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 testDynamoDBServiceRun(self, amqpClientClassMock, connectDynamoDB,
_gracefulCreateTable):
""" Very basic test to validate that the service follows AMQP protocol.
Upon `run()`, it should:
1. Connecto to RabbitMQ
2. Open a channel
3. Declare two exchanges; one for model results, and one for non-metric
data
4. Declare a durable "dynamodb" queue
5. Bind the "dynamodb" queue to the two exchanges
6. Start consuming.
"""
amqpClientMock = MagicMock(spec_set=(
dynamodb_service.amqp.synchronous_amqp_client.SynchronousAmqpClient
))
amqpClientMock.__enter__.return_value = amqpClientMock
amqpClientClassMock.return_value = amqpClientMock
DynamoDBService().run()
self.assertTrue(amqpClientClassMock.called,
"Service did not connect to rabbitmq")
self.assertTrue(amqpClientMock.declareExchange.called)
amqpClientMock.declareExchange.assert_any_call(
durable=True,
exchangeType="fanout",
exchange=taurus.engine.config.get("metric_streamer",
"results_exchange_name"))
amqpClientMock.declareExchange.assert_any_call(
durable=True,
exchangeType="topic",
exchange=taurus.engine.config.get("non_metric_data",
"exchange_name"))
amqpClientMock.declareQueue.assert_called_once_with(ANY, durable=True)
amqpClientMock.bindQueue.assert_any_call(
queue=amqpClientMock.declareQueue.return_value.queue,
exchange=taurus.engine.config.get("metric_streamer",
"results_exchange_name"),
routingKey="")
amqpClientMock.bindQueue.assert_any_call(
exchange=taurus.engine.config.get("non_metric_data",
"exchange_name"),
queue=amqpClientMock.declareQueue.return_value.queue,
routingKey="#")
self.assertTrue(amqpClientMock.readEvents.called)
def testDynamoDBServiceInit(self, connectDynamoDB, _gracefulCreateTable):
service = DynamoDBService()
self.assertTrue(hasattr(service, "run"))
self.assertTrue(
connectDynamoDB.called, "Service did not attempt to "
"authenticate with DynamoDB API during initialization")
self.assertTrue(
_gracefulCreateTable.called, "Service did not attempt to "
"create any dynamodb tables")
for callArgs, _ in _gracefulCreateTable.call_args_list:
self.assertIsInstance(
callArgs[0], DynamoDBDefinition, "Service "
"attempted to create a table using something that isn't a subclass of "
"DynamoDBDefinition")
def testMessageHandlerRoutesTweetDataToDynamoDB(self, _amqpUtilsMock,
connectDynamoDB,
_gracefulCreateTable):
""" Simple test for twitter interface
"""
## channel = Mock()
## method = Mock(routing_key="taurus.data.non-metric.twitter")
## properties = Mock()
tweetData = [{
"metric_name": "Metric Name",
"tweet_uid": "3b035a5916994f2bb950f5717138f94b",
"created_at": "2015-02-19T19:43:24.870109",
"agg_ts": "2015-02-19T19:43:24.870118",
"text": "Tweet text",
"userid": "10",
"username": "******",
"retweet_count": "0"
}]
message = amqp.messages.ConsumerMessage(
body=json.dumps(tweetData),
properties=Mock(),
methodInfo=amqp.messages.MessageDeliveryInfo(
consumerTag=Mock(),
deliveryTag=Mock(),
redelivered=False,
exchange=Mock(),
routingKey="taurus.data.non-metric.twitter"),
ackImpl=Mock(),
nackImpl=Mock())
service = DynamoDBService()
service.messageHandler(message)
(service._metric_tweets.batch_write.return_value.__enter__.
