sadpandajoe commented on code in PR #44406:
URL: https://github.com/apache/superset/pull/44406#discussion_r4243433681
##########
superset/common/query_context_processor.py:
##########
@@ -72,7 +74,6 @@
get_column_name,
get_column_names_from_columns,
get_column_names_from_metrics,
- get_user_id,
is_adhoc_column,
Review Comment:
Dropping `get_user_id` here leaves the rest of the old annotation-context
imports orphaned: `DatasourceDAO`, `DatasourceNotFound`,
`DatasourceTypeNotSupportedError`, `DatasourceValueIsIncorrect`, `json` and the
`TYPE_CHECKING` `Datasource` no longer have any reference in this file now that
`_annotation_source_scope` resolves the datasource through the chart's query
context. Ruff's F401 will fail lint/pre-commit on the changed file. Can these
be removed in the same pass?
##########
superset/common/query_context_processor.py:
##########
@@ -343,6 +355,32 @@ def get_df_payload_result(
# nonce reads the freshly-cached result instead of recomputing
it.
self._mark_force_executed(query_obj, cache_key,
cache.result_persisted)
+ # Annotation data is fetched per requesting user (and, for chart-backed
+ # layers, scoped by the referenced chart datasource's RLS), so it is
+ # resolved and cached under its own entry — independent of whether the
+ # (shareable) dataframe above was a hit or a miss — rather than forcing
+ # every viewer of the same chart onto their own full dataframe copy.
+ annotation_data: dict[str, Any] = {}
+ if (
+ query_obj
+ and query_obj.annotation_layers
+ and cache.status != QueryStatus.FAILED
Review Comment:
The caller-level wiring around this block has no direct coverage: every new
`get_df_payload_result` test either supplies a successful dataframe cache or
replaces `_get_annotation_data_cached` with a mock. If someone drops the
`cache.status != QueryStatus.FAILED` guard, a failed dataframe would still
trigger the annotation fetch, and an annotation exception could replace the
original failure response; likewise nothing asserts the annotation nonce marker
is written only when `set_and_log_cache` actually persisted (the existing
marker tests call `_mark_force_executed` directly). Could a test drive
`get_df_payload_result` with annotation layers through the real helper for both
a failed dataframe (annotation helper not called, `annotation_data == {}`,
original error kept) and a non-persisting annotation write (no marker, next
same-nonce read still forces)?
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