def _handleModelCommandResult(self, body):
""" ModelCommandResult handler. Handles model creation/deletion events and
makes the associated put_item() and delete() calls to appropriate dynamodb
tables
:param body: Incoming message payload
:type body: str
"""
try:
modelCommandResult = AnomalyService.deserializeModelResult(body)
except Exception:
g_log.exception("Error deserializing model command result")
raise
if modelCommandResult["status"] != htmengineerrno.SUCCESS:
return # Ignore...
if modelCommandResult["method"] == "defineModel":
g_log.info("Handling `defineModel` for %s",
modelCommandResult.get("modelId"))
metricItem = convertDefineModelResultToMetricItem(modelCommandResult)
g_log.info("Saving %r to dynamodb", metricItem)
self._metric.put_item(data=metricItem._asdict(), overwrite=True)
elif modelCommandResult["method"] == "deleteModel":
self._purgeMetricFromDynamoDB(modelCommandResult["modelId"])
def _handleModelCommandResult(self, body):
""" ModelCommandResult handler. Handles model creation/deletion events and
makes the associated put_item() and delete() calls to appropriate dynamodb
tables
:param body: Incoming message payload
:type body: str
"""
try:
modelCommandResult = AnomalyService.deserializeModelResult(body)
except Exception:
g_log.exception("Error deserializing model command result")
raise
if modelCommandResult["status"] != htmengineerrno.SUCCESS:
return # Ignore...
if modelCommandResult["method"] == "defineModel":
g_log.info("Handling `defineModel` for %s",
modelCommandResult.get("modelId"))
metricItem = convertDefineModelResultToMetricItem(
modelCommandResult)
g_log.info("Saving %r to dynamodb", metricItem)
self._metric.put_item(data=metricItem._asdict(), overwrite=True)
elif modelCommandResult["method"] == "deleteModel":
self._purgeMetricFromDynamoDB(modelCommandResult["modelId"])
def handleModelInferenceResults(body):
""" Model results batch handler.
:param body: Serialized message payload; the message is compliant with
htmengine/runtime/json_schema/model_inference_results_msg_schema.json.
:type body: str
"""
try:
batch = AnomalyService.deserializeModelResult(body)
except Exception:
print "Error deserializing model result"
raise
metricId = batch["metric"]["uid"]
metricName = batch["metric"]["name"]
print "Handling %d model result(s) for %s - %s" % (len(batch["results"]),
metricId,
metricName)
if not batch["results"]:
print "Empty results in model inference results batch; model=%s" % metricId
return
print metricId, batch["results"]
def handleModelCommandResult(body):
""" ModelCommandResult handler. Handles model creation/deletion events
:param body: Incoming message payload
:type body: str
"""
try:
modelCommandResult = AnomalyService.deserializeModelResult(body)
except Exception:
print "Error deserializing model command result"
raise
if modelCommandResult["status"] != htmengineerrno.SUCCESS:
return # Ignore...
if modelCommandResult["method"] == "defineModel":
print "Handling `defineModel` for %s" % modelCommandResult.get("modelId")
print modelCommandResult
elif modelCommandResult["method"] == "deleteModel":
print "Handling `deleteModel` for %s" % modelCommandResult.get("modelId")
print modelCommandResult
def handleModelCommandResult(body):
""" ModelCommandResult handler. Handles model creation/deletion events
:param body: Incoming message payload
:type body: str
"""
try:
modelCommandResult = AnomalyService.deserializeModelResult(body)
except Exception:
print "Error deserializing model command result"
raise
if modelCommandResult["status"] != htmengineerrno.SUCCESS:
return # Ignore...
if modelCommandResult["method"] == "defineModel":
print "Handling `defineModel` for %s" % modelCommandResult.get(
"modelId")
print modelCommandResult
elif modelCommandResult["method"] == "deleteModel":
print "Handling `deleteModel` for %s" % modelCommandResult.get(
"modelId")
print modelCommandResult
def handleModelInferenceResults(body):
""" Model results batch handler.
:param body: Serialized message payload; the message is compliant with
htmengine/runtime/json_schema/model_inference_results_msg_schema.json.
:type body: str
"""
try:
batch = AnomalyService.deserializeModelResult(body)
except Exception:
print "Error deserializing model result"
raise
metricId = batch["metric"]["uid"]
metricName = batch["metric"]["name"]
print "Handling %d model result(s) for %s - %s" % (len(
batch["results"]), metricId, metricName)
if not batch["results"]:
print "Empty results in model inference results batch; model=%s" % metricId
return
print metricId, batch["results"]
def _reapAnomalyServiceResults(self, metricId, numRowsExpected):
""" Retrieve likelihood results from our AMQP message queue that is bound to
Anomaly Service's results fanout exchange
NOTE that Anomaly Service fans out all results for all models via "fanout"
exchange, so our queue might contain results from additional models, which
we filter out.
