875 lines
34 KiB
Python
875 lines
34 KiB
Python
#
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# Copyright 2012 New Dream Network, LLC (DreamHost)
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# Copyright 2013 eNovance
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# Copyright 2014 Red Hat, Inc
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#
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# Authors: Doug Hellmann <doug.hellmann@dreamhost.com>
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# Julien Danjou <julien@danjou.info>
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# Eoghan Glynn <eglynn@redhat.com>
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#
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# Licensed under the Apache License, Version 2.0 (the "License"); you may
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# not use this file except in compliance with the License. You may obtain
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# a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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# License for the specific language governing permissions and limitations
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# under the License.
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"""MongoDB storage backend"""
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import calendar
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import copy
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import datetime
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import json
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import operator
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import uuid
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import bson.code
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import bson.objectid
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from oslo_config import cfg
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from oslo_log import log
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from oslo_utils import timeutils
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import pymongo
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import six
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import ceilometer
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from ceilometer.i18n import _
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from ceilometer import storage
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from ceilometer.storage import base
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from ceilometer.storage import models
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from ceilometer.storage.mongo import utils as pymongo_utils
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from ceilometer.storage import pymongo_base
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from ceilometer import utils
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LOG = log.getLogger(__name__)
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AVAILABLE_CAPABILITIES = {
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'resources': {'query': {'simple': True,
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'metadata': True}},
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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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'selectable': {'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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'stddev': True,
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'cardinality': True}}}
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}
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class Connection(pymongo_base.Connection):
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"""Put the data into a MongoDB database
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Collections::
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- meter
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- the raw incoming data
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- resource
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- the metadata for resources
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- { _id: uuid of resource,
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metadata: metadata dictionaries
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user_id: uuid
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project_id: uuid
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meter: [ array of {counter_name: string, counter_type: string,
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counter_unit: string} ]
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}
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"""
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CAPABILITIES = utils.update_nested(pymongo_base.Connection.CAPABILITIES,
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AVAILABLE_CAPABILITIES)
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CONNECTION_POOL = pymongo_utils.ConnectionPool()
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STANDARD_AGGREGATES = dict(
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emit_initial=dict(
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sum='',
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count='',
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avg='',
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min='',
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max=''
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),
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emit_body=dict(
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sum='sum: this.counter_volume,',
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count='count: NumberInt(1),',
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avg='acount: NumberInt(1), asum: this.counter_volume,',
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min='min: this.counter_volume,',
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max='max: this.counter_volume,'
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),
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reduce_initial=dict(
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sum='',
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count='',
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avg='',
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min='',
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max=''
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),
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reduce_body=dict(
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sum='sum: values[0].sum,',
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count='count: values[0].count,',
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avg='acount: values[0].acount, asum: values[0].asum,',
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min='min: values[0].min,',
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max='max: values[0].max,'
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),
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reduce_computation=dict(
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sum='res.sum += values[i].sum;',
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count='res.count = NumberInt(res.count + values[i].count);',
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avg=('res.acount = NumberInt(res.acount + values[i].acount);'
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'res.asum += values[i].asum;'),
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min='if ( values[i].min < res.min ) {res.min = values[i].min;}',
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max='if ( values[i].max > res.max ) {res.max = values[i].max;}'
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),
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finalize=dict(
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sum='',
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count='',
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avg='value.avg = value.asum / value.acount;',
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min='',
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max=''
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),
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)
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UNPARAMETERIZED_AGGREGATES = dict(
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emit_initial=dict(
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stddev=(
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''
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)
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),
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emit_body=dict(
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stddev='sdsum: this.counter_volume,'
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'sdcount: 1,'
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'weighted_distances: 0,'
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'stddev: 0,'
