rusackas commented on code in PR #44406:
URL: https://github.com/apache/superset/pull/44406#discussion_r4332206832
##########
superset/common/query_context_processor.py:
##########
@@ -449,34 +481,189 @@ def query_cache_key(self, query_obj: QueryObject,
**kwargs: Any) -> str | None:
)
return cache_key
- def _annotation_cache_context(self, query_obj: QueryObject) -> dict[str,
Any]:
+ def annotation_cache_key(self, query_obj: QueryObject) -> str | None:
+ """
+ Cache key for this query's annotation-layer payload, or ``None`` when
+ the query has no annotation layers.
+
+ Annotation payloads are fetched under the requesting user's access
+ scope, which is a stricter security requirement than the dataframe
+ itself has. Keying them separately from :meth:`query_cache_key` keeps
+ that scoping from forcing every distinct viewer of an annotated chart
+ onto their own full copy of the (potentially much larger) shared
+ dataframe: users with the same access scope share this key too.
"""
- Cache-key material binding cached annotation data to its security
- context.
+ if not query_obj or not query_obj.annotation_layers:
+ return None
+ return self.query_cache_key(
+ query_obj,
annotation_context=self._annotation_cache_context(query_obj)
+ )
- Annotation payloads are fetched per requesting user and stored on the
- same cache entry as the dataframe, so the key also binds the requesting
- user and, for chart-backed layers, the RLS clauses of the referenced
- chart's datasource.
+ def _annotation_cache_context(self, query_obj: QueryObject) -> dict[str,
Any]:
+ """
+ Cache-key material binding annotation data to its security *scope* so
+ users with the same access share a cache entry and users with a
+ different scope — or no access — never read each other's data.
+
+ * NATIVE layers: the ``can_read`` permission on ``Annotation``, the
+ only user-dependent dimension of these global records.
+ * Chart-backed (``line``/``table``) layers: see
+ :meth:`_annotation_source_scope`.
"""
- source_rls: dict[str, list[str] | None] = {}
+ context: dict[str, Any] = {}
+
+ if any(
+ layer.get("sourceType") == "NATIVE" for layer in
query_obj.annotation_layers
+ ):
+ context["annotation_read"] = security_manager.can_access(
+ "can_read", "Annotation"
+ )
+
+ source_scope: dict[str, Any] = {}
for layer in query_obj.annotation_layers:
if (
layer.get("sourceType")
not in ANNOTATION_SOURCE_TYPES_WITH_CHART_REFERENCE
):
continue
layer_value = layer.get("value")
+ source_scope[str(layer_value)] =
self._annotation_source_scope(layer)
+ if source_scope:
+ context["source_scope"] = source_scope
+
+ return context
+
+ def _annotation_source_scope(self, layer: dict[str, Any]) -> dict[str,
Any]:
+ """
+ Access and data-identity cache-key material for one chart-backed
+ annotation layer.
+
+ ``access`` keeps a user denied the referenced chart's datasource from
+ reading an authorized user's cached payload. When the chart has a
+ saved query context, this runs the *same* authorization path
+ :meth:`get_viz_annotation_data` executes
+ (``QueryContext.raise_for_access``) rather than the coarser
+ :meth:`SecurityManager.can_access_datasource` — a requester whose
+ access comes from a dashboard/viewer-promiscuous-mode bypass (which
+ depends on the chart's own saved ``form_data``, e.g. its
+ ``slice_id``/``dashboardId``) would otherwise still fail that coarser,
+ context-free check and collapse onto the same denied scope as a
+ genuinely unauthorized requester, letting the latter read the
+ former's cached payload. ``data_key`` is the annotation chart's own
+ query cache key(s) — derived from the same, override-applied query
+ objects actually executed (see :meth:`_apply_annotation_overrides`),
+ so it captures the datasource version, RLS clauses, and any per-user
+ Jinja/virtual-dataset RLS material exactly as the live fetch would,
+ including material an override only introduces at a finer grain.
+ Reusing this logic avoids re-deriving it and automatically inherits
+ any future correctness fixes made there.
