunit.process()
self.running = False
class Pool(object):
"""The Pool class represents a pool of worker threads.
It has methods which allows tasks to be offloaded to the
worker processes in a few different ways.
"""
def __init__(self, nworkers, name="Pool"):
"""\param nworkers (integer) number of worker threads to start
\param name (string) prefix for the worker threads' name
"""
self._workq = Queue.Queue()
self._closed = False
self._workers = []
for idx in xrange(nworkers):
thr = PoolWorker(self._workq, name="Worker-%s-%d" % (name, idx))
try:
thr.start()
except Exception:
# If one thread has a problem, undo everything
self.terminate()
raise
else:
self._workers.append(thr)
def get_nworkers(self):
return len([w for w in self._workers if w.running])
def apply(self, func, args=(), kwds=None):
"""Equivalent of the apply() builtin function.
It blocks till the result is ready.
"""
if not kwds:
kwds = dict()
return self.apply_async(func, args, kwds).get()
def map(self, func, iterable, chunksize=None):
"""A parallel equivalent of the map() builtin function.
It blocks till the result is ready.
This method chops the iterable into a number of chunks which
it submits to the process pool as separate tasks. The
(approximate) size of these chunks can be specified by setting
chunksize to a positive integer.
"""
return self.map_async(func, iterable, chunksize).get()
def imap(self, func, iterable, chunksize=1):
"""An equivalent of itertools.imap().
The chunksize argument is the same as the one used by the
map() method. For very long iterables using a large value for
chunksize can make make the job complete much faster than
using the default value of 1.
Also if chunksize is 1 then the next() method of the iterator
returned by the imap() method has an optional timeout
parameter: next(timeout) will raise processing.TimeoutError if
the result cannot be returned within timeout seconds.
"""
collector = OrderedResultCollector(as_iterator=True)
self._create_sequences(func, iterable, chunksize, collector)
return iter(collector)
def imap_unordered(self, func, iterable, chunksize=1):
"""The same as imap() except that the ordering of the results
from the returned iterator should be considered arbitrary.
(Only when there is only one worker process is the order
guaranteed to be "correct".)
"""
collector = UnorderedResultCollector()
self._create_sequences(func, iterable, chunksize, collector)
return iter(collector)
def apply_async(self, func, args=(), kwds=None, callback=None):
"""A variant of the apply() method which returns an ApplyResult object.
If callback is specified then it should be a callable which
accepts a single argument. When the result becomes ready,
callback is applied to it (unless the call failed). callback
should complete immediately since otherwise the thread which
handles the results will get blocked.
"""
if not kwds:
kwds = dict()
assert not self._closed # No lock here. We assume it's atomic...
apply_result = ApplyResult(callback=callback)
job = Job(func, args, kwds, apply_result)
self._workq.put(job)
return apply_result
def map_async(self, func, iterable, chunksize=None, callback=None):
"""A variant of the map() method which returns a ApplyResult object.
If callback is specified then it should be a callable which
accepts a single argument. When the result becomes ready
callback is applied to it (unless the call failed). callback
should complete immediately since otherwise the thread which
handles the results will get blocked.
"""
apply_result = ApplyResult(callback=callback)
collector = OrderedResultCollector(apply_result, as_iterator=False)
self._create_sequences(func, iterable, chunksize, collector)
return apply_result
def imap_async(self, func, iterable, chunksize=None, callback=None):
"""A variant of the imap() method which returns an ApplyResult
object that provides an iterator (next method(timeout)
available).
If callback is specified then it should be a callable which
accepts a single argument. When the resulting iterator becomes
ready, callback is applied to it (unless the call
failed). callback should complete immediately since otherwise
the thread which handles the results will get blocked.
"""
apply_result = ApplyResult(callback=callback)
collector = OrderedResultCollector(apply_result, as_iterator=True)
self._create_sequences(func, iterable, chunksize, collector)
return apply_result
def imap_unordered_async(self, func, iterable, chunksize=None,
callback=None):
"""A variant of the imap_unordered() method which returns an
ApplyResult object that provides an iterator (next method(timeout)
available).
