superset2/superset/commands/datasource/list.py

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"""Command for the combined dataset + semantic view list endpoint."""

from __future__ import annotations

import logging
from typing import Any, cast

from sqlalchemy import union_all

from superset.commands.base import BaseCommand
from superset.connectors.sqla.models import SqlaTable
from superset.daos.datasource import DatasourceDAO
from superset.datasource.schemas import DatasetListSchema, SemanticViewListSchema
from superset.semantic_layers.models import SemanticView

logger = logging.getLogger(__name__)

_dataset_schema = DatasetListSchema()
_semantic_view_schema = SemanticViewListSchema()


class GetCombinedDatasourceListCommand(BaseCommand):
    """
    Fetch and serialize a paginated, combined list of datasets and semantic views.

    Callers are responsible for checking access permissions before constructing
    this command and for passing the appropriate ``can_read_*`` flags.
    """

    def __init__(
        self,
        args: dict[str, Any],
        can_read_datasets: bool,
        can_read_semantic_views: bool,
    ) -> None:
        self._args = args
        self._can_read_datasets = can_read_datasets
        self._can_read_semantic_views = can_read_semantic_views

    def run(self) -> dict[str, Any]:
        self.validate()

        page = self._args.get("page", 0)
        page_size = self._args.get("page_size", 25)
        order_column = self._args.get("order_column", "changed_on")
        order_direction = self._args.get("order_direction", "desc")
        filters = self._args.get("filters", [])

        source_type, name_filter, sql_filter, type_filter = self._parse_filters(filters)
        source_type = self._resolve_source_type(source_type, sql_filter, type_filter)

        if source_type == "empty":
            return {"count": 0, "result": []}

        ds_q = DatasourceDAO.build_dataset_query(name_filter, sql_filter)
        sv_q = DatasourceDAO.build_semantic_view_query(name_filter)

        if source_type == "database":
            combined = ds_q.subquery()
        elif source_type == "semantic_layer":
            combined = sv_q.subquery()
        else:
            combined = union_all(ds_q, sv_q).subquery()

        total_count, rows = DatasourceDAO.paginate_combined_query(
            combined, order_column, order_direction, page, page_size
        )

        datasets_map = DatasourceDAO.fetch_datasets_by_ids(
            [r.item_id for r in rows if r.source_type == "database"]
        )
        sv_map = DatasourceDAO.fetch_semantic_views_by_ids(
            [r.item_id for r in rows if r.source_type == "semantic_layer"]
        )

        result: list[dict[str, Any]] = []
        for row in rows:
            if row.source_type == "database":
                ds_obj = cast(SqlaTable | None, datasets_map.get(row.item_id))
                if ds_obj:
                    result.append(_dataset_schema.dump(ds_obj))
            else:
                sv_obj = cast(SemanticView | None, sv_map.get(row.item_id))
                if sv_obj:
                    result.append(_semantic_view_schema.dump(sv_obj))

        return {"count": total_count, "result": result}

    def validate(self) -> None:
        pass  # access checks are performed by the caller (API layer)

    def _resolve_source_type(
        self,
        source_type: str,
        sql_filter: bool | None,
        type_filter: str | None,
    ) -> str:
        """Narrow source_type based on access flags, sql filter, and type filter.

        Returns one of: "database", "semantic_layer", "all", or "empty".
        "empty" signals that the caller should short-circuit and return no results
        (used when the user explicitly requests semantic views but lacks access).
        """
        if not self._can_read_semantic_views:
            # If the user explicitly asked for semantic views but cannot read them,
            # return "empty" so the caller yields zero results rather than silently
            # falling back to the full dataset list.
            if source_type == "semantic_layer" or type_filter == "semantic_view":
                return "empty"
            return "database"
        if not self._can_read_datasets:
            return "semantic_layer"
        # sql_filter (physical/virtual toggle) only applies to datasets
        if sql_filter is not None:
            return "database"
        # Explicit semantic-view type filter
        if type_filter == "semantic_view":
            return "semantic_layer"
        return source_type

    @staticmethod
    def _parse_filters(
        filters: list[dict[str, Any]],
    ) -> tuple[str, str | None, bool | None, str | None]:
        """
        Translate raw rison filter dicts into typed query parameters.

        Returns:
            source_type:  "all" | "database" | "semantic_layer"
            name_filter:  substring to match against name/table_name
            sql_filter:   True → physical only, False → virtual only, None → both
            type_filter:  "semantic_view" when the caller wants only semantic views
        """
        source_type = "all"
        name_filter: str | None = None
        sql_filter: bool | None = None
        type_filter: str | None = None

        for f in filters:
            col = f.get("col")
            opr = f.get("opr")
            value = f.get("value")

            if col == "source_type":
                source_type = value or "all"
            elif col == "table_name" and f.get("opr") == "ct":
                name_filter = value
            elif col == "sql":
                if opr == "dataset_is_null_or_empty" and value == "semantic_view":
                    type_filter = "semantic_view"
                elif opr == "dataset_is_null_or_empty" and isinstance(value, bool):
                    sql_filter = value

        return source_type, name_filter, sql_filter, type_filter