fill series when aggregating cross metrics
this fills in series so that overlap of all series is not required to aggregate. we only fill in if at least one of the metrics contains a point in the timeslot. Closes-Bug: #1642661 Change-Id: I1a29f0095387c326bdc87af760a3ccb4a95de828
This commit is contained in:
gord chung
gordon chung
parent
fb312488dd
commit
5edc39ad91
@ -533,16 +533,29 @@ well.
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Resampling is done prior to any reaggregation if both parameters are
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specified.
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Also aggregation across metrics have different behavior depending
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on if boundary are set ('start' and 'stop') and if 'needed_overlap' is set.
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Also, aggregation across metrics have different behavior depending
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on whether boundary values are set ('start' and 'stop') and if 'needed_overlap'
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is set.
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If boundaries are not set, Gnocchi makes the aggregation only with points
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at timestamp present in all timeseries.
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at timestamp present in all timeseries. When boundaries are set, Gnocchi
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expects that we have certain percent of timestamps common between timeseries,
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this percent is controlled by needed_overlap (defaulted with 100%). If this
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percent is not reached an error is returned.
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The ability to fill in points missing from a subset of timeseries is supported
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by specifying a `fill` value. Valid fill values include any valid float or
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`null` which will compute aggregation with only the points that exist. The
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`fill` parameter will not backfill timestamps which contain no points in any
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of the timeseries. Only timestamps which have datapoints in at least one of
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the timeseries is returned.
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.. note::
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A granularity must be specified when using the `fill` parameter.
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{{ scenarios['get-across-metrics-measures-by-metric-ids-fill']['doc'] }}
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But when boundaries are set, Gnocchi expects that we have certain
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percent of timestamps common between timeseries, this percent is controlled
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by needed_overlap (defaulted with 100%). If this percent is not reached an
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error is returned.
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Capabilities
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============
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@ -726,6 +726,10 @@
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request: |
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GET /v1/aggregation/metric?metric={{ scenarios['create-resource-instance-with-metrics']['response'].json['metrics']['cpu.util'] }}&metric={{ scenarios['create-resource-instance-with-dynamic-metrics']['response'].json['metrics']['cpu.util'] }}&aggregation=mean&reaggregation=min HTTP/1.1
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- name: get-across-metrics-measures-by-metric-ids-fill
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request: |
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GET /v1/aggregation/metric?metric={{ scenarios['create-resource-instance-with-metrics']['response'].json['metrics']['cpu.util'] }}&metric={{ scenarios['create-resource-instance-with-dynamic-metrics']['response'].json['metrics']['cpu.util'] }}&fill=0&granularity=1 HTTP/1.1
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- name: append-metrics-to-resource
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request: |
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POST /v1/resource/generic/{{ scenarios['create-resource-instance-with-metrics']['response'].json['id'] }}/metric HTTP/1.1
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@ -713,7 +713,8 @@ class AggregatedTimeSerie(TimeSerie):
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@staticmethod
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def aggregated(timeseries, aggregation, from_timestamp=None,
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to_timestamp=None, needed_percent_of_overlap=100.0):
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to_timestamp=None, needed_percent_of_overlap=100.0,
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fill=None):
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index = ['timestamp', 'granularity']
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columns = ['timestamp', 'granularity', 'value']
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@ -737,57 +738,65 @@ class AggregatedTimeSerie(TimeSerie):
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set(ts.sampling for ts in timeseries)
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)
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grouped = pandas.concat(dataframes).groupby(level=index)
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left_boundary_ts = None
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right_boundary_ts = None
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maybe_next_timestamp_is_left_boundary = False
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if fill is not None:
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fill_df = pandas.concat(dataframes, axis=1)
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if fill != 'null':
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fill_df = fill_df.fillna(fill)
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single_df = pandas.concat([series for __, series in
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fill_df.iteritems()]).to_frame()
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grouped = single_df.groupby(level=index)
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else:
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grouped = pandas.concat(dataframes).groupby(level=index)
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maybe_next_timestamp_is_left_boundary = False
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left_holes = 0
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right_holes = 0
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holes = 0
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for (timestamp, __), group in grouped:
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if group.count()['value'] != number_of_distinct_datasource:
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maybe_next_timestamp_is_left_boundary = True
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if left_boundary_ts is not None:
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right_holes += 1
