def test_aggregated_different_archive_overlap_edge_missing2(self):
tsc1 = {
'sampling': numpy.timedelta64(60, 's'),
'size': 10,
'agg': 'mean'
}
tsb1 = carbonara.BoundTimeSerie(block_size=tsc1['sampling'])
tsc2 = {
'sampling': numpy.timedelta64(60, 's'),
'size': 10,
'agg': 'mean'
}
tsb2 = carbonara.BoundTimeSerie(block_size=tsc2['sampling'])
tsb1.set_values(numpy.array([(datetime64(2014, 1, 1, 12, 3, 0), 4)],
dtype=carbonara.TIMESERIES_ARRAY_DTYPE),
before_truncate_callback=functools.partial(
self._resample_and_merge, agg_dict=tsc1))
tsb2.set_values(numpy.array([(datetime64(2014, 1, 1, 11, 0, 0), 4),
(datetime64(2014, 1, 1, 12, 3, 0), 4)],
dtype=carbonara.TIMESERIES_ARRAY_DTYPE),
before_truncate_callback=functools.partial(
self._resample_and_merge, agg_dict=tsc2))
output = cross_metric.aggregated([tsc1['return'], tsc2['return']],
aggregation='mean')
self.assertEqual([
(datetime64(2014, 1, 1, 12, 3,
0), numpy.timedelta64(60000000000, 'ns'), 4.0),
], list(output))
def test_aggregated_different_archive_no_overlap(self):
tsc1 = {
'sampling': numpy.timedelta64(60, 's'),
'size': 50,
'agg': 'mean'
}
tsb1 = carbonara.BoundTimeSerie(block_size=tsc1['sampling'])
tsc2 = {
'sampling': numpy.timedelta64(60, 's'),
'size': 50,
'agg': 'mean'
}
tsb2 = carbonara.BoundTimeSerie(block_size=tsc2['sampling'])
tsb1.set_values(numpy.array([(datetime64(2014, 1, 1, 11, 46, 4), 4)],
dtype=carbonara.TIMESERIES_ARRAY_DTYPE),
before_truncate_callback=functools.partial(
self._resample_and_merge, agg_dict=tsc1))
tsb2.set_values(numpy.array([(datetime64(2014, 1, 1, 9, 1, 4), 4)],
dtype=carbonara.TIMESERIES_ARRAY_DTYPE),
before_truncate_callback=functools.partial(
self._resample_and_merge, agg_dict=tsc2))
dtfrom = datetime64(2014, 1, 1, 11, 0, 0)
self.assertRaises(cross_metric.UnAggregableTimeseries,
cross_metric.aggregated,
[tsc1['return'], tsc2['return']],
from_timestamp=dtfrom,
aggregation='mean')
def test_aggregated_different_archive_overlap_edge_missing2(self):
tsc1 = carbonara.AggregatedTimeSerie(sampling=60,
max_size=10,
aggregation_method='mean')
tsb1 = carbonara.BoundTimeSerie(block_size=tsc1.sampling)
tsc2 = carbonara.AggregatedTimeSerie(sampling=60,
max_size=10,
aggregation_method='mean')
tsb2 = carbonara.BoundTimeSerie(block_size=tsc2.sampling)
tsb1.set_values([
(datetime.datetime(2014, 1, 1, 12, 3, 0), 4),
],
before_truncate_callback=tsc1.update)
tsb2.set_values([
(datetime.datetime(2014, 1, 1, 11, 0, 0), 4),
(datetime.datetime(2014, 1, 1, 12, 3, 0), 4),
],
before_truncate_callback=tsc2.update)
output = carbonara.AggregatedTimeSerie.aggregated([tsc1, tsc2],
aggregation='mean')
self.assertEqual([
(pandas.Timestamp('2014-01-01 12:03:00'), 60.0, 4.0),
], output)
def test_aggregated_some_overlap_with_fill_null(self):
tsc1 = {
'sampling': numpy.timedelta64(60, 's'),
'size': 10,
'agg': 'mean'
}
tsb1 = carbonara.BoundTimeSerie(block_size=tsc1['sampling'])
tsc2 = {
'sampling': numpy.timedelta64(60, 's'),
'size': 10,
'agg': 'mean'
}
tsb2 = carbonara.BoundTimeSerie(block_size=tsc2['sampling'])
tsb1.set_values(numpy.array([(datetime64(2014, 1, 1, 12, 3, 0), 9),
(datetime64(2014, 1, 1, 12, 4, 0), 1),
(datetime64(2014, 1, 1, 12, 5, 0), 2),
(datetime64(2014, 1, 1, 12, 6, 0), 7),
(datetime64(2014, 1, 1, 12, 7, 0), 5),
(datetime64(2014, 1, 1, 12, 8, 0), 3)],
dtype=carbonara.TIMESERIES_ARRAY_DTYPE),
before_truncate_callback=functools.partial(
self._resample_and_merge, agg_dict=tsc1))
tsb2.set_values(numpy.array([(datetime64(2014, 1, 1, 12, 0, 0), 6),
(datetime64(2014, 1, 1, 12, 1, 0), 2),
(datetime64(2014, 1, 1, 12, 2, 0), 13),
(datetime64(2014, 1, 1, 12, 3, 0), 24),
(datetime64(2014, 1, 1, 12, 4, 0), 4),
(datetime64(2014, 1, 1, 12, 5, 0), 16),
(datetime64(2014, 1, 1, 12, 6, 0), 12)],
dtype=carbonara.TIMESERIES_ARRAY_DTYPE),
before_truncate_callback=functools.partial(
self._resample_and_merge, agg_dict=tsc2))
output = cross_metric.aggregated([tsc1['return'], tsc2['return']],
aggregation='mean',
fill='null')
self.assertEqual([
(datetime64(2014, 1, 1, 12, 0,
0), numpy.timedelta64(60000000000, 'ns'), 6.0),
(datetime64(2014, 1, 1, 12, 1,
0), numpy.timedelta64(60000000000, 'ns'), 2.0),
(datetime64(2014, 1, 1, 12, 2,
0), numpy.timedelta64(60000000000, 'ns'), 13.0),
(datetime64(2014, 1, 1, 12, 3,
0), numpy.timedelta64(60000000000, 'ns'), 16.5),
(datetime64(2014, 1, 1, 12, 4,
0), numpy.timedelta64(60000000000, 'ns'), 2.5),
(datetime64(2014, 1, 1, 12, 5,
0), numpy.timedelta64(60000000000, 'ns'), 9.0),
(datetime64(2014, 1, 1, 12, 6,
0), numpy.timedelta64(60000000000, 'ns'), 9.5),
(datetime64(2014, 1, 1, 12, 7,
0), numpy.timedelta64(60000000000, 'ns'), 5.0),
(datetime64(2014, 1, 1, 12, 8,
0), numpy.timedelta64(60000000000, 'ns'), 3.0),
], list(output))
def test_aggregated_different_archive_overlap_edge_missing1(self):
tsc1 = {
'sampling': numpy.timedelta64(60, 's'),
'size': 10,
'agg': 'mean'
}
tsb1 = carbonara.BoundTimeSerie(block_size=tsc1['sampling'])
tsc2 = {
'sampling': numpy.timedelta64(60, 's'),
'size': 10,
'agg': 'mean'
}
tsb2 = carbonara.BoundTimeSerie(block_size=tsc2['sampling'])
tsb1.set_values(numpy.array([(datetime64(2014, 1, 1, 12, 3, 0), 9),
