/usr/lib/python2.7/dist-packages/gnocchi/storage/_carbonara.py is in python-gnocchi 3.0.4-3.
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#
# Copyright © 2016 Red Hat, Inc.
# Copyright © 2014-2015 eNovance
#
# 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.
import collections
import datetime
import itertools
import operator
import struct
import uuid
from concurrent import futures
import iso8601
import msgpack
from oslo_config import cfg
from oslo_log import log
from oslo_serialization import msgpackutils
from oslo_utils import timeutils
import pandas
import six
import six.moves
from tooz import coordination
from gnocchi import carbonara
from gnocchi import storage
from gnocchi import utils
OPTS = [
cfg.IntOpt('aggregation_workers_number',
default=1, min=1,
help='Number of workers to run during adding new measures for '
'pre-aggregation needs. Due to the Python GIL, '
'1 is usually faster, unless you have high latency I/O'),
cfg.StrOpt('coordination_url',
secret=True,
help='Coordination driver URL'),
]
LOG = log.getLogger(__name__)
class CorruptionError(ValueError):
"""Data corrupted, damn it."""
def __init__(self, message):
super(CorruptionError, self).__init__(message)
class CarbonaraBasedStorage(storage.StorageDriver):
MEASURE_PREFIX = "measure"
UPGRADE_BATCH_SIZE = 1000
def __init__(self, conf):
super(CarbonaraBasedStorage, self).__init__(conf)
self.aggregation_workers_number = conf.aggregation_workers_number
if self.aggregation_workers_number == 1:
# NOTE(jd) Avoid using futures at all if we don't want any threads.
self._map_in_thread = self._map_no_thread
else:
self._map_in_thread = self._map_in_futures_threads
self.coord, my_id = utils.get_coordinator_and_start(
conf.coordination_url)
def stop(self):
self.coord.stop()
def _lock(self, metric_id):
lock_name = b"gnocchi-" + str(metric_id).encode('ascii')
return self.coord.get_lock(lock_name)
@staticmethod
def _get_measures(metric, timestamp_key, aggregation, granularity,
version=3):
raise NotImplementedError
@staticmethod
def _get_unaggregated_timeserie(metric, version=3):
raise NotImplementedError
def _get_unaggregated_timeserie_and_unserialize(
self, metric, block_size, back_window):
"""Retrieve unaggregated timeserie for a metric and unserialize it.
Returns a gnocchi.carbonara.BoundTimeSerie object. If the data cannot
be retrieved, returns None.
"""
with timeutils.StopWatch() as sw:
raw_measures = (
self._get_unaggregated_timeserie(
metric)
)
LOG.debug(
"Retrieve unaggregated measures "
"for %s in %.2fs"
% (metric.id, sw.elapsed()))
try:
return carbonara.BoundTimeSerie.unserialize(
raw_measures, block_size, back_window)
except ValueError:
raise CorruptionError(
"Data corruption detected for %s "
"unaggregated timeserie" % metric.id)
@staticmethod
def _store_unaggregated_timeserie(metric, data, version=3):
raise NotImplementedError
@staticmethod
def _store_metric_measures(metric, timestamp_key, aggregation,
granularity, data, offset=None, version=3):
raise NotImplementedError
@staticmethod
def _delete_unaggregated_timeserie(metric, version=3):
raise NotImplementedError
@staticmethod
def _list_split_keys_for_metric(metric, aggregation, granularity,
version=None):
raise NotImplementedError
@staticmethod
def _version_check(name, v):
"""Validate object matches expected version.
Version should be last attribute and start with 'v'
"""
attrs = name.split("_")
return not v or (not attrs[-1].startswith('v') if v == 2
else attrs[-1] == 'v%s' % v)
def get_measures(self, metric, from_timestamp=None, to_timestamp=None,
aggregation='mean', granularity=None):
super(CarbonaraBasedStorage, self).get_measures(
metric, from_timestamp, to_timestamp, aggregation)
if granularity is None:
agg_timeseries = self._map_in_thread(
self._get_measures_timeserie,
((metric, aggregation, ap.granularity,
from_timestamp, to_timestamp)
for ap in reversed(metric.archive_policy.definition)))
else:
agg_timeseries = [self._get_measures_timeserie(
metric, aggregation, granularity,
from_timestamp, to_timestamp)]
return [(timestamp.replace(tzinfo=iso8601.iso8601.UTC), r, v)
for ts in agg_timeseries
for timestamp, r, v in ts.fetch(from_timestamp, to_timestamp)]
def _get_measures_and_unserialize(self, metric, key,
aggregation, granularity):
data = self._get_measures(metric, key, aggregation, granularity)
try:
return carbonara.AggregatedTimeSerie.unserialize(
data, key, aggregation, granularity)
except carbonara.InvalidData:
LOG.error("Data corruption detected for %s "
"aggregated `%s' timeserie, granularity `%s' "
"around time `%s', ignoring."