return_value.put_item.assert_called_once_with(data=OrderedDict([
("metric_name_tweet_uid",
"Metric Name-3b035a5916994f2bb950f5717138f94b"),
("metric_name", "Metric Name"),
("tweet_uid", "3b035a5916994f2bb950f5717138f94b"),
("created_at", "2015-02-19T19:43:24.870109"),
("agg_ts", "2015-02-19T19:43:24.870118"), ("text", "Tweet text"),
("userid", "10"), ("username", "Tweet username"),
("retweet_count", "0")
]),
overwrite=True))
def testPublishInstanceDataHourly(self, connectDynamoDB,
_gracefulCreateTable):
connectionMock = Mock(spec_set=DynamoDBConnection)
connectionMock.update_item.side_effect = ResourceNotFoundException(
400, "item not found")
connectDynamoDB.return_value = connectionMock
tableName = InstanceDataHourlyDynamoDBDefinition().tableName
instanceName = "testName"
condition = "attribute_not_exists(instance_id)"
rows = [
dict(rowid=99,
ts=epochFromNaiveUTCDatetime(datetime(2015, 2, 20, 0, 46,
28)),
value=10305.0,
rawAnomaly=0.275,
anomaly=0.999840891),
dict(rowid=100,
ts=epochFromNaiveUTCDatetime(datetime(2015, 2, 20, 0, 51,
28)),
value=9305.0,
rawAnomaly=0.975,
anomaly=0.999990891),
dict(rowid=101,
ts=epochFromNaiveUTCDatetime(datetime(2015, 2, 20, 0, 56,
20)),
value=6111.0,
rawAnomaly=0.775,
anomaly=0.999940891),
dict(rowid=102,
ts=epochFromNaiveUTCDatetime(datetime(2015, 2, 20, 1, 1, 38)),
value=7092.0,
rawAnomaly=0.775,
anomaly=0.999640891)
]
service = DynamoDBService()
# Run the function under test
service._publishInstanceDataHourly(instanceName, "TwitterVolume", rows)
# Validate results
self.assertEqual(connectionMock.update_item.call_count, 2)
self.assertEqual(connectionMock.put_item.call_count, 2)
calls = connectionMock.put_item.call_args_list
kwargs0 = calls[0][1]
item0 = kwargs0["item"]
self.assertDictEqual(item0["instance_id"], {"S": instanceName})
self.assertEqual(item0["date_hour"], {"S": "2015-02-20T00"})
self.assertEqual(item0["date"], {"S": "2015-02-20"})
self.assertEqual(item0["hour"], {"S": "00"})
self.assertDictEqual(item0["anomaly_score"]["M"]["TwitterVolume"],
{"N": "0.99999"})
self.assertEqual(kwargs0["condition_expression"], condition)
kwargs1 = calls[1][1]
item1 = kwargs1["item"]
self.assertEqual(item1["instance_id"], {"S": instanceName})
self.assertEqual(item1["date_hour"], {"S": "2015-02-20T01"})
self.assertEqual(item1["date"], {"S": "2015-02-20"})
self.assertEqual(item1["hour"], {"S": "01"})
self.assertDictEqual(item1["anomaly_score"]["M"]["TwitterVolume"],
{"N": "0.99964"})
self.assertEqual(kwargs1["condition_expression"], condition)
def testModelResultHandlerSkipsStaleBatch(self, _amqpUtilsMock,
deserializeModelResult,
connectDynamoDB,
_gracefulCreateTable):
""" Given a stale batch of model inference results, verify that it isn't
saved to DynamoDB
"""
# 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
ts = epochFromNaiveUTCDatetime(
datetime.utcnow().replace(microsecond=0) -
timedelta(days=DynamoDBService._FRESH_DATA_THRESHOLD_DAYS + 1))
resultRow = dict(rowid=4790,
ts=ts,
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()
publishMetricDataPatch = patch.object(
service, "_publishMetricData", spec_set=service._publishMetricData)
publishInstancePatch = patch.object(
service,
"_publishInstanceDataHourly",
spec_set=service._publishInstanceDataHourly)
with publishMetricDataPatch as publishMetricDataMock, \
publishInstancePatch as publishInstanceMock:
service.messageHandler(message)
deserializeModelResult.assert_called_once_with(message.body)
self.assertEqual(publishMetricDataMock.call_count, 0)
self.assertEqual(publishInstanceMock.call_count, 0)
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)