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),
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reduce_initial=dict(
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stddev=''
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),
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reduce_body=dict(
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stddev='sdsum: values[0].sdsum,'
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'sdcount: values[0].sdcount,'
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'weighted_distances: values[0].weighted_distances,'
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'stddev: values[0].stddev,'
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),
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reduce_computation=dict(
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stddev=(
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'var deviance = (res.sdsum / res.sdcount) - values[i].sdsum;'
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'var weight = res.sdcount / ++res.sdcount;'
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'res.weighted_distances += (Math.pow(deviance, 2) * weight);'
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'res.sdsum += values[i].sdsum;'
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)
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),
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finalize=dict(
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stddev=(
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'value.stddev = Math.sqrt(value.weighted_distances /'
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' value.sdcount);'
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)
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),
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)
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PARAMETERIZED_AGGREGATES = dict(
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validate=dict(
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cardinality=lambda p: p in ['resource_id', 'user_id', 'project_id',
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'source']
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),
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emit_initial=dict(
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cardinality=(
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'aggregate["cardinality/%(aggregate_param)s"] = 1;'
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'var distinct_%(aggregate_param)s = {};'
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'distinct_%(aggregate_param)s[this["%(aggregate_param)s"]]'
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' = true;'
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)
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),
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emit_body=dict(
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cardinality=(
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'distinct_%(aggregate_param)s : distinct_%(aggregate_param)s,'
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'%(aggregate_param)s : this["%(aggregate_param)s"],'
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)
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),
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reduce_initial=dict(
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cardinality=''
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),
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reduce_body=dict(
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cardinality=(
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'aggregate : values[0].aggregate,'
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'distinct_%(aggregate_param)s:'
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' values[0].distinct_%(aggregate_param)s,'
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'%(aggregate_param)s : values[0]["%(aggregate_param)s"],'
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)
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),
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reduce_computation=dict(
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cardinality=(
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'if (!(values[i]["%(aggregate_param)s"] in'
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' res.distinct_%(aggregate_param)s)) {'
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' res.distinct_%(aggregate_param)s[values[i]'
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' ["%(aggregate_param)s"]] = true;'
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' res.aggregate["cardinality/%(aggregate_param)s"] += 1;}'
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)
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),
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finalize=dict(
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cardinality=''
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),
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)
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EMIT_STATS_COMMON = """
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var aggregate = {};
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%(aggregate_initial_placeholder)s
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emit(%(key_val)s, { unit: this.counter_unit,
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aggregate : aggregate,
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%(aggregate_body_placeholder)s
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groupby : %(groupby_val)s,
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duration_start : this.timestamp,
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duration_end : this.timestamp,
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period_start : %(period_start_val)s,
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period_end : %(period_end_val)s} )
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"""
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MAP_STATS_PERIOD_VAR = """
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var period = %(period)d * 1000;
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var period_first = %(period_first)d * 1000;
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var period_start = period_first
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+ (Math.floor(new Date(this.timestamp.getTime()
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- period_first) / period)
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* period);
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"""
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MAP_STATS_GROUPBY_VAR = """
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var groupby_fields = %(groupby_fields)s;
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var groupby = {};
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var groupby_key = {};
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for ( var i=0; i<groupby_fields.length; i++ ) {
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if (groupby_fields[i].search("resource_metadata") != -1) {
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var key = "resource_metadata";
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var j = groupby_fields[i].indexOf('.');
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var value = groupby_fields[i].slice(j+1, groupby_fields[i].length);
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groupby[groupby_fields[i]] = this[key][value];
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groupby_key[groupby_fields[i]] = this[key][value];
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} else {
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groupby[groupby_fields[i]] = this[groupby_fields[i]]
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groupby_key[groupby_fields[i]] = this[groupby_fields[i]]
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}
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}
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"""
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PARAMS_MAP_STATS = {
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'key_val': '\'statistics\'',
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'groupby_val': 'null',
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'period_start_val': 'this.timestamp',
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'period_end_val': 'this.timestamp',
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'aggregate_initial_placeholder': '%(aggregate_initial_val)s',
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'aggregate_body_placeholder': '%(aggregate_body_val)s'
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}
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MAP_STATS = bson.code.Code("function () {" +
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EMIT_STATS_COMMON % PARAMS_MAP_STATS +
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"}")
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PARAMS_MAP_STATS_PERIOD = {
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'key_val': 'period_start',
