def __init__(self):
self._log = grok_logging.getExtendedLogger(self.__class__.__name__)
self._profiling = (
grok.app.config.getboolean("debugging", "profiling") or
self._log.isEnabledFor(logging.DEBUG))
self._pollInterval = grok.app.config.getfloat(
"metric_collector", "poll_interval")
self._metricErrorGracePeriod = grok.app.config.getfloat(
"metric_collector", "metric_error_grace_period")
# Interval for periodic garbage collection of our caches (e.g.,
# self._metricInfoCache and self._resourceInfoCache)
self._cacheGarbageCollectionIntervalSec = self._metricErrorGracePeriod * 2
# Time (unix epoch) when to run the next garbage collection of
# self._metricInfoCache and self._resourceInfoCache; 0 triggers it ASAP as a
# quick test of the logic. See self._cacheGarbageCollectionIntervalSec.
self._nextCacheGarbageCollectionTime = 0
# We use this to cache info about metrics that helps us avoid unnecessary
# queries to the datasource. The keys are metric uid's and corresponding
# values are _MetricInfoCacheItem objects.
self._metricInfoCache = defaultdict(_MetricInfoCacheItem)
# We use this to cache info about resources referenced by metrics to help
# us avoid unnecessary resource-status queries to the datasource. The keys
# are resource cananonical identifiers (from Metric.server) and
# corresponding values are _ResourceInfoCacheItem objects.
self._resourceInfoCache = defaultdict(_ResourceInfoCacheItem)
self.metricStreamer = MetricStreamer()
def __init__(self):
# NOTE: the EC2InstanceMetricGetter instance and its process pool must be
# created BEFORE this main (parent) process creates any global or
# class-level shared resources that are also used by the pool workers (e.g.,
# boto connections) that would have undersirable consequences when
# replicated into and used by forked child processes (e.g., the same
# connection socket file descriptor used by multiple processes).
self._metricGetter = EC2InstanceMetricGetter()
self._log = _getLogger()
self.metricStreamer = MetricStreamer()
def __init__(self):
self._log = YOMP_logging.getExtendedLogger(self.__class__.__name__)
self._profiling = (
YOMP.app.config.getboolean("debugging", "profiling") or
self._log.isEnabledFor(logging.DEBUG))
self._pollInterval = YOMP.app.config.getfloat(
"metric_collector", "poll_interval")
self._metricErrorGracePeriod = YOMP.app.config.getfloat(
"metric_collector", "metric_error_grace_period")
# Interval for periodic garbage collection of our caches (e.g.,
# self._metricInfoCache and self._resourceInfoCache)
self._cacheGarbageCollectionIntervalSec = self._metricErrorGracePeriod * 2
# Time (unix epoch) when to run the next garbage collection of
# self._metricInfoCache and self._resourceInfoCache; 0 triggers it ASAP as a
# quick test of the logic. See self._cacheGarbageCollectionIntervalSec.
self._nextCacheGarbageCollectionTime = 0
# We use this to cache info about metrics that helps us avoid unnecessary
# queries to the datasource. The keys are metric uid's and corresponding
# values are _MetricInfoCacheItem objects.
self._metricInfoCache = defaultdict(_MetricInfoCacheItem)
# We use this to cache info about resources referenced by metrics to help
# us avoid unnecessary resource-status queries to the datasource. The keys
# are resource cananonical identifiers (from Metric.server) and
# corresponding values are _ResourceInfoCacheItem objects.
self._resourceInfoCache = defaultdict(_ResourceInfoCacheItem)
self.metricStreamer = MetricStreamer()
def runServer():
# Get the current list of custom metrics
appConfig = Config("application.conf",
os.environ["APPLICATION_CONFIG_PATH"])
engine = repository.engineFactory(appConfig)
global gCustomMetrics
now = datetime.datetime.utcnow()
with engine.connect() as conn:
gCustomMetrics = dict(
(m.name, [m, now]) for m in repository.getCustomMetrics(conn))
queueName = appConfig.get("metric_listener", "queue_name")
global gProfiling
gProfiling = (appConfig.getboolean("debugging", "profiling") or
LOGGER.isEnabledFor(logging.DEBUG))
del appConfig
metricStreamer = MetricStreamer()
modelSwapper = ModelSwapperInterface()
with MessageBusConnector() as bus:
if not bus.isMessageQeueuePresent(queueName):
bus.createMessageQueue(mqName=queueName, durable=True)
LOGGER.info("Waiting for messages. To exit, press CTRL+C")
with bus.consume(queueName) as consumer:
messages = []
messageRxTimes = []
while True:
message = consumer.pollOneMessage()
if message is not None:
messages.append(message)
if gProfiling:
messageRxTimes.append(time.time())
if message is None or len(messages) >= MAX_MESSAGES_PER_BATCH:
if messages:
# Process the batch
try:
_handleBatch(engine,
messages,
messageRxTimes,
metricStreamer,
modelSwapper)
except Exception: # pylint: disable=W0703
LOGGER.exception("Unknown failure in processing messages.")
