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Observability

The BookWorm application implements comprehensive observability using OpenTelemetry, providing insights into application performance, behavior, and health across the distributed system.

Observability Pillars​

Metrics​

  • Application Metrics - Business KPIs and application-specific counters
  • System Metrics - CPU, memory, disk, and network utilization
  • Runtime Metrics - .NET runtime performance indicators
  • Custom Metrics - Domain-specific measurements and business metrics

Logging​

  • Structured Logging - JSON-formatted logs with consistent schema
  • Correlation IDs - Request tracking across service boundaries
  • Context Propagation - Maintain context throughout request lifecycle
  • Log Aggregation - Centralized logging for distributed system analysis

Distributed Tracing​

  • Request Tracing - End-to-end request flow visualization
  • Service Dependencies - Understand service interaction patterns
  • Performance Analysis - Identify bottlenecks and optimization opportunities
  • Error Correlation - Link errors to specific request contexts

OpenTelemetry Integration​

Core Components​

  • OpenTelemetry.Extensions.Hosting - Host integration for automatic setup
  • OpenTelemetry.Exporter.OpenTelemetryProtocol - OTLP export for observability platforms
  • Custom Instrumentation - Application-specific telemetry collection
  • Auto-Instrumentation - Automatic instrumentation for common libraries

Instrumentation Libraries​

  • OpenTelemetry.Instrumentation.AspNetCore - HTTP request/response tracing
  • OpenTelemetry.Instrumentation.Http - HTTP client instrumentation
  • OpenTelemetry.Instrumentation.GrpcNetClient - gRPC client tracing
  • OpenTelemetry.Instrumentation.Runtime - .NET runtime metrics

Telemetry Configuration​

Trace Configuration​

  • Activity Sources - Custom trace sources for application components
  • Sampling Strategies - Intelligent sampling to manage trace volume
  • Span Enrichment - Add contextual information to traces
  • Custom Processors - Process and filter telemetry data

Metrics Configuration​

  • Meter Providers - Metric collection and aggregation
  • Histogram Buckets - Configurable histogram boundaries
  • Counter Aggregation - Sum and rate calculations
  • Gauge Metrics - Point-in-time measurements

Export Configuration​

  • Multiple Exporters - Send telemetry to multiple backends
  • Batch Processing - Efficient batching of telemetry data
  • Retry Logic - Handle export failures gracefully
  • Compression - Reduce network overhead for telemetry data

Custom Telemetry​

Activity Scopes​

  • Request Scopes - Track request lifecycle and context
  • Business Operations - Trace domain-specific operations
  • Performance Monitoring - Measure critical path performance
  • Resource Utilization - Track resource consumption patterns

Telemetry Tags​

  • Standard Tags - Consistent tagging across all services
  • Custom Tags - Application-specific metadata
  • Dynamic Tags - Context-dependent tag values
  • Cardinality Control - Manage tag cardinality for performance

Telemetry Propagation​

  • Context Propagation - Maintain trace context across services
  • Baggage - Carry application-specific data in trace context
  • Custom Propagators - Support for custom trace context formats
  • Header Management - HTTP header-based context propagation

Performance Optimization​

Instrumentation Performance​

  • Sampling Strategies - Reduce overhead with intelligent sampling
  • Conditional Instrumentation - Enable/disable instrumentation based on context
  • Batch Processing - Efficient telemetry data processing
  • Memory Management - Optimize memory usage for telemetry collection

Data Volume Management​

  • Attribute Limits - Control span and metric attribute counts
  • Event Limits - Manage span event volumes
  • Link Limits - Control span link counts
  • Sampling Configuration - Balance observability needs with performance

Monitoring Integration​

Observability Platforms​

  • Prometheus - Metrics collection and alerting
  • Grafana - Visualization and dashboarding
  • Jaeger - Distributed tracing analysis
  • Elastic Stack - Log aggregation and search

Cloud Platforms​

  • Azure Monitor - Azure-native observability
  • AWS X-Ray - AWS distributed tracing
  • Google Cloud Monitoring - GCP observability suite
  • Datadog - Third-party observability platform

Alerting and Notifications​

  • Metric-Based Alerts - Threshold-based alerting on key metrics
  • Trace-Based Alerts - Alerting based on trace patterns
  • Log-Based Alerts - Error pattern detection in logs
  • Composite Alerts - Multi-signal alerting strategies

Best Practices​

Telemetry Design​

  • Meaningful Names - Use descriptive names for metrics and traces
  • Consistent Units - Standardize units across all metrics
  • Appropriate Cardinality - Balance detail with performance
  • Context Enrichment - Add relevant context to telemetry data

Performance Guidelines​

  • Minimize Overhead - Keep instrumentation lightweight
  • Lazy Initialization - Initialize telemetry components on demand
  • Resource Cleanup - Properly dispose of telemetry resources
  • Batch Operations - Group telemetry operations efficiently

Operational Considerations​

  • Data Retention - Configure appropriate data retention policies
  • Security - Protect sensitive information in telemetry data
  • Compliance - Ensure telemetry practices meet regulatory requirements
  • Cost Management - Monitor and optimize observability costs