:param metricId: unique id of our metric/model
:param numRowsExpected: number of result rows expected by caller
:returns: a sequence of dicts conforming to the schema of the results items
per model_inference_results_msg_schema.json
"""
rows = []
@test_case_base.retry(duration=30)
def getBatch(amqpClient):
message = amqpClient.getOneMessage(self.resultsQueueName,
noAck=False)
try:
self.assertIsNotNone(message)
except AssertionError:
LOGGER.info("Got %d rows so far, waiting for %d more",
len(rows), numRowsExpected - len(rows))
raise
return message
amqp.connection.getRabbitmqConnectionParameters()
with amqp.synchronous_amqp_client.SynchronousAmqpClient(
amqp.connection.getRabbitmqConnectionParameters(
)) as amqpClient:
lastMessage = None
while len(rows) < numRowsExpected:
message = getBatch(amqpClient)
lastMessage = message
batch = AnomalyService.deserializeModelResult(message.body)
dataType = (message.properties.headers.get("dataType")
if message.properties.headers else None)
if dataType:
continue # Not a model inference result
# batch is a dict compliant with model_inference_results_msg_schema.json
if batch["metric"]["uid"] != metricId:
# Another model's result
continue
# Extract data rows; each row is a dict from the "results" attribute per
# model_inference_results_msg_schema.json
rows.extend(batch["results"])
lastMessage.ack(multiple=True)
return rows
def _handleModelInferenceResults(self, body):
""" Model results batch handler. Publishes metric data to DynamoDB for a
given model inference results batch pulled off of the `dynamodb` queue.
:param body: Serialized message payload; the message is compliant with
htmengine/runtime/json_schema/model_inference_results_msg_schema.json.
:type body: str
"""
try:
batch = AnomalyService.deserializeModelResult(body)
except Exception:
g_log.exception("Error deserializing model result")
raise
metricId = batch["metric"]["uid"]
metricName = batch["metric"]["name"]
g_log.info("Handling %d model result(s) for %s - %s",
len(batch["results"]), metricId, metricName)
if not batch["results"]:
g_log.error(
"Empty results in model inference results batch; model=%s",
metricId)
return
lastRow = batch["results"][-1]
if (datetime.utcfromtimestamp(lastRow["ts"]) <
(datetime.utcnow() -
timedelta(days=self._FRESH_DATA_THRESHOLD_DAYS))):
g_log.info(
"Dropping stale result batch from model=%s; first=%s; last=%s",
metricId, batch["results"][0], lastRow)
return
instanceName = batch["metric"]["resource"]
metricSpec = batch["metric"]["spec"]
userInfo = metricSpec.get("userInfo", {})
metricType = userInfo.get("metricType")
metricTypeName = userInfo.get("metricTypeName")
symbol = userInfo.get("symbol")
# Although not relevant in a production setting, since dynamodb service
# sits atop htmengine and is running during htmengine integration tests
# there are inbound custom metrics that lack crucial Taurus-specific
# user-data not intended to be published on dynamodb. If the metric lacks
# any of the Taurus-required `metricType`, `metricTypeName`, or `symbol`
# userInfo keys, log it as a warning and don't publish to dynamodb.
if not metricType:
g_log.warning("Missing value for metricType, uid=%s, name=%s",
metricId, metricName)
return
if not metricTypeName:
g_log.warning("Missing value for metricTypeName, uid=%s, name=%s",
metricId, metricName)
return
if not symbol:
g_log.warning("Missing value for symbol, uid=%s, name=%s",
metricId, metricName)
return
self._publishMetricData(metricId, batch["results"])
self._publishInstanceDataHourly(instanceName, metricType,
batch["results"])
def messageHandler(self, message):
""" Inspect all inbound model results in a batch for anomaly thresholds and
trigger notifications where applicable.
