mirror of
https://github.com/pacnpal/thrillwiki_django_no_react.git
synced 2026-02-05 06:05:18 -05:00
feat: Implement email change cancellation, location search, and admin anomaly detection endpoints.
This commit is contained in:
pacnpal
@@ -34,4 +34,22 @@ urlpatterns = [
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views.CeleryTaskStatusView.as_view(),
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name="task_status",
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),
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# Anomaly Detection
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path(
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"anomalies/detect/",
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views.DetectAnomaliesView.as_view(),
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name="detect_anomalies",
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),
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# Metrics Collection
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path(
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"metrics/collect/",
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views.CollectMetricsView.as_view(),
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name="collect_metrics",
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),
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# Pipeline Integrity Scan
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path(
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"pipeline/integrity-scan/",
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views.PipelineIntegrityScanView.as_view(),
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name="pipeline_integrity_scan",
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),
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]
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@@ -708,3 +708,558 @@ class CeleryTaskStatusView(APIView):
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{"detail": "Failed to fetch task status"},
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status=status.HTTP_500_INTERNAL_SERVER_ERROR,
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)
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class DetectAnomaliesView(APIView):
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"""
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POST /admin/anomalies/detect/
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Detect data anomalies for admin dashboard.
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Full parity with Supabase Edge Function: detect-anomalies
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The original Edge Function implements 7 ML detection algorithms:
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1. Z-Score (standard deviation outliers)
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2. Moving Average (trend deviation)
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3. Rate of Change (sudden changes)
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4. Isolation Forest (ML-based outlier detection)
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5. Seasonal Decomposition (periodic pattern anomalies)
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6. Predictive (Holt-Winters exponential smoothing)
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7. Ensemble (combines all algorithms)
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This implementation provides:
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- Config-driven detection framework
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- Data quality checks (orphaned records, duplicates, incomplete data)
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- Auto-alerting for critical anomalies
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TODO: Implement full ML algorithms with numpy/scipy in follow-up task.
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"""
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permission_classes = [IsAdminUser]
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# Severity score thresholds
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SEVERITY_THRESHOLDS = {
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"critical": 4.0,
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"high": 3.0,
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"medium": 2.0,
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"low": 1.0,
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}
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def post(self, request):
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"""Detect and return data anomalies."""
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try:
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# ================================================================
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# Input validation with safe type handling
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# ================================================================
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raw_type = request.data.get("type", "all")
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anomaly_type = raw_type if isinstance(raw_type, str) else "all"
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anomaly_type = anomaly_type.strip().lower()[:50]
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# Validate anomaly_type against allowed values
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allowed_types = {"all", "orphaned", "duplicates", "incomplete", "data_quality"}
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if anomaly_type not in allowed_types:
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anomaly_type = "all"
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# Safe sensitivity parsing with bounds
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try:
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sensitivity = float(request.data.get("sensitivity", 2.5))
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sensitivity = max(0.1, min(sensitivity, 10.0)) # Clamp to [0.1, 10.0]
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except (ValueError, TypeError):
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sensitivity = 2.5
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# Safe lookback_minutes parsing with bounds
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try:
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lookback_minutes = int(request.data.get("lookback_minutes", 60))
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lookback_minutes = max(1, min(lookback_minutes, 10080)) # 1 min to 1 week
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except (ValueError, TypeError):
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lookback_minutes = 60
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anomalies = []
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# ================================================================
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# Data Quality Anomalies (immediate checks)
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# ================================================================
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# Check for orphaned records
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if anomaly_type in ["all", "orphaned", "data_quality"]:
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try:
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Park = apps.get_model("parks", "Park")
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Ride = apps.get_model("rides", "Ride")
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# Rides without parks
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orphaned_rides = Ride.objects.filter(park__isnull=True).count()
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if orphaned_rides > 0:
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severity = "high" if orphaned_rides > 10 else "medium"
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anomalies.append({
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"id": f"orphaned_rides_{timezone.now().timestamp()}",
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"metric_name": "orphaned_records",
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"metric_category": "data_quality",
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"anomaly_type": "orphaned_rides",
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"severity": severity,
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"baseline_value": 0,
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"anomaly_value": orphaned_rides,
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"deviation_score": orphaned_rides / 5.0, # Score based on count
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"confidence_score": 1.0, # 100% confidence for exact counts
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"detection_algorithm": "rule_based",
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"description": f"{orphaned_rides} rides without associated park",
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"detected_at": timezone.now().isoformat(),
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})
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except LookupError:
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pass
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# Check for duplicate slugs
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if anomaly_type in ["all", "duplicates", "data_quality"]:
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try:
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Park = apps.get_model("parks", "Park")
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duplicate_slugs = (
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Park.objects.values("slug")
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.annotate(count=Count("id"))
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.filter(count__gt=1)
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)
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dup_count = duplicate_slugs.count()
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if dup_count > 0:
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anomalies.append({
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"id": f"duplicate_slugs_{timezone.now().timestamp()}",
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"metric_name": "duplicate_slugs",
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"metric_category": "data_quality",
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"anomaly_type": "duplicate_values",
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"severity": "high" if dup_count > 5 else "medium",
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"baseline_value": 0,
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"anomaly_value": dup_count,
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"deviation_score": dup_count / 2.0,
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"confidence_score": 1.0,
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"detection_algorithm": "rule_based",
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"description": f"{dup_count} duplicate park slugs detected",
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"detected_at": timezone.now().isoformat(),
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})
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except LookupError:
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pass
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# Check for missing required fields
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if anomaly_type in ["all", "incomplete", "data_quality"]:
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try:
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Park = apps.get_model("parks", "Park")
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parks_no_location = Park.objects.filter(
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Q(latitude__isnull=True) | Q(longitude__isnull=True)
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).count()
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if parks_no_location > 0:
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anomalies.append({
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"id": f"incomplete_parks_{timezone.now().timestamp()}",
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"metric_name": "incomplete_data",
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"metric_category": "data_quality",
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"anomaly_type": "missing_required_fields",
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"severity": "low" if parks_no_location < 10 else "medium",
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"baseline_value": 0,
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"anomaly_value": parks_no_location,
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"deviation_score": parks_no_location / 10.0,
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"confidence_score": 1.0,
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"detection_algorithm": "rule_based",
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"description": f"{parks_no_location} parks missing location data",
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"detected_at": timezone.now().isoformat(),
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})
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except LookupError:
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pass
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# ================================================================
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# TODO: Implement ML-based anomaly detection
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# ================================================================
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# The original Supabase Edge Function reads from:
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# - anomaly_detection_config table (enabled metrics, sensitivity, algorithms)
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# - metric_time_series table (historical metric data)
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#
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# Then applies these algorithms:
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# 1. z_score: (value - mean) / std_dev
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# 2. moving_average: deviation from rolling average
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# 3. rate_of_change: delta compared to previous values
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# 4. isolation_forest: sklearn-style outlier detection
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# 5. seasonal_decomposition: detect periodic pattern breaks
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# 6. predictive: Holt-Winters forecasting comparison
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# 7. ensemble: weighted combination of all methods
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#
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# For now, we return data quality anomalies. Full ML implementation
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# requires numpy/scipy dependencies and metric_time_series data.
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# ================================================================
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# Auto-create alerts for critical/high severity
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# ================================================================
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for anomaly in anomalies:
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if anomaly.get("severity") in ["critical", "high"]:
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# Log critical anomaly (would create SystemAlert in full impl)
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logger.warning(
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f"Critical anomaly detected: {anomaly.get('description')}",
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extra={
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"anomaly_type": anomaly.get("anomaly_type"),
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"severity": anomaly.get("severity"),
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"deviation_score": anomaly.get("deviation_score"),
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}
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)
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# Calculate summary counts
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detected_count = len(anomalies)
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critical_count = sum(1 for a in anomalies if a.get("severity") in ["critical", "high"])
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return Response({
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"success": True,
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"detected_count": detected_count,
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"critical_count": critical_count,
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"anomalies": anomalies,
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"scanned_at": timezone.now().isoformat(),
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"config": {
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"sensitivity": sensitivity,
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"lookback_minutes": lookback_minutes,
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"algorithms_available": [
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"rule_based",
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# TODO: Add when implemented
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# "z_score", "moving_average", "rate_of_change",
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# "isolation_forest", "seasonal", "predictive", "ensemble"
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],
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},
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})
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except Exception as e:
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capture_and_log(e, "Detect anomalies - error", source="api")
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return Response(
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{"detail": "Failed to detect anomalies"},
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status=status.HTTP_500_INTERNAL_SERVER_ERROR,
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)
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class CollectMetricsView(APIView):
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"""
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POST /admin/metrics/collect/
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Collect system metrics for admin dashboard.