% (metric.id, aggregation, granularity, key))
def _get_measures_timeserie(self, metric,
aggregation, granularity,
from_timestamp=None, to_timestamp=None):
# Find the number of point
for d in metric.archive_policy.definition:
if d.granularity == granularity:
points = d.points
break
else:
raise storage.GranularityDoesNotExist(metric, granularity)
all_keys = None
try:
all_keys = self._list_split_keys_for_metric(
metric, aggregation, granularity)
except storage.MetricDoesNotExist:
for d in metric.archive_policy.definition:
if d.granularity == granularity:
return carbonara.AggregatedTimeSerie(
sampling=granularity,
aggregation_method=aggregation,
max_size=d.points)
raise storage.GranularityDoesNotExist(metric, granularity)
if from_timestamp:
from_timestamp = str(
carbonara.SplitKey.from_timestamp_and_sampling(
from_timestamp, granularity))
if to_timestamp:
to_timestamp = str(
carbonara.SplitKey.from_timestamp_and_sampling(
to_timestamp, granularity))
timeseries = filter(
lambda x: x is not None,
self._map_in_thread(
self._get_measures_and_unserialize,
((metric, key, aggregation, granularity)
for key in all_keys
if ((not from_timestamp or key >= from_timestamp)
and (not to_timestamp or key <= to_timestamp))))
)
return carbonara.AggregatedTimeSerie.from_timeseries(
sampling=granularity,
aggregation_method=aggregation,
timeseries=timeseries,
max_size=points)
def _store_timeserie_split(self, metric, key, split,
aggregation, archive_policy_def,
oldest_mutable_timestamp):
# NOTE(jd) We write the full split only if the driver works that way
# (self.WRITE_FULL) or if the oldest_mutable_timestamp is out of range.
write_full = self.WRITE_FULL or next(key) <= oldest_mutable_timestamp
key_as_str = str(key)
if write_full:
try:
existing = self._get_measures_and_unserialize(
metric, key_as_str, aggregation,
archive_policy_def.granularity)
except storage.AggregationDoesNotExist:
pass
else:
if existing is not None:
if split is None:
split = existing
else:
split.merge(existing)
if split is None:
# `split' can be none if existing is None and no split was passed
# in order to rewrite and compress the data; in that case, it means
# the split key is present and listed, but some aggregation method
# or granularity is missing. That means data is corrupted, but it
# does not mean we have to fail, we can just do nothing and log a
# warning.
LOG.warning("No data found for metric %s, granularity %f "
"and aggregation method %s (split key %s): "
"possible data corruption",
metric, archive_policy_def.granularity,
aggregation, key)
return
offset, data = split.serialize(key, compressed=write_full)
return self._store_metric_measures(
metric, key_as_str, aggregation, archive_policy_def.granularity,
data, offset=offset)
def _add_measures(self, aggregation, archive_policy_def,
metric, grouped_serie,
previous_oldest_mutable_timestamp,
oldest_mutable_timestamp):
ts = carbonara.AggregatedTimeSerie.from_grouped_serie(
grouped_serie, archive_policy_def.granularity,
aggregation, max_size=archive_policy_def.points)
# Don't do anything if the timeserie is empty
if not ts:
return
# We only need to check for rewrite if driver is not in WRITE_FULL mode
# and if we already stored splits once
need_rewrite = (
not self.WRITE_FULL
and previous_oldest_mutable_timestamp is not None
)
if archive_policy_def.timespan or need_rewrite:
existing_keys = self._list_split_keys_for_metric(
metric, aggregation, archive_policy_def.granularity)
# First delete old splits
if archive_policy_def.timespan:
oldest_point_to_keep = ts.last - datetime.timedelta(
seconds=archive_policy_def.timespan)
oldest_key_to_keep = ts.get_split_key(oldest_point_to_keep)
oldest_key_to_keep_s = str(oldest_key_to_keep)
for key in list(existing_keys):
# NOTE(jd) Only delete if the key is strictly inferior to
# the timestamp; we don't delete any timeserie split that
# contains our timestamp, so we prefer to keep a bit more
# than deleting too much
if key < oldest_key_to_keep_s:
self._delete_metric_measures(
metric, key, aggregation,
archive_policy_def.granularity)
existing_keys.remove(key)
else:
oldest_key_to_keep = carbonara.SplitKey(0, 0)