# Make sure that we ack messages when there is an unexpected error
# to avoid getting hung forever on one bad record.
# Ack all the messages
messages[-1].ack(multiple=True)
# Clear the message buffer
messages = []
messageRxTimes = []
else:
# Queue is empty, wait before retrying
time.sleep(POLL_DELAY_SEC)
def __init__(self):
# NOTE: the EC2InstanceMetricGetter instance and its process pool must be
# created BEFORE this main (parent) process creates any global or
# class-level shared resources that are also used by the pool workers (e.g.,
# boto connections) that would have undersirable consequences when
# replicated into and used by forked child processes (e.g., the same
# connection socket file descriptor used by multiple processes).
self._metricGetter = EC2InstanceMetricGetter()
self._log = _getLogger()
self.metricStreamer = MetricStreamer()
class AggregatorService(object):
_NOTHING_READY_SLEEP_TIME_SEC = 0.5
def __init__(self):
# NOTE: the EC2InstanceMetricGetter instance and its process pool must be
# created BEFORE this main (parent) process creates any global or
# class-level shared resources that are also used by the pool workers (e.g.,
# boto connections) that would have undersirable consequences when
# replicated into and used by forked child processes (e.g., the same
# connection socket file descriptor used by multiple processes).
self._metricGetter = EC2InstanceMetricGetter()
self._log = _getLogger()
self.metricStreamer = MetricStreamer()
def close(self):
self._metricGetter.close()
def run(self):
with ModelSwapperInterface() as modelSwapper:
engine = repository.engineFactory()
while True:
with engine.connect() as conn:
pendingStacks = repository.retryOnTransientErrors(
repository.getAutostackMetricsPendingDataCollection)(conn)
if not pendingStacks:
time.sleep(self._NOTHING_READY_SLEEP_TIME_SEC)
continue
# Build a sequence of autostack metric requests
requests = []
for autostack, metrics in pendingStacks:
refBase = len(requests)
requests.extend(
AutostackMetricRequest(refID=refBase + i,
autostack=autostack,
metric=metric)
for i, metric in enumerate(metrics))
# Collect, aggregate, and stream metric data
self._processAutostackMetricRequests(engine, requests, modelSwapper)
def _processAutostackMetricRequests(self, engine, requests, modelSwapper):
""" Execute autostack metric requests, aggregate and stream
collected metric data
:param engine: SQLAlchemy engine object
:type engine: sqlalchemy.engine.Engine
:param requests: sequence of AutostackMetricRequest objects
:param modelSwapper: Model Swapper
"""
# Start collecting requested metric data
collectionIter = self._metricGetter.collectMetricData(requests)
# Aggregate each collection and dispatch to app MetricStreamer
for metricCollection in collectionIter:
request = requests[metricCollection.refID]
metricObj = request.metric
data = None
if metricCollection.slices:
aggregationFn = getAggregationFn(metricObj)
if aggregationFn:
data = aggregate(metricCollection.slices,
aggregationFn=aggregationFn)
else:
data = aggregate(metricCollection.slices)
try:
with engine.connect() as conn:
repository.retryOnTransientErrors(repository.setMetricLastTimestamp)(
conn, metricObj.uid, metricCollection.nextMetricTime)
except ObjectNotFoundError:
self._log.warning("Processing autostack data collection results for "
"unknown model=%s (model deleted?)", metricObj.uid)
continue
if data:
try:
self.metricStreamer.streamMetricData(data,
metricID=metricObj.uid,
modelSwapper=modelSwapper)
except ObjectNotFoundError:
# We expect that the model exists but in the odd case that it has
# already been deleted we don't want to crash the process.
self._log.info("Metric not found when adding data. metric=%s" %
metricObj.uid)
self._log.debug(
"{TAG:APP.AGG.DATA.PUB} Published numItems=%d for metric=%s;"
"timeRange=[%sZ-%sZ]; headTS=%sZ; tailTS=%sZ",
len(data), getMetricLogPrefix(metricObj),
metricCollection.timeRange.start.isoformat(),
metricCollection.timeRange.end.isoformat(),
data[0][0].isoformat(), data[-1][0].isoformat())
else:
self._log.info(
"{TAG:APP.AGG.DATA.NONE} No data for metric=%s;"
"timeRange=[%sZ-%sZ]", getMetricLogPrefix(metricObj),
metricCollection.timeRange.start.isoformat(),
metricCollection.timeRange.end.isoformat())
class MetricCollector(object):
"""
This process is responsible for collecting data from all the metrics at
a specified time interval
"""
# Number of concurrent worker processes used for querying metrics
_WORKER_PROCESS_POOL_SIZE = 10
# Amount of time to sleep when there are no metrics pending data collection
_NO_PENDING_METRICS_SLEEP_SEC = 10
# Metric's period times this number is the duration of the metric's quarantine
# time after a mettic errors out or returns empty data
_METRIC_QUARANTINE_DURATION_RATIO = 0.5
# The approximate time between status queries of a metric's resource to
# avoid too many of the costly calls (e.g., Cloudwatch), especially during a
# metric's catch-up phase
_RESOURCE_STATUS_UPDATE_INTERVAL_SEC = 2.5 * 60
# An self._metricInfoCache item may be garbage-collected when this much time
# passes since last access to the item
_METRIC_INFO_CACHE_ITEM_EXPIRATION_SEC = (60 * 60)
_SENTINEL = None
def __init__(self):
self._log = grok_logging.getExtendedLogger(self.__class__.__name__)
self._profiling = (
grok.app.config.getboolean("debugging", "profiling") or
self._log.isEnabledFor(logging.DEBUG))
self._pollInterval = grok.app.config.getfloat(
"metric_collector", "poll_interval")
self._metricErrorGracePeriod = grok.app.config.getfloat(
"metric_collector", "metric_error_grace_period")
