| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you 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. |
| import logging |
| from collections import Counter |
| from typing import Any, Dict, List, Optional |
| |
| from flask_appbuilder.models.sqla import Model |
| from flask_appbuilder.security.sqla.models import User |
| from marshmallow import ValidationError |
| |
| from superset.commands.base import BaseCommand |
| from superset.commands.utils import populate_owners |
| from superset.connectors.sqla.models import SqlaTable |
| from superset.dao.exceptions import DAOUpdateFailedError |
| from superset.datasets.commands.exceptions import ( |
| DatabaseChangeValidationError, |
| DatasetColumnNotFoundValidationError, |
| DatasetColumnsDuplicateValidationError, |
| DatasetColumnsExistsValidationError, |
| DatasetExistsValidationError, |
| DatasetForbiddenError, |
| DatasetInvalidError, |
| DatasetMetricsDuplicateValidationError, |
| DatasetMetricsExistsValidationError, |
| DatasetMetricsNotFoundValidationError, |
| DatasetNotFoundError, |
| DatasetUpdateFailedError, |
| ) |
| from superset.datasets.dao import DatasetDAO |
| from superset.exceptions import SupersetSecurityException |
| from superset.views.base import check_ownership |
| |
| logger = logging.getLogger(__name__) |
| |
| |
| class UpdateDatasetCommand(BaseCommand): |
| def __init__( |
| self, |
| user: User, |
| model_id: int, |
| data: Dict[str, Any], |
| override_columns: bool = False, |
| ): |
| self._actor = user |
| self._model_id = model_id |
| self._properties = data.copy() |
| self._model: Optional[SqlaTable] = None |
| self.override_columns = override_columns |
| |
| def run(self) -> Model: |
| self.validate() |
| if self._model: |
| try: |
| dataset = DatasetDAO.update( |
| model=self._model, |
| properties=self._properties, |
| override_columns=self.override_columns, |
| ) |
| return dataset |
| except DAOUpdateFailedError as ex: |
| logger.exception(ex.exception) |
| raise DatasetUpdateFailedError() |
| raise DatasetUpdateFailedError() |
| |
| def validate(self) -> None: |
| exceptions: List[ValidationError] = list() |
| owner_ids: Optional[List[int]] = self._properties.get("owners") |
| # Validate/populate model exists |
| self._model = DatasetDAO.find_by_id(self._model_id) |
| if not self._model: |
| raise DatasetNotFoundError() |
| # Check ownership |
| try: |
| check_ownership(self._model) |
| except SupersetSecurityException: |
| raise DatasetForbiddenError() |
| |
| database_id = self._properties.get("database", None) |
| table_name = self._properties.get("table_name", None) |
| # Validate uniqueness |
| if not DatasetDAO.validate_update_uniqueness( |
| self._model.database_id, self._model_id, table_name |
| ): |
| exceptions.append(DatasetExistsValidationError(table_name)) |
| # Validate/Populate database not allowed to change |
| if database_id and database_id != self._model: |
| exceptions.append(DatabaseChangeValidationError()) |
| # Validate/Populate owner |
| try: |
| owners = populate_owners(self._actor, owner_ids) |
| self._properties["owners"] = owners |
| except ValidationError as ex: |
| exceptions.append(ex) |
| |
| # Validate columns |
| columns = self._properties.get("columns") |
| if columns: |
| self._validate_columns(columns, exceptions) |
| |
| # Validate metrics |
| metrics = self._properties.get("metrics") |
| if metrics: |
| self._validate_metrics(metrics, exceptions) |
| |
| if exceptions: |
| exception = DatasetInvalidError() |
| exception.add_list(exceptions) |
| raise exception |
| |
| def _validate_columns( |
| self, columns: List[Dict[str, Any]], exceptions: List[ValidationError] |
| ) -> None: |
| # Validate duplicates on data |
| if self._get_duplicates(columns, "column_name"): |
| exceptions.append(DatasetColumnsDuplicateValidationError()) |
| else: |
| # validate invalid id's |
| columns_ids: List[int] = [ |
| column["id"] for column in columns if "id" in column |
| ] |
| if not DatasetDAO.validate_columns_exist(self._model_id, columns_ids): |
| exceptions.append(DatasetColumnNotFoundValidationError()) |
| |
| # validate new column names uniqueness |
| if not self.override_columns: |
| columns_names: List[str] = [ |
| column["column_name"] for column in columns if "id" not in column |
| ] |
| if not DatasetDAO.validate_columns_uniqueness( |
| self._model_id, columns_names |
| ): |
| exceptions.append(DatasetColumnsExistsValidationError()) |
| |
| def _validate_metrics( |
| self, metrics: List[Dict[str, Any]], exceptions: List[ValidationError] |
| ) -> None: |
| if self._get_duplicates(metrics, "metric_name"): |
| exceptions.append(DatasetMetricsDuplicateValidationError()) |
| else: |
| # validate invalid id's |
| metrics_ids: List[int] = [ |
| metric["id"] for metric in metrics if "id" in metric |
| ] |
| if not DatasetDAO.validate_metrics_exist(self._model_id, metrics_ids): |
| exceptions.append(DatasetMetricsNotFoundValidationError()) |
| # validate new metric names uniqueness |
| metric_names: List[str] = [ |
| metric["metric_name"] for metric in metrics if "id" not in metric |
| ] |
| if not DatasetDAO.validate_metrics_uniqueness(self._model_id, metric_names): |
| exceptions.append(DatasetMetricsExistsValidationError()) |
| |
| @staticmethod |
| def _get_duplicates(data: List[Dict[str, Any]], key: str) -> List[str]: |
| duplicates = [ |
| name |
| for name, count in Counter([item[key] for item in data]).items() |
| if count > 1 |
| ] |
| return duplicates |