OpenStack Telemetry (Ceilometer)
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ceilometer/ceilometer/storage/impl_sqlalchemy.py

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31 KiB

#
# Author: John Tran <jhtran@att.com>
# Julien Danjou <julien@danjou.info>
#
# 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.
"""SQLAlchemy storage backend."""
from __future__ import absolute_import
import datetime
import hashlib
import os
from oslo.config import cfg
from oslo.db import exception as dbexc
from oslo.db.sqlalchemy import session as db_session
from oslo.serialization import jsonutils
from oslo.utils import timeutils
import six
import sqlalchemy as sa
from sqlalchemy import and_
from sqlalchemy import distinct
from sqlalchemy import func
from sqlalchemy.orm import aliased
import ceilometer
from ceilometer.openstack.common.gettextutils import _
from ceilometer.openstack.common import log
from ceilometer import storage
from ceilometer.storage import base
from ceilometer.storage import models as api_models
from ceilometer.storage.sqlalchemy import models
from ceilometer.storage.sqlalchemy import utils as sql_utils
from ceilometer import utils
LOG = log.getLogger(__name__)
STANDARD_AGGREGATES = dict(
avg=func.avg(models.Sample.volume).label('avg'),
sum=func.sum(models.Sample.volume).label('sum'),
min=func.min(models.Sample.volume).label('min'),
max=func.max(models.Sample.volume).label('max'),
count=func.count(models.Sample.volume).label('count')
)
UNPARAMETERIZED_AGGREGATES = dict(
stddev=func.stddev_pop(models.Sample.volume).label('stddev')
)
PARAMETERIZED_AGGREGATES = dict(
validate=dict(
cardinality=lambda p: p in ['resource_id', 'user_id', 'project_id']
),
compute=dict(
cardinality=lambda p: func.count(
distinct(getattr(models.Resource, p))
).label('cardinality/%s' % p)
)
)
AVAILABLE_CAPABILITIES = {
'meters': {'query': {'simple': True,
'metadata': True}},
'resources': {'query': {'simple': True,
'metadata': True}},
'samples': {'pagination': True,
'groupby': True,
'query': {'simple': True,
'metadata': True,
'complex': 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}}
},
}
AVAILABLE_STORAGE_CAPABILITIES = {
'storage': {'production_ready': True},
}
def apply_metaquery_filter(session, query, metaquery):
"""Apply provided metaquery filter to existing query.
:param session: session used for original query
:param query: Query instance
:param metaquery: dict with metadata to match on.
"""
for k, value in six.iteritems(metaquery):
key = k[9:] # strip out 'metadata.' prefix
try:
_model = sql_utils.META_TYPE_MAP[type(value)]
except KeyError:
raise ceilometer.NotImplementedError(
'Query on %(key)s is of %(value)s '
'type and is not supported' %
{"key": k, "value": type(value)})
else:
meta_alias = aliased(_model)
on_clause = and_(models.Resource.internal_id == meta_alias.id,
meta_alias.meta_key == key)
# outer join is needed to support metaquery
# with or operator on non existent metadata field
# see: test_query_non_existing_metadata_with_result
# test case.
query = query.outerjoin(meta_alias, on_clause)
query = query.filter(meta_alias.value == value)
return query
def make_query_from_filter(session, query, sample_filter, require_meter=True):
"""Return a query dictionary based on the settings in the filter.
:param session: session used for original query
:param query: Query instance
:param sample_filter: SampleFilter instance
:param require_meter: If true and the filter does not have a meter,
raise an error.
"""
if sample_filter.meter:
query = query.filter(models.Meter.name == sample_filter.meter)
elif require_meter:
raise RuntimeError('Missing required meter specifier')
if sample_filter.source:
query = query.filter(
models.Resource.source_id == sample_filter.source)
if sample_filter.start:
ts_start = sample_filter.start
if sample_filter.start_timestamp_op == 'gt':
query = query.filter(models.Sample.timestamp > ts_start)
else:
query = query.filter(models.Sample.timestamp >= ts_start)
if sample_filter.end:
ts_end = sample_filter.end
if sample_filter.end_timestamp_op == 'le':
query = query.filter(models.Sample.timestamp <= ts_end)
else:
query = query.filter(models.Sample.timestamp < ts_end)
if sample_filter.user:
query = query.filter(models.Resource.user_id == sample_filter.user)
if sample_filter.project:
query = query.filter(
models.Resource.project_id == sample_filter.project)
if sample_filter.resource:
query = query.filter(
models.Resource.resource_id == sample_filter.resource)
if sample_filter.message_id:
query = query.filter(
models.Sample.message_id == sample_filter.message_id)
if sample_filter.metaquery:
query = apply_metaquery_filter(session, query,
sample_filter.metaquery)
return query
class Connection(base.Connection):
"""Put the data into a SQLAlchemy database.
