The idempotency_key detail is what makes this non-trivial.
Use a window function to rank rows within each (user_id, idempotency_key) partition, ordering by event_time ascending and event_id ascending as a tiebreaker. Then filter to rows where the rank equals 1, returning all columns.
Pro tip: Mention that this pattern is a classic deduplication technique and that the choice of ROW_NUMBER() ensures exactly one row per group, unlike RANK() or DENSE_RANK() which could return ties. Also note that if the table is huge, partitioning by (user_id, idempotency_key) and ordering by (event_time, event_id) can leverage sorting and avoid a full shuffle if the data is already clustered.
Clarify that we need to deduplicate based on (user_id, idempotency_key) and keep the row with the earliest event_time, using event_id as a tiebreaker. All columns must be returned.
Select ROW_NUMBER() because it assigns a unique sequential number to each row within the partition, ensuring exactly one row per group. Avoid RANK() or DENSE_RANK() as they can produce ties.
Partition by user_id and idempotency_key, and order by event_time ASC, event_id ASC. This orders rows so the earliest event_time and smallest event_id get row number 1.
Wrap the window function in a subquery or CTE, then filter for rows where the row number equals 1. Select all columns from the original table.
Discuss indexing on (user_id, idempotency_key, event_time, event_id) to speed up the window function. Handle NULLs in idempotency_key if necessary, and ensure the query scales for large datasets.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Chaining a second CTE off the first was straightforward enough.
First, write a correct SQL query that filters purchases to 2025-09-01 UTC, counts distinct buyers per product, ranks products using ROW_NUMBER() with tie-breaking by product_id, and selects the top 2. Then, discuss scalability by explaining how to handle a billion rows through partitioning, indexing, and query optimization techniques.
Pro tip: When explaining scalability, emphasize that the query should be written to leverage indexes and partitions, and mention that using approximate algorithms like HyperLogLog for distinct counts can be a trade-off for extreme scale, but exact counts are preferred when feasible.
Restate the problem: top 2 products by distinct purchasing users on 2025-09-01 UTC, with tie-breaking by product_id ascending. Confirm that 'purchasing users' means users who made at least one purchase of that product on that date, and that the date is based on UTC.
Construct a query that filters the de-duplicated purchase data for the target date, groups by product_id and product_name, counts distinct user IDs, then uses ROW_NUMBER() OVER (ORDER BY distinct_buyers DESC, product_id ASC) to rank, and finally selects the top 2.
Discuss how the query can be scaled: use partitioning by date to prune irrelevant data, ensure indexes on (purchase_date, product_id, user_id) to speed up filtering and grouping, and consider columnar storage or pre-aggregation for performance.
Recommend a composite index on (purchase_date, product_id, user_id) to cover the query, and possibly a materialized view or summary table for daily distinct counts if the query is frequent. Mention that for a billion rows, distributing the query across partitions and using approximate distinct counts (e.g., HyperLogLog) can be considered if exact counts are not strictly required.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.