sadpandajoe commented on code in PR #35662:
URL: https://github.com/apache/superset/pull/35662#discussion_r4037767412
##########
superset/jinja_context.py:
##########
@@ -1101,28 +1104,91 @@ def get_template_processor(
def dataset_macro(
- dataset_id: int,
+ dataset_id: Union[int, str],
include_metrics: bool = False,
columns: list[str] | None = None,
from_dttm: datetime | None = None,
to_dttm: datetime | None = None,
+ schema: str | None = None,
+ catalog: str | None = None,
+ database_id: Union[int, str] | None = None,
+ alias: str | None = None,
) -> str:
"""
- Given a dataset ID, return the SQL that represents it.
+ Given a dataset ID or name, return the SQL that represents it.
+
+ If ``dataset_id`` is an integer, it is treated as the unique dataset ID and
+ the optional ``schema``, ``catalog`` and ``database_id`` parameters are
+ ignored.
+
+ If ``dataset_id`` is a string, it is treated as a dataset name. The
optional
+ ``schema``, ``catalog`` and ``database_id`` parameters are used to narrow
+ down the search when provided. If multiple datasets match the provided
+ criteria, an error is raised because the dataset name is ambiguous.
The generated SQL includes all columns (including computed) by default.
Optionally
the user can also request metrics to be included, and columns to group by.
- The from_dttm and to_dttm parameters are filled in from filter values in
explore
- views, and we take them to make those properties available to jinja
templates in
- the underlying dataset.
+ The ``from_dttm`` and ``to_dttm`` parameters are filled in from filter
values in
+ explore views, and we take them to make those properties available to jinja
+ templates in the underlying dataset.
+
+ The ``alias`` parameter allows the user to specify an explicit alias for
the
+ returned subquery.
"""
# pylint: disable=import-outside-toplevel
+ from sqlalchemy.orm.exc import MultipleResultsFound
+
from superset.daos.dataset import DatasetDAO
- dataset = DatasetDAO.find_by_id(dataset_id)
+ filters = {
+ key: value
+ for key, value in {
+ "database_id": database_id,
+ "catalog": catalog,
+ "schema": schema,
+ }.items()
+ if value is not None
+ }
+
+ if isinstance(dataset_id, str):
+ try:
+ dataset = DatasetDAO.get_table_by_catalog_schema_and_name(
Review Comment:
The name path bypasses `DatasetDAO`'s `DatasourceFilter`, so a Gamma user
with SQL Lab access can resolve an ungranted virtual dataset; tableless
datasets or ones built only from otherwise granted tables can also pass the
later physical-table check and expose their saved SQL/output. Should this
lookup apply the DAO base filter or explicitly check access to the selected
dataset before expanding it?
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