+ """
+ layer_value = layer.get("value")
+ datasource = None
+ try:
chart = (
ChartDAO.find_by_id(layer_value) if layer_value is not None
else None
)
- annotation_datasource = chart.datasource if chart else None
- source_rls[str(layer.get("value"))] = (
- security_manager.get_rls_cache_key(annotation_datasource)
- if annotation_datasource
- else None
+ # resolved_datasource, not datasource: the latter is pinned to
+ # table-backed datasources and resolves to None for a
+ # semantic-view-backed chart, which would otherwise collapse
+ # every requester onto the same {access: None, data_key: None}
+ # scope below regardless of their actual access.
+ datasource = chart.resolved_datasource if chart else None
+ if chart is None or datasource is None:
+ return {"access": None, "data_key": None}
+
+ annotation_query_context = chart.get_query_context()
+ if annotation_query_context is not None:
+ self._apply_annotation_overrides(annotation_query_context,
layer)
+ try:
+ annotation_query_context.raise_for_access()
+ access: Any = True
+ except SupersetSecurityException:
+ access = False
+ data_key: Any = [
+ annotation_query_context.query_cache_key(query_object)
Review Comment:
You are right, I had the host chart in my head. The key is now derived from
the query after result-type preparation (`prepare_query_object`, shared with
the result handler), with a test covering a samples source chart whose metrics
get cleared.
##########
superset/common/query_context_processor.py:
##########
@@ -474,73 +503,226 @@ def query_cache_key(self, query_obj: QueryObject,
**kwargs: Any) -> str | None:
)
return cache_key
- def _annotation_cache_context(self, query_obj: QueryObject) -> dict[str,
Any]:
+ def annotation_cache_key(self, query_obj: QueryObject) -> str | None:
"""
- Cache-key material binding cached annotation data to its security
- context.
-
- Annotation payloads are fetched per requesting user and stored on the
- same cache entry as the dataframe, so the key also binds the requesting
- user and, for chart-backed layers, the RLS clauses of the referenced
- chart's datasource.
+ Cache key for this query's annotation-layer payload, or ``None`` when
+ the query has no annotation layers or when some layer's access scope
+ could not be resolved (the payload is then never cached, see
+ :meth:`_annotation_source_scope`).
+
+ Annotation payloads are fetched under the requesting user's access
+ scope, which is a stricter security requirement than the dataframe
+ itself has. Keying them separately from :meth:`query_cache_key` keeps
+ that scoping from forcing every distinct viewer of an annotated chart
+ onto their own full copy of the (potentially much larger) shared
+ dataframe: users with the same access scope share this key too.
"""
- source_rls: dict[str, list[str] | None] = {}
- source_versions: dict[str, tuple[str, str]] = {}
- metadata_versions: dict[str, str] = {}
+ if not query_obj or not query_obj.annotation_layers:
+ return None
+ if (annotation_context := self._annotation_cache_context(query_obj))
is None:
+ return None
+ return self.query_cache_key(query_obj,
annotation_context=annotation_context)
+
+ def _annotation_cache_context(
+ self, query_obj: QueryObject
+ ) -> dict[str, Any] | None:
+ """
+ Cache-key material binding annotation data to its security *scope* so
+ users with the same access share a cache entry and users with a
+ different scope — or no access — never read each other's data.
+
+ * NATIVE layers: the ``can_read`` permission on ``Annotation``, the
+ only user-dependent dimension of these global records.
+ * Chart-backed (``line``/``table``) layers: see
+ :meth:`_annotation_source_scope`, recorded per *layer* (not per
+ referenced chart) since two layers can reference the same chart
+ with different overrides, and so different scopes.
+
+ Returns ``None`` when any layer's scope cannot be resolved, meaning
+ the payload must not be cached at all.
