def test_get_closest_datapoint(self):
expect(get_closest_datapoint(self.timeseries, 10)).to.be.equal(
TimeSeriesTuple('_', 10, 1))
expect(get_closest_datapoint(self.timeseries, 40)).to.be.equal(
TimeSeriesTuple('_', 30, 1))
expect(get_closest_datapoint(self.timeseries, 99)).to.be.equal(
TimeSeriesTuple('_', 100, 1))
def read(self):
quantile_25 = self.params['quantile_25']
quantile_75 = self.params['quantile_75']
metrics = self.params['metrics']
delay = self.params.get('offset', 0)
maximum_delay = self.params.get('maximum_delay', 600)
# read metrics from metric_sink
quantile_25 = [i for i in self.metric_sink.iread(quantile_25)]
quantile_75 = [i for i in self.metric_sink.iread(quantile_75)]
metrics = [i for i in self.metric_sink.iread(metrics)]
if not (len(quantile_25) * len(quantile_75) * len(metrics)):
self.logger.error(
'No data found for quantile/to be checked metrics. Exiting')
return None
# sort TimeSeriesTuples by timestamp
quantile_25 = sorted(quantile_25, key=lambda tup: tup.timestamp)
quantile_75 = sorted(quantile_75, key=lambda tup: tup.timestamp)
metrics = sorted(metrics, key=lambda tup: (tup.name, tup.timestamp))
# find closest datapoint to now() (corrected by delay) if not too old
time_now = time() - delay
quantile_25 = get_closest_datapoint(quantile_25, time_now)
if time_now - quantile_25.timestamp > maximum_delay:
self.logger.error(
'Quantile25 Value is too old (Timestamp: %d) of: %s. Exiting' %
(quantile_25.timestamp, quantile_25.name))
return None
quantile_25 = quantile_25.value
quantile_75 = get_closest_datapoint(quantile_75, time_now)
if time_now - quantile_75.timestamp > maximum_delay:
self.logger.error(
'Quantile75 Value is too old (Timestamp: %d) of: %s. Exiting' %
(quantile_75.timestamp, quantile_75.name))
return None
quantile_75 = quantile_75.value
if quantile_25 > quantile_75:
self.logger.error(
'Inconsistent Quantile Values (Q25: %f, Q75: %f). Exiting' %
(quantile_25, quantile_75))
return None
# group by metric (e.g. instance) first and find then closest datapoint
distribution = {}
grouped = itertools.groupby(metrics, key=lambda tup: tup.name)
for key, metrics in grouped:
closest_datapoint = get_closest_datapoint(
[metric for metric in metrics], time_now)
if time_now - closest_datapoint.timestamp < maximum_delay:
distribution[key] = closest_datapoint.value
if len(distribution) == 0:
self.logger.error('No Distribution Values. Exiting')
return None
return quantile_25, quantile_75, distribution
def read(self):
quantile_25 = self.params['quantile_25']
quantile_75 = self.params['quantile_75']
metrics = self.params['metrics']
delay = self.params.get('offset', 0)
maximum_delay = self.params.get('maximum_delay', 600)
# read metrics from metric_sink
quantile_25 = [i for i in self.metric_sink.iread(quantile_25)]
quantile_75 = [i for i in self.metric_sink.iread(quantile_75)]
metrics = [i for i in self.metric_sink.iread(metrics)]
if not (len(quantile_25) * len(quantile_75) * len(metrics)):
self.logger.error(
'No data found for quantile/to be checked metrics. Exiting')
return None
# sort TimeSeriesTuples by timestamp
quantile_25 = sorted(quantile_25, key=lambda tup: tup.timestamp)
quantile_75 = sorted(quantile_75, key=lambda tup: tup.timestamp)
metrics = sorted(metrics, key=lambda tup: (tup.name, tup.timestamp))
# find closest datapoint to now() (corrected by delay) if not too old
time_now = time() - delay
quantile_25 = get_closest_datapoint(quantile_25, time_now)
if time_now - quantile_25.timestamp > maximum_delay:
self.logger.error('Quantile25 Value is too old (Timestamp: %d) of: %s. Exiting' % (
quantile_25.timestamp, quantile_25.name))
return None
quantile_25 = quantile_25.value
quantile_75 = get_closest_datapoint(quantile_75, time_now)
if time_now - quantile_75.timestamp > maximum_delay:
self.logger.error('Quantile75 Value is too old (Timestamp: %d) of: %s. Exiting' % (
quantile_75.timestamp, quantile_75.name))
return None
quantile_75 = quantile_75.value
if quantile_25 > quantile_75:
self.logger.error('Inconsistent Quantile Values (Q25: %f, Q75: %f). Exiting' % (
quantile_25, quantile_75))
return None
# group by metric (e.g. instance) first and find then closest datapoint
distribution = {}
grouped = itertools.groupby(metrics, key=lambda tup: tup.name)
for key, metrics in grouped:
closest_datapoint = get_closest_datapoint(
[metric for metric in metrics], time_now)
if time_now - closest_datapoint.timestamp < maximum_delay:
distribution[key] = closest_datapoint.value
if len(distribution) == 0:
self.logger.error('No Distribution Values. Exiting')
return None
return quantile_25, quantile_75, distribution
def test_get_closest_datapoint(self):
expect(get_closest_datapoint(self.timeseries, 10)).to.be.equal(TimeSeriesTuple('_', 10, 1))
expect(get_closest_datapoint(self.timeseries, 40)).to.be.equal(TimeSeriesTuple('_', 30, 1))
expect(get_closest_datapoint(self.timeseries, 99)).to.be.equal(TimeSeriesTuple('_', 100, 1))