openstack/deb-ceilometer
Code Issues Proposed changes
552c63849e
deb-ceilometer/ceilometer/storage/impl_mongodb.py
Igor Degtiarov 262e2cf2f3 [MongoDB] Improves get_meter_statistics method 
Now period_end that returns in get_meter_statistic method
doesn't have correct value on MongoDB backend if period
doesn't set in query.

This patch improves that.

Change-Id: I980f630e0a113638d4b06608ae999ae56d92338f
Closes-Bug: #1372442
2015-01-22 16:25:36 +02:00

948 lines
38 KiB
Python
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#
# Copyright 2012 New Dream Network, LLC (DreamHost)
# Copyright 2013 eNovance
# Copyright 2014 Red Hat, Inc
#
# Authors: Doug Hellmann <doug.hellmann@dreamhost.com>
# Julien Danjou <julien@danjou.info>
# Eoghan Glynn <eglynn@redhat.com>
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
"""MongoDB storage backend"""
import calendar
import copy
import datetime
import json
import operator
import uuid
import bson.code
import bson.objectid
from oslo_config import cfg
from oslo_utils import timeutils
import pymongo
import six
import ceilometer
from ceilometer.i18n import _
from ceilometer.openstack.common import log
from ceilometer import storage
from ceilometer.storage import base
from ceilometer.storage import models
from ceilometer.storage.mongo import utils as pymongo_utils
from ceilometer.storage import pymongo_base
from ceilometer import utils
LOG = log.getLogger(__name__)
AVAILABLE_CAPABILITIES = {
'resources': {'query': {'simple': True,
'metadata': True}},
'statistics': {'groupby': True,
'query': {'simple': True,
'metadata': True},
'aggregation': {'standard': True,
'selectable': {'max': True,
'min': True,
'sum': True,
'avg': True,
'count': True,
'stddev': True,
'cardinality': True}}}
}
class Connection(pymongo_base.Connection):
"""Put the data into a MongoDB database
Collections::
- meter
- the raw incoming data
- resource
- the metadata for resources
- { _id: uuid of resource,
metadata: metadata dictionaries
user_id: uuid
project_id: uuid
meter: [ array of {counter_name: string, counter_type: string,
counter_unit: string} ]
}
"""
CAPABILITIES = utils.update_nested(pymongo_base.Connection.CAPABILITIES,
AVAILABLE_CAPABILITIES)
CONNECTION_POOL = pymongo_utils.ConnectionPool()
STANDARD_AGGREGATES = dict(
emit_initial=dict(
sum='',
count='',
avg='',
min='',
max=''
),
emit_body=dict(
sum='sum: this.counter_volume,',
count='count: NumberInt(1),',
avg='acount: NumberInt(1), asum: this.counter_volume,',
min='min: this.counter_volume,',
max='max: this.counter_volume,'
),
reduce_initial=dict(
sum='',
count='',
avg='',
min='',
max=''
),
reduce_body=dict(
sum='sum: values[0].sum,',
count='count: values[0].count,',
avg='acount: values[0].acount, asum: values[0].asum,',
min='min: values[0].min,',
max='max: values[0].max,'
),
reduce_computation=dict(
sum='res.sum += values[i].sum;',
count='res.count = NumberInt(res.count + values[i].count);',
avg=('res.acount = NumberInt(res.acount + values[i].acount);'
'res.asum += values[i].asum;'),
min='if ( values[i].min < res.min ) {res.min = values[i].min;}',
max='if ( values[i].max > res.max ) {res.max = values[i].max;}'
),
finalize=dict(
sum='',
count='',
avg='value.avg = value.asum / value.acount;',
min='',
max=''
),
)
UNPARAMETERIZED_AGGREGATES = dict(
emit_initial=dict(
stddev=(
''
)
),
emit_body=dict(
stddev='sdsum: this.counter_volume,'