(datetime64(2014, 1, 1, 12, 4, 0), 1),
(datetime64(2014, 1, 1, 12, 5, 0), 2),
(datetime64(2014, 1, 1, 12, 6, 0), 7),
(datetime64(2014, 1, 1, 12, 7, 0), 5),
(datetime64(2014, 1, 1, 12, 8, 0), 3)],
dtype=carbonara.TIMESERIES_ARRAY_DTYPE),
before_truncate_callback=functools.partial(
self._resample_and_merge, agg_dict=tsc1))
tsb2.set_values(numpy.array([(datetime64(2014, 1, 1, 11, 0, 0), 6),
(datetime64(2014, 1, 1, 12, 1, 0), 2),
(datetime64(2014, 1, 1, 12, 2, 0), 13),
(datetime64(2014, 1, 1, 12, 3, 0), 24),
(datetime64(2014, 1, 1, 12, 4, 0), 4),
(datetime64(2014, 1, 1, 12, 5, 0), 16),
(datetime64(2014, 1, 1, 12, 6, 0), 12)],
dtype=carbonara.TIMESERIES_ARRAY_DTYPE),
before_truncate_callback=functools.partial(
self._resample_and_merge, agg_dict=tsc2))
# By default we require 100% of point that overlap
# but we allow that the last datapoint is missing
# of the precisest granularity
output = cross_metric.aggregated([tsc1['return'], tsc2['return']],
aggregation='sum')
self.assertEqual([
(datetime64(2014, 1, 1, 12, 3, 0), numpy.timedelta64(60,
's'), 33.0),
(datetime64(2014, 1, 1, 12, 4, 0), numpy.timedelta64(60,
's'), 5.0),
(datetime64(2014, 1, 1, 12, 5, 0), numpy.timedelta64(60,
's'), 18.0),
(datetime64(2014, 1, 1, 12, 6, 0), numpy.timedelta64(60,
's'), 19.0),
], list(output))
def test_fetch_nano(self):
ts = {'sampling': numpy.timedelta64(200, 'ms'),
'size': 10, 'agg': 'mean'}
tsb = carbonara.BoundTimeSerie(block_size=ts['sampling'])
tsb.set_values(numpy.array([
(datetime64(2014, 1, 1, 11, 46, 0, 200123), 4),
(datetime64(2014, 1, 1, 11, 46, 0, 340000), 8),
(datetime64(2014, 1, 1, 11, 47, 0, 323154), 50),
(datetime64(2014, 1, 1, 11, 48, 0, 590903), 4),
(datetime64(2014, 1, 1, 11, 48, 0, 903291), 4)],
dtype=carbonara.TIMESERIES_ARRAY_DTYPE),
before_truncate_callback=functools.partial(
self._resample_and_merge, agg_dict=ts))
tsb.set_values(numpy.array([
(datetime64(2014, 1, 1, 11, 48, 0, 821312), 5)],
dtype=carbonara.TIMESERIES_ARRAY_DTYPE),
before_truncate_callback=functools.partial(
self._resample_and_merge, agg_dict=ts))
self.assertEqual([
(datetime64(2014, 1, 1, 11, 46, 0, 200000), 6.0),
(datetime64(2014, 1, 1, 11, 47, 0, 200000), 50.0),
(datetime64(2014, 1, 1, 11, 48, 0, 400000), 4.0),
(datetime64(2014, 1, 1, 11, 48, 0, 800000), 4.5)
], list(ts['return'].fetch()))
self.assertEqual(numpy.timedelta64(200000000, 'ns'),
ts['return'].aggregation.granularity)
def test_fetch_agg_std(self):
# NOTE (gordc): this is a good test to ensure we drop NaN entries
# 2014-01-01 12:00:00 will appear if we don't dropna()
ts = {'sampling': numpy.timedelta64(60, 's'),
'size': 60, 'agg': 'std'}
tsb = carbonara.BoundTimeSerie(block_size=ts['sampling'])
tsb.set_values(numpy.array([(datetime64(2014, 1, 1, 12, 0, 0), 3),
(datetime64(2014, 1, 1, 12, 1, 4), 4),
(datetime64(2014, 1, 1, 12, 1, 9), 7),
(datetime64(2014, 1, 1, 12, 2, 1), 15),
(datetime64(2014, 1, 1, 12, 2, 12), 1)],
dtype=carbonara.TIMESERIES_ARRAY_DTYPE),
before_truncate_callback=functools.partial(
self._resample_and_merge, agg_dict=ts))
self.assertEqual([
(datetime64(2014, 1, 1, 12, 1, 0), 2.1213203435596424),
(datetime64(2014, 1, 1, 12, 2, 0), 9.8994949366116654),
], list(ts['return'].fetch(datetime64(2014, 1, 1, 12, 0, 0))))
tsb.set_values(numpy.array([(datetime64(2014, 1, 1, 12, 2, 13), 110)],
dtype=carbonara.TIMESERIES_ARRAY_DTYPE),
before_truncate_callback=functools.partial(
self._resample_and_merge, agg_dict=ts))
self.assertEqual([
(datetime64(2014, 1, 1, 12, 1, 0), 2.1213203435596424),
(datetime64(2014, 1, 1, 12, 2, 0), 59.304300012730948),
], list(ts['return'].fetch(datetime64(2014, 1, 1, 12, 0, 0))))
def test_fetch_agg_max(self):
ts = carbonara.AggregatedTimeSerie(sampling=60,
max_size=60,
aggregation_method='max')
tsb = carbonara.BoundTimeSerie(block_size=ts.sampling)
tsb.set_values([(datetime.datetime(2014, 1, 1, 12, 0, 0), 3),
(datetime.datetime(2014, 1, 1, 12, 1, 4), 4),
(datetime.datetime(2014, 1, 1, 12, 1, 9), 7),
(datetime.datetime(2014, 1, 1, 12, 2, 1), 15),
(datetime.datetime(2014, 1, 1, 12, 2, 12), 1)],
before_truncate_callback=ts.update)
self.assertEqual([
(pandas.Timestamp('2014-01-01 12:00:00'), 60.0, 3),
(pandas.Timestamp('2014-01-01 12:01:00'), 60.0, 7),
(pandas.Timestamp('2014-01-01 12:02:00'), 60.0, 15),
], ts.fetch(datetime.datetime(2014, 1, 1, 12, 0, 0)))
tsb.set_values([(datetime.datetime(2014, 1, 1, 12, 2, 13), 110)],
before_truncate_callback=ts.update)
self.assertEqual([
(pandas.Timestamp('2014-01-01 12:00:00'), 60.0, 3),
(pandas.Timestamp('2014-01-01 12:01:00'), 60.0, 7),
(pandas.Timestamp('2014-01-01 12:02:00'), 60.0, 110),
], ts.fetch(datetime.datetime(2014, 1, 1, 12, 0, 0)))
def test_fetch_agg_std(self):
ts = carbonara.AggregatedTimeSerie(sampling=60,
max_size=60,
aggregation_method='std')
tsb = carbonara.BoundTimeSerie(block_size=ts.sampling)
tsb.set_values([(datetime.datetime(2014, 1, 1, 12, 0, 0), 3),
(datetime.datetime(2014, 1, 1, 12, 1, 4), 4),
(datetime.datetime(2014, 1, 1, 12, 1, 9), 7),
(datetime.datetime(2014, 1, 1, 12, 2, 1), 15),
(datetime.datetime(2014, 1, 1, 12, 2, 12), 1)],
before_truncate_callback=ts.update)
self.assertEqual([
