# Aurora AI Framework - Monitoring and Analytics Guide

## 🌟 Overview

This comprehensive monitoring and analytics guide covers all aspects of monitoring, observability, and analytics for the Aurora AI framework. With 27 integrated systems and 74 API endpoints, this guide provides enterprise-grade monitoring strategies, analytics implementations, and operational intelligence.

## 📊 Monitoring Architecture

### Monitoring Stack Components
```yaml
# monitoring/stack.yaml
monitoring_stack:
  data_collection:
    - prometheus_server
    - grafana_dashboards
    - elk_stack
    - jaeger_tracing
  
  alerting:
    - alertmanager
    - pagerduty_integration
    - slack_notifications
    - email_alerts
  
  analytics:
    - time_series_database
    - machine_learning_analytics
    - anomaly_detection
    - predictive_analytics
  
  visualization:
    - grafana_dashboards
    - kibana_dashboards
    - custom_dashboards
    - real_time_visualizations
```

### Prometheus Configuration
```yaml
# monitoring/prometheus.yml
global:
  scrape_interval: 15s
  evaluation_interval: 15s
  external_labels:
    cluster: 'aurora-ai'
    replica: 'prometheus-1'

rule_files:
  - "aurora_rules.yml"
  - "security_rules.yml"
  - "performance_rules.yml"

alerting:
  alertmanagers:
    - static_configs:
      - targets:
        - alertmanager:9093

scrape_configs:
  # Aurora AI Application Metrics
  - job_name: 'aurora-web'
    static_configs:
      - targets: ['aurora-web:8080']
    metrics_path: '/api/monitoring/metrics'
    scrape_interval: 30s
    scrape_timeout: 10s
    
    # Relabeling for better metric organization
    relabel_configs:
      - source_labels: [__address__]
        target_label: instance
        replacement: 'aurora-web-1'
      - source_labels: [__address__]
        target_label: service
        replacement: 'aurora-ai'

  # Database Metrics
  - job_name: 'postgres-exporter'
    static_configs:
      - targets: ['postgres-exporter:9187']
    scrape_interval: 30s
    
  # Redis Metrics
  - job_name: 'redis-exporter'
    static_configs:
      - targets: ['redis-exporter:9121']
    scrape_interval: 30s
    
  # Kubernetes Metrics
  - job_name: 'kubernetes-pods'
    kubernetes_sd_configs:
      - role: pod
    relabel_configs:
      - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
        action: keep
        regex: true
      - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_path]
        action: replace
        target_label: __metrics_path__
        regex: (.+)
      - source_labels: [__address__, __meta_kubernetes_pod_annotation_prometheus_io_port]
        action: replace
        regex: ([^:]+)(?::\d+)?;(\d+)
        replacement: $1:$2
        target_label: __address__

  # Node Exporter
  - job_name: 'node-exporter'
    static_configs:
      - targets: ['node-exporter:9100']
    scrape_interval: 30s
```

### Grafana Dashboard Configuration
```json
{
  "dashboard": {
    "id": null,
    "title": "Aurora AI - System Overview",
    "tags": ["aurora-ai", "system"],
    "timezone": "browser",
    "panels": [
      {
        "id": 1,
        "title": "System Health Score",
        "type": "stat",
        "targets": [
          {
            "expr": "aurora_system_health_score",
            "refId": "A"
          }
        ],
        "fieldConfig": {
          "defaults": {
            "color": {
              "mode": "thresholds"
            },
            "thresholds": {
              "steps": [
                {"color": "red", "value": 0},
                {"color": "yellow", "value": 70},
                {"color": "green", "value": 90}
              ]
            }
          }
        },
        "gridPos": {"h": 8, "w": 12, "x": 0, "y": 0}
      },
      {
        "id": 2,
        "title": "API Request Rate",
        "type": "graph",
        "targets": [
          {
            "expr": "rate(aurora_api_requests_total[5m])",
            "refId": "A",
            "legendFormat": "{{method}} {{endpoint}}"
          }
        ],
        "gridPos": {"h": 8, "w": 12, "x": 12, "y": 0}
      },
      {
        "id": 3,
        "title": "Response Time Distribution",
        "type": "heatmap",
        "targets": [
          {
            "expr": "rate(aurora_api_request_duration_seconds_bucket[5m])",
            "refId": "A"
          }
        ],
        "gridPos": {"h": 8, "w": 24, "x": 0, "y": 8}
      },
      {
        "id": 4,
        "title": "Error Rate",
        "type": "graph",
        "targets": [
          {
            "expr": "rate(aurora_api_requests_total{status=~\"5..\"}[5m]) / rate(aurora_api_requests_total[5m])",
            "refId": "A",
            "legendFormat": "Error Rate"
          }
        ],
        "gridPos": {"h": 8, "w": 12, "x": 0, "y": 16}
      },
      {
        "id": 5,
        "title": "Resource Utilization",
        "type": "graph",
        "targets": [
          {
            "expr": "rate(container_cpu_usage_seconds_total[5m])",
            "refId": "A",
            "legendFormat": "CPU Usage"
          },
          {
            "expr": "container_memory_usage_bytes / 1024 / 1024",
            "refId": "B",
            "legendFormat": "Memory Usage (MB)"
          }
        ],
        "gridPos": {"h": 8, "w": 12, "x": 12, "y": 16}
      }
    ],
    "time": {"from": "now-1h", "to": "now"},
    "refresh": "30s"
  }
}
```

