def updateQuota(args):
helpString = (
"This script updates Grok app quotas in %s.\n"
"%%prog\n\n"
"IT MUST BE CALLED AFTER set_edition.py, BUT BEFORE STARTING GROK SERVICES"
) % (QuotaConfig.CONFIG_NAME,)
parser = OptionParser(helpString)
(_options, posArgs) = parser.parse_args(args)
if len(posArgs) != 0:
parser.error("Expected no positional args, but got %s" % (
len(posArgs),))
Quota.init()
def updateQuota(args):
helpString = (
"This script updates Grok app quotas in %s.\n"
"%%prog\n\n"
"IT MUST BE CALLED AFTER set_edition.py, BUT BEFORE STARTING GROK SERVICES"
) % (QuotaConfig.CONFIG_NAME, )
parser = OptionParser(helpString)
(_options, posArgs) = parser.parse_args(args)
if len(posArgs) != 0:
parser.error("Expected no positional args, but got %s" %
(len(posArgs), ))
Quota.init()
def testCollectAndPublishMetrics(self):
# Start Metric Collector, create a set of Metrics, wait for it to collect
# some metrics and to publish them to the metric_exchange, then validate
# attributes of the published metrics.
#
# TODO Add more metric types
# TODO Deeper validation of the published metrics
# Start our own instance of metric collector and wait for data points
with self._startModelSchedulerSubprocess() as modelSchedulerSubprocess, \
self._startMetricCollectorSubprocess() as metricCollectorSubprocess:
# Create some models for metric collector to harvest
region = "us-west-2"
namespace = "AWS/EC2"
resourceType = ResourceTypeNames.EC2_INSTANCE
engine = repository.engineFactory()
adapter = createCloudwatchDatasourceAdapter()
ec2Instances = adapter.describeResources(region=region,
resourceType=resourceType)
self.assertGreater(len(ec2Instances), 0)
maxModels = 10
ec2Instances = ec2Instances[:min(maxModels, Quota.getInstanceQuota())]
metricInstances = []
_LOGGER.info("Starting %d models", len(ec2Instances))
self.assertGreater(len(ec2Instances), 0)
for ec2Instance in ec2Instances:
metricSpec = {"region": region,
"namespace": namespace,
"metric": "CPUUtilization",
"dimensions": {"InstanceId": ec2Instance["resID"]}}
modelSpec = {"datasource": "cloudwatch",
"metricSpec": metricSpec}
metricId = adapter.monitorMetric(modelSpec)
with engine.connect() as conn:
repository.setMetricStatus(conn, metricId, MetricStatus.ACTIVE)
metricInstances.append(metricId)
_LOGGER.info("Waiting for results from models...")
seenMetricIDs = set()
allMetricIDs = set(metricInstances)
# Register a timeout so we won't deadlock the test
def onTimeout(resultsQueueName):
_LOGGER.error(
"Timed out waiting to get results from models; numResults=%d; "
"expected=%d", len(seenMetricIDs), len(allMetricIDs))
# HACK delete model swapper results queue to abort the consumer
try:
with MessageBusConnector() as bus:
bus.deleteMessageQueue(resultsQueueName)
except Exception:
_LOGGER.exception("Failed to delete results mq=%s", resultsQueueName)
raise
with ModelSwapperInterface() as modelSwapper:
with modelSwapper.consumeResults() as consumer:
timer = threading.Timer(120, onTimeout,
args=[modelSwapper._resultsQueueName])
timer.start()
try:
for batch in consumer:
seenMetricIDs.add(batch.modelID)
batch.ack()
if seenMetricIDs == allMetricIDs:
break
else:
self.fail(
"Expected %d results, but got only %d: %s"
% (len(allMetricIDs), len(seenMetricIDs), seenMetricIDs,))
_LOGGER.info("Got %d results from models", len(seenMetricIDs))
finally:
timer.cancel()
# Terminate metric_collector subprocess gracefully to avoid too much
# error logging junk on the terminal
metricCollectorSubprocess.send_signal(signal.SIGINT)
# Terminate metric_collector subprocess gracefully to avoid too much
# error logging junk on the terminal
modelSchedulerSubprocess.send_signal(signal.SIGINT)
def testCollectAndPublishMetrics(self):
# Start Metric Collector, create a set of Metrics, wait for it to collect
# some metrics and to publish them to the metric_exchange, then validate
# attributes of the published metrics.
#
# TODO Add more metric types
# TODO Deeper validation of the published metrics
# Start our own instance of metric collector and wait for data points
with self._startModelSchedulerSubprocess() as modelSchedulerSubprocess, \
self._startMetricCollectorSubprocess() as metricCollectorSubprocess:
# Create some models for metric collector to harvest
region = "us-west-2"
namespace = "AWS/EC2"
resourceType = ResourceTypeNames.EC2_INSTANCE
engine = repository.engineFactory()
adapter = createCloudwatchDatasourceAdapter()
ec2Instances = adapter.describeResources(region=region,
resourceType=resourceType)
self.assertGreater(len(ec2Instances), 0)
maxModels = 10
ec2Instances = ec2Instances[:min(maxModels, Quota.getInstanceQuota(
))]
metricInstances = []
_LOGGER.info("Starting %d models", len(ec2Instances))
self.assertGreater(len(ec2Instances), 0)
for ec2Instance in ec2Instances:
metricSpec = {
"region": region,
"namespace": namespace,
"metric": "CPUUtilization",
"dimensions": {
"InstanceId": ec2Instance["resID"]
}
}
modelSpec = {
"datasource": "cloudwatch",
"metricSpec": metricSpec
}
metricId = adapter.monitorMetric(modelSpec)
with engine.connect() as conn:
repository.setMetricStatus(conn, metricId,
MetricStatus.ACTIVE)
metricInstances.append(metricId)
_LOGGER.info("Waiting for results from models...")
seenMetricIDs = set()
allMetricIDs = set(metricInstances)
# Register a timeout so we won't deadlock the test
def onTimeout(resultsQueueName):
_LOGGER.error(
"Timed out waiting to get results from models; numResults=%d; "
"expected=%d", len(seenMetricIDs), len(allMetricIDs))
# HACK delete model swapper results queue to abort the consumer
try:
with MessageBusConnector() as bus:
bus.deleteMessageQueue(resultsQueueName)
except Exception:
_LOGGER.exception("Failed to delete results mq=%s",
resultsQueueName)
raise
with ModelSwapperInterface() as modelSwapper:
with modelSwapper.consumeResults() as consumer:
timer = threading.Timer(
120, onTimeout, args=[modelSwapper._resultsQueueName])
timer.start()
try:
for batch in consumer:
seenMetricIDs.add(batch.modelID)
batch.ack()
if seenMetricIDs == allMetricIDs:
break
else:
self.fail(
"Expected %d results, but got only %d: %s" % (
len(allMetricIDs),
len(seenMetricIDs),
seenMetricIDs,
))
_LOGGER.info("Got %d results from models",
len(seenMetricIDs))
finally:
timer.cancel()
# Terminate metric_collector subprocess gracefully to avoid too much
# error logging junk on the terminal
metricCollectorSubprocess.send_signal(signal.SIGINT)
# Terminate metric_collector subprocess gracefully to avoid too much
# error logging junk on the terminal
modelSchedulerSubprocess.send_signal(signal.SIGINT)