(pandas.Timestamp('2014-01-01 12:01:00'), 60.0,
2.1213203435596424),
(pandas.Timestamp('2014-01-01 12:02:00'), 60.0,
9.8994949366116654),
], ts.fetch(datetime.datetime(2014, 1, 1, 12, 0, 0)))
tsb.set_values([(datetime.datetime(2014, 1, 1, 12, 2, 13), 110)],
before_truncate_callback=ts.update)
self.assertEqual([
(pandas.Timestamp('2014-01-01 12:01:00'), 60.0,
2.1213203435596424),
(pandas.Timestamp('2014-01-01 12:02:00'), 60.0,
59.304300012730948),
], ts.fetch(datetime.datetime(2014, 1, 1, 12, 0, 0)))
def test_fetch_nano(self):
ts = carbonara.AggregatedTimeSerie(sampling=0.2,
max_size=10,
aggregation_method='mean')
tsb = carbonara.BoundTimeSerie(block_size=ts.sampling)
tsb.set_values([
(datetime.datetime(2014, 1, 1, 11, 46, 0, 200123), 4),
(datetime.datetime(2014, 1, 1, 11, 46, 0, 340000), 8),
(datetime.datetime(2014, 1, 1, 11, 47, 0, 323154), 50),
(datetime.datetime(2014, 1, 1, 11, 48, 0, 590903), 4),
(datetime.datetime(2014, 1, 1, 11, 48, 0, 903291), 4),
],
before_truncate_callback=ts.update)
tsb.set_values([
(datetime.datetime(2014, 1, 1, 11, 48, 0, 821312), 5),
],
before_truncate_callback=ts.update)
self.assertEqual(
[(datetime.datetime(2014, 1, 1, 11, 46, 0, 200000), 0.2, 6.0),
(datetime.datetime(2014, 1, 1, 11, 47, 0, 200000), 0.2, 50.0),
(datetime.datetime(2014, 1, 1, 11, 48, 0, 400000), 0.2, 4.0),
(datetime.datetime(2014, 1, 1, 11, 48, 0, 800000), 0.2, 4.5)],
ts.fetch())
def test_fetch_agg_max(self):
ts = {'sampling': numpy.timedelta64(60, 's'),
'size': 60, 'agg': 'max'}
tsb = carbonara.BoundTimeSerie(block_size=ts['sampling'])
tsb.set_values(numpy.array([(datetime64(2014, 1, 1, 12, 0, 0), 3),
(datetime64(2014, 1, 1, 12, 1, 4), 4),
(datetime64(2014, 1, 1, 12, 1, 9), 7),
(datetime64(2014, 1, 1, 12, 2, 1), 15),
(datetime64(2014, 1, 1, 12, 2, 12), 1)],
dtype=carbonara.TIMESERIES_ARRAY_DTYPE),
before_truncate_callback=functools.partial(
self._resample_and_merge, agg_dict=ts))
self.assertEqual([
(datetime64(2014, 1, 1, 12, 0, 0), 3),
(datetime64(2014, 1, 1, 12, 1, 0), 7),
(datetime64(2014, 1, 1, 12, 2, 0), 15),
], list(ts['return'].fetch(datetime64(2014, 1, 1, 12, 0, 0))))
tsb.set_values(numpy.array([(datetime64(2014, 1, 1, 12, 2, 13), 110)],
dtype=carbonara.TIMESERIES_ARRAY_DTYPE),
before_truncate_callback=functools.partial(
self._resample_and_merge, agg_dict=ts))
self.assertEqual([
(datetime64(2014, 1, 1, 12, 0, 0), 3),
(datetime64(2014, 1, 1, 12, 1, 0), 7),
(datetime64(2014, 1, 1, 12, 2, 0), 110),
], list(ts['return'].fetch(datetime64(2014, 1, 1, 12, 0, 0))))
def test_back_window(self):
"""Back window testing.
Test the back window on an archive is not longer than the window we
aggregate on.
"""
ts = {'sampling': numpy.timedelta64(1, 's'), 'size': 60, 'agg': 'mean'}
tsb = carbonara.BoundTimeSerie(block_size=ts['sampling'])
tsb.set_values(numpy.array([
(datetime64(2014, 1, 1, 12, 0, 1, 2300), 1),
(datetime64(2014, 1, 1, 12, 0, 1, 4600), 2),
(datetime64(2014, 1, 1, 12, 0, 2, 4500), 3),
(datetime64(2014, 1, 1, 12, 0, 2, 7800), 4),
(datetime64(2014, 1, 1, 12, 0, 3, 8), 2.5)],
dtype=carbonara.TIMESERIES_ARRAY_DTYPE),
before_truncate_callback=functools.partial(
self._resample_and_merge, agg_dict=ts))
self.assertEqual(
[
(datetime64(2014, 1, 1, 12, 0, 1), 1.5),
(datetime64(2014, 1, 1, 12, 0, 2), 3.5),
(datetime64(2014, 1, 1, 12, 0, 3), 2.5),
],
list(ts['return'].fetch()))
def test_cross_metric_with_random_holes_derived_boundaries(self):
tsc1 = {
'sampling': numpy.timedelta64(60, 's'),
'size': 10,
'agg': 'mean'
}
tsb1 = carbonara.BoundTimeSerie(block_size=tsc1['sampling'])
tsc2 = {
'sampling': numpy.timedelta64(60, 's'),
'size': 10,
'agg': 'mean'
}
tsb2 = carbonara.BoundTimeSerie(block_size=tsc2['sampling'])
tsb1.set_values(numpy.array([(datetime64(2014, 1, 1, 12, 1, 0), 9),
(datetime64(2014, 1, 1, 12, 2, 0), 1),
(datetime64(2014, 1, 1, 12, 4, 0), 5),
(datetime64(2014, 1, 1, 12, 6, 0), 3)],
dtype=carbonara.TIMESERIES_ARRAY_DTYPE),
before_truncate_callback=functools.partial(
self._resample_and_merge, agg_dict=tsc1))
tsb2.set_values(numpy.array([(datetime64(2014, 1, 1, 12, 0, 0), 6),
(datetime64(2014, 1, 1, 12, 1, 0), 2),
(datetime64(2014, 1, 1, 12, 2, 0), 13),
(datetime64(2014, 1, 1, 12, 3, 0), 24),
(datetime64(2014, 1, 1, 12, 4, 0), 4)],
dtype=carbonara.TIMESERIES_ARRAY_DTYPE),
before_truncate_callback=functools.partial(
self._resample_and_merge, agg_dict=tsc2))
output = cross_metric.aggregated([tsc1['return'], tsc2['return']],
aggregation='mean',
needed_percent_of_overlap=50.0)
self.assertEqual([
(datetime64(2014, 1, 1, 12, 1,
0), numpy.timedelta64(60000000000, 'ns'), 5.5),
(datetime64(2014, 1, 1, 12, 2,
0), numpy.timedelta64(60000000000, 'ns'), 7.0),
(datetime64(2014, 1, 1, 12, 3,
0), numpy.timedelta64(60000000000, 'ns'), 24.0),
(datetime64(2014, 1, 1, 12, 4,
0), numpy.timedelta64(60000000000, 'ns'), 4.5),
], list(output))
def test_aggregated_different_archive_overlap_edge_missing1(self):
tsc1 = carbonara.AggregatedTimeSerie(sampling=60,
max_size=10,
aggregation_method='mean')
tsb1 = carbonara.BoundTimeSerie(block_size=tsc1.sampling)
tsc2 = carbonara.AggregatedTimeSerie(sampling=60,
max_size=10,