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left_holes = 0
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right_holes = 0
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holes = 0
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for (timestamp, __), group in grouped:
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if group.count()['value'] != number_of_distinct_datasource:
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maybe_next_timestamp_is_left_boundary = True
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if left_boundary_ts is not None:
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right_holes += 1
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else:
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left_holes += 1
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elif maybe_next_timestamp_is_left_boundary:
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left_boundary_ts = timestamp
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maybe_next_timestamp_is_left_boundary = False
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else:
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left_holes += 1
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elif maybe_next_timestamp_is_left_boundary:
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left_boundary_ts = timestamp
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maybe_next_timestamp_is_left_boundary = False
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else:
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right_boundary_ts = timestamp
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right_boundary_ts = timestamp
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holes += right_holes
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right_holes = 0
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if to_timestamp is not None:
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holes += left_holes
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if from_timestamp is not None:
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holes += right_holes
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right_holes = 0
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if to_timestamp is not None:
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holes += left_holes
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if from_timestamp is not None:
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holes += right_holes
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if to_timestamp is not None or from_timestamp is not None:
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maximum = len(grouped)
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percent_of_overlap = (float(maximum - holes) * 100.0 /
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float(maximum))
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if percent_of_overlap < needed_percent_of_overlap:
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raise UnAggregableTimeseries(
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'Less than %f%% of datapoints overlap in this '
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'timespan (%.2f%%)' % (needed_percent_of_overlap,
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percent_of_overlap))
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if (needed_percent_of_overlap > 0 and
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(right_boundary_ts == left_boundary_ts or
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(right_boundary_ts is None
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and maybe_next_timestamp_is_left_boundary))):
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LOG.debug("We didn't find points that overlap in those "
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"timeseries. "
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"right_boundary_ts=%(right_boundary_ts)s, "
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"left_boundary_ts=%(left_boundary_ts)s, "
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"groups=%(groups)s", {
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'right_boundary_ts': right_boundary_ts,
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'left_boundary_ts': left_boundary_ts,
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'groups': list(grouped)
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})
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raise UnAggregableTimeseries('No overlap')
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if to_timestamp is not None or from_timestamp is not None:
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maximum = len(grouped)
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percent_of_overlap = (float(maximum - holes) * 100.0 /
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float(maximum))
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if percent_of_overlap < needed_percent_of_overlap:
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raise UnAggregableTimeseries(
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'Less than %f%% of datapoints overlap in this '
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'timespan (%.2f%%)' % (needed_percent_of_overlap,
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percent_of_overlap))
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if (needed_percent_of_overlap > 0 and
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(right_boundary_ts == left_boundary_ts or
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(right_boundary_ts is None
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and maybe_next_timestamp_is_left_boundary))):
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LOG.debug("We didn't find points that overlap in those "
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"timeseries. "
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"right_boundary_ts=%(right_boundary_ts)s, "
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"left_boundary_ts=%(left_boundary_ts)s, "
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"groups=%(groups)s", {
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'right_boundary_ts': right_boundary_ts,
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'left_boundary_ts': left_boundary_ts,
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'groups': list(grouped)
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})
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raise UnAggregableTimeseries('No overlap')
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# NOTE(sileht): this call the aggregation method on already
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# aggregated values, for some kind of aggregation this can
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@ -1496,7 +1496,7 @@ class AggregationResourceController(rest.RestController):
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@pecan.expose('json')
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def post(self, start=None, stop=None, aggregation='mean',
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reaggregation=None, granularity=None, needed_overlap=100.0,
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groupby=None, refresh=False, resample=None):
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groupby=None, fill=None, refresh=False, resample=None):
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# First, set groupby in the right format: a sorted list of unique
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# strings.
