The distance part tripped me up more than the pricing logic.
Start by clarifying the problem: define distance metric (e.g., Euclidean or Manhattan), confirm if basket items are exactly matched or can be substituted, and discuss data scale. Then outline an algorithm: for each restaurant, compute total cost by summing prices of basket items (handle missing items), and compute distance; track the best (lowest cost, then nearest). Finally, analyze time and space complexity, and discuss potential optimizations like pre-filtering by distance or using spatial indexes.
Pro tip: Mention that in a real system, you'd likely precompute distances or use a geospatial index (e.g., geohash) to avoid O(n) distance calculations per query, and consider caching frequent baskets. This shows awareness of production-scale trade-offs.
Ask about distance metric (straight-line vs. road), whether all basket items must be available, and if prices are static. Confirm input/output format and scale.
Choose a metric (e.g., Euclidean for simplicity, Manhattan for grid-like cities) and explain how to compute it from user location to each restaurant. Mention that for large-scale, Haversine is more accurate.
Iterate through restaurants, compute total cost for the basket (sum item prices, handle missing items), and compute distance. Track the best restaurant based on cost, then distance.
State time complexity O(R * B) where R is restaurants and B is basket size, and space O(1). Discuss optimizations like pre-filtering by distance or using a spatial index.
Cover missing items, ties, no restaurants available, and trade-offs between accuracy and performance (e.g., Euclidean vs. road distance).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went to a sliding window with a deque pretty fast.
Start by clarifying requirements: define the sliding window semantics (e.g., tumbling vs. hopping, event-time vs. processing-time), whether windows are fixed-size or arbitrary, and the expected throughput and latency. Then propose a data structure like a balanced BST or a deque with prefix sums to maintain per-window aggregates, and discuss how to handle multiple overlapping windows efficiently, possibly using a segment tree or a time-indexed ring buffer with incremental updates.
Pro tip: Emphasize the trade-offs between exact and approximate solutions (e.g., using t-digests for percentiles) and mention how you'd handle out-of-order events and late data, as these are critical in financial systems like Coinbase.
Ask about window types (fixed vs. sliding, overlapping), time semantics (event-time vs. processing-time), data volume, latency requirements, and whether exact or approximate results are acceptable.
Select appropriate structures: a deque for maintaining a single window's orders, a balanced BST or Fenwick tree for prefix sums to compute aggregates over arbitrary ranges, and a segment tree for multiple overlapping windows.
Outline an incremental algorithm: for each new order, update the relevant windows by adding the order's price and count, and remove expired orders. For multiple windows, use a time-indexed array or tree to query sums over any interval in O(log n).
Address out-of-order events, late data, window boundaries, and empty windows. Discuss watermarks and allowed lateness if using event-time processing.
Analyze time and space complexity per operation and for multiple windows. Discuss distributed processing (e.g., using Apache Flink or Kafka Streams) if data volume is high.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements: define the time window (sliding vs. tumbling), whether K is fixed, and how ties should be broken (e.g., by order ID or timestamp). Then propose a hybrid data structure: a hash map for O(1) updates and a balanced BST or heap for maintaining top K, with careful handling of ties via composite keys. Discuss trade-offs between exact and approximate solutions, and how to handle real-time updates efficiently.
Pro tip: Mention that ties can be broken deterministically by including a unique identifier (like order ID) in the sort key, ensuring stable and reproducible results. Also, consider using a min-heap of size K for top K queries to achieve O(log K) updates, which is efficient for large streams.
Ask about the time window semantics (sliding vs. tumbling), whether K is fixed, expected data volume, and tie-breaking rules. Confirm if approximate results are acceptable.
Propose a hash map for O(1) access to order/item totals, and a balanced BST or min-heap of size K for maintaining top K. For ties, use composite keys (e.g., total price + order ID).
On each insertion, update the hash map and adjust the top-K structure. For sliding windows, use a time-ordered queue to expire old entries and update aggregates accordingly.
State time complexity: O(1) average for hash map updates, O(log K) for heap/BST adjustments. Space: O(N) for hash map and O(K) for top-K structure. Discuss trade-offs with alternative approaches.
Explain tie-breaking strategy (e.g., by order ID or timestamp) and how to handle empty windows, K larger than data size, and concurrent updates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.