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BULLETPROOFED: Safe input parsing with validation.
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"""
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permission_classes = [IsAdminUser]
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# Allowed values
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ALLOWED_METRIC_TYPES = {"all", "database", "users", "moderation", "performance"}
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ALLOWED_TIME_RANGES = {"24h", "7d", "30d", "1h", "12h"}
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def post(self, request):
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"""Collect and return system metrics."""
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try:
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# ================================================================
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# Input validation with safe type handling
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# ================================================================
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raw_type = request.data.get("type", "all")
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metric_type = raw_type if isinstance(raw_type, str) else "all"
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metric_type = metric_type.strip().lower()[:50]
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if metric_type not in self.ALLOWED_METRIC_TYPES:
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metric_type = "all"
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raw_time_range = request.data.get("timeRange", "24h")
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time_range = raw_time_range if isinstance(raw_time_range, str) else "24h"
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time_range = time_range.strip().lower()[:10]
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if time_range not in self.ALLOWED_TIME_RANGES:
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time_range = "24h"
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# Parse time range to cutoff
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time_range_map = {
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"1h": timedelta(hours=1),
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"12h": timedelta(hours=12),
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"24h": timedelta(hours=24),
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"7d": timedelta(days=7),
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"30d": timedelta(days=30),
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}
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cutoff = timezone.now() - time_range_map.get(time_range, timedelta(hours=24))
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metrics = {
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"collectedAt": timezone.now().isoformat(),
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"timeRange": time_range,
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}
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# Database metrics
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if metric_type in ["all", "database"]:
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try:
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Park = apps.get_model("parks", "Park")
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Ride = apps.get_model("rides", "Ride")
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metrics["database"] = {
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"totalParks": Park.objects.count(),
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"totalRides": Ride.objects.count(),
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"recentParks": Park.objects.filter(created_at__gte=cutoff).count(),
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"recentRides": Ride.objects.filter(created_at__gte=cutoff).count(),
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}
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except (LookupError, Exception):
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metrics["database"] = {
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"error": "Could not fetch database metrics"
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}
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# User metrics
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if metric_type in ["all", "users"]:
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try:
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metrics["users"] = {
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"totalUsers": User.objects.count(),
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"activeUsers": User.objects.filter(
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last_login__gte=cutoff
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).count(),
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"newUsers": User.objects.filter(
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date_joined__gte=cutoff
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).count(),
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}
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except Exception:
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metrics["users"] = {
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"error": "Could not fetch user metrics"
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}
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# Moderation metrics
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if metric_type in ["all", "moderation"]:
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try:
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EditSubmission = apps.get_model("moderation", "EditSubmission")
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metrics["moderation"] = {
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"pendingSubmissions": EditSubmission.objects.filter(
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status="PENDING"
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).count(),
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"recentSubmissions": EditSubmission.objects.filter(
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created_at__gte=cutoff
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).count(),
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}
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except (LookupError, Exception):
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metrics["moderation"] = {
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"error": "Could not fetch moderation metrics"
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}
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return Response({
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"success": True,
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"metrics": metrics,
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})
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except Exception as e:
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capture_and_log(e, "Collect metrics - error", source="api")
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return Response(
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{"detail": "Failed to collect metrics"},
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status=status.HTTP_500_INTERNAL_SERVER_ERROR,
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)
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class PipelineIntegrityScanView(APIView):
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"""
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POST /admin/pipeline/integrity-scan/
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Scan data pipeline for integrity issues.
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BULLETPROOFED: Safe input parsing with validation.
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"""
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permission_classes = [IsAdminUser]
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# Allowed values
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ALLOWED_SCAN_TYPES = {"full", "referential", "status", "media", "submissions", "stuck", "versions"}
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MAX_HOURS_BACK = 720 # 30 days max
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def post(self, request):
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"""Run integrity scan on data pipeline."""
