# 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_index_scan_queries(database: DatabaseInstance,
                                     collections: Sequence[CollectionInfo]):
    collection = [ci for ci in collections if ci.name.startswith('index_scan')][0]
    fields = [f for f in collection.fields if f.name == 'choice']

    requests = []

    for field in fields:
        for val in field.distribution.get_values():
            if val.startswith('_'):
                continue
            keys_length = len(val) + 2
            requests.append(
                Query(pipeline=[{'$match': {field.name: val}}], keys_length_in_bytes=keys_length,
                      note='IndexScan'))

    await workload_execution.execute(database, main_config.workload_execution, [collection],
                                     requests)


async def execute_physical_scan_queries(database: DatabaseInstance,
                                        collections: Sequence[CollectionInfo]):
    collections = [ci for ci in collections if ci.name.startswith('physical_scan')]
    fields = [f for f in collections[0].fields if f.name == 'choice']
    requests = []
    for field in fields:
        for val in field.distribution.get_values()[::3]:
            if val.startswith('_'):
                continue
            keys_length = len(val) + 2
            requests.append(
                Query(pipeline=[{'$match': {field.name: val}}, {"$limit": 10}],
                      keys_length_in_bytes=keys_length, note='PhysicalScan'))

    await workload_execution.execute(database, main_config.workload_execution, collections,
                                     requests)


async def execute_index_intersections_with_requests(database: DatabaseInstance,
                                                    collections: Sequence[CollectionInfo],
                                                    requests: Sequence[Query]):
    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_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))

    await execute_index_intersections_with_requests(database, collections, requests)


async def execute_evaluation(database: DatabaseInstance, collections: Sequence[CollectionInfo]):
    collections = [ci for ci in collections if ci.name.startswith('c_int_05')]
    requests = []

    for i in [100, 500, 1000]:
        requests.append(
            Query(pipeline=[{'$project': {'uniform1': 1, 'mixed2': 1}}, {"$limit": i}],
                  keys_length_in_bytes=1, number_of_fields=1, note='Evaluation'))

    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 execute_unique(database: DatabaseInstance, collections: Sequence[CollectionInfo]):
    collections = [ci for ci in collections if ci.name.startswith('c_arr_01')]
    requests = []

    for i in range(500, 1000, 200):
        requests.append(Query(pipeline=[{"$match": {"as": {"$gt": i}}}]))

    await workload_execution.execute(database, main_config.workload_execution, collections,
                                     requests)


async def execute_limitskip(database: DatabaseInstance, collections: Sequence[CollectionInfo]):
    collection = [ci for ci in collections if ci.name.startswith('index_scan')][0]
    limits = [5, 10, 15, 20]
    skips = [5, 10, 15, 20]
    requests = []

    for limit in limits:
        for skip in skips:
            requests.append(Query(pipeline=[{"$skip": skip}, {"$limit": limit}], note="LimitSkip"))

    await workload_execution.execute(database, main_config.workload_execution, [collection],
                                     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.
        execution_query_functions = [
            execute_index_scan_queries,
            execute_physical_scan_queries,
            execute_index_intersections,
            execute_evaluation,
            execute_unwind,
            execute_unique,
            execute_limitskip,
        ]
        for execute_query in execution_query_functions:
            await execute_query(database, generator.collection_infos)
            main_config.workload_execution.write_mode = WriteMode.APPEND

        # 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.new_event_loop()
    asyncio.set_event_loop(loop)
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        pass