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_different_archive_no_overlap2(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')
tsb1.set_values([(datetime.datetime(2014, 1, 1, 12, 3, 0), 4)],
before_truncate_callback=tsc1.update)
self.assertRaises(carbonara.UnAggregableTimeseries,
carbonara.AggregatedTimeSerie.aggregated,
[tsc1, tsc2],
aggregation='mean')
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 = 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_74_percentile_serialized(self):
ts = carbonara.AggregatedTimeSerie(sampling='1Min',
aggregation_method='74pct')
ts.update(
carbonara.TimeSerie.from_tuples([
(datetime.datetime(2014, 1, 1, 12, 0, 0), 3),
(datetime.datetime(2014, 1, 1, 12, 0, 4), 5),
(datetime.datetime(2014, 1, 1, 12, 0, 9), 6)
]))
self.assertEqual(1, len(ts))
self.assertEqual(5.48, ts[datetime.datetime(2014, 1, 1, 12, 0, 0)])
# Serialize and unserialize
ts = carbonara.AggregatedTimeSerie.unserialize(ts.serialize())
ts.update(
carbonara.TimeSerie.from_tuples([
(datetime.datetime(2014, 1, 1, 12, 0, 0), 3),
(datetime.datetime(2014, 1, 1, 12, 0, 4), 5),
(datetime.datetime(2014, 1, 1, 12, 0, 9), 6)
]))
self.assertEqual(1, len(ts))
self.assertEqual(5.48, ts[datetime.datetime(2014, 1, 1, 12, 0, 0)])
def _get_splits_and_unserialize(self, metrics_aggregations_keys):
"""Get splits and unserialize them
:param metrics_aggregations_keys: A dict where keys are
`storage.Metric` and values are dict
of {Aggregation: [SplitKey]} to
retrieve.
:return: A dict where keys are `storage.Metric` and values are dict
{aggregation: [`carbonara.AggregatedTimeSerie`]}.
"""
raw_measures = self._get_splits(metrics_aggregations_keys)
results = collections.defaultdict(
lambda: collections.defaultdict(list))
for metric, aggregations_and_raws in six.iteritems(raw_measures):
for aggregation, raws in six.iteritems(aggregations_and_raws):
for key, raw in six.moves.zip(
metrics_aggregations_keys[metric][aggregation], raws):
try:
ts = carbonara.AggregatedTimeSerie.unserialize(
raw, key, aggregation)
except carbonara.InvalidData:
LOG.error(
"Data corruption detected for %s "
"aggregated `%s' timeserie, granularity "
"`%s' around time `%s', ignoring.", metric.id,
aggregation.method, key.sampling, key)
ts = carbonara.AggregatedTimeSerie(aggregation)
results[metric][aggregation].append(ts)
return results
def _get_splits_and_unserialize(self, metric, keys_and_aggregations):
"""Get splits and unserialize them
:param metric: The metric to retrieve.
:param keys_and_aggregations: A list of tuple (SplitKey, Aggregation)
to retrieve.
:return: A list of AggregatedTimeSerie.
"""
if not keys_and_aggregations:
return []
raw_measures = self._get_measures(metric, keys_and_aggregations)
results = []
for (key, aggregation), raw in six.moves.zip(
keys_and_aggregations, raw_measures):
try:
ts = carbonara.AggregatedTimeSerie.unserialize(
raw, key, aggregation)
except carbonara.InvalidData:
LOG.error("Data corruption detected for %s "
"aggregated `%s' timeserie, granularity `%s' "
"around time `%s', ignoring.",
metric.id, aggregation.method, key.sampling, key)
ts = carbonara.AggregatedTimeSerie(aggregation)
results.append(ts)
return results
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 _get_measures_timeserie(storage, ref, granularity, *args, **kwargs):
agg = ref.metric.archive_policy.get_aggregation(ref.aggregation,
granularity)
try:
data = storage.get_aggregated_measures({ref.metric: [agg]}, *args,
**kwargs)[ref.metric][agg]
except gnocchi_storage.MetricDoesNotExist:
data = carbonara.AggregatedTimeSerie(
carbonara.Aggregation(ref.aggregation, granularity, None))
return (ref, data)
def test_down_sampling(self):
ts = carbonara.AggregatedTimeSerie(sampling='5Min',
aggregation_method='mean')
ts.update(
carbonara.TimeSerie.from_data([
datetime.datetime(2014, 1, 1, 12, 0, 0),
datetime.datetime(2014, 1, 1, 12, 0, 4),
datetime.datetime(2014, 1, 1, 12, 0, 9)
], [3, 5, 7]))
self.assertEqual(1, len(ts))
self.assertEqual(5, ts[datetime.datetime(2014, 1, 1, 12, 0, 0)])
def test_before_epoch(self):
ts = carbonara.AggregatedTimeSerie(sampling='1Min',
aggregation_method='74pct')
self.assertRaises(
carbonara.BeforeEpochError, ts.update,
carbonara.TimeSerie.from_tuples([
(datetime.datetime(1950, 1, 1, 12, 0, 0), 3),