## 📈 Custom Metrics Implementation

### Application Metrics Collector
```python
# monitoring/metrics_collector.py
from prometheus_client import Counter, Histogram, Gauge, start_http_server
import time
import functools
from flask import request
import psutil
import threading

class AuroraMetricsCollector:
    def __init__(self):
        # Request metrics
        self.request_count = Counter(
            'aurora_api_requests_total',
            'Total API requests',
            ['method', 'endpoint', 'status']
        )
        
        self.request_duration = Histogram(
            'aurora_api_request_duration_seconds',
            'API request duration',
            ['method', 'endpoint'],
            buckets=[0.1, 0.25, 0.5, 1.0, 2.5, 5.0, 10.0]
        )
        
        # System metrics
        self.system_health = Gauge(
            'aurora_system_health_score',
            'System health score (0-100)'
        )
        
        self.active_connections = Gauge(
            'aurora_active_connections',
            'Number of active connections'
        )
        
        self.model_performance = Gauge(
            'aurora_model_performance_score',
            'Model performance score',
            ['model_id']
        )
        
        # Business metrics
        self.predictions_made = Counter(
            'aurora_predictions_total',
            'Total predictions made',
            ['model_id', 'prediction_type']
        )
        
        self.data_validations = Counter(
            'aurora_data_validations_total',
            'Total data validations',
            ['validation_type', 'result']
        )
        
        self.training_jobs = Counter(
            'aurora_training_jobs_total',
            'Total training jobs',
            ['status', 'algorithm']
        )
        
        # Start metrics server
        start_http_server(8000)
        
        # Start background metrics collection
        self.start_background_collection()
    
    def track_request(self, func):
        """Decorator to track API requests"""
        @functools.wraps(func)
        def wrapper(*args, **kwargs):
            start_time = time.time()
            
            try:
                response = func(*args, **kwargs)
                status = getattr(response, 'status_code', 200)
                return response
            except Exception as e:
                status = 500
                raise
            finally:
                duration = time.time() - start_time
                
                # Record metrics
                self.request_count.labels(
                    method=request.method,
                    endpoint=request.endpoint,
                    status=status
                ).inc()
                
                self.request_duration.labels(
                    method=request.method,
                    endpoint=request.endpoint
                ).observe(duration)
        
        return wrapper
    
    def track_prediction(self, model_id, prediction_type, success=True):
        """Track prediction metrics"""
        self.predictions_made.labels(
            model_id=model_id,
            prediction_type=prediction_type
        ).inc()
    
    def track_data_validation(self, validation_type, result):
        """Track data validation metrics"""
        self.data_validations.labels(
            validation_type=validation_type,
            result=result
        ).inc()
    
    def track_training_job(self, status, algorithm):
        """Track training job metrics"""
        self.training_jobs.labels(
            status=status,
            algorithm=algorithm
        ).inc()
    
    def update_system_health(self, health_score):
        """Update system health score"""
        self.system_health.set(health_score)
    
    def update_active_connections(self, count):
        """Update active connections count"""
        self.active_connections.set(count)
    
    def update_model_performance(self, model_id, performance_score):
        """Update model performance score"""
        self.model_performance.labels(model_id=model_id).set(performance_score)
    
    def start_background_collection(self):
        """Start background metrics collection"""
        def collect_system_metrics():
            while True:
                try:
                    # CPU usage
                    cpu_percent = psutil.cpu_percent(interval=1)
                    
                    # Memory usage
                    memory = psutil.virtual_memory()
                    memory_percent = memory.percent
                    
                    # Disk usage
                    disk = psutil.disk_usage('/')
                    disk_percent = disk.percent
                    
                    # Network I/O
                    network = psutil.net_io_counters()
                    
                    # Update custom metrics
                    self.update_system_health(
                        self.calculate_health_score(cpu_percent, memory_percent, disk_percent)
                    )
                    
                except Exception as e:
                    print(f"Error collecting system metrics: {e}")
                
                time.sleep(30)  # Collect every 30 seconds
        
        # Start background thread
        thread = threading.Thread(target=collect_system_metrics, daemon=True)
        thread.start()
    
    def calculate_health_score(self, cpu_percent, memory_percent, disk_percent):
        """Calculate overall system health score"""
        # Weight different factors
        cpu_weight = 0.3
        memory_weight = 0.3
        disk_weight = 0.4
        
        # Calculate individual scores (inverse of usage)
        cpu_score = max(0, 100 - cpu_percent)
        memory_score = max(0, 100 - memory_percent)
        disk_score = max(0, 100 - disk_percent)
        
        # Calculate weighted average
        health_score = (
            cpu_score * cpu_weight +
            memory_score * memory_weight +
            disk_score * disk_weight
        )
        
        return round(health_score, 2)
```

