def activateModel(self, metricId):
""" Start a model that is PENDING_DATA, creating the OPF/CLA model
NOTE: used by MetricStreamer when model is in PENDING_DATA state and
sufficient data samples are available to get statistics and complete model
creation.
:param metricId: unique identifier of the metric row
:raises grok.app.exceptions.ObjectNotFoundError: if metric with the
referenced metric uid doesn't exist
:raises grok.app.exceptions.MetricStatisticsNotReadyError:
"""
with self.connectionFactory() as conn:
# TODO: This function is identical to custom metric activateModel()
metricObj = repository.getMetric(
conn, metricId, fields=[schema.metric.c.datasource, schema.metric.c.parameters]
)
if metricObj.datasource != self._DATASOURCE:
raise TypeError("activateModel: not a cloudwatch metric=%r" % (metricObj,))
if metricObj.parameters:
parameters = htmengine.utils.jsonDecode(metricObj.parameters)
else:
parameters = {}
stats = self._getMetricStatistics(parameters["metricSpec"])
self._log.info("activateModel: metric=%s, stats=%r", metricId, stats)
swarmParams = scalar_metric_utils.generateSwarmParams(stats)
scalar_metric_utils.startModel(metricId, swarmParams=swarmParams, logger=self._log)
def activateModel(self, metricId):
""" Start a model that is PENDING_DATA, creating the OPF/CLA model
NOTE: used by MetricStreamer when model is in PENDING_DATA state and
sufficient data samples are available to get statistics and complete model
creation.
:param metricId: unique identifier of the metric row
:raises htmengine.exceptions.ObjectNotFoundError: if metric with the
referenced metric uid doesn't exist
:raises htmengine.exceptions.MetricStatisticsNotReadyError:
"""
# Load the existing metric
with self.connectionFactory() as conn:
metricObj = repository.getMetric(conn,
metricId,
fields=[schema.metric.c.datasource])
if metricObj.datasource != self._DATASOURCE:
raise TypeError(
"activateModel: not an HTM metric=%s; datasource=%s" %
(metricId, metricObj.datasource))
stats = self._getMetricStatistics(metricId)
swarmParams = scalar_metric_utils.generateSwarmParams(stats)
scalar_metric_utils.startModel(metricId,
swarmParams=swarmParams,
logger=self._log)
def activateModel(self, metricId):
""" Start a model that is PENDING_DATA, creating the OPF/CLA model
NOTE: used by MetricStreamer when model is in PENDING_DATA state and
sufficient data samples are available to get statistics and complete model
creation.
:param metricId: unique identifier of the metric row
:raises htmengine.exceptions.ObjectNotFoundError: if metric with the
referenced metric uid doesn't exist
:raises htmengine.exceptions.MetricStatisticsNotReadyError:
"""
# Load the existing metric
with self.connectionFactory() as conn:
metricObj = repository.getMetric(
conn, metricId, fields=[schema.metric.c.datasource])
if metricObj.datasource != self._DATASOURCE:
raise TypeError(
"activateModel: not an HTM metric=%s; datasource=%s" %
(metricId, metricObj.datasource))
stats = self._getMetricStatistics(metricId)
swarmParams = scalar_metric_utils.generateSwarmParams(stats)
scalar_metric_utils.startModel(metricId,
swarmParams=swarmParams,
logger=self._log)
def activateModel(self, metricId):
""" Start a model that is PENDING_DATA, creating the OPF/CLA model
NOTE: used by MetricStreamer when model is in PENDING_DATA state and
sufficient data samples are available to get statistics and complete model
creation.
:param metricId: unique identifier of the metric row
:raises htm.it.app.exceptions.ObjectNotFoundError: if metric with the
referenced metric uid doesn't exist
:raises htm.it.app.exceptions.MetricStatisticsNotReadyError:
"""
with self.connectionFactory() as conn:
# TODO: This function is identical to custom metric activateModel()
metricObj = repository.getMetric(conn,
metricId,
fields=[
schema.metric.c.datasource,
schema.metric.c.parameters
])
if metricObj.datasource != self._DATASOURCE:
raise TypeError("activateModel: not a cloudwatch metric=%r" %
(metricObj, ))
if metricObj.parameters:
parameters = htmengine.utils.jsonDecode(metricObj.parameters)
else:
parameters = {}
stats = self._getMetricStatistics(parameters["metricSpec"])
self._log.info("activateModel: metric=%s, stats=%r", metricId, stats)
swarmParams = scalar_metric_utils.generateSwarmParams(stats)
scalar_metric_utils.startModel(metricId,
swarmParams=swarmParams,
logger=self._log)
