← Jane Street Interview Insights
This took me a while to even frame correctly.
First, clarify the requirements: O(1) view_result() and dynamic global filter changes. Then propose maintaining incremental aggregates (e.g., total PnL, count) for the active set, updating them when trades are added/removed or when the global filter changes. For filter changes, compute the delta between old and new active sets efficiently, possibly using precomputed per-filter aggregates or a data structure that supports fast set difference.
Pro tip: Emphasize that O(1) view_result() is achievable only if updates (add/remove trade, filter change) are amortized or handled in O(k) where k is the size of the delta; discuss trade-offs between update latency and query latency.
Confirm that view_result() must be O(1) and that set_global_filter() can be called at any time. Ask about the frequency of updates vs. queries and whether filters can overlap arbitrarily.
Maintain a running aggregate (e.g., sum of PnL) for the currently active set of trades. When a trade is added or removed, update the aggregate in O(1).
When the global filter changes, compute the set of trades that are newly included and those that are excluded. Update the aggregate by subtracting the PnL of excluded trades and adding that of included trades. To do this efficiently, maintain per-filter aggregates or an index that allows fast retrieval of trades in a filter.
If filters are known in advance, precompute aggregates for each filter. Then a global filter change can be handled by swapping the active aggregate to the precomputed one, but careful: overlapping groups mean a trade may belong to multiple filters, so the global filter might be a union/intersection. Consider maintaining a data structure that supports fast union/intersection of filter sets.
Acknowledge that O(1) view_result() may come at the cost of slower updates or higher memory. Discuss how to handle concurrent updates and filter changes, and ensure correctness when trades are modified.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Part 2 is where things got genuinely hard.
Start by clarifying the constraints: get_pnl is async and idempotent per (trade, time) pair, so caching is safe if we key by that pair and never call it twice. Then design a cache that stores promises (or results) keyed by (trade, time), and invalidate or recompute only for trades affected when set_global_filter changes the active trade set. Emphasize correctness under concurrency and the trade-off between memory and recomputation.
Pro tip: Mention that you'd cache the in-flight promise, not just the resolved value, to deduplicate concurrent requests for the same (trade, time) pair—this prevents duplicate calls and is a common pitfall in async caching.
Confirm that get_pnl is idempotent per (trade, time) and that set_global_filter changes the active trade set. Identify whether time is a discrete timestamp or a range, and how often set_global_filter is called.
Use a two-level map: trade -> time -> Promise<result> (or result). Store the promise immediately upon first call to deduplicate concurrent requests. Consider eviction policy (e.g., LRU) if memory is a concern.
When the active trade set changes, invalidate cache entries only for trades that are no longer active (or newly active). For newly active trades, recompute lazily on demand. For removed trades, optionally evict to free memory.
Use a mutex or atomic operations to guard cache reads/writes and invalidation, especially if set_global_filter can be called concurrently with get_pnl. Ensure that invalidation doesn't race with in-flight requests.
Weigh memory vs. recomputation, staleness vs. freshness, and complexity of invalidation. Mention handling of errors (e.g., failed promises should be evicted) and time-based expiry if data becomes stale.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.