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Pretty straightforward GROUP BY with a CASE to bucket phone and tab together as mobile.
Start by writing a SQL query that groups by device group and computes COUNT(DISTINCT content_id) and COUNT(*) (or SUM of view events). Then explain that distinct content IDs and total view events are typically not equal because a single content ID can be viewed multiple times by the same or different users on the same device group.
Pro tip: Mention that the ratio of total views to distinct content IDs gives an average views per content metric, which is useful for understanding content popularity and engagement depth.
Identify the relevant columns: device group (e.g., device_type), content ID (e.g., content_id), and view event identifier (e.g., view_id or timestamp). Clarify that each row represents a view event.
Use GROUP BY device group and compute COUNT(DISTINCT content_id) for distinct content IDs and COUNT(*) for total view events. Ensure proper filtering if needed (e.g., date range).
Explain that the two numbers will typically differ because total view events count every view, while distinct content IDs count unique content items. Multiple views of the same content inflate the total view count.
Mention scenarios where they could be equal: if each content ID is viewed exactly once per device group, or if the data is deduplicated. Also note that if there are no repeat views, the numbers match.
Relate the metrics to product analytics: distinct content IDs indicate content breadth, while total views indicate engagement volume. The ratio can inform content strategy.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The ranking part was fine, used ROW_NUMBER with EXTRACT(HOUR FROM viewed_at) and partitioned by device_type.
Start by clarifying the data schema and defining 'view volume' and 'hour of day'. Then outline a two-step process: aggregate views by device type and hour, then for each device type sort hours by total views descending and hour ascending, and select the top 3. Finally, discuss efficient implementation using window functions or sorting.
Pro tip: Mention that you would validate the result by checking for ties and ensuring the tie-breaking rule is applied correctly, and consider if the data is large enough to require a distributed approach like Spark.
Ask about the data source, schema (e.g., columns: device_type, timestamp, view_count), and whether 'view volume' means count of views or sum of a metric. Confirm that 'hour of day' is based on a specific timezone.
Group the data by device_type and hour of day (extracted from timestamp), summing the view volume. This yields total views per device per hour.
For each device_type, sort the hours by total views descending, and for ties, by hour ascending. Use a window function like ROW_NUMBER() with ORDER BY total_views DESC, hour ASC.
Filter to rows where rank <= 3 for each device_type. Present the results clearly, perhaps as a table with device_type, hour, and total views.
Mention how to handle large datasets (e.g., using partitioning in Spark or indexing in SQL) and edge cases like missing hours or devices with fewer than 3 hours of data.
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Start by clarifying the table schema and the definition of 'viewed' events. Then write a single SQL query that uses conditional aggregation: COUNT(DISTINCT CASE WHEN content_id IS NOT NULL THEN content_id END) for distinct non-null content IDs, and SUM(CASE WHEN content_id IS NULL THEN 1 ELSE 0 END) for null events. Explain that this approach scans the table once and returns both metrics in one row.
Pro tip: Mention that COUNT(DISTINCT) ignores NULLs by default, so you could simplify the first metric to COUNT(DISTINCT content_id), but explicitly using a CASE statement makes the logic clear and avoids ambiguity. Also note that if the table has duplicate event rows, you may need to deduplicate before counting nulls depending on the definition of 'events'.
Ask or confirm what table contains the view events and what constitutes a 'viewed' event (e.g., event_type = 'view'). Ensure you understand whether the table has one row per event or per user-content interaction.
Break down the request: (1) count of distinct non-null content IDs ever viewed, and (2) count of events where content_id is null. Note that the first is a distinct count and the second is a row count.
Use a single SELECT with COUNT(DISTINCT CASE WHEN content_id IS NOT NULL THEN content_id END) and SUM(CASE WHEN content_id IS NULL THEN 1 ELSE 0 END). This avoids multiple table scans and returns both numbers in one row.
Mention that COUNT(DISTINCT) can be expensive on large tables; if performance is a concern, consider approximate distinct counts or pre-aggregation. Also check if null content_id events should be filtered out for the first metric.
Run the query and sanity-check the numbers: the distinct count should be less than or equal to the total number of non-null events. Present the two numbers clearly, perhaps with a brief interpretation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Break the problem into two stages: first, use a window function (ROW_NUMBER() OVER (PARTITION BY agent_id, date ORDER BY timestamp, view_id)) to identify each agent's first page view per day, then aggregate the resulting first-views to compute the distribution of content types across agent-days, including per-date shares. Validate the tiebreaker logic and handle edge cases like missing timestamps or duplicate view_ids.
Pro tip: Explicitly state your tiebreaker logic and how you'd validate it—e.g., checking for duplicate (agent_id, date, timestamp) pairs—because interviewers at The New York Times care about data quality and reproducibility. Also, mention that you'd compute both absolute counts and per-date shares to avoid misleading conclusions from uneven daily volumes.
Confirm the grain (one row per agent per calendar date), the definition of 'first page view' (earliest timestamp, then smallest view_id), and what 'distribution of first-seen content types' means (count and share of agent-days per content type, per date).
Use a window function like ROW_NUMBER() OVER (PARTITION BY agent_id, DATE(timestamp) ORDER BY timestamp ASC, view_id ASC) to assign a rank to each page view within each agent-day, ensuring deterministic tiebreaking.
Filter the ranked dataset to keep only rows where rank = 1, yielding one first page view per agent per day.
Group the first page views by date and content_type to count agent-days, then compute each type's share of total agent-days for that date using a window function or a self-join to the daily total.
Check for anomalies (e.g., dates with zero agent-days, unexpected content types) and present both counts and shares, noting any assumptions or data quality caveats.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.