superset2/superset/daos/dataset.py

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# to you under the Apache License, Version 2.0 (the
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from __future__ import annotations

import logging
from datetime import datetime
from typing import Any, Dict, List

import dateutil.parser
from sqlalchemy.exc import SQLAlchemyError

from superset.connectors.sqla.models import SqlaTable, SqlMetric, TableColumn
from superset.daos.base import BaseDAO
from superset.extensions import db
from superset.models.core import Database
from superset.models.dashboard import Dashboard
from superset.models.slice import Slice
from superset.sql.parse import Table
from superset.utils.core import DatasourceType
from superset.views.base import DatasourceFilter

logger = logging.getLogger(__name__)

# Custom filterable fields for datasets
DATASET_CUSTOM_FIELDS: dict[str, list[str]] = {}


class DatasetDAO(BaseDAO[SqlaTable]):
    """
    DAO for datasets. Supports filtering on model fields, hybrid properties, and custom
    fields:
    - tags: list of tags (eq, in_, like)
    - owner: user id (eq, in_)
    """

    base_filter = DatasourceFilter

    @staticmethod
    def get_database_by_id(database_id: int) -> Database | None:
        try:
            return db.session.query(Database).filter_by(id=database_id).one_or_none()
        except SQLAlchemyError as ex:  # pragma: no cover
            logger.error("Could not get database by id: %s", str(ex), exc_info=True)
            return None

    @staticmethod
    def get_related_objects(database_id: int) -> dict[str, Any]:
        charts = (
            db.session.query(Slice)
            .filter(
                Slice.datasource_id == database_id,
                Slice.datasource_type == DatasourceType.TABLE,
            )
            .all()
        )
        chart_ids = [chart.id for chart in charts]

        dashboards = (
            (
                db.session.query(Dashboard)
                .join(Dashboard.slices)
                .filter(Slice.id.in_(chart_ids))
            )
            .distinct()
            .all()
        )
        return {"charts": charts, "dashboards": dashboards}

    @staticmethod
    def validate_table_exists(
        database: Database,
        table: Table,
    ) -> bool:
        try:
            database.get_table(table)
            return True
        except SQLAlchemyError as ex:  # pragma: no cover
            logger.warning("Got an error %s validating table: %s", str(ex), table)
            return False

    @staticmethod
    def validate_uniqueness(
        database: Database,
        table: Table,
        dataset_id: int | None = None,
    ) -> bool:
        # The catalog might not be set even if the database supports catalogs, in case
        # multi-catalog is disabled.
        catalog = table.catalog or database.get_default_catalog()

        dataset_query = db.session.query(SqlaTable).filter(
            SqlaTable.table_name == table.table,
            SqlaTable.schema == table.schema,
            SqlaTable.catalog == catalog,
            SqlaTable.database_id == database.id,
        )

        if dataset_id:
            # make sure the dataset found is different from the target (if any)
            dataset_query = dataset_query.filter(SqlaTable.id != dataset_id)

        return not db.session.query(dataset_query.exists()).scalar()

    @staticmethod
    def validate_update_uniqueness(
        database: Database,
        table: Table,
        dataset_id: int,
    ) -> bool:
        # The catalog might not be set even if the database supports catalogs, in case
        # multi-catalog is disabled.
        catalog = table.catalog or database.get_default_catalog()

        dataset_query = db.session.query(SqlaTable).filter(
            SqlaTable.table_name == table.table,
            SqlaTable.database_id == database.id,
            SqlaTable.schema == table.schema,
            SqlaTable.catalog == catalog,
            SqlaTable.id != dataset_id,
        )
        return not db.session.query(dataset_query.exists()).scalar()

    @staticmethod
    def validate_columns_exist(dataset_id: int, columns_ids: list[int]) -> bool:
        dataset_query = (
            db.session.query(TableColumn.id).filter(
                TableColumn.table_id == dataset_id, TableColumn.id.in_(columns_ids)
            )
        ).all()
        return len(columns_ids) == len(dataset_query)

    @staticmethod
    def validate_columns_uniqueness(dataset_id: int, columns_names: list[str]) -> bool:
        dataset_query = (
            db.session.query(TableColumn.id).filter(
                TableColumn.table_id == dataset_id,
                TableColumn.column_name.in_(columns_names),
            )
        ).all()
        return len(dataset_query) == 0

    @staticmethod
    def validate_metrics_exist(dataset_id: int, metrics_ids: list[int]) -> bool:
        dataset_query = (
            db.session.query(SqlMetric.id).filter(
                SqlMetric.table_id == dataset_id, SqlMetric.id.in_(metrics_ids)
            )
        ).all()
        return len(metrics_ids) == len(dataset_query)

    @staticmethod
    def validate_metrics_uniqueness(dataset_id: int, metrics_names: list[str]) -> bool:
        dataset_query = (
            db.session.query(SqlMetric.id).filter(
                SqlMetric.table_id == dataset_id,
                SqlMetric.metric_name.in_(metrics_names),
            )
        ).all()
        return len(dataset_query) == 0

    @staticmethod
    def validate_python_date_format(dt_format: str) -> bool:
        if dt_format in ("epoch_s", "epoch_ms"):
            return True
        try:
            dt_str = datetime.now().strftime(dt_format)
            dateutil.parser.isoparse(dt_str)
            return True
        except ValueError:
            return False

