mirror of https://github.com/mongodb/mongo
642 lines
21 KiB
Python
642 lines
21 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 os
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import random
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import config
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import numpy as np
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import pandas as pd
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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=os.getenv("MONGODB_URI", "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_coll_scan_collection_template(
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name: str, cardinalities: list[int], 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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config.FieldTemplate(
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name="int_uniform",
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data_type=config.DataType.INTEGER,
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distribution=RandomDistribution.uniform(
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RangeGenerator(DataType.INTEGER, 0, 100_000)
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),
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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=cardinalities,
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)
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# 10 more unindexed fields whose value is always 1.
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filter_fields = [
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config.FieldTemplate(
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name=f"int_uniform_unindexed_{i}",
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data_type=config.DataType.INTEGER,
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distribution=RandomDistribution.uniform(RangeGenerator(DataType.INTEGER, 1, 2)),
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indexed=False,
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)
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for i in range(10)
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]
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template.fields.extend(filter_fields)
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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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def create_intersection_collection_template(
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name: str, cardinalities: list[int], distribution: str, value_range: int = 10
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) -> config.CollectionTemplate:
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distribution_fn = (
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RandomDistribution.normal if distribution == "normal" else RandomDistribution.uniform
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)
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fields = [
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config.FieldTemplate(
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name="a",
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data_type=config.DataType.INTEGER,
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distribution=distribution_fn(RangeGenerator(DataType.INTEGER, 1, value_range + 1)),
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indexed=True,
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),
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config.FieldTemplate(
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name="b",
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data_type=config.DataType.INTEGER,
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distribution=distribution_fn(RangeGenerator(DataType.INTEGER, 1, value_range + 1)),
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indexed=True,
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),
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]
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return config.CollectionTemplate(
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name=name,
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fields=fields,
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compound_indexes=[],
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cardinalities=cardinalities,
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)
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# Creates a collection with fields "a", "b", ... "j" (if 'num_fields' is 10) and an
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# additional field "sort_field" if 'include_sort_field' is true.
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# If 'every_field_indexed' is false then only "a" will be indexed.
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# 'end_of_range_is_card' requires that there is only one cardinality in
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# 'cardinalities' and sets the end of the range for the field values to be the cardinality.
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def create_indexed_fields_template(
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name: str,
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cardinalities: list[int],
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end_of_range_is_card,
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every_field_indexed,
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include_sort_field,
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num_base_fields: int = 10,
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) -> config.CollectionTemplate:
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# Generate fields "a", "b", ... "j" (if num_merge_fields is 10)
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field_names = [chr(ord("a") + i) for i in range(num_base_fields)]
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dist_end_range = num_base_fields + 1
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if end_of_range_is_card:
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assert len(cardinalities) == 1
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dist_end_range = cardinalities[0]
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fields = [
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config.FieldTemplate(
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name=field_name,
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data_type=config.DataType.INTEGER,
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distribution=RandomDistribution.uniform(
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RangeGenerator(DataType.INTEGER, 1, dist_end_range)
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),
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indexed=True if every_field_indexed else (field_name == "a"),
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)
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for field_name in field_names
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]
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compound_indexes = []
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if include_sort_field:
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fields.append(
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config.FieldTemplate(
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name="sort_field",
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data_type=config.DataType.STRING,
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distribution=random_strings_distr(10, 1000),
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indexed=False,
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)
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)
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compound_indexes = [{field_name: 1, "sort_field": 1} for field_name in field_names]
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elif not every_field_indexed:
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assert num_base_fields == 10
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compound_indexes = [
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# Note the single field index is created in the FieldTemplate for 'a' above.