# Interval for periodic garbage collection of our caches (e.g.,
# self._metricInfoCache and self._resourceInfoCache)
self._cacheGarbageCollectionIntervalSec = self._metricErrorGracePeriod * 2
# Time (unix epoch) when to run the next garbage collection of
# self._metricInfoCache and self._resourceInfoCache; 0 triggers it ASAP as a
# quick test of the logic. See self._cacheGarbageCollectionIntervalSec.
self._nextCacheGarbageCollectionTime = 0
# We use this to cache info about metrics that helps us avoid unnecessary
# queries to the datasource. The keys are metric uid's and corresponding
# values are _MetricInfoCacheItem objects.
self._metricInfoCache = defaultdict(_MetricInfoCacheItem)
# We use this to cache info about resources referenced by metrics to help
# us avoid unnecessary resource-status queries to the datasource. The keys
# are resource cananonical identifiers (from Metric.server) and
# corresponding values are _ResourceInfoCacheItem objects.
self._resourceInfoCache = defaultdict(_ResourceInfoCacheItem)
self.metricStreamer = MetricStreamer()
def _collectDataForMetrics(self, metricsToUpdate, processPool, resultsQueue):
""" Collect data for the given metrics
:param metricsToUpdate: a dict of Metric instances which are due for
an update
:param processPool: Process poll in which collection tasks are mapped.
:type processPool: multiprocessing.Pool
:param resultsQueue: Results Queue onto which collection results are
published
:type resultsQueue: multiprocessing.JoinableQueue
:returns: Result of calling `processPool.map_async()`. Call .wait() on the
return value to block until all map tasks have been completed.
Meanwhile, you may consume resultsQueue for the results as they become
available.
:rtype: multiprocessing.pool.AsyncResult
"""
# Create task parameters for concurrent processing
now = time.time()
tasks = []
for metricObj in metricsToUpdate.values():
metricSpec = utils.jsonDecode(metricObj.parameters)["metricSpec"]
resourceCacheItem = self._resourceInfoCache[metricObj.server]
if now >= resourceCacheItem.nextResourceStatusUpdateTime:
updateResourceStatus = True
resourceCacheItem.nextResourceStatusUpdateTime = (
now + self._RESOURCE_STATUS_UPDATE_INTERVAL_SEC)
else:
updateResourceStatus = False
task = _DataCollectionTask(
metricID=metricObj.uid,
datasource=metricObj.datasource,
metricSpec=metricSpec,
rangeStart=metricObj.last_timestamp,
metricPeriod=metricObj.poll_interval,
updateResourceStatus=updateResourceStatus,
resultsQueue=resultsQueue)
tasks.append(task)
# Process the tasks concurrently, return immediately
return processPool.map_async(_collect, tasks)
def _garbageCollectInfoCache(self):
""" Remove stale items from self._metricInfoCache and clear
self._resourceInfoCache
TODO: unit-test
"""
now = time.time()
self._nextCacheGarbageCollectionTime = (
now + self._cacheGarbageCollectionIntervalSec)
# Clear resource info cache
self._resourceInfoCache.clear()
self._log.info("Garbage-collected resource info cache")
# Gargabe-collect stale metric info cache items
staleProperties = tuple(
k for k, v in self._metricInfoCache.iteritems()
if now > (v.lastAccessTime + self._METRIC_INFO_CACHE_ITEM_EXPIRATION_SEC))
for k in staleProperties:
self._metricInfoCache.pop(k)
self._log.info("Garbage-collected stale=%d metric cache info items",
len(staleProperties))
def _getCandidateMetrics(self, engine):
""" Return the metrics that are due for an update
:param engine: SQLAlchemy engine object
:type engine: sqlalchemy.engine.Engine
:returns: a (possibly empty) sequence of Metric instances that are due for
an update (after removing quarantined indexes)
TODO: unit-test
"""
with engine.connect() as conn:
metricsToUpdate = repository.retryOnTransientErrors(
repository.getCloudwatchMetricsPendingDataCollection)(conn)
# Remove quarantined metrics
quarantinedIndexes = None
if metricsToUpdate:
now = time.time()
quarantinedIndexes = set(
i for i, m in enumerate(metricsToUpdate)
if now < self._metricInfoCache[m.uid].quarantineEndTime)
metricsToUpdate = OrderedDict((m.uid, m)
for i, m in enumerate(metricsToUpdate)
if i not in quarantinedIndexes)
if not metricsToUpdate:
# TODO: unit-test
self._log.debug("All candidate numMetrics=%d are quarantined",
len(quarantinedIndexes))
return metricsToUpdate
def _handleMetricCollectionError(self, engine, metricObj, startTime, error):
""" Update the metric's collector_error member and promote the metric
to ERROR state if needed
:param engine: SQLAlchemy engine object
:type engine: sqlalchemy.engine.Engine
:param metricObj: Metric instance
:param startTime: metric collection start time; unix epoch timestamp
:param error: exception object that is the result of metric data collection
or (possibly pending) resource status check
"""
# Quarantine the metric temporarily
self._metricInfoCache[metricObj.uid].quarantineEndTime = (
startTime +
metricObj.poll_interval * self._METRIC_QUARANTINE_DURATION_RATIO)
try:
if metricObj.collector_error is None:
# TODO: unit-test
# Begin error grace period for this metric
deadline = time.time() + self._metricErrorGracePeriod
with engine.connect() as conn:
repository.retryOnTransientErrors(repository.setMetricCollectorError)(
conn,
metricObj.uid,
utils.jsonEncode(dict(deadline=deadline,
message=repr(error))))
self._log.error(
"Started error grace period on metric=<%r> through %sZ due to "
"error=%r",
metricObj, datetime.utcfromtimestamp(deadline).isoformat(), error)
elif (time.time() >
utils.jsonDecode(metricObj.collector_error)["deadline"]):
# TODO: unit-test
# Error grace period expired: promote the metric to ERROR state
with engine.connect() as conn:
repository.retryOnTransientErrors(repository.setMetricStatus)(
conn, metricObj.uid, MetricStatus.ERROR, repr(error))
self._log.error(
"Metric Collector: grace period expired; placed metric=<%r> in "
"ERROR state due to error=%r", metricObj, error)
except app_exceptions.ObjectNotFoundError:
# TODO: unit-test
self._log.warning("Metric deleted? metric=%r", metricObj, exc_info=True)
def _processCollectedData(self, engine, metricsToUpdate, modelSwapper,
collectResult):
""" Process collected metrics data; publish it on the destination
message queue, and update metric instance status and metric collection
state.
