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# Copyright (c) 2011-present, Facebook, Inc. All rights reserved.
# This source code is licensed under both the GPLv2 (found in the
# COPYING file in the root directory) and Apache 2.0 License
# (found in the LICENSE.Apache file in the root directory).
from abc import ABC, abstractmethod
from advisor.db_log_parser import DataSource, NO_COL_FAMILY
from advisor.db_timeseries_parser import TimeSeriesData
from enum import Enum
from advisor.ini_parser import IniParser
import re
class Section(ABC):
def __init__(self, name):
self.name = name
@abstractmethod
def set_parameter(self, key, value):
pass
@abstractmethod
def perform_checks(self):
pass
class Rule(Section):
def __init__(self, name):
super().__init__(name)
self.conditions = None
self.suggestions = None
self.overlap_time_seconds = None
self.trigger_entities = None
self.trigger_column_families = None
def set_parameter(self, key, value):
# If the Rule is associated with a single suggestion/condition, then
# value will be a string and not a list. Hence, convert it to a single
# element list before storing it in self.suggestions or
# self.conditions.
if key == 'conditions':
if isinstance(value, str):
self.conditions = [value]
else:
self.conditions = value
elif key == 'suggestions':
if isinstance(value, str):
self.suggestions = [value]
else:
self.suggestions = value
elif key == 'overlap_time_period':
self.overlap_time_seconds = value
def get_suggestions(self):
return self.suggestions
def perform_checks(self):
if not self.conditions or len(self.conditions) < 1:
raise ValueError(
self.name + ': rule must have at least one condition'
)
if not self.suggestions or len(self.suggestions) < 1:
raise ValueError(
self.name + ': rule must have at least one suggestion'
)
if self.overlap_time_seconds:
if len(self.conditions) != 2:
raise ValueError(
self.name + ": rule must be associated with 2 conditions\
in order to check for a time dependency between them"
)
time_format = '^\d+[s|m|h|d]$'
if (
not
re.match(time_format, self.overlap_time_seconds, re.IGNORECASE)
):
raise ValueError(
self.name + ": overlap_time_seconds format: \d+[s|m|h|d]"
)
else: # convert to seconds
in_seconds = int(self.overlap_time_seconds[:-1])
if self.overlap_time_seconds[-1] == 'm':
in_seconds *= 60
elif self.overlap_time_seconds[-1] == 'h':
in_seconds *= (60 * 60)
elif self.overlap_time_seconds[-1] == 'd':
in_seconds *= (24 * 60 * 60)
self.overlap_time_seconds = in_seconds
def get_overlap_timestamps(self, key1_trigger_epochs, key2_trigger_epochs):
# this method takes in 2 timeseries i.e. timestamps at which the
# rule's 2 TIME_SERIES conditions were triggered and it finds
# (if present) the first pair of timestamps at which the 2 conditions
# were triggered within 'overlap_time_seconds' of each other
key1_lower_bounds = [
epoch - self.overlap_time_seconds
for epoch in key1_trigger_epochs
]
key1_lower_bounds.sort()
key2_trigger_epochs.sort()
trigger_ix = 0
overlap_pair = None
for key1_lb in key1_lower_bounds:
while (
key2_trigger_epochs[trigger_ix] < key1_lb and
trigger_ix < len(key2_trigger_epochs)
):
trigger_ix += 1
if trigger_ix >= len(key2_trigger_epochs):
break
if (
key2_trigger_epochs[trigger_ix] <=
