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of 25What is Jaeger and what problem does it solve?
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Jaeger is an open-source, end-to-end distributed tracing system originally developed by Uber. It helps monitor and troubleshoot complex microservices architectures by tracking requests as they flow through multiple services.
Problems it solves:
- Performance bottlenecks: Identifies slow services in request chains
- Error tracking: Pinpoints where failures occur in distributed systems
- Dependency analysis: Maps service interactions and dependencies
- Root cause analysis: Helps debug issues across multiple services
- Service optimization: Provides insights for performance improvements
Jaeger follows the OpenTracing standard and is now part of the Cloud Native Computing Foundation (CNCF).
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Explain the concept of distributed tracing and its key components.
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Distributed tracing tracks requests as they travel through multiple services in a distributed system. It creates a complete picture of how a request is processed across different components.
Key components:
- Trace: Complete journey of a request through the system
- Span: Individual unit of work (e.g., HTTP request, database call)
- SpanContext: Carries trace information between services
- Tags: Key-value pairs that add metadata to spans
- Logs: Timestamped events within spans
- Baggage: Cross-service propagated key-value data
Example flow:
User Request → Service A → Service B → Database
| | | |
Trace ID Span 1 Span 2 Span 3
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What are the differences between logging, metrics, and tracing?
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These are the three pillars of observability, each serving different purposes:
Logging:
- Records discrete events with timestamps
- Good for debugging specific issues
- High volume, text-based
- Example: "User 123 failed login at 2023-10-15 14:30:00"
Metrics:
- Numerical measurements over time
- Good for alerting and dashboards
- Low volume, aggregated data
- Example: CPU usage: 75%, Request rate: 100/sec
Tracing:
- Shows request flow across services
- Good for understanding system behavior
- Medium volume, structured data
- Example: Request path: Frontend → API → Database (150ms total)
Integration: Modern observability uses all three together - traces provide context for logs and metrics.
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What are the different deployment strategies in Jaeger?
How do you instrument a microservice to work with Jaeger?
Describe Jaeger's architecture and its main components.
What is the role of Jaeger Agent and why is it important?
Explain the difference between Jaeger's push and pull models.
How would you deploy Jaeger in a Kubernetes environment?
How do you configure sampling in Jaeger?
Explain how trace context propagation works across services.
How do you handle tracing in asynchronous operations?
What are the performance considerations when implementing Jaeger?
What are the storage backend options and their trade-offs?
How do you troubleshoot missing or incomplete traces in Jaeger?
Explain how to perform trace analysis and create custom dashboards.
How do you scale Jaeger collectors?
What are the security considerations for Jaeger deployment?
What are the best practices for Jaeger in production?
How do you integrate Jaeger with service mesh (Istio)?
Explain how to implement custom span processors and samplers.
How do you monitor Jaeger itself and set up alerting?
How do you implement distributed tracing for batch jobs and scheduled tasks?
How do you handle trace data retention and archival?
What are the common anti-patterns and how to avoid them?
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