The year-boundary thing is what trips people up.
Start by identifying the granularity of the source data (minute-level rows per streamer) and clarify what constitutes a 'streaming minute' to avoid double-counting. Then aggregate by extracting the year-month from the timestamp, summing total minutes across all streamers, and converting to hours. Finally, ensure the output is sorted chronologically using the YYYY-MM label, not lexicographically on a raw string.
Pro tip: At Twitch's scale, minute-level data is enormous — mention partitioning the source table by date and filtering only necessary partitions to avoid full table scans, which signals production-aware thinking beyond just correctness.
Confirm the source table structure — e.g., columns like streamer_id, timestamp, and whether each row represents one minute of streaming or a session with a duration field. Clarifying this prevents logic errors in the aggregation.
Use DATE_FORMAT(timestamp, '%Y-%m') or equivalent (e.g., TO_CHAR in PostgreSQL, FORMAT_DATE in BigQuery) to derive the month label. Avoid casting to just MONTH() alone, as that collapses data across multiple years.
SUM the minute-level rows grouped by the YYYY-MM period to get total minutes, then divide by 60 to convert to hours. Consider whether to ROUND or keep decimals based on business requirements.
Order the results by the raw timestamp truncated to month (e.g., DATE_TRUNC('month', timestamp)) or by the YYYY-MM string, which sorts correctly due to its ISO format. Avoid ordering by a plain MONTH() integer, which breaks across years.
Check for NULL timestamps, duplicate rows, or timezone inconsistencies (e.g., UTC vs. local time) that could misassign minutes to the wrong month. Mention that at Twitch's scale, late-arriving data or reprocessing windows may also need to be addressed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The case-insensitive part is easy to miss if you just do category = cat_keyword.
Break the problem into two aggregation layers: first compute total streamed minutes per streamer, then compute keyword-matched minutes per streamer using a case-insensitive filter, and finally join the two to derive the ratio. Use SQL window functions or CTEs to keep the logic clean and readable, which is especially important in a data-heavy company like Twitch.
Pro tip: Explicitly handle edge cases like streamers with zero total minutes (division by zero) and NULL categories upfront — interviewers at Twitch look for production-ready thinking, not just a working query on the happy path.
Confirm the table structure (e.g., streamer_id, category, minutes_streamed) and how the keyword is passed — as a parameter or hardcoded. Ask whether a streamer can appear in multiple rows with different categories.
Use a GROUP BY on streamer_id with SUM(minutes_streamed) to get each streamer's total minutes. Store this in a CTE for reuse.
In a second CTE, apply a case-insensitive substring match (e.g., LOWER(category) LIKE LOWER('%keyword%') or ILIKE in PostgreSQL) and SUM the matching minutes per streamer.
LEFT JOIN the keyword-matched CTE onto the total-minutes CTE so streamers with no matching categories still appear. Divide keyword minutes by total minutes, using COALESCE to handle NULLs and NULLIF to prevent division by zero.
Walk through edge cases: streamers with zero total minutes, NULL category values, and keywords that match no rows. Mention that the ratio should be bounded between 0 and 1 as a sanity check.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use a window function (LAG) partitioned by streamer and ordered by calendar month to fetch the previous month's streamed hours, while using a complete month spine or COALESCE to handle months with no activity as zero. Then filter the result set to only include rows where the current month's hours exceed the previous month's hours. This approach cleanly handles gaps in the data and avoids complex self-joins.
Pro tip: Explicitly address the 'missing month as zero' requirement by generating a full calendar month spine (e.g., using a date dimension table or a series generator) and LEFT JOINing streaming data onto it — this ensures LAG correctly sees a zero for inactive months rather than skipping over them, which is a common pitfall interviewers look for.
Confirm the definition of 'calendar month' (truncated date vs. year-month integer), the grain of the source data (event-level vs. pre-aggregated), and whether the very first month for a streamer should be included if it has activity (previous month would be zero).
Group the raw streaming data by streamer_id and calendar month (using DATE_TRUNC or equivalent), summing streamed hours to get a monthly total per streamer.