+ """
+ context: dict[str, Any] = {}
+
+ if any(
+ layer.get("sourceType") == "NATIVE" for layer in
query_obj.annotation_layers
+ ):
+ context["annotation_read"] = security_manager.can_access(
+ "can_read", "Annotation"
+ )
+
+ source_scope: list[dict[str, Any]] = []
for layer in query_obj.annotation_layers:
if (
layer.get("sourceType")
not in ANNOTATION_SOURCE_TYPES_WITH_CHART_REFERENCE
):
continue
- layer_value = layer.get("value")
+ if (scope := self._annotation_source_scope(layer)) is None:
+ return None
+ source_scope.append({"value": str(layer.get("value")), **scope})
+ if source_scope:
+ context["source_scope"] = source_scope
+
+ return context
+
+ def _annotation_source_scope(self, layer: dict[str, Any]) -> dict[str,
Any] | None:
+ """
+ Access and data-identity cache-key material for one chart-backed
+ annotation layer, or ``None`` when it cannot be resolved.
+
+ ``None`` means "do not cache": a missing chart, a chart with neither
+ a saved query context nor a resolvable datasource, or any error while
+ deriving the scope would otherwise collapse an authorized and a
+ denied requester onto one shared identity, letting the latter read
+ the former's warmed payload. The caller fetches live instead.
+
+ ``access`` keeps a user denied the referenced chart's datasource from
+ reading an authorized user's cached payload. When the chart has a
+ saved query context, this runs the *same* authorization path
+ :meth:`get_viz_annotation_data` executes
+ (``QueryContext.raise_for_access``) rather than the coarser
+ :meth:`SecurityManager.can_access_datasource` — a requester whose
+ access comes from a dashboard/viewer-promiscuous-mode bypass (which
+ depends on the chart's own saved ``form_data``, e.g. its
+ ``slice_id``/``dashboardId``) would otherwise still fail that coarser,
+ context-free check and collapse onto the same denied scope as a
+ genuinely unauthorized requester, letting the latter read the
+ former's cached payload. ``data_key`` is the annotation chart's own
+ query cache key(s) — derived from the same, override-applied query
+ objects actually executed (see :meth:`_apply_annotation_overrides`),
+ so it captures the datasource version, RLS clauses, and any per-user
+ Jinja/virtual-dataset RLS material exactly as the live fetch would,
+ including material an override only introduces at a finer grain.
+ Reusing this logic avoids re-deriving it and automatically inherits
+ any future correctness fixes made there.
+ """
+ layer_value = layer.get("value")
+ try:
chart = (
ChartDAO.find_by_id(layer_value) if layer_value is not None
else None
)
- annotation_datasource: Datasource | None = (
- chart.resolved_datasource if chart else None
- )
- if chart and isinstance(chart.query_context, str):
+ if chart is None:
+ return None
+
+ # The saved query context is what the live fetch executes and
+ # authorizes against, so it is consulted first, independent of
+ # the chart's own datasource pointer: a chart can have its
+ # ``datasource_id`` cleared while its saved query context still
+ # targets a valid dataset, and the live fetch would still work.
+ annotation_query_context = chart.get_query_context()
+ if annotation_query_context is not None:
+ self._apply_annotation_overrides(annotation_query_context,
layer)
try:
- source: dict[str, Any] = json.loads(chart.query_context)[
- "datasource"
- ]
- annotation_datasource = DatasourceDAO.get_datasource(
- source["type"], source["id"]
- )
- except (
- ValueError,
- TypeError,
- KeyError,
- DatasourceNotFound,
- DatasourceTypeNotSupportedError,
- DatasourceValueIsIncorrect,
- ):
- annotation_datasource = None
+ annotation_query_context.raise_for_access()
+ access: Any = True
+ except SupersetSecurityException:
+ access = False
+ data_key: Any = [
+ annotation_query_context.query_cache_key(query_object)
Review Comment:
Fair point on the `-1` case. A referenced chart (or its datasource) with
caching disabled now yields no scope at all, so its annotation payload is
fetched live and never cached under the host chart. Tests cover both the
saved-query-context path and the datasource fallback.
##########
superset/charts/data/api.py:
##########
@@ -448,6 +448,15 @@ def _run_async(
return self._send_chart_response(result)
except ChartDataCacheLoadError:
pass
+ except (
Review Comment:
Added `test_run_async_answers_400_when_cached_read_back_fails`:
`execute(force_cached=True)` raises `ChartDataQueryFailedError`, and it asserts
a 400 with no task submitted.
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