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'groupby_val': 'null',
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'period_start_val': 'new Date(period_start)',
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'period_end_val': 'new Date(period_start + period)',
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'aggregate_initial_placeholder': '%(aggregate_initial_val)s',
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'aggregate_body_placeholder': '%(aggregate_body_val)s'
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}
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MAP_STATS_PERIOD = bson.code.Code(
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"function () {" +
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MAP_STATS_PERIOD_VAR +
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EMIT_STATS_COMMON % PARAMS_MAP_STATS_PERIOD +
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"}")
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PARAMS_MAP_STATS_GROUPBY = {
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'key_val': 'groupby_key',
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'groupby_val': 'groupby',
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'period_start_val': 'this.timestamp',
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'period_end_val': 'this.timestamp',
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'aggregate_initial_placeholder': '%(aggregate_initial_val)s',
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'aggregate_body_placeholder': '%(aggregate_body_val)s'
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}
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MAP_STATS_GROUPBY = bson.code.Code(
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"function () {" +
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MAP_STATS_GROUPBY_VAR +
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EMIT_STATS_COMMON % PARAMS_MAP_STATS_GROUPBY +
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"}")
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PARAMS_MAP_STATS_PERIOD_GROUPBY = {
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'key_val': 'groupby_key',
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'groupby_val': 'groupby',
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'period_start_val': 'new Date(period_start)',
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'period_end_val': 'new Date(period_start + period)',
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'aggregate_initial_placeholder': '%(aggregate_initial_val)s',
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'aggregate_body_placeholder': '%(aggregate_body_val)s'
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}
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MAP_STATS_PERIOD_GROUPBY = bson.code.Code(
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"function () {" +
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MAP_STATS_PERIOD_VAR +
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MAP_STATS_GROUPBY_VAR +
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" groupby_key['period_start'] = period_start\n" +
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EMIT_STATS_COMMON % PARAMS_MAP_STATS_PERIOD_GROUPBY +
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"}")
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REDUCE_STATS = bson.code.Code("""
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function (key, values) {
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%(aggregate_initial_val)s
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var res = { unit: values[0].unit,
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aggregate: values[0].aggregate,
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%(aggregate_body_val)s
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groupby: values[0].groupby,
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period_start: values[0].period_start,
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period_end: values[0].period_end,
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duration_start: values[0].duration_start,
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duration_end: values[0].duration_end };
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for ( var i=1; i<values.length; i++ ) {
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%(aggregate_computation_val)s
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if ( values[i].duration_start < res.duration_start )
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res.duration_start = values[i].duration_start;
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if ( values[i].duration_end > res.duration_end )
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res.duration_end = values[i].duration_end;
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if ( values[i].period_start < res.period_start )
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res.period_start = values[i].period_start;
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if ( values[i].period_end > res.period_end )
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res.period_end = values[i].period_end; }
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return res;
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}
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""")
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FINALIZE_STATS = bson.code.Code("""
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function (key, value) {
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%(aggregate_val)s
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value.duration = (value.duration_end - value.duration_start) / 1000;
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value.period = NumberInt(%(period)d);
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return value;
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}""")
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SORT_OPERATION_MAPPING = {'desc': (pymongo.DESCENDING, '$lt'),
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'asc': (pymongo.ASCENDING, '$gt')}
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MAP_RESOURCES = bson.code.Code("""
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function () {
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emit(this.resource_id,
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{user_id: this.user_id,
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project_id: this.project_id,
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source: this.source,
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first_timestamp: this.timestamp,
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last_timestamp: this.timestamp,
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metadata: this.resource_metadata})
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}""")
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REDUCE_RESOURCES = bson.code.Code("""
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function (key, values) {
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var merge = {user_id: values[0].user_id,
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project_id: values[0].project_id,
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source: values[0].source,
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first_timestamp: values[0].first_timestamp,
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last_timestamp: values[0].last_timestamp,
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metadata: values[0].metadata}
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values.forEach(function(value) {
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if (merge.first_timestamp - value.first_timestamp > 0) {
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merge.first_timestamp = value.first_timestamp;
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merge.user_id = value.user_id;
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merge.project_id = value.project_id;
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merge.source = value.source;
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} else if (merge.last_timestamp - value.last_timestamp <= 0) {
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merge.last_timestamp = value.last_timestamp;
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merge.metadata = value.metadata;
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}
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});
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return merge;
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}""")
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_GENESIS = datetime.datetime(year=datetime.MINYEAR, month=1, day=1)
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_APOCALYPSE = datetime.datetime(year=datetime.MAXYEAR, month=12, day=31,
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hour=23, minute=59, second=59)
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def __init__(self, url):
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# NOTE(jd) Use our own connection pooling on top of the Pymongo one.