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groupby = sorted(set(arg_to_list(groupby)))
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@ -1520,7 +1520,7 @@ class AggregationResourceController(rest.RestController):
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for r in resources)))
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return AggregationController.get_cross_metric_measures_from_objs(
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metrics, start, stop, aggregation, reaggregation,
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granularity, needed_overlap, refresh, resample)
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granularity, needed_overlap, fill, refresh, resample)
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def groupper(r):
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return tuple((attr, r[attr]) for attr in groupby)
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@ -1534,7 +1534,7 @@ class AggregationResourceController(rest.RestController):
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"group": dict(key),
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"measures": AggregationController.get_cross_metric_measures_from_objs( # noqa
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metrics, start, stop, aggregation, reaggregation,
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granularity, needed_overlap, refresh, resample)
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granularity, needed_overlap, fill, refresh, resample)
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})
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return results
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@ -1564,7 +1564,7 @@ class AggregationController(rest.RestController):
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aggregation='mean',
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reaggregation=None,
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granularity=None,
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needed_overlap=100.0,
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needed_overlap=100.0, fill=None,
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refresh=False, resample=None):
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try:
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needed_overlap = float(needed_overlap)
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@ -1611,6 +1611,15 @@ class AggregationController(rest.RestController):
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except ValueError as e:
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abort(400, e)
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if fill is not None:
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if granularity is None:
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abort(400, "Unable to fill without a granularity")
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try:
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fill = float(fill)
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except ValueError as e:
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if fill != 'null':
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abort(400, "fill must be a float or \'null\': %s" % e)
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try:
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if strutils.bool_from_string(refresh):
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pecan.request.storage.process_new_measures(
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@ -1625,7 +1634,7 @@ class AggregationController(rest.RestController):
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else:
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measures = pecan.request.storage.get_cross_metric_measures(
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metrics, start, stop, aggregation,
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reaggregation, resample, granularity, needed_overlap)
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reaggregation, resample, granularity, needed_overlap, fill)
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# Replace timestamp keys by their string versions
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return [(timestamp.isoformat(), offset, v)
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for timestamp, offset, v in measures]
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@ -1640,7 +1649,8 @@ class AggregationController(rest.RestController):
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@pecan.expose('json')
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def get_metric(self, metric=None, start=None, stop=None,
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aggregation='mean', reaggregation=None, granularity=None,
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needed_overlap=100.0, refresh=False, resample=None):
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needed_overlap=100.0, fill=None,
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refresh=False, resample=None):
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# Check RBAC policy
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metric_ids = arg_to_list(metric)
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metrics = pecan.request.indexer.list_metrics(ids=metric_ids)
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@ -1652,7 +1662,7 @@ class AggregationController(rest.RestController):
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missing_metric_ids.pop()))
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return self.get_cross_metric_measures_from_objs(
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metrics, start, stop, aggregation, reaggregation,
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granularity, needed_overlap, refresh, resample)
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granularity, needed_overlap, fill, refresh, resample)
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class CapabilityController(rest.RestController):
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@ -257,8 +257,8 @@ class StorageDriver(object):
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def get_cross_metric_measures(metrics, from_timestamp=None,
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to_timestamp=None, aggregation='mean',
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reaggregation=None, resample=None,
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granularity=None,
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needed_overlap=None):
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granularity=None, needed_overlap=None,
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fill=None):
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"""Get aggregated measures of multiple entities.
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:param entities: The entities measured to aggregate.
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@ -269,6 +269,7 @@ class StorageDriver(object):
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:param reaggregation: The type of aggregation to compute
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on the retrieved measures.
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:param resample: The granularity to resample to.
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:param fill: The value to use to fill in missing data in series.