If callback is specified then it should be a callable which
accepts a single argument. When the resulting iterator becomes
ready, callback is applied to it (unless the call
failed). callback should complete immediately since otherwise
the thread which handles the results will get blocked.
"""
apply_result = ApplyResult(callback=callback)
collector = UnorderedResultCollector(apply_result)
self._create_sequences(func, iterable, chunksize, collector)
return apply_result
def close(self):
"""Prevents any more tasks from being submitted to the pool.
Once all the tasks have been completed the worker
processes will exit.
"""
# No lock here. We assume it's sufficiently atomic...
self._closed = True
def terminate(self):
"""Stops the worker processes immediately without completing outstanding work.
When the pool object is garbage collected terminate() will be called immediately.
"""
self.close()
# Clearing the job queue
try:
while True:
self._workq.get_nowait()
except Queue.Empty:
pass
# Send one sentinel for each worker thread: each thread will die
# eventually, leaving the next sentinel for the next thread
for thr in self._workers:
self._workq.put(SENTINEL)
def join(self):
"""Wait for the worker processes to exit.
One must call close() or terminate() before using join().
"""
for thr in self._workers:
thr.join()
def _create_sequences(self, func, iterable, chunksize, collector=None):
"""Create the WorkUnit objects to process and pushes them on the work queue.
Each work unit is meant to process a slice of iterable of size chunksize.
If collector is specified, then the ApplyResult objects associated with
the jobs will notify collector when their result becomes ready.
\return the list of WorkUnit objects (basically: JobSequences)
pushed onto the work queue
"""
assert not self._closed # No lock here. We assume it's atomic...
sequences = []
results = []
it_ = iter(iterable)
exit_loop = False
while not exit_loop:
seq = []
for i in xrange(chunksize or 1):
try:
arg = it_.next()
except StopIteration:
exit_loop = True
break
apply_result = ApplyResult(collector)
job = Job(func, (arg,), {}, apply_result)
seq.append(job)
results.append(apply_result)
sequences.append(JobSequence(seq))
for seq in sequences:
self._workq.put(seq)
return sequences
class WorkUnit(object):
"""ABC for a unit of work submitted to the worker threads.
It's basically just an object equipped with a process() method
"""
def process(self):
"""Do the work. Shouldn't raise any exception"""
raise NotImplementedError("Children must override Process")
class Job(WorkUnit):
"""A work unit that corresponds to the execution of a single function.
"""
def __init__(self, func, args, kwds, apply_result):
"""\param func/args/kwds used to call the function
\param apply_result ApplyResult object that holds the result
of the function call
"""
WorkUnit.__init__(self)
self._func = func
self._args = args
self._kwds = kwds
self._result = apply_result
def process(self):
"""Call the function with the args/kwds and tell the ApplyResult
that its result is ready. Correctly handles the exceptions
happening during the execution of the function.
"""
try:
result = self._func(*self._args, **self._kwds)
except Exception:
self._result._set_exception()
else:
self._result._set_value(result)
class JobSequence(WorkUnit):
"""A work unit that corresponds to the processing of a continuous
sequence of Job objects
"""
def __init__(self, jobs):
WorkUnit.__init__(self)
self._jobs = jobs
def process(self):
"""Call process() on all the Job objects that have been specified.
"""
for job in self._jobs:
job.process()
class ApplyResult(object):
"""An object associated with a Job object that holds its result:
it's available during the whole life the Job and after, even when
the Job didn't process yet. It's possible to use this object to
wait for the result/exception of the job to be available.
The result objects returns by the Pool::*_async() methods are of
this type
"""
def __init__(self, collector=None, callback=None):
"""\param collector when not None, the notify_ready() method of
the collector will be called when the result from the Job is
ready
\param callback when not None, function to call when the
result becomes available (this is the paramater passed to the
Pool::*_async() methods.