'sdcount: 1,'
'weighted_distances: 0,'
'stddev: 0,'
),
reduce_initial=dict(
stddev=''
),
reduce_body=dict(
stddev='sdsum: values[0].sdsum,'
'sdcount: values[0].sdcount,'
'weighted_distances: values[0].weighted_distances,'
'stddev: values[0].stddev,'
),
reduce_computation=dict(
stddev=(
'var deviance = (res.sdsum / res.sdcount) - values[i].sdsum;'
'var weight = res.sdcount / ++res.sdcount;'
'res.weighted_distances += (Math.pow(deviance, 2) * weight);'
'res.sdsum += values[i].sdsum;'
)
),
finalize=dict(
stddev=(
'value.stddev = Math.sqrt(value.weighted_distances /'
' value.sdcount);'
)
),
)
PARAMETERIZED_AGGREGATES = dict(
validate=dict(
cardinality=lambda p: p in ['resource_id', 'user_id', 'project_id',
'source']
),
emit_initial=dict(
cardinality=(
'aggregate["cardinality/%(aggregate_param)s"] = 1;'
'var distinct_%(aggregate_param)s = {};'
'distinct_%(aggregate_param)s[this["%(aggregate_param)s"]]'
' = true;'
)
),
emit_body=dict(
cardinality=(
'distinct_%(aggregate_param)s : distinct_%(aggregate_param)s,'
'%(aggregate_param)s : this["%(aggregate_param)s"],'
)
),
reduce_initial=dict(
cardinality=''
),
reduce_body=dict(
cardinality=(
'aggregate : values[0].aggregate,'
'distinct_%(aggregate_param)s:'
' values[0].distinct_%(aggregate_param)s,'
'%(aggregate_param)s : values[0]["%(aggregate_param)s"],'
)
),
reduce_computation=dict(
cardinality=(
'if (!(values[i]["%(aggregate_param)s"] in'
' res.distinct_%(aggregate_param)s)) {'
' res.distinct_%(aggregate_param)s[values[i]'
' ["%(aggregate_param)s"]] = true;'
' res.aggregate["cardinality/%(aggregate_param)s"] += 1;}'
)
),
finalize=dict(
cardinality=''
),
)
EMIT_STATS_COMMON = """
var aggregate = {};
%(aggregate_initial_placeholder)s
emit(%(key_val)s, { unit: this.counter_unit,
aggregate : aggregate,
%(aggregate_body_placeholder)s
groupby : %(groupby_val)s,
duration_start : this.timestamp,
duration_end : this.timestamp,
period_start : %(period_start_val)s,
period_end : %(period_end_val)s} )
"""
MAP_STATS_PERIOD_VAR = """
var period = %(period)d * 1000;
var period_first = %(period_first)d * 1000;
var period_start = period_first
+ (Math.floor(new Date(this.timestamp.getTime()
- period_first) / period)
* period);
"""
MAP_STATS_GROUPBY_VAR = """
var groupby_fields = %(groupby_fields)s;
var groupby = {};
var groupby_key = {};
for ( var i=0; i<groupby_fields.length; i++ ) {
if (groupby_fields[i].search("resource_metadata") != -1) {
var key = "resource_metadata";
var j = groupby_fields[i].indexOf('.');
var value = groupby_fields[i].slice(j+1, groupby_fields[i].length);
groupby[groupby_fields[i]] = this[key][value];
groupby_key[groupby_fields[i]] = this[key][value];
} else {
groupby[groupby_fields[i]] = this[groupby_fields[i]]
groupby_key[groupby_fields[i]] = this[groupby_fields[i]]
}
}
"""
PARAMS_MAP_STATS = {
'key_val': '\'statistics\'',
'groupby_val': 'null',
'period_start_val': 'this.timestamp',
'period_end_val': 'this.timestamp',
'aggregate_initial_placeholder': '%(aggregate_initial_val)s',
'aggregate_body_placeholder': '%(aggregate_body_val)s'
}
MAP_STATS = bson.code.Code("function () {" +
EMIT_STATS_COMMON % PARAMS_MAP_STATS +
"}")
PARAMS_MAP_STATS_PERIOD = {
'key_val': 'period_start',
'groupby_val': 'null',
'period_start_val': 'new Date(period_start)',
'period_end_val': 'new Date(period_start + period)',
'aggregate_initial_placeholder': '%(aggregate_initial_val)s',