aggregation_method='mean')
tsb2 = carbonara.BoundTimeSerie(block_size=tsc2.sampling)
tsb1.set_values([
(datetime.datetime(2014, 1, 1, 12, 3, 0), 9),
(datetime.datetime(2014, 1, 1, 12, 4, 0), 1),
(datetime.datetime(2014, 1, 1, 12, 5, 0), 2),
(datetime.datetime(2014, 1, 1, 12, 6, 0), 7),
(datetime.datetime(2014, 1, 1, 12, 7, 0), 5),
(datetime.datetime(2014, 1, 1, 12, 8, 0), 3),
],
before_truncate_callback=tsc1.update)
tsb2.set_values([
(datetime.datetime(2014, 1, 1, 11, 0, 0), 6),
(datetime.datetime(2014, 1, 1, 12, 1, 0), 2),
(datetime.datetime(2014, 1, 1, 12, 2, 0), 13),
(datetime.datetime(2014, 1, 1, 12, 3, 0), 24),
(datetime.datetime(2014, 1, 1, 12, 4, 0), 4),
(datetime.datetime(2014, 1, 1, 12, 5, 0), 16),
(datetime.datetime(2014, 1, 1, 12, 6, 0), 12),
],
before_truncate_callback=tsc2.update)
# By default we require 100% of point that overlap
# but we allow that the last datapoint is missing
# of the precisest granularity
output = carbonara.AggregatedTimeSerie.aggregated([tsc1, tsc2],
aggregation='sum')
self.assertEqual([
(pandas.Timestamp('2014-01-01 12:03:00'), 60.0, 33.0),
(pandas.Timestamp('2014-01-01 12:04:00'), 60.0, 5.0),
(pandas.Timestamp('2014-01-01 12:05:00'), 60.0, 18.0),
(pandas.Timestamp('2014-01-01 12:06:00'), 60.0, 19.0),
], output)
def test_fetch(self):
ts = {'sampling': numpy.timedelta64(60, 's'),
'size': 10, 'agg': 'mean'}
tsb = carbonara.BoundTimeSerie(block_size=ts['sampling'])
tsb.set_values(numpy.array([
(datetime64(2014, 1, 1, 11, 46, 4), 4),
(datetime64(2014, 1, 1, 11, 47, 34), 8),
(datetime64(2014, 1, 1, 11, 50, 54), 50),
(datetime64(2014, 1, 1, 11, 54, 45), 4),
(datetime64(2014, 1, 1, 11, 56, 49), 4),
(datetime64(2014, 1, 1, 11, 57, 22), 6),
(datetime64(2014, 1, 1, 11, 58, 22), 5),
(datetime64(2014, 1, 1, 12, 1, 4), 4),
(datetime64(2014, 1, 1, 12, 1, 9), 7),
(datetime64(2014, 1, 1, 12, 2, 1), 15),
(datetime64(2014, 1, 1, 12, 2, 12), 1),
(datetime64(2014, 1, 1, 12, 3, 0), 3),
(datetime64(2014, 1, 1, 12, 4, 9), 7),
(datetime64(2014, 1, 1, 12, 5, 1), 15),
(datetime64(2014, 1, 1, 12, 5, 12), 1),
(datetime64(2014, 1, 1, 12, 6, 0, 2), 3)],
dtype=carbonara.TIMESERIES_ARRAY_DTYPE),
before_truncate_callback=functools.partial(
self._resample_and_merge, agg_dict=ts))
tsb.set_values(numpy.array([(datetime64(2014, 1, 1, 12, 6), 5)],
dtype=carbonara.TIMESERIES_ARRAY_DTYPE),
before_truncate_callback=functools.partial(
self._resample_and_merge, agg_dict=ts))
self.assertEqual([
(numpy.datetime64('2014-01-01T11:46:00.000000000'), 4.0),
(numpy.datetime64('2014-01-01T11:47:00.000000000'), 8.0),
(numpy.datetime64('2014-01-01T11:50:00.000000000'), 50.0),
(datetime64(2014, 1, 1, 11, 54), 4.0),
(datetime64(2014, 1, 1, 11, 56), 4.0),
(datetime64(2014, 1, 1, 11, 57), 6.0),
(datetime64(2014, 1, 1, 11, 58), 5.0),
(datetime64(2014, 1, 1, 12, 1), 5.5),
(datetime64(2014, 1, 1, 12, 2), 8.0),
(datetime64(2014, 1, 1, 12, 3), 3.0),
(datetime64(2014, 1, 1, 12, 4), 7.0),
(datetime64(2014, 1, 1, 12, 5), 8.0),
(datetime64(2014, 1, 1, 12, 6), 4.0)
], list(ts['return'].fetch()))
self.assertEqual([
(datetime64(2014, 1, 1, 12, 1), 5.5),
(datetime64(2014, 1, 1, 12, 2), 8.0),
(datetime64(2014, 1, 1, 12, 3), 3.0),
(datetime64(2014, 1, 1, 12, 4), 7.0),
(datetime64(2014, 1, 1, 12, 5), 8.0),
(datetime64(2014, 1, 1, 12, 6), 4.0)
], list(ts['return'].fetch(datetime64(2014, 1, 1, 12, 0, 0))))
def test_aggregated_different_archive_no_overlap(self):
tsc1 = carbonara.AggregatedTimeSerie(sampling=60,
max_size=50,
aggregation_method='mean')
tsb1 = carbonara.BoundTimeSerie(block_size=tsc1.sampling)
tsc2 = carbonara.AggregatedTimeSerie(sampling=60,
max_size=50,
aggregation_method='mean')
tsb2 = carbonara.BoundTimeSerie(block_size=tsc2.sampling)
tsb1.set_values([(datetime.datetime(2014, 1, 1, 11, 46, 4), 4)],
before_truncate_callback=tsc1.update)
tsb2.set_values([(datetime.datetime(2014, 1, 1, 9, 1, 4), 4)],
before_truncate_callback=tsc2.update)
dtfrom = datetime.datetime(2014, 1, 1, 11, 0, 0)
self.assertRaises(carbonara.UnAggregableTimeseries,
carbonara.AggregatedTimeSerie.aggregated,
[tsc1, tsc2],
from_timestamp=dtfrom,
aggregation='mean')
def test_no_truncation(self):
ts = carbonara.AggregatedTimeSerie(sampling=60,
aggregation_method='mean')
tsb = carbonara.BoundTimeSerie()
for i in six.moves.range(1, 11):
tsb.set_values(
[(datetime.datetime(2014, 1, 1, 12, i, i), float(i))],
before_truncate_callback=ts.update)
tsb.set_values(
[(datetime.datetime(2014, 1, 1, 12, i, i + 1), float(i + 1))],
before_truncate_callback=ts.update)
self.assertEqual(i, len(ts.fetch()))
def test_fetch_agg_pct(self):
ts = {'sampling': numpy.timedelta64(1, 's'),
'size': 3600 * 24, 'agg': '90pct'}
tsb = carbonara.BoundTimeSerie(block_size=ts['sampling'])
tsb.set_values(numpy.array([(datetime64(2014, 1, 1, 12, 0, 0), 3),
(datetime64(2014, 1, 1, 12, 0, 0, 123), 4),
(datetime64(2014, 1, 1, 12, 0, 2), 4)],
dtype=carbonara.TIMESERIES_ARRAY_DTYPE),
before_truncate_callback=functools.partial(
self._resample_and_merge, agg_dict=ts))
result = ts['return'].fetch(datetime64(2014, 1, 1, 12, 0, 0))
reference = [
(datetime64(
2014, 1, 1, 12, 0, 0
), 3.9),
(datetime64(
