First, deduplicate the event table using SELECT DISTINCT or a window function to remove exact duplicate rows. Then, convert each event into a +1 (start) or -1 (stop) delta, and compute a running sum ordered by event timestamp to get concurrent viewer counts. Finally, identify the peak concurrency per stream and the time window by finding the maximum running sum and the timestamps where that peak occurs.
Pro tip: Mention that you would validate the event stream for missing stop events (e.g., sessions that never end) and handle them by capping at stream end or using a default timeout, as this is a common data quality issue in streaming platforms.
Remove exact duplicate rows using SELECT DISTINCT or ROW_NUMBER() over all columns to ensure each event is counted once.
Assign +1 for view start and -1 for view stop, then union all events into a single stream with their timestamps.
Use a window function SUM(delta) OVER (PARTITION BY stream_id ORDER BY event_time) to calculate concurrent viewers at each event time.
For each stream, find the maximum concurrency and the earliest and latest timestamps where that maximum occurs to define the peak window.
Ensure ordering by event_time (not ingestion time) and consider ties, missing stops, or overlapping sessions when defining the window.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the data schema and define what constitutes a 'stream' and 'concurrent watching'. Then, for each user and date, generate all pairs of streams they watched, compute the overlap duration between each pair, filter overlaps >= 5 minutes, and sum these durations per user.
Pro tip: Mention that you would validate the logic with edge cases like back-to-back streams with no overlap or exactly 5-minute overlaps, and discuss how to handle time zones and session boundaries.
Ask questions to understand the data: what fields are available (user_id, stream_id, start_time, end_time), whether streams can be watched on multiple devices, and how to handle time zones. Confirm that 'concurrently' means the user was actively watching both streams at the same time.
For each user and date, extract all stream watching sessions. Ensure each session has a clear start and end time. If a user watched the same stream multiple times, treat each session separately or merge if contiguous.
For each user-date, generate all unique pairs of streams. For each pair, compute the overlap duration between their watching intervals. If a stream has multiple sessions, consider all combinations of sessions from the two streams.
Filter out overlaps shorter than 5 minutes. Sum the remaining overlap durations for each user. Be careful not to double-count if a user watched three streams simultaneously; decide whether to count pairwise overlaps or distinct concurrent watching time.
Test with edge cases: no overlap, exactly 5-minute overlap, multiple overlapping streams, and streams spanning midnight. Discuss how to handle time zone conversions and whether to consider only the given date or allow overlaps that cross date boundaries.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Retention queries are familiar territory but the '2 continuous minutes' condition adds a layer.
First, clarify the exact definitions of 'first-ever view', 'D+7', and '2 continuous minutes' to ensure alignment. Then, outline a SQL-based approach: identify the cohort of users with first view in the date range, compute their 7th day after first view, and check if they have a qualifying watch session on that day. Finally, calculate the retention rate as the proportion of retained users in the cohort.
Pro tip: Mention that you would validate the retention metric by checking edge cases, such as users with multiple sessions on D+7 or sessions that span midnight, and consider time zone handling.
Confirm what constitutes a 'view' (e.g., any stream watch), how to determine 'first-ever view', and the exact meaning of 'exactly their 7th day after first view' (e.g., calendar day vs. 24-hour periods). Also clarify if '2 continuous minutes' means a single uninterrupted session or can be aggregated.
Write a query to select users whose first-ever view timestamp falls within the specified date range. This involves finding the minimum view timestamp per user and filtering.
For each user in the cohort, calculate the date that is exactly 7 days after their first view date (e.g., using DATE_ADD or equivalent). Ensure consistent date truncation.
For each user, determine if they have any watch session on their D+7 date that lasts at least 2 continuous minutes. This may require sessionization logic to identify continuous watch periods.
Count the number of retained users and divide by the total cohort size to get the D+7 retention rate. Consider breaking down by dimensions if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Tying this back to the peak concurrency result from the first question is a nice touch.
Start by clarifying the metric definition: average per-viewer watch time = total watch time / unique viewers, computed over a 7-day window and filtered to US viewers. Then outline a SQL query that aggregates watch time and unique viewers per creator, applies a HAVING clause for at least 100 unique US viewers, and orders by average watch time descending, using peak concurrency as a tiebreaker. Finally, discuss how to handle edge cases like ties in peak concurrency and ensure the window is correctly applied.
Pro tip: Mention that you would validate the 7-day window by checking if it's a rolling window or a fixed calendar week, and confirm with stakeholders whether 'unique viewers' should be deduplicated across the entire window or daily. This shows attention to metric definition and business context.
Confirm what 'average per-viewer watch time' means (total watch time / unique viewers), the exact 7-day window (rolling vs. fixed), and that 'US viewers' is based on viewer location. Also confirm that 'unique US viewers' counts distinct viewers over the window.
Write a SQL query that joins watch time data with viewer location, filters to US viewers, groups by creator, and computes total watch time and unique viewers. Apply a HAVING clause to include only creators with at least 100 unique US viewers.
Calculate average per-viewer watch time as total watch time / unique viewers. Order creators by this metric descending. For ties, incorporate peak concurrency from the earlier query as a secondary sort key.
If peak concurrency also ties, decide on a deterministic tiebreaker (e.g., creator ID) or report ties. Ensure the final output includes creator, average watch time, unique viewers, and peak concurrency for transparency.
Check for data quality issues (e.g., nulls, bot traffic), consider time zone handling for the 7-day window, and discuss how the metric might be gamed. Also mention performance considerations for large-scale data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.