The base sorting part was fine, I talked through a custom comparator and parsing the timestamp out of each string.
Start by clarifying the business logic for sorting and the expected log formats, then propose a normalization pipeline that parses each log into a canonical structure before sorting. Discuss trade-offs between strict parsing, lenient parsing, and fallback strategies, and how to handle unparseable logs.
Pro tip: Mention that you would log and monitor parsing failures to detect format drift over time, and consider a schema registry or versioned parsers for long-term maintainability.
Ask about the business logic for sorting, the expected log formats, and the scale of data. Determine if sorting is by timestamp, severity, or a custom key, and whether logs are streaming or batch.
Propose parsing each log into a canonical structure (e.g., a LogEntry object) with a unified timestamp format. Use a parser that tries multiple formats and falls back to a default or flags unparseable logs.
Sort the normalized entries using the business logic. For inconsistent formats, ensure the comparator handles missing or invalid fields gracefully, perhaps by placing them at the end or using a secondary sort key.
Decide how to treat logs that cannot be parsed: skip, log an error, or assign a default timestamp. Discuss monitoring and alerting for format inconsistencies.
Compare approaches: pre-processing vs. on-the-fly parsing, strict vs. lenient parsing, and in-memory vs. external sorting. Consider performance implications for large datasets.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.