### Business Metrics Tracker
```python
# monitoring/business_metrics.py
class BusinessMetricsTracker:
    def __init__(self, metrics_collector):
        self.metrics = metrics_collector
        self.user_engagement = {}
        self.feature_usage = {}
    
    def track_user_engagement(self, user_id, action, feature):
        """Track user engagement metrics"""
        timestamp = time.time()
        
        if user_id not in self.user_engagement:
            self.user_engagement[user_id] = {
                'first_seen': timestamp,
                'last_seen': timestamp,
                'actions': [],
                'features_used': set()
            }
        
        self.user_engagement[user_id]['last_seen'] = timestamp
        self.user_engagement[user_id]['actions'].append({
            'timestamp': timestamp,
            'action': action,
            'feature': feature
        })
        self.user_engagement[user_id]['features_used'].add(feature)
        
        # Update Prometheus metrics
        self.metrics.user_engagement.labels(
            action=action,
            feature=feature
        ).inc()
    
    def track_feature_usage(self, feature, usage_type, value=1):
        """Track feature usage metrics"""
        if feature not in self.feature_usage:
            self.feature_usage[feature] = {
                'total_usage': 0,
                'usage_types': {},
                'first_used': time.time(),
                'last_used': time.time()
            }
        
        self.feature_usage[feature]['total_usage'] += value
        self.feature_usage[feature]['last_used'] = time.time()
        
        if usage_type not in self.feature_usage[feature]['usage_types']:
            self.feature_usage[feature]['usage_types'][usage_type] = 0
        
        self.feature_usage[feature]['usage_types'][usage_type] += value
        
        # Update Prometheus metrics
        self.metrics.feature_usage.labels(
            feature=feature,
            usage_type=usage_type
        ).inc()
    
    def generate_engagement_report(self, hours=24):
        """Generate user engagement report"""
        cutoff_time = time.time() - (hours * 3600)
        
        active_users = 0
        total_actions = 0
        feature_usage = {}
        
        for user_id, engagement in self.user_engagement.items():
            if engagement['last_seen'] > cutoff_time:
                active_users += 1
                total_actions += len(engagement['actions'])
                
                for feature in engagement['features_used']:
                    if feature not in feature_usage:
                        feature_usage[feature] = 0
                    feature_usage[feature] += 1
        
        return {
            'period_hours': hours,
            'active_users': active_users,
            'total_actions': total_actions,
            'actions_per_user': total_actions / active_users if active_users > 0 else 0,
            'feature_usage': feature_usage,
            'most_used_feature': max(feature_usage.items(), key=lambda x: x[1])[0] if feature_usage else None
        }
```

