john-bodley commented on code in PR #23269:
URL: https://github.com/apache/superset/pull/23269#discussion_r1171934332
##########
superset/utils/dashboard_filter_scopes_converter.py:
##########
@@ -88,3 +90,249 @@ def copy_filter_scopes(
if int(slice_id) in old_to_new_slc_id_dict
]
return new_filter_scopes
+
+
+def convert_filter_scopes_to_native_filters( # pylint:
disable=invalid-name,too-many-branches,too-many-locals,too-many-nested-blocks,too-many-statements
+ json_metadata: Dict[str, Any],
+ position_json: Dict[str, Any],
+ filter_boxes: List[Slice],
+) -> List[Dict[str, Any]]:
+ """
+ Convert the legacy filter scopes et al. to the native filter configuration.
+
+ Dashboard filter scopes are implicitly defined where an undefined scope
implies
+ no immunity, i.e., they apply to all applicable charts. The
`convert_filter_scopes`
+ method provides an explicit definition by extracting the underlying
filter-box
+ configurations.
+
+ Hierarchical legacy filters are defined via non-exclusion of peer or
children
+ filter-box charts whereas native hierarchical filters are defined via
explicit
+ parental relationships, i.e., the inverse.
+
+ :param json_metata: The dashboard metadata
+ :param position_json: The dashboard layout
+ :param filter_boxes: The filter-box charts associated with the dashboard
+ :returns: The native filter configuration
+ :see: convert_filter_scopes
+ """
+
+ shortid = ShortId()
+ default_filters = json.loads(json_metadata.get("default_filters") or "{}")
+ filter_scopes = json_metadata.get("filter_scopes", {})
+ filter_box_ids = {filter_box.id for filter_box in filter_boxes}
+
+ filter_scope_by_key_and_field: Dict[str, Dict[str, Dict[str, Any]]] =
defaultdict(
+ dict
+ )
+
+ filter_by_key_and_field: Dict[str, Dict[str, Dict[str, Any]]] =
defaultdict(dict)
+
+ # Dense representation of filter scopes, falling back to chart level
filter configs
+ # if the respective filter scope is not defined at the dashboard level.
+ for filter_box in filter_boxes:
+ key = str(filter_box.id)
+
+ filter_scope_by_key_and_field[key] = {
+ **(
+ convert_filter_scopes(
+ json_metadata,
+ filter_boxes=[filter_box],
+ ).get(filter_box.id, {})
+ ),
+ **(filter_scopes.get(key, {})),
+ }
+
+ # Contruct the native filters.
+ for filter_box in filter_boxes:
+ key = str(filter_box.id)
+ params = json.loads(filter_box.params or "{}")
+
+ for field, filter_scope in filter_scope_by_key_and_field[key].items():
+ default = default_filters.get(key, {}).get(field)
+
+ fltr: Dict[str, Any] = {
+ "cascadeParentIds": [],
+ "id": f"NATIVE_FILTER-{shortid.generate()}",
+ "scope": {
+ "rootPath": filter_scope["scope"],
+ "excluded": [
+ id_
+ for id_ in filter_scope["immune"]
+ if id_ not in filter_box_ids
+ ],
+ },
+ "type": "NATIVE_FILTER",
+ }
+
+ if field == "__time_col" and params.get("show_sqla_time_column"):
+ fltr.update(
+ {
+ "filterType": "filter_timecolumn",
+ "name": "Time Column",
+ "targets": [{"datasetId": filter_box.datasource_id}],
+ }
+ )
+
+ if not default:
+ default = params.get("granularity_sqla")
+
+ if default:
+ fltr["defaultDataMask"] = {
+ "extraFormData": {"granularity_sqla": default},
+ "filterState": {"value": [default]},
+ }
+ elif field == "__time_grain" and
params.get("show_sqla_time_granularity"):
+ fltr.update(
+ {
+ "filterType": "filter_timegrain",
+ "name": "Time Grain",
+ "targets": [{"datasetId": filter_box.datasource_id}],
+ }
+ )
+
+ if not default:
+ default = params.get("time_grain_sqla")
+
+ if default:
+ fltr["defaultDataMask"] = {
+ "extraFormData": {"time_grain_sqla": default},
+ "filterState": {"value": [default]},
+ }
+ elif field == "__time_range" and params.get("date_filter"):
+ fltr.update(
+ {
+ "filterType": "filter_time",
+ "name": "Time Range",
+ "targets": [{}],
+ }
+ )
+
+ if not default:
+ default = params.get("time_range")
+
+ if default and default != "No filter":
+ fltr["defaultDataMask"] = {
+ "extraFormData": {"time_range": default},
+ "filterState": {"value": default},
+ }
+ else:
+ for config in params.get("filter_configs") or []:
+ if config["column"] == field:
+ fltr.update(
+ {
+ "controlValues": {
+ "defaultToFirstItem": False,
+ "enableEmptyFilter": not config.get(
+ "clearable",
+ True,
+ ),
+ "inverseSelection": False,
+ "multiSelect": config.get(
+ "multiple",
+ False,
+ ),
+ "searchAllOptions": config.get(
+ "searchAllOptions",
+ False,
+ ),
+ },
+ "filterType": "filter_select",
+ "name": config.get("label") or field,
+ "targets": [
+ {
+ "column": {"name": field},
+ "datasetId": filter_box.datasource_id,
+ },
+ ],
+ }
+ )
+
+ if "metric" in config:
+ fltr["sortMetric"] = config["metric"]
+ fltr["controlValues"]["sortAscending"] =
config["asc"]
+
+ if params.get("adhoc_filters"):
+ fltr["adhoc_filters"] = params["adhoc_filters"]
+
+ # Pre-filter available values based on time
range/column.
+ time_range = params.get("time_range")
+
+ if time_range and time_range != "No filter":
+ fltr.update(
+ {
+ "time_range": time_range,
+ "granularity_sqla":
params.get("granularity_sqla"),
+ }
+ )
+
+ if not default:
+ default = config.get("defaultValue")
+
+ if default:
+ if config["multiple"]:
+ default = default.split(";")
+ else:
+ default = [default]
+
+ if default:
+ fltr["defaultDataMask"] = {
+ "extraFormData": {
+ "filters": [
+ {
+ "col": field,
+ "op": "IN",
+ "val": default,
+ }
+ ],
+ },
+ "filterState": {"value": default},
+ }
+
+ break
+
+ if "filterType" in fltr:
+ filter_by_key_and_field[key][field] = fltr
+
+ # Ancestors of filter-box charts.
Review Comment:
@michael-s-molina I generally agree that long functions aren't great and
though I took a pass at extracting this into sub functions the refactor wasn't
great, i.e., assembling the filters really contains the hierarchical wiring.
I also previous refactored fetching the defaults into a function, but there
needed to be lots of special case handling due to where/when the defaults were
used, i.e., a `filter_select` filter may require the time range/granularity but
these defaults are at the chart rather than dashboard level.
The TL;DR is granted the formulation isn't great I sense it's acceptable,
especially in the context of a migration.
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