Tables::
- meter
- meter definition
- { id: meter id
name: meter name
type: meter type
unit: meter unit
}
- resource
- resource definition
- { internal_id: resource id
resource_id: resource uuid
user_id: user uuid
project_id: project uuid
source_id: source id
resource_metadata: metadata dictionary
metadata_hash: metadata dictionary hash
}
- sample
- the raw incoming data
- { id: sample id
meter_id: meter id (->meter.id)
resource_id: resource id (->resource.internal_id)
volume: sample volume
timestamp: datetime
recorded_at: datetime
message_signature: message signature
message_id: message uuid
}
"""
CAPABILITIES = utils.update_nested(base.Connection.CAPABILITIES,
AVAILABLE_CAPABILITIES)
STORAGE_CAPABILITIES = utils.update_nested(
base.Connection.STORAGE_CAPABILITIES,
AVAILABLE_STORAGE_CAPABILITIES,
)
def __init__(self, url):
self._engine_facade = db_session.EngineFacade(
url,
**dict(cfg.CONF.database.items())
)
def upgrade(self):
# NOTE(gordc): to minimise memory, only import migration when needed
from oslo.db.sqlalchemy import migration
path = os.path.join(os.path.abspath(os.path.dirname(__file__)),
'sqlalchemy', 'migrate_repo')
migration.db_sync(self._engine_facade.get_engine(), path)
def clear(self):
engine = self._engine_facade.get_engine()
for table in reversed(models.Base.metadata.sorted_tables):
engine.execute(table.delete())
self._engine_facade._session_maker.close_all()
engine.dispose()
@staticmethod
def _create_meter(conn, name, type, unit):
# TODO(gordc): implement lru_cache to improve performance
try:
meter = models.Meter.__table__
trans = conn.begin_nested()
if conn.dialect.name == 'sqlite':
trans = conn.begin()
with trans:
meter_row = conn.execute(
sa.select([meter.c.id])
.where(sa.and_(meter.c.name == name,
meter.c.type == type,
meter.c.unit == unit))).first()
meter_id = meter_row[0] if meter_row else None
if meter_id is None:
result = conn.execute(meter.insert(), name=name,
type=type, unit=unit)
meter_id = result.inserted_primary_key[0]
except dbexc.DBDuplicateEntry:
# retry function to pick up duplicate committed object
meter_id = Connection._create_meter(conn, name, type, unit)
return meter_id
@staticmethod
def _create_resource(conn, res_id, user_id, project_id, source_id,
rmeta):
# TODO(gordc): implement lru_cache to improve performance
try:
res = models.Resource.__table__
m_hash = hashlib.md5(jsonutils.dumps(rmeta,
sort_keys=True)).hexdigest()
trans = conn.begin_nested()
if conn.dialect.name == 'sqlite':
trans = conn.begin()
with trans:
res_row = conn.execute(
sa.select([res.c.internal_id])
.where(sa.and_(res.c.resource_id == res_id,
res.c.user_id == user_id,
res.c.project_id == project_id,
res.c.source_id == source_id,
res.c.metadata_hash == m_hash))).first()
internal_id = res_row[0] if res_row else None
if internal_id is None:
result = conn.execute(res.insert(), resource_id=res_id,
user_id=user_id,
project_id=project_id,
source_id=source_id,
resource_metadata=rmeta,
metadata_hash=m_hash)
internal_id = result.inserted_primary_key[0]
if rmeta and isinstance(rmeta, dict):
meta_map = {}
for key, v in utils.dict_to_keyval(rmeta):
try:
_model = sql_utils.META_TYPE_MAP[type(v)]
if meta_map.get(_model) is None:
meta_map[_model] = []
meta_map[_model].append(
{'id': internal_id, 'meta_key': key,
'value': v})
except KeyError:
LOG.warn(_("Unknown metadata type. Key (%s) "
"will not be queryable."), key)
for _model in meta_map.keys():
conn.execute(_model.__table__.insert(),
meta_map[_model])
except dbexc.DBDuplicateEntry:
# retry function to pick up duplicate committed object
internal_id = Connection._create_resource(
conn, res_id, user_id, project_id, source_id, rmeta)
return internal_id
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
"""
engine = self._engine_facade.get_engine()
with engine.begin() as conn:
# Record the raw data for the sample.
m_id = self._create_meter(conn,
data['counter_name'],
data['counter_type'],
data['counter_unit'])
res_id = self._create_resource(conn,
data['resource_id'],
data['user_id'],
data['project_id'],
data['source'],
data['resource_metadata'])
sample = models.Sample.__table__
conn.execute(sample.insert(), meter_id=m_id,
resource_id=res_id,
timestamp=data['timestamp'],
volume=data['counter_volume'],
message_signature=data['message_signature'],
message_id=data['message_id'])
def clear_expired_metering_data(self, ttl):
"""Clear expired data from the backend storage system.
Clearing occurs according to the time-to-live.
:param ttl: Number of seconds to keep records for.
"""
session = self._engine_facade.get_session()
with session.begin():
end = timeutils.utcnow() - datetime.timedelta(seconds=ttl)
sample_q = (session.query(models.Sample)
.filter(models.Sample.timestamp < end))
sample_subq = sample_q.subquery()
for table in [models.MetaText, models.MetaBigInt,
models.MetaFloat, models.MetaBool]:
(session.query(table)
.join(sample_subq, sample_subq.c.id == table.id)
.delete())
rows = sample_q.delete()
# remove Meter definitions with no matching samples
(session.query(models.Meter)
.filter(~models.Meter.samples.any())
.delete(synchronize_session='fetch'))
(session.query(models.Resource)
.filter(~models.Resource.samples.any())
.delete(synchronize_session='fetch'))
LOG.info(_("%d samples removed from database"), rows)
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 api_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')
s_filter = storage.SampleFilter(user=user,
project=project,
source=source,
start=start_timestamp,
start_timestamp_op=start_timestamp_op,
end=end_timestamp,
end_timestamp_op=end_timestamp_op,
metaquery=metaquery,
resource=resource)
session = self._engine_facade.get_session()
# get list of resource_ids
res_q = session.query(distinct(models.Resource.resource_id)).join(
models.Sample,
models.Sample.resource_id == models.Resource.internal_id)
res_q = make_query_from_filter(session, res_q, s_filter,
require_meter=False)
for res_id in res_q.all():
# get latest Sample
max_q = (session.query(models.Sample)
.join(models.Resource,
models.Resource.internal_id ==
models.Sample.resource_id)
.filter(models.Resource.resource_id == res_id[0]))
max_q = make_query_from_filter(session, max_q, s_filter,
require_meter=False)
max_q = max_q.order_by(models.Sample.timestamp.desc(),
models.Sample.id.desc()).limit(1)
# get the min timestamp value.
min_q = (session.query(models.Sample.timestamp)
.join(models.Resource,
models.Resource.internal_id ==
models.Sample.resource_id)
.filter(models.Resource.resource_id == res_id[0]))
min_q = make_query_from_filter(session, min_q, s_filter,
require_meter=False)
min_q = min_q.order_by(models.Sample.timestamp.asc()).limit(1)
sample = max_q.first()
if sample:
yield api_models.Resource(
resource_id=sample.resource.resource_id,
project_id=sample.resource.project_id,
first_sample_timestamp=min_q.first().timestamp,
last_sample_timestamp=sample.timestamp,
source=sample.resource.source_id,
user_id=sample.resource.user_id,
metadata=sample.resource.resource_metadata
)
def get_meters(self, user=None, project=None, resource=None, source=None,
metaquery=None, pagination=None):
"""Return an iterable of api_models.Meter instances
:param user: Optional ID for user that owns the resource.
:param project: Optional ID for project that owns the resource.
:param resource: Optional ID of the resource.
:param source: Optional source filter.
:param metaquery: Optional dict with metadata to match on.
:param pagination: Optional pagination query.
