def createTestData(self):
print "Searching for instances in us-west-2 that are running since 15 days or more."
instances = aws.getLongRunningEC2Instances("us-west-2", os.environ["AWS_ACCESS_KEY_ID"],
os.environ["AWS_SECRET_ACCESS_KEY"], 15)
print "Found %s instance(s)." % len(instances)
metrics = ["CPUUtilization", "DiskReadBytes", "DiskWriteBytes", "NetworkIn", "NetworkOut"]
testData = []
for instance in instances:
for metric in metrics:
model = {
"region": "us-west-2",
"namespace": "AWS/EC2",
"datasource": "cloudwatch",
"metric": metric,
"dimensions": {
"InstanceId": instance.id
}
}
testData.append(model)
return testData
def setUpClass(cls):
"""
Setup steps for all test cases.
Focus for these is to cover all API checks for ModelDataHandler.
Hence, this does all setup creating metric, waiting for
metricData across all testcases, all API call for querying metricData
will be against single metric created in setup
Setup Process
1) Update conf with aws credentials, ManagedTempRepository will not
work in this test
2) Select test instance such that its running from longer time,
We are using instance older than 15 days
3) Create Metric, wait for min metricData rows to become available
Set to 100, configurable
4) Pick testRowId, set it lower value this will make sure to have
Non NULL value for anomaly_score field for given row while invoking
GET with consitions, set to 5
5) Decide queryParams for anomalyScore, to and from timestamp
"""
cls.headers = getDefaultHTTPHeaders(grok.app.config)
# All other sevices needs AWS credentials to work
# Set AWS credentials
grok.app.config.loadConfig()
# Select test instance such that its running from longer time
g_logger.info("Getting long-running EC2 Instances")
instances = aws_utils.getLongRunningEC2Instances("us-west-2",
grok.app.config.get("aws", "aws_access_key_id"),
grok.app.config.get("aws", "aws_secret_access_key"), 15)
testInstance = instances[randrange(1, len(instances))]
createModelData = {
"region": "us-west-2",
"namespace": "AWS/EC2",
"datasource": "cloudwatch",
"metric": "CPUUtilization",
"dimensions": {
"InstanceId": testInstance.id
}
}
# Number of minimum rows
cls.minDataRows = 100
cls.app = TestApp(models_api.app.wsgifunc())
# create test metric
g_logger.info("Creating test metric; modelSpec=%s", createModelData)
response = cls.app.put("/", utils.jsonEncode(createModelData),
headers=cls.headers)
postResult = utils.jsonDecode(response.body)
maxWaitTime = 600
waitTimeMetricData = 0
waitAnomalyScore = 0
# Wait for enough metric data to be available
cls.uid = postResult[0]["uid"]
engine = repository.engineFactory()
with engine.connect() as conn:
cls.metricData = [row for row
in repository.getMetricData(conn, cls.uid)]
with engine.connect() as conn:
cls.testMetric = repository.getMetric(conn, cls.uid)
# Confirm that we have enough metricData
g_logger.info("Waiting for metric data")
while (len(cls.metricData) < cls.minDataRows and
waitTimeMetricData < maxWaitTime):
g_logger.info("not ready, waiting for metric data: got %d of %d ...",
len(cls.metricData), cls.minDataRows)
time.sleep(5)
waitTimeMetricData += 5
with engine.connect() as conn:
cls.metricData = [row for row
in repository.getMetricData(conn, cls.uid)]
# taking lower value for testRowId, this will make sure to have
# Non NULL value for anomaly_score field for given row
cls.testRowId = 5
with engine.connect() as conn:
cls.testMetricRow = (repository.getMetricData(conn,
cls.uid,
rowid=cls.testRowId)
.fetchone())
# Make sure we did not receive None etc for anomaly score
g_logger.info("cls.testMetricRow.anomaly_score=%r",
cls.testMetricRow.anomaly_score)
g_logger.info("waitAnomalyScore=%r", waitAnomalyScore)
while (cls.testMetricRow.anomaly_score is None and
waitAnomalyScore < maxWaitTime):
g_logger.info("anomaly_score not ready, sleeping...")
time.sleep(5)
waitAnomalyScore += 5
with engine.connect() as conn:
cls.testMetricRow = (repository.getMetricData(conn,
cls.uid,
rowid=cls.testRowId)
.fetchone())
# Decide queryParams for anomalyScore, to and from timestamp
cls.testAnomalyScore = cls.testMetricRow.anomaly_score
cls.testTimeStamp = cls.testMetricRow.timestamp
def setUpClass(cls):
"""
Setup steps for all test cases.
