The fact that they let you look things up sounds chill until you realize you're burning time googling Jackson vs Gson while they're watching.
First, clarify the API client's structure and the JSON response format, then choose a suitable JSON parsing library like Jackson or Gson. Write clean, modular code to extract the required fields, handling potential errors and edge cases.
Pro tip: Mention that you'd check the API documentation for field names and types, and consider using Jackson's tree model for flexibility if the schema might change. Also, discuss trade-offs between different libraries (e.g., Jackson vs Gson) in terms of performance and ease of use.
Review the API client code and sample JSON response to identify the exact fields (stop name, description) and their paths. Note any nested objects or arrays.
Select a library like Jackson or Gson based on project needs, performance, and familiarity. Explain your choice briefly.
Write code to parse the JSON string into a tree or POJO, then extract the required fields. Use appropriate methods (e.g., path(), get()) and handle nulls.
Add error handling for malformed JSON, missing fields, or network issues. Consider logging and fallback values.
Write unit tests with sample JSON to verify extraction works correctly, including edge cases like empty responses.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the code's purpose and context, then systematically address each area (error handling, retries, timeouts, schema validation, observability, configuration, testability) with concrete improvements. Prioritize changes based on impact and explain trade-offs, emphasizing reliability, maintainability, and operational readiness.
Pro tip: Frame your answer around the principle of 'failing fast but recovering gracefully'—show that you balance robustness with simplicity and avoid over-engineering. Mention specific libraries or patterns (e.g., exponential backoff with jitter, structured logging) to demonstrate hands-on experience.
Ask about the code's role, expected load, dependencies, and SLAs to tailor your recommendations. This shows you don't apply a one-size-fits-all solution.
Discuss adding explicit error types, retries with exponential backoff and jitter, circuit breakers, and timeouts for all external calls. Emphasize idempotency and graceful degradation.
Validate inputs/outputs against schemas (e.g., JSON Schema, Protobuf) and externalize configuration with environment variables or a config service. Highlight validation at boundaries and secure secret handling.
Add structured logging, metrics (e.g., latency, error rates), and distributed tracing. Ensure logs are actionable and include correlation IDs for debugging.
Refactor for dependency injection, write unit/integration tests with mocks, and add health checks and readiness probes. Discuss CI/CD and canary deployments.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.