:param engine: SQLAlchemy engine object
:type engine: sqlalchemy.engine.Engine
:param metricsToUpdate: a sequence of Metric instances which were due for
an update
:type metricsToUpdate: collections.OrderedDict
:param modelSwapper: ModelSwapperInterface object for running models
:param collectResult: _DataCollectionResult object
:returns: 2-tuple (numEmpty, numErrors), where numEmpty is the count of
metrics with empty data (not ready or data gap) numErrors is the count
of metrics with errors
"""
numEmpty = numErrors = 0
metricObj = metricsToUpdate[collectResult.metricID]
# Update cached resource status
resourceInfoCacheItem = self._resourceInfoCache[metricObj.server]
newResourceStatus = None
if collectResult.resourceStatus is not None:
if isinstance(collectResult.resourceStatus, Exception):
resourceInfoCacheItem.error = collectResult.resourceStatus
else:
newResourceStatus = collectResult.resourceStatus
resourceInfoCacheItem.error = None
if isinstance(collectResult.data, Exception):
dataError = collectResult.data
newData = None
nextStartTime = None
else:
dataError = None
newData = collectResult.data
nextStartTime = collectResult.nextCallStart
if newData is not None or newResourceStatus is not None:
rangeStart = metricObj.last_timestamp
if not newData:
# Data is not ready yet or data gap
numEmpty += 1
self._metricInfoCache[metricObj.uid].quarantineEndTime = (
collectResult.creationTime +
metricObj.poll_interval * self._METRIC_QUARANTINE_DURATION_RATIO)
else:
headTS = newData[0][0]
tailTS = newData[-1][0]
startTime = time.time()
# Update resource status and next metric start time
@repository.retryOnTransientErrors
def runSQL(engine):
with engine.begin() as conn:
try:
# Save resource status
if newResourceStatus is not None:
repository.saveMetricInstanceStatus(conn,
metricObj.server,
newResourceStatus)
# Save starting time for the next data query in metric row
if nextStartTime is not None:
repository.setMetricLastTimestamp(conn,
metricObj.uid,
nextStartTime)
except app_exceptions.ObjectNotFoundError:
self._log.warning("Metric deleted?", exc_info=True)
if newResourceStatus is not None or nextStartTime is not None:
runSQL(engine)
if newData:
# This is done outside the cursor context to minimize lock duration.
#
# NOTE: It is still possible for duplicate data to be sent to the
# exchange so the recipient will need to de-dupe it. It is also
# possible for the metric to be updated in the database but data not
# sent to the exchange.
try:
self.metricStreamer.streamMetricData(newData,
metricID=metricObj.uid,
modelSwapper=modelSwapper)
except ValueError:
self._log.exception("Failed to stream metric data %r" % newData)
except app_exceptions.ObjectNotFoundError:
# We expect that the model exists but in the odd case that it has
# already been deleted we don't want to crash the process.
self._log.info("Metric not found when adding data.")
if self._profiling:
self._log.info(
"{TAG:APP.MC.DATA.TO_STRM.DONE} numItems=%d for metric=%r; "
"rangeStart=%s; ts=[%s]; duration=%.4fs; ",
len(newData), metricObj,
rangeStart.isoformat() + "Z" if rangeStart is not None else None,
(("%sZ..%sZ" % (headTS.isoformat(), tailTS.isoformat()))
if len(newData) > 1 else (headTS.isoformat() + "Z")),
time.time() - startTime + collectResult.duration)
else:
if self._log.isEnabledFor(logging.DEBUG):
self._log.debug(
"{TAG:APP.MC.DATA.NONE} No data for metric=%r; rangeStart=%s; "
"duration=%.4fs", metricObj,
rangeStart.isoformat() + "Z" if rangeStart is not None else None,
time.time() - startTime + collectResult.duration)
# Take care of errors
error = dataError or resourceInfoCacheItem.error
if error is not None:
# Metric data or resource status collection error
numErrors += 1
# Update metric error grace period and quarantine info
self._handleMetricCollectionError(engine,
metricObj,
startTime=collectResult.creationTime,
error=error)
else:
if metricObj.collector_error is not None:
oldErrorInfo = metricObj.collector_error
# There was pending collector error before, but no error this time, so
# clear metric's collector_error
try:
with engine.connect() as conn:
repository.retryOnTransientErrors(
repository.setMetricCollectorError)(conn, metricObj.uid, None)
except app_exceptions.ObjectNotFoundError:
self._log.warning("Metric deleted?", exc_info=True)
self._log.info("metric=<%r> exited error grace state %s",
metricObj, oldErrorInfo)
return (numEmpty, numErrors)
@abortProgramOnAnyException(
_EXIT_CODE_ON_UNHANDLED_EXCEPTION_IN_THREAD,
logger=_getLogger())
@logExceptions(_getLogger)
def _processAndDispatchThreadTarget(self, engine, metricsToUpdate,
resultsQueue, modelSwapper, statsPipe):
""" Process collected metrics data; publish it on the destination
message queue, and update metric instance status and metric collection
state.