(datetime.datetime(2014, 1, 1, 12, 0, 4), 5),
(datetime.datetime(2014, 1, 1, 12, 0, 9), 6)
]))
def _get_measures_timeserie(self,
metric,
aggregation,
granularity,
from_timestamp=None,
to_timestamp=None):
# Find the number of point
for d in metric.archive_policy.definition:
if d.granularity == granularity:
points = d.points
break
else:
raise storage.GranularityDoesNotExist(metric, granularity)
all_keys = None
try:
all_keys = self._list_split_keys_for_metric(
metric, aggregation, granularity)
except storage.MetricDoesNotExist:
for d in metric.archive_policy.definition:
if d.granularity == granularity:
return carbonara.AggregatedTimeSerie(
sampling=granularity,
aggregation_method=aggregation,
max_size=d.points)
raise storage.GranularityDoesNotExist(metric, granularity)
if from_timestamp:
from_timestamp = str(
carbonara.SplitKey.from_timestamp_and_sampling(
from_timestamp, granularity))
if to_timestamp:
to_timestamp = str(
carbonara.SplitKey.from_timestamp_and_sampling(
to_timestamp, granularity))
timeseries = list(
filter(
lambda x: x is not None,
self._map_in_thread(
self._get_measures_and_unserialize,
((metric, key, aggregation, granularity)
for key in sorted(all_keys)
if ((not from_timestamp or key >= from_timestamp) and (
not to_timestamp or key <= to_timestamp))))))
return carbonara.AggregatedTimeSerie.from_timeseries(
sampling=granularity,
aggregation_method=aggregation,
timeseries=timeseries,
max_size=points)
def test_95_percentile(self):
ts = carbonara.AggregatedTimeSerie(sampling='1Min',
aggregation_method='95pct')
ts.update(
carbonara.TimeSerie.from_tuples([
(datetime.datetime(2014, 1, 1, 12, 0, 0), 3),
(datetime.datetime(2014, 1, 1, 12, 0, 4), 5),
(datetime.datetime(2014, 1, 1, 12, 0, 9), 6)
]))
self.assertEqual(1, len(ts))
self.assertEqual(5.9000000000000004,
ts[datetime.datetime(2014, 1, 1, 12, 0, 0)])
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_max_size(self):
ts = carbonara.AggregatedTimeSerie(sampling=1,
max_size=2,
aggregation_method='mean')
ts.update(
carbonara.TimeSerie.from_data([
datetime.datetime(2014, 1, 1, 12, 0, 0),
datetime.datetime(2014, 1, 1, 12, 0, 4),
datetime.datetime(2014, 1, 1, 12, 0, 9)
], [3, 5, 6]))
self.assertEqual(2, len(ts))
self.assertEqual(5, ts[0])
self.assertEqual(6, ts[1])
def test_to_dict_from_dict(self):
ts = carbonara.AggregatedTimeSerie(sampling='1Min',
max_size=2,
aggregation_method='max')
ts.update(
carbonara.TimeSerie.from_data([
datetime.datetime(2014, 1, 1, 12, 0, 0),
datetime.datetime(2014, 1, 1, 12, 1, 4),
datetime.datetime(2014, 1, 1, 12, 1, 9),
datetime.datetime(2014, 1, 1, 12, 2, 12)
], [3, 5, 7, 1]))
ts2 = carbonara.AggregatedTimeSerie.from_dict(ts.to_dict())
self.assertEqual(ts, ts2)
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_down_sampling_with_max_size_and_method_max(self):
ts = carbonara.AggregatedTimeSerie(sampling='1Min',
max_size=2,
aggregation_method='max')
ts.update(
carbonara.TimeSerie.from_data([
datetime.datetime(2014, 1, 1, 12, 0, 0),
datetime.datetime(2014, 1, 1, 12, 1, 4),
datetime.datetime(2014, 1, 1, 12, 1, 9),
datetime.datetime(2014, 1, 1, 12, 2, 12)
], [3, 5, 70, 1]))
self.assertEqual(2, len(ts))
self.assertEqual(70, ts[datetime.datetime(2014, 1, 1, 12, 1, 0)])
self.assertEqual(1, ts[datetime.datetime(2014, 1, 1, 12, 2, 0)])
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 _get_measures_timeserie(self,
metric,
aggregation,
from_timestamp=None,
to_timestamp=None):
try:
all_keys = self._list_split_keys_for_metric(
metric, aggregation.method, aggregation.granularity)
except MetricDoesNotExist:
return carbonara.AggregatedTimeSerie(
sampling=aggregation.granularity,
aggregation_method=aggregation.method)
if from_timestamp:
from_timestamp = carbonara.SplitKey.from_timestamp_and_sampling(
from_timestamp, aggregation.granularity)
if to_timestamp:
to_timestamp = carbonara.SplitKey.from_timestamp_and_sampling(
to_timestamp, aggregation.granularity)
keys = [
key for key in sorted(all_keys)
if ((not from_timestamp or key >= from_timestamp) and (
not to_timestamp or key <= to_timestamp))
]
timeseries = self._get_measures_and_unserialize(
metric, keys, aggregation.method)
ts = carbonara.AggregatedTimeSerie.from_timeseries(
sampling=aggregation.granularity,
aggregation_method=aggregation.method,
timeseries=timeseries)
