⚡ Bolt: [performance improvement] Optimize Civic Intelligence Engine with column projection

backend/civic_intelligence.py

@@ -51,7 +51,12 @@ def run_daily_cycle(self):
 
             # 1. Fetch Data
             # Get issues created in the last 24 hours
-            issues_24h = db.query(Issue).filter(Issue.created_at >= last_24h).all()
+            # Performance Optimization: Use column projection to avoid loading full ORM models,
+            # since trend analyzer only needs specific attributes (id, description, category, lat, lon, upvotes, created_at)
+            issues_24h = db.query(
+                Issue.id, Issue.description, Issue.category,
+                Issue.latitude, Issue.longitude, Issue.upvotes, Issue.created_at
+            ).filter(Issue.created_at >= last_24h).all()
 
             # 2. Trend Analysis
             trends = trend_analyzer.analyze(issues_24h)

backend/tests/test_civic_intelligence.py

@@ -152,15 +152,18 @@ def open_side_effect(file, mode='r', *args, **kwargs):
 
     # Define query side effects
     def query_side_effect(*args):
-        if len(args) == 1:
+        if len(args) > 0:
             model = args[0]
-            if getattr(model, '__name__', '') == 'Issue':
+            # Handle column projection (InstrumentedAttribute) by checking class_
+            class_name = getattr(model, 'class_', model).__name__ if hasattr(model, 'class_') else getattr(model, '__name__', '')
+
+            if class_name == 'Issue':
                 return mock_query_issues
             elif hasattr(model, 'name') and model.name == 'count':
                 return mock_query_issues
-            elif getattr(model, '__name__', '') == 'EscalationAudit':
+            elif class_name == 'EscalationAudit':
                 return mock_query_upgrades
-            elif getattr(model, '__name__', '') == 'Grievance':
+            elif class_name == 'Grievance':
                 return mock_query_grievance
         return MagicMock()