2014, 1, 1, 12, 0, 2
), 4)
]
self.assertEqual(len(reference), len(list(result)))
for ref, res in zip(reference, result):
self.assertEqual(ref[0], res[0])
# Rounding \o/
self.assertAlmostEqual(ref[1], res[1])
tsb.set_values(numpy.array([
(datetime64(2014, 1, 1, 12, 0, 2, 113), 110)],
dtype=carbonara.TIMESERIES_ARRAY_DTYPE),
before_truncate_callback=functools.partial(
self._resample_and_merge, agg_dict=ts))
result = ts['return'].fetch(datetime64(2014, 1, 1, 12, 0, 0))
reference = [
(datetime64(
2014, 1, 1, 12, 0, 0
), 3.9),
(datetime64(
2014, 1, 1, 12, 0, 2
), 99.4)
]
self.assertEqual(len(reference), len(list(result)))
for ref, res in zip(reference, result):
self.assertEqual(ref[0], res[0])
# Rounding \o/
self.assertAlmostEqual(ref[1], res[1])
def test_fetch(self):
ts = carbonara.AggregatedTimeSerie(sampling=60,
max_size=10,
aggregation_method='mean')
tsb = carbonara.BoundTimeSerie(block_size=ts.sampling)
tsb.set_values([
(datetime.datetime(2014, 1, 1, 11, 46, 4), 4),
(datetime.datetime(2014, 1, 1, 11, 47, 34), 8),
(datetime.datetime(2014, 1, 1, 11, 50, 54), 50),
(datetime.datetime(2014, 1, 1, 11, 54, 45), 4),
(datetime.datetime(2014, 1, 1, 11, 56, 49), 4),
(datetime.datetime(2014, 1, 1, 11, 57, 22), 6),
(datetime.datetime(2014, 1, 1, 11, 58, 22), 5),
(datetime.datetime(2014, 1, 1, 12, 1, 4), 4),
(datetime.datetime(2014, 1, 1, 12, 1, 9), 7),
(datetime.datetime(2014, 1, 1, 12, 2, 1), 15),
(datetime.datetime(2014, 1, 1, 12, 2, 12), 1),
(datetime.datetime(2014, 1, 1, 12, 3, 0), 3),
(datetime.datetime(2014, 1, 1, 12, 4, 9), 7),
(datetime.datetime(2014, 1, 1, 12, 5, 1), 15),
(datetime.datetime(2014, 1, 1, 12, 5, 12), 1),
(datetime.datetime(2014, 1, 1, 12, 6, 0, 2), 3),
],
before_truncate_callback=ts.update)
tsb.set_values([
(datetime.datetime(2014, 1, 1, 12, 6), 5),
],
before_truncate_callback=ts.update)
self.assertEqual([(datetime.datetime(2014, 1, 1, 11, 54), 60.0, 4.0),
(datetime.datetime(2014, 1, 1, 11, 56), 60.0, 4.0),
(datetime.datetime(2014, 1, 1, 11, 57), 60.0, 6.0),
(datetime.datetime(2014, 1, 1, 11, 58), 60.0, 5.0),
(datetime.datetime(2014, 1, 1, 12, 1), 60.0, 5.5),
(datetime.datetime(2014, 1, 1, 12, 2), 60.0, 8.0),
(datetime.datetime(2014, 1, 1, 12, 3), 60.0, 3.0),
(datetime.datetime(2014, 1, 1, 12, 4), 60.0, 7.0),
(datetime.datetime(2014, 1, 1, 12, 5), 60.0, 8.0),
(datetime.datetime(2014, 1, 1, 12, 6), 60.0, 4.0)],
ts.fetch())
self.assertEqual([(datetime.datetime(2014, 1, 1, 12, 1), 60.0, 5.5),
(datetime.datetime(2014, 1, 1, 12, 2), 60.0, 8.0),
(datetime.datetime(2014, 1, 1, 12, 3), 60.0, 3.0),
(datetime.datetime(2014, 1, 1, 12, 4), 60.0, 7.0),
(datetime.datetime(2014, 1, 1, 12, 5), 60.0, 8.0),
(datetime.datetime(2014, 1, 1, 12, 6), 60.0, 4.0)],
ts.fetch(datetime.datetime(2014, 1, 1, 12, 0, 0)))
def test_back_window_ignore(self):
"""Back window testing.
Test the back window on an archive is not longer than the window we
aggregate on.
"""
ts = carbonara.AggregatedTimeSerie(sampling=1,
max_size=60,
aggregation_method='mean')
tsb = carbonara.BoundTimeSerie(block_size=ts.sampling)
tsb.set_values([
(datetime.datetime(2014, 1, 1, 12, 0, 1, 2300), 1),
(datetime.datetime(2014, 1, 1, 12, 0, 1, 4600), 2),
(datetime.datetime(2014, 1, 1, 12, 0, 2, 4500), 3),
(datetime.datetime(2014, 1, 1, 12, 0, 2, 7800), 4),
(datetime.datetime(2014, 1, 1, 12, 0, 3, 8), 2.5),
],
before_truncate_callback=ts.update)
self.assertEqual([
(pandas.Timestamp('2014-01-01 12:00:01'), 1.0, 1.5),
(pandas.Timestamp('2014-01-01 12:00:02'), 1.0, 3.5),
(pandas.Timestamp('2014-01-01 12:00:03'), 1.0, 2.5),
], ts.fetch())
tsb.set_values([
(datetime.datetime(2014, 1, 1, 12, 0, 2, 99), 9),
],
ignore_too_old_timestamps=True,
before_truncate_callback=ts.update)
self.assertEqual([
(pandas.Timestamp('2014-01-01 12:00:01'), 1.0, 1.5),
(pandas.Timestamp('2014-01-01 12:00:02'), 1.0, 3.5),
(pandas.Timestamp('2014-01-01 12:00:03'), 1.0, 2.5),
], ts.fetch())
tsb.set_values([
(datetime.datetime(2014, 1, 1, 12, 0, 2, 99), 9),
(datetime.datetime(2014, 1, 1, 12, 0, 3, 9), 4.5),
],
ignore_too_old_timestamps=True,
before_truncate_callback=ts.update)
self.assertEqual([
(pandas.Timestamp('2014-01-01 12:00:01'), 1.0, 1.5),
(pandas.Timestamp('2014-01-01 12:00:02'), 1.0, 3.5),
(pandas.Timestamp('2014-01-01 12:00:03'), 1.0, 3.5),
], ts.fetch())
def test_no_truncation(self):
ts = {'sampling': numpy.timedelta64(60, 's'), 'agg': 'mean'}
tsb = carbonara.BoundTimeSerie()
for i in six.moves.range(1, 11):
tsb.set_values(numpy.array([
(datetime64(2014, 1, 1, 12, i, i), float(i))],
dtype=carbonara.TIMESERIES_ARRAY_DTYPE),
before_truncate_callback=functools.partial(
self._resample_and_merge, agg_dict=ts))
tsb.set_values(numpy.array([
(datetime64(2014, 1, 1, 12, i, i + 1), float(i + 1))],
dtype=carbonara.TIMESERIES_ARRAY_DTYPE),
before_truncate_callback=functools.partial(
self._resample_and_merge, agg_dict=ts))
self.assertEqual(i, len(list(ts['return'].fetch())))
def test_serialize(self):
ts = carbonara.AggregatedTimeSerie(sampling=0.5,
aggregation_method='mean')
tsb = carbonara.BoundTimeSerie(block_size=ts.sampling)
tsb.set_values([
(datetime.datetime(2014, 1, 1, 12, 0, 0, 1234), 3),
