Distributed tracing in HLD - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Purpose of Distributed Tracing in Microservices

Which of the following best describes the primary purpose of distributed tracing in a microservices architecture?

A To automatically scale microservices based on request volume.

B To monitor and visualize the flow of requests across multiple services to identify latency and errors.

C To encrypt all data transmitted between microservices for security.

D To store all logs from microservices in a centralized database.

💡 Hint

Think about how distributed tracing helps understand request paths and performance.

Distributed tracing tracks requests as they travel through different services, helping identify where delays or errors occur.

❓ Architecture

intermediate

Key Components of a Distributed Tracing System

Which set of components is essential for implementing a distributed tracing system?

A Load balancers, API gateways, and service registries.

B Database replicas, cache servers, and message queues.

C Trace context propagators, trace collectors, and a trace storage backend.

D Authentication servers, firewalls, and VPN gateways.

💡 Hint

Focus on components that handle trace data collection and storage.

Distributed tracing requires propagating trace context, collecting trace data, and storing it for analysis.

❓ scaling

advanced

Scaling Trace Data Collection in High-Traffic Systems

In a high-traffic distributed system, what is the best approach to scale trace data collection without overwhelming storage and processing?

A Send trace data synchronously with each request to ensure completeness.

B Store all trace data indefinitely without filtering.

C Disable tracing during peak traffic to reduce load.

D Implement sampling to collect traces only for a subset of requests.

💡 Hint

Consider how to reduce data volume while keeping useful information.

Sampling reduces the amount of trace data collected, balancing detail and resource use.

❓ tradeoff

advanced

Tradeoffs Between Synchronous and Asynchronous Trace Reporting

What is a key tradeoff when choosing between synchronous and asynchronous trace reporting in distributed tracing?

A Synchronous reporting ensures trace completeness but increases request latency; asynchronous reduces latency but may lose trace data on failure.

B Synchronous reporting reduces request latency but may lose trace data; asynchronous increases latency but guarantees trace completeness.

C Both synchronous and asynchronous reporting have the same impact on latency and data loss.

D Synchronous reporting is only used for error traces; asynchronous is used for all other traces.

💡 Hint

Think about how reporting method affects user experience and data reliability.

Synchronous reporting waits for trace data to be sent, adding latency but ensuring data is captured. Asynchronous sends data in background, reducing latency but risking data loss if failures occur.

❓ estimation

expert

Estimating Storage Requirements for Distributed Tracing Data

A system handles 1 million requests per minute. Each trace generates approximately 10 KB of data. If the system stores traces for 7 days, what is the approximate storage needed for trace data?

A Approximately 100 TB

B Approximately 70 TB

C Approximately 1 TB

D Approximately 10 TB

💡 Hint

Calculate total data per minute, then multiply by minutes in 7 days.

1 million requests/min × 10 KB = 10 GB/min. 7 days = 7 × 24 × 60 = 10080 minutes. Total = 10 GB × 10080 = 100800 GB ≈ 100 TB.