"""
self._success = False
self._event = threading.Event()
self._data = None
self._collector = None
self._callback = callback
if collector is not None:
collector.register_result(self)
self._collector = collector
def get(self, timeout=None):
"""Returns the result when it arrives.
If timeout is not None and the result does not arrive within timeout
seconds then TimeoutError is raised. If the remote call raised an
exception then that exception will be re-raised by get().
"""
if not self.wait(timeout):
raise TimeoutError("Result not available within %fs" % timeout)
if self._success:
return self._data
raise self._data[0], self._data[1], self._data[2]
def wait(self, timeout=None):
"""Waits until the result is available or until timeout seconds pass.
"""
self._event.wait(timeout)
return self._event.isSet()
def ready(self):
"""Returns whether the call has completed.
"""
return self._event.isSet()
def successful(self):
"""Returns whether the call completed without raising an exception.
Will raise AssertionError if the result is not ready.
"""
assert self.ready()
return self._success
def _set_value(self, value):
"""Called by a Job object to tell the result is ready, and
provides the value of this result. The object will become
ready and successful. The collector's notify_ready() method
will be called, and the callback method too.
"""
assert not self.ready()
self._data = value
self._success = True
self._event.set()
if self._collector is not None:
self._collector.notify_ready(self)
if self._callback is not None:
try:
self._callback(value)
except Exception:
traceback.print_exc()
def _set_exception(self):
"""Called by a Job object to tell that an exception occurred
during the processing of the function. The object will become
ready but not successful. The collector's notify_ready()
method will be called, but NOT the callback method
"""
assert not self.ready()
self._data = sys.exc_info()
self._success = False
self._event.set()
if self._collector is not None:
self._collector.notify_ready(self)
class AbstractResultCollector(object):
"""ABC to define the interface of a ResultCollector object.
It is basically an object which knows whuich results it's waiting for,
and which is able to get notify when they get available. It is
also able to provide an iterator over the results when they are
available.
"""
def __init__(self, to_notify):
"""\param to_notify ApplyResult object to notify when all the
results we're waiting for become available. Can be None.
"""
self._to_notify = to_notify
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
"""
raise NotImplementedError("Children classes must implement it")
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
"""
raise NotImplementedError("Children classes must implement it")
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)
"""
raise NotImplementedError("Children classes must implement it")
def __iter__(self):
"""Return a new CollectorIterator object for this collector.
"""
return CollectorIterator(self)
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/ }}}

71
monasca_agent/collector/checks/services_checks.py
View File

@ -5,8 +5,11 @@ import Queue
import threading import threading
import time 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
import monasca_agent.collector.checks.libs.thread_pool
DEFAULT_TIMEOUT = 180 DEFAULT_TIMEOUT = 180
@ -18,6 +21,7 @@ FAILURE = "FAILURE"
up_down = collections.namedtuple('up_down', ['UP', 'DOWN']) up_down = collections.namedtuple('up_down', ['UP', 'DOWN'])
Status = up_down('UP', 'DOWN') Status = up_down('UP', 'DOWN')
EventType = up_down("servicecheck.state_change.up", "servicecheck.state_change.down") EventType = up_down("servicecheck.state_change.up", "servicecheck.state_change.down")
monkey.patch_all()
class ServicesCheck(monasca_agent.collector.checks.AgentCheck): class ServicesCheck(monasca_agent.collector.checks.AgentCheck):
@ -25,7 +29,7 @@ class ServicesCheck(monasca_agent.collector.checks.AgentCheck):
"""Services checks inherits from this class. """Services checks inherits from this class.
This class should never be directly instanciated. This class should never be directly instantiated.
