Scalability and Code Organization Questions

Focuses on designing software and codebases that remain maintainable and performant as features and user load grow. Areas include modularity and separation of concerns, component and API boundaries, when and how to refactor, trade offs between monolith and service oriented architectures, data partitioning and caching strategies, performance optimization, testing strategies, dependency management, code review practices, and patterns for maintainability and evolvability. Interview questions may ask candidates to reason about design choices, identify coupling and cohesion issues, and propose practical steps to evolve an existing codebase safely.

HardTechnical

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A dashboard query plan shows a hotspot where multiple dashboards repeatedly compute the same intermediate aggregation. Explain how you would detect this hotspot in production and propose a solution that minimizes duplicate work. Include considerations for correctness, cost, and cache eviction.

HardSystem Design

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Design a blue-green or canary deployment strategy for a new version of a metrics API used by dashboards. Include traffic routing, database/schema compatibility checks, rollback plan, and how to run parallel runs with a shadow traffic approach to validate computations without impacting users.

MediumTechnical

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Write a SQL-ish query (pseudocode is fine) for a metrics microservice that supports pagination and time-based aggregation. The API should accept page_token, page_size, start_time, end_time, and group_by fields. Describe how you would structure the pagination token and how the service can remain stateless while supporting consistent pagination across changing underlying data.

MediumTechnical

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For a BI microservice deployed in Kubernetes, propose practical observability instrumentation to detect scalability regressions: metrics, logs, traces, and alerts. Specify at least five signals you would track and explain why each is important for dashboard performance.

MediumTechnical

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A set of metric calculation scripts depend on many shared utility functions and a shared 'utils.py'. Changing a small utility often triggers a large test run and deploy. Propose concrete strategies to reduce coupling and speed up deploys while preserving code reuse and consistency of metrics.

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