'aggregate_body_placeholder': '%(aggregate_body_val)s'
}
MAP_STATS_PERIOD = bson.code.Code(
"function () {" +
MAP_STATS_PERIOD_VAR +
EMIT_STATS_COMMON % PARAMS_MAP_STATS_PERIOD +
"}")
PARAMS_MAP_STATS_GROUPBY = {
'key_val': 'groupby_key',
'groupby_val': 'groupby',
'period_start_val': 'this.timestamp',
'period_end_val': 'this.timestamp',
'aggregate_initial_placeholder': '%(aggregate_initial_val)s',
'aggregate_body_placeholder': '%(aggregate_body_val)s'
}
MAP_STATS_GROUPBY = bson.code.Code(
"function () {" +
MAP_STATS_GROUPBY_VAR +
EMIT_STATS_COMMON % PARAMS_MAP_STATS_GROUPBY +
"}")
PARAMS_MAP_STATS_PERIOD_GROUPBY = {
'key_val': 'groupby_key',
'groupby_val': 'groupby',
'period_start_val': 'new Date(period_start)',
'period_end_val': 'new Date(period_start + period)',
'aggregate_initial_placeholder': '%(aggregate_initial_val)s',
'aggregate_body_placeholder': '%(aggregate_body_val)s'
}
MAP_STATS_PERIOD_GROUPBY = bson.code.Code(
"function () {" +
MAP_STATS_PERIOD_VAR +
MAP_STATS_GROUPBY_VAR +
" groupby_key['period_start'] = period_start\n" +
EMIT_STATS_COMMON % PARAMS_MAP_STATS_PERIOD_GROUPBY +
"}")
REDUCE_STATS = bson.code.Code("""
function (key, values) {
%(aggregate_initial_val)s
var res = { unit: values[0].unit,
aggregate: values[0].aggregate,
%(aggregate_body_val)s
groupby: values[0].groupby,
period_start: values[0].period_start,
period_end: values[0].period_end,
duration_start: values[0].duration_start,
duration_end: values[0].duration_end };
for ( var i=1; i<values.length; i++ ) {
%(aggregate_computation_val)s
if ( values[i].duration_start < res.duration_start )
res.duration_start = values[i].duration_start;
if ( values[i].duration_end > res.duration_end )
res.duration_end = values[i].duration_end;
if ( values[i].period_start < res.period_start )
res.period_start = values[i].period_start;
if ( values[i].period_end > res.period_end )
res.period_end = values[i].period_end; }
return res;
}
""")
FINALIZE_STATS = bson.code.Code("""
function (key, value) {
%(aggregate_val)s
value.duration = (value.duration_end - value.duration_start) / 1000;
value.period = NumberInt(%(period)d);
return value;
}""")
SORT_OPERATION_MAPPING = {'desc': (pymongo.DESCENDING, '$lt'),
'asc': (pymongo.ASCENDING, '$gt')}
MAP_RESOURCES = bson.code.Code("""
function () {
emit(this.resource_id,
{user_id: this.user_id,
project_id: this.project_id,
source: this.source,
first_timestamp: this.timestamp,
last_timestamp: this.timestamp,
metadata: this.resource_metadata})
}""")
REDUCE_RESOURCES = bson.code.Code("""
function (key, values) {
var merge = {user_id: values[0].user_id,
project_id: values[0].project_id,
source: values[0].source,
first_timestamp: values[0].first_timestamp,
last_timestamp: values[0].last_timestamp,
metadata: values[0].metadata}
values.forEach(function(value) {
if (merge.first_timestamp - value.first_timestamp > 0) {
merge.first_timestamp = value.first_timestamp;
merge.user_id = value.user_id;
merge.project_id = value.project_id;
merge.source = value.source;
} else if (merge.last_timestamp - value.last_timestamp <= 0) {
merge.last_timestamp = value.last_timestamp;
merge.metadata = value.metadata;
}
});
return merge;
}""")
_GENESIS = datetime.datetime(year=datetime.MINYEAR, month=1, day=1)
_APOCALYPSE = datetime.datetime(year=datetime.MAXYEAR, month=12, day=31,
hour=23, minute=59, second=59)
def __init__(self, url):
