def _updateAnomalyLikelihoodParams(cls, conn, metricId, modelParamsJson,
likelihoodParams):
"""Update and save anomaly_params with the given likelihoodParams if the
metric is ACTIVE.
:param conn: Transactional SQLAlchemy connection object
:type conn: sqlalchemy.engine.base.Connection
:param metricId: Metric uid
:param modelParamsJson: Model params JSON object (from model_params metric
column)
:param likelihoodParams: anomaly likelihood params dict
:raises: htmengine.exceptions.MetricNotActiveError if metric's status is not
MetricStatus.ACTIVE
"""
lockedRow = repository.getMetricWithUpdateLock(
conn,
metricId,
fields=[schema.metric.c.status])
if lockedRow.status != MetricStatus.ACTIVE:
raise MetricNotActiveError(
"_updateAnomalyLikelihoodParams failed because metric=%s is not "
"ACTIVE; status=%s" % (metricId, lockedRow.status,))
modelParams = json.loads(modelParamsJson)
modelParams["anomalyLikelihoodParams"] = likelihoodParams
repository.updateMetricColumns(conn,
metricId,
{"model_params": json.dumps(modelParams)})
def _updateAnomalyLikelihoodParams(cls, conn, metricId, modelParamsJson,
likelihoodParams):
"""Update and save anomaly_params with the given likelyhoodParams if the
metric is ACTIVE.
:param conn: Transactional SQLAlchemy connection object
:type conn: sqlalchemy.engine.base.Connection
:param metricId: Metric uid
:param modelParamsJson: Model params JSON object (from model_params metric
column)
:param likelihoodParams: anomaly likelihood params dict
:raises: htmengine.exceptions.MetricNotActiveError if metric's status is not
MetricStatus.ACTIVE
"""
lockedRow = repository.getMetricWithUpdateLock(
conn, metricId, fields=[schema.metric.c.status])
if lockedRow.status != MetricStatus.ACTIVE:
raise MetricNotActiveError(
"_updateAnomalyLikelihoodParams failed because metric=%s is not "
"ACTIVE; status=%s" % (
metricId,
lockedRow.status,
))
modelParams = json.loads(modelParamsJson)
modelParams["anomalyLikelihoodParams"] = likelihoodParams
repository.updateMetricColumns(
conn, metricId, {"model_params": json.dumps(modelParams)})
def _startMonitoringWithRetries(self, metricId, modelSpec, swarmParams):
""" Perform the start-monitoring operation atomically/reliably
:param metricId: unique identifier of the metric row
:param modelSpec: same as `modelSpec`` from `monitorMetric`
:param swarmParams: object returned by
scalar_metric_utils.generateSwarmParams()
:raises htmengine.exceptions.ObjectNotFoundError: if referenced metric
doesn't exist
:raises htmengine.exceptions.MetricNotSupportedError: if requested metric
isn't supported
:raises htmengine.exceptions.MetricAlreadyMonitored: if the metric is
already being monitored
"""
with self.connectionFactory() as conn:
with conn.begin():
# Lock the metric to synchronize with metric streamer; must be first
# call at start of transaction
metricObj = repository.getMetricWithUpdateLock(conn, metricId)
if metricObj.datasource != self._DATASOURCE:
raise TypeError("Not an HTM metric=%r; modelSpec=%r" %
(metricObj, modelSpec))
if metricObj.status != MetricStatus.UNMONITORED:
self._log.info(
"monitorMetric: already monitored; metric=%r",
metricObj)
raise app_exceptions.MetricAlreadyMonitored(
("Custom metric=%s is already monitored by model=%r" %
(
metricObj.name,
metricObj,
)),
uid=metricId)
# Save model specification in metric row
update = {"parameters": htmengine.utils.jsonEncode(modelSpec)}
instanceName = self.getInstanceNameForModelSpec(modelSpec)
if instanceName is not None:
update["server"] = instanceName
repository.updateMetricColumns(conn, metricId, update)
modelStarted = scalar_metric_utils.startMonitoring(
conn=conn,
metricId=metricId,
swarmParams=swarmParams,
logger=self._log)
if modelStarted:
scalar_metric_utils.sendBacklogDataToModel(
conn=conn, metricId=metricId, logger=self._log)
def _startMonitoringWithRetries(self, metricId, modelSpec, swarmParams):
""" Perform the start-monitoring operation atomically/reliably
:param metricId: unique identifier of the metric row
:param modelSpec: same as `modelSpec`` from `monitorMetric`
:param swarmParams: object returned by
scalar_metric_utils.generateSwarmParams()
:raises htmengine.exceptions.ObjectNotFoundError: if referenced metric
doesn't exist
:raises htmengine.exceptions.MetricNotSupportedError: if requested metric
isn't supported
:raises htmengine.exceptions.MetricAlreadyMonitored: if the metric is
already being monitored
"""
with self.connectionFactory() as conn:
with conn.begin():
# Lock the metric to synchronize with metric streamer; must be first
# call at start of transaction
metricObj = repository.getMetricWithUpdateLock(conn, metricId)
if metricObj.datasource != self._DATASOURCE:
raise TypeError("Not an HTM metric=%r; modelSpec=%r"
% (metricObj, modelSpec))
if metricObj.status != MetricStatus.UNMONITORED:
self._log.info("monitorMetric: already monitored; metric=%r",
metricObj)
raise app_exceptions.MetricAlreadyMonitored(
("Custom metric=%s is already monitored by model=%r"
% (metricObj.name, metricObj,)),
uid=metricId)
# Save model specification in metric row
update = {"parameters": htmengine.utils.jsonEncode(modelSpec)}
instanceName = self.getInstanceNameForModelSpec(modelSpec)
if instanceName is not None:
update["server"] = instanceName
repository.updateMetricColumns(conn, metricId, update)
modelStarted = scalar_metric_utils.startMonitoring(
conn=conn,
metricId=metricId,
swarmParams=swarmParams,
logger=self._log)
if modelStarted:
scalar_metric_utils.sendBacklogDataToModel(
conn=conn,
metricId=metricId,
logger=self._log)
