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ADVANCED TELEMETRY FEATURES

Documentation for ADVANCED_TELEMETRY_FEATURES from the Foundation repository.

Advanced Telemetry Features

This document outlines the advanced telemetry features that can be built upon the foundation established in Phases 1-4.

Phase 5: Advanced Analytics & Alerting

Performance Tracking and Analysis System

Real-Time Performance Metrics

  • Sliding Window Aggregations: Calculate metrics over configurable time windows (1m, 5m, 15m, 1h)
  • Percentile Calculations: Track p50, p90, p95, p99 latencies for all operations
  • Rate Calculations: Requests per second, errors per minute, cache hit rates
  • Resource Utilization Tracking: Memory, CPU, ETS table sizes, process counts

Historical Trend Analysis

  • Time Series Storage: Store metrics in ETS/DETS for historical analysis
  • Trend Detection: Identify performance degradation over time
  • Capacity Planning: Project future resource needs based on growth patterns
  • Seasonal Pattern Recognition: Detect daily, weekly, monthly patterns

Pattern Detection and Anomaly Detection

Statistical Anomaly Detection

  • Z-Score Based Detection: Flag metrics that deviate >3 standard deviations
  • Moving Average Comparison: Detect sudden changes in behavior
  • Rate of Change Monitoring: Alert on rapid metric changes
  • Correlation Analysis: Detect related metric anomalies

Machine Learning Integration

  • Clustering: Group similar performance patterns
  • Prediction Models: Forecast expected metric values
  • Anomaly Scoring: ML-based anomaly confidence scores
  • Root Cause Analysis: Correlate anomalies with system events

Alert System with Configurable Thresholds

Alert Configuration

%{
  id: "high_error_rate",
  metric: [:foundation, :service, :error],
  condition: %{
    type: :threshold,
    operator: :gt,
    value: 100,
    window: :last_5_minutes
  },
  severity: :critical,
  actions: [:log, :email, :webhook],
  cooldown: 300_000  # 5 minutes
}

Alert Types

  • Threshold Alerts: Simple greater/less than conditions
  • Rate Alerts: Changes in metric rates
  • Absence Alerts: Missing expected events
  • Composite Alerts: Multiple conditions combined
  • Predictive Alerts: Based on forecast violations

Alert Actions

  • Logging: Structured alert logs with context
  • Email Notifications: Configurable recipients and templates
  • Webhook Integration: POST to external services
  • Circuit Breaker Triggers: Automatic service protection
  • Auto-Remediation: Trigger corrective actions

Integration with Monitoring Systems

Prometheus Integration

  • Metric Export: Expose metrics in Prometheus format
  • Push Gateway Support: For short-lived processes
  • Custom Labels: Service, node, environment tags
  • Grafana Dashboards: Pre-built dashboard templates

StatsD/DataDog Integration

  • Metric Forwarding: Send metrics to StatsD daemon
  • Custom Tags: Rich metadata support
  • APM Integration: Distributed tracing support
  • Service Maps: Automatic dependency mapping

OpenTelemetry Support

  • Trace Export: OTLP protocol support
  • Metric Export: OpenMetrics format
  • Log Correlation: Trace IDs in logs
  • Sampling: Configurable trace sampling

Implementation Considerations

Performance Optimization

  • Metric Batching: Reduce overhead with batch processing
  • Sampling Strategies: Configurable sampling for high-volume events
  • Async Processing: Non-blocking metric processing
  • Memory Management: Automatic metric expiration and cleanup

Scalability

  • Distributed Aggregation: Cluster-wide metric aggregation
  • Sharded Storage: Distribute metrics across nodes
  • Federation: Multi-cluster metric federation
  • Edge Computing: Process metrics at the edge

Security

  • Metric Encryption: Encrypt sensitive metrics
  • Access Control: Role-based metric access
  • Audit Logging: Track metric access and modifications
  • PII Filtering: Automatic PII detection and removal

Future Enhancements

Advanced Analytics

  • Complex Event Processing: Pattern matching across event streams
  • Business Intelligence: Custom metric dashboards and reports
  • Cost Analysis: Track and optimize resource costs
  • SLA Monitoring: Automatic SLA compliance tracking

AI/ML Integration

  • Automated Optimization: ML-driven configuration tuning
  • Predictive Maintenance: Predict component failures
  • Intelligent Alerting: Reduce alert fatigue with ML
  • Natural Language Queries: Query metrics with natural language

Visualization

  • Real-Time Dashboards: WebSocket-based live updates
  • 3D Visualizations: Complex system topology views
  • AR/VR Support: Immersive monitoring experiences
  • Mobile Apps: Native mobile monitoring apps