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# We need that otherwise we overflow the MongoDB instance with new
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# connection since we instantiate a Pymongo client each time someone
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# requires a new storage connection.
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self.conn = self.CONNECTION_POOL.connect(url)
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# Require MongoDB 2.4 to use $setOnInsert
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if self.conn.server_info()['versionArray'] < [2, 4]:
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raise storage.StorageBadVersion("Need at least MongoDB 2.4")
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connection_options = pymongo.uri_parser.parse_uri(url)
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self.db = getattr(self.conn, connection_options['database'])
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if connection_options.get('username'):
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self.db.authenticate(connection_options['username'],
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connection_options['password'])
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# NOTE(jd) Upgrading is just about creating index, so let's do this
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# on connection to be sure at least the TTL is correctly updated if
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# needed.
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self.upgrade()
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@staticmethod
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def update_ttl(ttl, ttl_index_name, index_field, coll):
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"""Update or ensure time_to_live indexes.
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:param ttl: time to live in seconds.
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:param ttl_index_name: name of the index we want to update or ensure.
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:param index_field: field with the index that we need to update.
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:param coll: collection which indexes need to be updated.
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"""
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indexes = coll.index_information()
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if ttl <= 0:
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if ttl_index_name in indexes:
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coll.drop_index(ttl_index_name)
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return
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if ttl_index_name in indexes:
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return coll.database.command(
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'collMod', coll.name,
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index={'keyPattern': {index_field: pymongo.ASCENDING},
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'expireAfterSeconds': ttl})
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coll.create_index([(index_field, pymongo.ASCENDING)],
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expireAfterSeconds=ttl,
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name=ttl_index_name)
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def upgrade(self):
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# Establish indexes
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#
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# We need variations for user_id vs. project_id because of the
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# way the indexes are stored in b-trees. The user_id and
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# project_id values are usually mutually exclusive in the
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# queries, so the database won't take advantage of an index
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# including both.
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# create collection if not present
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if 'resource' not in self.db.conn.collection_names():
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self.db.conn.create_collection('resource')
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if 'meter' not in self.db.conn.collection_names():
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self.db.conn.create_collection('meter')
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name_qualifier = dict(user_id='', project_id='project_')
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background = dict(user_id=False, project_id=True)
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for primary in ['user_id', 'project_id']:
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name = 'resource_%sidx' % name_qualifier[primary]
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self.db.resource.create_index([
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(primary, pymongo.ASCENDING),
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('source', pymongo.ASCENDING),
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], name=name, background=background[primary])
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name = 'meter_%sidx' % name_qualifier[primary]
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self.db.meter.create_index([
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('resource_id', pymongo.ASCENDING),
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(primary, pymongo.ASCENDING),
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('counter_name', pymongo.ASCENDING),
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('timestamp', pymongo.ASCENDING),
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('source', pymongo.ASCENDING),
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], name=name, background=background[primary])
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self.db.resource.create_index([('last_sample_timestamp',
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pymongo.DESCENDING)],
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name='last_sample_timestamp_idx',
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sparse=True)
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self.db.meter.create_index([('timestamp', pymongo.DESCENDING)],
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name='timestamp_idx')
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# update or ensure time_to_live index
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ttl = cfg.CONF.database.metering_time_to_live
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self.update_ttl(ttl, 'meter_ttl', 'timestamp', self.db.meter)
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self.update_ttl(ttl, 'resource_ttl', 'last_sample_timestamp',
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self.db.resource)
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def clear(self):
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self.conn.drop_database(self.db.name)
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# Connection will be reopened automatically if needed
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self.conn.close()
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def record_metering_data(self, data):
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"""Write the data to the backend storage system.
