This one was more about window functions than anything else.
Start by identifying the necessary joins: Impressions to Posts to PostHashtags to get hashtags for each impression, then filter impressions to the last 7 days using a date function like CURRENT_DATE - INTERVAL '7 days'. Count distinct users per hashtag, rank them using a window function with deterministic tie-breaking (e.g., by count descending, then hashtag ascending), and return the top results.
Pro tip: Clarify whether 'unique viewers' means distinct users who saw any post with that hashtag, and ensure you handle ties deterministically by adding a secondary sort key like hashtag name. Also, consider if impressions can have multiple hashtags per post and whether that affects distinct counts.
Identify the relevant tables and columns: Users (user_id), Posts (post_id, user_id), PostHashtags (post_id, hashtag), Impressions (impression_id, post_id, viewer_id, timestamp). Clarify that 'unique viewers' means distinct viewer_id per hashtag.
Use a date function to dynamically filter impressions, e.g., WHERE timestamp >= CURRENT_DATE - INTERVAL '7 days'. Avoid hardcoded dates.
Join Impressions to Posts on post_id, then to PostHashtags on post_id to get hashtag for each impression. Ensure you keep viewer_id for distinct counting.
Group by hashtag and count distinct viewer_id to get unique viewer count. Use COUNT(DISTINCT viewer_id).
Use a window function like RANK() or DENSE_RANK() OVER (ORDER BY unique_viewers DESC, hashtag ASC) to assign ranks. The secondary sort on hashtag ensures deterministic tie-breaking.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
CTR queries sound simple but this one had a real trap around the denominator.
Start by clarifying the definitions of exposure and click events, then outline a SQL-based approach that joins exposure and click tables on hashtag and user, using COUNT(DISTINCT user_id) for both numerator and denominator. Emphasize the need to filter exposures to only those posts containing the specific hashtag and to deduplicate users per hashtag before computing the ratio.
Pro tip: Mention that you would validate the metric by checking for edge cases like users who clicked without exposure or hashtags with zero exposures, and consider segmenting by user demographics or hashtag popularity to uncover actionable insights.
Confirm what constitutes an exposure (e.g., post impression) and a click (e.g., hashtag click), and ensure the time window is exactly the last 7 days. Ask if there are any bot filters or minimum exposure thresholds to apply.
Locate the exposure table (with post_id, user_id, timestamp, and hashtags array) and the click table (with user_id, hashtag, timestamp). Ensure both tables have the necessary join keys and time filters.
For each hashtag, explode the hashtags array in the exposure table, filter to the last 7 days, and count distinct user_ids. This gives the denominator, ensuring each user is counted once per hashtag even if exposed multiple times.
From the click table, filter to the last 7 days, group by hashtag, and count distinct user_ids. This gives the numerator. Optionally, join with exposures to ensure clicks are attributed only to users who were exposed to that hashtag.
Join the numerator and denominator by hashtag, compute CTR as clicks/exposures, and handle hashtags with zero exposures (e.g., exclude or set CTR to 0). Validate results by checking for anomalies and consider adding filters for statistical significance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, outline the SQL query structure: a CTE or subquery to aggregate click counts per post and hashtag over the last 30 days, then join to posts from the last 7 days using LEFT JOIN with IS NULL to exclude existing hashtags, and rank to pick top 2. Then, explain the semantic difference between LEFT JOIN and INNER JOIN, focusing on which rows are dropped and why that matters for posts with no hashtags or no clicks.
Pro tip: Emphasize that the LEFT JOIN with IS NULL is not just a filter but a deliberate choice to preserve posts that have zero eligible hashtags, ensuring they appear in the output with NULL recommendations—this is crucial for product completeness and avoids silently dropping content.
Identify the tables involved (posts, hashtag_clicks, post_hashtags) and confirm the time windows: posts from last 7 days, clicks from last 30 days. Define 'viewers clicked most' as the hashtags with the highest click counts by viewers of that post.
Write a subquery to count clicks per post per hashtag in the last 30 days. Then LEFT JOIN this to the posts from the last 7 days, and use a second LEFT JOIN to the post's existing hashtags with a WHERE clause that filters out matches (IS NULL) to exclude already-used hashtags.
Use a window function like ROW_NUMBER() OVER (PARTITION BY post_id ORDER BY click_count DESC) to rank hashtags per post, then filter to rank <= 2. Ensure posts with no eligible hashtags still appear with NULLs.
Explain that INNER JOIN would drop any post that has no matching rows in the joined table. Specifically, posts with no hashtags at all (no rows in post_hashtags) and posts whose viewers never clicked any hashtag (no rows in the click aggregation) would be lost entirely from the result set.
Highlight that using INNER JOIN would silently exclude posts that might still be relevant for recommendation (e.g., new posts with no clicks yet), leading to incomplete recommendations and potential bias against new or low-engagement content. LEFT JOIN ensures all posts are considered, even if no hashtags are recommended.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the schema and the definition of 'impression' (e.g., a row in an Impressions table). Then write a query that LEFT JOINs Posts to PostHashtags, filters for NULL hashtag IDs to find posts with zero hashtags, and joins to an impressions table to count impressions in the last 7 days, using HAVING to filter for more than one impression. Finally, explain why a RIGHT JOIN is unnecessary: a LEFT JOIN from Posts to PostHashtags already preserves all posts, and the NULL check identifies those without hashtags.
Pro tip: Mention that using NOT EXISTS or a subquery with NOT IN can be an alternative to LEFT JOIN for finding posts with no hashtags, but LEFT JOIN with IS NULL is often more efficient and easier to read, especially when combined with other joins. Also, clarify the grain of the impressions data to avoid double-counting.
Ask about the tables involved (Posts, PostHashtags, Impressions) and confirm the definition of 'impression' and 'last 7 days' (e.g., based on current date or a specific timestamp).
Use a LEFT JOIN from Posts to PostHashtags and filter WHERE PostHashtags.PostID IS NULL to get posts that have no matching hashtag rows.
Join the result to an Impressions table (or equivalent) on PostID, filter for impressions within the last 7 days, and group by post to count impressions.
Use HAVING COUNT(*) > 1 (or SUM(impression_count) > 1) to keep only posts with more than one impression.
Explain that a LEFT JOIN from Posts to PostHashtags already includes all posts, and the NULL check identifies those without hashtags. A RIGHT JOIN would return all PostHashtags rows, which is not needed because we want posts, not hashtags.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.