aminghadersohi commented on code in PR #46045:
URL: https://github.com/apache/superset/pull/46045#discussion_r4488648466
##########
superset/daos/dataset.py:
##########
@@ -192,8 +191,64 @@ def get_related_objects_for_datasets(dataset_ids:
list[int]) -> dict[str, Any]:
.distinct()
.all()
)
+ seen_ids = {dashboard.id for dashboard in dashboards}
+ extra_ids = [
+ dashboard_id
+ for dashboard_id in DatasetDAO._dashboard_ids_targeting_datasets(
+ dataset_ids
+ )
+ if dashboard_id not in seen_ids
+ ]
+ if extra_ids:
+ dashboards.extend(
+
db.session.query(Dashboard).filter(Dashboard.id.in_(extra_ids)).all()
+ )
return {"charts": charts, "dashboards": dashboards}
+ @staticmethod
+ def get_charts_for_datasets(dataset_ids: list[int]) -> list[Slice]:
+ """
+ Return the charts built on any of the datasets. Callers that only need
+ charts should use this rather than ``get_related_objects``, which also
+ scans dashboard metadata.
+ """
+ if not dataset_ids:
+ return []
+ return (
+ db.session.query(Slice)
+ .filter(
+ Slice.datasource_id.in_(dataset_ids),
+ Slice.datasource_type == DatasourceType.TABLE,
+ )
+ .all()
+ )
+
+ @staticmethod
+ def _dashboard_ids_targeting_datasets(dataset_ids: list[int]) -> list[int]:
+ """
+ Ids of the dashboards whose native filters or display controls target
+ any of the datasets.
+
+ Targets live in ``json_metadata`` text, so a cheap ``LIKE`` prefilter
+ narrows the rows and the parsed metadata is then checked exactly.
+ Dashboards with missing or malformed metadata never match.
+ """
+ wanted = set(dataset_ids)
+ rows = (
+ db.session.query(Dashboard.id, Dashboard.json_metadata)
+ .filter(
+ Dashboard.json_metadata.like('%"datasetId"%'),
+ Dashboard.deleted_at.is_(None),
+ )
+ .order_by(Dashboard.id)
+ .all()
+ )
Review Comment:
Acceptable as is: filter targets exist only in json_metadata, so no index
can serve any correct lookup. It runs only on delete/usage lookups, never on
list pages; I measured 0.15 s at 1k and 2.7 s at 20k dashboards (14 KB
metadata), against the indexed chart query's ~2 ms.
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