:param engine: SQLAlchemy engine object
:type engine: sqlalchemy.engine.Engine
:param metricsToUpdate: a sequence of Metric instances which were due for
an update
:type metricsToUpdate: collections.OrderedDict
:param resultsQueue: Queue from which a sequence of _DataCollectionResult
instances are processed
:type resultsQueue: multiprocessing.JoinableQueue
:param modelSwapper: ModelSwapperInterface object for running models
:param statsPipe: Pipe in which the final statistics are conveyed in the
form of a 2-tuple (numEmpty, numErrors), where numEmpty is the count of
metrics with empty data (not ready or data gap) numErrors is the count
of metrics with errors
:type statsPipe: multiprocessing.Pipe (non-duplex)
:returns: None
"""
numEmpty = numErrors = 0
while True:
try:
collectResult = resultsQueue.get(True)
if collectResult is self._SENTINEL:
# There are no more results to process
break
stats = self._processCollectedData(engine,
metricsToUpdate,
modelSwapper,
collectResult)
numEmpty += stats[0]
numErrors += stats[1]
finally:
resultsQueue.task_done()
statsPipe.send((numEmpty, numErrors))
def run(self):
""" Collect metric data and status for active metrics
"""
# NOTE: the process pool must be created BEFORE this main (parent) process
# creates any global or class-level shared resources (e.g., boto
# connection) that would have undersirable consequences when
# replicated into and used by forked child processes (e.g., the same MySQL
# connection socket file descriptor used by multiple processes). And we
# can't take advantage of the process Pool's maxtasksperchild feature
# either (for the same reason)
self._log.info("Starting grok Metric Collector")
resultsQueue = multiprocessing.Manager().JoinableQueue()
recvPipe, sendPipe = multiprocessing.Pipe(False)
processPool = multiprocessing.Pool(
processes=self._WORKER_PROCESS_POOL_SIZE,
maxtasksperchild=None)
try:
with ModelSwapperInterface() as modelSwapper:
engine = repository.engineFactory()
while True:
startTime = time.time()
if startTime > self._nextCacheGarbageCollectionTime:
# TODO: unit-test
self._garbageCollectInfoCache()
# Determine which metrics are due for an update
metricsToUpdate = self._getCandidateMetrics(engine)
filterDuration = time.time() - startTime
if not metricsToUpdate:
time.sleep(self._NO_PENDING_METRICS_SLEEP_SEC)
continue
# Collect metric data
collectionStartTime = time.time()
poolResults = self._collectDataForMetrics(metricsToUpdate,
processPool,
resultsQueue)
# Process/dispatch results in parallel in another thread as results
# become available in resultsQueue
dispatchThread = (
threading.Thread(target=self._processAndDispatchThreadTarget,
args=(engine,
metricsToUpdate,
resultsQueue,
modelSwapper,
sendPipe)))
dispatchStartTime = time.time()
dispatchThread.start()
# Syncronize with processPool
poolResults.wait() # Wait for collection tasks to complete
metricPollDuration = time.time() - collectionStartTime
resultsQueue.join() # Block until all tasks completed...
# Syncronize with dispatchThread
resultsQueue.put(self._SENTINEL) # Signal to dispatchThread that
# there are no more results to
# process.
resultsQueue.join()
numEmpty, numErrors = recvPipe.recv() # Get dispatchThread stats
dispatchDuration = time.time() - dispatchStartTime
self._log.info(
"Processed numMetrics=%d; numEmpty=%d; numErrors=%d; "
"duration=%.4fs (filter=%.4fs; query=%.4fs; dispatch=%.4fs)",
len(metricsToUpdate), numEmpty, numErrors,
time.time() - startTime, filterDuration,
metricPollDuration, dispatchDuration)
finally:
self._log.info("Exiting Metric Collector run-loop")
processPool.terminate()
processPool.join()
class MetricCollector(object):
"""
This process is responsible for collecting data from all the metrics at
a specified time interval
"""
# Number of concurrent worker processes used for querying metrics
_WORKER_PROCESS_POOL_SIZE = 10
# Amount of time to sleep when there are no metrics pending data collection
_NO_PENDING_METRICS_SLEEP_SEC = 10
# Metric's period times this number is the duration of the metric's quarantine
# time after a mettic errors out or returns empty data
_METRIC_QUARANTINE_DURATION_RATIO = 0.5
# The approximate time between status queries of a metric's resource to
# avoid too many of the costly calls (e.g., Cloudwatch), especially during a
# metric's catch-up phase
_RESOURCE_STATUS_UPDATE_INTERVAL_SEC = 2.5 * 60
# An self._metricInfoCache item may be garbage-collected when this much time
# passes since last access to the item
_METRIC_INFO_CACHE_ITEM_EXPIRATION_SEC = (60 * 60)
_SENTINEL = None
def __init__(self):
self._log = YOMP_logging.getExtendedLogger(self.__class__.__name__)
self._profiling = (
YOMP.app.config.getboolean("debugging", "profiling") or
self._log.isEnabledFor(logging.DEBUG))
self._pollInterval = YOMP.app.config.getfloat(
"metric_collector", "poll_interval")
self._metricErrorGracePeriod = YOMP.app.config.getfloat(
"metric_collector", "metric_error_grace_period")
