Selectable aggregation functions for statistics
Addresses: BP selectable-aggregates
Previously, the statistics API always computed and returned a
standard set of aggregate functions.
Now, individual aggregation functions can be selected and even
parameterized via syntax of form:
/v2/meters/<meter_name>/statistics?aggregate.func=<name>&aggregate.param=<value>
For example in order to calculate the average CPU util only:
GET /v2/meters/cpu_util/statistics?aggregate.func=avg
HTTP/1.0 200 OK
[{"aggregate": {"avg": 0.6858829535841072},
"avg": 0.6858829535841072,
"duration_start": "2014-01-30T11:13:23",
"duration_end": "2014-01-31T16:07:13",
"duration": 104030.0,
"period": 0,
"period_start": "2014-01-30T11:13:23",
"period_end": "2014-01-31T16:07:13",
"groupby": null,
"unit": "%"}]
In the current patch, selectable aggregates are provided by the
sqlalchemy driver only, with support in the mongodb driver to
follow in a subsequent patch.
Change-Id: I6cc095ba5ae16dea3f6b404e72a070ab9ac49c9a
This commit is contained in:
Eoghan Glynn
@@ -108,6 +108,14 @@ META_TYPE_MAP = {bool: models.MetaBool,
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long: models.MetaBigInt,
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float: models.MetaFloat}
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STANDARD_AGGREGATES = dict(
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avg=func.avg(models.Sample.volume).label('avg'),
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sum=func.sum(models.Sample.volume).label('sum'),
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min=func.min(models.Sample.volume).label('min'),
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max=func.max(models.Sample.volume).label('max'),
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count=func.count(models.Sample.volume).label('count')
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)
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def apply_metaquery_filter(session, query, metaquery):
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"""Apply provided metaquery filter to existing query.
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@@ -634,18 +642,32 @@ class Connection(base.Connection):
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limit,
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models.MeterSample)
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def _make_stats_query(self, sample_filter, groupby):
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@staticmethod
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def _get_aggregate_functions(aggregate):
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if not aggregate:
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return [f for f in STANDARD_AGGREGATES.values()]
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functions = []
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for a in aggregate:
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if a.func in STANDARD_AGGREGATES:
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functions.append(STANDARD_AGGREGATES[a.func])
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else:
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raise NotImplementedError(_('Selectable aggregate function %s'
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' is not supported') % a.func)
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return functions
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def _make_stats_query(self, sample_filter, groupby, aggregate):
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select = [
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models.Meter.unit,
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func.min(models.Sample.timestamp).label('tsmin'),
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func.max(models.Sample.timestamp).label('tsmax'),
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func.avg(models.Sample.volume).label('avg'),
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func.sum(models.Sample.volume).label('sum'),
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func.min(models.Sample.volume).label('min'),
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func.max(models.Sample.volume).label('max'),
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func.count(models.Sample.volume).label('count')
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]
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select.extend(self._get_aggregate_functions(aggregate))
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session = self._get_db_session()
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if groupby:
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@@ -659,30 +681,41 @@ class Connection(base.Connection):
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return make_query_from_filter(session, query, sample_filter)
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@staticmethod
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def _stats_result_aggregates(result, aggregate):
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stats_args = {}
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if isinstance(result.count, (int, long)):
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stats_args['count'] = result.count
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for attr in ['min', 'max', 'sum', 'avg']:
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if hasattr(result, attr):
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stats_args[attr] = getattr(result, attr)
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if aggregate:
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stats_args['aggregate'] = dict(
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('%s%s' % (a.func, '/%s' % a.param if a.param else ''),
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getattr(result, a.func)) for a in aggregate
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)
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return stats_args
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@staticmethod
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def _stats_result_to_model(result, period, period_start,
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period_end, groupby):
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period_end, groupby, aggregate):
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stats_args = Connection._stats_result_aggregates(result, aggregate)
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stats_args['unit'] = result.unit
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duration = (timeutils.delta_seconds(result.tsmin, result.tsmax)
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if result.tsmin is not None and result.tsmax is not None
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else None)
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return api_models.Statistics(
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unit=result.unit,
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count=int(result.count),
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min=result.min,
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max=result.max,
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avg=result.avg,
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sum=result.sum,
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duration_start=result.tsmin,
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duration_end=result.tsmax,
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duration=duration,
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period=period,
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period_start=period_start,
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period_end=period_end,
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groupby=(dict((g, getattr(result, g)) for g in groupby)
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if groupby else None)
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)
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stats_args['duration'] = duration
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stats_args['duration_start'] = result.tsmin
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stats_args['duration_end'] = result.tsmax
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stats_args['period'] = period
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stats_args['period_start'] = period_start
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stats_args['period_end'] = period_end
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stats_args['groupby'] = (dict(
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(g, getattr(result, g)) for g in groupby) if groupby else None)
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return api_models.Statistics(**stats_args)
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def get_meter_statistics(self, sample_filter, period=None, groupby=None):
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def get_meter_statistics(self, sample_filter, period=None, groupby=None,
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aggregate=None):
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"""Return an iterable of api_models.Statistics instances containing
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meter statistics described by the query parameters.
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@@ -696,17 +729,22 @@ class Connection(base.Connection):
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_("Unable to group by these fields"))
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if not period:
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for res in self._make_stats_query(sample_filter, groupby):
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for res in self._make_stats_query(sample_filter,
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groupby,
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aggregate):
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if res.count:
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yield self._stats_result_to_model(res, 0,
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res.tsmin, res.tsmax,
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groupby)
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groupby,
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aggregate)
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return
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if not sample_filter.start or not sample_filter.end:
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res = self._make_stats_query(sample_filter, None).first()
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res = self._make_stats_query(sample_filter,
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None,
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aggregate).first()
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query = self._make_stats_query(sample_filter, groupby)
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query = self._make_stats_query(sample_filter, groupby, aggregate)
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# HACK(jd) This is an awful method to compute stats by period, but
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# since we're trying to be SQL agnostic we have to write portable
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# code, so here it is, admire! We're going to do one request to get
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@@ -726,7 +764,8 @@ class Connection(base.Connection):
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period_end)),
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period_start=period_start,
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period_end=period_end,
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groupby=groupby
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groupby=groupby,
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aggregate=aggregate
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)
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@staticmethod
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@@ -1299,7 +1338,14 @@ class QueryTransformer(object):
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'statistics': {'groupby': True,
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'query': {'simple': True,
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'metadata': True},
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'aggregation': {'standard': True}},
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'aggregation': {'standard': True,
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'selectable': {
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'max': True,
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'min': True,
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'sum': True,
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'avg': True,
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'count': True}}
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},
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'alarms': {'query': {'simple': True,
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'complex': True},
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'history': {'query': {'simple': True,
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