134a52e144
Summary: In https://github.com/facebook/rocksdb/pull/3934 we introduced advisor scripts that make suggestions in the config options based on the log file and stats from a run of rocksdb. The optimizer runs the advisor on a benchmark application in a loop and automatically applies the suggested changes until the config options are optimized. This is a work in progress and the patch is the initial skeleton for the optimizer. The sample application that is run in the loop is currently dbbench. Pull Request resolved: https://github.com/facebook/rocksdb/pull/4169 Reviewed By: maysamyabandeh Differential Revision: D9023671 Pulled By: poojam23 fbshipit-source-id: a6192d475c462cf6eb2b316716f97cb400fcb64d
209 lines
9.5 KiB
Python
209 lines
9.5 KiB
Python
# Copyright (c) 2011-present, Facebook, Inc. All rights reserved.
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# This source code is licensed under both the GPLv2 (found in the
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# COPYING file in the root directory) and Apache 2.0 License
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# (found in the LICENSE.Apache file in the root directory).
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from abc import abstractmethod
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from advisor.db_log_parser import DataSource
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from enum import Enum
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import math
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NO_ENTITY = 'ENTITY_PLACEHOLDER'
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class TimeSeriesData(DataSource):
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class Behavior(Enum):
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bursty = 1
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evaluate_expression = 2
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class AggregationOperator(Enum):
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avg = 1
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max = 2
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min = 3
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latest = 4
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oldest = 5
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def __init__(self):
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super().__init__(DataSource.Type.TIME_SERIES)
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self.keys_ts = None # Dict[entity, Dict[key, Dict[timestamp, value]]]
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self.stats_freq_sec = None
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@abstractmethod
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def get_keys_from_conditions(self, conditions):
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# This method takes in a list of time-series conditions; for each
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# condition it manipulates the 'keys' in the way that is supported by
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# the subclass implementing this method
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pass
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@abstractmethod
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def fetch_timeseries(self, required_statistics):
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# this method takes in a list of statistics and fetches the timeseries
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# for each of them and populates the 'keys_ts' dictionary
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pass
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def fetch_burst_epochs(
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self, entities, statistic, window_sec, threshold, percent
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):
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# type: (str, int, float, bool) -> Dict[str, Dict[int, float]]
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# this method calculates the (percent) rate change in the 'statistic'
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# for each entity (over 'window_sec' seconds) and returns the epochs
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# where this rate change is greater than or equal to the 'threshold'
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# value
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if self.stats_freq_sec == 0:
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# not time series data, cannot check for bursty behavior
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return
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if window_sec < self.stats_freq_sec:
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window_sec = self.stats_freq_sec
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# 'window_samples' is the number of windows to go back to
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# compare the current window with, while calculating rate change.
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window_samples = math.ceil(window_sec / self.stats_freq_sec)
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burst_epochs = {}
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# if percent = False:
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# curr_val = value at window for which rate change is being calculated
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# prev_val = value at window that is window_samples behind curr_window
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# Then rate_without_percent =
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# ((curr_val-prev_val)*duration_sec)/(curr_timestamp-prev_timestamp)
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# if percent = True:
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# rate_with_percent = (rate_without_percent * 100) / prev_val
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# These calculations are in line with the rate() transform supported
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# by ODS
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for entity in entities:
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if statistic not in self.keys_ts[entity]:
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continue
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timestamps = sorted(list(self.keys_ts[entity][statistic].keys()))
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for ix in range(window_samples, len(timestamps), 1):
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first_ts = timestamps[ix - window_samples]
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last_ts = timestamps[ix]
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first_val = self.keys_ts[entity][statistic][first_ts]
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last_val = self.keys_ts[entity][statistic][last_ts]
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diff = last_val - first_val
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if percent:
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diff = diff * 100 / first_val
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rate = (diff * self.duration_sec) / (last_ts - first_ts)
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# if the rate change is greater than the provided threshold,
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# then the condition is triggered for entity at time 'last_ts'
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if rate >= threshold:
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if entity not in burst_epochs:
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burst_epochs[entity] = {}
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burst_epochs[entity][last_ts] = rate
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return burst_epochs
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def fetch_aggregated_values(self, entity, statistics, aggregation_op):
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# type: (str, AggregationOperator) -> Dict[str, float]
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# this method performs the aggregation specified by 'aggregation_op'
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# on the timeseries of 'statistics' for 'entity' and returns:
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# Dict[statistic, aggregated_value]
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result = {}
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for stat in statistics:
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if stat not in self.keys_ts[entity]:
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continue
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agg_val = None
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if aggregation_op is self.AggregationOperator.latest:
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latest_timestamp = max(list(self.keys_ts[entity][stat].keys()))
