sadpandajoe commented on code in PR #35662:
URL: https://github.com/apache/superset/pull/35662#discussion_r3685924251
##########
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
Review Comment:
This drops an explicitly supplied `schema=None` or `catalog=None`, even
though the DAO's new sentinel treats `None` as an `IS NULL` qualifier. When a
table name exists in both the default schema/catalog and a named one, the macro
remains ambiguous with no way to select the NULL-qualified dataset by name;
should the macro preserve explicit `None` separately from an omitted argument?
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