## 🔍 Advanced Analytics

### Anomaly Detection System
```python
# monitoring/anomaly_detection.py
import numpy as np
from sklearn.ensemble import IsolationForest
from sklearn.preprocessing import StandardScaler
import pandas as pd
from datetime import datetime, timedelta

class AnomalyDetectionEngine:
    def __init__(self):
        self.models = {}
        self.scalers = {}
        self.anomaly_thresholds = {
            'response_time': 5.0,  # seconds
            'error_rate': 0.05,   # 5%
            'cpu_usage': 0.90,   # 90%
            'memory_usage': 0.85  # 85%
        }
        self.anomaly_history = []
    
    def train_anomaly_models(self, metrics_data):
        """Train anomaly detection models on historical data"""
        df = pd.DataFrame(metrics_data)
        
        # Train models for different metric types
        metric_columns = ['cpu_usage', 'memory_usage', 'disk_usage', 
                         'response_time', 'error_rate', 'throughput']
        
        for metric in metric_columns:
            if metric in df.columns:
                # Prepare data
                data = df[metric].values.reshape(-1, 1)
                
                # Scale data
                scaler = StandardScaler()
                scaled_data = scaler.fit_transform(data)
                
                # Train Isolation Forest
                model = IsolationForest(
                    contamination=0.1,
                    random_state=42,
                    n_estimators=100
                )
                model.fit(scaled_data)
                
                # Store model and scaler
                self.models[metric] = model
                self.scalers[metric] = scaler
    
    def detect_anomalies(self, current_metrics):
        """Detect anomalies in current metrics"""
        anomalies = []
        
        for metric, value in current_metrics.items():
            if metric in self.models:
                # Prepare data
                data = np.array([[value]])
                scaled_data = self.scalers[metric].transform(data)
                
                # Predict anomaly
                prediction = self.models[metric].predict(scaled_data)[0]
                anomaly_score = self.models[metric].decision_function(scaled_data)[0]
                
                # Check if anomaly
                is_anomaly = prediction == -1
                
                # Also check against static thresholds
                threshold_anomaly = False
                if metric in self.anomaly_thresholds:
                    threshold = self.anomaly_thresholds[metric]
                    if metric in ['response_time', 'error_rate', 'cpu_usage', 'memory_usage']:
                        threshold_anomaly = value > threshold
                
                if is_anomaly or threshold_anomaly:
                    anomalies.append({
                        'metric': metric,
                        'value': value,
                        'anomaly_score': anomaly_score,
                        'threshold_violation': threshold_anomaly,
                        'severity': self._calculate_severity(metric, value, anomaly_score),
                        'timestamp': datetime.utcnow().isoformat()
                    })
        
        # Store anomalies
        self.anomaly_history.extend(anomalies)
        
        # Keep only last 1000 anomalies
        if len(self.anomaly_history) > 1000:
            self.anomaly_history = self.anomaly_history[-1000:]
        
        return anomalies
    
    def _calculate_severity(self, metric, value, anomaly_score):
        """Calculate anomaly severity"""
        base_severity = abs(anomaly_score)
        
        # Adjust based on metric importance
        critical_metrics = ['error_rate', 'cpu_usage', 'memory_usage']
        if metric in critical_metrics:
            base_severity *= 1.5
        
        # Determine severity level
        if base_severity > 0.8:
            return 'critical'
        elif base_severity > 0.5:
            return 'high'
        elif base_severity > 0.3:
            return 'medium'
        else:
            return 'low'
    
    def get_anomaly_summary(self, hours=24):
        """Get anomaly summary for specified period"""
        cutoff_time = datetime.utcnow() - timedelta(hours=hours)
        
        recent_anomalies = [
            anomaly for anomaly in self.anomaly_history
            if datetime.fromisoformat(anomaly['timestamp']) > cutoff_time
        ]
        
        if not recent_anomalies:
            return {
                'period_hours': hours,
                'total_anomalies': 0,
                'severity_breakdown': {},
                'metric_breakdown': {},
                'trend': 'stable'
            }
        
        # Analyze anomalies
        severity_breakdown = {}
        metric_breakdown = {}
        
        for anomaly in recent_anomalies:
            severity = anomaly['severity']
            metric = anomaly['metric']
            
            severity_breakdown[severity] = severity_breakdown.get(severity, 0) + 1
            metric_breakdown[metric] = metric_breakdown.get(metric, 0) + 1
        
        # Determine trend
        if len(recent_anomalies) > 10:
            recent_count = len([a for a in recent_anomalies 
                             if datetime.fromisoformat(a['timestamp']) > 
                             datetime.utcnow() - timedelta(hours=1)])
            if recent_count > 5:
                trend = 'increasing'
            elif recent_count < 2:
                trend = 'decreasing'
            else:
                trend = 'stable'
        else:
            trend = 'insufficient_data'
        
        return {
            'period_hours': hours,
            'total_anomalies': len(recent_anomalies),
            'severity_breakdown': severity_breakdown,
            'metric_breakdown': metric_breakdown,
            'trend': trend,
            'recommendations': self._generate_anomaly_recommendations(recent_anomalies)
        }
    
    def _generate_anomaly_recommendations(self, anomalies):
        """Generate recommendations based on anomalies"""
        recommendations = []
        
        # Group anomalies by type
        critical_anomalies = [a for a in anomalies if a['severity'] == 'critical']
        high_anomalies = [a for a in anomalies if a['severity'] == 'high']
        
        if critical_anomalies:
            recommendations.append({
                'priority': 'critical',
                'issue': 'Critical anomalies detected',
                'action': 'Immediate investigation required',
                'affected_metrics': list(set(a['metric'] for a in critical_anomalies))
            })
        
        if len(anomalies) > 20:
            recommendations.append({
                'priority': 'high',
                'issue': 'High anomaly volume',
                'action': 'Review system performance and capacity',
                'affected_metrics': list(set(a['metric'] for a in anomalies))
            })
        
        # Check for specific metric patterns
        error_anomalies = [a for a in anomalies if a['metric'] == 'error_rate']
        if len(error_anomalies) > 5:
            recommendations.append({
                'priority': 'high',
                'issue': 'Elevated error rate anomalies',
                'action': 'Investigate application errors and logs',
                'affected_metrics': ['error_rate']
            })
        
        return recommendations
```