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:param data: a dictionary such as returned by
|
|
ceilometer.meter.meter_message_from_counter
|
|
"""
|
|
# Record the updated resource metadata - we use $setOnInsert to
|
|
# unconditionally insert sample timestamps and resource metadata
|
|
# (in the update case, this must be conditional on the sample not
|
|
# being out-of-order)
|
|
data = copy.deepcopy(data)
|
|
data['resource_metadata'] = pymongo_utils.improve_keys(
|
|
data.pop('resource_metadata'))
|
|
resource = self.db.resource.find_one_and_update(
|
|
{'_id': data['resource_id']},
|
|
{'$set': {'project_id': data['project_id'],
|
|
'user_id': data['user_id'],
|
|
'source': data['source'],
|
|
},
|
|
'$setOnInsert': {'metadata': data['resource_metadata'],
|
|
'first_sample_timestamp': data['timestamp'],
|
|
'last_sample_timestamp': data['timestamp'],
|
|
},
|
|
'$addToSet': {'meter': {'counter_name': data['counter_name'],
|
|
'counter_type': data['counter_type'],
|
|
'counter_unit': data['counter_unit'],
|
|
},
|
|
},
|
|
},
|
|
upsert=True,
|
|
return_document=pymongo.ReturnDocument.AFTER,
|
|
)
|
|
|
|
# only update last sample timestamp if actually later (the usual
|
|
# in-order case)
|
|
last_sample_timestamp = resource.get('last_sample_timestamp')
|
|
if (last_sample_timestamp is None or
|
|
last_sample_timestamp <= data['timestamp']):
|
|
self.db.resource.update_one(
|
|
{'_id': data['resource_id']},
|
|
{'$set': {'metadata': data['resource_metadata'],
|
|
'last_sample_timestamp': data['timestamp']}}
|
|
)
|
|
|
|
# only update first sample timestamp if actually earlier (the unusual
|
|
# out-of-order case)
|
|
# NOTE: a null first sample timestamp is not updated as this indicates
|
|
# a pre-existing resource document dating from before we started
|
|
# recording these timestamps in the resource collection
|
|
first_sample_timestamp = resource.get('first_sample_timestamp')
|
|
if (first_sample_timestamp is not None and
|
|
first_sample_timestamp > data['timestamp']):
|
|
self.db.resource.update_one(
|
|
{'_id': data['resource_id']},
|
|
{'$set': {'first_sample_timestamp': data['timestamp']}}
|
|
)
|
|
|
|
# Record the raw data for the meter. Use a copy so we do not
|
|
# modify a data structure owned by our caller (the driver adds
|
|
# a new key '_id').
|
|
record = copy.copy(data)
|
|
record['recorded_at'] = timeutils.utcnow()
|
|
self.db.meter.insert_one(record)
|
|
|
|
def clear_expired_metering_data(self, ttl):
|
|
"""Clear expired data from the backend storage system.
|
|
|
|
Clearing occurs with native MongoDB time-to-live feature.
|
|
"""
|
|
LOG.debug(_("Clearing expired metering data is based on native "
|
|
"MongoDB time to live feature and going in background."))
|
|
|
|
@staticmethod
|
|
def _get_marker(db_collection, marker_pairs):
|
|
"""Return the mark document according to the attribute-value pairs.
|
|
|
|
:param db_collection: Database collection that be query.
|
|
:param maker_pairs: Attribute-value pairs filter.
|
|
"""
|
|
if db_collection is None:
|
|
return
|
|
if not marker_pairs:
|
|
return
|
|
ret = db_collection.find(marker_pairs, limit=2)
|
|
|
|
if ret.count() == 0:
|
|
raise base.NoResultFound
|
|
elif ret.count() > 1:
|
|
raise base.MultipleResultsFound
|
|
else:
|
|
_ret = ret.__getitem__(0)
|
|
return _ret
|
|
|
|
@classmethod
|
|
def _recurse_sort_keys(cls, sort_keys, marker, flag):
|
|
_first = sort_keys[0]
|
|
value = marker[_first]
|
|
if len(sort_keys) == 1:
|
|
return {_first: {flag: value}}
|
|
else:
|
|
criteria_equ = {_first: {'eq': value}}
|
|
criteria_cmp = cls._recurse_sort_keys(sort_keys[1:], marker, flag)
|
|
return dict(criteria_equ, ** criteria_cmp)
|
|
|
|
@classmethod
|
|
def _build_sort_instructions(cls, sort_keys=None, sort_dir='desc'):
|
|
"""Returns a sort_instruction and paging operator.