(datetime.datetime(2014, 1, 1, 12, 0, 0, 321), 6),
(datetime.datetime(2014, 1, 1, 12, 1, 4, 234), 5),
(datetime.datetime(2014, 1, 1, 12, 1, 9, 32), 7),
(datetime.datetime(2014, 1, 1, 12, 2, 12, 532), 1),
],
before_truncate_callback=ts.update)
self.assertEqual(
ts, carbonara.AggregatedTimeSerie.unserialize(ts.serialize()))
def test_serialize(self):
ts = {'sampling': numpy.timedelta64(500, 'ms'), 'agg': 'mean'}
tsb = carbonara.BoundTimeSerie(block_size=ts['sampling'])
tsb.set_values(numpy.array([
(datetime64(2014, 1, 1, 12, 0, 0, 1234), 3),
(datetime64(2014, 1, 1, 12, 0, 0, 321), 6),
(datetime64(2014, 1, 1, 12, 1, 4, 234), 5),
(datetime64(2014, 1, 1, 12, 1, 9, 32), 7),
(datetime64(2014, 1, 1, 12, 2, 12, 532), 1)],
dtype=carbonara.TIMESERIES_ARRAY_DTYPE),
before_truncate_callback=functools.partial(
self._resample_and_merge, agg_dict=ts))
key = ts['return'].get_split_key()
o, s = ts['return'].serialize(key)
self.assertEqual(ts['return'],
carbonara.AggregatedTimeSerie.unserialize(
s, key, ts['return'].aggregation))
def test_back_window(self):
"""Back window testing.
Test the back window on an archive is not longer than the window we
aggregate on.
"""
ts = carbonara.AggregatedTimeSerie(sampling=1,
max_size=60,
aggregation_method='mean')
tsb = carbonara.BoundTimeSerie(block_size=ts.sampling)
tsb.set_values([
(datetime.datetime(2014, 1, 1, 12, 0, 1, 2300), 1),
(datetime.datetime(2014, 1, 1, 12, 0, 1, 4600), 2),
(datetime.datetime(2014, 1, 1, 12, 0, 2, 4500), 3),
(datetime.datetime(2014, 1, 1, 12, 0, 2, 7800), 4),
(datetime.datetime(2014, 1, 1, 12, 0, 3, 8), 2.5),
],
before_truncate_callback=ts.update)
self.assertEqual([
(pandas.Timestamp('2014-01-01 12:00:01'), 1.0, 1.5),
(pandas.Timestamp('2014-01-01 12:00:02'), 1.0, 3.5),
(pandas.Timestamp('2014-01-01 12:00:03'), 1.0, 2.5),
], ts.fetch())
try:
tsb.set_values([
(datetime.datetime(2014, 1, 1, 12, 0, 2, 99), 9),
])
except carbonara.NoDeloreanAvailable as e:
self.assertEqual(
six.text_type(e),
u"2014-01-01 12:00:02.000099 is before 2014-01-01 12:00:03")
self.assertEqual(datetime.datetime(2014, 1, 1, 12, 0, 2, 99),
e.bad_timestamp)
self.assertEqual(datetime.datetime(2014, 1, 1, 12, 0, 3),
e.first_timestamp)
else:
self.fail("No exception raised")
def test_fetch_agg_pct(self):
ts = carbonara.AggregatedTimeSerie(sampling=1,
max_size=3600 * 24,
aggregation_method='90pct')
tsb = carbonara.BoundTimeSerie(block_size=ts.sampling)
tsb.set_values([(datetime.datetime(2014, 1, 1, 12, 0, 0), 3),
(datetime.datetime(2014, 1, 1, 12, 0, 0, 123), 4),
(datetime.datetime(2014, 1, 1, 12, 0, 2), 4)],
before_truncate_callback=ts.update)
result = ts.fetch(datetime.datetime(2014, 1, 1, 12, 0, 0))
reference = [(pandas.Timestamp('2014-01-01 12:00:00'), 1.0, 3.9),
(pandas.Timestamp('2014-01-01 12:00:02'), 1.0, 4)]
self.assertEqual(len(reference), len(result))
for ref, res in zip(reference, result):
self.assertEqual(ref[0], res[0])
self.assertEqual(ref[1], res[1])
# Rounding \o/
self.assertAlmostEqual(ref[2], res[2])
tsb.set_values([(datetime.datetime(2014, 1, 1, 12, 0, 2, 113), 110)],
before_truncate_callback=ts.update)
result = ts.fetch(datetime.datetime(2014, 1, 1, 12, 0, 0))
reference = [(pandas.Timestamp('2014-01-01 12:00:00'), 1.0, 3.9),
(pandas.Timestamp('2014-01-01 12:00:02'), 1.0, 99.4)]
self.assertEqual(len(reference), len(result))
for ref, res in zip(reference, result):
self.assertEqual(ref[0], res[0])
self.assertEqual(ref[1], res[1])
# Rounding \o/
self.assertAlmostEqual(ref[2], res[2])
def add_measures_to_metrics(self, metrics_and_measures):
"""Update a metric with a new measures, computing new aggregations.
:param metrics_and_measures: A dict there keys are `storage.Metric`
objects and values are timeseries array of
the new measures.
"""
with self.statistics.time("raw measures fetch"):
raw_measures = self._get_or_create_unaggregated_timeseries(
metrics_and_measures.keys())
self.statistics["raw measures fetch"] += len(metrics_and_measures)
self.statistics["processed measures"] += sum(
map(len, metrics_and_measures.values()))
new_boundts = []
splits_to_delete = {}
splits_to_update = {}
for metric, measures in six.iteritems(metrics_and_measures):
measures = numpy.sort(measures, order='timestamps')
agg_methods = list(metric.archive_policy.aggregation_methods)
block_size = metric.archive_policy.max_block_size
back_window = metric.archive_policy.back_window
# NOTE(sileht): We keep one more blocks to calculate rate of change
# correctly
if any(filter(lambda x: x.startswith("rate:"), agg_methods)):
back_window += 1
if raw_measures[metric] is None:
ts = None
else:
try:
ts = carbonara.BoundTimeSerie.unserialize(
raw_measures[metric], block_size, back_window)
except carbonara.InvalidData:
LOG.error(
"Data corruption detected for %s "
"unaggregated timeserie, creating a new one",
metric.id)
ts = None
if ts is None:
# This is the first time we treat measures for this
# metric, or data are corrupted, create a new one
ts = carbonara.BoundTimeSerie(block_size=block_size,
back_window=back_window)
current_first_block_timestamp = None
else:
current_first_block_timestamp = ts.first_block_timestamp()
