The join logic itself isn't hard but I kept second-guessing the direction.
First, clarify the schema and define what constitutes a friend-sourced impression (viewer and author are friends) versus unconnected (no friendship). Then, write a SQL query that joins impressions with friendships to classify each impression, and finally aggregate by user and date to compute the fraction of friend-sourced impressions.
Pro tip: Remember that friendships are bidirectional; ensure your join condition captures both directions (user_id = friend_id OR friend_id = user_id) to avoid missing friendships. Also, consider using a LEFT JOIN to handle cases where there is no friendship, labeling those as unconnected.
Identify the relevant tables and columns: users, friendships (likely with user_id and friend_id), posts (with author_id), and impressions (with viewer_id, post_id, impression_date). Define a friend as a user who has a mutual friendship with the viewer.
Join impressions with posts to get the author, then left join with friendships to check if the viewer and author are friends. Use a CASE statement to label each impression as 'friend' if a friendship exists, else 'unconnected'.
Group by viewer_id and impression_date, and compute the count of friend impressions and total impressions. Then calculate the fraction as friend_count / total_count.
Combine the classification and aggregation into a single query, ensuring proper handling of NULLs and using appropriate aggregation functions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one tripped me up more than I expected.
Start by clarifying the grain: one row per (user_id, date, content_source). Then walk through the aggregation logic: join impressions to interactions, pivot interaction types into counts, apply weights, and sum to get the weighted score. Emphasize that impressions without interactions still appear with zero counts and zero score.
Pro tip: Mention that you would validate the output by checking that the sum of interaction counts never exceeds impression count and that the weighted score is zero when all interaction counts are zero. Also note that you would handle potential duplicate interactions by deduplicating on interaction_id if available.
Confirm that the output is one row per user_id, date, and content_source (friend vs unconnected). Define what constitutes an impression and an interaction, and ensure you understand how content_source is determined (e.g., from the impression or interaction metadata).
From the impressions table, count distinct impressions per user-day-source. From the interactions table, count interactions by type (like, comment, share) per user-day-source. Use a left join from impressions to interactions to retain impression-only rows.
Apply weights: like=1, comment=3, share=5. Multiply each interaction count by its weight and sum to get the weighted social engagement score. Ensure that rows with no interactions have a score of 0.
Select user_id, date, content_source, impression_count, like_count, comment_count, share_count, and weighted_score. Order by user_id, date, and content_source for readability.
Check for negative or missing values, ensure interaction counts are non-negative integers, and verify that the weighted score is consistent with the counts. Consider how to handle multiple interactions of the same type by the same user on the same content (e.g., multiple likes) — typically count each interaction event.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, filter the posts to the specific date and compute the weighted engagement score for each post using the given weights. Then, use a window function with dense_rank() partitioned by user_id and ordered by weighted score descending, post_id ascending, to rank posts per user. Finally, select rows where the dense rank is <= 2, ensuring ties at the cutoff are included.
Pro tip: Clarify the tie-breaking logic: dense_rank() with ORDER BY weighted_score DESC, post_id ASC ensures deterministic ranking and includes all tied posts at the cutoff. Also, confirm whether the date filter should be applied before or after computing scores, as it affects performance.
Filter the posts table to the specific date and calculate the weighted engagement score for each post using the provided weights.
Use a window function to assign a dense rank to each post within each user, ordering by weighted score descending and post_id ascending.
Retrieve only the rows where the dense rank is less than or equal to 2, which includes all tied posts at the cutoff.
Check for users with fewer than 2 posts, null scores, or missing weights, and ensure the result is deterministic.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The CTE calendar spine is the part I think most people skip and then wonder why their LAG returns NULL instead of a clean 0.
Start by clarifying the metric definition and assumptions, then outline a SQL solution that uses a month calendar CTE to fill missing months with zero, computes weighted engagement per source per month, and applies LAG to calculate month-over-month percent change. Emphasize how you handle multiple interactions and timezone boundaries to ensure accurate aggregation.
Pro tip: Explicitly state that you would validate the calendar CTE against the date range of the data and confirm that zero-filling is appropriate for missing months (e.g., no activity vs. missing data). Also, mention that you would test edge cases like division by zero when computing percent change.
Define weighted engagement (e.g., sum of weighted interactions) and state assumptions about multiple interactions on the same post (e.g., each interaction counts separately or deduplicated per user per post) and timezone boundaries (e.g., UTC or user-local).
Create a CTE that generates a series of months covering the relevant period, ensuring all months are represented even if no data exists, to handle missing months as zero.
Join the calendar CTE with the engagement data, group by month and content source, and compute the sum of weighted engagement, filling missing months with zero.
Use the LAG window function to get the previous month's weighted engagement for each source, then calculate the month-over-month percent change, handling division by zero.
Discuss how you would validate the results, handle timezone conversions, and address potential issues like multiple interactions and missing data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.