/usr/lib/python3/dist-packages/gnocchi/rest/aggregates/processor.py is in python3-gnocchi 4.2.0-0ubuntu5.
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#
# Copyright © 2016-2017 Red Hat, Inc.
# Copyright © 2014-2015 eNovance
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
"""Timeseries cross-aggregation."""
import collections
import daiquiri
import numpy
import six
from gnocchi import carbonara
from gnocchi.rest.aggregates import exceptions
from gnocchi.rest.aggregates import operations as agg_operations
from gnocchi import storage as gnocchi_storage
from gnocchi import utils
LOG = daiquiri.getLogger(__name__)
class MetricReference(object):
def __init__(self, metric, aggregation, resource=None, wildcard=None):
self.metric = metric
self.aggregation = aggregation
self.resource = resource
self.timeseries = {}
if self.resource is None:
self.name = str(self.metric.id)
else:
self.name = self.metric.name
self.lookup_key = [wildcard or self.name, self.aggregation]
def __eq__(self, other):
return (self.metric == other.metric and
self.resource == other.resource and
self.aggregation == other.aggregation)
def _get_measures_timeserie(storage, ref, granularity, *args, **kwargs):
return (ref, storage._get_measures_timeserie(
ref.metric,
ref.metric.archive_policy.get_aggregation(
ref.aggregation, granularity),
*args, **kwargs))
def get_measures(storage, references, operations,
from_timestamp=None, to_timestamp=None,
granularities=None, needed_overlap=100.0,
fill=None):
"""Get aggregated measures of multiple entities.
:param storage: The storage driver.
:param metrics_and_aggregations: List of metric+agg_method tuple
measured to aggregate.
:param from timestamp: The timestamp to get the measure from.
:param to timestamp: The timestamp to get the measure to.
:param granularities: The granularities to retrieve.
:param fill: The value to use to fill in missing data in series.
"""
if granularities is None:
all_granularities = (
definition.granularity
for ref in references
for definition in ref.metric.archive_policy.definition
)
# granularities_in_common
granularities = [
g
for g, occurrence in six.iteritems(
collections.Counter(all_granularities))
if occurrence == len(references)
]
if not granularities:
raise exceptions.UnAggregableTimeseries(
list((ref.name, ref.aggregation)
for ref in references),
'No granularity match')
references_with_missing_granularity = []
for ref in references:
if (ref.aggregation not in
ref.metric.archive_policy.aggregation_methods):
raise gnocchi_storage.AggregationDoesNotExist(
ref.metric, ref.aggregation,
# Use the first granularity, that should be good enough since
# they are all missing anyway
ref.metric.archive_policy.definition[0].granularity)
available_granularities = [
d.granularity
for d in ref.metric.archive_policy.definition
]
for g in granularities:
if g not in available_granularities:
references_with_missing_granularity.append(
(ref.name, ref.aggregation, g))
break
if references_with_missing_granularity:
raise exceptions.UnAggregableTimeseries(
references_with_missing_granularity,
"Granularities are missing")
tss = utils.parallel_map(_get_measures_timeserie,
[(storage, ref, g, from_timestamp, to_timestamp)
for ref in references
for g in granularities])
return aggregated(tss, operations, from_timestamp, to_timestamp,
needed_overlap, fill)
def aggregated(refs_and_timeseries, operations, from_timestamp=None,
to_timestamp=None, needed_percent_of_overlap=100.0, fill=None):
series = collections.defaultdict(list)
references = collections.defaultdict(list)
lookup_keys = collections.defaultdict(list)
for (ref, timeserie) in refs_and_timeseries:
from_ = (None if from_timestamp is None else
carbonara.round_timestamp(from_timestamp, timeserie.sampling))
references[timeserie.sampling].append(ref)
lookup_keys[timeserie.sampling].append(ref.lookup_key)
series[timeserie.sampling].append(timeserie[from_:to_timestamp])
result = []
is_aggregated = False
result = {}
for sampling in sorted(series, reverse=True):
combine = numpy.concatenate(series[sampling])
# np.unique sorts results for us
times, indices = numpy.unique(combine['timestamps'],
return_inverse=True)
# create nd-array (unique series x unique times) and fill
filler = (numpy.NaN if fill in [None, 'null', 'dropna']
else fill)
val_grid = numpy.full((len(series[sampling]), len(times)), filler)
start = 0
for i, split in enumerate(series[sampling]):
size = len(split)
val_grid[i][indices[start:start + size]] = split['values']
start += size
values = val_grid.T
if fill is None:
overlap = numpy.flatnonzero(~numpy.any(numpy.isnan(values),
axis=1))
if overlap.size == 0 and needed_percent_of_overlap > 0:
raise exceptions.UnAggregableTimeseries(lookup_keys[sampling],
'No overlap')
if times.size:
# if no boundary set, use first/last timestamp which overlap
if to_timestamp is None and overlap.size:
times = times[:overlap[-1] + 1]
values = values[:overlap[-1] + 1]
if from_timestamp is None and overlap.size:
times = times[overlap[0]:]
values = values[overlap[0]:]
percent_of_overlap = overlap.size * 100.0 / times.size
if percent_of_overlap < needed_percent_of_overlap:
raise exceptions.UnAggregableTimeseries(
lookup_keys[sampling],
'Less than %f%% of datapoints overlap in this '
'timespan (%.2f%%)' % (needed_percent_of_overlap,
percent_of_overlap))
granularity, times, values, is_aggregated = (
agg_operations.evaluate(operations, sampling, times, values,
False, lookup_keys[sampling]))
values = values.T
result[sampling] = (granularity, times, values, references[sampling])
if is_aggregated:
output = {"aggregated": []}
for sampling in sorted(result, reverse=True):
granularity, times, values, references = result[sampling]
if fill == "dropna":
pos = ~numpy.isnan(values[0])
v = values[0][pos]
t = times[pos]
else:
v = values[0]
t = times
g = [granularity] * len(t)
output["aggregated"].extend(six.moves.zip(t, g, v))
return output
else:
r_output = collections.defaultdict(
lambda: collections.defaultdict(
lambda: collections.defaultdict(list)))
m_output = collections.defaultdict(
lambda: collections.defaultdict(list))
for sampling in sorted(result, reverse=True):
granularity, times, values, references = result[sampling]
for i, ref in enumerate(references):
if fill == "dropna":
pos = ~numpy.isnan(values[i])
v = values[i][pos]
t = times[pos]
else:
v = values[i]
t = times
g = [granularity] * len(t)
measures = six.moves.zip(t, g, v)
if ref.resource is None:
m_output[ref.name][ref.aggregation].extend(measures)
else:
r_output[str(ref.resource.id)][
ref.metric.name][ref.aggregation].extend(measures)
return r_output if r_output else m_output
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