feat(ingest/looker): Do not emit usage for non-ingested dashboards an…

…d charts (datahub-project#11647)

metadata-ingestion/src/datahub/ingestion/source/looker/looker_common.py

@@ -1408,6 +1408,15 @@ class LookerDashboardSourceReport(StaleEntityRemovalSourceReport):
     dashboards_with_activity: LossySet[str] = dataclasses_field(
         default_factory=LossySet
     )
+
+    # Entities that don't seem to exist, so we don't emit usage aspects for them despite having usage data
+    dashboards_skipped_for_usage: LossySet[str] = dataclasses_field(
+        default_factory=LossySet
+    )
+    charts_skipped_for_usage: LossySet[str] = dataclasses_field(
+        default_factory=LossySet
+    )
+
     stage_latency: List[StageLatency] = dataclasses_field(default_factory=list)
     _looker_explore_registry: Optional[LookerExploreRegistry] = None
     total_explores: int = 0

metadata-ingestion/src/datahub/ingestion/source/looker/looker_source.py

@@ -68,6 +68,7 @@
     ViewField,
     ViewFieldType,
     gen_model_key,
+    get_urn_looker_element_id,
 )
 from datahub.ingestion.source.looker.looker_config import LookerDashboardSourceConfig
 from datahub.ingestion.source.looker.looker_lib_wrapper import LookerAPI
@@ -165,6 +166,9 @@ def __init__(self, config: LookerDashboardSourceConfig, ctx: PipelineContext):
         # Required, as we do not ingest all folders but only those that have dashboards/looks
         self.processed_folders: List[str] = []
 
+        # Keep track of ingested chart urns, to omit usage for non-ingested entities
+        self.chart_urns: Set[str] = set()
+
     @staticmethod
     def test_connection(config_dict: dict) -> TestConnectionReport:
         test_report = TestConnectionReport()
@@ -642,6 +646,7 @@ def _make_chart_metadata_events(
         chart_urn = self._make_chart_urn(
             element_id=dashboard_element.get_urn_element_id()
         )
+        self.chart_urns.add(chart_urn)
         chart_snapshot = ChartSnapshot(
             urn=chart_urn,
             aspects=[Status(removed=False)],
@@ -1380,7 +1385,9 @@ def _get_folder_and_ancestors_workunits(
         yield from self._emit_folder_as_container(folder)
 
     def extract_usage_stat(
-        self, looker_dashboards: List[looker_usage.LookerDashboardForUsage]
+        self,
+        looker_dashboards: List[looker_usage.LookerDashboardForUsage],
+        ingested_chart_urns: Set[str],
     ) -> List[MetadataChangeProposalWrapper]:
         looks: List[looker_usage.LookerChartForUsage] = []
         # filter out look from all dashboard
@@ -1391,6 +1398,15 @@ def extract_usage_stat(
 
         # dedup looks
         looks = list({str(look.id): look for look in looks}.values())
+        filtered_looks = []
+        for look in looks:
+            if not look.id:
+                continue
+            chart_urn = self._make_chart_urn(get_urn_looker_element_id(look.id))
+            if chart_urn in ingested_chart_urns:
+                filtered_looks.append(look)
+            else:
+                self.reporter.charts_skipped_for_usage.add(look.id)
 
         # Keep stat generators to generate entity stat aspect later
         stat_generator_config: looker_usage.StatGeneratorConfig = (
@@ -1414,7 +1430,7 @@ def extract_usage_stat(
             stat_generator_config,
             self.reporter,
             self._make_chart_urn,
-            looks,
+            filtered_looks,
         )
 
         mcps: List[MetadataChangeProposalWrapper] = []
@@ -1669,7 +1685,7 @@ def get_workunits_internal(self) -> Iterable[MetadataWorkUnit]:
         if self.source_config.extract_usage_history:
             self.reporter.report_stage_start("usage_extraction")
             usage_mcps: List[MetadataChangeProposalWrapper] = self.extract_usage_stat(
-                looker_dashboards_for_usage
+                looker_dashboards_for_usage, self.chart_urns
             )
             for usage_mcp in usage_mcps:
                 yield usage_mcp.as_workunit()

metadata-ingestion/src/datahub/ingestion/source/looker/looker_usage.py

@@ -42,6 +42,7 @@
     TimeWindowSizeClass,
     _Aspect as AspectAbstract,
 )
+from datahub.utilities.lossy_collections import LossySet
 
 logger = logging.getLogger(__name__)
 
@@ -170,7 +171,7 @@ def __init__(
         self.config = config
         self.looker_models = looker_models
         # Later it will help to find out for what are the looker entities from query result
-        self.id_vs_model: Dict[str, ModelForUsage] = {
+        self.id_to_model: Dict[str, ModelForUsage] = {
             self.get_id(looker_object): looker_object for looker_object in looker_models
         }
         self.post_filter = len(self.looker_models) > 100
@@ -225,6 +226,10 @@ def get_id(self, looker_object: ModelForUsage) -> str:
     def get_id_from_row(self, row: dict) -> str:
         pass
 
