Add best_run_id to REST/search API results (#2696)

Author: mbertrand

frontends/api/src/generated/v0/api.ts

@@ -367,6 +367,7 @@ def for_serialization(self, *, user: Optional["User"] = None):
                 queryset=LearningResourceRun.objects.filter(published=True)
                 .order_by("start_date", "enrollment_start", "id")
                 .for_serialization(),
+                to_attr="_published_runs",
             ),
             Prefetch(
                 "parents",
@@ -537,36 +538,68 @@ def audience(self) -> str | None:
     @cached_property
     def best_run(self) -> Optional["LearningResourceRun"]:
         """Returns the most current/upcoming enrollable run for the learning resource"""
-        published_runs = self.runs.filter(published=True)
+        if hasattr(self, "_published_runs"):
+            published_runs = self._published_runs
+        else:
+            published_runs = list(self.runs.filter(published=True))
+
+        if not published_runs:
+            return None
+
         now = now_in_utc()
+
         # Find the most recent run with a currently active enrollment period
-        best_lr_run = (
-            published_runs.filter(
-                (
-                    Q(enrollment_start__lte=now)
-                    | (Q(enrollment_start__isnull=True) & Q(start_date__lte=now))
-                )
-                & (
-                    Q(enrollment_end__gt=now)
-                    | (Q(enrollment_end__isnull=True) & Q(end_date__gt=now))
+        enrollable_runs = [
+            run
+            for run in published_runs
+            if (
+                (run.enrollment_start and run.enrollment_start <= now)
+                or (
+                    not run.enrollment_start
+                    and run.start_date
+                    and run.start_date <= now
                 )
             )
-            .order_by("start_date", "end_date")
-            .first()
-        )
-        if not best_lr_run:
-            # If no current enrollable run found, find the next upcoming run
-            best_lr_run = (
-                self.runs.filter(Q(published=True) & Q(start_date__gte=timezone.now()))
-                .order_by("start_date")
-                .first()
+            and (
+                (run.enrollment_end and run.enrollment_end > now)
+                or (not run.enrollment_end and run.end_date and run.end_date > now)
             )
-        if not best_lr_run:
-            # If current_run is still null, return the run with the latest start date
-            best_lr_run = (
-                self.runs.filter(Q(published=True)).order_by("-start_date").first()
+        ]
+        if enrollable_runs:
+            return min(
+                enrollable_runs,
+                key=lambda r: (
+                    r.start_date or timezone.now(),
+                    r.end_date or timezone.now(),
+                ),
             )
-        return best_lr_run
+
+        # If no enrollable runs found, find the next upcoming run
+        upcoming_runs = [
+            run
+            for run in published_runs
+            if run.start_date and run.start_date >= timezone.now()
+        ]
+        if upcoming_runs:
+            return min(upcoming_runs, key=lambda r: r.start_date)
+
+        # No enrollable/upcoming runs, return run with the latest start date
+        runs_with_dates = [run for run in published_runs if run.start_date]
+        if runs_with_dates:
+            return max(runs_with_dates, key=lambda r: r.start_date)
+
+        return published_runs[0] if published_runs else None
+
+    @cached_property
+    def published_runs(self) -> list["LearningResourceRun"]:
+        """Return a list of published runs for the resource"""
+        if hasattr(self, "_published_runs"):
+            return self._published_runs
+        return list(
+            self.runs.filter(published=True)
+            .order_by("start_date", "enrollment_start", "id")
+            .for_serialization()
+        )
 
     @cached_property
     def views_count(self) -> int:

frontends/api/src/generated/v1/api.ts

@@ -900,7 +900,9 @@ class LearningResourceBaseSerializer(serializers.ModelSerializer, WriteableTopic
         read_only=True,
     )
     resource_prices = LearningResourcePriceSerializer(read_only=True, many=True)
-    runs = LearningResourceRunSerializer(read_only=True, many=True, allow_null=True)
+    runs = LearningResourceRunSerializer(
+        source="published_runs", read_only=True, many=True, allow_null=True
+    )
     image = serializers.SerializerMethodField()
     learning_path_parents = MicroLearningPathRelationshipSerializer(
         many=True, read_only=True
@@ -915,13 +917,21 @@ class LearningResourceBaseSerializer(serializers.ModelSerializer, WriteableTopic
     format = serializers.ListField(child=FormatSerializer(), read_only=True)
     pace = serializers.ListField(child=PaceSerializer(), read_only=True)
     children = serializers.SerializerMethodField(allow_null=True)
+    best_run_id = serializers.SerializerMethodField(allow_null=True)
 
     @extend_schema_field(LearningResourceRelationshipChildField(allow_null=True))
     def get_children(self, instance):
         return LearningResourceRelationshipChildField(
             instance.children, many=True, read_only=True
         ).data
 
+    def get_best_run_id(self, instance) -> int | None:
+        """Return the best run id for the resource, if it has runs"""
+        best_run = instance.best_run
+        if best_run:
+            return best_run.id
+        return None
+
     def get_resource_category(self, instance) -> str:
         """Return the resource category of the resource"""
         if instance.resource_type in [

learning_resources/models.py

@@ -324,7 +324,7 @@ def test_learning_resource_serializer(  # noqa: PLR0913
         "ocw_topics": sorted(resource.ocw_topics),
         "runs": [
             serializers.LearningResourceRunSerializer(instance=run).data
-            for run in resource.runs.all()
+            for run in resource.published_runs
         ],
         detail_key: detail_serializer_cls(instance=getattr(resource, detail_key)).data,
         "views": resource.views.count(),
@@ -351,6 +351,7 @@ def test_learning_resource_serializer(  # noqa: PLR0913
         "max_weekly_hours": resource.max_weekly_hours,
         "min_weeks": resource.min_weeks,
         "max_weeks": resource.max_weeks,
+        "best_run_id": resource.best_run.id if resource.best_run else None,
     }
 
 
@@ -857,6 +858,11 @@ def test_instructors_display():
     load_instructors(
         run, [{"full_name": instructor.full_name} for instructor in instructors]
     )
+    # Clear cached properties so they pick up the new run with instructors
+    if hasattr(resource, "_published_runs"):
+        delattr(resource, "_published_runs")
+    if hasattr(resource, "published_runs"):
+        del resource.__dict__["published_runs"]
     serialized_resource = serializers.LearningResourceSerializer(resource).data
     metadata_serializer = serializers.LearningResourceMetadataDisplaySerializer(
         serialized_resource

learning_resources/serializers.py

@@ -300,6 +300,7 @@ class FilterConfig:
             "location": {"type": "keyword"},
         },
     },
+    "best_run_id": {"type": "long"},
     "next_start_date": {"type": "date"},
     "resource_age_date": {"type": "date"},
     "featured_rank": {"type": "float"},