### Predictive Analytics
```python
# monitoring/predictive_analytics.py
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error, mean_squared_error
import numpy as np

class PredictiveAnalyticsEngine:
    def __init__(self):
        self.models = {}
        self.feature_columns = [
            'hour_of_day', 'day_of_week', 'cpu_usage', 'memory_usage',
            'active_connections', 'requests_per_minute'
        ]
        self.target_columns = ['response_time', 'error_rate', 'throughput']
    
    def train_predictive_models(self, historical_data):
        """Train predictive models on historical data"""
        df = pd.DataFrame(historical_data)
        
        # Prepare features
        df['hour_of_day'] = pd.to_datetime(df['timestamp']).dt.hour
        df['day_of_week'] = pd.to_datetime(df['timestamp']).dt.dayofweek
        
        # Train models for each target
        for target in self.target_columns:
            if target in df.columns:
                # Prepare training data
                X = df[self.feature_columns].fillna(0)
                y = df[target].fillna(0)
                
                # Split data (80% train, 20% test)
                split_idx = int(len(X) * 0.8)
                X_train, X_test = X[:split_idx], X[split_idx:]
                y_train, y_test = y[:split_idx], y[split_idx:]
                
                # Train Random Forest model
                model = RandomForestRegressor(
                    n_estimators=100,
                    random_state=42,
                    max_depth=10
                )
                model.fit(X_train, y_train)
                
                # Evaluate model
                y_pred = model.predict(X_test)
                mae = mean_absolute_error(y_test, y_pred)
                mse = mean_squared_error(y_test, y_pred)
                
                # Store model
                self.models[target] = {
                    'model': model,
                    'mae': mae,
                    'mse': mse,
                    'feature_importance': dict(zip(self.feature_columns, model.feature_importances_))
                }
    
    def predict_metrics(self, current_data, horizon_minutes=60):
        """Predict metrics for specified horizon"""
        predictions = {}
        
        # Prepare features from current data
        features = self._prepare_features(current_data)
        
        for target, model_info in self.models.items():
            model = model_info['model']
            
            # Predict for future time points
            future_predictions = []
            
            for minute_ahead in range(0, horizon_minutes + 1, 15):  # Every 15 minutes
                # Add time-based features
                future_time = datetime.utcnow() + timedelta(minutes=minute_ahead)
                future_features = features.copy()
                future_features['hour_of_day'] = future_time.hour
                future_features['day_of_week'] = future_time.weekday()
                
                # Make prediction
                feature_array = np.array([list(future_features.values())])
                prediction = model.predict(feature_array)[0]
                
                future_predictions.append({
                    'timestamp': (future_time).isoformat(),
                    'minutes_ahead': minute_ahead,
                    'predicted_value': max(0, prediction),  # Ensure non-negative
                    'confidence': self._calculate_confidence(model_info, prediction)
                })
            
            predictions[target] = {
                'predictions': future_predictions,
                'model_accuracy': {
                    'mae': model_info['mae'],
                    'mse': model_info['mse']
                },
                'feature_importance': model_info['feature_importance']
            }
        
        return predictions
    
    def _prepare_features(self, current_data):
        """Prepare features from current data"""
        features = {}
        
        # Current metrics
        features['cpu_usage'] = current_data.get('cpu_usage', 0)
        features['memory_usage'] = current_data.get('memory_usage', 0)
        features['active_connections'] = current_data.get('active_connections', 0)
        features['requests_per_minute'] = current_data.get('requests_per_minute', 0)
        
        # Time-based features
        now = datetime.utcnow()
        features['hour_of_day'] = now.hour
        features['day_of_week'] = now.weekday()
        
        return features
    
    def _calculate_confidence(self, model_info, prediction):
        """Calculate prediction confidence based on model accuracy"""
        mae = model_info['mae']
        
        # Simple confidence calculation based on MAE
        if mae < 0.1:
            return 0.9  # High confidence
        elif mae < 0.5:
            return 0.7  # Medium confidence
        else:
            return 0.5  # Low confidence
    
    def generate_insights(self, predictions):
        """Generate insights from predictions"""
        insights = []
        
        for target, prediction_data in predictions.items():
            future_values = [p['predicted_value'] for p in prediction_data['predictions']]
            
            # Calculate trend
            if len(future_values) > 1:
                trend = np.polyfit(range(len(future_values)), future_values, 1)[0]
                
                if trend > 0.1:
                    trend_direction = 'increasing'
                elif trend < -0.1:
                    trend_direction = 'decreasing'
                else:
                    trend_direction = 'stable'
                
                # Check for concerning trends
                if target == 'error_rate' and trend_direction == 'increasing':
                    insights.append({
                        'type': 'warning',
                        'metric': target,
                        'message': f'Error rate predicted to {trend_direction}',
                        'severity': 'high' if trend > 0.5 else 'medium'
                    })
                
                if target == 'response_time' and trend_direction == 'increasing':
                    insights.append({
                        'type': 'warning',
                        'metric': target,
                        'message': f'Response time predicted to {trend_direction}',
                        'severity': 'medium'
                    })
                
                if target == 'throughput' and trend_direction == 'decreasing':
                    insights.append({
                        'type': 'warning',
                        'metric': target,
                        'message': f'Throughput predicted to {trend_direction}',
                        'severity': 'medium'
                    })
        
        return insights
```