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["a", "b"],
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["a", "b", "c"],
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["a", "b", "c", "d"],
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["a", "b", "c", "d", "e"],
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["a", "b", "c", "d", "e", "f"],
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["a", "b", "c", "d", "e", "f", "g"],
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["a", "b", "c", "d", "e", "f", "g", "h"],
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["a", "b", "c", "d", "e", "f", "g", "h", "i"],
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["a", "b", "c", "d", "e", "f", "g", "h", "i", "j"],
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]
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return config.CollectionTemplate(
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name=name,
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fields=fields,
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compound_indexes=compound_indexes,
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cardinalities=cardinalities,
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)
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projection_collection = config.CollectionTemplate(
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name="projection",
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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=[30000],
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)
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doc_scan_collection = create_coll_scan_collection_template(
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"doc_scan", cardinalities=[100_000, 200_000], payload_size=2000
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)
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sort_collections = create_coll_scan_collection_template(
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"sort",
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# We add '2' here to calibrate the startup cost in qsn_calibrator
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cardinalities=[2] + list(range(1000, 10_001, 1000)),
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payload_size=1000,
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)
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large_sort_collections = create_coll_scan_collection_template(
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"large_sort",
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cardinalities=list(range(100_000, 150_001, 10_000)),
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payload_size=1000,
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)
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merge_sort_collections = create_indexed_fields_template(
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"merge_sort",
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cardinalities=[5, 10, 50, 75, 100, 150, 300, 400, 500, 750, 1000],
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end_of_range_is_card=False,
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every_field_indexed=False,
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include_sort_field=True,
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num_base_fields=10,
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)
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or_collections = create_indexed_fields_template(
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"or",
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cardinalities=[5, 10, 50, 75, 100, 150, 300, 400, 500, 750] + list(range(1000, 10001, 1000)),
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end_of_range_is_card=False,
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every_field_indexed=True,
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include_sort_field=False,
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num_base_fields=2,
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)
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intersection_sorted_collections = create_intersection_collection_template(
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"intersection_sorted",
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distribution="normal",
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cardinalities=[5, 100, 1000, 5000],
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value_range=10,
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)
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intersection_hash_collection = create_intersection_collection_template(
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"intersection_hash",
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distribution="normal",
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cardinalities=[1000],
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value_range=10,
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)
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index_scan_collection = create_indexed_fields_template(
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"index_scan",
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cardinalities=[10000],
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end_of_range_is_card=True,
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every_field_indexed=False,
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include_sort_field=False,
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num_base_fields=10,
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)
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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=[
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index_scan_collection,
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doc_scan_collection,
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sort_collections,
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large_sort_collections,
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merge_sort_collections,
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or_collections,
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intersection_sorted_collections,
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intersection_hash_collection,
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projection_collection,
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],
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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=10,
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runs=100,
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)
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qsn_nodes = [
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config.QsNodeCalibrationConfig(name="COLLSCAN_FORWARD", type="COLLSCAN"),
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config.QsNodeCalibrationConfig(name="COLLSCAN_BACKWARD", type="COLLSCAN"),
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config.QsNodeCalibrationConfig(name="COLLSCAN_W_FILTER", type="COLLSCAN"),
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config.QsNodeCalibrationConfig(
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name="IXSCAN_FORWARD",
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type="IXSCAN",
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variables_override=lambda df: pd.concat(
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[df["n_processed"].rename("Keys Examined"), df["seeks"].rename("Number of seeks")],
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axis=1,
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),
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),
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config.QsNodeCalibrationConfig(
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name="IXSCAN_BACKWARD",
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type="IXSCAN",
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variables_override=lambda df: pd.concat(
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[df["n_processed"].rename("Keys Examined"), df["seeks"].rename("Number of seeks")],
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axis=1,
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),