# Interval for periodic garbage collection of our caches (e.g.,
# self._metricInfoCache and self._resourceInfoCache)
self._cacheGarbageCollectionIntervalSec = self._metricErrorGracePeriod * 2
# Time (unix epoch) when to run the next garbage collection of
# self._metricInfoCache and self._resourceInfoCache; 0 triggers it ASAP as a
# quick test of the logic. See self._cacheGarbageCollectionIntervalSec.
self._nextCacheGarbageCollectionTime = 0
# We use this to cache info about metrics that helps us avoid unnecessary
# queries to the datasource. The keys are metric uid's and corresponding
# values are _MetricInfoCacheItem objects.
self._metricInfoCache = defaultdict(_MetricInfoCacheItem)
# We use this to cache info about resources referenced by metrics to help
# us avoid unnecessary resource-status queries to the datasource. The keys
# are resource cananonical identifiers (from Metric.server) and
# corresponding values are _ResourceInfoCacheItem objects.
self._resourceInfoCache = defaultdict(_ResourceInfoCacheItem)
self.metricStreamer = MetricStreamer()
def _collectDataForMetrics(self, metricsToUpdate, processPool, resultsQueue):
""" Collect data for the given metrics
:param metricsToUpdate: a dict of Metric instances which are due for
an update
:param processPool: Process poll in which collection tasks are mapped.
:type processPool: multiprocessing.Pool
:param resultsQueue: Results Queue onto which collection results are
published
:type resultsQueue: multiprocessing.JoinableQueue
:returns: Result of calling `processPool.map_async()`. Call .wait() on the
return value to block until all map tasks have been completed.
Meanwhile, you may consume resultsQueue for the results as they become
available.
:rtype: multiprocessing.pool.AsyncResult
"""
# Create task parameters for concurrent processing
now = time.time()
tasks = []
for metricObj in metricsToUpdate.values():
metricSpec = utils.jsonDecode(metricObj.parameters)["metricSpec"]
resourceCacheItem = self._resourceInfoCache[metricObj.server]
if now >= resourceCacheItem.nextResourceStatusUpdateTime:
updateResourceStatus = True
resourceCacheItem.nextResourceStatusUpdateTime = (
now + self._RESOURCE_STATUS_UPDATE_INTERVAL_SEC)
else:
updateResourceStatus = False
task = _DataCollectionTask(
metricID=metricObj.uid,
datasource=metricObj.datasource,
metricSpec=metricSpec,
rangeStart=metricObj.last_timestamp,
metricPeriod=metricObj.poll_interval,
updateResourceStatus=updateResourceStatus,
resultsQueue=resultsQueue)
tasks.append(task)
# Process the tasks concurrently, return immediately
return processPool.map_async(_collect, tasks)
def _garbageCollectInfoCache(self):
""" Remove stale items from self._metricInfoCache and clear
self._resourceInfoCache
TODO: unit-test
"""
now = time.time()
self._nextCacheGarbageCollectionTime = (
now + self._cacheGarbageCollectionIntervalSec)
# Clear resource info cache
self._resourceInfoCache.clear()
self._log.info("Garbage-collected resource info cache")
# Gargabe-collect stale metric info cache items
staleProperties = tuple(
k for k, v in self._metricInfoCache.iteritems()
if now > (v.lastAccessTime + self._METRIC_INFO_CACHE_ITEM_EXPIRATION_SEC))
for k in staleProperties:
self._metricInfoCache.pop(k)
self._log.info("Garbage-collected stale=%d metric cache info items",
len(staleProperties))
def _getCandidateMetrics(self, engine):
""" Return the metrics that are due for an update
:param engine: SQLAlchemy engine object
:type engine: sqlalchemy.engine.Engine
:returns: a (possibly empty) sequence of Metric instances that are due for
an update (after removing quarantined indexes)
TODO: unit-test
"""
with engine.connect() as conn:
metricsToUpdate = repository.retryOnTransientErrors(
repository.getCloudwatchMetricsPendingDataCollection)(conn)
# Remove quarantined metrics
quarantinedIndexes = None
if metricsToUpdate:
now = time.time()
quarantinedIndexes = set(
i for i, m in enumerate(metricsToUpdate)
if now < self._metricInfoCache[m.uid].quarantineEndTime)
metricsToUpdate = OrderedDict((m.uid, m)
for i, m in enumerate(metricsToUpdate)
if i not in quarantinedIndexes)
if not metricsToUpdate:
# TODO: unit-test
self._log.debug("All candidate numMetrics=%d are quarantined",
len(quarantinedIndexes))
return metricsToUpdate
def _handleMetricCollectionError(self, engine, metricObj, startTime, error):
""" Update the metric's collector_error member and promote the metric
to ERROR state if needed
:param engine: SQLAlchemy engine object
:type engine: sqlalchemy.engine.Engine
:param metricObj: Metric instance