|
|
|
|
Sort instructions are used in the query to determine what attributes
|
|
to sort on and what direction to use.
|
|
:param q: The query dict passed in.
|
|
:param sort_keys: array of attributes by which results be sorted.
|
|
:param sort_dir: direction in which results be sorted (asc, desc).
|
|
:return: sort instructions and paging operator
|
|
"""
|
|
sort_keys = sort_keys or []
|
|
sort_instructions = []
|
|
_sort_dir, operation = cls.SORT_OPERATION_MAPPING.get(
|
|
sort_dir, cls.SORT_OPERATION_MAPPING['desc'])
|
|
|
|
for _sort_key in sort_keys:
|
|
_instruction = (_sort_key, _sort_dir)
|
|
sort_instructions.append(_instruction)
|
|
|
|
return sort_instructions, operation
|
|
|
|
def _get_time_constrained_resources(self, query,
|
|
start_timestamp, start_timestamp_op,
|
|
end_timestamp, end_timestamp_op,
|
|
metaquery, resource):
|
|
"""Return an iterable of models.Resource instances
|
|
|
|
Items are constrained by sample timestamp.
|
|
:param query: project/user/source query
|
|
:param start_timestamp: modified timestamp start range.
|
|
:param start_timestamp_op: start time operator, like gt, ge.
|
|
:param end_timestamp: modified timestamp end range.
|
|
:param end_timestamp_op: end time operator, like lt, le.
|
|
:param metaquery: dict with metadata to match on.
|
|
:param resource: resource filter.
|
|
"""
|
|
if resource is not None:
|
|
query['resource_id'] = resource
|
|
|
|
# Add resource_ prefix so it matches the field in the db
|
|
query.update(dict(('resource_' + k, v)
|
|
for (k, v) in six.iteritems(metaquery)))
|
|
|
|
# FIXME(dhellmann): This may not perform very well,
|
|
# but doing any better will require changing the database
|
|
# schema and that will need more thought than I have time
|
|
# to put into it today.
|
|
# Look for resources matching the above criteria and with
|
|
# samples in the time range we care about, then change the
|
|
# resource query to return just those resources by id.
|
|
ts_range = pymongo_utils.make_timestamp_range(start_timestamp,
|
|
end_timestamp,
|
|
start_timestamp_op,
|
|
end_timestamp_op)
|
|
if ts_range:
|
|
query['timestamp'] = ts_range
|
|
|
|
sort_keys = base._handle_sort_key('resource')
|
|
sort_instructions = self._build_sort_instructions(sort_keys)[0]
|
|
|
|
# use a unique collection name for the results collection,
|
|
# as result post-sorting (as oppposed to reduce pre-sorting)
|
|
# is not possible on an inline M-R
|
|
out = 'resource_list_%s' % uuid.uuid4()
|
|
self.db.meter.map_reduce(self.MAP_RESOURCES,
|
|
self.REDUCE_RESOURCES,
|
|
out=out,
|
|
sort={'resource_id': 1},
|
|
query=query)
|
|
|
|
try:
|
|
for r in self.db[out].find(sort=sort_instructions):
|
|
resource = r['value']
|
|
yield models.Resource(
|
|
resource_id=r['_id'],
|
|
user_id=resource['user_id'],
|
|
project_id=resource['project_id'],
|
|
first_sample_timestamp=resource['first_timestamp'],
|
|
last_sample_timestamp=resource['last_timestamp'],
|
|
source=resource['source'],
|
|
metadata=pymongo_utils.unquote_keys(resource['metadata']))
|
|
finally:
|
|
self.db[out].drop()
|
|
|
|
def _get_floating_resources(self, query, metaquery, resource):
|
|
"""Return an iterable of models.Resource instances
|
|
|
|
Items are unconstrained by timestamp.
|
|
:param query: project/user/source query
|
|
:param metaquery: dict with metadata to match on.
|
|
:param resource: resource filter.
