def test_round_timestamp(self):
t = time()
for unit in Period.UNITS:
period = Period(**{unit: 1})
st = period.round_timestamp(t)
self.assertEqual(t, st)
def test_intervals(self):
"""Test calculate on different intervals."""
now = time()
# let a period of 1 day
period = Period(day=1)
oneday = period.total_seconds()
rnow = period.round_timestamp(now)
# let a timewindow of 10+1/4 days
timewindow = TimeWindow(start=now - oneday, stop=now + 45/4 * oneday)
nan = float('nan')
points = [
# the first interval is empty
(rnow, nan), # the second interval contains nan at start
(rnow + oneday + 1, nan), # the third interval contains nan at start + 1
(rnow + 2 * oneday, 1), # the fourth interval contains 1 at start
(rnow + 3 * oneday + 1, 1), # the fourth interval contains 1 at start + 1
(rnow + 4 * oneday, nan), (rnow + 4 * oneday + 1, 1), # the fith interval contains 1 and nan
(rnow + 5 * oneday, 1), (rnow + 5 * oneday + 1, 1), # the sixth interval contains 1 and 1
(rnow + 6 * oneday, 1), (rnow + 6 * oneday, 1), # the sixth interval contains 1 and 1 at the same time
(rnow + 7 * oneday, nan), (rnow + 7 * oneday, nan), # the sixth interval contains nan and nan at the same time
]
timeserie = TimeSerie(
config=self.conf,
aggregation='sum',
period=period,
round_time=True
)
_points = timeserie.calculate(points, timewindow)
for i in [0, 1, 2, 5, 8, 9, 10, 11, 12]:
self.assertEqual(_points[i][0], rnow + (i - 1) * oneday)
self.assertTrue(isnan(_points[i][1]))
for i in [3, 4]:
self.assertEqual(_points[i][0], rnow + (i - 1) * oneday)
self.assertEqual(_points[i][1], 1)
for i in [6, 7]:
self.assertEqual(_points[i][0], rnow + (i - 1) * oneday)
self.assertEqual(_points[i][1], 2)
self.assertEqual(len(_points), len(points) + 1)
def prepare_event(
self,
display_name,
sla_measures,
output,
sla_state,
alerts_percent,
alerts_duration,
avail_duration,
timewindow_dict,
now
):
perf_data_array = []
# Compute metrics to publish
for state in self.states:
perf_data_array.append({
'metric': 'cps_pct_by_{}'.format(state),
'value': round(sla_measures[state] * 100.0, 2),
'max': 100
})
availability = (1.0 - alerts_percent) * 100.0
perf_data_array.append({
'metric': 'cps_avail',
'value': round(availability, 2),
'max': 100,
SLIDING_TIME: True
})
perf_data_array.append({
'metric': 'cps_avail_duration',
'value': avail_duration,
SLIDING_TIME: True
})
perf_data_array.append({
'metric': 'cps_alerts_duration',
'value': alerts_duration,
SLIDING_TIME: True
})
period_options = {
timewindow_dict['durationType']: timewindow_dict['value']
}
self.logger.debug(u'period options {}, now {}'.format(
period_options,
now
))
period = Period(**period_options)
periodic_timestamp = period.round_timestamp(now, next_period=True)
self.logger.debug(u'periodic timestamp {}'.format(periodic_timestamp))
event = forger(
connector='sla',
connector_name='engine',
event_type='sla',
source_type='resource',
component=display_name,
resource='sla',
state=sla_state,
output=output,
perf_data_array=perf_data_array,
display_name=display_name,
timestamp=periodic_timestamp
)
self.logger.info(u'publishing sla {}, states {}'.format(
display_name,
sla_measures
))
self.logger.debug(u'event : {}'.format(pp.pformat(event)))
return event
def prepare_event(
self,
display_name,
sla_measures,
output,
sla_state,
alerts_percent,
alerts_duration,
avail_duration,
timewindow_dict,
now
):
perf_data_array = []
# Compute metrics to publish
for state in self.states:
perf_data_array.append({
'metric': 'cps_pct_by_{}'.format(state),
'value': round(sla_measures[state] * 100.0, 2),
'max': 100
})
availability = (1.0 - alerts_percent) * 100.0
perf_data_array.append({
'metric': 'cps_avail',
'value': round(availability, 2),
'max': 100,
SLIDING_TIME: True
})
perf_data_array.append({
'metric': 'cps_avail_duration',
'value': avail_duration,
SLIDING_TIME: True
})
perf_data_array.append({
'metric': 'cps_alerts_duration',
'value': alerts_duration,
SLIDING_TIME: True
})
period_options = {
timewindow_dict['durationType']: timewindow_dict['value']
}
self.logger.debug(u'period options {}, now {}'.format(
period_options,
now
))
period = Period(**period_options)
periodic_timestamp = period.round_timestamp(now, next_period=True)
self.logger.debug(u'periodic timestamp {}'.format(periodic_timestamp))
event = forger(
connector='sla',
connector_name='engine',
event_type='sla',
source_type='resource',
component=display_name,
resource='sla',
state=sla_state,
output=output,
perf_data_array=perf_data_array,
display_name=display_name,
timestamp=periodic_timestamp
)
self.logger.info(u'publishing sla {}, states {}'.format(
display_name,
sla_measures
))
self.logger.debug(u'event : {}'.format(pp.pformat(event)))
return event