+    @abstractmethod
+    def report_skip_set(self) -> LossySet[str]:
+        pass
+
     def create_mcp(
         self, model: ModelForUsage, aspect: Aspect
     ) -> MetadataChangeProposalWrapper:
@@ -258,20 +263,11 @@ def _process_entity_timeseries_rows(
 
         return entity_stat_aspect
 
-    def _process_absolute_aspect(self) -> List[Tuple[ModelForUsage, AspectAbstract]]:
-        aspects: List[Tuple[ModelForUsage, AspectAbstract]] = []
-        for looker_object in self.looker_models:
-            aspects.append(
-                (looker_object, self.to_entity_absolute_stat_aspect(looker_object))
-            )
-
-        return aspects
-
     def _fill_user_stat_aspect(
         self,
         entity_usage_stat: Dict[Tuple[str, str], Aspect],
         user_wise_rows: List[Dict],
-    ) -> Iterable[Tuple[ModelForUsage, Aspect]]:
+    ) -> Iterable[Tuple[str, Aspect]]:
         logger.debug("Entering fill user stat aspect")
 
         # We first resolve all the users using a threadpool to warm up the cache
@@ -300,7 +296,7 @@ def _fill_user_stat_aspect(
 
         for row in user_wise_rows:
             # Confirm looker object was given for stat generation
-            looker_object = self.id_vs_model.get(self.get_id_from_row(row))
+            looker_object = self.id_to_model.get(self.get_id_from_row(row))
             if looker_object is None:
                 logger.warning(
                     "Looker object with id({}) was not register with stat generator".format(
@@ -338,7 +334,7 @@ def _fill_user_stat_aspect(
         logger.debug("Starting to yield answers for user-wise counts")
 
         for (id, _), aspect in entity_usage_stat.items():
-            yield self.id_vs_model[id], aspect
+            yield id, aspect
 
     def _execute_query(self, query: LookerQuery, query_name: str) -> List[Dict]:
         rows = []
@@ -357,7 +353,7 @@ def _execute_query(self, query: LookerQuery, query_name: str) -> List[Dict]:
             )
             if self.post_filter:
                 logger.debug("post filtering")
-                rows = [r for r in rows if self.get_id_from_row(r) in self.id_vs_model]
+                rows = [r for r in rows if self.get_id_from_row(r) in self.id_to_model]
                 logger.debug("Filtered down to %d rows", len(rows))
         except Exception as e:
             logger.warning(f"Failed to execute {query_name} query: {e}")
@@ -378,7 +374,8 @@ def generate_usage_stat_mcps(self) -> Iterable[MetadataChangeProposalWrapper]:
             return
 
         # yield absolute stat for looker entities
-        for looker_object, aspect in self._process_absolute_aspect():  # type: ignore
+        for looker_object in self.looker_models:
+            aspect = self.to_entity_absolute_stat_aspect(looker_object)
             yield self.create_mcp(looker_object, aspect)
 
         # Execute query and process the raw json which contains stat information
@@ -399,10 +396,13 @@ def generate_usage_stat_mcps(self) -> Iterable[MetadataChangeProposalWrapper]:
         )
         user_wise_rows = self._execute_query(user_wise_query_with_filters, "user_query")
         # yield absolute stat for entity
-        for looker_object, aspect in self._fill_user_stat_aspect(
+        for object_id, aspect in self._fill_user_stat_aspect(
             entity_usage_stat, user_wise_rows
         ):
-            yield self.create_mcp(looker_object, aspect)
+            if object_id in self.id_to_model:
+                yield self.create_mcp(self.id_to_model[object_id], aspect)
+            else:
+                self.report_skip_set().add(object_id)
 
 
 class DashboardStatGenerator(BaseStatGenerator):
@@ -425,6 +425,9 @@ def __init__(
     def get_stats_generator_name(self) -> str:
         return "DashboardStats"
 
+    def report_skip_set(self) -> LossySet[str]:
+        return self.report.dashboards_skipped_for_usage
+
     def get_filter(self) -> Dict[ViewField, str]:
         return {
             HistoryViewField.HISTORY_DASHBOARD_ID: ",".join(
@@ -541,6 +544,9 @@ def __init__(
     def get_stats_generator_name(self) -> str:
         return "ChartStats"
 
+    def report_skip_set(self) -> LossySet[str]:
+        return self.report.charts_skipped_for_usage
+
     def get_filter(self) -> Dict[ViewField, str]:
         return {
             LookViewField.LOOK_ID: ",".join(