# Rewrite all read-only splits just for fun (and compression). This
# only happens if `previous_oldest_mutable_timestamp' exists, which
# means we already wrote some splits at some point – so this is not the
# first time we treat this timeserie.
if need_rewrite:
previous_oldest_mutable_key = str(ts.get_split_key(
previous_oldest_mutable_timestamp))
oldest_mutable_key = str(ts.get_split_key(
oldest_mutable_timestamp))
if previous_oldest_mutable_key != oldest_mutable_key:
for key in existing_keys:
if previous_oldest_mutable_key <= key < oldest_mutable_key:
# NOTE(jd) Rewrite it entirely for fun (and later for
# compression). For that, we just pass None as split.
self._store_timeserie_split(
metric, carbonara.SplitKey(
float(key), archive_policy_def.granularity),
None, aggregation, archive_policy_def,
oldest_mutable_timestamp)
for key, split in ts.split():
if key >= oldest_key_to_keep:
self._store_timeserie_split(
metric, key, split, aggregation, archive_policy_def,
oldest_mutable_timestamp)
def add_measures(self, metric, measures):
measures = list(measures)
data = struct.pack(
"<" + self._MEASURE_SERIAL_FORMAT * len(measures),
*list(
itertools.chain(
# NOTE(jd) int(10e8) to avoid rounding errors
*((int(utils.datetime_to_unix(timestamp) * int(10e8)),
value)
for timestamp, value in measures))))
self._store_new_measures(metric, data)
@staticmethod
def _store_new_measures(metric, data):
raise NotImplementedError
@staticmethod
def _delete_metric(metric):
raise NotImplementedError
@staticmethod
def list_metric_with_measures_to_process(size, part, full=False):
raise NotImplementedError
@staticmethod
def _pending_measures_to_process_count(metric_id):
raise NotImplementedError
def delete_metric(self, metric, sync=False):
with self._lock(metric.id)(blocking=sync):
# If the metric has never been upgraded, we need to delete this
# here too
self._delete_metric(metric)
@staticmethod
def _delete_metric_measures(metric, timestamp_key,
aggregation, granularity, version=3):
raise NotImplementedError
_MEASURE_SERIAL_FORMAT = "Qd"
_MEASURE_SERIAL_LEN = struct.calcsize(_MEASURE_SERIAL_FORMAT)
def _unserialize_measures(self, measure_id, data):
nb_measures = len(data) // self._MEASURE_SERIAL_LEN
try:
measures = struct.unpack(
"<" + self._MEASURE_SERIAL_FORMAT * nb_measures, data)
except struct.error:
# This either a corruption, either a v2 measures
try:
return msgpackutils.loads(data)
except ValueError:
LOG.error(
"Unable to decode measure %s, possible data corruption",
measure_id)
raise
return six.moves.zip(
pandas.to_datetime(measures[::2], unit='ns'),
itertools.islice(measures, 1, len(measures), 2))
def measures_report(self, details=True):
metrics, measures, full_details = self._build_report(details)
report = {'summary': {'metrics': metrics, 'measures': measures}}
if full_details is not None:
report['details'] = full_details
return report
def _check_for_metric_upgrade(self, metric):
lock = self._lock(metric.id)
with lock:
try:
old_unaggregated = self._get_unaggregated_timeserie_and_unserialize_v2( # noqa
metric)
except (storage.MetricDoesNotExist, CorruptionError) as e:
# NOTE(jd) This case is not really possible – you can't
# have archives with splits and no unaggregated
# timeserie…
LOG.error(
"Unable to find unaggregated timeserie for "
"metric %s, unable to upgrade data: %s",
metric.id, e)
return
unaggregated = carbonara.BoundTimeSerie(
ts=old_unaggregated.ts,
block_size=metric.archive_policy.max_block_size,
back_window=metric.archive_policy.back_window)
# Upgrade unaggregated timeserie to v3
self._store_unaggregated_timeserie(
metric, unaggregated.serialize())
oldest_mutable_timestamp = (
unaggregated.first_block_timestamp()
)
for agg_method, d in itertools.product(
metric.archive_policy.aggregation_methods,
metric.archive_policy.definition):
LOG.debug(
"Checking if the metric %s needs migration for %s"
% (metric, agg_method))
try:
all_keys = self._list_split_keys_for_metric(
metric, agg_method, d.granularity, version=2)
except storage.MetricDoesNotExist:
# Just try the next metric, this one has no measures
break
else:
LOG.info("Migrating metric %s to new format" % metric)
timeseries = filter(
lambda x: x is not None,
self._map_in_thread(
self._get_measures_and_unserialize_v2,
((metric, key, agg_method, d.granularity)
for key in all_keys))
)
ts = carbonara.AggregatedTimeSerie.from_timeseries(
sampling=d.granularity,
aggregation_method=agg_method,
timeseries=timeseries, max_size=d.points)
for key, split in ts.split():
self._store_timeserie_split(
metric, key, split,
ts.aggregation_method,
d, oldest_mutable_timestamp)
for key in all_keys:
self._delete_metric_measures(
metric, key, agg_method,
d.granularity, version=None)
self._delete_unaggregated_timeserie(metric, version=None)
LOG.info("Migrated metric %s to new format" % metric)
def upgrade(self, index):
marker = None
while True:
metrics = [(metric,) for metric in
index.list_metrics(limit=self.UPGRADE_BATCH_SIZE,
marker=marker)]
self._map_in_thread(self._check_for_metric_upgrade, metrics)
if len(metrics) == 0:
break
marker = metrics[-1][0].id
def process_new_measures(self, indexer, metrics_to_process, sync=False):
metrics = indexer.list_metrics(ids=metrics_to_process)
