Start by clarifying the schema and definitions: what table(s) hold watch events, what columns represent user, video, timestamp, and watch duration, and whether watch-time is stored as a duration or must be derived from start/end times. Then write a query that truncates the event timestamp to the day and sums the watch duration, grouping by that day. If events can span multiple days, decide whether to attribute watch-time to the start day or split it across days, and state your assumption.
Pro tip: Mention that you would check for duplicate events or bot traffic and consider timezone consistency (e.g., UTC vs. local) before finalizing the query, because at Meta's scale, data quality and timezone handling can significantly affect daily watch-time metrics.
Ask which table contains watch events and what columns represent user, video, timestamp, and watch duration. Confirm whether watch-time is stored directly or must be computed from start/end times.
Decide how to assign each watch event to a day: use the event timestamp truncated to day (e.g., DATE(timestamp) or DATE_TRUNC('day', timestamp)). Clarify timezone (e.g., UTC) and whether events spanning midnight should be split.
Sum the watch duration per day using GROUP BY on the day bucket. If duration is in seconds, consider converting to minutes or hours for readability.
Filter out invalid or duplicate events (e.g., negative durations, test accounts, bot traffic). If events can span multiple days, either split the duration proportionally or state that you attribute to the start day.
Present the final SQL query clearly, explaining each clause. Optionally include a sample output or discuss how you would validate the results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the data model and the definition of 'cumulative daily metric' with mismatched IDs. Then propose a full outer join on the date and ID, using COALESCE to handle missing values, and compute the cumulative sum with a window function. Finally, discuss how to handle ID mismatches by either treating them as separate entities or aggregating at a higher level.
Pro tip: Mention that in production, you'd likely use a calendar table to ensure all dates are present and avoid gaps, and that you'd validate the cumulative logic with a running total check.
Ask about the grain of the tables, whether IDs are supposed to match, and what the cumulative metric represents. Confirm if the cumulative is per ID or overall.
Use a FULL OUTER JOIN on date and ID to include all records from both days. If IDs are not expected to match, consider aggregating at a higher level (e.g., total per day) or using a UNION ALL with a date dimension.
Replace NULLs from the join with 0 for the metric value, so that missing IDs contribute nothing to the cumulative sum.
Use SUM(metric) OVER (PARTITION BY id ORDER BY date) to calculate the running total. If aggregating overall, partition by nothing or by a constant.
Mention how to handle duplicate IDs, negative values, and date gaps. Suggest using a calendar table to fill missing dates and ensure correct cumulative logic.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.