← Newyorktimes Interview Insights
The NULL handling is what trips people up here.
Start by clarifying the table schema and definitions of 'unique contents' and 'view events'. Then write a SQL query that groups by device type (mobile vs desktop), counts distinct content IDs for unique contents, and counts all rows for total view events. Use conditional aggregation or a CASE statement to handle device grouping if needed.
Pro tip: Mention that you would confirm whether 'unique contents' means distinct content IDs per device group or overall, and whether view events should include all rows or only valid views. Also, consider using APPROX_COUNT_DISTINCT for large datasets to improve performance.
Ask about the table columns (e.g., device_type, content_id, user_id, timestamp) and define 'unique contents' and 'view events'. Confirm if device_type values are exactly 'mobile' and 'desktop' or need mapping.
Decide to group by device type and compute two metrics: COUNT(DISTINCT content_id) for unique contents and COUNT(*) for total view events. Consider if any filters (e.g., date range) are needed.
Use a GROUP BY on device_type with COUNT(DISTINCT content_id) AS unique_contents and COUNT(*) AS total_views. If device_type needs normalization, use a CASE expression to create a device_group column.
Check for NULLs or unexpected device types, and consider using APPROX_COUNT_DISTINCT for scalability. Ensure the query returns one row per device group.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Got the RANK vs DENSE_RANK question almost immediately after I wrote it.
Start by aggregating view volume per device group and hour, then use a window function like ROW_NUMBER() or RANK() partitioned by device group and ordered by total views descending. Finally, filter to the top 3 hours per group, ensuring ties are handled appropriately.
Pro tip: Clarify whether ties should be included (use RANK/DENSE_RANK) or exactly 3 rows (use ROW_NUMBER), and mention that hour should be extracted from a timestamp with proper timezone handling.
Write a subquery or CTE that groups the raw event data by device_group and hour of day, summing the view counts. Use EXTRACT(HOUR FROM timestamp) or equivalent.
In an outer query, use ROW_NUMBER() OVER (PARTITION BY device_group ORDER BY total_views DESC) to assign a rank to each hour within each device group.
Wrap the ranked result in another CTE or subquery and filter WHERE rank <= 3. This yields the top 3 hours by view volume for each device group.
If ties are possible, consider using RANK() or DENSE_RANK() instead of ROW_NUMBER() to include all tied hours. Also, ensure hours with zero views are handled if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use conditional aggregation with COUNT(DISTINCT CASE WHEN content_id IS NOT NULL THEN content_id END) to count unique non-NULL content IDs, and SUM(CASE WHEN content_id IS NULL THEN 1 ELSE 0 END) to count NULL view events. Combine both in a single SELECT statement to return one row with two columns.
Pro tip: Always clarify whether 'view events' means all rows or only those with NULL content_id; if the latter, the second count is simply the number of rows where content_id IS NULL. Also, confirm if the table might have duplicate view events that need deduplication.
Identify the two metrics: count of unique non-NULL content IDs and count of view events with NULL content_id. Clarify if 'view events' refers to all rows or only those with NULL content_id.
For unique non-NULL content IDs, use COUNT(DISTINCT content_id) with a filter or CASE. For NULL view events, use COUNT(*) with a filter or SUM(CASE WHEN content_id IS NULL THEN 1 ELSE 0 END).
Construct a single SELECT statement using CASE expressions or FILTER clauses (if supported) to compute both counts in one pass over the data.
Run the query on a sample dataset to ensure it returns the expected counts. Check edge cases like all NULLs or no NULLs.
Articulate the logic clearly and mention potential performance considerations, such as indexing on content_id.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one took me a minute to think through.
Break the problem into two stages: first, use a window function (ROW_NUMBER) partitioned by agent and calendar day, ordered by event timestamp and then event_id, to isolate each agent's first view event per day. Then, aggregate the resulting set by content type to compute the distribution (counts and percentages) across all agent-days.
Pro tip: Always clarify the definition of 'first view event'—whether it's based on event timestamp or ingestion time—and confirm how to handle ties (e.g., event_id ascending). Also, consider whether the distribution should be weighted by agent-days or by unique agents, as this can change the interpretation.
Confirm the definition of 'first view event' (e.g., earliest timestamp, tie-break by event_id), the time zone for calendar day, and whether to include only view events or all events. Ask about handling nulls or duplicate events.
Filter the events table to only 'view' events. Use a window function like ROW_NUMBER() OVER (PARTITION BY agent_id, DATE(event_timestamp) ORDER BY event_timestamp ASC, event_id ASC) to assign a rank to each event within each agent-day.
Retain only rows where the rank equals 1. This yields one row per agent per calendar day, representing the first view event.
Group the resulting set by content_type and count the number of agent-days. Calculate the percentage of total agent-days for each content type to get the distribution.
Check for anomalies (e.g., missing days, unexpected content types) and ensure the total count matches the number of agent-days. Present the distribution in a clear table or chart, highlighting key insights.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.