Two parts and they hit you with the second one right after you think you're done with the first.
Start by clarifying requirements and edge cases, then design a clean data model and processing pipeline. Implement the core report generation first, then extend it to handle streaming rules with efficient lookups, and finally write comprehensive unit tests covering normal and edge cases.
Pro tip: Demonstrate production thinking by discussing how to handle out-of-order events, rule updates, and scalability, and mention that you would use a time-ordered data structure like a sorted list or priority queue for the stream.
Ask about input format, timestamp granularity, rule matching semantics (exact match, case sensitivity), and how to handle multiple rules or conflicting rules. Confirm expected output format and any performance constraints.
Propose a data model for requests and rules. For the report, sort requests by timestamp. For streaming rules, consider maintaining an index (e.g., hash map keyed by field) for O(1) rule lookup per request, and handle rules that arrive after some requests have already been processed.
Write a function that takes a list of requests and outputs the formatted report. Then extend it to accept a stream of rules, applying them to requests that occur after the rule's timestamp. Ensure the output is chronologically ordered and human-readable.
Cover scenarios: no rules, single rule, multiple rules, rules that don't match, rules with timestamps before/after requests, and edge cases like empty input, duplicate timestamps, and invalid data. Use a testing framework like JUnit or pytest.
Talk about time/space complexity, potential bottlenecks (e.g., sorting large data), and how to handle high-volume streams. Mention alternatives like using a database or stream processing framework for production.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.