85e3678ec6
- this patch adds ability to store raw notification. it does not support indexing/querying against it as this is not expected to be performant. - use conf fixture to ensure test isolation. Change-Id: Ic35bb81d1ed1b538280cb9c2e87aced22bdff207
271 lines
11 KiB
Python
271 lines
11 KiB
Python
#
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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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import datetime
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import operator
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import elasticsearch as es
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from elasticsearch import helpers
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from oslo_utils import netutils
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from oslo_utils import timeutils
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import six
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from ceilometer.event.storage import base
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from ceilometer.event.storage import models
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from ceilometer import utils
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AVAILABLE_CAPABILITIES = {
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'events': {'query': {'simple': True}},
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}
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AVAILABLE_STORAGE_CAPABILITIES = {
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'storage': {'production_ready': True},
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}
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class Connection(base.Connection):
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"""Put the event data into an ElasticSearch db.
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Events in ElasticSearch are indexed by day and stored by event_type.
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An example document::
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{"_index":"events_2014-10-21",
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"_type":"event_type0",
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"_id":"dc90e464-65ab-4a5d-bf66-ecb956b5d779",
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"_score":1.0,
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"_source":{"timestamp": "2014-10-21T20:02:09.274797"
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"traits": {"id4_0": "2014-10-21T20:02:09.274797",
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"id3_0": 0.7510790937279408,
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"id2_0": 5,
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"id1_0": "18c97ba1-3b74-441a-b948-a702a30cbce2"}
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}
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}
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"""
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CAPABILITIES = utils.update_nested(base.Connection.CAPABILITIES,
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AVAILABLE_CAPABILITIES)
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STORAGE_CAPABILITIES = utils.update_nested(
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base.Connection.STORAGE_CAPABILITIES,
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AVAILABLE_STORAGE_CAPABILITIES,
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)
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index_name = 'events'
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# NOTE(gordc): mainly for testing, data is not searchable after write,
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# it is only searchable after periodic refreshes.
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_refresh_on_write = False
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def __init__(self, url):
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url_split = netutils.urlsplit(url)
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self.conn = es.Elasticsearch(url_split.netloc)
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def upgrade(self):
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iclient = es.client.IndicesClient(self.conn)
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ts_template = {
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'template': '*',
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'mappings': {'_default_':
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{'_timestamp': {'enabled': True,
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'store': True},
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'properties': {'traits': {'type': 'nested'}}}}}
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iclient.put_template(name='enable_timestamp', body=ts_template)
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def record_events(self, events):
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def _build_bulk_index(event_list):
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for ev in event_list:
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traits = {t.name: t.value for t in ev.traits}
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yield {'_op_type': 'create',
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'_index': '%s_%s' % (self.index_name,
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ev.generated.date().isoformat()),
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'_type': ev.event_type,
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'_id': ev.message_id,
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'_source': {'timestamp': ev.generated.isoformat(),
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'traits': traits,
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'raw': ev.raw}}
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problem_events = []
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for ok, result in helpers.streaming_bulk(
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self.conn, _build_bulk_index(events)):
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if not ok:
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__, result = result.popitem()
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if result['status'] == 409:
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problem_events.append((models.Event.DUPLICATE,
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result['_id']))
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else:
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problem_events.append((models.Event.UNKNOWN_PROBLEM,
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result['_id']))
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if self._refresh_on_write:
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self.conn.indices.refresh(index='%s_*' % self.index_name)
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while self.conn.cluster.pending_tasks(local=True)['tasks']:
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pass
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return problem_events
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def _make_dsl_from_filter(self, indices, ev_filter):
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q_args = {}
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filters = []
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if ev_filter.start_timestamp:
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filters.append({'range': {'timestamp':
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{'ge': ev_filter.start_timestamp.isoformat()}}})
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while indices[0] < (
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'%s_%s' % (self.index_name,
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ev_filter.start_timestamp.date().isoformat())):
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del indices[0]
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if ev_filter.end_timestamp:
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filters.append({'range': {'timestamp':
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{'le': ev_filter.end_timestamp.isoformat()}}})
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while indices[-1] > (
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'%s_%s' % (self.index_name,
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ev_filter.end_timestamp.date().isoformat())):
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del indices[-1]
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q_args['index'] = indices
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if ev_filter.event_type:
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q_args['doc_type'] = ev_filter.event_type
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if ev_filter.message_id:
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filters.append({'term': {'_id': ev_filter.message_id}})
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if ev_filter.traits_filter:
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trait_filters = []
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for t_filter in ev_filter.traits_filter:
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value = None
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for val_type in ['integer', 'string', 'float', 'datetime']:
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if t_filter.get(val_type):
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value = t_filter.get(val_type)
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if isinstance(value, six.string_types):
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value = value.lower()
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elif isinstance(value, datetime.datetime):
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value = value.isoformat()
