mirror of https://github.com/mongodb/mongo
366 lines
11 KiB
Python
366 lines
11 KiB
Python
# Copyright (C) 2022-present MongoDB, Inc.
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#
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the Server Side Public License, version 1,
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# as published by MongoDB, Inc.
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#
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# Server Side Public License for more details.
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#
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# You should have received a copy of the Server Side Public License
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# along with this program. If not, see
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# <http://www.mongodb.com/licensing/server-side-public-license>.
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#
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# As a special exception, the copyright holders give permission to link the
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# code of portions of this program with the OpenSSL library under certain
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# conditions as described in each individual source file and distribute
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# linked combinations including the program with the OpenSSL library. You
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# must comply with the Server Side Public License in all respects for
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# all of the code used other than as permitted herein. If you modify file(s)
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# with this exception, you may extend this exception to your version of the
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# file(s), but you are not obligated to do so. If you do not wish to do so,
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# delete this exception statement from your version. If you delete this
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# exception statement from all source files in the program, then also delete
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# it in the license file.
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#
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"""Calibration configuration."""
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import random
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from typing import Any
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import config
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from random_generator import ArrayRandomDistribution, DataType, RandomDistribution, RangeGenerator
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__all__ = ["main_config", "distributions"]
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# A string value to fill up collections and not used in queries.
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HIDDEN_STRING_VALUE = "__hidden_string_value"
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# Data distributions settings.
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distributions = {}
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string_choice_values = [
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"h",
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"hi",
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"hi!",
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"hola",
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"hello",
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"square",
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"squared",
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"gaussian",
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"chisquare",
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"chisquared",
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"hello world",
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"distribution",
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]
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string_choice_weights = [10, 20, 5, 17, 30, 7, 9, 15, 40, 2, 12, 1]
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distributions["string_choice"] = RandomDistribution.choice(
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string_choice_values, string_choice_weights
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)
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small_query_weights = [i for i in range(10, 201, 10)]
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small_query_cardinality = sum(small_query_weights)
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int_choice_values = [i for i in range(1, 1000, 50)]
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random.shuffle(int_choice_values)
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distributions["int_choice"] = RandomDistribution.choice(int_choice_values, small_query_weights)
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distributions["random_string"] = ArrayRandomDistribution(
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RandomDistribution.uniform(RangeGenerator(DataType.INTEGER, 5, 10, 2)),
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RandomDistribution.uniform(RangeGenerator(DataType.STRING, "a", "z")),
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)
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def generate_random_str(num: int):
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strs = distributions["random_string"].generate(num)
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str_list = []
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for char_array in strs:
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str_res = "".join(char_array)
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str_list.append(str_res)
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return str_list
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def random_strings_distr(size: int, count: int):
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distr = ArrayRandomDistribution(
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RandomDistribution.uniform([size]),
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RandomDistribution.uniform(RangeGenerator(DataType.STRING, "a", "z")),
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)
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return RandomDistribution.uniform(["".join(s) for s in distr.generate(count)])
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small_string_choice = generate_random_str(20)
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distributions["string_choice_small"] = RandomDistribution.choice(
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small_string_choice, small_query_weights
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)
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string_range_4 = RandomDistribution.normal(RangeGenerator(DataType.STRING, "abca", "abc_"))
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string_range_5 = RandomDistribution.normal(RangeGenerator(DataType.STRING, "abcda", "abcd_"))
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string_range_7 = RandomDistribution.normal(RangeGenerator(DataType.STRING, "hello_a", "hello__"))
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string_range_12 = RandomDistribution.normal(
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RangeGenerator(DataType.STRING, "helloworldaa", "helloworldd_")
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)
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distributions["string_mixed"] = RandomDistribution.mixed(
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[string_range_4, string_range_5, string_range_7, string_range_12], [0.1, 0.15, 0.25, 0.5]
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)
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distributions["string_uniform"] = RandomDistribution.uniform(
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RangeGenerator(DataType.STRING, "helloworldaa", "helloworldd_")
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)
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distributions["int_normal"] = RandomDistribution.normal(
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RangeGenerator(DataType.INTEGER, 0, 1000, 2)
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)
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lengths_distr = RandomDistribution.uniform(RangeGenerator(DataType.INTEGER, 1, 10))
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distributions["array_small"] = ArrayRandomDistribution(lengths_distr, distributions["int_normal"])
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# Database settings
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database = config.DatabaseConfig(
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connection_string="mongodb://localhost",
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database_name="qsn_calibration",
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dump_path="~/mongo/buildscripts/cost_model",
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restore_from_dump=config.RestoreMode.NEVER,
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dump_on_exit=False,
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)
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# Collection template settings
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def create_index_scan_collection_template(name: str, cardinality: int) -> config.CollectionTemplate:
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values = [
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"iqtbr5b5is",
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"vt5s3tf8o6",
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"b0rgm58qsn",
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"9m59if353m",
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"biw2l9ok17",
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"b9ct0ue14d",
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"oxj0vxjsti",
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"f3k8w9vb49",
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"ec7v82k6nk",
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"f49ufwaqx7",
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]
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start_weight = 10
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step_weight = 25
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finish_weight = start_weight + len(values) * step_weight
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weights = list(range(start_weight, finish_weight, step_weight))
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fill_up_weight = cardinality - sum(weights)
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if fill_up_weight > 0:
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values.append(HIDDEN_STRING_VALUE)
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weights.append(fill_up_weight)
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distr = RandomDistribution.choice(values, weights)
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return config.CollectionTemplate(
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name=name,
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fields=[
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config.FieldTemplate(
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name="choice", data_type=config.DataType.STRING, distribution=distr, indexed=True
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),
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config.FieldTemplate(
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name="mixed1",
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data_type=config.DataType.STRING,
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distribution=distributions["string_mixed"],
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indexed=False,
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),
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config.FieldTemplate(
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name="uniform1",
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data_type=config.DataType.STRING,
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distribution=distributions["string_uniform"],
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indexed=False,
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),
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config.FieldTemplate(
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name="choice2",
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data_type=config.DataType.STRING,
