# Copyright (C) 2022-present MongoDB, Inc. # # This program is free software: you can redistribute it and/or modify # it under the terms of the Server Side Public License, version 1, # as published by MongoDB, Inc. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # Server Side Public License for more details. # # You should have received a copy of the Server Side Public License # along with this program. If not, see # . # # As a special exception, the copyright holders give permission to link the # code of portions of this program with the OpenSSL library under certain # conditions as described in each individual source file and distribute # linked combinations including the program with the OpenSSL library. You # must comply with the Server Side Public License in all respects for # all of the code used other than as permitted herein. If you modify file(s) # with this exception, you may extend this exception to your version of the # file(s), but you are not obligated to do so. If you do not wish to do so, # delete this exception statement from your version. If you delete this # exception statement from all source files in the program, then also delete # it in the license file. # """Cost Model Calibrator entry point.""" import os import json from data_generator import DataGenerator from database_instance import DatabaseInstance from config import Config import abt_calibrator import workload_execution __all__ = [] def main(): """Entry point function.""" script_directory = os.path.abspath(os.path.dirname(__file__)) os.chdir(script_directory) with open("config.json") as config_file: config = Config.create(json.load(config_file)) # 1. Database Instance provides connectivity to a MongoDB instance, it loads data optionally # from the dump on creating and stores data optionally to the dump on closing. with DatabaseInstance(config.database) as database: # 2. Data generation (optional), generates random data and populates collections with it. generator = DataGenerator(database, config.data_generator) generator.populate_collections() collection_names = list(generator.list_collection_names()) # 3. Collecting data for calibration (optional). # It runs the pipelines and stores explains to the database. pipelines = [ [{'$match': {'f_5': 7}}], [{'$match': {'f_1': 5}}], [{'$match': {'f_7': 4}}], [{'$match': {'f_5': 7}}], [{'$match': {'f_1': 5}}], [{'$match': {'f_2': generator.gen_random_string()}}], [{'$match': {'f_5': generator.gen_random_string()}}], ] workload_execution.execute(database, config.workload_execution, collection_names, pipelines) # Calibration phase (optional). # Reads the explains stored on the previous step (this run and/or previous runs), # aparses the explains, nd calibrates the cost model for the ABT nodes. models = abt_calibrator.calibrate(config.abt_calibrator, database, ['IndexScan', 'Seek']) for abt, model in models.items(): print(abt) print(model) if __name__ == '__main__': main()