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try:
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# ================================================================
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# Input validation with safe type handling
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# ================================================================
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# Safe hours_back parsing with bounds
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try:
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hours_back = int(request.data.get("hours_back", 48))
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hours_back = max(1, min(hours_back, self.MAX_HOURS_BACK))
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except (ValueError, TypeError):
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hours_back = 48
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# Safe scan_type validation
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raw_type = request.data.get("type", "full")
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scan_type = raw_type if isinstance(raw_type, str) else "full"
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scan_type = scan_type.strip().lower()[:50]
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if scan_type not in self.ALLOWED_SCAN_TYPES:
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scan_type = "full"
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# Safe fix_issues parsing (boolean)
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raw_fix = request.data.get("fix", False)
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fix_issues = raw_fix is True or str(raw_fix).lower() in ("true", "1", "yes")
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# Calculate cutoff time based on hours_back
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cutoff_time = timezone.now() - timedelta(hours=hours_back)
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issues = []
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fixed_count = 0
|
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# Check for referential integrity
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if scan_type in ["full", "referential"]:
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try:
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Ride = apps.get_model("rides", "Ride")
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Park = apps.get_model("parks", "Park")
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# Rides pointing to non-existent parks
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valid_park_ids = Park.objects.values_list("id", flat=True)
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invalid_rides = Ride.objects.exclude(
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park_id__in=valid_park_ids
|
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).exclude(park_id__isnull=True)
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|
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if invalid_rides.exists():
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for ride in invalid_rides[:10]: # Limit to 10 examples
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issues.append({
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"issue_type": "broken_reference",
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"entity_type": "ride",
|
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"entity_id": str(ride.id),
|
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"submission_id": "",
|
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"severity": "critical",
|
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"description": f"Ride '{ride.name}' has invalid park reference",
|
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"detected_at": timezone.now().isoformat(),
|
||||
})
|
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|
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if fix_issues:
|
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# Set invalid park references to null
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invalid_rides.update(park_id=None)
|
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fixed_count += invalid_rides.count()
|
||||
except LookupError:
|
||||
pass
|
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|
||||
# Check for status consistency
|
||||
if scan_type in ["full", "status"]:
|
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try:
|
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Park = apps.get_model("parks", "Park")
|
||||
|
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# Parks with invalid status values
|
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valid_statuses = ["operating", "closed", "under_construction", "announced", "deleted"]
|
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invalid_status_parks = Park.objects.exclude(status__in=valid_statuses)
|
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|
||||
for park in invalid_status_parks[:10]: # Limit to 10 examples
|
||||
issues.append({
|
||||
"issue_type": "invalid_status",