# NOTE(jd) Use our own connection pooling on top of the Pymongo one.
# We need that otherwise we overflow the MongoDB instance with new
# connection since we instantiate a Pymongo client each time someone
# requires a new storage connection.
self.conn = self.CONNECTION_POOL.connect(url)
# Require MongoDB 2.4 to use $setOnInsert
if self.conn.server_info()['versionArray'] < [2, 4]:
raise storage.StorageBadVersion("Need at least MongoDB 2.4")
connection_options = pymongo.uri_parser.parse_uri(url)
self.db = getattr(self.conn, connection_options['database'])
if connection_options.get('username'):
self.db.authenticate(connection_options['username'],
connection_options['password'])
# NOTE(jd) Upgrading is just about creating index, so let's do this
# on connection to be sure at least the TTL is correctly updated if
# needed.
self.upgrade()
@staticmethod
def update_ttl(ttl_index_name, index_field, coll):
"""Update or ensure time_to_live indexes.
:param ttl_index_name: name of the index we want to update or ensure.
:param index_field: field with the index that we need to update.
:param coll: collection which indexes need to be updated.
"""
ttl = cfg.CONF.database.time_to_live
indexes = coll.index_information()
if ttl <= 0:
if ttl_index_name in indexes:
coll.drop_index(ttl_index_name)
return
if ttl_index_name in indexes:
return coll.database.command(
'collMod', coll.name,
index={'keyPattern': {index_field: pymongo.ASCENDING},
'expireAfterSeconds': ttl})
coll.ensure_index([(index_field, pymongo.ASCENDING)],
expireAfterSeconds=ttl,
name=ttl_index_name)
def upgrade(self):
# Establish indexes
#
# We need variations for user_id vs. project_id because of the
# way the indexes are stored in b-trees. The user_id and
# project_id values are usually mutually exclusive in the
# queries, so the database won't take advantage of an index
# including both.
name_qualifier = dict(user_id='', project_id='project_')
background = dict(user_id=False, project_id=True)
for primary in ['user_id', 'project_id']:
name = 'resource_%sidx' % name_qualifier[primary]
self.db.resource.ensure_index([
(primary, pymongo.ASCENDING),
('source', pymongo.ASCENDING),
], name=name, background=background[primary])
name = 'meter_%sidx' % name_qualifier[primary]
self.db.meter.ensure_index([
('resource_id', pymongo.ASCENDING),
(primary, pymongo.ASCENDING),
('counter_name', pymongo.ASCENDING),
('timestamp', pymongo.ASCENDING),
('source', pymongo.ASCENDING),
], name=name, background=background[primary])
self.db.resource.ensure_index([('last_sample_timestamp',
pymongo.DESCENDING)],
name='last_sample_timestamp_idx',
sparse=True)
self.db.meter.ensure_index([('timestamp', pymongo.DESCENDING)],
name='timestamp_idx')
# remove API v1 related table
self.db.user.drop()
self.db.project.drop()
# update or ensure time_to_live index
self.update_ttl('meter_ttl', 'timestamp', self.db.meter)
self.update_ttl('resource_ttl', 'last_sample_timestamp',
self.db.resource)
def clear(self):
self.conn.drop_database(self.db.name)
# Connection will be reopened automatically if needed
self.conn.close()
def record_metering_data(self, data):
"""Write the data to the backend storage system.
