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# 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
# <http://www.mongodb.com/licensing/server-side-public-license>.
#
# 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 dataclasses
import os
import csv
import asyncio
from typing import Mapping, Sequence
from config import WriteMode
from cost_estimator import ExecutionStats, CostModelParameters
from data_generator import CollectionInfo, DataGenerator
from database_instance import DatabaseInstance
import abt_calibrator
import workload_execution
from workload_execution import Query, QueryParameters
import parameters_extractor
from calibration_settings import distributions, main_config
__all__ = []
def save_to_csv(parameters: Mapping[str, Sequence[CostModelParameters]], filepath: str) -> None:
"""Save model input parameters to a csv file."""
abt_type_name = 'abt_type'
fieldnames = [
abt_type_name, *[f.name for f in dataclasses.fields(ExecutionStats)],
*[f.name for f in dataclasses.fields(QueryParameters)]
]
with open(filepath, 'w', newline='') as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writeheader()
for abt_type, type_params_list in parameters.items():
for type_params in type_params_list:
fields = dataclasses.asdict(type_params.execution_stats) | dataclasses.asdict(
type_params.query_params)
fields[abt_type_name] = abt_type
writer.writerow(fields)
async def execute_general(database: DatabaseInstance, collections: Sequence[CollectionInfo]):
requests = []
for val in distributions['string_choice'].get_values()[::3]:
keys_length = len(val) + 2
for i in range(1, 5):
requests.append(
Query(pipeline=[{'$match': {f'choice{i}': val}}], keys_length_in_bytes=keys_length))
await workload_execution.execute(database, main_config.workload_execution,
[ci for ci in collections if ci.name.startswith('c_str')],
requests)
async def execute_index_intersections(database: DatabaseInstance,
collections: Sequence[CollectionInfo]):
collections = [ci for ci in collections if ci.name.startswith('c_int')]
requests = []
for i in range(0, 1000, 100):
requests.append(Query(pipeline=[{'$match': {'in1': i, 'in2': i}}], keys_length_in_bytes=1))
requests.append(
Query(pipeline=[{'$match': {'in1': i, 'in2': 1000 - i}}], keys_length_in_bytes=1))
requests.append(
Query(pipeline=[{'$match': {'in1': {'$lte': i}, 'in2': 1000 - i}}],
keys_length_in_bytes=1))
requests.append(
Query(pipeline=[{'$match': {'in1': i, 'in2': {'$gt': 1000 - i}}}],
keys_length_in_bytes=1))
try:
await database.set_parameter('internalCostModelCoefficients',
'{"filterIncrementalCost": 10000.0}')
await database.set_parameter('internalCascadesOptimizerDisableMergeJoinRIDIntersect', False)
await database.set_parameter('internalCascadesOptimizerDisableHashJoinRIDIntersect', False)
await workload_execution.execute(database, main_config.workload_execution, collections,
requests)
await database.set_parameter('internalCascadesOptimizerDisableMergeJoinRIDIntersect', True)
await database.set_parameter('internalCascadesOptimizerDisableHashJoinRIDIntersect', True)
main_config.workload_execution.write_mode = WriteMode.APPEND
await workload_execution.execute(database, main_config.workload_execution, collections,
requests[::4])
finally:
await database.set_parameter('internalCascadesOptimizerDisableMergeJoinRIDIntersect', False)
await database.set_parameter('internalCascadesOptimizerDisableHashJoinRIDIntersect', False)
await database.set_parameter('internalCostModelCoefficients', '')
async def execute_evaluation(database: DatabaseInstance, collections: Sequence[CollectionInfo]):
collections = [ci for ci in collections if ci.name.startswith('c_int')]
requests = []
for i in range(500, 1000, 100):
requests.append(
Query(pipeline=[{"$match": {'in1': {"$gt": i}}}, {'$project': {'proj1': 1}}],
keys_length_in_bytes=1, number_of_fields=1))
await workload_execution.execute(database, main_config.workload_execution, collections,
requests)
async def execute_unwind(database: DatabaseInstance, collections: Sequence[CollectionInfo]):
collections = [ci for ci in collections if ci.name.startswith('c_arr_01')]
requests = []
# average size of arrays in the collection
average_size_of_arrays = 10
for _ in range(500, 1000, 100):
requests.append(
Query(pipeline=[{"$unwind": "$as"}], number_of_fields=average_size_of_arrays))
await workload_execution.execute(database, main_config.workload_execution, collections,
requests)
async def main():
"""Entry point function."""
script_directory = os.path.abspath(os.path.dirname(__file__))
os.chdir(script_directory)
# 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(main_config.database) as database:
# 2. Data generation (optional), generates random data and populates collections with it.
generator = DataGenerator(database, main_config.data_generator)
await generator.populate_collections()
# 3. Collecting data for calibration (optional).
# It runs the pipelines and stores explains to the database.
await execute_general(database, generator.collection_infos)
main_config.workload_execution.write_mode = WriteMode.APPEND
await execute_index_intersections(database, generator.collection_infos)
await execute_evaluation(database, generator.collection_infos)
await execute_unwind(database, generator.collection_infos)
# 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 = await abt_calibrator.calibrate(main_config.abt_calibrator, database)
for abt, model in models.items():
print(f'{abt}\t\t{model}')
parameters = await parameters_extractor.extract_parameters(main_config.abt_calibrator,
database, [])
save_to_csv(parameters, 'parameters.csv')
print("DONE!")
if __name__ == '__main__':
loop = asyncio.get_event_loop()
loop.run_until_complete(main())
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