Went straight to COUNT(*)/60 grouped by month and forgot to include year in the group-by key.
Start by clarifying the schema of minute_streamed (e.g., timestamp column, minutes streamed per row). Then write a SQL query that truncates the timestamp to the month level, sums the minutes, converts to hours, and orders by month. Finally, explain how the date truncation naturally handles multi-year data by including the year in the grouping.
Pro tip: Mention that using DATE_TRUNC('month', timestamp) is more robust than extracting month alone because it preserves the year, and consider time zone implications if the data is stored in UTC.
Ask or state assumptions about the columns in minute_streamed, such as a timestamp column (e.g., streamed_at) and a numeric column for minutes streamed (e.g., minutes).
Use DATE_TRUNC('month', streamed_at) to group records into calendar months, then SUM(minutes) to get total minutes per month.
Divide the total minutes by 60 to get hours, and order the results by the truncated month in ascending order.
Highlight that DATE_TRUNC includes the year, so months from different years are separate groups, ensuring chronological ordering across years.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the data model and assumptions, then outline a SQL or pandas solution that aggregates total hours per streamer and computes the percentage of hours in matching categories using a case-insensitive keyword match. Emphasize handling edge cases like nulls, multiple categories per stream, and ensuring the percentage is calculated correctly as a ratio of sums.
Pro tip: Mention that you would validate the keyword match logic with a quick sample query and consider performance implications for large datasets, such as using indexing or pre-filtering. Also, discuss how you might handle multiple keywords or partial matches if the requirement evolves.
Ask clarifying questions about the data schema: how stream sessions are recorded, whether each session has a single category, and how hours are calculated. Confirm that 'total streamed hours' means sum of hours across all sessions per streamer.
Plan to group by streamer to compute total hours. For the percentage, sum hours where the category matches the keyword (case-insensitive) and divide by total hours, multiplying by 100.
Use LOWER() or ILIKE in SQL, or str.lower() in pandas, to match the keyword against the category. Ensure the keyword is also lowercased for consistency.
Address null categories, streamers with zero hours, and potential duplicate sessions. Validate results with a small sample or by cross-checking totals.
Write the final query or code clearly, and mention performance considerations like indexing on streamer_id and category, or using approximate methods for large-scale data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the data schema and defining 'streamed hours' and 'month' precisely. Then outline an algorithm that aggregates hours per streamer per month, handles missing months by treating them as zero, and compares each month's total to the prior month's total, ensuring correct year-over-year handling. Finally, discuss edge cases like streamers with no activity in either month and how to efficiently process large datasets.
Pro tip: Mention that you would validate the results by spot-checking a few streamers and also consider the business context—e.g., whether to include only live hours or also reruns—to show you think beyond the code.
Ask questions to confirm the definition of 'streamed hours' (e.g., live vs. total watch time), the time granularity (month), and the data sources. Ensure you understand how to handle multiple years and missing months.
Group the data by streamer ID and month (including year) and sum the streamed hours. This creates a complete timeline for each streamer, filling in missing months with zero hours.
For each streamer, compute the previous month's total (using a window function like LAG or a self-join) and filter for rows where the current month's hours exceed the previous month's hours.
Ensure that streamers with no activity in the prior month are treated as having zero hours, and that year boundaries are handled correctly. Validate results with sample checks and consider performance optimizations for large datasets.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Straightforward join but the two aggregations live on different tables so you have to be careful not to fan out rows before aggregating.
First clarify the grain of each table and the join key (likely streamer_id and minute timestamp). Then filter both tables to 2019, join on streamer_id and minute, and compute the two metrics: average concurrent viewers per streamer and total US watch minutes. Use appropriate aggregation and handle potential duplicates or missing data.
Pro tip: Be explicit about the join type and grain: an inner join on streamer_id and minute ensures you only count minutes where both streaming and viewing occurred, avoiding inflated averages. Also, clarify whether 'average concurrent viewers' should be computed as total viewer-minutes divided by total streamed minutes or as an average of per-minute counts—the former is more accurate for concurrency.
Examine the schema of minute_streamed and minute_viewed to identify join keys, time fields, and viewer counts. Clarify definitions: average concurrent viewers = total viewer-minutes / total streamed minutes per streamer; total US watch minutes = sum of minutes watched by US viewers.
Filter both tables to records in 2019 using the appropriate date/timestamp column. Ensure streamer_id and minute are consistent types for joining.
Join minute_streamed and minute_viewed on streamer_id and minute (inner join) to align streaming minutes with viewing minutes. This ensures each row represents a minute where the streamer was live and viewers were watching.
Group by streamer_id. For average concurrent viewers, compute SUM(viewer_count) / COUNT(DISTINCT minute) or SUM(viewer_count) / SUM(stream_minutes) depending on table structure. For US watch minutes, filter viewer_country = 'US' and sum viewer_minutes (or count of minutes).
Check for anomalies (e.g., streamers with zero minutes, negative values). Present results clearly, noting any assumptions made about the data or metric definitions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.