|
|
"""
|
|
if resource is not None:
|
|
query['_id'] = resource
|
|
|
|
query.update(dict((k, v)
|
|
for (k, v) in six.iteritems(metaquery)))
|
|
|
|
keys = base._handle_sort_key('resource')
|
|
sort_keys = ['last_sample_timestamp' if i == 'timestamp' else i
|
|
for i in keys]
|
|
sort_instructions = self._build_sort_instructions(sort_keys)[0]
|
|
|
|
for r in self.db.resource.find(query, sort=sort_instructions):
|
|
yield models.Resource(
|
|
resource_id=r['_id'],
|
|
user_id=r['user_id'],
|
|
project_id=r['project_id'],
|
|
first_sample_timestamp=r.get('first_sample_timestamp',
|
|
self._GENESIS),
|
|
last_sample_timestamp=r.get('last_sample_timestamp',
|
|
self._APOCALYPSE),
|
|
source=r['source'],
|
|
metadata=pymongo_utils.unquote_keys(r['metadata']))
|
|
|
|
def get_resources(self, user=None, project=None, source=None,
|
|
start_timestamp=None, start_timestamp_op=None,
|
|
end_timestamp=None, end_timestamp_op=None,
|
|
metaquery=None, resource=None):
|
|
"""Return an iterable of models.Resource instances
|
|
|
|
:param user: Optional ID for user that owns the resource.
|
|
:param project: Optional ID for project that owns the resource.
|
|
:param source: Optional source filter.
|
|
:param start_timestamp: Optional modified timestamp start range.
|
|
:param start_timestamp_op: Optional start time operator, like gt, ge.
|
|
:param end_timestamp: Optional modified timestamp end range.
|
|
:param end_timestamp_op: Optional end time operator, like lt, le.
|
|
:param metaquery: Optional dict with metadata to match on.
|
|
:param resource: Optional resource filter.
|
|
"""
|
|
metaquery = pymongo_utils.improve_keys(metaquery, metaquery=True) or {}
|
|
|
|
query = {}
|
|
if user is not None:
|
|
query['user_id'] = user
|
|
if project is not None:
|
|
query['project_id'] = project
|
|
if source is not None:
|
|
query['source'] = source
|
|
|
|
if start_timestamp or end_timestamp:
|
|
return self._get_time_constrained_resources(query,
|
|
start_timestamp,
|
|
start_timestamp_op,
|
|
end_timestamp,
|
|
end_timestamp_op,
|
|
metaquery, resource)
|
|
else:
|
|
return self._get_floating_resources(query, metaquery, resource)
|
|
|
|
def _aggregate_param(self, fragment_key, aggregate):
|
|
fragment_map = self.STANDARD_AGGREGATES[fragment_key]
|
|
|
|
if not aggregate:
|
|
return ''.join([f for f in fragment_map.values()])
|
|
|
|
fragments = ''
|
|
|
|
for a in aggregate:
|
|
if a.func in self.STANDARD_AGGREGATES[fragment_key]:
|
|
fragment_map = self.STANDARD_AGGREGATES[fragment_key]
|
|
fragments += fragment_map[a.func]
|
|
elif a.func in self.UNPARAMETERIZED_AGGREGATES[fragment_key]:
|
|
fragment_map = self.UNPARAMETERIZED_AGGREGATES[fragment_key]
|
|
fragments += fragment_map[a.func]
|
|
elif a.func in self.PARAMETERIZED_AGGREGATES[fragment_key]:
|
|
fragment_map = self.PARAMETERIZED_AGGREGATES[fragment_key]
|
|
v = self.PARAMETERIZED_AGGREGATES['validate'].get(a.func)
|
|
if not (v and v(a.param)):
|
|
raise storage.StorageBadAggregate('Bad aggregate: %s.%s'
|
|
% (a.func, a.param))
|
|
params = dict(aggregate_param=a.param)
|
|
fragments += (fragment_map[a.func] % params)
|
|
else:
|
|
raise ceilometer.NotImplementedError(
|
|
'Selectable aggregate function %s'
|
|
' is not supported' % a.func)
|
|
|
|
return fragments
|
|
|
|
def get_meter_statistics(self, sample_filter, period=None, groupby=None,
|
|
aggregate=None):
|
|
"""Return an iterable of models.Statistics instance.
|
|
|
|
Items are containing meter statistics described by the query
|
|
parameters. The filter must have a meter value set.
