Start by clarifying the schema and definitions (e.g., what table stores streaming events, how duration is measured, and what 'month' means). Then write a SQL query that aggregates total streamed hours per month, using date functions to extract year and month, and ensure it handles multi-year data by grouping on both year and month or by using a date truncation function. Finally, discuss edge cases like time zones, incomplete months, and performance considerations.
Pro tip: Mention that you would use a date truncation function (e.g., DATE_TRUNC('month', event_date)) to group by month, which automatically handles multiple years and is more efficient than extracting year and month separately. Also, note that for large datasets like TikTok's, you might pre-aggregate or partition by date to improve performance.
Ask about the table structure, the definition of 'streamed hours' (e.g., sum of watch time per user per video), and how months should be defined (calendar month, time zone).
Determine that you need to group by month and year. Use a date truncation function or extract year and month to create a unique month identifier across years.
Construct a query that sums the streamed hours (or duration) and groups by the month identifier. Ensure the query includes all necessary filters (e.g., valid events).
Explain that grouping by year and month (or using DATE_TRUNC) ensures months from different years are separate. Optionally, format the output to show 'YYYY-MM' for clarity.
Mention handling of time zones, incomplete months, and performance tips like partitioning or indexing on date columns.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I fumbled a bit on the case sensitivity piece.
Start by clarifying the schema and business context, then outline a SQL-based solution using aggregation and conditional logic. Emphasize handling case sensitivity and parameterization to ensure robustness and reusability.
Pro tip: Mention that case sensitivity depends on the database collation and show awareness of performance implications when filtering on category. Also, suggest using a parameterized query or a CTE to avoid hardcoding the category name.
Ask about the table structure, data types, and whether the category name is case-sensitive in the database. Confirm if the query needs to be dynamic or if the category is fixed.
Write a SQL query that groups by streamer, sums the duration for total streaming time, and conditionally sums duration for the specific category. Use a CASE statement or FILTER clause to isolate the category.
Use LOWER() or UPPER() on both the column and the input parameter to ensure case-insensitive matching, or rely on the database's collation settings. For parameterization, use a prepared statement or a variable to pass the category name safely.
Calculate the ratio as category_duration / total_duration, handling division by zero. Validate results with sample data and consider edge cases like streamers with no streams in the category.
Suggest indexing on streamer_id and category, and mention that for large datasets, pre-aggregation or materialized views may be beneficial. Discuss trade-offs between readability and performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where LAG() comes in and I knew that much.
First, clarify the data schema and definitions (e.g., what constitutes a stream, how months are defined). Then, outline a SQL-based solution using a self-join or window function to compare each streamer's monthly stream count with the previous month, explicitly handling year boundaries and NULLs. Finally, discuss edge cases and potential optimizations.
Pro tip: Mention that you would validate the results by spot-checking streamers with known activity and ensure that the comparison is done on a per-streamer basis, not globally. Also, consider using a calendar table to handle missing months.
Ask about the data schema, definition of a stream, and how months are represented. Confirm whether the comparison is month-over-month for each streamer and how to handle missing months.
Write a query to count streams per streamer per month, ensuring that months with no streams are included with a count of 0 (e.g., using a calendar table or generating a series).
Use a window function like LAG to get the previous month's stream count for each streamer, handling year boundaries by ordering by year and month correctly.
Filter for streamers where current month's streams > previous month's streams. For NULLs (e.g., first month or missing data), decide whether to treat as 0 or exclude, and document the assumption.
Mention validation steps (e.g., spot-checking) and discuss edge cases like streamers with no data in the previous month, leap years, and timezone considerations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The query joins minute_streamed and minute_viewed on streamer_username without a time join condition, so it creates a many-to-many mess.
First, restate the query's intent to confirm understanding, then systematically verify each clause against the requirements. Check for common SQL pitfalls like incorrect aggregation, missing filters, and join errors, and propose corrections with explanations.
Pro tip: Always clarify ambiguous terms like 'average concurrent viewers' and 'total view time' before diving into code review, as these definitions drive the correct SQL logic. Mention that you'd validate the query with sample data or edge cases to ensure accuracy.
Restate the goal: calculate average concurrent viewers per streamer in 2019 and total view time from US viewers. Confirm definitions of 'concurrent viewers', 'view time', and 'US viewers'.
Examine the SELECT, FROM, WHERE, GROUP BY, and JOIN clauses to ensure they align with the requirements. Check for correct aggregation and filtering.
Look for issues like missing date filters, incorrect join conditions, wrong aggregation functions, or improper handling of time zones and session overlaps.
Suggest specific fixes, such as adding WHERE clauses for year and country, using appropriate window functions for concurrency, and ensuring correct grouping.
Recommend testing the corrected query with sample data or edge cases to ensure it produces accurate results.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.