## 📊 Real-Time Analytics Dashboard

### Dashboard Data Provider
```python
# monitoring/dashboard_provider.py
class DashboardDataProvider:
    def __init__(self, metrics_collector, anomaly_detector, predictive_analytics):
        self.metrics_collector = metrics_collector
        self.anomaly_detector = anomaly_detector
        self.predictive_analytics = predictive_analytics
    
    def get_dashboard_data(self):
        """Get comprehensive dashboard data"""
        return {
            'overview': self._get_overview_metrics(),
            'performance': self._get_performance_metrics(),
            'anomalies': self._get_anomaly_data(),
            'predictions': self._get_prediction_data(),
            'alerts': self._get_active_alerts(),
            'system_health': self._get_system_health()
        }
    
    def _get_overview_metrics(self):
        """Get overview metrics"""
        return {
            'total_requests': self._get_metric_value('aurora_api_requests_total'),
            'active_users': self._get_active_users_count(),
            'system_health': self._get_system_health_score(),
            'uptime_percentage': self._get_uptime_percentage(),
            'error_rate': self._get_current_error_rate(),
            'average_response_time': self._get_average_response_time()
        }
    
    def _get_performance_metrics(self):
        """Get detailed performance metrics"""
        return {
            'request_rate': self._get_request_rate(),
            'response_time_distribution': self._get_response_time_distribution(),
            'throughput': self._get_throughput(),
            'resource_utilization': self._get_resource_utilization(),
            'database_performance': self._get_database_performance(),
            'cache_performance': self._get_cache_performance()
        }
    
    def _get_anomaly_data(self):
        """Get anomaly detection data"""
        anomaly_summary = self.anomaly_detector.get_anomaly_summary(hours=24)
        
        return {
            'summary': anomaly_summary,
            'recent_anomalies': self.anomaly_detector.anomaly_history[-10:],
            'anomaly_trends': self._get_anomaly_trends(),
            'recommendations': anomaly_summary.get('recommendations', [])
        }
    
    def _get_prediction_data(self):
        """Get predictive analytics data"""
        current_data = self._get_current_metrics()
        predictions = self.predictive_analytics.predict_metrics(current_data, horizon_minutes=60)
        
        return {
            'predictions': predictions,
            'insights': self.predictive_analytics.generate_insights(predictions),
            'model_accuracy': self._get_model_accuracy()
        }
    
    def _get_active_alerts(self):
        """Get active alerts"""
        return {
            'critical_alerts': self._get_critical_alerts(),
            'warning_alerts': self._get_warning_alerts(),
            'info_alerts': self._get_info_alerts(),
            'alert_trends': self._get_alert_trends()
        }
    
    def _get_system_health(self):
        """Get detailed system health"""
        return {
            'overall_health': self._get_system_health_score(),
            'component_health': {
                'web_server': self._get_component_health('web_server'),
                'database': self._get_component_health('database'),
                'cache': self._get_component_health('cache'),
                'ml_models': self._get_component_health('ml_models')
            },
            'health_trends': self._get_health_trends(),
            'health_recommendations': self._get_health_recommendations()
        }
    
    def _get_metric_value(self, metric_name):
        """Get current value for a metric"""
        # This would integrate with Prometheus client
        return 0  # Placeholder
    
    def _get_active_users_count(self):
        """Get active users count"""
        return 1250  # Placeholder
    
    def _get_system_health_score(self):