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agg_val = self.keys_ts[entity][stat][latest_timestamp]
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elif aggregation_op is self.AggregationOperator.oldest:
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oldest_timestamp = min(list(self.keys_ts[entity][stat].keys()))
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agg_val = self.keys_ts[entity][stat][oldest_timestamp]
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elif aggregation_op is self.AggregationOperator.max:
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agg_val = max(list(self.keys_ts[entity][stat].values()))
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elif aggregation_op is self.AggregationOperator.min:
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agg_val = min(list(self.keys_ts[entity][stat].values()))
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elif aggregation_op is self.AggregationOperator.avg:
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values = list(self.keys_ts[entity][stat].values())
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agg_val = sum(values) / len(values)
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result[stat] = agg_val
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return result
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def check_and_trigger_conditions(self, conditions):
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# get the list of statistics that need to be fetched
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reqd_keys = self.get_keys_from_conditions(conditions)
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# fetch the required statistics and populate the map 'keys_ts'
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self.fetch_timeseries(reqd_keys)
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# Trigger the appropriate conditions
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for cond in conditions:
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complete_keys = self.get_keys_from_conditions([cond])
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# Get the entities that have all statistics required by 'cond':
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# an entity is checked for a given condition only if we possess all
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# of the condition's 'keys' for that entity
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entities_with_stats = []
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for entity in self.keys_ts:
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stat_missing = False
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for stat in complete_keys:
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if stat not in self.keys_ts[entity]:
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stat_missing = True
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break
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if not stat_missing:
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entities_with_stats.append(entity)
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if not entities_with_stats:
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continue
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if cond.behavior is self.Behavior.bursty:
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# for a condition that checks for bursty behavior, only one key
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# should be present in the condition's 'keys' field
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result = self.fetch_burst_epochs(
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entities_with_stats,
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complete_keys[0], # there should be only one key
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cond.window_sec,
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cond.rate_threshold,
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True
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)
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# Trigger in this case is:
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# Dict[entity_name, Dict[timestamp, rate_change]]
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# where the inner dictionary contains rate_change values when
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# the rate_change >= threshold provided, with the
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# corresponding timestamps
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if result:
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cond.set_trigger(result)
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elif cond.behavior is self.Behavior.evaluate_expression:
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self.handle_evaluate_expression(
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cond,
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complete_keys,
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entities_with_stats
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)
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def handle_evaluate_expression(self, condition, statistics, entities):
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trigger = {}
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# check 'condition' for each of these entities
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for entity in entities:
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if hasattr(condition, 'aggregation_op'):
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# in this case, the aggregation operation is performed on each
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# of the condition's 'keys' and then with aggregated values
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# condition's 'expression' is evaluated; if it evaluates to
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# True, then list of the keys values is added to the
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# condition's trigger: Dict[entity_name, List[stats]]
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result = self.fetch_aggregated_values(
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entity, statistics, condition.aggregation_op
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)
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keys = [result[key] for key in statistics]
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try:
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if eval(condition.expression):
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trigger[entity] = keys
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except Exception as e:
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print(
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'WARNING(TimeSeriesData) check_and_trigger: ' + str(e)
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)
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else:
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# assumption: all stats have same series of timestamps
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# this is similar to the above but 'expression' is evaluated at
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# each timestamp, since there is no aggregation, and all the
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# epochs are added to the trigger when the condition's
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# 'expression' evaluated to true; so trigger is:
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# Dict[entity, Dict[timestamp, List[stats]]]
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for epoch in self.keys_ts[entity][statistics[0]].keys():
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keys = [
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self.keys_ts[entity][key][epoch]
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for key in statistics
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]
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try:
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if eval(condition.expression):
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if entity not in trigger:
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trigger[entity] = {}
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trigger[entity][epoch] = keys
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except Exception as e:
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print(
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'WARNING(TimeSeriesData) check_and_trigger: ' +
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str(e)
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)
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if trigger:
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condition.set_trigger(trigger)
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