|
|
"""
|
|
if (groupby and set(groupby) -
|
|
set(['user_id', 'project_id', 'resource_id', 'source',
|
|
'resource_metadata.instance_type'])):
|
|
raise ceilometer.NotImplementedError(
|
|
"Unable to group by these fields")
|
|
|
|
q = pymongo_utils.make_query_from_filter(sample_filter)
|
|
|
|
if period:
|
|
if sample_filter.start_timestamp:
|
|
period_start = sample_filter.start_timestamp
|
|
else:
|
|
period_start = self.db.meter.find(
|
|
limit=1, sort=[('timestamp',
|
|
pymongo.ASCENDING)])[0]['timestamp']
|
|
period_start = int(calendar.timegm(period_start.utctimetuple()))
|
|
map_params = {'period': period,
|
|
'period_first': period_start,
|
|
'groupby_fields': json.dumps(groupby)}
|
|
if groupby:
|
|
map_fragment = self.MAP_STATS_PERIOD_GROUPBY
|
|
else:
|
|
map_fragment = self.MAP_STATS_PERIOD
|
|
else:
|
|
if groupby:
|
|
map_params = {'groupby_fields': json.dumps(groupby)}
|
|
map_fragment = self.MAP_STATS_GROUPBY
|
|
else:
|
|
map_params = dict()
|
|
map_fragment = self.MAP_STATS
|
|
|
|
sub = self._aggregate_param
|
|
|
|
map_params['aggregate_initial_val'] = sub('emit_initial', aggregate)
|
|
map_params['aggregate_body_val'] = sub('emit_body', aggregate)
|
|
|
|
map_stats = map_fragment % map_params
|
|
|
|
reduce_params = dict(
|
|
aggregate_initial_val=sub('reduce_initial', aggregate),
|
|
aggregate_body_val=sub('reduce_body', aggregate),
|
|
aggregate_computation_val=sub('reduce_computation', aggregate)
|
|
)
|
|
reduce_stats = self.REDUCE_STATS % reduce_params
|
|
|
|
finalize_params = dict(aggregate_val=sub('finalize', aggregate),
|
|
period=(period if period else 0))
|
|
finalize_stats = self.FINALIZE_STATS % finalize_params
|
|
|
|
results = self.db.meter.map_reduce(
|
|
map_stats,
|
|
reduce_stats,
|
|
{'inline': 1},
|
|
finalize=finalize_stats,
|
|
query=q,
|
|
)
|
|
|
|
# FIXME(terriyu) Fix get_meter_statistics() so we don't use sorted()
|
|
# to return the results
|
|
return sorted(
|
|
(self._stats_result_to_model(r['value'], groupby, aggregate)
|
|
for r in results['results']),
|
|
key=operator.attrgetter('period_start'))
|
|
|
|
@staticmethod
|
|
def _stats_result_aggregates(result, aggregate):
|
|
stats_args = {}
|
|
for attr in ['count', 'min', 'max', 'sum', 'avg']:
|
|
if attr in result:
|
|
stats_args[attr] = result[attr]
|
|
|
|
if aggregate:
|
|
stats_args['aggregate'] = {}
|
|
for a in aggregate:
|
|
ak = '%s%s' % (a.func, '/%s' % a.param if a.param else '')
|
|
if ak in result:
|
|
stats_args['aggregate'][ak] = result[ak]
|
|
elif 'aggregate' in result:
|
|
stats_args['aggregate'][ak] = result['aggregate'].get(ak)
|
|
return stats_args
|
|
|
|
@staticmethod
|
|
def _stats_result_to_model(result, groupby, aggregate):
|
|
stats_args = Connection._stats_result_aggregates(result, aggregate)
|
|
stats_args['unit'] = result['unit']
|
|
stats_args['duration'] = result['duration']
|
|
stats_args['duration_start'] = result['duration_start']
|
|
stats_args['duration_end'] = result['duration_end']
|
|
stats_args['period'] = result['period']
|
|
stats_args['period_start'] = result['period_start']
|
|
stats_args['period_end'] = result['period_end']
|
|
stats_args['groupby'] = (dict(
|
|
(g, result['groupby'][g]) for g in groupby) if groupby else None)
|
|
return models.Statistics(**stats_args)
|