The price filtering part was straightforward but I fumbled the tie-breaking logic at first.
Clarify the problem constraints (e.g., number of restaurants, query frequency, data format) and propose an efficient solution using a hash map to group restaurants by menu item and price, then sort by price and distance. Discuss trade-offs between pre-processing and on-demand computation, and consider edge cases like ties and missing items.
Pro tip: Demonstrate awareness of real-world scalability by suggesting indexing strategies (e.g., database indexes or in-memory caches) and mentioning how you'd handle frequent queries with precomputed results.
Ask about input size, query frequency, data format, and whether the solution should be optimized for read-heavy or write-heavy workloads. Confirm tie-breaking rules and distance metric.
Propose a hash map mapping menu items to a list of (restaurant_id, price, coordinates). For efficient queries, consider sorting each list by price and then distance, or using a priority queue.
For a given item, retrieve the list, filter by lowest price, compute Euclidean distances for ties, and return the restaurant(s) with minimum distance. If multiple, return all or the first based on requirements.
Discuss time and space complexity: pre-processing O(N log N) per item, query O(1) if pre-sorted, or O(K) if scanning. Compare with on-demand sorting O(K log K). Mention trade-offs between memory and speed.
Address missing items, multiple restaurants with same price and distance, and scaling to many queries. Suggest caching or database indexing for production.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the data model and time window semantics (inclusive/exclusive, timezone), then propose an efficient aggregation strategy using filtering and grouping. Discuss trade-offs between batch and streaming, and how to handle edge cases like empty windows or refunds.
Pro tip: Mention that average order value should be computed as total revenue divided by total order count, not as an average of averages, to avoid Simpson's paradox. Also, consider using a single pass over the data for efficiency.
Ask about the definition of 'order', time window boundaries (inclusive/exclusive), timezone, and whether revenue includes refunds or taxes. Confirm the expected output format and scale of data.
Decide between batch processing (e.g., SQL GROUP BY) or streaming (e.g., windowed aggregation). Consider if the data is already partitioned by time to optimize filtering.
Filter orders within the time window, then compute total revenue (sum of order amounts), total order count, and average order value (total revenue / total order count). Use a single pass if possible.
Address empty windows (return zeros or nulls), duplicate orders, refunds, and timezone conversions. Validate results with sanity checks (e.g., AOV between min and max order values).
If data is large, suggest indexing on timestamp, using columnar storage, or pre-aggregating. For real-time needs, discuss streaming with watermarks and late data handling.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where things got interesting and also where I probably lost the most points.
Start by clarifying the requirements: define the time window, what 'top' means (by total price for orders, total quantity for items), and tie-breaking rules. Then propose an efficient algorithm using hash maps to aggregate totals and a min-heap of size K to find the top K, discussing time and space complexity. Finally, address trade-offs and potential optimizations for large-scale data.
Pro tip: Explicitly state your tie-breaking rules (e.g., if totals are equal, break ties by most recent order or lexicographically by ID) and justify them; this shows attention to detail and prevents ambiguity in production systems.
Ask about the time window format, data volume, whether the data fits in memory, and if the results need to be sorted. Confirm tie-breaking rules and whether K is small relative to the number of unique orders/items.
Use hash maps to aggregate total price per order and total quantity per item. Use a min-heap of size K to efficiently track the top K elements, or sort if K is large relative to the dataset.
Iterate through the data, updating aggregates. For each aggregate, push to the heap if it qualifies for the top K, maintaining the heap size. Define tie-breaking rules (e.g., by order ID or timestamp) and apply them consistently.
Time: O(N + M log K) where N is number of records, M is number of unique orders/items. Space: O(M + K). Discuss how this scales and potential bottlenecks.
Mention alternatives like sorting all aggregates (O(M log M)) if K is large, or using approximate algorithms for massive data. Consider distributed processing if data doesn't fit in memory.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.