fix(workflow_engine): Cleanup malformed anomaly conditions

migrations_lockfile.txt

@@ -43,4 +43,4 @@ tempest: 0001_squashed_0003_use_encrypted_char_field
 
 uptime: 0001_squashed_0055_backfill_2xx_status_assertion
 
-workflow_engine: 0120_replace_boolean_deescalation_comparisons
+workflow_engine: 0121_repair_mixed_anomaly_detection_condition_groups

src/sentry/workflow_engine/migrations/0121_repair_mixed_anomaly_detection_condition_groups.py

@@ -0,0 +1,70 @@
+# Generated by Django 5.2.16 on 2026-09-17 20:53
+
+from django.db import migrations
+from django.db.backends.base.schema import BaseDatabaseSchemaEditor
+from django.db.migrations.state import StateApps
+from django.db.models import Count, Q
+
+from sentry.new_migrations.migrations import CheckedMigration
+from sentry.utils.query import RangeQuerySetWrapperWithProgressBar
+
+ANOMALY_DETECTION = "anomaly_detection"
+DEFAULT_ANOMALY_COMPARISON = {
+    "seasonality": "auto",
+    "sensitivity": "low",
+    "threshold_type": 2,
+}
+
+
+def repair_mixed_anomaly_detection_condition_groups(
+    apps: StateApps, schema_editor: BaseDatabaseSchemaEditor
+) -> None:
+    DataCondition = apps.get_model("workflow_engine", "DataCondition")
+    DataConditionGroup = apps.get_model("workflow_engine", "DataConditionGroup")
+
+    condition_groups = DataConditionGroup.objects.annotate(
+        condition_count=Count("conditions"),
+        anomaly_count=Count(
+            "conditions",
+            filter=Q(conditions__type=ANOMALY_DETECTION),
+        ),
+    ).filter(condition_count__gt=1, anomaly_count__gt=0)
+
+    for condition_group in RangeQuerySetWrapperWithProgressBar(condition_groups):
+        conditions = DataCondition.objects.filter(condition_group_id=condition_group.id)
+        conditions.filter(type=ANOMALY_DETECTION).update(comparison=DEFAULT_ANOMALY_COMPARISON)
+        conditions.exclude(type=ANOMALY_DETECTION).delete()
+
+
+class Migration(CheckedMigration):
+    # This flag is used to mark that a migration shouldn't be automatically run in production.
+    # This should only be used for operations where it's safe to run the migration after your
+    # code has deployed. So this should not be used for most operations that alter the schema
+    # of a table.
+    # Here are some things that make sense to mark as post deployment:
+    # - Large data migrations. Typically we want these to be run manually so that they can be
+    #   monitored and not block the deploy for a long period of time while they run.
+    # - Adding indexes to large tables. Since this can take a long time, we'd generally prefer to
+    #   run this outside deployments so that we don't block them. Note that while adding an index
+    #   is a schema change, it's completely safe to run the operation after the code has deployed.
+    # Once deployed, run these manually via: https://develop.sentry.dev/database-migrations/#migration-deployment
+
+    is_post_deployment = True
+
+    dependencies = [
+        ("workflow_engine", "0120_replace_boolean_deescalation_comparisons"),
+    ]
+
+    operations = [
+        migrations.RunPython(
+            repair_mixed_anomaly_detection_condition_groups,
+            reverse_code=migrations.RunPython.noop,
+            hints={
+                "tables": [
+                    "workflow_engine_datacondition",
+                    "workflow_engine_dataconditiongroup",
+                    "workflow_engine_dataconditionalertruletrigger",
+                ]
+            },
+        ),
+    ]

tests/sentry/migrations/test_0121_repair_mixed_anomaly_detection_condition_groups.py

@@ -0,0 +1,104 @@
+from sentry.testutils.cases import TestMigrations
+
+
+class RepairMixedAnomalyDetectionConditionGroupsTest(TestMigrations):
+    app = "workflow_engine"
+    migrate_from = "0120_replace_boolean_deescalation_comparisons"  # pyright: ignore[reportAssignmentType]
+    migrate_to = "0121_repair_mixed_anomaly_detection_condition_groups"  # pyright: ignore[reportAssignmentType]
+
+    def setup_before_migration(self, apps):
+        DataCondition = apps.get_model("workflow_engine", "DataCondition")
+        DataConditionGroup = apps.get_model("workflow_engine", "DataConditionGroup")
+
+        mixed_group = DataConditionGroup.objects.create(organization_id=self.organization.id)
+        self.mixed_anomaly_id = DataCondition.objects.create(
+            condition_group_id=mixed_group.id,
+            type="anomaly_detection",
+            comparison=0.0,
+            condition_result=2,
+        ).id
+        self.mixed_static_ids = [
+            DataCondition.objects.create(
+                condition_group_id=mixed_group.id,
+                type=condition_type,
+                comparison=comparison,
+                condition_result=condition_result,
+            ).id
+            for condition_type, comparison, condition_result in (
+                ("lte", 0.0, 0),
+                ("gt", 100.0, 2),
+            )
+        ]
+
+        anomalies_only_group = DataConditionGroup.objects.create(
+            organization_id=self.organization.id
+        )
+        self.anomalies_only_ids = [
+            DataCondition.objects.create(
+                condition_group_id=anomalies_only_group.id,
+                type="anomaly_detection",
+                comparison={
+                    "seasonality": "weekly",
+                    "sensitivity": "high",
+                    "threshold_type": 0,
+                },
+                condition_result=condition_result,
+            ).id
+            for condition_result in (1, 2)
+        ]
+
+        single_anomaly_group = DataConditionGroup.objects.create(
+            organization_id=self.organization.id
+        )
+        self.single_anomaly_id = DataCondition.objects.create(
+            condition_group_id=single_anomaly_group.id,
+            type="anomaly_detection",
+            comparison={
+                "seasonality": "daily",
+                "sensitivity": "medium",
+                "threshold_type": 1,
+            },
+            condition_result=2,
+        ).id
+
+        static_group = DataConditionGroup.objects.create(organization_id=self.organization.id)
+        self.static_condition_ids = [
+            DataCondition.objects.create(
+                condition_group_id=static_group.id,
+                type=condition_type,
+                comparison=comparison,
+                condition_result=condition_result,
+            ).id
+            for condition_type, comparison, condition_result in (
+                ("gt", 10.0, 2),
+                ("lte", 10.0, 0),
+            )
+        ]
+
+    def test_repair_mixed_anomaly_detection_condition_groups(self):
+        DataCondition = self.apps.get_model("workflow_engine", "DataCondition")
+        default_comparison = {
+            "seasonality": "auto",
+            "sensitivity": "low",
+            "threshold_type": 2,
+        }
+
+        mixed_anomaly = DataCondition.objects.get(id=self.mixed_anomaly_id)
+        assert mixed_anomaly.comparison == default_comparison
+        assert not DataCondition.objects.filter(id__in=self.mixed_static_ids).exists()
+
+        anomalies_only = DataCondition.objects.filter(id__in=self.anomalies_only_ids).order_by("id")
+        assert anomalies_only.count() == 2
+        assert list(anomalies_only.values_list("comparison", flat=True)) == [
+            default_comparison,
+            default_comparison,
+        ]
+
+        single_anomaly = DataCondition.objects.get(id=self.single_anomaly_id)
+        assert single_anomaly.comparison == {
+            "seasonality": "daily",
+            "sensitivity": "medium",
+            "threshold_type": 1,
+        }
+
+        assert DataCondition.objects.filter(id__in=self.static_condition_ids).count() == 2