Start by identifying the relevant tables and columns needed — likely an orders or transactions table joined or filtered against the ads table to restrict dates. Use GROUP BY on both date and creation_source, then aggregate revenue with SUM, ensuring the date filter is driven by an EXISTS or JOIN on the ads table. Finally, apply ORDER BY on date and creation_source to satisfy the output requirement.
Pro tip: Explicitly clarify what 'creation source' maps to in the schema before writing code — at Meta this could be a column like `created_source` or an enum; naming it correctly signals you understand the data model, and using a JOIN rather than a subquery on the ads date filter is often more performant and readable at scale.
Identify the tables involved — typically an orders/revenue table with columns like order_date, creation_source, and revenue, plus an ads table that contains the valid dates. Confirm column names and data types before writing any SQL.
Use an INNER JOIN or WHERE EXISTS clause to restrict the result set to only dates that appear in the ads table, ensuring no revenue rows from dates absent in ads are included.
Apply GROUP BY on the date column and creation_source column, then use SUM(revenue) to compute total daily revenue for each combination.
Add ORDER BY date ASC, creation_source ASC at the end of the query to satisfy the requirement of ordering by date first, then creation source alphabetically.
Review the query for edge cases such as NULL values in creation_source or revenue, and consider whether a DATE_TRUNC is needed if the timestamp is more granular than a day. Mention indexing on date and creation_source for production performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The zero-count requirement is what makes this annoying.
Start by identifying the relevant tables (countries, advertisers, ads/revenue) and clarify the date window logic using a relative 30-day filter. Use a LEFT JOIN from a countries reference table to ensure countries with zero active advertisers still appear, then aggregate, rank, and apply tie-breaking alphabetically.
Pro tip: Explicitly calling out the need for a countries reference table (or CROSS JOIN seed) to surface zero-advertiser countries signals strong data modeling instinct — many candidates miss this edge case and only return countries that already appear in the ads table.
Identify the tables involved — typically a countries table, an advertisers table, and an ads/revenue table with timestamps. Confirm what 'last 30 days' means (e.g., CURRENT_DATE - 30 vs. a rolling window) and whether 'distinct active advertisers' means unique advertiser IDs.
Write a subquery or CTE that filters the ads table to the last 30 days and revenue > 0, then selects distinct advertiser_id and country combinations to define 'active' advertisers per country.
LEFT JOIN the active advertiser CTE onto a full countries reference table so that countries with no active advertisers appear with a count of zero. Use COUNT(DISTINCT advertiser_id) — which naturally returns 0 for NULLs after a LEFT JOIN — to get the active advertiser count per country.
Order the results by active_advertiser_count ASC, then by country_name ASC to handle alphabetical tie-breaking. Use LIMIT 10 (or RANK/ROW_NUMBER if you need to handle ties at the boundary explicitly).
Mention edge cases such as advertisers operating in multiple countries (counted separately per country), ads with NULL revenue (excluded by revenue > 0), and whether the 30-day window is inclusive of today. Briefly walk through expected output shape to confirm correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by aggregating advertiser spend for each period (Jan–Aug 2024 and Jan–Aug 2025) grouped by creation source, using FULL OUTER JOIN or UNION-based logic to ensure advertisers present in only one period are retained with zero spend for the missing period. Then compute the spend delta per advertiser per source, flag those with a delta of at least 1000, and divide by the total distinct advertisers per source to get the proportion.
Pro tip: Explicitly handle the 'missing advertiser' edge case by using a FULL OUTER JOIN or COALESCE on a unioned list of advertiser-source pairs — interviewers at Meta specifically look for candidates who anticipate data completeness issues rather than silently dropping advertisers who appear in only one period.
Write two CTEs (or subqueries) that each sum spend per advertiser and creation source, one filtered to Jan 1 – Aug 31 2024 and the other to Jan 1 – Aug 31 2025. This isolates the two comparison windows cleanly.
Build a complete universe of (advertiser_id, creation_source) pairs by taking the UNION (not UNION ALL) of both period CTEs, ensuring every advertiser that appears in either period is represented. This forms the denominator base.
LEFT JOIN the 2024 and 2025 spend CTEs onto the universe CTE, using COALESCE(spend, 0) for any advertiser missing from a given period. This correctly treats absent advertisers as having zero spend.
Calculate spend_2025 - spend_2024 for each advertiser-source pair and create a binary flag (1 if delta >= 1000, else 0). This separates the numerator logic from the aggregation step.
GROUP BY creation_source and compute AVG(growth_flag) — or equivalently SUM(growth_flag) / COUNT(*) — to get the proportion of qualifying advertisers per source. Order results for readability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.