The window function part I got right, COUNT(DISTINCT user_id) over a RANGE between 6 preceding and current row.
First, clarify the grain: compute daily unique viewers per post, then apply a 7-day rolling average over those daily counts. Use a window function with ROWS BETWEEN 6 PRECEDING AND CURRENT ROW, partitioned by post and ordered by date, and handle missing days by generating a date spine or using RANGE with date arithmetic.
Pro tip: Mention that you'd confirm whether the rolling average should include days with zero viewers (which affects the denominator) and whether the window is calendar-based or event-based—this shows you think about metric definitions, not just syntax.
Define 'daily unique viewers' as COUNT(DISTINCT viewer_id) per post per day, and confirm whether the rolling average is over calendar days or active days.
Write a subquery or CTE that groups by post_id and view_date, counting distinct viewers to get the daily metric.
Generate a date spine for each post (or use a calendar table) and left join the daily counts so that days with zero viewers are included as 0.
Use AVG(daily_unique_viewers) OVER (PARTITION BY post_id ORDER BY view_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) to get the 7-day rolling average.
Select post_id, view_date, daily_unique_viewers, and rolling_avg, then order by view_date (and optionally post_id) as requested.
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 data format, then propose a query using case-insensitive pattern matching (e.g., ILIKE or LOWER with LIKE) with proper wildcards to match hashtags containing the term. Discuss performance considerations and edge cases like partial matches and delimiters.
Pro tip: Mention that hashtags are often stored as delimited strings, so using wildcards on both sides can cause false positives (e.g., 'Apple' matching 'Pineapple'); suggest using delimiter-aware patterns or a normalized hashtag table for production.
Ask about the table structure, data type of the hashtags column, and whether hashtags are stored as a single string or an array. Confirm the expected output (post IDs) and case-insensitivity.
Select an appropriate SQL function based on the database (e.g., ILIKE in PostgreSQL, LOWER with LIKE in MySQL). Explain why case-insensitivity is needed and how to achieve it.
Write a query that uses wildcards (e.g., '%apple%') to match the term anywhere in the hashtags string. Ensure the pattern is case-insensitive.
Discuss potential false positives (e.g., 'pineapple' matching 'apple') and suggest delimiter-aware patterns (e.g., '%#apple#%' or using string functions). Mention performance implications of leading wildcards and possible indexing strategies.
Present the SQL query clearly, explaining each part. Optionally, mention alternative approaches like full-text search or normalized tables for scalability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
FROM, WHERE, GROUP BY, HAVING, SELECT, ORDER BY, LIMIT.
Start by clearly stating the logical order of SQL clauses, then explain how this order differs from the written syntax. Use concrete examples to show how this knowledge helps in debugging unexpected results and optimizing query performance, especially in large-scale data environments like TikTok.
Pro tip: Mention that window functions are evaluated after WHERE but before ORDER BY, and that this explains why you can't use them in WHERE—a detail that often trips up even experienced practitioners.
List the logical execution order: FROM/JOIN, WHERE, GROUP BY, HAVING, SELECT, DISTINCT, ORDER BY, LIMIT/OFFSET. Emphasize that this is the order in which the database engine processes the query, not the order in which it is written.
Explain that SQL is written in a different order (SELECT first) for readability, but the logical order determines what data is available at each stage. This mismatch is a common source of confusion.
Give examples of how this order affects debugging: e.g., why you can't use a column alias in WHERE (because SELECT is evaluated after WHERE), or why filtering in WHERE before GROUP BY reduces the data processed.
Discuss how understanding the order helps optimize queries: e.g., pushing filters early (WHERE before JOIN), using HAVING only when necessary, and leveraging indexes effectively.
Provide a concrete example from a data science context, such as calculating user engagement metrics, to illustrate how the logical order impacts query results and performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.