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"""
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for metric in metrics:
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if aggregation not in metric.archive_policy.aggregation_methods:
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@ -538,7 +538,8 @@ class CarbonaraBasedStorage(storage.StorageDriver):
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def get_cross_metric_measures(self, metrics, from_timestamp=None,
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to_timestamp=None, aggregation='mean',
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reaggregation=None, resample=None,
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granularity=None, needed_overlap=100.0):
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granularity=None, needed_overlap=100.0,
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fill=None):
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super(CarbonaraBasedStorage, self).get_cross_metric_measures(
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metrics, from_timestamp, to_timestamp,
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aggregation, reaggregation, resample, granularity, needed_overlap)
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@ -584,7 +585,7 @@ class CarbonaraBasedStorage(storage.StorageDriver):
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for timestamp, r, v
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in carbonara.AggregatedTimeSerie.aggregated(
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tss, reaggregation, from_timestamp, to_timestamp,
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needed_overlap)]
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needed_overlap, fill)]
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except carbonara.UnAggregableTimeseries as e:
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raise storage.MetricUnaggregatable(metrics, e.reason)
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@ -59,6 +59,8 @@ tests:
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value: 3.1
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- timestamp: "2015-03-06T14:34:12"
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value: 2
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- timestamp: "2015-03-06T14:35:12"
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value: 5
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status: 202
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- name: get metric list to get aggregates
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@ -128,6 +130,43 @@ tests:
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- ['2015-03-06T14:33:00+00:00', 60.0, 23.1]
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- ['2015-03-06T14:34:00+00:00', 60.0, 7.0]
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- name: get metric list to push metric 6
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GET: /v1/metric
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- name: get measure aggregates with fill zero
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GET: /v1/aggregation/metric?metric=$RESPONSE['$[0].id']&metric=$RESPONSE['$[1].id']&granularity=1&fill=0
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response_json_paths:
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$:
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- ['2015-03-06T14:33:57+00:00', 1.0, 23.1]
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- ['2015-03-06T14:34:12+00:00', 1.0, 7.0]
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- ['2015-03-06T14:35:12+00:00', 1.0, 2.5]
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- name: get metric list to push metric 7
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GET: /v1/metric
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- name: get measure aggregates with fill null
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GET: /v1/aggregation/metric?metric=$RESPONSE['$[0].id']&metric=$RESPONSE['$[1].id']&granularity=1&fill=null
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response_json_paths:
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$:
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- ['2015-03-06T14:33:57+00:00', 1.0, 23.1]
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- ['2015-03-06T14:34:12+00:00', 1.0, 7.0]
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- ['2015-03-06T14:35:12+00:00', 1.0, 5.0]
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- name: get metric list to push metric 8
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GET: /v1/metric
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- name: get measure aggregates with fill missing granularity