:param amqp.messages.ConsumerMessage message: ``message.body`` is a
serialized batch of model inference results generated in
``AnomalyService`` and must be deserialized using
``AnomalyService.deserializeModelResult()``. The message conforms to
htmengine/runtime/json_schema/model_inference_results_msg_schema.json
"""
if message.properties.headers and "dataType" in message.properties.headers:
# Not a model inference result
message.ack()
return
htm.it.app.config.loadConfig() # reload config on every batch
engine = repository.engineFactory()
# Cache minimum threshold to trigger any notification to avoid permuting
# settings x metricDataRows
try:
try:
batch = AnomalyService.deserializeModelResult(message.body)
except Exception:
self._log.exception("Error deserializing model result")
raise
# Load all settings for all users (once per incoming batch)
with engine.connect() as conn:
settings = repository.retryOnTransientErrors(
repository.getAllNotificationSettings)(conn)
self._log.debug("settings: %r" % settings)
if settings:
minThreshold = min(setting.sensitivity for setting in settings)
else:
minThreshold = 0.99999
metricInfo = batch["metric"]
metricId = metricInfo["uid"]
resource = metricInfo["resource"]
for row in batch["results"]:
if row["anomaly"] >= minThreshold:
rowDatetime = datetime.utcfromtimestamp(row["ts"])
if not settings:
# There are no device notification settings stored on this server,
# no notifications will be generated. However, log that a
# an anomaly was detected and notification would be sent if there
# were any configured devices
self._log.info("<%r>" % (metricInfo) +
("{TAG:APP.NOTIFICATION} Anomaly "
"detected at %s, but no devices are "
"configured.") % rowDatetime)
continue
for settingObj in settings:
if row["rowid"] <= 1000:
continue # Not enough data
if rowDatetime < datetime.utcnow() - timedelta(
seconds=3600):
continue # Skip old
if row["anomaly"] >= settingObj.sensitivity:
# First let's clear any old users out of the database.
with engine.connect() as conn:
repository.retryOnTransientErrors(
repository.deleteStaleNotificationDevices)(
conn, _NOTIFICATION_DEVICE_STALE_DAYS)
# If anomaly_score meets or exceeds any of the device
# notification sensitivity settings, trigger notification.
# repository.addNotification() will handle throttling.
notificationId = str(uuid.uuid4())
with engine.connect() as conn:
result = repository.retryOnTransientErrors(
repository.addNotification)(
conn,
uid=notificationId,
server=resource,
metric=metricId,
rowid=row["rowid"],
device=settingObj.uid,
windowsize=(settingObj.windowsize),
timestamp=rowDatetime,
acknowledged=0,
seen=0)
self._log.info(
"NOTIFICATION=%s SERVER=%s METRICID=%s DEVICE=%s "
"Notification generated. " %
(notificationId, resource, metricId,
settingObj.uid))
if (result is not None and result.rowcount > 0
and settingObj.email_addr):
# Notification was generated. Attempt to send email
with engine.connect() as conn:
notificationObj = repository.getNotification(
conn, notificationId)
self.sendNotificationEmail(
engine, settingObj, notificationObj)
finally:
message.ack()
# Do cleanup
with engine.connect() as conn:
repository.clearOldNotifications(
conn) # Delete all notifications outside
def messageHandler(self, message):
""" Inspect all inbound model results in a batch for anomaly thresholds and
trigger notifications where applicable.
:param amqp.messages.ConsumerMessage message: ``message.body`` is a
serialized batch of model inference results generated in
``AnomalyService`` and must be deserialized using
``AnomalyService.deserializeModelResult()``. The message conforms to
htmengine/runtime/json_schema/model_inference_results_msg_schema.json
"""
if message.properties.headers and "dataType" in message.properties.headers:
# Not a model inference result
return
grok.app.config.loadConfig() # reload config on every batch
engine = repository.engineFactory()
# Cache minimum threshold to trigger any notification to avoid permuting
# settings x metricDataRows
try:
try:
batch = AnomalyService.deserializeModelResult(message.body)
except Exception:
self._log.exception("Error deserializing model result")
raise
# Load all settings for all users (once per incoming batch)
with engine.connect() as conn:
settings = repository.retryOnTransientErrors(
repository.getAllNotificationSettings)(conn)
self._log.debug("settings: %r" % settings)
if settings:
minThreshold = min(setting.sensitivity for setting in settings)
else:
minThreshold = 0.99999
metricInfo = batch["metric"]
metricId = metricInfo["uid"]
resource = metricInfo["resource"]
for row in batch["results"]:
if row["anomaly"] >= minThreshold:
for settingObj in settings:
if row["rowid"] <= 1000:
continue # Not enough data
rowDatetime = datetime.utcfromtimestamp(row["ts"])
if rowDatetime < datetime.utcnow() - timedelta(seconds=3600):
continue # Skip old
if row["anomaly"] >= settingObj.sensitivity:
# First let's clear any old users out of the database.
with engine.connect() as conn:
repository.retryOnTransientErrors(
repository.deleteStaleNotificationDevices)(
conn, _NOTIFICATION_DEVICE_STALE_DAYS)
