I jumped straight into the geo part because it felt more interesting, which was probably the wrong move.
Clarify requirements and constraints first, then propose a data model and algorithms for both queries. For lowest total price, consider indexing menus and using a min-heap or precomputed combinations; for closest restaurant, use a spatial index like a k-d tree or geohash. Discuss trade-offs between precomputation and on-the-fly computation, and how to handle streaming orders.
Pro tip: Mention that for Coinbase, low-latency and high-throughput are critical, so caching frequent queries and using in-memory data stores (e.g., Redis) for spatial indexes can be a game-changer. Also, consider partitioning by geography to scale.
Ask about scale (number of restaurants, orders per second), latency requirements, consistency needs, and whether the set of menu items is arbitrary or limited. Clarify if prices can change and how often.
Propose a schema: restaurants with menus (item->price), and location (lat/long). For price queries, index items to restaurants; for location, use a spatial index (e.g., R-tree, geohash). Consider denormalization for fast reads.
For a given set of items, find restaurants that have all items and compute total price. Use inverted index from item to restaurants, intersect sets, and compute sums. Optimize with precomputed combinations if item sets are limited.
Use a spatial index to find nearest restaurant to user's location. For streaming orders, maintain a dynamic index or use a geohash-based lookup with neighbor cells.
Discuss how to handle incoming orders: update indexes if restaurants change, cache frequent queries, and shard by geography. Consider using a distributed system with eventual consistency if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the input format, time window boundaries, and definitions (e.g., revenue per order, handling of refunds). Then outline an efficient algorithm, such as sorting orders by timestamp and using a sliding window or prefix sums, and discuss how to compute the three metrics in one pass. Finally, analyze time and space complexity and consider edge cases like empty windows or invalid data.
Pro tip: At Coinbase, data accuracy and real-time processing are critical. Mention that you would validate the time window boundaries (inclusive/exclusive) and consider using a streaming approach for large-scale data, showing awareness of production constraints.
Ask about the input format (e.g., list of orders with timestamps and prices), time window definition (inclusive/exclusive), and whether refunds or discounts affect revenue. Confirm the output format and any constraints.
Propose sorting orders by timestamp and using a sliding window or prefix sums to compute metrics in O(n log n) time due to sorting, or O(n) if already sorted. Alternatively, use a single pass with a hash map if the window is fixed.
Iterate through orders within the window, accumulating total revenue and order count, then derive average order value as revenue divided by count. Handle division by zero if no orders.
Discuss time and space complexity, and address edge cases such as empty window, orders exactly on boundaries, negative prices (refunds), and large datasets requiring streaming.
Walk through a small example to verify correctness, including boundary conditions and expected outputs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the data model and constraints first, then propose an efficient algorithm using hash maps for aggregation and heaps for top-K selection. Discuss trade-offs between time/space complexity and scalability, and consider edge cases like ties and large data volumes.
Pro tip: Mention that you would use a min-heap of size K for each ranking to achieve O(n log K) time, and highlight that this is optimal for streaming data. Also, discuss how to handle ties by defining a secondary sort key (e.g., order ID or item name) to ensure deterministic results.
Ask about data size, time window definition, whether K is fixed, and if ties need special handling. Confirm input format and expected output.
Use hash maps to aggregate total price per order and transaction volume per menu item within the time window. Consider if data fits in memory or needs streaming.
For each ranking, use a min-heap of size K to keep the top K elements. Iterate through aggregated data, pushing and popping to maintain the heap.
Define a tie-breaking rule (e.g., by ID) and ensure the output is sorted descending by the ranking metric. Return two separate lists.
Discuss time complexity O(n log K) and space O(n + K). Compare with alternative approaches like sorting all items (O(n log n)) and explain why heap is better for large n and small K.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.