Two sub-questions built on top of each other, which I didn't fully anticipate.
Start by clarifying the requirements: what attributes to filter/group by, expected data volume, and whether the log is static or streaming. Then propose a clean, extensible design using a data model for requests and a pipeline of operations (filter, group, aggregate), discussing time/space trade-offs and potential optimizations like indexing or parallel processing.
Pro tip: Mention that you'd design the solution to be composable and testable, and that you'd consider using a streaming approach if the log is large, to avoid loading everything into memory.
Ask about the log format, size, and whether operations need to be real-time or batch. Confirm which attributes are most important and if multiple aggregations are needed.
Define a Request class/struct with fields like timestamp, user, endpoint, status code, etc. Consider using a schema that allows easy addition of new attributes.
Write functions for filtering (e.g., by user or status), grouping (e.g., by endpoint), and aggregating (e.g., count, average response time). Use functional composition for flexibility.
Discuss performance: indexing for fast lookups, streaming for large logs, parallel processing for aggregations. Mention trade-offs between memory and speed.
Outline unit tests for each operation, edge cases (empty log, invalid entries), and integration tests. Consider using sample data to demonstrate correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.