This is the kind of question where you know the shape of the answer immediately but the details trip you up.
Use a window function like ROW_NUMBER() or RANK() partitioned by the UTC date of order_ts and ordered by order_ts ascending to identify the earliest completed order(s) per day. Filter for completed orders and select rows where the rank equals 1, ensuring ties are included. Emphasize that this avoids correlated subqueries and scales well with proper indexing and partitioning.
Pro tip: Mention that partitioning the table by date and clustering by order_ts can drastically reduce the data scanned, and that using RANK() instead of ROW_NUMBER() correctly handles ties without extra logic.
Confirm that 'completed order' means status = 'completed', and that the date is derived from order_ts in UTC. Discuss tie-handling: if multiple orders share the exact earliest timestamp, all should be returned.
Select RANK() (or DENSE_RANK()) over ROW_NUMBER() because it assigns the same rank to ties, ensuring all earliest orders are captured. Partition by the UTC date and order by order_ts ascending.
Construct a subquery or CTE that computes the rank for each completed order, then filter for rank = 1 in the outer query. Select date, order_id, merchant_id, and order_ts.
Suggest partitioning the table by date and clustering by order_ts to enable partition pruning and efficient sorting. Mention that window functions can leverage sorted data and that indexes on (status, order_ts) may help.
Test the query on a sample and explain why this approach avoids correlated subqueries and performs well on large datasets. Discuss potential alternatives and their trade-offs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Much more straightforward once you've done part A.
Start by clarifying the schema and definitions (e.g., what constitutes a 'completed' order and how to identify the 'first' one). Then, outline a SQL query using a window function like ROW_NUMBER() partitioned by merchant and date, ordered by order timestamp, and filter for completed orders. Finally, discuss trade-offs such as performance, edge cases, and alternative approaches.
Pro tip: Mention that you would confirm whether 'first' means earliest by order creation time or completion time, as this ambiguity could lead to incorrect results. Also, highlight the importance of handling ties or duplicate timestamps to ensure deterministic output.
Ask questions to understand the table structure, what 'completed' means (e.g., status = 'completed'), and how to determine the 'first' order (e.g., by order timestamp). Confirm the granularity of merchant and date.
Use a window function like ROW_NUMBER() OVER (PARTITION BY merchant_id, order_date ORDER BY order_timestamp) to rank orders within each merchant-date group. Filter for completed orders before ranking.
Construct the query with a subquery or CTE that assigns row numbers, then select rows where row_number = 1. Ensure proper filtering for completed status and correct date extraction.
Address performance considerations (e.g., indexing on merchant_id, order_date, status), handling ties (e.g., using additional tiebreaker like order_id), and alternative approaches (e.g., self-join with MIN).
Mention how you would test the query with sample data, including edge cases like no completed orders, multiple orders with same timestamp, and timezone considerations for date extraction.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the query patterns and data scale, then justify partitioning and sorting choices based on access patterns and data distribution. Explain how indexes (e.g., composite, covering) support these queries, and discuss trade-offs like write overhead and storage.
Pro tip: Tie your choices to Amazon's leadership principles: insist on the highest standards by quantifying performance gains, and think big by designing for 10x scale. Also, mention how you'd monitor and iterate on index usage.
Ask about query patterns, data volume, latency SLAs, and update frequency to ground your design in real needs.
Choose partition key based on common filters (e.g., date, customer_id) to enable partition pruning and even data distribution.
Select sort key to optimize range scans and ordering for frequent queries, minimizing expensive sorts.
Propose composite indexes covering filter and sort columns, and consider covering indexes to avoid table lookups.
Acknowledge costs: write amplification, storage, maintenance; and suggest monitoring and periodic review.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.