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GET: /v1/aggregation/metric?metric=$RESPONSE['$[0].id']&metric=$RESPONSE['$[1].id']&fill=0
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status: 400
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- name: get metric list to push metric 9
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GET: /v1/metric
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- name: get measure aggregates with bad fill
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GET: /v1/aggregation/metric?metric=$RESPONSE['$[0].id']&metric=$RESPONSE['$[1].id']&granularity=1&fill=asdf
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status: 400
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# Aggregation by resource and metric_name
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- name: post a resource
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@ -180,6 +219,8 @@ tests:
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value: 3.1
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- timestamp: "2015-03-06T14:34:12"
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value: 2
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- timestamp: "2015-03-06T14:35:12"
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value: 5
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status: 202
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- name: get measure aggregates by granularity from resources with refresh
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@ -264,6 +305,19 @@ tests:
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- ['2015-03-06T14:33:57+00:00', 1.0, 3.1]
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- ['2015-03-06T14:34:12+00:00', 1.0, 2.0]
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- name: get measure aggregates from resources with fill zero
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POST: /v1/aggregation/resource/generic/metric/agg_meter?granularity=1&fill=0
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request_headers:
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x-user-id: 0fbb231484614b1a80131fc22f6afc9c
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x-project-id: f3d41b770cc14f0bb94a1d5be9c0e3ea
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content-type: application/json
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response_json_paths:
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$:
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- ['2015-03-06T14:33:57+00:00', 1.0, 23.1]
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- ['2015-03-06T14:34:12+00:00', 1.0, 7.0]
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- ['2015-03-06T14:35:12+00:00', 1.0, 2.5]
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# Some negative tests
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- name: get measure aggregates with wrong GET
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@ -511,6 +511,126 @@ class TestAggregatedTimeSerie(base.BaseTestCase):
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(datetime.datetime(2014, 1, 1, 12, 6), 60.0, 4.0)
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], ts['return'].fetch(datetime.datetime(2014, 1, 1, 12, 0, 0)))
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def test_aggregated_some_overlap_with_fill_zero(self):
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tsc1 = {'sampling': 60, 'size': 10, 'agg': 'mean'}
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tsb1 = carbonara.BoundTimeSerie(block_size=tsc1['sampling'])
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tsc2 = {'sampling': 60, 'size': 10, 'agg': 'mean'}
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tsb2 = carbonara.BoundTimeSerie(block_size=tsc2['sampling'])
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tsb1.set_values([
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(datetime.datetime(2014, 1, 1, 12, 3, 0), 9),
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(datetime.datetime(2014, 1, 1, 12, 4, 0), 1),
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(datetime.datetime(2014, 1, 1, 12, 5, 0), 2),
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(datetime.datetime(2014, 1, 1, 12, 6, 0), 7),
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(datetime.datetime(2014, 1, 1, 12, 7, 0), 5),
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(datetime.datetime(2014, 1, 1, 12, 8, 0), 3),
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], before_truncate_callback=functools.partial(
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self._resample_and_merge, agg_dict=tsc1))
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tsb2.set_values([