:param startTime: metric collection start time; unix epoch timestamp
:param error: exception object that is the result of metric data collection
or (possibly pending) resource status check
"""
# Quarantine the metric temporarily
self._metricInfoCache[metricObj.uid].quarantineEndTime = (
startTime +
metricObj.poll_interval * self._METRIC_QUARANTINE_DURATION_RATIO)
try:
if metricObj.collector_error is None:
# TODO: unit-test
# Begin error grace period for this metric
deadline = time.time() + self._metricErrorGracePeriod
with engine.connect() as conn:
repository.retryOnTransientErrors(repository.setMetricCollectorError)(
conn,
metricObj.uid,
utils.jsonEncode(dict(deadline=deadline,
message=repr(error))))
self._log.error(
"Started error grace period on metric=<%r> through %sZ due to "
"error=%r",
metricObj, datetime.utcfromtimestamp(deadline).isoformat(), error)
elif (time.time() >
utils.jsonDecode(metricObj.collector_error)["deadline"]):
# TODO: unit-test
# Error grace period expired: promote the metric to ERROR state
with engine.connect() as conn:
repository.retryOnTransientErrors(repository.setMetricStatus)(
conn, metricObj.uid, MetricStatus.ERROR, repr(error))
self._log.error(
"Metric Collector: grace period expired; placed metric=<%r> in "
"ERROR state due to error=%r", metricObj, error)
except app_exceptions.ObjectNotFoundError:
# TODO: unit-test
self._log.warning("Metric deleted? metric=%r", metricObj, exc_info=True)
def _processCollectedData(self, engine, metricsToUpdate, modelSwapper,
collectResult):
""" Process collected metrics data; publish it on the destination
message queue, and update metric instance status and metric collection
state.
:param engine: SQLAlchemy engine object
:type engine: sqlalchemy.engine.Engine
:param metricsToUpdate: a sequence of Metric instances which were due for
an update
:type metricsToUpdate: collections.OrderedDict
:param modelSwapper: ModelSwapperInterface object for running models
:param collectResult: _DataCollectionResult object
:returns: 2-tuple (numEmpty, numErrors), where numEmpty is the count of
metrics with empty data (not ready or data gap) numErrors is the count
of metrics with errors
"""
numEmpty = numErrors = 0
metricObj = metricsToUpdate[collectResult.metricID]
# Update cached resource status
resourceInfoCacheItem = self._resourceInfoCache[metricObj.server]
newResourceStatus = None
if collectResult.resourceStatus is not None:
if isinstance(collectResult.resourceStatus, Exception):
resourceInfoCacheItem.error = collectResult.resourceStatus
else:
newResourceStatus = collectResult.resourceStatus
resourceInfoCacheItem.error = None
if isinstance(collectResult.data, Exception):
dataError = collectResult.data
newData = None
nextStartTime = None
else:
dataError = None
newData = collectResult.data
nextStartTime = collectResult.nextCallStart
if newData is not None or newResourceStatus is not None:
rangeStart = metricObj.last_timestamp
if not newData:
# Data is not ready yet or data gap
numEmpty += 1
self._metricInfoCache[metricObj.uid].quarantineEndTime = (
collectResult.creationTime +
metricObj.poll_interval * self._METRIC_QUARANTINE_DURATION_RATIO)
else:
headTS = newData[0][0]
tailTS = newData[-1][0]
startTime = time.time()
# Update resource status and next metric start time
@repository.retryOnTransientErrors
def runSQL(engine):
with engine.begin() as conn:
try:
# Save resource status
if newResourceStatus is not None:
repository.saveMetricInstanceStatus(conn,
metricObj.server,
newResourceStatus)
# Save starting time for the next data query in metric row
if nextStartTime is not None:
repository.setMetricLastTimestamp(conn,
metricObj.uid,
nextStartTime)
except app_exceptions.ObjectNotFoundError:
self._log.warning("Metric deleted?", exc_info=True)
if newResourceStatus is not None or nextStartTime is not None:
runSQL(engine)
if newData:
# This is done outside the cursor context to minimize lock duration.
#
# NOTE: It is still possible for duplicate data to be sent to the
# exchange so the recipient will need to de-dupe it. It is also
# possible for the metric to be updated in the database but data not
# sent to the exchange.
try:
self.metricStreamer.streamMetricData(newData,
metricID=metricObj.uid,
modelSwapper=modelSwapper)
except ValueError:
self._log.exception("Failed to stream metric data %r" % newData)
except app_exceptions.ObjectNotFoundError:
# We expect that the model exists but in the odd case that it has
# already been deleted we don't want to crash the process.
self._log.info("Metric not found when adding data.")
