← Jane Street Interview Insights
Felt straightforward at first and it basically was.
Clarify the data types and constraints, then propose a comparator-based sort that applies the three keys in order. Discuss stability, performance, and edge cases, and consider whether a custom comparator or a composite key is more appropriate.
Pro tip: Mention that a stable sort is not required because the comparator fully orders the records, but if using a language with an unstable sort, ensure the comparator is total. Also, highlight that timestamps should be compared as numeric values, not strings, to avoid lexicographic pitfalls.
Ask about the data types (e.g., timestamp format, symbol and trade ID types), input size, and whether the array can be sorted in place. Confirm that the ordering is total and deterministic.
Define a comparator that first compares timestamps numerically, then symbols lexicographically, then trade IDs lexicographically. Ensure each comparison handles equality correctly to proceed to the next key.
Select an efficient sorting algorithm (e.g., O(n log n) like merge sort or quicksort) available in the language's standard library. If using a language with a stable sort, note that stability is not required but harmless.
Write the function, then test with edge cases: empty array, single element, duplicate timestamps, duplicate symbols, and duplicate trade IDs. Verify the ordering is correct.
Discuss time and space complexity, and mention alternative approaches like sorting by a composite key or using a radix sort if timestamps are bounded. Consider stability and in-place sorting trade-offs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the business impact of duplicates—whether they cause incorrect state, double-counting, or just noise—and then propose a layered defense: idempotent processing, deduplication windows, and reconciliation. Emphasize that the solution must balance correctness, latency, and operational simplicity, and be ready to discuss trade-offs between in-memory vs. persistent deduplication and exactly-once vs. at-least-once semantics.
Pro tip: Mention that you'd first check if the feed can include a sequence number or timestamp to define ordering and deduplication windows, because without a monotonic identifier, you need a time-based or content-based approach. Also, highlight that you'd measure duplicate rates and set up alerts to avoid over-engineering for a rare edge case.
Ask what 'duplicate' means in this context: exact same record, same trade with different fields, or replays after a failure? Determine the consequences: financial misstatements, double-counted positions, or just wasted processing.
Decide between stateful deduplication (e.g., keeping a set of seen IDs in memory or a database) and stateless heuristics (e.g., hashing the full record and comparing against a recent window). Consider using a composite key (e.g., trade ID + timestamp + counterparty) if IDs are not unique.
Make downstream processing idempotent so that even if duplicates slip through, the final state is correct. If the feed has no ordering guarantee, use timestamps or sequence numbers to define a deduplication window and handle late-arriving data.
For high-throughput feeds, an in-memory cache with TTL may suffice, but for long-term correctness, consider a persistent store (e.g., Redis, RocksDB) with a time-based eviction policy. Discuss the trade-off between memory usage and the risk of missing duplicates outside the window.
Instrument duplicate detection rates, set up alerts, and run reconciliation jobs to catch any missed duplicates. Test with simulated duplicate and replay scenarios to validate the approach.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.