:param data: a dictionary such as returned by
ceilometer.meter.meter_message_from_counter
"""
# Record the updated resource metadata - we use $setOnInsert to
# unconditionally insert sample timestamps and resource metadata
# (in the update case, this must be conditional on the sample not
# being out-of-order)
data = copy.deepcopy(data)
data['resource_metadata'] = pymongo_utils.improve_keys(
data.pop('resource_metadata'))
resource = self.db.resource.find_and_modify(
{'_id': data['resource_id']},
{'$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,
new=True,
)
# 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(
{'_id': data['resource_id']},
{'$set': {'metadata': data['resource_metadata'],
'last_sample_timestamp': data['timestamp']}}
)
# only update first sample timestamp if actually earlier (the unusual
# out-of-order case)
# NOTE: a null first sample timestamp is not updated as this indicates
# a pre-existing resource document dating from before we started
# recording these timestamps in the resource collection
first_sample_timestamp = resource.get('first_sample_timestamp')
if (first_sample_timestamp is not None and
first_sample_timestamp > data['timestamp']):
self.db.resource.update(
{'_id': data['resource_id']},
{'$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(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_paginate_query(cls, marker, sort_keys=None, sort_dir='desc'):
"""Returns a query with sorting / pagination.
Pagination works by requiring sort_key and sort_dir.
We use the last item in previous page as the 'marker' for pagination.
So we return values that follow the passed marker in the order.
:param q: The query dict passed in.
:param marker: the last item of the previous page; we return the next
results after this item.
:param sort_keys: array of attributes by which results be sorted.
:param sort_dir: direction in which results be sorted (asc, desc).
:return: sort parameters, query to use
"""
all_sort = []
sort_keys = sort_keys or []
all_sort, _op = cls._build_sort_instructions(sort_keys, sort_dir)
if marker is not None:
sort_criteria_list = []
for i in range(len(sort_keys)):
# NOTE(fengqian): Generate the query criteria recursively.
# sort_keys=[k1, k2, k3], maker_value=[v1, v2, v3]
# sort_flags = ['$lt', '$gt', 'lt'].
# The query criteria should be
# {'k3': {'$lt': 'v3'}, 'k2': {'eq': 'v2'}, 'k1':
# {'eq': 'v1'}},
# {'k2': {'$gt': 'v2'}, 'k1': {'eq': 'v1'}},
# {'k1': {'$lt': 'v1'}} with 'OR' operation.
# Each recurse will generate one items of three.
sort_criteria_list.append(cls._recurse_sort_keys(
sort_keys[:(len(sort_keys) - i)],
marker, _op))
metaquery = {"$or": sort_criteria_list}
else:
metaquery = {}
return all_sort, metaquery
@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
@classmethod
def paginate_query(cls, q, db_collection, limit=None, marker=None,
sort_keys=None, sort_dir='desc'):
"""Returns a query result with sorting / pagination.
Pagination works by requiring sort_key and sort_dir.
We use the last item in previous page as the 'marker' for pagination.
So we return values that follow the passed marker in the order.
:param q: the query dict passed in.
:param db_collection: Database collection that be query.
:param limit: maximum number of items to return.
:param marker: the last item of the previous page; we return the next
results after this item.
:param sort_keys: array of attributes by which results be sorted.
:param sort_dir: direction in which results be sorted (asc, desc).
:return: The query with sorting/pagination added.
"""
sort_keys = sort_keys or []
all_sort, query = cls._build_paginate_query(marker,
sort_keys,
sort_dir)
q.update(query)
# NOTE(Fengqian): MongoDB collection.find can not handle limit
# when it equals None, it will raise TypeError, so we treat
# None as 0 for the value of limit.
if limit is None:
limit = 0
return db_collection.find(q, limit=limit, sort=all_sort)
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, pagination=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.
:param pagination: Optional pagination query.
"""
if pagination:
raise ceilometer.NotImplementedError('Pagination not implemented')
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)
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