Generate all (streamer, month) combinations across the full date range and LEFT JOIN the aggregated data onto this spine, replacing NULLs with zero using COALESCE to correctly represent inactive months.
Use LAG(hours, 1, 0) OVER (PARTITION BY streamer_id ORDER BY month) to retrieve the previous calendar month's hours for each row, defaulting to zero if no prior row exists.
Wrap the windowed query in a CTE or subquery and apply a WHERE clause to retain only rows where current_month_hours > previous_month_hours, then select the relevant output columns.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The join-on-both-columns instruction is a hint that they've seen people mess this up by joining only on streamer and getting a cartesian product on time.
Start by identifying the two core tables — minute_streamed (one row per streamer per broadcast minute) and minute_viewed (one row per viewer per streamer per minute) — and join them on both streamer and timestamp before aggregating to prevent a Cartesian explosion. Filter to 2019 at the earliest possible stage, then compute concurrent viewers as the average across all streamed minutes and US viewer-minutes as the sum of US viewer rows (each row representing one viewer-minute). For the follow-up, add a conditional COUNT DISTINCT on timestamp where US viewers exist.
Pro tip: Explicitly call out the fan-out risk in your explanation — interviewers at Twitch will be impressed if you proactively note that summing viewer counts before joining, or joining without both keys, would multiply rows and inflate metrics, and that aggregating on the viewer side first (or using a proper keyed join) is the correct guard.
Confirm that minute_streamed has grain (streamer, timestamp) and minute_viewed has grain (viewer_id, streamer, timestamp), and identify which columns carry concurrent_viewers and US viewer flags. This prevents assumptions that lead to incorrect joins.
Apply a WHERE clause filtering timestamp to the year 2019 on both tables before or within the join to minimize data scanned and avoid carrying unnecessary rows into aggregation.
Join minute_streamed to minute_viewed on streamer AND timestamp together; joining on only one key would create a cross-product between minutes and viewers, inflating all downstream metrics.
Group by streamer and compute AVG(concurrent_viewers) from the streamed side for average concurrent viewers, and SUM of a US-viewer indicator (e.g., SUM(CASE WHEN country = 'US' THEN 1 ELSE 0 END)) for total US viewer-minutes.
Extend the query with COUNT(DISTINCT CASE WHEN country = 'US' THEN ms.timestamp END) per streamer, which counts only the unique broadcast minutes during which at least one US viewer was present.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
AVG happens after the JOIN, in the GROUP BY and SELECT phase.
Start by clearly enumerating SQL's logical processing order from FROM through SELECT, then precisely locate where aggregate functions like AVG() fall in that sequence relative to JOINs. Ground your explanation in a concrete, Twitch-relevant example (e.g., average watch time per streamer after joining a users table) to make the answer tangible and demonstrate applied understanding.
Pro tip: Distinguish between logical processing order and physical execution order — the optimizer may reorder operations for performance, but the logical order governs correctness and is what determines scoping rules for aliases and filters. Mentioning this nuance signals senior-level SQL maturity.
Enumerate all clauses in logical order: FROM → JOIN → WHERE → GROUP BY → HAVING → SELECT → DISTINCT → ORDER BY → LIMIT/OFFSET. Be explicit and sequential to show command of the full pipeline.
Clarify that AVG() is an aggregate function resolved during the GROUP BY/aggregation phase (step 5 logically), which occurs after FROM and JOIN (steps 1-2) have already produced the combined result set.
Emphasize that the JOIN first expands or filters the row set, and AVG() then operates on that already-joined dataset — meaning the join condition directly influences which rows are included in the average calculation.
Use a Twitch-relevant scenario, such as joining a 'streams' table with a 'channels' table and computing AVG(viewer_count) per category, to show how an INNER JOIN vs. LEFT JOIN would yield different AVG() results because the row set differs.
Note that because AVG() is computed after WHERE but before HAVING, you cannot filter on an aggregate in a WHERE clause — you must use HAVING — which is a direct consequence of the logical processing order.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.