taipy/taipy/core/data/sql_table.py

# Copyright 2021-2024 Avaiga Private Limited
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
#        http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
# an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
# specific language governing permissions and limitations under the License.

from datetime import datetime, timedelta
from typing import Any, Dict, List, Optional, Set, Union

import pandas as pd
from sqlalchemy import MetaData, Table

from taipy.config.common.scope import Scope

from .._version._version_manager_factory import _VersionManagerFactory
from ..exceptions.exceptions import MissingRequiredProperty
from ._abstract_sql import _AbstractSQLDataNode
from .data_node_id import DataNodeId, Edit


class SQLTableDataNode(_AbstractSQLDataNode):
    """Data Node stored in a SQL table.

    Attributes:
        config_id (str): Identifier of the data node configuration. It must be a valid Python
            identifier.
        scope (Scope^): The scope of this data node.
        id (str): The unique identifier of this data node.
        owner_id (str): The identifier of the owner (sequence_id, scenario_id, cycle_id) or
            None.
        parent_ids (Optional[Set[str]]): The identifiers of the parent tasks or `None`.
        last_edit_date (datetime): The date and time of the last modification.
        edits (List[Edit^]): The ordered list of edits for that job.
        version (str): The string indicates the application version of the data node to instantiate. If not provided,
            the current version is used.
        validity_period (Optional[timedelta]): The duration implemented as a timedelta since the last edit date for
            which the data node can be considered up-to-date. Once the validity period has passed, the data node is
            considered stale and relevant tasks will run even if they are skippable (see the
            [Task management page](../core/entities/task-mgt.md) for more details).
            If _validity_period_ is set to `None`, the data node is always up-to-date.
        edit_in_progress (bool): True if a task computing the data node has been submitted
            and not completed yet. False otherwise.
        editor_id (Optional[str]): The identifier of the user who is currently editing the data node.
        editor_expiration_date (Optional[datetime]): The expiration date of the editor lock.
        properties (dict[str, Any]): A dictionary of additional properties. Note that the
            _properties_ parameter must at least contain an entry for _"db_name"_, _"db_engine"_, _"table_name"_:

            - _"db_name"_ `(str)`: The database name, or the name of the SQLite database file.
            - _"db_engine"_ `(str)`: The database engine. For now, the accepted values are _"sqlite"_, _"mssql"_,
                _"mysql"_, or _"postgresql"_.
            - _"table_name"_ `(str)`: The name of the SQL table.
            - _"db_username"_ `(str)`: The database username.
            - _"db_password"_ `(str)`: The database password.
            - _"db_host"_ `(str)`: The database host. The default value is _"localhost"_.
            - _"db_port"_ `(int)`: The database port. The default value is 1433.
            - _"db_driver"_ `(str)`: The database driver.
            - _"sqlite_folder_path"_ (str): The path to the folder that contains SQLite file. The default value
                is the current working folder.
            - _"sqlite_file_extension"_ (str): The filename extension of the SQLite file. The default value is ".db".
            - _"db_extra_args"_ `(Dict[str, Any])`: A dictionary of additional arguments to be passed into database
                connection string.
            - _"exposed_type"_: The exposed type of the data read from SQL query. The default value is `pandas`.
    """

    __STORAGE_TYPE = "sql_table"
    __TABLE_KEY = "table_name"

    def __init__(
        self,
        config_id: str,
        scope: Scope,
        id: Optional[DataNodeId] = None,
        owner_id: Optional[str] = None,
        parent_ids: Optional[Set[str]] = None,
        last_edit_date: Optional[datetime] = None,
        edits: Optional[List[Edit]] = None,
        version: Optional[str] = None,
        validity_period: Optional[timedelta] = None,
        edit_in_progress: bool = False,
        editor_id: Optional[str] = None,
        editor_expiration_date: Optional[datetime] = None,
        properties: Optional[Dict] = None,
    ) -> None:
        if properties is None:
            properties = {}
        if properties.get(self.__TABLE_KEY) is None:
            raise MissingRequiredProperty(f"Property {self.__TABLE_KEY} is not informed and is required.")
        super().__init__(
            config_id,
            scope,
            id=id,
            owner_id=owner_id,
            parent_ids=parent_ids,
            last_edit_date=last_edit_date,
            edits=edits,
            version=version or _VersionManagerFactory._build_manager()._get_latest_version(),
            validity_period=validity_period,
            edit_in_progress=edit_in_progress,
            editor_id=editor_id,
            editor_expiration_date=editor_expiration_date,
            properties=properties,
        )
        self._TAIPY_PROPERTIES.update({self.__TABLE_KEY})

    @classmethod
    def storage_type(cls) -> str:
        return cls.__STORAGE_TYPE

    def _get_base_read_query(self) -> str:
        return f"SELECT * FROM {self.properties[self.__TABLE_KEY]}"

    def _do_append(self, data, engine, connection) -> None:
        self.__insert_data(data, engine, connection)

    def _do_write(self, data, engine, connection) -> None:
        self.__insert_data(data, engine, connection, delete_table=True)

    def __insert_data(self, data, engine, connection, delete_table: bool = False) -> None:
        table = self._create_table(engine)
        self.__insert_dataframe(
            self._convert_data_to_dataframe(self.properties[self._EXPOSED_TYPE_PROPERTY], data),
            table,
            connection,
            delete_table,
        )

    def _create_table(self, engine) -> Table:
        return Table(
            self.properties[self.__TABLE_KEY],
            MetaData(),
            autoload_with=engine,
        )

    @classmethod
    def __insert_dicts(cls, data: List[Dict], table: Any, connection: Any, delete_table: bool) -> None:
        """
        This method will insert the data contained in a list of dictionaries into a table. The query itself is handled
        by SQLAlchemy, so it's only needed to pass the correct data type.
        """
        cls.__delete_all_rows(table, connection, delete_table)
        connection.execute(table.insert(), data)

    @classmethod
    def __insert_dataframe(
        cls, df: Union[pd.DataFrame, pd.Series], table: Any, connection: Any, delete_table: bool
    ) -> None:
        if isinstance(df, pd.Series):
            data = [df.to_dict()]
        elif isinstance(df, pd.DataFrame):
            data = df.to_dict(orient="records")
        cls.__insert_dicts(data, table, connection, delete_table)

    @classmethod
    def __delete_all_rows(cls, table: Any, connection: Any, delete_table: bool) -> None:
        if delete_table:
            connection.execute(table.delete())