def checkStats(self, metricName, mn, mx):
"""Check that stats are computed correctly from the database"""
engine = repository.engineFactory()
with engine.begin() as conn:
metricObj = (
repository.getCustomMetricByName(conn,
metricName,
fields=[schema.metric.c.uid,
schema.metric.c.parameters]))
stats = repository.getMetricStats(conn, metricObj.uid)
self.assertSetEqual(set(stats.keys()), set(("min", "max")))
self.assertAlmostEqual(stats["min"], mn)
self.assertAlmostEqual(stats["max"], mx)
def getStatistics(metric):
"""Get aggregate statistics for an Autostack metric.
The metric must belong to an Autostack or a ValueError will be raised. If AWS
returns no stats and there is no data in the database then an
ObjectNotFoundError will be raised.
:param metric: the Autostack metric to get statistics for
:type metric: TODO
:returns: metric statistics
:rtype: dict {"min": minVal, "max": maxVal}
:raises: ValueError if the metric doesn't not belong to an Autostack
:raises: htm-it.app.exceptions.ObjectNotFoundError if the metric or the
corresponding autostack doesn't exist; this may happen if it got deleted
by another process in the meantime.
:raises: htm-it.app.exceptions.MetricStatisticsNotReadyError if there are no or
insufficent samples at this time; this may also happen if the metric and
its data were deleted by another process in the meantime
"""
engine = repository.engineFactory()
if metric.datasource != "autostack":
raise ValueError(
"Metric must belong to an Autostack but has datasource=%r"
% metric.datasource)
metricGetter = EC2InstanceMetricGetter()
try:
with engine.connect() as conn:
autostack = repository.getAutostackFromMetric(conn, metric.uid)
instanceMetricList = metricGetter.collectMetricStatistics(autostack, metric)
finally:
metricGetter.close()
n = 0
mins = 0.0
maxs = 0.0
for instanceMetric in instanceMetricList:
assert len(instanceMetric.records) == 1
metricRecord = instanceMetric.records[0]
stats = metricRecord.value
if (not isinstance(stats["min"], numbers.Number) or
math.isnan(stats["min"]) or
not isinstance(stats["max"], numbers.Number) or
math.isnan(stats["max"])):
# Cloudwatch gave us bogus data for this metric so we will exclude it
continue
mins += stats["min"]
maxs += stats["max"]
n += 1
if n == 0:
# Fall back to metric_data when we don't get anything from AWS. This may
# raise an MetricStatisticsNotReadyError if there is no or not enough data.
with engine.connect() as conn:
dbStats = repository.getMetricStats(conn, metric.uid)
minVal = dbStats["min"]
maxVal = dbStats["max"]
else:
minVal = mins / n
maxVal = maxs / n
# Now add the 20% buffer on the range
buff = (maxVal - minVal) * 0.2
minVal -= buff
maxVal += buff
return {"min": minVal,
"max": maxVal}