if self._profiling:
self._log.info(
"{TAG:APP.MC.DATA.TO_STRM.DONE} numItems=%d for metric=%r; "
"rangeStart=%s; ts=[%s]; duration=%.4fs; ",
len(newData), metricObj,
rangeStart.isoformat() + "Z" if rangeStart is not None else None,
(("%sZ..%sZ" % (headTS.isoformat(), tailTS.isoformat()))
if len(newData) > 1 else (headTS.isoformat() + "Z")),
time.time() - startTime + collectResult.duration)
else:
if self._log.isEnabledFor(logging.DEBUG):
self._log.debug(
"{TAG:APP.MC.DATA.NONE} No data for metric=%r; rangeStart=%s; "
"duration=%.4fs", metricObj,
rangeStart.isoformat() + "Z" if rangeStart is not None else None,
time.time() - startTime + collectResult.duration)
# Take care of errors
error = dataError or resourceInfoCacheItem.error
if error is not None:
# Metric data or resource status collection error
numErrors += 1
# Update metric error grace period and quarantine info
self._handleMetricCollectionError(engine,
metricObj,
startTime=collectResult.creationTime,
error=error)
else:
if metricObj.collector_error is not None:
oldErrorInfo = metricObj.collector_error
# There was pending collector error before, but no error this time, so
# clear metric's collector_error
try:
with engine.connect() as conn:
repository.retryOnTransientErrors(
repository.setMetricCollectorError)(conn, metricObj.uid, None)
except app_exceptions.ObjectNotFoundError:
self._log.warning("Metric deleted?", exc_info=True)
self._log.info("metric=<%r> exited error grace state %s",
metricObj, oldErrorInfo)
return (numEmpty, numErrors)
@abortProgramOnAnyException(
_EXIT_CODE_ON_UNHANDLED_EXCEPTION_IN_THREAD,
logger=_getLogger())
@logExceptions(_getLogger)
def _processAndDispatchThreadTarget(self, engine, metricsToUpdate,
resultsQueue, modelSwapper, statsPipe):
""" Process collected metrics data; publish it on the destination
message queue, and update metric instance status and metric collection
state.
:param engine: SQLAlchemy engine object
:type engine: sqlalchemy.engine.Engine
:param metricsToUpdate: a sequence of Metric instances which were due for
an update
:type metricsToUpdate: collections.OrderedDict
:param resultsQueue: Queue from which a sequence of _DataCollectionResult
instances are processed
:type resultsQueue: multiprocessing.JoinableQueue
:param modelSwapper: ModelSwapperInterface object for running models
:param statsPipe: Pipe in which the final statistics are conveyed in the
form of a 2-tuple (numEmpty, numErrors), where numEmpty is the count of
metrics with empty data (not ready or data gap) numErrors is the count
of metrics with errors
:type statsPipe: multiprocessing.Pipe (non-duplex)
:returns: None
"""
numEmpty = numErrors = 0
while True:
try:
collectResult = resultsQueue.get(True)
if collectResult is self._SENTINEL:
# There are no more results to process
break
stats = self._processCollectedData(engine,
metricsToUpdate,
modelSwapper,
collectResult)
numEmpty += stats[0]
numErrors += stats[1]
finally:
resultsQueue.task_done()
statsPipe.send((numEmpty, numErrors))
def run(self):
""" Collect metric data and status for active metrics
"""
# NOTE: the process pool must be created BEFORE this main (parent) process
# creates any global or class-level shared resources (e.g., boto
# connection) that would have undersirable consequences when
# replicated into and used by forked child processes (e.g., the same MySQL
# connection socket file descriptor used by multiple processes). And we
# can't take advantage of the process Pool's maxtasksperchild feature
# either (for the same reason)
self._log.info("Starting YOMP Metric Collector")
resultsQueue = multiprocessing.Manager().JoinableQueue()
recvPipe, sendPipe = multiprocessing.Pipe(False)
processPool = multiprocessing.Pool(
processes=self._WORKER_PROCESS_POOL_SIZE,
maxtasksperchild=None)
try:
with ModelSwapperInterface() as modelSwapper:
engine = repository.engineFactory()
while True:
startTime = time.time()
if startTime > self._nextCacheGarbageCollectionTime:
# TODO: unit-test
self._garbageCollectInfoCache()
# Determine which metrics are due for an update
metricsToUpdate = self._getCandidateMetrics(engine)
filterDuration = time.time() - startTime
if not metricsToUpdate:
time.sleep(self._NO_PENDING_METRICS_SLEEP_SEC)
continue
# Collect metric data
collectionStartTime = time.time()
poolResults = self._collectDataForMetrics(metricsToUpdate,
processPool,
resultsQueue)
# Process/dispatch results in parallel in another thread as results
# become available in resultsQueue
dispatchThread = (
threading.Thread(target=self._processAndDispatchThreadTarget,
args=(engine,
metricsToUpdate,
resultsQueue,
modelSwapper,
sendPipe)))
dispatchStartTime = time.time()
dispatchThread.start()
# Syncronize with processPool
poolResults.wait() # Wait for collection tasks to complete
metricPollDuration = time.time() - collectionStartTime
resultsQueue.join() # Block until all tasks completed...
# Syncronize with dispatchThread
resultsQueue.put(self._SENTINEL) # Signal to dispatchThread that
# there are no more results to
# process.
resultsQueue.join()
numEmpty, numErrors = recvPipe.recv() # Get dispatchThread stats
dispatchDuration = time.time() - dispatchStartTime
self._log.info(
"Processed numMetrics=%d; numEmpty=%d; numErrors=%d; "
"duration=%.4fs (filter=%.4fs; query=%.4fs; dispatch=%.4fs)",
len(metricsToUpdate), numEmpty, numErrors,
time.time() - startTime, filterDuration,
metricPollDuration, dispatchDuration)
finally:
self._log.info("Exiting Metric Collector run-loop")
processPool.terminate()
processPool.join()