    @classmethod
    def update(
        cls,
        item: SqlaTable | None = None,
        attributes: dict[str, Any] | None = None,
    ) -> SqlaTable:
        """
        Updates a Dataset model on the metadata DB
        """

        if item and attributes:
            force_update: bool = False
            if "columns" in attributes:
                cls.update_columns(
                    item,
                    attributes.pop("columns"),
                    override_columns=bool(attributes.get("override_columns")),
                )
                force_update = True

            if "metrics" in attributes:
                cls.update_metrics(item, attributes.pop("metrics"))
                force_update = True

            if force_update:
                attributes["changed_on"] = datetime.now()

        return super().update(item, attributes)

    @classmethod
    def update_columns(
        cls,
        model: SqlaTable,
        property_columns: list[dict[str, Any]],
        override_columns: bool = False,
    ) -> None:
        """
        Creates/updates and/or deletes a list of columns, based on a
        list of Dict.

        - If a column Dict has an `id` property then we update.
        - If a column Dict does not have an `id` then we create a new metric.
        - If there are extra columns on the metadata db that are not defined on the List
        then we delete.
        """

        for column in property_columns:
            if (
                "python_date_format" in column
                and column["python_date_format"] is not None
            ):
                if not DatasetDAO.validate_python_date_format(
                    column["python_date_format"]
                ):
                    raise ValueError(
                        "python_date_format is an invalid date/timestamp format."
                    )

        if override_columns:
            db.session.query(TableColumn).filter(
                TableColumn.table_id == model.id
            ).delete(synchronize_session="fetch")

            db.session.bulk_insert_mappings(
                TableColumn,
                [
                    {**properties, "table_id": model.id}
                    for properties in property_columns
                ],
            )
        else:
            columns_by_id = {column.id: column for column in model.columns}

            property_columns_by_id = {
                properties["id"]: properties
                for properties in property_columns
                if "id" in properties
            }

            db.session.bulk_insert_mappings(
                TableColumn,
                [
                    {**properties, "table_id": model.id}
                    for properties in property_columns
                    if "id" not in properties
                ],
            )

            db.session.bulk_update_mappings(
                TableColumn,
                [
                    {**columns_by_id[properties["id"]].__dict__, **properties}
                    for properties in property_columns_by_id.values()
                ],
            )

            db.session.query(TableColumn).filter(
                TableColumn.id.in_(
                    {column.id for column in model.columns}
                    - property_columns_by_id.keys()
                )
            ).delete(synchronize_session="fetch")

    @classmethod
    def update_metrics(
        cls,
        model: SqlaTable,
        property_metrics: list[dict[str, Any]],
    ) -> None:
        """
        Creates/updates and/or deletes a list of metrics, based on a
        list of Dict.

        - If a metric Dict has an `id` property then we update.
        - If a metric Dict does not have an `id` then we create a new metric.
        - If there are extra metrics on the metadata db that are not defined on the List
        then we delete.
        """

        metrics_by_id = {metric.id: metric for metric in model.metrics}

        property_metrics_by_id = {
            properties["id"]: properties
            for properties in property_metrics
            if "id" in properties
        }

        db.session.bulk_insert_mappings(
            SqlMetric,
            [
                {**properties, "table_id": model.id}
                for properties in property_metrics
                if "id" not in properties
            ],
        )

        db.session.bulk_update_mappings(
            SqlMetric,
            [
                {**metrics_by_id[properties["id"]].__dict__, **properties}
                for properties in property_metrics_by_id.values()
            ],
        )

        db.session.query(SqlMetric).filter(
            SqlMetric.id.in_(
                {metric.id for metric in model.metrics} - property_metrics_by_id.keys()
            )
        ).delete(synchronize_session="fetch")

    @classmethod
    def find_dataset_column(cls, dataset_id: int, column_id: int) -> TableColumn | None:
        # We want to apply base dataset filters
        dataset = DatasetDAO.find_by_id(dataset_id)
        if not dataset:
            return None
        return (
            db.session.query(TableColumn)
            .filter(TableColumn.table_id == dataset_id, TableColumn.id == column_id)
            .one_or_none()
        )

    @classmethod
    def find_dataset_metric(cls, dataset_id: int, metric_id: int) -> SqlMetric | None:
        # We want to apply base dataset filters
        dataset = DatasetDAO.find_by_id(dataset_id)
        if not dataset:
            return None
        return db.session.query(SqlMetric).get(metric_id)

    @staticmethod
    def get_table_by_name(database_id: int, table_name: str) -> SqlaTable | None:
        return (
            db.session.query(SqlaTable)
            .filter_by(database_id=database_id, table_name=table_name)
            .one_or_none()
        )

    @classmethod
    def get_filterable_columns_and_operators(cls) -> Dict[str, List[str]]:
        filterable = super().get_filterable_columns_and_operators()
        # Add custom fields
        filterable.update(DATASET_CUSTOM_FIELDS)
        return filterable


class DatasetColumnDAO(BaseDAO[TableColumn]):
    pass


class DatasetMetricDAO(BaseDAO[SqlMetric]):
    pass