# NOTE(jd) This is Python where you need such
# hack to pass a variable around a closure,
# sorry.
computed_points = {"number": 0}
def _map_compute_splits_operations(bound_timeserie):
# NOTE (gordc): bound_timeserie is entire set of
# unaggregated measures matching largest
# granularity. the following takes only the points
# affected by new measures for specific granularity
tstamp = max(bound_timeserie.first, measures['timestamps'][0])
new_first_block_timestamp = (
bound_timeserie.first_block_timestamp())
computed_points['number'] = len(bound_timeserie)
aggregations = metric.archive_policy.aggregations
grouped_timeseries = {
granularity: bound_timeserie.group_serie(
granularity,
carbonara.round_timestamp(tstamp, granularity))
for granularity, aggregations
# No need to sort the aggregation, they are already
in itertools.groupby(aggregations, ATTRGETTER_GRANULARITY)
}
aggregations_and_timeseries = {
aggregation:
carbonara.AggregatedTimeSerie.from_grouped_serie(
grouped_timeseries[aggregation.granularity],
aggregation)
for aggregation in aggregations
}
deleted_keys, keys_and_split_to_store = (
self._compute_split_operations(
metric, aggregations_and_timeseries,
current_first_block_timestamp,
new_first_block_timestamp))
return (new_first_block_timestamp, deleted_keys,
keys_and_split_to_store)
with self.statistics.time("aggregated measures compute"):
(new_first_block_timestamp, deleted_keys,
keys_and_splits_to_store) = ts.set_values(
measures,
before_truncate_callback=_map_compute_splits_operations,
)
splits_to_delete[metric] = deleted_keys
splits_to_update[metric] = (keys_and_splits_to_store,
new_first_block_timestamp)
new_boundts.append((metric, ts.serialize()))
with self.statistics.time("splits delete"):
self._delete_metric_splits(splits_to_delete)
self.statistics["splits delete"] += len(splits_to_delete)
with self.statistics.time("splits update"):
self._update_metric_splits(splits_to_update)
self.statistics["splits update"] += len(splits_to_update)
with self.statistics.time("raw measures store"):
self._store_unaggregated_timeseries(new_boundts)
self.statistics["raw measures store"] += len(new_boundts)
def process_measures(self, indexer, block_size, sync=False):
metrics_to_process = self._list_metric_with_measures_to_process(
block_size, full=sync)
metrics = indexer.list_metrics(ids=metrics_to_process)
# This build the list of deleted metrics, i.e. the metrics we have
# measures to process for but that are not in the indexer anymore.
deleted_metrics_id = (set(map(uuid.UUID, metrics_to_process)) -
set(m.id for m in metrics))
for metric_id in deleted_metrics_id:
# NOTE(jd): We need to lock the metric otherwise we might delete
# measures that another worker might be processing. Deleting
# measurement files under its feet is not nice!
with self._lock(metric_id)(blocking=sync):
self._delete_unprocessed_measures_for_metric_id(metric_id)
for metric in metrics:
lock = self._lock(metric.id)
agg_methods = list(metric.archive_policy.aggregation_methods)
# Do not block if we cannot acquire the lock, that means some other
# worker is doing the job. We'll just ignore this metric and may
# get back later to it if needed.
if lock.acquire(blocking=sync):
try:
LOG.debug("Processing measures for %s" % metric)
with self._process_measure_for_metric(metric) as measures:
# NOTE(mnaser): The metric could have been handled by
# another worker, ignore if no measures.
if len(measures) == 0:
LOG.debug("Skipping %s (already processed)" %
metric)
continue
try:
with timeutils.StopWatch() as sw:
raw_measures = (
self._get_unaggregated_timeserie(metric))
LOG.debug("Retrieve unaggregated measures "
"for %s in %.2fs" %
(metric.id, sw.elapsed()))
except storage.MetricDoesNotExist:
try:
self._create_metric(metric)
except storage.MetricAlreadyExists:
# Created in the mean time, do not worry
pass
ts = None
else:
try:
ts = carbonara.BoundTimeSerie.unserialize(
raw_measures)
except ValueError:
ts = None
LOG.error("Data corruption detected for %s "
"unaggregated timeserie, "
"recreating an empty one." %
metric.id)
if ts is None:
# This is the first time we treat measures for this
# metric, or data are corrupted, create a new one
mbs = metric.archive_policy.max_block_size
ts = carbonara.BoundTimeSerie(
block_size=mbs,
back_window=metric.archive_policy.back_window)
def _map_add_measures(bound_timeserie):
self._map_in_thread(
self._add_measures,
((aggregation, d, metric, bound_timeserie)
for aggregation in agg_methods
for d in metric.archive_policy.definition))
with timeutils.StopWatch() as sw:
ts.set_values(
measures,
before_truncate_callback=_map_add_measures,
ignore_too_old_timestamps=True)
LOG.debug(
"Computed new metric %s with %d new measures "
"in %.2f seconds" %
(metric.id, len(measures), sw.elapsed()))
self._store_unaggregated_timeserie(
metric, ts.serialize())
except Exception:
if sync:
raise
LOG.error("Error processing new measures", exc_info=True)
finally:
lock.release()
def _compute_and_store_timeseries(self, metric, measures):
# NOTE(mnaser): The metric could have been handled by
# another worker, ignore if no measures.
if len(measures) == 0:
LOG.debug("Skipping %s (already processed)", metric)
return
measures.sort(order='timestamps')
agg_methods = list(metric.archive_policy.aggregation_methods)
block_size = metric.archive_policy.max_block_size
back_window = metric.archive_policy.back_window
definition = metric.archive_policy.definition
# NOTE(sileht): We keep one more blocks to calculate rate of change
# correctly
if any(filter(lambda x: x.startswith("rate:"), agg_methods)):
back_window += 1
try:
ts = self._get_unaggregated_timeserie_and_unserialize(
metric, block_size=block_size, back_window=back_window)
except MetricDoesNotExist:
try:
self._create_metric(metric)
except MetricAlreadyExists:
# Created in the mean time, do not worry
pass
ts = None
except CorruptionError as e:
LOG.error(e)
ts = None
if ts is None:
# This is the first time we treat measures for this
# metric, or data are corrupted, create a new one
ts = carbonara.BoundTimeSerie(block_size=block_size,
back_window=back_window)
current_first_block_timestamp = None
else:
current_first_block_timestamp = ts.first_block_timestamp()
# NOTE(jd) This is Python where you need such
# hack to pass a variable around a closure,
# sorry.
computed_points = {"number": 0}
def _map_add_measures(bound_timeserie):
# NOTE (gordc): bound_timeserie is entire set of
# unaggregated measures matching largest
# granularity. the following takes only the points
# affected by new measures for specific granularity
tstamp = max(bound_timeserie.first, measures['timestamps'][0])
new_first_block_timestamp = bound_timeserie.first_block_timestamp()
computed_points['number'] = len(bound_timeserie)
for d in definition:
ts = bound_timeserie.group_serie(
d.granularity, carbonara.round_timestamp(
tstamp, d.granularity))
self._map_in_thread(
self._add_measures,
((aggregation, d, metric, ts,
current_first_block_timestamp,
new_first_block_timestamp)
for aggregation in agg_methods))
with utils.StopWatch() as sw:
ts.set_values(measures,
before_truncate_callback=_map_add_measures)
number_of_operations = (len(agg_methods) * len(definition))
perf = ""
elapsed = sw.elapsed()
if elapsed > 0:
perf = " (%d points/s, %d measures/s)" % (
((number_of_operations * computed_points['number']) /
elapsed),
((number_of_operations * len(measures)) / elapsed)
)
LOG.debug("Computed new metric %s with %d new measures "
"in %.2f seconds%s",
metric.id, len(measures), elapsed, perf)
self._store_unaggregated_timeserie(metric, ts.serialize())
def compute_and_store_timeseries(self, metric, measures):
