Straightforward filter-group-having pattern.
Start by clarifying the schema and definitions (e.g., what 'unconnected' means, how to handle NULLs, time zone for 'past 7 days'). Then write a SQL query that filters views from the last 7 days where connection_type = 'unconnected', groups by post_id, sums view_duration, and counts distinct post_ids where the sum exceeds 60 seconds.
Pro tip: Mention that you would validate the query by checking edge cases like posts with exactly 60 seconds (should be excluded) and ensuring the date filter uses the correct time zone. Also, discuss performance considerations such as indexing on date and connection_type.
Ask about the table schema, definition of 'unconnected' (e.g., connection_type = 'unconnected'), time zone for 'past 7 days', and whether view_duration is in seconds.
Use a WHERE clause to select views from the last 7 days and where connection_type indicates unconnected viewers.
Group by post_id and sum view_duration to get total view time per post.
Use a HAVING clause to filter posts with total view time > 60 seconds, then count distinct post_ids.
Check edge cases (e.g., exactly 60 seconds) and discuss indexing or partitioning for performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one required joining the views table to the actions table to pull in connection type alongside reaction counts.
First, clarify the definitions of 'friend-connected creators' and 'unconnected-creator posts' and confirm the time window. Then, write a SQL query that joins posts with friendship data, filters to the last 7 days, and computes the average reactions per post for each group, ensuring proper aggregation and handling of edge cases.
Pro tip: Mention that you would validate the friendship connection using the most recent state (e.g., current friends) and consider using a LEFT JOIN to include posts with zero reactions, as excluding them could bias the average upward.
Define what constitutes a 'friend-connected creator' (e.g., the post creator is a friend of the viewer) and 'unconnected-creator posts'. Confirm the time window (last 7 days) and the reaction metric (e.g., total reactions per post).
Determine the tables needed: posts (with creator_id, post_id, timestamp), reactions (post_id, reaction_type), and friendships (user_id, friend_id, status). Ensure you have the necessary join keys.
Join posts with friendships on creator_id = friend_id and user_id = viewer_id (or similar), filter posts to the last 7 days, and left join reactions to count reactions per post. Use a CASE statement to flag connected vs. unconnected.
Group by the connection flag and compute the average number of reactions per post. Ensure you handle posts with zero reactions by using LEFT JOIN and COALESCE to treat nulls as zero.
Check for data quality issues (e.g., duplicate friendships, missing timestamps) and consider if the average should be weighted by user or post. Discuss potential confounders and how to interpret the difference.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.