|
||||
"entity_type": "park",
|
||||
"entity_id": str(park.id),
|
||||
"submission_id": "",
|
||||
"severity": "warning",
|
||||
"description": f"Park '{park.name}' has invalid status: {park.status}",
|
||||
"detected_at": timezone.now().isoformat(),
|
||||
})
|
||||
except LookupError:
|
||||
pass
|
||||
|
||||
# Check for orphaned media
|
||||
if scan_type in ["full", "media"]:
|
||||
try:
|
||||
Photo = apps.get_model("media", "Photo")
|
||||
|
||||
orphaned_photos = Photo.objects.filter(
|
||||
entity_id__isnull=True,
|
||||
entity_type__isnull=True,
|
||||
)
|
||||
|
||||
for photo in orphaned_photos[:10]: # Limit to 10 examples
|
||||
issues.append({
|
||||
"issue_type": "orphaned_media",
|
||||
"entity_type": "photo",
|
||||
"entity_id": str(photo.id),
|
||||
"submission_id": "",
|
||||
"severity": "info",
|
||||
"description": "Photo without associated entity",
|
||||
"detected_at": timezone.now().isoformat(),
|
||||
})
|
||||
except LookupError:
|
||||
pass
|
||||
|
||||
# ================================================================
|
||||
# Check 3: Stuck submissions with expired locks (from original)
|
||||
# ================================================================
|
||||
if scan_type in ["full", "submissions", "stuck"]:
|
||||
try:
|
||||
EditSubmission = apps.get_model("moderation", "EditSubmission")
|
||||
|
||||
# Find submissions that are claimed but claim has expired
|
||||
# Assuming a claim expires after 30 minutes of inactivity
|
||||
claim_expiry = timezone.now() - timedelta(minutes=30)
|
||||
|
||||
stuck_submissions = EditSubmission.objects.filter(
|
||||
status__in=["CLAIMED", "claimed", "reviewing"],
|
||||
claimed_at__lt=claim_expiry,
|
||||
).exclude(claimed_at__isnull=True)
|
||||
|
||||
for sub in stuck_submissions[:10]: # Limit to 10 examples
|
||||
hours_stuck = (timezone.now() - sub.claimed_at).total_seconds() / 3600
|
||||
issues.append({
|
||||
"issue_type": "stuck_submission",
|
||||
"entity_type": "edit_submission",
|
||||
"entity_id": str(sub.id),
|
||||
"submission_id": str(sub.id),
|
||||
"severity": "warning" if hours_stuck < 4 else "critical",
|
||||
"description": (
|
||||
f"Submission claimed by {sub.claimed_by.username if sub.claimed_by else 'unknown'} "
|
||||
f"but stuck for {hours_stuck:.1f} hours"
|
||||
),
|
||||
"detected_at": timezone.now().isoformat(),
|
||||
"metadata": {
|
||||
"claimed_at": sub.claimed_at.isoformat() if sub.claimed_at else None,
|
||||
"claimed_by": sub.claimed_by.username if sub.claimed_by else None,
|
||||
"hours_stuck": round(hours_stuck, 1),
|
||||
},
|
||||
})
|
||||
|
||||
if fix_issues:
|
||||
# Unclaim stuck submissions
|
||||
sub.claimed_by = None
|
||||
sub.claimed_at = None
|
||||
sub.status = "PENDING"
|
||||
sub.save(update_fields=["claimed_by", "claimed_at", "status"])
|
||||
fixed_count += 1
|
||||
|
||||
except LookupError:
|
||||
pass
|
||||
|
||||
# ================================================================
|
||||
# Check: Entities with approvals but no version records (from original)
|
||||
# Uses pghistory events as proxy for version records
|
||||
# ================================================================
|
||||
if scan_type in ["full", "versions"]:
|
||||
try:
|
||||
# Check if pghistory events exist for recently approved submissions
|
||||
EditSubmission = apps.get_model("moderation", "EditSubmission")
|
||||
|
||||
recently_approved = EditSubmission.objects.filter(
|
||||
status__in=["APPROVED", "approved"],
|
||||
handled_at__gte=cutoff_time,
|
||||
)
|
||||
|
||||
for sub in recently_approved[:10]:
|
||||
# Check if the target object has history
|
||||
target = sub.content_object
|
||||
if target and hasattr(target, 'events'):
|
||||
try:
|
||||
event_count = target.events.count()
|
||||
if event_count == 0:
|
||||
issues.append({
|
||||
"issue_type": "missing_version_record",
|
||||
"entity_type": sub.content_type.model,
|
||||
"entity_id": str(sub.object_id),
|
||||
"submission_id": str(sub.id),
|
||||
"severity": "critical",
|
||||
"description": f"Approved {sub.content_type.model} has no version history",
|
||||
"detected_at": timezone.now().isoformat(),
|
||||
})
|
||||
except Exception:
|
||||
pass
|
||||
except LookupError:
|
||||
pass
|
||||
|
||||
# Calculate summary counts
|
||||
critical_count = sum(1 for i in issues if i.get("severity") == "critical")
|
||||
warning_count = sum(1 for i in issues if i.get("severity") == "warning")
|
||||
info_count = sum(1 for i in issues if i.get("severity") == "info")
|
||||
|
||||
# Return in frontend-expected format
|
||||
return Response({
|
||||
"success": True,
|
||||
"scan_timestamp": timezone.now().isoformat(),
|
||||
"hours_scanned": hours_back,
|
||||
"issues_found": len(issues),
|
||||
"issues": issues,
|
||||
"summary": {
|
||||
"critical": critical_count,
|
||||
"warning": warning_count,
|
||||
"info": info_count,
|
||||
},
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
capture_and_log(e, "Pipeline integrity scan - error", source="api")
|
||||
return Response(
|
||||
{"detail": "Failed to run integrity scan"},
|
||||
status=status.HTTP_500_INTERNAL_SERVER_ERROR,
|
||||
)
|
||||
|
||||
Reference in New Issue
Block a user