rocksdb

# 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).

import copy
import random

from advisor.db_log_parser import NO_COL_FAMILY
from advisor.db_options_parser import DatabaseOptions
from advisor.rule_parser import Suggestion


class ConfigOptimizer:
    SCOPE = "scope"
    SUGG_VAL = "suggested values"

    @staticmethod
    def apply_action_on_value(old_value, action, suggested_values):
        chosen_sugg_val = None
        if suggested_values:
            chosen_sugg_val = random.choice(list(suggested_values))
        new_value = None
        if action is Suggestion.Action.set or not old_value:
            assert chosen_sugg_val
            new_value = chosen_sugg_val
        else:
            # For increase/decrease actions, currently the code tries to make
            # a 30% change in the option's value per iteration. An addend is
            # also present (+1 or -1) to handle the cases when the option's
            # old value was 0 or the final int() conversion suppressed the 30%
            # change made to the option
            old_value = float(old_value)
            mul = 0
            add = 0
            if action is Suggestion.Action.increase:
                if old_value < 0:
                    mul = 0.7
                    add = 2
                else:
                    mul = 1.3
                    add = 2
            elif action is Suggestion.Action.decrease:
                if old_value < 0:
                    mul = 1.3
                    add = -2
                else:
                    mul = 0.7
                    add = -2
            new_value = int(old_value * mul + add)
        return new_value

    @staticmethod
    def improve_db_config(options, rule, suggestions_dict):
        # this method takes ONE 'rule' and applies all its suggestions on the
        # appropriate options
        required_options = []
        rule_suggestions = []
        for sugg_name in rule.get_suggestions():
            option = suggestions_dict[sugg_name].option
            action = suggestions_dict[sugg_name].action
            # A Suggestion in the rules spec must have the 'option' and
            # 'action' fields defined, always call perform_checks() method
            # after parsing the rules file using RulesSpec
            assert option
            assert action
            required_options.append(option)
            rule_suggestions.append(suggestions_dict[sugg_name])
        current_config = options.get_options(required_options)
        # Create the updated configuration from the rule's suggestions
        updated_config = {}
        for sugg in rule_suggestions:
            # case: when the option is not present in the current configuration
            if sugg.option not in current_config:
                try:
                    new_value = ConfigOptimizer.apply_action_on_value(
                        None, sugg.action, sugg.suggested_values
                    )
                    if sugg.option not in updated_config:
                        updated_config[sugg.option] = {}
                    if DatabaseOptions.is_misc_option(sugg.option):
                        # this suggestion is on an option that is not yet
                        # supported by the Rocksdb OPTIONS file and so it is
                        # not prefixed by a section type.
                        updated_config[sugg.option][NO_COL_FAMILY] = new_value
                    else:
                        for col_fam in rule.get_trigger_column_families():
                            updated_config[sugg.option][col_fam] = new_value
                except AssertionError:
                    print(
                        "WARNING(ConfigOptimizer): provide suggested_values "
                        + "for "
                        + sugg.option
                    )
                continue
            # case: when the option is present in the current configuration
            if NO_COL_FAMILY in current_config[sugg.option]:
                old_value = current_config[sugg.option][NO_COL_FAMILY]
                try:
                    new_value = ConfigOptimizer.apply_action_on_value(
                        old_value, sugg.action, sugg.suggested_values
                    )
                    if sugg.option not in updated_config:
                        updated_config[sugg.option] = {}
                    updated_config[sugg.option][NO_COL_FAMILY] = new_value
                except AssertionError:
                    print(
                        "WARNING(ConfigOptimizer): provide suggested_values "
                        + "for "
                        + sugg.option
                    )
            else:
                for col_fam in rule.get_trigger_column_families():
                    old_value = None
                    if col_fam in current_config[sugg.option]:
                        old_value = current_config[sugg.option][col_fam]
                    try:
                        new_value = ConfigOptimizer.apply_action_on_value(
                            old_value, sugg.action, sugg.suggested_values
                        )
                        if sugg.option not in updated_config:
                            updated_config[sugg.option] = {}
                        updated_config[sugg.option][col_fam] = new_value
                    except AssertionError:
                        print(
                            "WARNING(ConfigOptimizer): provide "
                            + "suggested_values for "
                            + sugg.option
                        )
        return current_config, updated_config

    @staticmethod
    def pick_rule_to_apply(rules, last_rule_name, rules_tried, backtrack):
        if not rules:
            print("\nNo more rules triggered!")
            return None
        # if the last rule provided an improvement in the database performance,
        # and it was triggered again (i.e. it is present in 'rules'), then pick
        # the same rule for this iteration too.
        if last_rule_name and not backtrack:
            for rule in rules:
                if rule.name == last_rule_name:
                    return rule
        # there was no previous rule OR the previous rule did not improve db
        # performance OR it was not triggered for this iteration,
        # then pick another rule that has not been tried yet
        for rule in rules:
            if rule.name not in rules_tried:
                return rule
        print("\nAll rules have been exhausted")
        return None