        """Get system health score"""
        return 92.5  # Placeholder
    
    def _get_uptime_percentage(self):
        """Get uptime percentage"""
        return 99.9  # Placeholder
    
    def _get_current_error_rate(self):
        """Get current error rate"""
        return 0.02  # Placeholder
    
    def _get_average_response_time(self):
        """Get average response time"""
        return 0.8  # Placeholder
    
    def _get_request_rate(self):
        """Get current request rate"""
        return 850  # Placeholder
    
    def _get_response_time_distribution(self):
        """Get response time distribution"""
        return {
            'p50': 0.5,
            'p95': 1.2,
            'p99': 2.8,
            'max': 5.5
        }
    
    def _get_throughput(self):
        """Get current throughput"""
        return 850  # Placeholder
    
    def _get_resource_utilization(self):
        """Get resource utilization"""
        return {
            'cpu': 45.2,
            'memory': 67.8,
            'disk': 23.4,
            'network': 12.1
        }
    
    def _get_database_performance(self):
        """Get database performance metrics"""
        return {
            'connections': 25,
            'query_time_avg': 0.15,
            'slow_queries': 2,
            'cache_hit_rate': 94.2
        }
    
    def _get_cache_performance(self):
        """Get cache performance metrics"""
        return {
            'hit_rate': 89.5,
            'miss_rate': 10.5,
            'evictions': 5,
            'memory_usage': 67.3
        }
    
    def _get_current_metrics(self):
        """Get current system metrics"""
        return {
            'cpu_usage': 45.2,
            'memory_usage': 67.8,
            'active_connections': 25,
            'requests_per_minute': 850
        }
    
    def _get_model_accuracy(self):
        """Get model accuracy metrics"""
        return {
            'response_time_model': {'mae': 0.12, 'mse': 0.08},
            'error_rate_model': {'mae': 0.05, 'mse': 0.03},
            'throughput_model': {'mae': 25.5, 'mse': 18.2}
        }
```

## 🚨 Alerting System

### Alert Manager Configuration
```yaml
# monitoring/alertmanager.yml
global:
  smtp_smarthost: 'localhost:587'
  smtp_from: 'alerts@aurora-ai.com'
  smtp_auth_username: 'alerts@aurora-ai.com'
  smtp_auth_password: 'password'

route:
  group_by: ['alertname', 'cluster', 'service']
  group_wait: 10s
  group_interval: 10s
  repeat_interval: 1h
  receiver: 'web.hook'
  routes:
  - match:
      severity: critical
    receiver: 'critical-alerts'
  - match:
      severity: warning
    receiver: 'warning-alerts'
  - match:
      severity: info
    receiver: 'info-alerts'

receivers:
- name: 'web.hook'
  webhook_configs:
  - url: 'http://aurora-web:8080/api/alerts/webhook'

- name: 'critical-alerts'
  email_configs:
  - to: 'admin@aurora-ai.com'
    subject: '[CRITICAL] Aurora AI Alert'
    body: |
      {{ range .Alerts }}
      Alert: {{ .Annotations.summary }}
      Description: {{ .Annotations.description }}
      {{ end }}
  
  slack_configs:
  - api_url: 'https://hooks.slack.com/services/YOUR/SLACK/WEBHOOK'
    channel: '#aurora-alerts-critical'
    title: 'Critical Alert: {{ .GroupLabels.alertname }}'
    text: '{{ range .Alerts }}{{ .Annotations.description }}{{ end }}'

- name: 'warning-alerts'
  email_configs:
  - to: 'team@aurora-ai.com'
    subject: '[WARNING] Aurora AI Alert'
    body: |
      {{ range .Alerts }}
      Alert: {{ .Annotations.summary }}
      Description: {{ .Annotations.description }}
      {{ end }}

- name: 'info-alerts'
  slack_configs:
  - api_url: 'https://hooks.slack.com/services/YOUR/SLACK/WEBHOOK'
    channel: '#aurora-alerts-info'
    title: 'Info: {{ .GroupLabels.alertname }}'
    text: '{{ range .Alerts }}{{ .Annotations.description }}{{ end }}'

inhibit_rules:
- source_match:
    severity: 'critical'
  target_match:
    severity: 'warning'
  equal: ['alertname', 'cluster', 'service']
```

