mirror of https://github.com/mongodb/mongo
280 lines
9.9 KiB
Python
280 lines
9.9 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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"""Cost Model Calibrator entry point."""
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import asyncio
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import csv
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import dataclasses
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import os
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from typing import Mapping, Sequence
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import abt_calibrator
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import parameters_extractor
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import workload_execution
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from calibration_settings import main_config
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from config import WriteMode
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from cost_estimator import CostModelParameters, ExecutionStats
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from data_generator import CollectionInfo, DataGenerator
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from database_instance import DatabaseInstance
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from workload_execution import Query, QueryParameters
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__all__ = []
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def save_to_csv(parameters: Mapping[str, Sequence[CostModelParameters]], filepath: str) -> None:
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"""Save model input parameters to a csv file."""
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abt_type_name = "abt_type"
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fieldnames = [
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abt_type_name,
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*[f.name for f in dataclasses.fields(ExecutionStats)],
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*[f.name for f in dataclasses.fields(QueryParameters)],
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]
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with open(filepath, "w", newline="") as csvfile:
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writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
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writer.writeheader()
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for abt_type, type_params_list in parameters.items():
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for type_params in type_params_list:
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fields = dataclasses.asdict(type_params.execution_stats) | dataclasses.asdict(
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type_params.query_params
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)
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fields[abt_type_name] = abt_type
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writer.writerow(fields)
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async def execute_index_scan_queries(
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database: DatabaseInstance, collections: Sequence[CollectionInfo]
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):
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collection = [ci for ci in collections if ci.name.startswith("index_scan")][0]
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fields = [f for f in collection.fields if f.name == "choice"]
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requests = []
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for field in fields:
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for val in field.distribution.get_values():
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if val.startswith("_"):
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continue
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keys_length = len(val) + 2
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requests.append(
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Query(
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pipeline=[{"$match": {field.name: val}}],
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keys_length_in_bytes=keys_length,
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note="IndexScan",
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)
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)
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await workload_execution.execute(
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database, main_config.workload_execution, [collection], requests
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)
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async def execute_physical_scan_queries(
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database: DatabaseInstance, collections: Sequence[CollectionInfo]
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):
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collections = [ci for ci in collections if ci.name.startswith("physical_scan")]
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fields = [f for f in collections[0].fields if f.name == "choice"]
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requests = []
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for field in fields:
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for val in field.distribution.get_values()[::3]:
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if val.startswith("_"):
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continue
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keys_length = len(val) + 2
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requests.append(
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Query(
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pipeline=[{"$match": {field.name: val}}, {"$limit": 10}],
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keys_length_in_bytes=keys_length,
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note="PhysicalScan",
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)
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)
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await workload_execution.execute(
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database, main_config.workload_execution, collections, requests
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)
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async def execute_index_intersections_with_requests(
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database: DatabaseInstance, collections: Sequence[CollectionInfo], requests: Sequence[Query]
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):
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try:
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await database.set_parameter(
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"internalCostModelCoefficients", '{"filterIncrementalCost": 10000.0}'
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)
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await workload_execution.execute(
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database, main_config.workload_execution, collections, requests
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)
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main_config.workload_execution.write_mode = WriteMode.APPEND
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await workload_execution.execute(
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database, main_config.workload_execution, collections, requests[::4]
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)
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finally:
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await database.set_parameter("internalCostModelCoefficients", "")
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async def execute_index_intersections(
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database: DatabaseInstance, collections: Sequence[CollectionInfo]
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):
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collections = [ci for ci in collections if ci.name.startswith("c_int")]
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requests = []
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for i in range(0, 1000, 100):
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requests.append(Query(pipeline=[{"$match": {"in1": i, "in2": i}}], keys_length_in_bytes=1))
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requests.append(
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Query(pipeline=[{"$match": {"in1": i, "in2": 1000 - i}}], keys_length_in_bytes=1)