    @staticmethod
    def apply_suggestions(
        triggered_rules,
        current_rule_name,
        rules_tried,
        backtrack,
        curr_options,
        suggestions_dict,
    ):
        curr_rule = ConfigOptimizer.pick_rule_to_apply(
            triggered_rules, current_rule_name, rules_tried, backtrack
        )
        if not curr_rule:
            return tuple([None] * 4)
        # if a rule has been picked for improving db_config, update rules_tried
        rules_tried.add(curr_rule.name)
        # get updated config based on the picked rule
        curr_conf, updated_conf = ConfigOptimizer.improve_db_config(
            curr_options, curr_rule, suggestions_dict
        )
        conf_diff = DatabaseOptions.get_options_diff(curr_conf, updated_conf)
        if not conf_diff:  # the current and updated configs are the same
            (
                curr_rule,
                rules_tried,
                curr_conf,
                updated_conf,
            ) = ConfigOptimizer.apply_suggestions(
                triggered_rules,
                None,
                rules_tried,
                backtrack,
                curr_options,
                suggestions_dict,
            )
        print("returning from apply_suggestions")
        return (curr_rule, rules_tried, curr_conf, updated_conf)

    # TODO(poojam23): check if this method is required or can we directly set
    # the config equal to the curr_config
    @staticmethod
    def get_backtrack_config(curr_config, updated_config):
        diff = DatabaseOptions.get_options_diff(curr_config, updated_config)
        bt_config = {}
        for option in diff:
            bt_config[option] = {}
            for col_fam in diff[option]:
                bt_config[option][col_fam] = diff[option][col_fam][0]
        print(bt_config)
        return bt_config

    def __init__(self, bench_runner, db_options, rule_parser, base_db):
        self.bench_runner = bench_runner
        self.db_options = db_options
        self.rule_parser = rule_parser
        self.base_db_path = base_db

    def run(self):
        # In every iteration of this method's optimization loop we pick ONE
        # RULE from all the triggered rules and apply all its suggestions to
        # the appropriate options.
        # bootstrapping the optimizer
        print("Bootstrapping optimizer:")
        options = copy.deepcopy(self.db_options)
        old_data_sources, old_metric = self.bench_runner.run_experiment(
            options, self.base_db_path
        )
        print("Initial metric: " + str(old_metric))
        self.rule_parser.load_rules_from_spec()
        self.rule_parser.perform_section_checks()
        triggered_rules = self.rule_parser.get_triggered_rules(
            old_data_sources, options.get_column_families()
        )
        print("\nTriggered:")
        self.rule_parser.print_rules(triggered_rules)
        backtrack = False
        rules_tried = set()
        (
            curr_rule,
            rules_tried,
            curr_conf,
            updated_conf,
        ) = ConfigOptimizer.apply_suggestions(
            triggered_rules,
            None,
            rules_tried,
            backtrack,
            options,
            self.rule_parser.get_suggestions_dict(),
        )
        # the optimizer loop
        while curr_rule:
            print("\nRule picked for next iteration:")
            print(curr_rule.name)
            print("\ncurrent config:")
            print(curr_conf)
            print("updated config:")
            print(updated_conf)
            options.update_options(updated_conf)
            # run bench_runner with updated config
            new_data_sources, new_metric = self.bench_runner.run_experiment(
                options, self.base_db_path
            )
            print("\nnew metric: " + str(new_metric))
            backtrack = not self.bench_runner.is_metric_better(new_metric, old_metric)
            # update triggered_rules, metric, data_sources, if required
            if backtrack:
                # revert changes to options config
                print("\nBacktracking to previous configuration")
                backtrack_conf = ConfigOptimizer.get_backtrack_config(
                    curr_conf, updated_conf
                )
                options.update_options(backtrack_conf)
            else:
                # run advisor on new data sources
                self.rule_parser.load_rules_from_spec()  # reboot the advisor
                self.rule_parser.perform_section_checks()
                triggered_rules = self.rule_parser.get_triggered_rules(
                    new_data_sources, options.get_column_families()
                )
                print("\nTriggered:")
                self.rule_parser.print_rules(triggered_rules)
                old_metric = new_metric
                old_data_sources = new_data_sources
                rules_tried = set()
            # pick rule to work on and set curr_rule to that
            (
                curr_rule,
                rules_tried,
                curr_conf,
                updated_conf,
            ) = ConfigOptimizer.apply_suggestions(
                triggered_rules,
                curr_rule.name,
                rules_tried,
                backtrack,
                options,
                self.rule_parser.get_suggestions_dict(),
            )
        # return the final database options configuration
        return options