### Alert Rules
```yaml
# monitoring/aurora_rules.yml
groups:
- name: aurora_alerts
  rules:
  # System Health Alerts
  - alert: SystemHealthLow
    expr: aurora_system_health_score < 70
    for: 5m
    labels:
      severity: warning
    annotations:
      summary: "System health score is low"
      description: "System health score is {{ $value }} (threshold < 70)"
  
  - alert: SystemHealthCritical
    expr: aurora_system_health_score < 50
    for: 2m
    labels:
      severity: critical
    annotations:
      summary: "System health score is critical"
      description: "System health score is {{ $value }} (threshold < 50)"
  
  # Performance Alerts
  - alert: HighResponseTime
    expr: histogram_quantile(0.95, rate(aurora_api_request_duration_seconds_bucket[5m])) > 2
    for: 5m
    labels:
      severity: warning
    annotations:
      summary: "High API response time"
      description: "95th percentile response time is {{ $value }}s (threshold > 2s)"
  
  - alert: CriticalResponseTime
    expr: histogram_quantile(0.95, rate(aurora_api_request_duration_seconds_bucket[5m])) > 5
    for: 2m
    labels:
      severity: critical
    annotations:
      summary: "Critical API response time"
      description: "95th percentile response time is {{ $value }}s (threshold > 5s)"
  
  # Error Rate Alerts
  - alert: HighErrorRate
    expr: rate(aurora_api_requests_total{status=~"5.."}[5m]) / rate(aurora_api_requests_total[5m]) > 0.05
    for: 5m
    labels:
      severity: warning
    annotations:
      summary: "High error rate"
      description: "Error rate is {{ $value | humanizePercentage }} (threshold > 5%)"
  
  - alert: CriticalErrorRate
    expr: rate(aurora_api_requests_total{status=~"5.."}[5m]) / rate(aurora_api_requests_total[5m]) > 0.10
    for: 2m
    labels:
      severity: critical
    annotations:
      summary: "Critical error rate"
      description: "Error rate is {{ $value | humanizePercentage }} (threshold > 10%)"
  
  # Resource Alerts
  - alert: HighCPUUsage
    expr: rate(container_cpu_usage_seconds_total[5m]) * 100 > 80
    for: 10m
    labels:
      severity: warning
    annotations:
      summary: "High CPU usage"
      description: "CPU usage is {{ $value }}% (threshold > 80%)"
  
  - alert: HighMemoryUsage
    expr: container_memory_usage_bytes / container_spec_memory_limit_bytes * 100 > 85
    for: 10m
    labels:
      severity: warning
    annotations:
      summary: "High memory usage"
      description: "Memory usage is {{ $value }}% (threshold > 85%)"
  
  # Business Metrics Alerts
  - alert: LowPredictionAccuracy
    expr: aurora_model_performance_score < 0.80
    for: 15m
    labels:
      severity: warning
    annotations:
      summary: "Low model performance"
      description: "Model performance score is {{ $value }} (threshold < 80%)"
  
  - alert: NoPredictions
    expr: rate(aurora_predictions_total[5m]) == 0
    for: 10m
    labels:
      severity: critical
    annotations:
      summary: "No predictions being made"
      description: "Prediction rate is 0 for the last 5 minutes"
```

---

**Aurora AI Monitoring and Analytics Guide**  
*Enterprise Monitoring • Real-time Analytics • Predictive Intelligence • Alert Management*