# We need to truncate because:
# - If the driver is not in WRITE_FULL mode, then it might read too
# much data that will be deleted once the split is rewritten. Just
# truncate so we don't return it.
# - If the driver is in WRITE_FULL but the archive policy has been
# resized, we might still have too much points stored, which will be
# deleted at a later point when new points will be procecessed.
# Truncate to be sure we don't return them.
if aggregation.timespan is not None:
ts.truncate(aggregation.timespan)
return ts
def test_aggregated_different_archive_no_overlap2(self):
tsc1 = {
'sampling': numpy.timedelta64(60, 's'),
'size': 50,
'agg': 'mean'
}
tsb1 = carbonara.BoundTimeSerie(block_size=tsc1['sampling'])
tsc2 = carbonara.AggregatedTimeSerie(sampling=numpy.timedelta64(
60, 's'),
max_size=50,
aggregation_method='mean')
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))
self.assertRaises(cross_metric.UnAggregableTimeseries,
cross_metric.aggregated, [tsc1['return'], tsc2],
aggregation='mean')
def test_from_timeseries(self):
sampling = 5
points = 100000
ts = carbonara.TimeSerie.from_data(timestamps=map(
datetime.datetime.utcfromtimestamp, six.moves.range(points)),
values=six.moves.range(points))
agg = carbonara.AggregatedTimeSerie(sampling=sampling,
aggregation_method='mean')
agg.update(ts)
split = [t[1] for t in list(agg.split())]
self.assertEqual(
agg,
carbonara.AggregatedTimeSerie.from_timeseries(
split,
sampling=agg.sampling,
max_size=agg.max_size,
aggregation_method=agg.aggregation_method))
def test_split(self):
sampling = 5
points = 100000
ts = carbonara.TimeSerie.from_data(timestamps=map(
datetime.datetime.utcfromtimestamp, six.moves.range(points)),
values=six.moves.range(points))
agg = carbonara.AggregatedTimeSerie(sampling=sampling,
aggregation_method='mean')
agg.update(ts)
grouped_points = list(agg.split())
self.assertEqual(
math.ceil((points / float(sampling)) /
carbonara.AggregatedTimeSerie.POINTS_PER_SPLIT),
len(grouped_points))
self.assertEqual("0.0", grouped_points[0][0])
# 14400 × 5s = 20 hours
self.assertEqual("72000.0", grouped_points[1][0])
self.assertEqual(carbonara.AggregatedTimeSerie.POINTS_PER_SPLIT,
len(grouped_points[0][1]))
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 _compute_split_operations(self, metric, aggregations_and_timeseries,
previous_oldest_mutable_timestamp,
oldest_mutable_timestamp):
"""Compute changes to a metric and return operations to be done.
Based on an aggregations list and a grouped timeseries, this computes
what needs to be deleted and stored for a metric and returns it.
:param metric: The metric
:param aggregations_and_timeseries: A dictionary of timeseries of the
form {aggregation: timeseries}.
:param previous_oldest_mutable_timestamp: The previous oldest storable
timestamp from the previous
backwindow.
:param oldest_mutable_timestamp: The current oldest storable timestamp
from the current backwindow.
:return: A tuple (keys_to_delete, keys_to_store) where keys_to_delete
is a set of `carbonara.SplitKey` to delete and where
keys_to_store is a dictionary of the form {key: aggts}
where key is a `carbonara.SplitKey` and aggts a
`carbonara.AggregatedTimeSerie` to be serialized.
