mongo/buildscripts/cost_model/data_generator.py

# Copyright (C) 2022-present MongoDB, Inc.
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"""Implements to populate MongoDB collections with generated data used to calibrate Cost Model."""

from __future__ import annotations
from dataclasses import dataclass
import time
from typing import Sequence
import asyncio
import pymongo
from pymongo import IndexModel
from motor.motor_asyncio import AsyncIOMotorCollection
from random_generator import RandomDistribution, DataType
from config import DataGeneratorConfig, WriteMode
from database_instance import DatabaseInstance

__all__ = ['DataGenerator']


@dataclass
class FieldInfo:
    """Field-related information."""

    name: str
    type: DataType
    distribution: RandomDistribution
    indexed: bool


@dataclass
class CollectionInfo:
    """Collection-related information."""

    name: str
    fields: Sequence[FieldInfo]
    documents_count: int
    compound_indexes: Sequence[Sequence[str]]


class DataGenerator:
    """Create and populate collections with generated data."""

    def __init__(self, database: DatabaseInstance, config: DataGeneratorConfig):
        """Create new DataGenerator.

        Keyword Arguments:
        database -- Instance of Database object
        stringlength -- Length of generated strings
        """

        self.database = database
        self.config = config

        self.collection_infos = list(self._generate_collection_infos())

    async def populate_collections(self) -> None:
        """Create and populate collections for each combination of size and data type in the corresponding 'docCounts' and 'dataTypes' input arrays.

        All collections have the same schema defined by one of the elements of 'collFields'.
        """

        if not self.config.enabled:
            return

        await self.database.enable_cascades(False)
        t0 = time.time()
        tasks = []
        for coll_info in self.collection_infos:
            coll = self.database.database[coll_info.name]
            if self.config.write_mode == WriteMode.REPLACE:
                await coll.drop()
            tasks.append(asyncio.create_task(self._populate_collection(coll, coll_info)))
            if self.config.create_indexes:
                tasks.append(
                    asyncio.create_task(create_single_field_indexes(coll, coll_info.fields)))
                tasks.append(asyncio.create_task(create_compound_indexes(coll, coll_info)))

        for task in tasks:
            await task

        t1 = time.time()
        print(f'\npopulate Collections took {t1-t0} s.')

    def _generate_collection_infos(self):
        for coll_template in self.config.collection_templates:
            fields = [
                FieldInfo(name=ft.name, type=ft.data_type, distribution=ft.distribution,
                          indexed=ft.indexed) for ft in coll_template.fields
            ]
            for doc_count in coll_template.cardinalities:
                name = f'{coll_template.name}'
                if self.config.collection_name_with_card is True:
                    name = f'{coll_template.name}_{doc_count}'
                yield CollectionInfo(name=name, fields=fields, documents_count=doc_count,
                                     compound_indexes=coll_template.compound_indexes)

    async def _populate_collection(self, coll: AsyncIOMotorCollection,
                                   coll_info: CollectionInfo) -> None:
        print(f'\nGenerating ${coll_info.name} ...')
        batch_size = self.config.batch_size
        tasks = []
        for _ in range(coll_info.documents_count // batch_size):
            tasks.append(asyncio.create_task(populate_batch(coll, batch_size, coll_info.fields)))
        if coll_info.documents_count % batch_size > 0:
            tasks.append(
                asyncio.create_task(
                    populate_batch(coll, coll_info.documents_count % batch_size, coll_info.fields)))

        for task in tasks:
            await task


async def populate_batch(coll: AsyncIOMotorCollection, documents_count: int,
                         fields: Sequence[FieldInfo]) -> None:
    """Generate collection data and write it to the collection."""

    await coll.insert_many(generate_collection_data(documents_count, fields), ordered=False)


def generate_collection_data(documents_count: int, fields: Sequence[FieldInfo]):
    """Generate random data for the specified fields of a collection."""

    documents = [{} for _ in range(documents_count)]
    for field in fields:
        for field_index, field_data in enumerate(field.distribution.generate(documents_count)):
            documents[field_index][field.name] = field_data
    return documents


async def create_single_field_indexes(coll: AsyncIOMotorCollection,
                                      fields: Sequence[FieldInfo]) -> None:
    """Create single-fields indexes on the given collection."""

    indexes = [IndexModel([(field.name, pymongo.ASCENDING)]) for field in fields if field.indexed]
    if len(indexes) > 0:
        await coll.create_indexes(indexes)
        print(f'create_single_field_indexes done. {[index.document for index in indexes]}')


async def create_compound_indexes(coll: AsyncIOMotorCollection, coll_info: CollectionInfo) -> None:
    """Create a coumpound indexes on the given collection."""

    indexes_spec = []
    index_specs = []
    for compound_index in coll_info.compound_indexes:
        index_spec = IndexModel([(field, pymongo.ASCENDING) for field in compound_index])
        indexes_spec.append(index_spec)
        index_specs.append([(field, pymongo.ASCENDING) for field in compound_index])
    if len(indexes_spec) > 0:
        await coll.create_indexes(indexes_spec)
        print(f'create_compound_indexes done. {index_specs}')