refactor db2 get_meter_statistics method to support mongodb and db2
db2 driver for get_meter_statistics method did not work against mongodb which caused many test cases being skipped. This patch set will fix that problem also made sure that all statistics functions are supported by db2 driver against db2 and mongodb database fixes: bug #1226079 Change-Id: I502aebbab51c51970ddb68641dab89944ce810d4
This commit is contained in:
Tong Li
Gerrit Code Review
parent
5bf2072ac7
commit
c60c129a6a
@ -21,16 +21,20 @@
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"""DB2 storage backend
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"""
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from __future__ import division
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import copy
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import weakref
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import itertools
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import bson.code
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import bson.objectid
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import datetime
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import pymongo
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import sys
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from ceilometer.openstack.common.gettextutils import _
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from ceilometer.openstack.common import log
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from ceilometer.openstack.common import timeutils
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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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@ -182,6 +186,8 @@ class Connection(base.Connection):
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SORT_OPERATION_MAP = {'desc': pymongo.DESCENDING, 'asc': pymongo.ASCENDING}
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SECONDS_IN_A_DAY = 86400
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def __init__(self, conf):
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url = conf.database.connection
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@ -291,6 +297,7 @@ class Connection(base.Connection):
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# removal of all the empty dbs created during the test runs since
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# test run is against mongodb on Jenkins
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self.conn.drop_database(self.db)
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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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@ -521,42 +528,81 @@ class Connection(base.Connection):
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statistics described by the query parameters.
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The filter must have a meter value set.
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"""
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if self._using_mongodb:
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raise NotImplementedError("Statistics not implemented.")
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if groupby:
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raise NotImplementedError("Group by not implemented.")
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if (groupby and
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set(groupby) - set(['user_id', 'project_id',
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'resource_id', 'source'])):
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raise NotImplementedError("Unable to group by these fields")
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q = make_query_from_filter(sample_filter)
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if period:
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raise NotImplementedError('Statistics for period not implemented.')
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if sample_filter.start:
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period_start = sample_filter.start
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else:
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period_start = self.db.meter.find(
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limit=1, sort=[('timestamp',
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pymongo.ASCENDING)])[0]['timestamp']
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results = self.db.meter.aggregate([
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{'$match': q},
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{'$group': self.GROUP},
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{'$project': self.PROJECT},
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])
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# Since there is no period grouping, there should be only one set in
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# the results
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rslt = results['result'][0]
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duration = rslt['duration_end'] - rslt['duration_start']
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if hasattr(duration, 'total_seconds'):
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rslt['duration'] = duration.total_seconds()
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if groupby:
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sort_keys = ['counter_name'] + groupby + ['timestamp']
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else:
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rslt['duration'] = duration.days * 3600 + duration.seconds
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sort_keys = ['counter_name', 'timestamp']
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rslt['period_start'] = rslt['duration_start']
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rslt['period_end'] = rslt['duration_end']
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# Period is not supported, set it to zero
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rslt['period'] = 0
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rslt['groupby'] = None
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sort_instructions = self._build_sort_instructions(sort_keys=sort_keys,
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sort_dir='asc')
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meters = self.db.meter.find(q, sort=sort_instructions)
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return [models.Statistics(**(rslt))]
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def _group_key(meter):
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# the method to define a key for groupby call
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key = {}
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for y in sort_keys:
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if y == 'timestamp' and period:
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key[y] = (timeutils.delta_seconds(period_start,
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meter[y]) // period)
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elif y != 'timestamp':
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key[y] = meter[y]
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return key
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def _to_offset(periods):
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return {'days': (periods * period) // self.SECONDS_IN_A_DAY,
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'seconds': (periods * period) % self.SECONDS_IN_A_DAY}
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for key, grouped_meters in itertools.groupby(meters, key=_group_key):
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stat = models.Statistics(None, sys.maxint, -sys.maxint, 0, 0, 0,
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0, 0, 0, 0, 0, 0, None)
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for meter in grouped_meters:
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stat.unit = meter.get('counter_unit', '')
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m_volume = meter.get('counter_volume')
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if stat.min > m_volume:
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stat.min = m_volume
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if stat.max < m_volume:
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stat.max = m_volume
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stat.sum += m_volume
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stat.count += 1
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if stat.duration_start == 0:
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stat.duration_start = meter['timestamp']
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stat.duration_end = meter['timestamp']
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if groupby and not stat.groupby:
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stat.groupby = {}
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for group_key in groupby:
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stat.groupby[group_key] = meter[group_key]
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stat.duration = timeutils.delta_seconds(stat.duration_start,
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stat.duration_end)
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stat.avg = stat.sum / stat.count
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if period:
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stat.period = period
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periods = key.get('timestamp')
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stat.period_start = period_start + \
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datetime.timedelta(**(_to_offset(periods)))
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stat.period_end = period_start + \
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datetime.timedelta(**(_to_offset(periods + 1)))
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else:
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stat.period_start = stat.duration_start
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stat.period_end = stat.duration_end
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yield stat
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@staticmethod
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def _decode_matching_metadata(matching_metadata):
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@ -46,5 +46,5 @@ MongoDB Yes Yes Yes
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MySQL Yes, except metadata querying Yes Yes
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PostgreSQL Yes, except metadata querying Yes Yes
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HBase Yes Yes, except groupby No
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DB2 Yes No No
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DB2 Yes Yes No
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================== ============================= =================== ======
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