codeant-ai-for-open-source[bot] commented on code in PR #43757:
URL: https://github.com/apache/superset/pull/43757#discussion_r4125845563
##########
superset/datasets/schemas.py:
##########
@@ -100,6 +100,17 @@ class DatasetColumnsPutSchema(Schema):
datetime_format = fields.String(
allow_none=True, validate=[Length(1, 100), validate_python_date_format]
)
+ partition_value_transform = fields.String(
+ allow_none=True,
+ metadata={
+ "description": (
+ "SQL expression containing a :value placeholder. Filters on "
+ "this column are mirrored onto the dataset's partition column "
+ "with the value passed through this transform."
+ )
+ },
+ )
+ partition_transform_is_monotonic = fields.Boolean(load_default=False)
Review Comment:
✅ **CodeAnt verified this suggestion was addressed in subsequent commits and
marked this thread resolved** as of `56a68c0`.
Removed the Boolean field's load default so omitted values remain absent
during deserialization and existing monotonic settings are preserved.
<sub>If that's not right, unresolve this thread and CodeAnt will leave it
open.</sub>
<!-- codeant-auto-resolve-reply -->
##########
superset/commands/dataset/update.py:
##########
@@ -399,6 +405,103 @@ def _validate_expressions(
)
)
+ def _validate_partition_mapping(self, exceptions: list[ValidationError])
-> None:
+ """
+ Validate the dataset's partition filter mapping.
+
+ Only the blocking (Tier 1) issues become validation errors. Tier 2
+ issues -- an unparseable transform, a transform missing `:value` --
+ deliberately let the save through and leave the mapping inactive, per
+ the PRD, so a half-written transform doesn't cost the owner the rest of
+ their edits. They are surfaced by the editor, not by rejecting the PUT.
+
+ The transform is authored by a dataset owner, the same principal and
+ trust level as a calculated-column expression, so it also goes through
+ `validate_stored_expression` -- the parser gate that already governs
+ stored expressions.
+ """
+ self._model = cast(SqlaTable, self._model)
+
+ columns = self._properties.get("columns")
+ column_names = (
+ {column["column_name"] for column in columns}
+ if columns is not None
+ else {column.column_name for column in self._model.columns}
+ )
+
+ partition_column = self._properties.get(
+ "partition_column", self._model.partition_column
+ )
+ partition_mapped_column = self._properties.get(
+ "partition_mapped_column", self._model.partition_mapped_column
+ )
+ main_dttm_col = self._properties.get("main_dttm_col",
self._model.main_dttm_col)
+ if not partition_column:
+ return
+
+ database = self._properties.get("database") or self._model.database
+ catalog = self._properties.get("catalog", self._model.catalog)
+ schema = self._properties.get("schema", self._model.schema)
+
+ effective_mapped_column = partition_mapped_column or main_dttm_col
+ transform = self._effective_transform(columns, effective_mapped_column)
+
+ for issue in validate_partition_mapping(
+ column_names=column_names,
+ partition_column=partition_column,
+ partition_mapped_column=partition_mapped_column,
+ main_dttm_col=main_dttm_col,
+ transform=transform,
+ engine=database.backend,
+ ):
+ if issue.blocking:
+ exceptions.append(
+ ValidationError(str(issue.message), field_name=issue.field)
+ )
+
+ if transform:
+ try:
+ validate_stored_expression(
+ database, catalog, schema, parse_skeleton(transform)
+ )
Review Comment:
✅ **CodeAnt verified this suggestion was addressed in subsequent commits and
marked this thread resolved** as of `56a68c0`.
The stored-expression validation is now guarded by `is_parseable(transform,
database.backend)`, so unparseable non-Jinja transforms do not produce a
blocking validation error.
<sub>If that's not right, unresolve this thread and CodeAnt will leave it
open.</sub>
<!-- codeant-auto-resolve-reply -->
##########
superset/connectors/sqla/models.py:
##########
@@ -1894,8 +1925,44 @@ def data(self) -> ExplorableData:
data_["extra"] = self.extra
data_["always_filter_main_dttm"] = self.always_filter_main_dttm
data_["normalize_columns"] = self.normalize_columns
+ data_["partition_column"] = self.partition_column
+ data_["partition_mapped_column"] = self.partition_mapped_column
+ data_["partition_filter_mapping"] =
self.partition_filter_mapping_summary
return data_
+ @property
+ def partition_filter_mapping_summary(self) -> dict[str, Any] | None:
+ """
+ Self-contained summary of the mapping for the Explore indicator.
+
+ Deliberately not a lookup into `columns`: `data_for_slices` prunes
+ columns no chart references, and the partition column is typically
+ referenced by none of them, so anything reading it out of
+ `datasource.columns` would work in Explore and break on dashboards.
+
+ `active` is derived from cheap signals only. This property is
serialized
+ on every chart and dashboard load, so parsing the transform here would
+ put a per-request cost on a hot path for a value that only changes on
+ save.
+ """
+ if not self.partition_column:
+ return None
+
+ columns_by_name = {column.column_name: column for column in
self.columns}
+ mapped_column_name = self.partition_mapped_column or self.main_dttm_col
+ mapped_column = columns_by_name.get(mapped_column_name or "")
+ active = bool(
+ self.partition_column in columns_by_name
+ and mapped_column is not None
+ and mapped_column_name != self.partition_column
+ and (mapped_column.partition_value_transform or "").strip()
+ )
Review Comment:
✅ **CodeAnt verified this suggestion was addressed in subsequent commits and
marked this thread resolved** as of `56a68c0`.
The summary now derives `active` through `is_transform_active(...)` instead
of treating any nonblank transform as active, while retaining the required
column checks.
<sub>If that's not right, unresolve this thread and CodeAnt will leave it
open.</sub>
<!-- codeant-auto-resolve-reply -->
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