# NOTE(mnaser): The metric could have been handled by
# another worker, ignore if no measures.
if len(measures) == 0:
LOG.debug("Skipping %s (already processed)", metric)
return
measures = numpy.sort(measures, order='timestamps')
agg_methods = list(metric.archive_policy.aggregation_methods)
block_size = metric.archive_policy.max_block_size
back_window = metric.archive_policy.back_window
definition = metric.archive_policy.definition
# NOTE(sileht): We keep one more blocks to calculate rate of change
# correctly
if any(filter(lambda x: x.startswith("rate:"), agg_methods)):
back_window += 1
with utils.StopWatch() as sw:
raw_measures = (
self._get_or_create_unaggregated_timeseries(
[metric])[metric]
)
LOG.debug("Retrieve unaggregated measures for %s in %.2fs",
metric.id, sw.elapsed())
if raw_measures is None:
ts = None
else:
try:
ts = carbonara.BoundTimeSerie.unserialize(
raw_measures, block_size, back_window)
except carbonara.InvalidData:
LOG.error("Data corruption detected for %s "
"unaggregated timeserie, creating a new one",
metric.id)
ts = None
if ts is None:
# This is the first time we treat measures for this
# metric, or data are corrupted, create a new one
ts = carbonara.BoundTimeSerie(block_size=block_size,
back_window=back_window)
current_first_block_timestamp = None
else:
current_first_block_timestamp = ts.first_block_timestamp()
# NOTE(jd) This is Python where you need such
# hack to pass a variable around a closure,
# sorry.
computed_points = {"number": 0}
def _map_add_measures(bound_timeserie):
# NOTE (gordc): bound_timeserie is entire set of
# unaggregated measures matching largest
# granularity. the following takes only the points
# affected by new measures for specific granularity
tstamp = max(bound_timeserie.first, measures['timestamps'][0])
new_first_block_timestamp = bound_timeserie.first_block_timestamp()
computed_points['number'] = len(bound_timeserie)
for granularity, aggregations in itertools.groupby(
# No need to sort the aggregation, they are already
metric.archive_policy.aggregations,
ATTRGETTER_GRANULARITY):
ts = bound_timeserie.group_serie(
granularity, carbonara.round_timestamp(
tstamp, granularity))
self._add_measures(metric, aggregations, ts,
current_first_block_timestamp,
new_first_block_timestamp)
with utils.StopWatch() as sw:
ts.set_values(measures,
before_truncate_callback=_map_add_measures)
number_of_operations = (len(agg_methods) * len(definition))
perf = ""
elapsed = sw.elapsed()
if elapsed > 0:
perf = " (%d points/s, %d measures/s)" % (
((number_of_operations * computed_points['number']) /
elapsed),
((number_of_operations * len(measures)) / elapsed)
)
LOG.debug("Computed new metric %s with %d new measures "
"in %.2f seconds%s",
metric.id, len(measures), elapsed, perf)
self._store_unaggregated_timeseries([(metric, ts.serialize())])
def test_aggregated_different_archive_overlap(self):
tsc1 = {
'sampling': numpy.timedelta64(60, 's'),
'size': 10,
'agg': 'mean'
}
tsb1 = carbonara.BoundTimeSerie(block_size=tsc1['sampling'])
tsc2 = {
'sampling': numpy.timedelta64(60, 's'),
'size': 10,
'agg': 'mean'
}
tsb2 = carbonara.BoundTimeSerie(block_size=tsc2['sampling'])
# NOTE(sileht): minute 8 is missing in both and
# minute 7 in tsc2 too, but it looks like we have
# enough point to do the aggregation
tsb1.set_values(numpy.array([(datetime64(2014, 1, 1, 11, 0, 0), 4),
(datetime64(2014, 1, 1, 12, 1, 0), 3),
(datetime64(2014, 1, 1, 12, 2, 0), 2),
(datetime64(2014, 1, 1, 12, 3, 0), 4),
(datetime64(2014, 1, 1, 12, 4, 0), 2),
(datetime64(2014, 1, 1, 12, 5, 0), 3),
(datetime64(2014, 1, 1, 12, 6, 0), 4),
(datetime64(2014, 1, 1, 12, 7, 0), 10),
(datetime64(2014, 1, 1, 12, 9, 0), 2)],
dtype=carbonara.TIMESERIES_ARRAY_DTYPE),
before_truncate_callback=functools.partial(
self._resample_and_merge, agg_dict=tsc1))
tsb2.set_values(numpy.array([(datetime64(2014, 1, 1, 12, 1, 0), 3),
(datetime64(2014, 1, 1, 12, 2, 0), 4),
(datetime64(2014, 1, 1, 12, 3, 0), 4),
(datetime64(2014, 1, 1, 12, 4, 0), 6),
(datetime64(2014, 1, 1, 12, 5, 0), 3),
(datetime64(2014, 1, 1, 12, 6, 0), 6),
(datetime64(2014, 1, 1, 12, 9, 0), 2),
(datetime64(2014, 1, 1, 12, 11, 0), 2),
(datetime64(2014, 1, 1, 12, 12, 0), 2)],
dtype=carbonara.TIMESERIES_ARRAY_DTYPE),
before_truncate_callback=functools.partial(
self._resample_and_merge, agg_dict=tsc2))
dtfrom = datetime64(2014, 1, 1, 12, 0, 0)
dtto = datetime64(2014, 1, 1, 12, 10, 0)
# By default we require 100% of point that overlap
# so that fail
self.assertRaises(cross_metric.UnAggregableTimeseries,
cross_metric.aggregated,
[tsc1['return'], tsc2['return']],
from_timestamp=dtfrom,
to_timestamp=dtto,
aggregation='mean')
# Retry with 80% and it works
output = cross_metric.aggregated([tsc1['return'], tsc2['return']],
from_timestamp=dtfrom,
to_timestamp=dtto,
aggregation='mean',
needed_percent_of_overlap=80.0)
self.assertEqual([
(datetime64(2014, 1, 1, 12, 1, 0), numpy.timedelta64(60,
's'), 3.0),
(datetime64(2014, 1, 1, 12, 2, 0), numpy.timedelta64(60,
's'), 3.0),
(datetime64(2014, 1, 1, 12, 3, 0), numpy.timedelta64(60,
's'), 4.0),
(datetime64(2014, 1, 1, 12, 4, 0), numpy.timedelta64(60,
's'), 4.0),
(datetime64(2014, 1, 1, 12, 5, 0), numpy.timedelta64(60,
's'), 3.0),
(datetime64(2014, 1, 1, 12, 6, 0), numpy.timedelta64(60,
's'), 5.0),
(datetime64(2014, 1, 1, 12, 7, 0), numpy.timedelta64(60,
's'), 10.0),
(datetime64(2014, 1, 1, 12, 9, 0), numpy.timedelta64(60,
's'), 2.0),
], list(output))
