Microservices Mastery

Distributed Tracing: OpenTelemetry & Jaeger

1 Views Updated 5/4/2026

Distributed Tracing

Logging tells you **What** happened. Tracing tells you **Where** the time was spent. If a request takes 5 seconds, was it slow because of the Database, a slow Network call to the Identity service, or a locked thread in the API? OpenTelemetry provides the answer.

1. Spans and Traces

  • Trace: The complete journey of a request.
  • Span: A single unit of work (e.g., a SQL query or an HTTP call).

2. Visualizing with Jaeger

Jaeger provides a Gantt-chart view of your request. You can see exactly how long each internal call took. If a SELECT query took 4.5 seconds of a 5-second request, you know exactly where to optimize without looking at a single line of log text.

4. Interview Mastery

Q: "What is the performance overhead of enabling tracing on every single request?"

Architect Answer: "Tracing can add significant overhead because it generates a lot of telemetry data. For high-traffic systems, we use **Sampling**. Instead of tracing 100% of requests, we might only trace 1% (or only trace requests that take longer than 500ms). This gives us enough data to identify trends and bottlenecks without slowing down the production cluster."

Microservices Mastery
1. Distributed Systems Fundamentals
Monolith vs Microservices: When to migrate? The 12-Factor App Methodology for Cloud-Native Apps Database Per Service: Handling distributed data consistency
2. Containerization & Orchestration
Docker Essentials: Building efficient .NET images Docker Compose: Orchestrating a multi-service environment Kubernetes Architecture: Pods, Services, and Deployments K8s ConfigMaps & Secrets: Managing environment variables Helm Charts: Packaging your microservices for K8s
3. Service Communication
Synchronous vs Asynchronous Communication: Pros and Cons REST APIs in a Microservices World: Best Practices Mastering gRPC: High-performance binary communication API Gateways: Implementing Ocelot for single-entry access BFF Pattern: Backend-for-Frontend (Mobile vs Web)
4. Event-Driven Architecture
Message Brokers: Introduction to RabbitMQ & Azure Service Bus Pub/Sub Pattern: Implementing MassTransit for .NET The Outbox Pattern: Ensuring 100% data consistency Dead Letter Queues: Handling message failure gracefully Distributed Transactions: The Saga Pattern (State Machines)
5. Resilience & Scalability
Distributed Caching with Redis: Optimizing global state Service Discovery: IdentityServer4 & Consul Load Balancing: Nginx vs Ingress Controllers The Sidecar Pattern: Offloading cross-cutting concerns
6. Observability & Security
Distributed Logging with Serilog & SEQ Distributed Tracing: OpenTelemetry & Jaeger Health Checks: Monitoring system vitals in real-time OAuth2 & OpenID Connect: Centralized Identity (AuthN/AuthZ) Rate Limiting & Throttling: Protecting your services
7. Advanced Cloud Topics
Infrastructure as Code (IaC): Introduction to Terraform CI/CD Pipelines for Microservices (GitHub Actions/Azure DevOps) C# Architect Interview: Microservices & System Design Focus