# If anomaly_score meets or exceeds any of the device
# notification sensitivity settings, trigger notification.
# repository.addNotification() will handle throttling.
notificationId = str(uuid.uuid4())
with engine.connect() as conn:
result = repository.retryOnTransientErrors(
repository.addNotification)(conn,
uid=notificationId,
server=resource,
metric=metricId,
rowid=row["rowid"],
device=settingObj.uid,
windowsize=(
settingObj.windowsize),
timestamp=rowDatetime,
acknowledged=0,
seen=0)
self._log.info("NOTIFICATION=%s SERVER=%s METRICID=%s DEVICE=%s "
"Notification generated. " % (notificationId,
resource, metricId,
settingObj.uid))
if (result is not None and
result.rowcount > 0 and
settingObj.email_addr):
# Notification was generated. Attempt to send email
with engine.connect() as conn:
notificationObj = repository.getNotification(conn,
notificationId)
self.sendNotificationEmail(engine,
settingObj,
notificationObj)
if not settings:
# There are no device notification settings stored on this server,
# no notifications will be generated. However, log that a
# an anomaly was detected and notification would be sent if there
# were any configured devices
self._log.info("<%r>" % (metricInfo) + (
"{TAG:APP.NOTIFICATION} Anomaly "
"detected at %s, but no devices are "
"configured.") % rowDatetime)
finally:
message.ack()
# Do cleanup
with engine.connect() as conn:
repository.clearOldNotifications(conn) # Delete all notifications outside
def _reapAnomalyServiceResults(self, metricId, numRowsExpected):
""" Retrieve likelihood results from our AMQP message queue that is bound to
Anomaly Service's results fanout exchange
NOTE that Anomaly Service fans out all results for all models via "fanout"
exchange, so our queue might contain results from additional models, which
we filter out.
:param metricId: unique id of our metric/model
:param numRowsExpected: number of result rows expected by caller
:returns: a sequence of dicts conforming to the schema of the results items
per model_inference_results_msg_schema.json
"""
rows = []
@test_case_base.retry(duration=30)
def getBatch(amqpClient):
message = amqpClient.getOneMessage(self.resultsQueueName, noAck=False)
try:
self.assertIsNotNone(message)
except AssertionError:
LOGGER.info("Got %d rows so far, waiting for %d more",
len(rows), numRowsExpected - len(rows))
raise
return message
connParams = amqp.connection.getRabbitmqConnectionParameters()
with amqp.synchronous_amqp_client.SynchronousAmqpClient(
amqp.connection.getRabbitmqConnectionParameters()) as amqpClient:
lastMessage = None
while len(rows) < numRowsExpected:
message = getBatch(amqpClient)
lastMessage = message
batch = AnomalyService.deserializeModelResult(message.body)
dataType = (message.properties.headers.get("dataType")
if message.properties.headers else None)
if dataType:
continue # Not a model inference result
# batch is a dict compliant with model_inference_results_msg_schema.json
if batch["metric"]["uid"] != metricId:
# Another model's result
continue
# Extract data rows; each row is a dict from the "results" attribute per
# model_inference_results_msg_schema.json
rows.extend(batch["results"])
lastMessage.ack(multiple=True)
return rows
def _handleModelInferenceResults(self, body):
""" Model results batch handler. Publishes metric data to DynamoDB for a
given model inference results batch pulled off of the `dynamodb` queue.
:param body: Serialized message payload; the message is compliant with
htmengine/runtime/json_schema/model_inference_results_msg_schema.json.
:type body: str
"""
try:
batch = AnomalyService.deserializeModelResult(body)
except Exception:
g_log.exception("Error deserializing model result")
raise
metricId = batch["metric"]["uid"]
metricName = batch["metric"]["name"]
g_log.info("Handling %d model result(s) for %s - %s",
len(batch["results"]), metricId, metricName)
if not batch["results"]:
g_log.error("Empty results in model inference results batch; model=%s",
metricId)
return
lastRow = batch["results"][-1]
if (datetime.utcfromtimestamp(lastRow["ts"]) <
(datetime.utcnow() -
timedelta(days=self._FRESH_DATA_THRESHOLD_DAYS))):
g_log.info("Dropping stale result batch from model=%s; first=%s; last=%s",
metricId, batch["results"][0], lastRow)
return
instanceName = batch["metric"]["resource"]
metricSpec = batch["metric"]["spec"]
userInfo = metricSpec.get("userInfo", {})
metricType = userInfo.get("metricType")
metricTypeName = userInfo.get("metricTypeName")
symbol = userInfo.get("symbol")
# Although not relevant in a production setting, since dynamodb service
# sits atop htmengine and is running during htmengine integration tests
# there are inbound custom metrics that lack crucial Taurus-specific
# user-data not intended to be published on dynamodb. If the metric lacks
# any of the Taurus-required `metricType`, `metricTypeName`, or `symbol`
# userInfo keys, log it as a warning and don't publish to dynamodb.
if not metricType:
g_log.warning("Missing value for metricType, uid=%s, name=%s",
metricId, metricName)
return
if not metricTypeName:
g_log.warning("Missing value for metricTypeName, uid=%s, name=%s",
metricId, metricName)
return
if not symbol:
g_log.warning("Missing value for symbol, uid=%s, name=%s",
metricId, metricName)
return
self._publishMetricData(metricId, batch["results"])
self._publishInstanceDataHourly(instanceName, metricType,
batch["results"])