"""
# We only need to check for rewrite if driver is not in WRITE_FULL mode
# and if we already stored splits once
need_rewrite = (not self.WRITE_FULL
and previous_oldest_mutable_timestamp is not None)
aggregations_needing_list_of_keys = set()
oldest_values = {}
for aggregation, ts in six.iteritems(aggregations_and_timeseries):
# Don't do anything if the timeseries is empty
if not ts:
continue
agg_oldest_values = {
'oldest_point_to_keep':
ts.truncate(aggregation.timespan)
if aggregation.timespan else None,
'prev_oldest_mutable_key':
None,
'oldest_mutable_key':
None
}
if previous_oldest_mutable_timestamp and (aggregation.timespan
or need_rewrite):
previous_oldest_mutable_key = ts.get_split_key(
previous_oldest_mutable_timestamp)
oldest_mutable_key = ts.get_split_key(oldest_mutable_timestamp)
# only cleanup if there is a new object, as there must be a new
# object for an old object to be cleanup
if previous_oldest_mutable_key != oldest_mutable_key:
aggregations_needing_list_of_keys.add(aggregation)
agg_oldest_values['prev_oldest_mutable_key'] = (
previous_oldest_mutable_key)
agg_oldest_values['oldest_mutable_key'] = (
oldest_mutable_key)
oldest_values[aggregation.granularity] = agg_oldest_values
all_existing_keys = self._list_split_keys(
{metric: aggregations_needing_list_of_keys})[metric]
# NOTE(jd) This dict uses (key, aggregation) tuples as keys because
# using just (key) would not carry the aggregation method and therefore
# would not be unique per aggregation!
keys_and_split_to_store = {}
deleted_keys = set()
for aggregation, ts in six.iteritems(aggregations_and_timeseries):
# Don't do anything if the timeseries is empty
if not ts:
continue
agg_oldest_values = oldest_values[aggregation.granularity]
oldest_key_to_keep = ts.get_split_key(
agg_oldest_values['oldest_point_to_keep'])
# If we listed the keys for the aggregation, that's because we need
# to check for cleanup and/or rewrite
if aggregation in all_existing_keys:
# FIXME(jd) This should be sorted by the driver and asserted it
# is in tests. It's likely backends already sort anyway.
existing_keys = sorted(all_existing_keys[aggregation])
# First, check for old splits to delete
if aggregation.timespan:
for key in list(existing_keys):
# NOTE(jd) Only delete if the key is strictly
# inferior the timestamp; we don't delete any
# timeserie split that contains our timestamp, so
# we prefer to keep a bit more than deleting too
# much
if key >= oldest_key_to_keep:
break
deleted_keys.add((key, aggregation))
existing_keys.remove(key)
# Rewrite all read-only splits just for fun (and
# compression). This only happens if
# `previous_oldest_mutable_timestamp' exists, which means
# we already wrote some splits at some point – so this is
# not the first time we treat this timeserie.
if need_rewrite:
for key in existing_keys:
if agg_oldest_values['prev_oldest_mutable_key'] <= key:
if key >= agg_oldest_values['oldest_mutable_key']:
break
LOG.debug(
"Compressing previous split %s (%s) for "
"metric %s", key, aggregation.method, metric)
# NOTE(jd) Rewrite it entirely for fun (and
# later for compression). For that, we just
# pass an empty split.
keys_and_split_to_store[(key, aggregation)] = (
carbonara.AggregatedTimeSerie(aggregation))
for key, split in ts.split():
if key >= oldest_key_to_keep:
LOG.debug("Storing split %s (%s) for metric %s", key,
aggregation.method, metric)
keys_and_split_to_store[(key, aggregation)] = split
return (deleted_keys, keys_and_split_to_store)
def _get_measures_timeserie(self,
metric,
aggregation,
granularity,
from_timestamp=None,
to_timestamp=None):
# Find the number of point
for d in metric.archive_policy.definition:
if d.granularity == granularity:
points = d.points
break
else:
raise storage.GranularityDoesNotExist(metric, granularity)
all_keys = None
try:
all_keys = self._list_split_keys_for_metric(
metric, aggregation, granularity)
except storage.MetricDoesNotExist:
# This can happen if it's an old metric with a TimeSerieArchive
all_keys = None
if not all_keys:
# It does not mean we have no data: it can be an old metric with a
# TimeSerieArchive.
try:
data = self._get_metric_archive(metric, aggregation)
except (storage.MetricDoesNotExist,
storage.AggregationDoesNotExist):
# It really does not exist
for d in metric.archive_policy.definition:
if d.granularity == granularity:
return carbonara.AggregatedTimeSerie(
sampling=granularity,
aggregation_method=aggregation,
max_size=d.points)
raise storage.GranularityDoesNotExist(metric, granularity)
else:
archive = carbonara.TimeSerieArchive.unserialize(data)
