def read(self):
metric = self.params['metric']
period_length = self.params['period_length']
seasons = self.params['seasons']
interval = self.params['interval']
# gather data and assure requirements
self._read_tdigest()
data = [el for el in self.metric_sink.read(metric)]
if not data[0]:
self.logger.error('%s :: No Datapoints. Exiting' % self.service)
return None
data = sorted(data, key=lambda tup: tup.timestamp)
if int(time()) - data[-1].timestamp > 3 * interval:
self.logger.error('%s :: Datapoints are too old (%d sec). Exiting' % (
self.service, (int(time()) - data[-1].timestamp)))
return None
data = insert_missing_datapoints(data, False, interval)
if len(data) < period_length * seasons + 1:
self.logger.error(
'%s :: Not enough (%d) datapoints. Exiting' % (
self.service, len(data)))
return None
data = data[-period_length * seasons - 1:-1]
return data
def read(self):
metric = self.params['metric']
period_length = self.params['period_length']
seasons = self.params['seasons']
default = self.params['default']
tdigest_json = [el for el in self.metric_sink.read(self.tdigest_key)]
if tdigest_json:
centroids = json.loads(tdigest_json[0])
[self.td.add(c[0], c[1]) for c in centroids]
# gather data and assure requirements
data = [el for el in self.metric_sink.read(metric)]
data = sorted(data, key=lambda tup: tup.timestamp)
step_size = find_step_size(data)
if not step_size:
self.logger.error(
'Datapoints have no common time grid or are not enough. Exiting')
return None
if data[-1].timestamp - int(time()) > 2 * step_size:
self.logger.error('Datapoints are too old (%d sec). Exiting' % (
data[-1].timestamp - int(time())))
return None
data = insert_missing_datapoints(data, default, step_size)
if len(data) < period_length * seasons:
self.logger.error(
'Not enough (%d) datapoints. Exiting' % len(data))
return None
data = data[-period_length * seasons - 1:-1]
return data
def read(self):
metric = self.params['metric']
period_length = self.params['period_length']
seasons = self.params['seasons']
interval = self.params['interval']
# gather data and assure requirements
self._read_tdigest()
data = [el for el in self.metric_sink.read(metric)]
if not data[0]:
self.logger.error('%s :: No Datapoints. Exiting' % self.service)
return None
data = sorted(data, key=lambda tup: tup.timestamp)
if int(time()) - data[-1].timestamp > 3 * interval:
self.logger.error(
'%s :: Datapoints are too old (%d sec). Exiting' %
(self.service, (int(time()) - data[-1].timestamp)))
return None
data = insert_missing_datapoints(data, False, interval)
if len(data) < period_length * seasons + 1:
self.logger.error('%s :: Not enough (%d) datapoints. Exiting' %
(self.service, len(data)))
return None
data = data[-period_length * seasons - 1:-1]
return data
def test_insert_missing_datapoints(self):
default = 111
step_size = 10
exp = [TimeSeriesTuple('_', 10, 1),
TimeSeriesTuple('_', 20, 1),
TimeSeriesTuple('_', 30, 1),
TimeSeriesTuple('_', 40, 111),
TimeSeriesTuple('_', 50, 1),
TimeSeriesTuple('_', 60, 1),
TimeSeriesTuple('_', 70, 111),
TimeSeriesTuple('_', 80, 111),
TimeSeriesTuple('_', 90, 111),
TimeSeriesTuple('_', 100, 1)]
expect(insert_missing_datapoints(self.timeseries, default, step_size)).to.be.equal(exp)
def test_insert_missing_datapoints(self):
default = 111
step_size = 10
exp = [
TimeSeriesTuple('_', 10, 1),
TimeSeriesTuple('_', 20, 1),
TimeSeriesTuple('_', 30, 1),
TimeSeriesTuple('_', 40, 111),
TimeSeriesTuple('_', 50, 1),
TimeSeriesTuple('_', 60, 1),
TimeSeriesTuple('_', 70, 111),
TimeSeriesTuple('_', 80, 111),
TimeSeriesTuple('_', 90, 111),
TimeSeriesTuple('_', 100, 1)
]
expect(insert_missing_datapoints(self.timeseries, default,
step_size)).to.be.equal(exp)
