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 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