# This build the list of deleted metrics, i.e. the metrics we have
# measures to process for but that are not in the indexer anymore.
deleted_metrics_id = (set(map(uuid.UUID, metrics_to_process))
- set(m.id for m in metrics))
for metric_id in deleted_metrics_id:
# NOTE(jd): We need to lock the metric otherwise we might delete
# measures that another worker might be processing. Deleting
# measurement files under its feet is not nice!
try:
with self._lock(metric_id)(blocking=sync):
self._delete_unprocessed_measures_for_metric_id(metric_id)
except coordination.LockAcquireFailed:
LOG.debug("Cannot acquire lock for metric %s, postponing "
"unprocessed measures deletion" % metric_id)
for metric in metrics:
lock = self._lock(metric.id)
agg_methods = list(metric.archive_policy.aggregation_methods)
# Do not block if we cannot acquire the lock, that means some other
# worker is doing the job. We'll just ignore this metric and may
# get back later to it if needed.
if lock.acquire(blocking=sync):
try:
locksw = timeutils.StopWatch().start()
LOG.debug("Processing measures for %s" % metric)
with self._process_measure_for_metric(metric) as measures:
# NOTE(mnaser): The metric could have been handled by
# another worker, ignore if no measures.
if len(measures) == 0:
LOG.debug("Skipping %s (already processed)"
% metric)
continue
measures = sorted(measures, key=operator.itemgetter(0))
block_size = metric.archive_policy.max_block_size
try:
ts = self._get_unaggregated_timeserie_and_unserialize( # noqa
metric,
block_size=block_size,
back_window=metric.archive_policy.back_window)
except storage.MetricDoesNotExist:
try:
self._create_metric(metric)
except storage.MetricAlreadyExists:
# Created in the mean time, do not worry
pass
ts = None
except CorruptionError as e:
LOG.error(e)
ts = None
if ts is None:
# This is the first time we treat measures for this
# metric, or data are corrupted, create a new one
ts = carbonara.BoundTimeSerie(
block_size=block_size,
back_window=metric.archive_policy.back_window)
current_first_block_timestamp = None
else:
current_first_block_timestamp = (
ts.first_block_timestamp()
)