Focus for these is to cover all API checks for ModelDataHandler.
Hence, this does all setup creating metric, waiting for
metricData across all testcases, all API call for querying metricData
will be against single metric created in setup
Setup Process
1) Update conf with aws credentials, ManagedTempRepository will not
work in this test
2) Select test instance such that its running from longer time,
We are using instance older than 15 days
3) Create Metric, wait for min metricData rows to become available
Set to 100, configurable
4) Pick testRowId, set it lower value this will make sure to have
Non NULL value for anomaly_score field for given row while invoking
GET with consitions, set to 5
5) Decide queryParams for anomalyScore, to and from timestamp
"""
cls.headers = getDefaultHTTPHeaders(grok.app.config)
# All other sevices needs AWS credentials to work
# Set AWS credentials
grok.app.config.loadConfig()
# Select test instance such that its running from longer time
g_logger.info("Getting long-running EC2 Instances")
instances = aws_utils.getLongRunningEC2Instances(
"us-west-2", grok.app.config.get("aws", "aws_access_key_id"),
grok.app.config.get("aws", "aws_secret_access_key"), 15)
testInstance = instances[randrange(1, len(instances))]
createModelData = {
"region": "us-west-2",
"namespace": "AWS/EC2",
"datasource": "cloudwatch",
"metric": "CPUUtilization",
"dimensions": {
"InstanceId": testInstance.id
}
}
# Number of minimum rows
cls.minDataRows = 100
cls.app = TestApp(models_api.app.wsgifunc())
# create test metric
g_logger.info("Creating test metric; modelSpec=%s", createModelData)
response = cls.app.put("/",
utils.jsonEncode(createModelData),
headers=cls.headers)
postResult = utils.jsonDecode(response.body)
maxWaitTime = 600
waitTimeMetricData = 0
waitAnomalyScore = 0
# Wait for enough metric data to be available
cls.uid = postResult[0]["uid"]
engine = repository.engineFactory()
with engine.connect() as conn:
cls.metricData = [
row for row in repository.getMetricData(conn, cls.uid)
]
with engine.connect() as conn:
cls.testMetric = repository.getMetric(conn, cls.uid)
# Confirm that we have enough metricData
g_logger.info("Waiting for metric data")
while (len(cls.metricData) < cls.minDataRows
and waitTimeMetricData < maxWaitTime):
g_logger.info(
"not ready, waiting for metric data: got %d of %d ...",
len(cls.metricData), cls.minDataRows)
time.sleep(5)
waitTimeMetricData += 5
with engine.connect() as conn:
cls.metricData = [
row for row in repository.getMetricData(conn, cls.uid)
]
# taking lower value for testRowId, this will make sure to have
# Non NULL value for anomaly_score field for given row
cls.testRowId = 5
with engine.connect() as conn:
cls.testMetricRow = (repository.getMetricData(
conn, cls.uid, rowid=cls.testRowId).fetchone())
# Make sure we did not receive None etc for anomaly score
g_logger.info("cls.testMetricRow.anomaly_score=%r",
cls.testMetricRow.anomaly_score)
g_logger.info("waitAnomalyScore=%r", waitAnomalyScore)
while (cls.testMetricRow.anomaly_score is None
and waitAnomalyScore < maxWaitTime):
g_logger.info("anomaly_score not ready, sleeping...")
time.sleep(5)
waitAnomalyScore += 5
with engine.connect() as conn:
cls.testMetricRow = (repository.getMetricData(
conn, cls.uid, rowid=cls.testRowId).fetchone())
# Decide queryParams for anomalyScore, to and from timestamp
cls.testAnomalyScore = cls.testMetricRow.anomaly_score
cls.testTimeStamp = cls.testMetricRow.timestamp