key1_lb + (2 * self.overlap_time_seconds)
):
overlap_pair = (
key2_trigger_epochs[trigger_ix],
key1_lb + self.overlap_time_seconds
)
break
return overlap_pair
def get_trigger_entities(self):
return self.trigger_entities
def get_trigger_column_families(self):
return self.trigger_column_families
def is_triggered(self, conditions_dict, column_families):
if self.overlap_time_seconds:
condition1 = conditions_dict[self.conditions[0]]
condition2 = conditions_dict[self.conditions[1]]
if not (
condition1.get_data_source() is DataSource.Type.TIME_SERIES and
condition2.get_data_source() is DataSource.Type.TIME_SERIES
):
raise ValueError(self.name + ': need 2 timeseries conditions')
map1 = condition1.get_trigger()
map2 = condition2.get_trigger()
if not (map1 and map2):
return False
self.trigger_entities = {}
is_triggered = False
entity_intersection = (
set(map1.keys()).intersection(set(map2.keys()))
)
for entity in entity_intersection:
overlap_timestamps_pair = (
self.get_overlap_timestamps(
list(map1[entity].keys()), list(map2[entity].keys())
)
)
if overlap_timestamps_pair:
self.trigger_entities[entity] = overlap_timestamps_pair
is_triggered = True
if is_triggered:
self.trigger_column_families = set(column_families)
return is_triggered
else:
all_conditions_triggered = True
self.trigger_column_families = set(column_families)
for cond_name in self.conditions:
cond = conditions_dict[cond_name]
if not cond.get_trigger():
all_conditions_triggered = False
break
if (
cond.get_data_source() is DataSource.Type.LOG or
cond.get_data_source() is DataSource.Type.DB_OPTIONS
):
cond_col_fam = set(cond.get_trigger().keys())
if NO_COL_FAMILY in cond_col_fam:
cond_col_fam = set(column_families)
self.trigger_column_families = (
self.trigger_column_families.intersection(cond_col_fam)
)
elif cond.get_data_source() is DataSource.Type.TIME_SERIES:
cond_entities = set(cond.get_trigger().keys())
if self.trigger_entities is None:
self.trigger_entities = cond_entities
else:
self.trigger_entities = (
self.trigger_entities.intersection(cond_entities)
)
if not (self.trigger_entities or self.trigger_column_families):
all_conditions_triggered = False
break
if not all_conditions_triggered: # clean up if rule not triggered
self.trigger_column_families = None
self.trigger_entities = None
return all_conditions_triggered
def __repr__(self):
# Append conditions
rule_string = "Rule: " + self.name + " has conditions:: "
is_first = True
for cond in self.conditions:
if is_first:
rule_string += cond
is_first = False
else:
rule_string += (" AND " + cond)
# Append suggestions
rule_string += "\nsuggestions:: "
is_first = True
for sugg in self.suggestions:
if is_first:
rule_string += sugg
is_first = False
else:
rule_string += (", " + sugg)
if self.trigger_entities:
rule_string += (', entities:: ' + str(self.trigger_entities))
if self.trigger_column_families:
rule_string += (', col_fam:: ' + str(self.trigger_column_families))
# Return constructed string
return rule_string
class Suggestion(Section):
class Action(Enum):
set = 1
increase = 2
decrease = 3
def __init__(self, name):
super().__init__(name)
self.option = None
self.action = None
self.suggested_values = None
self.description = None
def set_parameter(self, key, value):
if key == 'option':
