Distributed tracing (Jaeger, Zipkin) in Microservices - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding the purpose of distributed tracing

Which of the following best describes the main purpose of distributed tracing tools like Jaeger and Zipkin in a microservices architecture?

A To automatically scale microservices based on incoming traffic patterns.

B To replace traditional logging systems by storing all logs in a centralized database.

C To monitor and visualize the flow of requests across multiple microservices to identify latency and errors.

D To encrypt all communication between microservices for enhanced security.

💡 Hint

Think about what problem distributed tracing solves in complex systems.

Distributed tracing tools help track requests as they travel through multiple microservices, making it easier to find where delays or errors happen.

❓ Architecture

intermediate

Key components of a distributed tracing system

Which component is NOT typically part of a distributed tracing system like Jaeger or Zipkin?

A Load balancer that distributes incoming user requests to microservices.

B Collector that receives and processes trace data from instrumented services.

C Storage backend that saves trace data for querying and visualization.

D Agent that runs alongside services to capture and forward trace data.

💡 Hint

Consider which components are specific to tracing versus general infrastructure.

Load balancers distribute traffic but are not part of the tracing system itself.

❓ scaling

advanced

Scaling distributed tracing in high-traffic microservices

In a high-traffic microservices environment, what is the best approach to ensure distributed tracing data does not overwhelm storage and processing systems?

A Store all trace data indefinitely to ensure complete historical records.

B Disable tracing on all services during peak traffic to avoid overload.

C Send trace data directly from clients to the storage backend without processing.

D Sample traces by collecting only a subset of requests to reduce data volume.

💡 Hint

Think about balancing data completeness with system performance.

Sampling reduces the amount of trace data collected, helping systems handle high traffic without losing all visibility.

❓ tradeoff

advanced

Tradeoffs in choosing between Jaeger and Zipkin

Which statement correctly describes a tradeoff when choosing between Jaeger and Zipkin for distributed tracing?

A Jaeger offers better integration with Kubernetes but Zipkin has a simpler architecture and easier setup.

B Zipkin supports more programming languages natively than Jaeger.

C Jaeger does not support sampling, while Zipkin supports advanced sampling techniques.

D Zipkin requires proprietary storage solutions, whereas Jaeger only supports open-source databases.

💡 Hint

Consider differences in deployment and architecture complexity.

Jaeger is designed with Kubernetes in mind and offers rich features, while Zipkin is known for its simplicity and ease of setup.

❓ estimation

expert

Estimating storage needs for distributed tracing data

A microservices system handles 100,000 requests per minute. Each trace generates approximately 10KB of data. If you sample 10% of traces for storage, what is the estimated storage needed per day (in GB)?

A Approximately 864 GB

B Approximately 144 GB

C Approximately 1,440 GB

D Approximately 86.4 GB

💡 Hint

Calculate total data per minute, apply sampling, then scale to one day.

Calculate: 100,000 requests/min * 10KB = 1,000,000 KB/min = 1,000 MB/min. Sampling 10% means 100 MB/min stored. For one day: 100 MB/min * 60 min * 24 hours = 144,000 MB = 144 GB.