Start by clarifying the schema and definitions (e.g., what counts as a 'commenter', how to handle deleted comments, and the 7-day window relative to post date). Then build the query in stages: filter comments, join with posts and users, aggregate distinct commenter counts and countries per post, and finally rank and select the top 3 with the specified tie-breaking rules.
Pro tip: Explicitly state your assumptions about the schema (e.g., commenter_id, country, is_deleted, post_date) and the 7-day window (e.g., comments within 7 days after post creation). This shows you think about data nuances and prevents misalignment with the interviewer.
Ask about table structures, column names, and definitions: what is a 'commenter' (user who commented), how to identify deleted comments, and whether the 7-day window is relative to post creation or current date. Confirm tie-breaking rules.
Filter out deleted comments and restrict to comments within the 7-day window. Join comments with posts to get post details and with users to get commenter country.
Group by post_id and compute COUNT(DISTINCT commenter_id) as distinct_commenter_count and COUNT(DISTINCT country) as distinct_country_count. Ensure you handle potential NULLs or duplicates appropriately.
Use a window function (e.g., ROW_NUMBER() or RANK()) to order posts by distinct_commenter_count DESC, post_date DESC, post_id ASC. Then select the top 3 rows.
Walk through the query logic, check edge cases (e.g., posts with no comments, ties), and discuss performance considerations (indexes, partitioning).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Blanked for a second on the percentile_cont syntax.
Start by clarifying the schema and definitions: identify the post ID, comment timestamp, comment text, and deletion flag. Then write a SQL query that filters non-deleted comments, extracts the day from the comment timestamp, groups by post and day, and uses percentile_cont(0.5) and percentile_cont(0.9) to compute the P50 and P90 of text length. Ensure you only include days with at least one qualifying comment by using a HAVING clause or by filtering after aggregation.
Pro tip: Mention that percentile_cont is an ordered-set aggregate that interpolates between values, which is important for small sample sizes; also note that you should use the comment's creation date, not the post's date, to define the day.
Identify the relevant tables and columns: post ID, comment ID, comment text, comment timestamp, and deletion flag. Confirm what 'non-deleted' means (e.g., is_deleted = false) and how to compute text length (e.g., LENGTH(text) or CHAR_LENGTH(text)).
Filter out deleted comments and extract the day from the comment timestamp (e.g., DATE(created_at)). Ensure you only consider comments within the same 7-day window per post, if that is a requirement (though the question says 'within the same 7-day window' which might imply grouping by day within a 7-day period).
Group by post ID and day. Use percentile_cont(0.5) WITHIN GROUP (ORDER BY text_length) and percentile_cont(0.9) WITHIN GROUP (ORDER BY text_length) to compute P50 and P90. Apply a HAVING COUNT(*) > 0 to ensure at least one qualifying comment.
Select post ID, day, P50, and P90 as columns. Order the results by post ID and day for readability. Consider rounding the percentiles if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one was actually the cleanest to write.
Start by clarifying the schema: posts (post_id, user_id, post_timestamp) and comments (comment_id, post_id, user_id, comment_timestamp). Then, join comments to posts on post_id, filter for comments where the commenter is the post author and the comment timestamp falls within 7 days of the post timestamp (or within the last 7 days from a reference date, depending on interpretation). Finally, aggregate by user_id to count self-comments and find the max comment timestamp, then sort as specified.
Pro tip: Always clarify the definition of '7-day window'—whether it's relative to the post creation date or a fixed recent period—and confirm the time granularity (e.g., days vs. hours) to avoid off-by-one errors.
Identify the tables (e.g., posts, comments) and their columns. Confirm what '7-day window' means: is it the last 7 days from today, or within 7 days after the post was created? Also clarify if 'self-comment' means the post author commenting on their own post.
Join the comments table to the posts table on post_id to associate each comment with its post and the post author.
Apply conditions: commenter user_id equals post author user_id, and comment timestamp is within the specified 7-day window (e.g., comment_timestamp BETWEEN post_timestamp AND post_timestamp + INTERVAL '7 days' or comment_timestamp >= CURRENT_DATE - INTERVAL '7 days').
Group by user_id to compute COUNT(*) as self_comment_count and MAX(comment_timestamp) as most_recent_self_comment.
Sort by self_comment_count descending, then user_id ascending. Return the required columns: user_id, self_comment_count, most_recent_self_comment.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.