Start by clarifying the table schemas and the definition of 'viewed' (e.g., whether multiple views count). Then write a SQL query that joins the view events table with the article types table, filters for the specific date, and counts distinct article types per user using GROUP BY.
Pro tip: Mention that you would check for data quality issues like duplicate events or missing article type mappings, and consider using a subquery or CTE to filter views first for performance before joining.
Ask about the table structures, column names, and whether 'viewed' means any interaction or a specific event type. Confirm that the date is a single day and that we need distinct article types per user.
Use a WHERE clause to select only view events on '2019-01-01'. This reduces the dataset before joining, improving performance.
Join the filtered view events with the article types table on article_id to get the type for each viewed article.
Group by user_id and use COUNT(DISTINCT article_type) to get the number of unique article types each user viewed.
Consider users with no views (they won't appear), and verify that the join doesn't drop events due to missing mappings. Optionally, use a LEFT JOIN to include all views, but then handle NULL types.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, aggregate the data to count distinct article types viewed per user across all dates. Then, group users by that count and count how many users have each distinct count. Finally, present the results as a histogram.
Pro tip: Clarify whether 'article types' refers to categories or individual articles, and confirm if the histogram should include users with zero views. Also, consider using a subquery or CTE for readability and performance.
Confirm what 'article types' means (e.g., categories, topics) and whether to include users with zero views. Ask if the histogram should be based on all users or only those with at least one view.
Write a subquery that groups by user_id and counts distinct article_type (or equivalent) across all dates. This yields a table of user_id and their distinct count N.
Group the subquery results by N and count the number of users for each N. This gives the histogram data: each possible N and the user count.
Check for users with zero views (if needed, left join from users table) and ensure all possible N values are represented, possibly by generating a sequence of N values and left joining.
Format the output as a histogram (e.g., table or chart) and briefly interpret the distribution, noting skewness or common patterns.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.