def testPublishMetricDataWithDuplicateKeys(self, connectDynamoDB,
_gracefulCreateTable):
""" Test for elimination of rows with duplicate keys by _publishMetricData
"""
metricId = "3b035a5916994f2bb950f5717138f94b"
rowTemplate = dict(
rowid=99,
ts=epochFromNaiveUTCDatetime(datetime(2015, 3, 20, 0, 46, 28)),
value=10305.0,
rawAnomaly=0.275,
anomaly=0.999840891
)
row1 = dict(rowTemplate)
row2 = dict(rowTemplate)
row2["rowid"] = row1["rowid"] + 1
rows = [row1, row2]
service = DynamoDBService()
service._publishMetricData(metricId, rows)
data = dynamodb_service.convertInferenceResultRowToMetricDataItem(metricId,
row1)
mockPutItem = (service._metric_data.batch_write.return_value.__enter__
.return_value.put_item)
mockPutItem.assert_called_once_with(data=data._asdict(), overwrite=True)
def testPublishMetricDataWithDuplicateKeys(self, connectDynamoDB,
_gracefulCreateTable):
""" Test for elimination of rows with duplicate keys by _publishMetricData
"""
metricId = "3b035a5916994f2bb950f5717138f94b"
rowTemplate = dict(rowid=99,
ts=epochFromNaiveUTCDatetime(
datetime(2015, 3, 20, 0, 46, 28)),
value=10305.0,
rawAnomaly=0.275,
anomaly=0.999840891)
row1 = dict(rowTemplate)
row2 = dict(rowTemplate)
row2["rowid"] = row1["rowid"] + 1
rows = [row1, row2]
service = DynamoDBService()
service._publishMetricData(metricId, rows)
data = dynamodb_service.convertInferenceResultRowToMetricDataItem(
metricId, row1)
mockPutItem = (service._metric_data.batch_write.return_value.__enter__.
return_value.put_item)
mockPutItem.assert_called_once_with(data=data._asdict(),
overwrite=True)
def testMessageHandlerRoutesMetricDataToDynamoDB(
self, _amqpUtilsMock,
deserializeModelResult, connectDynamoDB, _gracefulCreateTable):
""" Given a batch of model inference results, send the appropriate data to
DynamoDB tables according to design in an environment where both rabbitmq
and dynamodb are mocked out
"""
# We're going to mostly mock out all of the arguments to
# DynamoDBService.messageHandler() since it is normally called by amqp lib.
# Then simulate the process of handling an inbound batch of model inference
# results and assert that the appropriate put_item() calls are made at the
# other end.
message = amqp.messages.ConsumerMessage(
body=Mock(),
properties=Mock(headers=dict()),
methodInfo=amqp.messages.MessageDeliveryInfo(consumerTag=Mock(),
deliveryTag=Mock(),
redelivered=False,
exchange=Mock(),
routingKey=""),
ackImpl=Mock(),
nackImpl=Mock())
# We will have to bypass the normal serialize/deserialize phases to avoid
# dependency on sqlalchemy rowproxy. Instead, we'll just mock out the
# AnomalyService.deserializeModelResult() call, returning an object that
# approximates a batch of model inference results as much as possible
now = int(time.time())
resultRow = dict(
rowid=4790,
ts=now,
value=9305.0,
rawAnomaly=0.775,
anomaly=0.999840891
)
metricId = "3b035a5916994f2bb950f5717138f94b"
deserializeModelResult.return_value = dict(
metric=dict(
uid=metricId,
name="XIGNITE.AGN.VOLUME",
description="XIGNITE.AGN.VOLUME",
resource="Resource-of-XIGNITE.AGN.VOLUME",
location = "",
datasource = "custom",
spec=dict(
userInfo=dict(
symbol="AGN",
metricType="StockVolume",
metricTypeName="Stock Volume"
)
)
),
results=[resultRow]
)
service = DynamoDBService()
service.messageHandler(message)
deserializeModelResult.assert_called_once_with(message.body)
mockMetricDataPutItem = (
service._metric_data.batch_write.return_value.__enter__
.return_value.put_item)
data = dynamodb_service.convertInferenceResultRowToMetricDataItem(
metricId, resultRow)
mockMetricDataPutItem.assert_called_once_with(data=data._asdict(),
overwrite=True)
self.assertFalse(service._metric_tweets.batch_write.called)
# Make sure that a model command result doesn't get mistaken for an
# inference result batch
deserializeModelResult.return_value = Mock()
message.properties = Mock(headers=dict(dataType="model-cmd-result"))
message.body = Mock()
service = DynamoDBService()
with patch.object(service, "_handleModelCommandResult",
spec_set=service._handleModelCommandResult):
service.messageHandler(message)
service._handleModelCommandResult.assert_called_once_with(message.body)
def testMessageHandlerRoutesMetricDataToDynamoDB(self, _amqpUtilsMock,
deserializeModelResult,
connectDynamoDB,
_gracefulCreateTable):
""" Given a batch of model inference results, send the appropriate data to
DynamoDB tables according to design in an environment where both rabbitmq
and dynamodb are mocked out
"""
# We're going to mostly mock out all of the arguments to
# DynamoDBService.messageHandler() since it is normally called by amqp lib.
# Then simulate the process of handling an inbound batch of model inference
# results and assert that the appropriate put_item() calls are made at the
# other end.
message = amqp.messages.ConsumerMessage(
body=Mock(),
properties=Mock(headers=dict()),
methodInfo=amqp.messages.MessageDeliveryInfo(consumerTag=Mock(),
deliveryTag=Mock(),
redelivered=False,
exchange=Mock(),
routingKey=""),
ackImpl=Mock(),
nackImpl=Mock())
# We will have to bypass the normal serialize/deserialize phases to avoid
# dependency on sqlalchemy rowproxy. Instead, we'll just mock out the
# AnomalyService.deserializeModelResult() call, returning an object that
# approximates a batch of model inference results as much as possible
now = int(time.time())
resultRow = dict(rowid=4790,
ts=now,
value=9305.0,
rawAnomaly=0.775,
anomaly=0.999840891)
metricId = "3b035a5916994f2bb950f5717138f94b"
deserializeModelResult.return_value = dict(metric=dict(
uid=metricId,
name="XIGNITE.AGN.VOLUME",
description="XIGNITE.AGN.VOLUME",
resource="Resource-of-XIGNITE.AGN.VOLUME",
location="",
datasource="custom",
spec=dict(userInfo=dict(symbol="AGN",
metricType="StockVolume",
metricTypeName="Stock Volume"))),
results=[resultRow])
service = DynamoDBService()
service.messageHandler(message)
deserializeModelResult.assert_called_once_with(message.body)
mockMetricDataPutItem = (service._metric_data.batch_write.return_value.
__enter__.return_value.put_item)
data = dynamodb_service.convertInferenceResultRowToMetricDataItem(
metricId, resultRow)
mockMetricDataPutItem.assert_called_once_with(data=data._asdict(),
overwrite=True)
self.assertFalse(service._metric_tweets.batch_write.called)
# Make sure that a model command result doesn't get mistaken for an
# inference result batch
deserializeModelResult.return_value = Mock()
message.properties = Mock(headers=dict(dataType="model-cmd-result"))
message.body = Mock()
service = DynamoDBService()
with patch.object(service,
"_handleModelCommandResult",
spec_set=service._handleModelCommandResult):
service.messageHandler(message)
service._handleModelCommandResult.assert_called_once_with(
message.body)