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),
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config.QsNodeCalibrationConfig(
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name="IXSCANS_W_DIFF_NUM_FIELDS",
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type="IXSCAN",
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variables_override=lambda df: pd.concat(
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[df["n_index_fields"].rename("Number of fields in index")],
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axis=1,
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),
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),
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config.QsNodeCalibrationConfig(
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name="FETCH_W_FILTERS_W_DIFF_NUM_LEAVES",
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type="FETCH",
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variables_override=lambda df: pd.concat(
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[df["n_top_level_and_children"].rename("Number of filters")],
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axis=1,
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),
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),
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config.QsNodeCalibrationConfig(
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name="COLLSCAN_W_FILTERS_W_DIFF_NUM_LEAVES",
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type="COLLSCAN",
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variables_override=lambda df: pd.concat(
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[df["n_top_level_and_children"].rename("Number of filters")],
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axis=1,
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),
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),
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config.QsNodeCalibrationConfig(
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name="IXSCAN_W_FILTERS_W_DIFF_NUM_LEAVES",
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type="IXSCAN",
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variables_override=lambda df: pd.concat(
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[df["n_top_level_and_children"].rename("Number of filters")],
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axis=1,
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),
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),
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config.QsNodeCalibrationConfig(
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name="IXSCAN_W_FILTER",
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type="IXSCAN",
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variables_override=lambda df: pd.concat(
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[df["n_processed"].rename("Keys Examined"), df["seeks"].rename("Number of seeks")],
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axis=1,
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),
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),
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config.QsNodeCalibrationConfig(type="FETCH"),
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config.QsNodeCalibrationConfig(name="FETCH_W_FILTER", type="FETCH"),
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config.QsNodeCalibrationConfig(
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type="AND_HASH",
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variables_override=lambda df: pd.concat(
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[
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df["n_processed_per_child"].str[0].rename("Documents from first child"),
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df["n_processed_per_child"].str[1].rename("Documents from second child"),
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df["n_returned"],
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],
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axis=1,
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),
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),
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config.QsNodeCalibrationConfig(
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type="AND_SORTED",
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variables_override=lambda df: pd.concat(
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[
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df["n_processed"],
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df["n_returned"],
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],
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axis=1,
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),
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),
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config.QsNodeCalibrationConfig(type="OR"),
|
|
config.QsNodeCalibrationConfig(
|
|
type="SORT_MERGE",
|
|
# Note: n_returned = n_processed - (amount of duplicates dropped)
|
|
variables_override=lambda df: pd.concat(
|
|
[
|
|
(df["n_returned"] * np.log2(df["n_children"])).rename(
|
|
"n_returned * log2(n_children)"
|
|
),
|
|
df["n_processed"],
|
|
],
|
|
axis=1,
|
|
),
|
|
),
|
|
config.QsNodeCalibrationConfig(
|
|
name="SORT_DEFAULT",
|
|
type="SORT",
|
|
# Calibration involves a combination of a linearithmic and linear factor
|
|
variables_override=lambda df: pd.concat(
|
|
[
|
|
(df["n_processed"] * np.log2(df["n_processed"])).rename(
|
|
"n_processed * log2(n_processed)"
|
|
)
|
|
],
|
|
axis=1,
|
|
),
|
|
),
|
|
config.QsNodeCalibrationConfig(
|
|
name="SORT_DEFAULT_SPILL",
|
|
type="SORT",
|
|
variables_override=lambda df: pd.concat(
|
|
[
|
|
(df["n_processed"] * np.log2(df["n_processed"])).rename(
|
|
"n_processed * log2(n_processed)"
|
|
)
|
|
],
|
|
axis=1,
|
|
),
|
|
),
|
|
config.QsNodeCalibrationConfig(
|
|
name="SORT_SIMPLE",
|
|
type="SORT",
|
|
# Calibration involves a combination of a linearithmic and linear factor
|
|
variables_override=lambda df: pd.concat(
|
|
[
|
|
(df["n_processed"] * np.log2(df["n_processed"])).rename(
|
|
"n_processed * log2(n_processed)"
|
|
),
|
|
],
|
|
axis=1,
|
|
),
|
|
),
|
|
config.QsNodeCalibrationConfig(
|
|
name="SORT_SIMPLE_SPILL",
|
|
type="SORT",
|
|
variables_override=lambda df: pd.concat(
|
|
[
|
|
(df["n_processed"] * np.log2(df["n_processed"])).rename(
|
|
"n_processed * log2(n_processed)"
|
|
)
|
|
],
|
|
axis=1,
|
|
),
|
|
),
|
|
config.QsNodeCalibrationConfig(
|
|
name="SORT_LIMIT_SIMPLE",
|
|
type="SORT",
|
|
# Note: n_returned = min(limitAmount, n_processed)
|
|
variables_override=lambda df: pd.concat(
|
|
[
|
|
df["n_processed"],
|
|
(df["n_processed"] * np.log2(df["n_returned"])).rename(
|
|
"n_processed * log2(n_returned)"
|
|
),
|
|
(df["n_returned"] * np.log2(df["n_returned"])).rename(
|
|
"n_returned * log2(n_returned)"
|
|
),
|
|
],
|
|
axis=1,
|
|
),
|
|
),
|
|
config.QsNodeCalibrationConfig(
|
|
name="SORT_LIMIT_DEFAULT",
|
|
type="SORT",
|
|
# Note: n_returned = min(limitAmount, n_processed)
|
|
variables_override=lambda df: pd.concat(
|
|
[
|
|
df["n_processed"],
|
|
(df["n_processed"] * np.log2(df["n_returned"])).rename(
|
|
"n_processed * log2(n_returned)"
|
|
),
|
|
(df["n_returned"] * np.log2(df["n_returned"])).rename(
|
|
"n_returned * log2(n_returned)"
|
|
),
|
|
],
|
|
axis=1,
|
|
),
|
|
),
|
|
config.QsNodeCalibrationConfig(type="LIMIT"),
|
|
config.QsNodeCalibrationConfig(
|
|
type="SKIP",
|
|
variables_override=lambda df: pd.concat(
|
|
[
|
|
df["n_returned"].rename("Documents Passed"),
|
|
(df["n_processed"] - df["n_returned"]).rename("Documents Skipped"),
|
|
],
|
|
axis=1,
|
|
),
|
|
),
|
|
config.QsNodeCalibrationConfig(type="PROJECTION_SIMPLE"),
|
|
config.QsNodeCalibrationConfig(type="PROJECTION_COVERED"),
|
|
config.QsNodeCalibrationConfig(type="PROJECTION_DEFAULT"),
|
|
]
|
|
# Calibrator settings
|
|
qs_calibrator = config.QuerySolutionCalibrationConfig(
|
|
enabled=True,
|
|
test_size=0.2,
|
|
input_collection_name=workload_execution.output_collection_name,
|
|
trace=False,
|
|
nodes=qsn_nodes,
|
|
)
|
|
|
|
|
|
main_config = config.Config(
|
|
database=database,
|
|
data_generator=data_generator,
|
|
qs_calibrator=qs_calibrator,
|
|
workload_execution=workload_execution,
|
|
)
|