# Note:
# case 1: 'option' is supported by Rocksdb OPTIONS file; in this
# case the option belongs to one of the sections in the config
# file and it's name is prefixed by "<section_type>."
# case 2: 'option' is not supported by Rocksdb OPTIONS file; the
# option is not expected to have the character '.' in its name
self.option = value
elif key == 'action':
if self.option and not value:
raise ValueError(self.name + ': provide action for option')
self.action = self.Action[value]
elif key == 'suggested_values':
if isinstance(value, str):
self.suggested_values = [value]
else:
self.suggested_values = value
elif key == 'description':
self.description = value
def perform_checks(self):
if not self.description:
if not self.option:
raise ValueError(self.name + ': provide option or description')
if not self.action:
raise ValueError(self.name + ': provide action for option')
if self.action is self.Action.set and not self.suggested_values:
raise ValueError(
self.name + ': provide suggested value for option'
)
def __repr__(self):
sugg_string = "Suggestion: " + self.name
if self.description:
sugg_string += (' description : ' + self.description)
else:
sugg_string += (
' option : ' + self.option + ' action : ' + self.action.name
)
if self.suggested_values:
sugg_string += (
' suggested_values : ' + str(self.suggested_values)
)
return sugg_string
class Condition(Section):
def __init__(self, name):
super().__init__(name)
self.data_source = None
self.trigger = None
def perform_checks(self):
if not self.data_source:
raise ValueError(self.name + ': condition not tied to data source')
def set_data_source(self, data_source):
self.data_source = data_source
def get_data_source(self):
return self.data_source
def reset_trigger(self):
self.trigger = None
def set_trigger(self, condition_trigger):
self.trigger = condition_trigger
def get_trigger(self):
return self.trigger
def is_triggered(self):
if self.trigger:
return True
return False
def set_parameter(self, key, value):
# must be defined by the subclass
raise NotImplementedError(self.name + ': provide source for condition')
class LogCondition(Condition):
@classmethod
def create(cls, base_condition):
base_condition.set_data_source(DataSource.Type['LOG'])
base_condition.__class__ = cls
return base_condition
def set_parameter(self, key, value):
if key == 'regex':
self.regex = value
def perform_checks(self):
super().perform_checks()
if not self.regex:
raise ValueError(self.name + ': provide regex for log condition')
def __repr__(self):
log_cond_str = "LogCondition: " + self.name
log_cond_str += (" regex: " + self.regex)
# if self.trigger:
# log_cond_str += (" trigger: " + str(self.trigger))
return log_cond_str
class OptionCondition(Condition):
@classmethod
def create(cls, base_condition):
base_condition.set_data_source(DataSource.Type['DB_OPTIONS'])
base_condition.__class__ = cls
return base_condition
def set_parameter(self, key, value):
if key == 'options':
if isinstance(value, str):
self.options = [value]
else:
self.options = value
elif key == 'evaluate':
self.eval_expr = value
def perform_checks(self):
super().perform_checks()
if not self.options:
raise ValueError(self.name + ': options missing in condition')
if not self.eval_expr:
raise ValueError(self.name + ': expression missing in condition')
def __repr__(self):
opt_cond_str = "OptionCondition: " + self.name
opt_cond_str += (" options: " + str(self.options))
opt_cond_str += (" expression: " + self.eval_expr)
if self.trigger:
opt_cond_str += (" trigger: " + str(self.trigger))
return opt_cond_str
class TimeSeriesCondition(Condition):
@classmethod
def create(cls, base_condition):
base_condition.set_data_source(DataSource.Type['TIME_SERIES'])
base_condition.__class__ = cls
return base_condition
def set_parameter(self, key, value):
if key == 'keys':
if isinstance(value, str):
self.keys = [value]
else:
self.keys = value
elif key == 'behavior':
self.behavior = TimeSeriesData.Behavior[value]
elif key == 'rate_threshold':
self.rate_threshold = float(value)
elif key == 'window_sec':
self.window_sec = int(value)
elif key == 'evaluate':
self.expression = value
elif key == 'aggregation_op':
self.aggregation_op = TimeSeriesData.AggregationOperator[value]
def perform_checks(self):
if not self.keys:
raise ValueError(self.name + ': specify timeseries key')
if not self.behavior:
raise ValueError(self.name + ': specify triggering behavior')
if self.behavior is TimeSeriesData.Behavior.bursty:
if not self.rate_threshold:
raise ValueError(self.name + ': specify rate burst threshold')
if not self.window_sec:
self.window_sec = 300 # default window length is 5 minutes
if len(self.keys) > 1:
raise ValueError(self.name + ': specify only one key')
elif self.behavior is TimeSeriesData.Behavior.evaluate_expression:
if not (self.expression):
raise ValueError(self.name + ': specify evaluation expression')
else:
raise ValueError(self.name + ': trigger behavior not supported')
def __repr__(self):
ts_cond_str = "TimeSeriesCondition: " + self.name
ts_cond_str += (" statistics: " + str(self.keys))
ts_cond_str += (" behavior: " + self.behavior.name)
if self.behavior is TimeSeriesData.Behavior.bursty:
ts_cond_str += (" rate_threshold: " + str(self.rate_threshold))
ts_cond_str += (" window_sec: " + str(self.window_sec))
if self.behavior is TimeSeriesData.Behavior.evaluate_expression:
ts_cond_str += (" expression: " + self.expression)
if hasattr(self, 'aggregation_op'):
ts_cond_str += (" aggregation_op: " + self.aggregation_op.name)
if self.trigger:
ts_cond_str += (" trigger: " + str(self.trigger))
return ts_cond_str
class RulesSpec:
def __init__(self, rules_path):
self.file_path = rules_path
def initialise_fields(self):
self.rules_dict = {}
self.conditions_dict = {}
self.suggestions_dict = {}
def perform_section_checks(self):
for rule in self.rules_dict.values():
rule.perform_checks()
for cond in self.conditions_dict.values():
cond.perform_checks()
for sugg in self.suggestions_dict.values():
sugg.perform_checks()
def load_rules_from_spec(self):
self.initialise_fields()
with open(self.file_path, 'r') as db_rules:
curr_section = None
for line in db_rules:
line = IniParser.remove_trailing_comment(line)
if not line:
continue
element = IniParser.get_element(line)
if element is IniParser.Element.comment:
continue
elif element is not IniParser.Element.key_val:
curr_section = element # it's a new IniParser header
section_name = IniParser.get_section_name(line)
if element is IniParser.Element.rule:
new_rule = Rule(section_name)
self.rules_dict[section_name] = new_rule
elif element is IniParser.Element.cond:
new_cond = Condition(section_name)
self.conditions_dict[section_name] = new_cond
elif element is IniParser.Element.sugg:
new_suggestion = Suggestion(section_name)
self.suggestions_dict[section_name] = new_suggestion
elif element is IniParser.Element.key_val:
key, value = IniParser.get_key_value_pair(line)
if curr_section is IniParser.Element.rule:
new_rule.set_parameter(key, value)
elif curr_section is IniParser.Element.cond:
if key == 'source':
if value == 'LOG':
new_cond = LogCondition.create(new_cond)
elif value == 'OPTIONS':
new_cond = OptionCondition.create(new_cond)
elif value == 'TIME_SERIES':
new_cond = TimeSeriesCondition.create(new_cond)
else:
new_cond.set_parameter(key, value)
elif curr_section is IniParser.Element.sugg:
new_suggestion.set_parameter(key, value)
def get_rules_dict(self):
return self.rules_dict
def get_conditions_dict(self):
return self.conditions_dict
def get_suggestions_dict(self):
return self.suggestions_dict
def get_triggered_rules(self, data_sources, column_families):
self.trigger_conditions(data_sources)
triggered_rules = []
for rule in self.rules_dict.values():
if rule.is_triggered(self.conditions_dict, column_families):
triggered_rules.append(rule)
return triggered_rules
def trigger_conditions(self, data_sources):
for source_type in data_sources:
cond_subset = [
cond
for cond in self.conditions_dict.values()
if cond.get_data_source() is source_type
]
if not cond_subset:
continue
for source in data_sources[source_type]:
source.check_and_trigger_conditions(cond_subset)
def print_rules(self, rules):
for rule in rules:
print('\nRule: ' + rule.name)
for cond_name in rule.conditions:
print(repr(self.conditions_dict[cond_name]))
for sugg_name in rule.suggestions:
print(repr(self.suggestions_dict[sugg_name]))
if rule.trigger_entities:
print('scope: entities:')
print(rule.trigger_entities)
if rule.trigger_column_families:
print('scope: col_fam:')
print(rule.trigger_column_families)