def getAggregationFn(metric):
fn = None
slaveDatasource = AutostackMetricAdapterBase.getMetricDatasource(metric)
metricAdapter = AutostackMetricAdapterBase.getMetricAdapter(slaveDatasource)
query = metricAdapter.getQueryParams(metric.name)
if "statistics" in query and query["statistics"] == "Sum":
fn = sum
return fn
def _createMetricDataCollectionTasks(cls, requests, instanceCache):
""" Create tasks to be executed concurrently from the given collection
requests.
:param requests: Metric collection requests
:type requests: A sequence of AutostackMetricRequest objects
:param instanceCache: Autostack instance cache. All Autostacks referenced in
requests are expected to be present in instance cache
:type instanceCache: a dict, where each key is an Autostack uid and the
corresponding value is an _InstanceCacheValue object
:returns: data collection tasks and request
refID-to-_MetricCollectionAccumulator mappings
:rtype: A two-tuple:
The first element is a sequence of _MetricCollectionTask objects with
refID values from the corresponding AutostackMetricRequest objects;
The second elment is a dict of the
refID-to-_MetricCollectionAccumulator mappings. The refID values are the
ones provided by user in the corresponding AutostackMetricRequest
objects.
"""
accumulatorMap = dict()
tasks = []
for request in requests:
refID = request.refID
autostack = request.autostack
metric = request.metric
period = metric.poll_interval
slaveDatasource = AutostackMetricAdapterBase.getMetricDatasource(
metric)
if slaveDatasource == "autostacks":
timeRange = cls._getMetricCollectionTimeSliceForAutostackMetric(
period=period)
else:
timeRange = cls._getMetricCollectionTimeSlice(
startTime=metric.last_timestamp, period=period)
instanceCacheItem = instanceCache[autostack.uid]
region = autostack.region
metricAdapter = AutostackMetricAdapterBase.getMetricAdapter(
slaveDatasource)
queryParams = metricAdapter.getQueryParams(metric.name)
metricName = metric.name.split("/")[-1]
stats = queryParams["statistics"]
unit = queryParams["unit"]
# Generate metric data collection tasks for the current request
for instance in instanceCacheItem.instances:
# instance is an aggregator_instances.InstanceInfo object
task = _MetricCollectionTask(refID=refID,
metricID=metric.uid,
region=region,
instanceID=instance.instanceID,
metricName=metricName,
stats=stats,
unit=unit,
period=period,
timeRange=timeRange)
tasks.append(task)
# Create the metric collection accumulator for the current request
assert refID not in accumulatorMap
accumulatorMap[refID] = _MetricCollectionAccumulator(
expectedNumSlices=len(instanceCacheItem.instances),
collection=MetricCollection(refID=refID,
slices=[],
timeRange=timeRange,
nextMetricTime=timeRange.end))
return tasks, accumulatorMap
def _createMetricDataCollectionTasks(cls, requests, instanceCache):
""" Create tasks to be executed concurrently from the given collection
requests.
:param requests: Metric collection requests
:type requests: A sequence of AutostackMetricRequest objects
:param instanceCache: Autostack instance cache. All Autostacks referenced in
requests are expected to be present in instance cache
:type instanceCache: a dict, where each key is an Autostack uid and the
corresponding value is an _InstanceCacheValue object
:returns: data collection tasks and request
refID-to-_MetricCollectionAccumulator mappings
:rtype: A two-tuple:
The first element is a sequence of _MetricCollectionTask objects with
refID values from the corresponding AutostackMetricRequest objects;
The second elment is a dict of the
refID-to-_MetricCollectionAccumulator mappings. The refID values are the
ones provided by user in the corresponding AutostackMetricRequest
objects.