# NOTE(jd) This is Python where you need such
# hack to pass a variable around a closure,
# sorry.
computed_points = {"number": 0}
def _map_add_measures(bound_timeserie):
# NOTE (gordc): bound_timeserie is entire set of
# unaggregated measures matching largest
# granularity. the following takes only the points
# affected by new measures for specific granularity
tstamp = max(bound_timeserie.first, measures[0][0])
computed_points['number'] = len(bound_timeserie)
for d in metric.archive_policy.definition:
ts = bound_timeserie.group_serie(
d.granularity, carbonara.round_timestamp(
tstamp, d.granularity * 10e8))
self._map_in_thread(
self._add_measures,
((aggregation, d, metric, ts,
current_first_block_timestamp,
bound_timeserie.first_block_timestamp())
for aggregation in agg_methods))
with timeutils.StopWatch() as sw:
ts.set_values(
measures,
before_truncate_callback=_map_add_measures,
ignore_too_old_timestamps=True)
elapsed = sw.elapsed()
number_of_operations = (
len(agg_methods)
* len(metric.archive_policy.definition)
)
if elapsed > 0:
perf = " (%d points/s, %d measures/s)" % (
((number_of_operations
* computed_points['number']) / elapsed),
((number_of_operations
* len(measures)) / elapsed)
)
else:
perf = ""
LOG.debug(
"Computed new metric %s with %d new measures "
"in %.2f seconds%s"
% (metric.id, len(measures), elapsed, perf))
self._store_unaggregated_timeserie(metric,
ts.serialize())
LOG.debug("Metric %s locked during %.2f seconds" %
(metric.id, locksw.elapsed()))
except Exception:
LOG.debug("Metric %s locked during %.2f seconds" %
(metric.id, locksw.elapsed()))
if sync:
raise
LOG.error("Error processing new measures", exc_info=True)
finally:
lock.release()
def get_cross_metric_measures(self, metrics, from_timestamp=None,
to_timestamp=None, aggregation='mean',
reaggregation=None,
granularity=None,
needed_overlap=100.0):
super(CarbonaraBasedStorage, self).get_cross_metric_measures(
metrics, from_timestamp, to_timestamp,
aggregation, reaggregation, granularity, needed_overlap)
if reaggregation is None:
reaggregation = aggregation
if granularity is None:
granularities = (
definition.granularity
for metric in metrics
for definition in metric.archive_policy.definition
)
granularities_in_common = [
g
for g, occurrence in six.iteritems(
collections.Counter(granularities))
if occurrence == len(metrics)
]
if not granularities_in_common:
raise storage.MetricUnaggregatable(
metrics, 'No granularity match')
else:
granularities_in_common = [granularity]
tss = self._map_in_thread(self._get_measures_timeserie,
[(metric, aggregation, g,
from_timestamp, to_timestamp)
for metric in metrics
for g in granularities_in_common])
try:
return [(timestamp.replace(tzinfo=iso8601.iso8601.UTC), r, v)
for timestamp, r, v
in carbonara.AggregatedTimeSerie.aggregated(
tss, reaggregation, from_timestamp, to_timestamp,
needed_overlap)]
except carbonara.UnAggregableTimeseries as e:
raise storage.MetricUnaggregatable(metrics, e.reason)
def _find_measure(self, metric, aggregation, granularity, predicate,
from_timestamp, to_timestamp):
timeserie = self._get_measures_timeserie(
metric, aggregation, granularity,
from_timestamp, to_timestamp)
values = timeserie.fetch(from_timestamp, to_timestamp)
return {metric:
[(timestamp.replace(tzinfo=iso8601.iso8601.UTC),
g, value)
for timestamp, g, value in values
if predicate(value)]}
# TODO(jd) Add granularity parameter here and in the REST API
# rather than fetching all granularities
def search_value(self, metrics, query, from_timestamp=None,
to_timestamp=None, aggregation='mean'):
predicate = storage.MeasureQuery(query)
results = self._map_in_thread(
self._find_measure,
[(metric, aggregation,
ap.granularity, predicate,
from_timestamp, to_timestamp)
for metric in metrics
for ap in metric.archive_policy.definition])
result = collections.defaultdict(list)
for r in results:
for metric, metric_result in six.iteritems(r):
result[metric].extend(metric_result)
# Sort the result
for metric, r in six.iteritems(result):
# Sort by timestamp asc, granularity desc
r.sort(key=lambda t: (t[0], - t[1]))
return result
@staticmethod
def _map_no_thread(method, list_of_args):
return list(itertools.starmap(method, list_of_args))
def _map_in_futures_threads(self, method, list_of_args):
with futures.ThreadPoolExecutor(
max_workers=self.aggregation_workers_number) as executor:
# We use 'list' to iterate all threads here to raise the first
# exception now, not much choice
return list(executor.map(lambda args: method(*args), list_of_args))
@staticmethod
def _unserialize_timeserie_v2(data):
return carbonara.TimeSerie.from_data(
*carbonara.TimeSerie._timestamps_and_values_from_dict(
msgpack.loads(data, encoding='utf-8')['values']))
def _get_unaggregated_timeserie_and_unserialize_v2(self, metric):
"""Unserialization method for unaggregated v2 timeseries."""
data = self._get_unaggregated_timeserie(metric, version=None)
try:
return self._unserialize_timeserie_v2(data)
except ValueError:
LOG.error("Data corruption detected for %s ignoring." % metric.id)
def _get_measures_and_unserialize_v2(self, metric, key,
aggregation, granularity):
"""Unserialization method for upgrading v2 objects. Upgrade only."""
data = self._get_measures(
metric, key, aggregation, granularity, version=None)
try:
return self._unserialize_timeserie_v2(data)
except ValueError:
LOG.error("Data corruption detected for %s "
"aggregated `%s' timeserie, granularity `%s' "
"around time `%s', ignoring."
% (metric.id, aggregation, granularity, key))
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