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break
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if t_filter.get('op') in ['gt', 'ge', 'lt', 'le']:
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op = (t_filter.get('op').replace('ge', 'gte')
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.replace('le', 'lte'))
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trait_filters.append(
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{'range': {t_filter['key']: {op: value}}})
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else:
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tf = {"query": {"query_string": {
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"query": "%s: \"%s\"" % (t_filter['key'], value)}}}
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if t_filter.get('op') == 'ne':
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tf = {"not": tf}
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trait_filters.append(tf)
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filters.append(
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{'nested': {'path': 'traits', 'query': {'filtered': {
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'filter': {'bool': {'must': trait_filters}}}}}})
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q_args['body'] = {'query': {'filtered':
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{'filter': {'bool': {'must': filters}}}}}
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return q_args
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def get_events(self, event_filter):
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iclient = es.client.IndicesClient(self.conn)
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indices = iclient.get_mapping('%s_*' % self.index_name).keys()
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if indices:
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filter_args = self._make_dsl_from_filter(indices, event_filter)
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results = self.conn.search(fields=['_id', 'timestamp',
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'_type', '_source'],
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sort='timestamp:asc',
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**filter_args)
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trait_mappings = {}
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for record in results['hits']['hits']:
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trait_list = []
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if not record['_type'] in trait_mappings:
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trait_mappings[record['_type']] = list(
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self.get_trait_types(record['_type']))
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for key in record['_source']['traits'].keys():
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value = record['_source']['traits'][key]
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for t_map in trait_mappings[record['_type']]:
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if t_map['name'] == key:
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dtype = t_map['data_type']
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break
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trait_list.append(models.Trait(
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name=key, dtype=dtype,
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value=models.Trait.convert_value(dtype, value)))
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gen_ts = timeutils.normalize_time(timeutils.parse_isotime(
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record['_source']['timestamp']))
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yield models.Event(message_id=record['_id'],
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event_type=record['_type'],
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generated=gen_ts,
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traits=sorted(
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trait_list,
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key=operator.attrgetter('dtype')),
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raw=record['_source']['raw'])
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def get_event_types(self):
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iclient = es.client.IndicesClient(self.conn)
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es_mappings = iclient.get_mapping('%s_*' % self.index_name)
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seen_types = set()
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for index in es_mappings.keys():
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for ev_type in es_mappings[index]['mappings'].keys():
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seen_types.add(ev_type)
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# TODO(gordc): tests assume sorted ordering but backends are not
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# explicitly ordered.
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# NOTE: _default_ is a type that appears in all mappings but is not
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# real 'type'
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seen_types.discard('_default_')
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return sorted(list(seen_types))
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@staticmethod
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def _remap_es_types(d_type):
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if d_type == 'string':
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d_type = 'text'
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elif d_type == 'long':
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d_type = 'int'
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elif d_type == 'double':
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d_type = 'float'
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elif d_type == 'date' or d_type == 'date_time':
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d_type = 'datetime'
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return d_type
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def get_trait_types(self, event_type):
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iclient = es.client.IndicesClient(self.conn)
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es_mappings = iclient.get_mapping('%s_*' % self.index_name)
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seen_types = []
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for index in es_mappings.keys():
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# if event_type exists in index and has traits
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if (es_mappings[index]['mappings'].get(event_type) and
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es_mappings[index]['mappings'][event_type]['properties']
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['traits'].get('properties')):
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for t_type in (es_mappings[index]['mappings'][event_type]
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['properties']['traits']['properties'].keys()):
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d_type = (es_mappings[index]['mappings'][event_type]
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['properties']['traits']['properties']
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[t_type]['type'])
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d_type = models.Trait.get_type_by_name(
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self._remap_es_types(d_type))
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if (t_type, d_type) not in seen_types:
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yield {'name': t_type, 'data_type': d_type}
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seen_types.append((t_type, d_type))
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def get_traits(self, event_type, trait_type=None):
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t_types = dict((res['name'], res['data_type'])
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for res in self.get_trait_types(event_type))
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if not t_types or (trait_type and trait_type not in t_types.keys()):
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return
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result = self.conn.search('%s_*' % self.index_name, event_type)
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for ev in result['hits']['hits']:
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if trait_type and ev['_source']['traits'].get(trait_type):
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yield models.Trait(
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name=trait_type,
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dtype=t_types[trait_type],
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value=models.Trait.convert_value(
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t_types[trait_type],
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ev['_source']['traits'][trait_type]))
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else:
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for trait in ev['_source']['traits'].keys():
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yield models.Trait(
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name=trait,
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dtype=t_types[trait],
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value=models.Trait.convert_value(
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t_types[trait],
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ev['_source']['traits'][trait]))
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