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distribution=distributions["string_choice"],
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indexed=False,
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),
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config.FieldTemplate(
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name="mixed2",
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data_type=config.DataType.STRING,
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distribution=distributions["string_mixed"],
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indexed=False,
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),
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],
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compound_indexes=[],
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cardinalities=[cardinality],
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)
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def create_physical_scan_collection_template(
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name: str, payload_size: int = 0
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) -> config.CollectionTemplate:
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template = config.CollectionTemplate(
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name=name,
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fields=[
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config.FieldTemplate(
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name="choice1",
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data_type=config.DataType.STRING,
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distribution=distributions["string_choice"],
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indexed=False,
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),
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config.FieldTemplate(
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name="mixed1",
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data_type=config.DataType.STRING,
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distribution=distributions["string_mixed"],
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indexed=False,
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),
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config.FieldTemplate(
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name="uniform1",
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data_type=config.DataType.STRING,
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distribution=distributions["string_uniform"],
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indexed=False,
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),
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config.FieldTemplate(
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name="choice",
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data_type=config.DataType.STRING,
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distribution=distributions["string_choice"],
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indexed=False,
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),
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config.FieldTemplate(
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name="mixed2",
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data_type=config.DataType.STRING,
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distribution=distributions["string_mixed"],
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indexed=False,
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),
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],
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compound_indexes=[],
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cardinalities=[1000, 5000, 10000],
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)
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if payload_size > 0:
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payload_distr = random_strings_distr(payload_size, 1000)
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template.fields.append(
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config.FieldTemplate(
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name="payload",
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data_type=config.DataType.STRING,
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distribution=payload_distr,
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indexed=False,
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)
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)
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return template
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collection_caridinalities = list(range(10000, 50001, 10000))
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c_int_05 = config.CollectionTemplate(
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name="c_int_05",
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fields=[
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config.FieldTemplate(
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name="in1",
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data_type=config.DataType.INTEGER,
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distribution=distributions["int_normal"],
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indexed=True,
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),
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config.FieldTemplate(
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name="mixed1",
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data_type=config.DataType.STRING,
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distribution=distributions["string_mixed"],
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indexed=False,
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),
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config.FieldTemplate(
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name="uniform1",
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data_type=config.DataType.STRING,
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distribution=distributions["string_uniform"],
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indexed=False,
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),
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config.FieldTemplate(
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name="in2",
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data_type=config.DataType.INTEGER,
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distribution=distributions["int_normal"],
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indexed=True,
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),
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config.FieldTemplate(
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name="mixed2",
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data_type=config.DataType.STRING,
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distribution=distributions["string_mixed"],
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indexed=False,
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),
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],
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compound_indexes=[],
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cardinalities=collection_caridinalities,
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)
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c_arr_01 = config.CollectionTemplate(
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name="c_arr_01",
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fields=[
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config.FieldTemplate(
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name="as",
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data_type=config.DataType.INTEGER,
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distribution=distributions["array_small"],
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indexed=True,
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)
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],
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compound_indexes=[],
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cardinalities=collection_caridinalities,
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)
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index_scan = create_index_scan_collection_template("index_scan", 1000000)
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physical_scan = create_physical_scan_collection_template("physical_scan", 2000)
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# Data Generator settings
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data_generator = config.DataGeneratorConfig(
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enabled=True,
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create_indexes=True,
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batch_size=10000,
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collection_templates=[index_scan, physical_scan, c_int_05, c_arr_01],
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write_mode=config.WriteMode.REPLACE,
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collection_name_with_card=True,
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)
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# Workload Execution settings
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workload_execution = config.WorkloadExecutionConfig(
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enabled=True,
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output_collection_name="calibrationData",
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write_mode=config.WriteMode.REPLACE,
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warmup_runs=5,
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runs=100,
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)
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def make_filter_by_note(note_value: Any):
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def impl(df):
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return df[df.note == note_value]
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return impl
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qsn_nodes = [
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config.QsNodeCalibrationConfig(type="SUBPLAN"),
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config.QsNodeCalibrationConfig(type="COLLSCAN"),
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config.QsNodeCalibrationConfig(type="IXSCAN"),
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config.QsNodeCalibrationConfig(type="FETCH"),
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config.QsNodeCalibrationConfig(type="AND_HASH"),
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config.QsNodeCalibrationConfig(type="AND_SORTED"),
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config.QsNodeCalibrationConfig(type="OR"),
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config.QsNodeCalibrationConfig(type="MERGE_SORT"),
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config.QsNodeCalibrationConfig(type="SORT_MERGE"),
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config.QsNodeCalibrationConfig(type="SORT"),
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config.QsNodeCalibrationConfig(type="LIMIT"),
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config.QsNodeCalibrationConfig(type="SKIP"),
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]
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# Calibrator settings
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qs_calibrator = config.QuerySolutionCalibrationConfig(
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enabled=True,
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test_size=0.2,
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input_collection_name=workload_execution.output_collection_name,
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trace=False,
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nodes=qsn_nodes,
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)
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main_config = config.Config(
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database=database,
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data_generator=data_generator,
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qs_calibrator=qs_calibrator,
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workload_execution=workload_execution,
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)
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