"""
accumulatorMap = dict()
tasks = []
for request in requests:
refID = request.refID
autostack = request.autostack
metric = request.metric
period = metric.poll_interval
slaveDatasource = AutostackMetricAdapterBase.getMetricDatasource(metric)
if slaveDatasource == "autostacks":
timeRange = cls._getMetricCollectionTimeSliceForAutostackMetric(period=period)
else:
timeRange = cls._getMetricCollectionTimeSlice(startTime=metric.last_timestamp, period=period)
instanceCacheItem = instanceCache[autostack.uid]
region = autostack.region
metricAdapter = AutostackMetricAdapterBase.getMetricAdapter(slaveDatasource)
queryParams = metricAdapter.getQueryParams(metric.name)
metricName = metric.name.split("/")[-1]
stats = queryParams["statistics"]
unit = queryParams["unit"]
# Generate metric data collection tasks for the current request
for instance in instanceCacheItem.instances:
# instance is an aggregator_instances.InstanceInfo object
task = _MetricCollectionTask(
refID=refID,
metricID=metric.uid,
region=region,
instanceID=instance.instanceID,
metricName=metricName,
stats=stats,
unit=unit,
period=period,
timeRange=timeRange,
)
tasks.append(task)
# Create the metric collection accumulator for the current request
assert refID not in accumulatorMap
accumulatorMap[refID] = _MetricCollectionAccumulator(
expectedNumSlices=len(instanceCacheItem.instances),
collection=MetricCollection(refID=refID, slices=[], timeRange=timeRange, nextMetricTime=timeRange.end),
)
return tasks, accumulatorMap
def monitorMetric(self, modelSpec):
""" Start monitoring a metric; create a model linked to an existing
Autostack
:param modelSpec: model specification for an Autostack-based model
:type modelSpec: dict
::
{
"datasource": "autostack",
"metricSpec": {
# TODO [MER-3533]: This should be autostack name instead
"autostackId": "a858c6990a444cd8a07466ec7f3cae16",
"slaveDatasource": "cloudwatch",
"slaveMetric": {
# specific to slaveDatasource
"namespace": "AWS/EC2",
"metric": "CPUUtilization"
},
"period": 300 # aggregation period; seconds
},
"modelParams": { # optional; specific to slave metric
"min": 0, # optional
"max": 100 # optional
}
}
:returns: datasource-specific unique model identifier
:raises grok.app.exceptions.ObjectNotFoundError: if referenced autostack
doesn't exist
:raises grok.app.exceptions.MetricNotSupportedError: if requested metric
isn't supported
:raises grok.app.exceptions.MetricAlreadyMonitored: if the metric is already
being monitored
"""
metricSpec = modelSpec["metricSpec"]
autostackId = metricSpec["autostackId"]
with self.connectionFactory() as conn:
autostack = repository.getAutostack(conn, autostackId)
slaveDatasource = metricSpec["slaveDatasource"]
slaveMetric = metricSpec["slaveMetric"]
canonicalResourceName = self.getInstanceNameForModelSpec(modelSpec)
metricAdapter = AutostackMetricAdapterBase.getMetricAdapter(
slaveDatasource)
nameColumnValue = metricAdapter.getMetricName(slaveMetric)
metricDescription = metricAdapter.getMetricDescription(
slaveMetric, autostack)
queryParams = metricAdapter.getQueryParams(nameColumnValue)
defaultMin = queryParams["min"]
defaultMax = queryParams["max"]
defaultPeriod = queryParams["period"]
modelParams = modelSpec.get("modelParams", dict())
explicitMin = modelParams.get("min")
explicitMax = modelParams.get("max")
explicitPeriod = metricSpec.get("period")
if (explicitMin is None) != (explicitMax is None):
raise ValueError(
"min and max params must both be None or non-None; modelSpec=%r"
% (modelSpec, ))
minVal = explicitMin if explicitMin is not None else defaultMin
maxVal = explicitMax if explicitMax is not None else defaultMax
period = explicitPeriod if explicitPeriod is not None else defaultPeriod
stats = {"min": minVal, "max": maxVal}
if minVal is None or maxVal is None:
raise ValueError("Expected min and max to be set")
swarmParams = scalar_metric_utils.generateSwarmParams(stats)
@repository.retryOnTransientErrors
def startMonitoringWithRetries():
"""
:returns: metricId
"""
with self.connectionFactory() as conn:
with conn.begin():
repository.lockOperationExclusive(
conn, repository.OperationLock.METRICS)
# Check if the metric is already monitored
matchingMetrics = repository.getAutostackMetricsWithMetricName(
conn,
autostackId,
nameColumnValue,
fields=[schema.metric.c.uid])
matchingMetric = next(iter(matchingMetrics), None)
if matchingMetric:
msg = (
"monitorMetric: Autostack modelId=%s is already "
"monitoring metric=%s on resource=%s; model=%r" %
(matchingMetric.uid, nameColumnValue,
canonicalResourceName, matchingMetric))
self._log.warning(msg)
raise grok.app.exceptions.MetricAlreadyMonitored(