Work flow: Work flow:
The main agent loop will call the check function for each instance for 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 # A dictionary to keep track of service statuses
self.statuses = {} self.statuses = {}
self.notified = {} self.notified = {}
self.resultsq = Queue.Queue()
self.nb_failures = 0 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 # 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' # and the DEFAULT_SIZE_POOL. It can also be overridden by the 'threads_count'
# parameter in the init_config of the check # parameter in the init_config of the check
self.log.info("Starting Thread Pool")
default_size = min(self.instance_count(), DEFAULT_SIZE_POOL) default_size = min(self.instance_count(), DEFAULT_SIZE_POOL)
self.pool_size = int(self.init_config.get('threads_count', default_size)) self.pool_size = int(self.init_config.get('threads_count', default_size))
self.timeout = int(self.agent_config.get('timeout', DEFAULT_TIMEOUT)) self.timeout = int(self.agent_config.get('timeout', DEFAULT_TIMEOUT))
self.pool = monasca_agent.collector.checks.libs.thread_pool.Pool(self.pool_size) def start_pool(self):
self.log.info("Starting Thread Pool")
self.resultsq = Queue.Queue() self.pool = ThreadPool(self.pool_size)
self.jobs_status = {} if threading.activeCount() > MAX_ALLOWED_THREADS:
self.pool_started = True self.log.error("Thread count ({0}) exceeds maximum ({1})".format(threading.activeCount(),
MAX_ALLOWED_THREADS))
self.running_jobs = set()
def stop_pool(self): def stop_pool(self):
self.log.info("Stopping Thread Pool") self.log.info("Stopping Thread Pool")
if self.pool_started: if self.pool:
self.pool.terminate() self.pool.close()
self.pool.join() self.pool.join()
self.jobs_status.clear() self.pool = None
assert self.pool.get_nworkers() == 0
def restart_pool(self): def restart_pool(self):
self.stop_pool() self.stop_pool()
self.start_pool() self.start_pool()
def check(self, instance): def check(self, instance):
if not self.pool_started: if not self.pool:
self.start_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._process_results()
self._clean()
name = instance.get('name', None) name = instance.get('name', None)
if name is None: if name is None:
self.log.error('Each service check must have a name') self.log.error('Each service check must have a name')
return 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 # 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,)) self.pool.apply_async(self._process, args=(instance,))
else: else:
self.log.info("Instance: %s skipped because it's already running." % name) self.log.info("Instance: %s skipped because it's already running." % name)
def _process(self, instance): def _process(self, instance):
name = instance.get('name', None) name = instance.get('name', None)
try: try:
return_value = self._check(instance) with Timeout(self.timeout):
return_value = self._check(instance)
if not return_value: if not return_value:
del self.jobs_status[name]
return return
status, msg = return_value status, msg = return_value
result = (status, msg, name, instance) result = (status, msg, name, instance)
# We put the results in the result queue # We put the results in the result queue
self.resultsq.put(result) self.resultsq.put(result)
except Timeout:
self.log.error('ServiceCheck {0} timed out'.format(name))
except Exception: except Exception:
self.log.exception('Failure in ServiceCheck {0}'.format(name)) self.log.exception('Failure in ServiceCheck {0}'.format(name))
result = (FAILURE, FAILURE, FAILURE, FAILURE) result = (FAILURE, FAILURE, FAILURE, FAILURE)
self.resultsq.put(result) self.resultsq.put(result)
finally:
self.running_jobs.remove(name)
def _process_results(self): def _process_results(self):
for i in range(MAX_LOOP_ITERATIONS): for i in range(MAX_LOOP_ITERATIONS):
@ -171,18 +164,8 @@ class ServicesCheck(monasca_agent.collector.checks.AgentCheck):
if event is not None: if event is not None:
self.events.append(event) self.events.append(event)
# The job is finished here, this instance can be re processed
del self.jobs_status[name]
def _check(self, instance): def _check(self, instance):
"""This function should be implemented by inherited classes. """This function should be implemented by inherited classes.
""" """
raise NotImplementedError 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()

1
requirements.txt
View File

@ -5,6 +5,7 @@ oslo.utils
oslo.vmware oslo.vmware
PyYAML PyYAML
gevent
gearman>=2.0.2,<2.1 gearman>=2.0.2,<2.1
httplib2 httplib2
netaddr netaddr