I knew the general shape: PARTITION BY user_id, ORDER BY day, then ROWS BETWEEN 6 PRECEDING AND CURRENT ROW.
First, aggregate the raw events into daily sums per user to reduce data volume, then apply a window function with a RANGE frame covering the current day and the preceding 6 days to compute the rolling 7-day sum. Ensure the window is partitioned by user_id and ordered by day, and handle missing days appropriately.
Pro tip: Mention that using RANGE with INTERVAL '6 days' PRECEDING correctly handles gaps in dates, unlike ROWS which would count rows instead of days. Also, clarify that the rolling sum should include the current day, so the frame is BETWEEN 6 PRECEDING AND CURRENT ROW.
Confirm the definition of 'rolling 7-day sum': does it include the current day? Should days with no events be included? What is the expected output granularity (per user per day)?
Write a subquery or CTE that groups by user_id and date (truncated from event_time) and sums the value column to get daily totals per user.
Use SUM(daily_value) OVER (PARTITION BY user_id ORDER BY day RANGE BETWEEN INTERVAL '6 days' PRECEDING AND CURRENT ROW) to compute the rolling 7-day sum.
Consider how to handle users with fewer than 7 days of data, missing dates, and time zone conversions. Optionally, generate a date spine to fill gaps if needed.
Discuss indexing on (user_id, event_time), partitioning strategies for large datasets, and validate results with sample data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use a window function like ROW_NUMBER() partitioned by user_id and ordered by event_timestamp with a deterministic tie-breaker (e.g., event_id). Explain that this ensures each event gets a unique rank per user, and discuss how the tie-breaker prevents non-deterministic results.
Pro tip: Mention that without a unique tie-breaker, the ranking can vary between executions, which is unacceptable in production. Also, consider performance implications: partitioning and ordering large datasets may require appropriate indexing or distribution strategies.
Confirm that 'rank' means assigning a unique sequential number per user, and that ties should be broken deterministically. Ask if there's a preferred tie-breaker column (e.g., event_id) or if one needs to be derived.
Select ROW_NUMBER() over RANK() or DENSE_RANK() because it guarantees unique ranks even with ties. Explain that RANK() would leave gaps and not provide a strict chronological order.
Partition by user_id and order by event_timestamp ascending, then by a unique column like event_id ascending to break ties. This ensures deterministic results.
Construct the query: SELECT user_id, event_id, event_timestamp, ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY event_timestamp, event_id) AS event_rank FROM events;
Address scenarios like duplicate timestamps, null timestamps, and large data volumes. Suggest indexing on (user_id, event_timestamp, event_id) for efficiency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the table schema, ordering column, and definition of 'last non-null value seen so far' (e.g., per user, ordered by event time). Then explain that you would use a window function like LAST_VALUE with IGNORE NULLS over a partition by user ordered by event time, and discuss the default window frame and why it must be adjusted to ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW. Finally, mention trade-offs such as performance and alternative approaches like self-joins or correlated subqueries.
Pro tip: Demonstrate awareness that the default window frame for LAST_VALUE is RANGE BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING, which would incorrectly return the last non-null value in the entire partition, not up to the current row. Explicitly stating this shows deep understanding of window framing.
Ask about the table structure, the ordering column (e.g., event timestamp), and what 'last non-null value seen so far' means (e.g., per user, ordered by time). Confirm whether ties in ordering are possible and how to handle them.
Select LAST_VALUE with IGNORE NULLS (if supported) over a partition by user ordered by event time. Explicitly set the window frame to ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW to ensure the function only looks at rows up to the current one.
Describe why the default frame (RANGE BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) is incorrect for this use case, as it would consider all rows in the partition. Emphasize that the frame must be restricted to the current row.
Mention alternative approaches such as using a self-join or correlated subquery, and compare their performance and readability. Note that window functions are generally more efficient and set-based.
Address handling of nulls, ties in ordering, and large datasets. Suggest indexing the partition and order columns to optimize performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.