This is the kind of question where the filtering criteria and the ranking criteria are separate and it's easy to conflate them.
Break the problem into three parts: first, aggregate visits and reports per advertiser over the last 7 days; second, filter advertisers meeting the thresholds of 1,000 visits and 50 unique reporters; third, compute the report rate and use a window function with deterministic tiebreakers to rank and select the top 5. Write a single SQL query using CTEs for clarity and ensure all aggregations are correct.
Pro tip: Always clarify the definition of 'unique reporters' and 'last 7 days' (e.g., relative to current date or a fixed date) and confirm whether visits and reports should be counted from the same time window. Also, use deterministic tiebreakers like advertiser_id to avoid non-deterministic results.
Use CTEs to compute total visits and total reports per advertiser from the page_visits and reports tables, filtering for the last 7 days. Also compute the number of unique reporters per advertiser.
Filter the aggregated results to only include advertisers with at least 1,000 page visits and at least 50 unique reporters.
Compute the report rate as total reports divided by total visits for each qualifying advertiser.
Use a window function (e.g., RANK() or DENSE_RANK()) to rank advertisers by report rate in descending order, with deterministic tiebreakers such as advertiser_id ascending.
Filter the ranked results to only include ranks 1 through 5 and return the advertiser details along with the rank.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Adding a per-advertiser worst ad without blowing up the row count is the tricky part.
First, compute the report rate for each ad over the last 7 days, filtering to ads with at least 100 visits. Then, for each advertiser, select the ad with the highest report rate, breaking ties by total reports descending and ad_id ascending. Finally, join this result with the previous advertiser ranking query to output each advertiser's ranking along with their worst ad.
Pro tip: Clarify the definition of 'report rate' (e.g., reports per visit) and ensure you handle ties correctly using ROW_NUMBER() with the specified ordering. Also, consider the time window: 'last 7 days' should be relative to the current date, not the ad's creation date.
Select ads with at least 100 visits in the last 7 days. Ensure the date range is correctly applied to the visits or reports data.
Compute report rate as total reports divided by total visits for each ad. Also compute total reports for tie-breaking.
Use a window function like ROW_NUMBER() partitioned by advertiser, ordered by report rate descending, total reports descending, and ad_id ascending.
Filter to rows where the row number equals 1 to get the single worst ad for each advertiser.
Combine the worst ad result with the existing advertiser ranking output, ensuring all advertisers from the ranking are included (use LEFT JOIN if necessary).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The zero-report case is a classic LEFT JOIN trap.
Start by clarifying the definitions of 'report rate' and 'visits', then outline a SQL query that aggregates daily visits and reports per advertiser, filters for at least 200 visits, and computes the rate. Use a date spine or calendar table to ensure all last 7 days are covered, and left join to include advertisers with zero reports. Finally, rank advertisers within each day by report rate.
Pro tip: Mention the importance of handling edge cases like advertisers with zero visits (excluded) and zero reports (included with rate 0), and discuss how to optimize the query for performance on large datasets, such as using partitioning or indexing.
Confirm definitions: 'report rate' likely means reports per visit (or percentage), 'visits' are user interactions, and 'last 7 days' includes today or yesterday. Ensure understanding of zero-report inclusion and zero-visit exclusion.
Plan to aggregate visits and reports per advertiser per day, then filter for visits >= 200. Use a calendar table to generate all dates in the last 7 days and left join to include advertisers with zero reports.
Calculate report rate as reports/visits (or reports per 100 visits). Use window functions like RANK() or DENSE_RANK() partitioned by date and ordered by report rate descending.
Ensure advertisers with zero reports appear with rate 0, and exclude days where an advertiser had no visits. Consider using COALESCE to handle nulls from left joins.
Test the query on sample data, check for correctness, and discuss potential performance improvements like indexing on date and advertiser_id.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Explain that the query should first aggregate visits and reports at the user-ad-minute grain, then combine them using a full outer join or union with deduplication logic. Use conditional aggregation to count a user-ad-minute as a single event if either a visit or report exists, avoiding double-counting. For report rows without a visit, ensure they are included by treating the report as the event of record in that minute.
Pro tip: Mention that in ad tech, a report often implies a visit, but not always; explicitly state your assumption about the relationship and how you'd validate it with data profiling. This shows you think about data quality and business context.
Clarify that the analysis is at the user-ad-minute level, and define a 'unique event' as any minute where a user either visited or reported the ad. This sets the foundation for deduplication.
Aggregate visits and reports separately to the user-ad-minute grain, using COUNT(DISTINCT) or GROUP BY to collapse multiple actions within the same minute into a single row per source.
Use a FULL OUTER JOIN on user_id, ad_id, and minute to merge the aggregated visits and reports, ensuring that report rows without visits are retained and vice versa.
Create a flag or use COALESCE to mark the combined row as a single event if either source exists, and count it once. For example, COUNT(DISTINCT CONCAT(user_id, ad_id, minute)) or SUM(CASE WHEN visit IS NOT NULL OR report IS NOT NULL THEN 1 ELSE 0 END).
Check edge cases like multiple reports in the same minute or reports without visits, and document assumptions about whether a report implies a visit. This ensures the logic is robust and transparent.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.