# It's an old metric with an TimeSerieArchive!
for ts in archive.agg_timeseries:
if ts.sampling == granularity:
return ts
raise storage.GranularityDoesNotExist(metric, granularity)
if from_timestamp:
from_timestamp = carbonara.AggregatedTimeSerie.get_split_key(
from_timestamp, granularity)
if to_timestamp:
to_timestamp = carbonara.AggregatedTimeSerie.get_split_key(
to_timestamp, granularity)
timeseries = filter(
lambda x: x is not None,
self._map_in_thread(
self._get_measures_and_unserialize,
((metric, key, aggregation, granularity) for key in all_keys
if ((not from_timestamp or key >= from_timestamp) and (
not to_timestamp or key <= to_timestamp)))))
return carbonara.AggregatedTimeSerie.from_timeseries(
sampling=granularity,
aggregation_method=aggregation,
timeseries=timeseries,
max_size=points)
def _add_measures(self, metric, aggregations, grouped_serie,
previous_oldest_mutable_timestamp,
oldest_mutable_timestamp):
# We only need to check for rewrite if driver is not in WRITE_FULL mode
# and if we already stored splits once
need_rewrite = (
not self.WRITE_FULL
and previous_oldest_mutable_timestamp is not None
)
timeseries = {}
aggregations_needing_list_of_keys = set()
for aggregation in aggregations:
ts = carbonara.AggregatedTimeSerie.from_grouped_serie(
grouped_serie, aggregation)
# Don't do anything if the timeserie is empty
if not ts:
continue
# Otherwise, store it for the next iteration
timeseries[aggregation] = ts
if aggregation.timespan:
oldest_point_to_keep = ts.truncate(aggregation.timespan)
else:
oldest_point_to_keep = None
if previous_oldest_mutable_timestamp and (aggregation.timespan or
need_rewrite):
previous_oldest_mutable_key = ts.get_split_key(
previous_oldest_mutable_timestamp)
oldest_mutable_key = ts.get_split_key(oldest_mutable_timestamp)
# only cleanup if there is a new object, as there must be a new
# object for an old object to be cleanup
if previous_oldest_mutable_key != oldest_mutable_key:
aggregations_needing_list_of_keys.add(aggregation)
all_existing_keys = self._list_split_keys(
metric, aggregations_needing_list_of_keys)
# NOTE(jd) This dict uses (key, aggregation) tuples as keys because
# using just (key) would not carry the aggregation method and therefore
# would not be unique per aggregation!
keys_and_split_to_store = {}
deleted_keys = set()
for aggregation, ts in six.iteritems(timeseries):
oldest_key_to_keep = ts.get_split_key(oldest_point_to_keep)
# If we listed the keys for the aggregation, that's because we need
# to check for cleanup and/or rewrite
if aggregation in all_existing_keys:
# FIXME(jd) This should be sorted by the driver and asserted it
# is in tests. It's likely backends already sort anyway.
existing_keys = sorted(all_existing_keys[aggregation])
# First, check for old splits to delete
if aggregation.timespan:
for key in list(existing_keys):
# NOTE(jd) Only delete if the key is strictly
# inferior the timestamp; we don't delete any
# timeserie split that contains our timestamp, so
# we prefer to keep a bit more than deleting too
# much
if key >= oldest_key_to_keep:
break
deleted_keys.add((key, aggregation))
existing_keys.remove(key)
# Rewrite all read-only splits just for fun (and
# compression). This only happens if
# `previous_oldest_mutable_timestamp' exists, which means
# we already wrote some splits at some point – so this is
# not the first time we treat this timeserie.
if need_rewrite:
for key in existing_keys:
if previous_oldest_mutable_key <= key:
if key >= oldest_mutable_key:
break
LOG.debug(
"Compressing previous split %s (%s) for "
"metric %s", key, aggregation.method,
metric)
# NOTE(jd) Rewrite it entirely for fun (and
# later for compression). For that, we just
# pass an empty split.
keys_and_split_to_store[
(key, aggregation)] = (
carbonara.AggregatedTimeSerie(
aggregation)
)
for key, split in ts.split():
if key >= oldest_key_to_keep:
LOG.debug(
"Storing split %s (%s) for metric %s",
key, aggregation.method, metric)
keys_and_split_to_store[(key, aggregation)] = split
self._delete_metric_splits(metric, deleted_keys)
self._store_timeserie_splits(
metric, keys_and_split_to_store, oldest_mutable_timestamp)