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(datetime.datetime(2014, 1, 1, 12, 0, 0), 6),
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(datetime.datetime(2014, 1, 1, 12, 1, 0), 2),
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(datetime.datetime(2014, 1, 1, 12, 2, 0), 13),
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(datetime.datetime(2014, 1, 1, 12, 3, 0), 24),
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(datetime.datetime(2014, 1, 1, 12, 4, 0), 4),
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(datetime.datetime(2014, 1, 1, 12, 5, 0), 16),
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(datetime.datetime(2014, 1, 1, 12, 6, 0), 12),
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], before_truncate_callback=functools.partial(
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self._resample_and_merge, agg_dict=tsc2))
|
||||
|
||||
output = carbonara.AggregatedTimeSerie.aggregated([
|
||||
tsc1['return'], tsc2['return']], aggregation='mean', fill=0)
|
||||
|
||||
self.assertEqual([
|
||||
(datetime.datetime(2014, 1, 1, 12, 0, 0), 60.0, 3.0),
|
||||
(datetime.datetime(2014, 1, 1, 12, 1, 0), 60.0, 1.0),
|
||||
(datetime.datetime(2014, 1, 1, 12, 2, 0), 60.0, 6.5),
|
||||
(datetime.datetime(2014, 1, 1, 12, 3, 0), 60.0, 16.5),
|
||||
(datetime.datetime(2014, 1, 1, 12, 4, 0), 60.0, 2.5),
|
||||
(datetime.datetime(2014, 1, 1, 12, 5, 0), 60.0, 9.0),
|
||||
(datetime.datetime(2014, 1, 1, 12, 6, 0), 60.0, 9.5),
|
||||
(datetime.datetime(2014, 1, 1, 12, 7, 0), 60.0, 2.5),
|
||||
(datetime.datetime(2014, 1, 1, 12, 8, 0), 60.0, 1.5),
|
||||
], output)
|
||||
|
||||
def test_aggregated_some_overlap_with_fill_null(self):
|
||||
tsc1 = {'sampling': 60, 'size': 10, 'agg': 'mean'}
|
||||
tsb1 = carbonara.BoundTimeSerie(block_size=tsc1['sampling'])
|
||||
tsc2 = {'sampling': 60, 'size': 10, 'agg': '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=functools.partial(
|
||||
self._resample_and_merge, agg_dict=tsc1))
|
||||
|
||||
tsb2.set_values([
|
||||
(datetime.datetime(2014, 1, 1, 12, 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=functools.partial(
|
||||
self._resample_and_merge, agg_dict=tsc2))
|
||||
|
||||
output = carbonara.AggregatedTimeSerie.aggregated([
|
||||
tsc1['return'], tsc2['return']], aggregation='mean', fill='null')
|
||||
|
||||
self.assertEqual([
|
||||
(datetime.datetime(2014, 1, 1, 12, 0, 0), 60.0, 6.0),
|
||||
(datetime.datetime(2014, 1, 1, 12, 1, 0), 60.0, 2.0),
|
||||
(datetime.datetime(2014, 1, 1, 12, 2, 0), 60.0, 13.0),
|
||||
(datetime.datetime(2014, 1, 1, 12, 3, 0), 60.0, 16.5),
|
||||
(datetime.datetime(2014, 1, 1, 12, 4, 0), 60.0, 2.5),
|
||||
(datetime.datetime(2014, 1, 1, 12, 5, 0), 60.0, 9.0),
|
||||
(datetime.datetime(2014, 1, 1, 12, 6, 0), 60.0, 9.5),
|
||||
(datetime.datetime(2014, 1, 1, 12, 7, 0), 60.0, 5.0),
|
||||
(datetime.datetime(2014, 1, 1, 12, 8, 0), 60.0, 3.0),
|
||||
], output)
|
||||
|
||||
def test_aggregate_no_points_with_fill_zero(self):
|
||||
tsc1 = {'sampling': 60, 'size': 10, 'agg': 'mean'}
|
||||
tsb1 = carbonara.BoundTimeSerie(block_size=tsc1['sampling'])
|
||||
tsc2 = {'sampling': 60, 'size': 10, 'agg': '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, 7, 0), 5),
|
||||
(datetime.datetime(2014, 1, 1, 12, 8, 0), 3),
|
||||
], before_truncate_callback=functools.partial(
|
||||
self._resample_and_merge, agg_dict=tsc1))
|
||||
|
||||
tsb2.set_values([
|
||||
(datetime.datetime(2014, 1, 1, 12, 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),
|
||||
], before_truncate_callback=functools.partial(
|
||||
self._resample_and_merge, agg_dict=tsc2))
|
||||
|
||||
output = carbonara.AggregatedTimeSerie.aggregated([
|
||||
tsc1['return'], tsc2['return']], aggregation='mean', fill=0)
|
||||
|
||||
self.assertEqual([
|
||||
(datetime.datetime(2014, 1, 1, 12, 0, 0), 60.0, 3.0),
|
||||
(datetime.datetime(2014, 1, 1, 12, 1, 0), 60.0, 1.0),
|
||||
(datetime.datetime(2014, 1, 1, 12, 2, 0), 60.0, 6.5),
|
||||
(datetime.datetime(2014, 1, 1, 12, 3, 0), 60.0, 16.5),
|
||||
(datetime.datetime(2014, 1, 1, 12, 4, 0), 60.0, 2.5),
|
||||
(datetime.datetime(2014, 1, 1, 12, 7, 0), 60.0, 2.5),
|
||||
(datetime.datetime(2014, 1, 1, 12, 8, 0), 60.0, 1.5),
|
||||
], output)
|
||||
|
||||
def test_fetch_agg_pct(self):
|
||||
ts = {'sampling': 1, 'size': 3600 * 24, 'agg': '90pct'}
|
||||
tsb = carbonara.BoundTimeSerie(block_size=ts['sampling'])
|
||||
|
||||
@ -0,0 +1,6 @@
|
||||
---
|
||||
features:
|
||||
- Add support to backfill timestamps with missing points in a subset of
|
||||
timeseries when computing aggregation across multiple metrics. User can
|
||||
specify `fill` value with either a float or `null` value. A granularity
|
||||
must be specified in addition to `fill`.
|
||||
Loading…
Reference in New Issue
Block a user