3ade0b9fe3
Generate random data with integers. The approach is as follows: - There is one collection for each different cardinality. All collections contain the same fields. - Each field contains the data generated from a certain data distribution. The data could be anything - same type, mixed types, same mathematical distribution (e.g. normal), or a mixed distribution. - The committed configuration file, and the corresponding data file are reduced to only two small collections. For actual experiments one needs to add more data sizes, and re-generate the data locally. This is done so that Evergreen tests can run fast, and to reduce the size of the git repository. - All data is saved in a single JavaScript file: jstests/query_golden/libs/data/ce_accuracy_test.data, with a corresponding schema file jstests/query_golden/libs/data/ce_accuracy_test.schema. - The data file is a JavaScript file that can be loaded directly inside a JS test. When loading this file, it creates a global variable dataSet. The reason is that this is the only way to load an external JSON file that doesn't need to install external tools in Evergreen.
152 lines
6.5 KiB
Python
152 lines
6.5 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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"""Data generation entry point."""
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import asyncio
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import dataclasses
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import json
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import os
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import subprocess
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from pathlib import Path
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from bson.json_util import dumps
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from config import CollectionTemplate, FieldTemplate, DataType
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from data_generator import CollectionInfo, DataGenerator
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from database_instance import DatabaseInstance
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import parameters_extractor
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#from ce_generate_data_settings import database_config, data_generator_config
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from ce_data_settings import database_config, data_generator_config
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__all__ = []
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class CollectionTemplateEncoder(json.JSONEncoder):
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def default(self, o):
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if isinstance(o, CollectionTemplate):
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collections = []
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for card in o.cardinalities:
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name = f'{o.name}_{card}'
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collections.append(
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dict(collectionName=name, fields=o.fields, compound_indexes=o.compound_indexes,
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cardinality=card))
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return collections
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elif isinstance(o, FieldTemplate):
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return dict(fieldName=o.name, data_type=o.data_type, indexed=o.indexed)
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elif isinstance(o, DataType):
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return o.name.lower()
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# Let the base class default method raise the TypeError
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return super(CollectionTemplateEncoder, self).default(o)
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class OidEncoder(json.JSONEncoder):
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cur_oid = -1
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def default(self, o):
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# TODO: doesn't work, what is the type of ObjectIds?
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#if isinstance(o, OectId):
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if hasattr(o, '__str__'): # This will handle ObjectIds
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#return str(o) this is the real OID of the document
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# Replace the OID with a consequtive int number as needed by the query generator
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OidEncoder.cur_oid += 1
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return OidEncoder.cur_oid
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return super(OidEncoder, self).default(o)
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async def dump_collection_to_json(db, dump_path, database_name, collections):
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with open(Path(dump_path) / f'{database_name}.data', "w") as data_file:
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data_file.write('// This is a generated file.\n')
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data_file.write('const dataSet = [\n')
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coll_pos = 1
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for coll_name in collections:
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collection = db[coll_name]
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doc_count = await collection.count_documents({})
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doc_pos = 1
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data_file.write(f'{{collName: "{coll_name}", collData: [\n')
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async for doc in collection.find({}):
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#data_file.write(dumps(doc))
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data_file.write(json.dumps(doc, cls=OidEncoder))
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if doc_pos < doc_count:
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data_file.write(',')
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data_file.write("\n")
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doc_pos += 1
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data_file.write(']}')
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if coll_pos < len(collections):
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data_file.write(",")
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data_file.write("]\n")
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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(database_config) as database_instance:
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# 2. Generate random data and populate collections with it.
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old_db_collections = await database_instance.database.list_collection_names()
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for coll_name in old_db_collections:
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collection = database_instance.database[coll_name]
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collection.drop()
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generator = DataGenerator(database_instance, data_generator_config)
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await generator.populate_collections()
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# 3. Export all collections in the database into json files.
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db_collections = await database_instance.database.list_collection_names()
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#for coll_name in db_collections:
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# subprocess.run([
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# 'mongoexport', f'--db={database_config.database_name}', f'--collection={coll_name}',
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# f'--out={coll_name}.dat'
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# ], cwd=database_config.dump_path, check=True)
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await dump_collection_to_json(database_instance.database, database_config.dump_path,
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database_config.database_name, db_collections)
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# 4. Export the collection templates used to create the test collections into JSON file
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with open(Path(database_config.dump_path) / f'{database_config.database_name}.schema',
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"w") as metadata_file:
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collections = []
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for coll_template in data_generator_config.collection_templates:
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for card in coll_template.cardinalities:
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name = f'{coll_template.name}_{card}'
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collections.append(
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dict(collectionName=name, fields=coll_template.fields,
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compound_indexes=coll_template.compound_indexes, cardinality=card))
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json_metadata = json.dumps(collections, indent=4, cls=CollectionTemplateEncoder)
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metadata_file.write("// This is a generated file.\nconst dbMetadata = ")
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metadata_file.write(json_metadata)
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metadata_file.write(";")
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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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asyncio.run(main())
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