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)
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requests.append(
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Query(
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pipeline=[{"$match": {"in1": {"$lte": i}, "in2": 1000 - i}}], keys_length_in_bytes=1
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)
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)
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requests.append(
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Query(
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pipeline=[{"$match": {"in1": i, "in2": {"$gt": 1000 - i}}}], keys_length_in_bytes=1
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)
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)
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await execute_index_intersections_with_requests(database, collections, requests)
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async def execute_evaluation(database: DatabaseInstance, collections: Sequence[CollectionInfo]):
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collections = [ci for ci in collections if ci.name.startswith("c_int_05")]
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requests = []
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for i in [100, 500, 1000]:
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requests.append(
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Query(
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pipeline=[{"$project": {"uniform1": 1, "mixed2": 1}}, {"$limit": i}],
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keys_length_in_bytes=1,
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number_of_fields=1,
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note="Evaluation",
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)
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)
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await workload_execution.execute(
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database, main_config.workload_execution, collections, requests
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)
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async def execute_unwind(database: DatabaseInstance, collections: Sequence[CollectionInfo]):
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collections = [ci for ci in collections if ci.name.startswith("c_arr_01")]
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requests = []
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# average size of arrays in the collection
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average_size_of_arrays = 10
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for _ in range(500, 1000, 100):
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requests.append(
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Query(pipeline=[{"$unwind": "$as"}], number_of_fields=average_size_of_arrays)
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)
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await workload_execution.execute(
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database, main_config.workload_execution, collections, requests
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)
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async def execute_unique(database: DatabaseInstance, collections: Sequence[CollectionInfo]):
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collections = [ci for ci in collections if ci.name.startswith("c_arr_01")]
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requests = []
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for i in range(500, 1000, 200):
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requests.append(Query(pipeline=[{"$match": {"as": {"$gt": i}}}]))
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await workload_execution.execute(
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database, main_config.workload_execution, collections, requests
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)
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async def execute_limitskip(database: DatabaseInstance, collections: Sequence[CollectionInfo]):
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collection = [ci for ci in collections if ci.name.startswith("index_scan")][0]
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limits = [5, 10, 15, 20]
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skips = [5, 10, 15, 20]
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requests = []
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for limit in limits:
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for skip in skips:
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requests.append(Query(pipeline=[{"$skip": skip}, {"$limit": limit}], note="LimitSkip"))
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await workload_execution.execute(
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database, main_config.workload_execution, [collection], requests
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)
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async def main():
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"""Entry point function."""
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script_directory = os.path.abspath(os.path.dirname(__file__))
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os.chdir(script_directory)
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# 1. Database Instance provides connectivity to a MongoDB instance, it loads data optionally
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# from the dump on creating and stores data optionally to the dump on closing.
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with DatabaseInstance(main_config.database) as database:
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# 2. Data generation (optional), generates random data and populates collections with it.
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generator = DataGenerator(database, main_config.data_generator)
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await generator.populate_collections()
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# 3. Collecting data for calibration (optional).
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# It runs the pipelines and stores explains to the database.
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execution_query_functions = [
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execute_index_scan_queries,
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execute_physical_scan_queries,
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execute_index_intersections,
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execute_evaluation,
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execute_unwind,
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execute_unique,
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execute_limitskip,
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]
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for execute_query in execution_query_functions:
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await execute_query(database, generator.collection_infos)
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main_config.workload_execution.write_mode = WriteMode.APPEND
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# Calibration phase (optional).
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# Reads the explains stored on the previous step (this run and/or previous runs),
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# aparses the explains, nd calibrates the cost model for the ABT nodes.
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models = await abt_calibrator.calibrate(main_config.abt_calibrator, database)
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for abt, model in models.items():
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print(f"{abt}\t\t{model}")
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parameters = await parameters_extractor.extract_parameters(
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main_config.abt_calibrator, database, []
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)
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save_to_csv(parameters, "parameters.csv")
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print("DONE!")
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if __name__ == "__main__":
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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try:
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asyncio.run(main())
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except KeyboardInterrupt:
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pass
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