"""
if pagination:
raise ceilometer.NotImplementedError('Pagination not implemented')
s_filter = storage.SampleFilter(user=user,
project=project,
source=source,
metaquery=metaquery,
resource=resource)
# NOTE(gordc): get latest sample of each meter/resource. we do not
# filter here as we want to filter only on latest record.
session = self._engine_facade.get_session()
subq = session.query(func.max(models.Sample.id).label('id')).join(
models.Resource,
models.Resource.internal_id == models.Sample.resource_id).group_by(
models.Sample.meter_id, models.Resource.resource_id)
if resource:
subq = subq.filter(models.Resource.resource_id == resource)
subq = subq.subquery()
# get meter details for samples.
query_sample = (session.query(models.Sample.meter_id,
models.Meter.name, models.Meter.type,
models.Meter.unit,
models.Resource.resource_id,
models.Resource.project_id,
models.Resource.source_id,
models.Resource.user_id).join(
subq, subq.c.id == models.Sample.id)
.join(models.Meter, models.Meter.id == models.Sample.meter_id)
.join(models.Resource,
models.Resource.internal_id == models.Sample.resource_id))
query_sample = make_query_from_filter(session, query_sample, s_filter,
require_meter=False)
for row in query_sample.all():
yield api_models.Meter(
name=row.name,
type=row.type,
unit=row.unit,
resource_id=row.resource_id,
project_id=row.project_id,
source=row.source_id,
user_id=row.user_id)
def _retrieve_samples(self, query):
samples = query.all()
for s in samples:
# Remove the id generated by the database when
# the sample was inserted. It is an implementation
# detail that should not leak outside of the driver.
yield api_models.Sample(
source=s.source_id,
counter_name=s.counter_name,
counter_type=s.counter_type,
counter_unit=s.counter_unit,
counter_volume=s.counter_volume,
user_id=s.user_id,
project_id=s.project_id,
resource_id=s.resource_id,
timestamp=s.timestamp,
recorded_at=s.recorded_at,
resource_metadata=s.resource_metadata,
message_id=s.message_id,
message_signature=s.message_signature,
)
def get_samples(self, sample_filter, limit=None):
"""Return an iterable of api_models.Samples.
:param sample_filter: Filter.
:param limit: Maximum number of results to return.
"""
if limit == 0:
return []
session = self._engine_facade.get_session()
query = session.query(models.Sample.timestamp,
models.Sample.recorded_at,
models.Sample.message_id,
models.Sample.message_signature,
models.Sample.volume.label('counter_volume'),
models.Meter.name.label('counter_name'),
models.Meter.type.label('counter_type'),
models.Meter.unit.label('counter_unit'),
models.Resource.source_id,
models.Resource.user_id,
models.Resource.project_id,
models.Resource.resource_metadata,
models.Resource.resource_id).join(
models.Meter, models.Meter.id == models.Sample.meter_id).join(
models.Resource,
models.Resource.internal_id == models.Sample.resource_id).order_by(
models.Sample.timestamp.desc())
query = make_query_from_filter(session, query, sample_filter,
require_meter=False)
if limit:
query = query.limit(limit)
return self._retrieve_samples(query)
def query_samples(self, filter_expr=None, orderby=None, limit=None):
if limit == 0:
return []
session = self._engine_facade.get_session()
query = session.query(models.FullSample)
transformer = sql_utils.QueryTransformer(models.FullSample, query)
if filter_expr is not None:
transformer.apply_filter(filter_expr)
transformer.apply_options(orderby, limit)
return self._retrieve_samples(transformer.get_query())
@staticmethod
def _get_aggregate_functions(aggregate):
if not aggregate:
return [f for f in STANDARD_AGGREGATES.values()]
functions = []
for a in aggregate:
if a.func in STANDARD_AGGREGATES:
functions.append(STANDARD_AGGREGATES[a.func])
elif a.func in UNPARAMETERIZED_AGGREGATES:
functions.append(UNPARAMETERIZED_AGGREGATES[a.func])
elif a.func in PARAMETERIZED_AGGREGATES['compute']:
validate = PARAMETERIZED_AGGREGATES['validate'].get(a.func)
if not (validate and validate(a.param)):
raise storage.StorageBadAggregate('Bad aggregate: %s.%s'
% (a.func, a.param))
compute = PARAMETERIZED_AGGREGATES['compute'][a.func]
functions.append(compute(a.param))
else:
raise ceilometer.NotImplementedError(
'Selectable aggregate function %s'
' is not supported' % a.func)
return functions
def _make_stats_query(self, sample_filter, groupby, aggregate):
select = [
func.min(models.Sample.timestamp).label('tsmin'),
func.max(models.Sample.timestamp).label('tsmax'),
models.Meter.unit
]
select.extend(self._get_aggregate_functions(aggregate))
session = self._engine_facade.get_session()
if groupby:
group_attributes = []
for g in groupby:
if g != 'resource_metadata.instance_type':
group_attributes.append(getattr(models.Resource, g))
else:
group_attributes.append(
getattr(models.MetaText, 'value')
.label('resource_metadata.instance_type'))
select.extend(group_attributes)
query = (
session.query(*select)
.join(models.Meter,
models.Meter.id == models.Sample.meter_id)
.join(models.Resource,
models.Resource.internal_id == models.Sample.resource_id)
.group_by(models.Meter.unit))
if groupby:
for g in groupby:
if g == 'resource_metadata.instance_type':
query = query.join(
models.MetaText,
models.Resource.internal_id == models.MetaText.id)
query = query.filter(
models.MetaText.meta_key == 'instance_type')
query = query.group_by(*group_attributes)
return make_query_from_filter(session, query, sample_filter)
@staticmethod
def _stats_result_aggregates(result, aggregate):
stats_args = {}
if isinstance(result.count, (int, long)):
stats_args['count'] = result.count
for attr in ['min', 'max', 'sum', 'avg']:
if hasattr(result, attr):
stats_args[attr] = getattr(result, attr)
if aggregate:
stats_args['aggregate'] = {}
for a in aggregate:
key = '%s%s' % (a.func, '/%s' % a.param if a.param else '')
stats_args['aggregate'][key] = getattr(result, key)
return stats_args
@staticmethod
def _stats_result_to_model(result, period, period_start,
period_end, groupby, aggregate):
stats_args = Connection._stats_result_aggregates(result, aggregate)
stats_args['unit'] = result.unit
duration = (timeutils.delta_seconds(result.tsmin, result.tsmax)
if result.tsmin is not None and result.tsmax is not None
else None)
stats_args['duration'] = duration
stats_args['duration_start'] = result.tsmin
stats_args['duration_end'] = result.tsmax
stats_args['period'] = period
stats_args['period_start'] = period_start
stats_args['period_end'] = period_end
stats_args['groupby'] = (dict(
(g, getattr(result, g)) for g in groupby) if groupby else None)
return api_models.Statistics(**stats_args)
def get_meter_statistics(self, sample_filter, period=None, groupby=None,
aggregate=None):
"""Return an iterable of api_models.Statistics instances.
Items are containing meter statistics described by the query
parameters. The filter must have a meter value set.
"""
if groupby:
for group in groupby:
if group not in ['user_id', 'project_id', 'resource_id',
'resource_metadata.instance_type']:
raise ceilometer.NotImplementedError('Unable to group by '
'these fields')
if not period:
for res in self._make_stats_query(sample_filter,
groupby,
aggregate):
if res.count:
yield self._stats_result_to_model(res, 0,
res.tsmin, res.tsmax,
groupby,
aggregate)
return
if not sample_filter.start or not sample_filter.end:
res = self._make_stats_query(sample_filter,
None,
aggregate).first()
if not res:
# NOTE(liusheng):The 'res' may be NoneType, because no
# sample has found with sample filter(s).
return
query = self._make_stats_query(sample_filter, groupby, aggregate)
# HACK(jd) This is an awful method to compute stats by period, but
# since we're trying to be SQL agnostic we have to write portable
# code, so here it is, admire! We're going to do one request to get
# stats by period. We would like to use GROUP BY, but there's no
# portable way to manipulate timestamp in SQL, so we can't.
for period_start, period_end in base.iter_period(
sample_filter.start or res.tsmin,
sample_filter.end or res.tsmax,
period):
q = query.filter(models.Sample.timestamp >= period_start)
q = q.filter(models.Sample.timestamp < period_end)
for r in q.all():
if r.count:
yield self._stats_result_to_model(
result=r,
period=int(timeutils.delta_seconds(period_start,
period_end)),
period_start=period_start,
period_end=period_end,
groupby=groupby,
aggregate=aggregate
)