msg, uid=matchingMetric.uid)
# Add a metric row for the requested metric
metricDict = repository.addMetric(
conn,
datasource=self._DATASOURCE,
name=nameColumnValue,
description=metricDescription,
server=canonicalResourceName,
location=autostack.region,
tag_name=autostack.name,
parameters=htmengine.utils.jsonEncode(modelSpec),
poll_interval=period,
status=MetricStatus.UNMONITORED)
metricId = metricDict["uid"]
repository.addMetricToAutostack(conn, autostackId,
metricId)
# Start monitoring
scalar_metric_utils.startMonitoring(
conn=conn,
metricId=metricId,
swarmParams=swarmParams,
logger=self._log)
self._log.info("monitorMetric: monitoring metric=%s, stats=%r",
metricId, stats)
return metricId
return startMonitoringWithRetries()
def monitorMetric(self, modelSpec):
""" Start monitoring a metric; create a model linked to an existing
Autostack
:param modelSpec: model specification for an Autostack-based model
:type modelSpec: dict
::
{
"datasource": "autostack",
"metricSpec": {
# TODO [MER-3533]: This should be autostack name instead
"autostackId": "a858c6990a444cd8a07466ec7f3cae16",
"slaveDatasource": "cloudwatch",
"slaveMetric": {
# specific to slaveDatasource
"namespace": "AWS/EC2",
"metric": "CPUUtilization"
},
"period": 300 # aggregation period; seconds
},
"modelParams": { # optional; specific to slave metric
"min": 0, # optional
"max": 100 # optional
}
}
:returns: datasource-specific unique model identifier
:raises grok.app.exceptions.ObjectNotFoundError: if referenced autostack
doesn't exist
:raises grok.app.exceptions.MetricNotSupportedError: if requested metric
isn't supported
:raises grok.app.exceptions.MetricAlreadyMonitored: if the metric is already
being monitored
"""
metricSpec = modelSpec["metricSpec"]
autostackId = metricSpec["autostackId"]
with self.connectionFactory() as conn:
autostack = repository.getAutostack(conn, autostackId)
slaveDatasource = metricSpec["slaveDatasource"]
slaveMetric = metricSpec["slaveMetric"]
canonicalResourceName = self.getInstanceNameForModelSpec(modelSpec)
metricAdapter = AutostackMetricAdapterBase.getMetricAdapter(slaveDatasource)
nameColumnValue = metricAdapter.getMetricName(slaveMetric)
metricDescription = metricAdapter.getMetricDescription(slaveMetric,
autostack)
queryParams = metricAdapter.getQueryParams(nameColumnValue)
defaultMin = queryParams["min"]
defaultMax = queryParams["max"]
defaultPeriod = queryParams["period"]
modelParams = modelSpec.get("modelParams", dict())
explicitMin = modelParams.get("min")
explicitMax = modelParams.get("max")
explicitPeriod = metricSpec.get("period")
if (explicitMin is None) != (explicitMax is None):
raise ValueError(
"min and max params must both be None or non-None; modelSpec=%r"
% (modelSpec,))
minVal = explicitMin if explicitMin is not None else defaultMin
maxVal = explicitMax if explicitMax is not None else defaultMax
period = explicitPeriod if explicitPeriod is not None else defaultPeriod
stats = {"min": minVal, "max": maxVal}
if minVal is None or maxVal is None:
raise ValueError("Expected min and max to be set")
swarmParams = scalar_metric_utils.generateSwarmParams(stats)
@repository.retryOnTransientErrors
def startMonitoringWithRetries():
"""
:returns: metricId
"""
with self.connectionFactory() as conn:
with conn.begin():
repository.lockOperationExclusive(conn,
repository.OperationLock.METRICS)
# Check if the metric is already monitored
matchingMetrics = repository.getAutostackMetricsWithMetricName(
conn,
autostackId,
nameColumnValue,
fields=[schema.metric.c.uid])
matchingMetric = next(iter(matchingMetrics), None)
if matchingMetric:
msg = ("monitorMetric: Autostack modelId=%s is already "
"monitoring metric=%s on resource=%s; model=%r"
% (matchingMetric.uid, nameColumnValue,
canonicalResourceName, matchingMetric))
self._log.warning(msg)
raise grok.app.exceptions.MetricAlreadyMonitored(
msg,
uid=matchingMetric.uid)
# Add a metric row for the requested metric
metricDict = repository.addMetric(
conn,
datasource=self._DATASOURCE,
name=nameColumnValue,
description=metricDescription,
server=canonicalResourceName,
location=autostack.region,
tag_name=autostack.name,
parameters=htmengine.utils.jsonEncode(modelSpec),
poll_interval=period,
status=MetricStatus.UNMONITORED)
metricId = metricDict["uid"]
repository.addMetricToAutostack(conn, autostackId, metricId)
# Start monitoring
scalar_metric_utils.startMonitoring(
conn=conn,
metricId=metricId,
swarmParams=swarmParams,
logger=self._log)
self._log.info("monitorMetric: monitoring metric=%s, stats=%r",
metricId, stats)
return metricId
return startMonitoringWithRetries()
