Straightforward GROUP BY with a HAVING filter, but I almost forgot the tie-breaking on total listings.
Start by clarifying the schema and assumptions (e.g., is_visible is 0/1, listings table has shop_id foreign key). Then write a SQL query that groups by shop_id, computes AVG(is_visible) and COUNT(*), filters with HAVING COUNT(*) >= 5, and orders by visibility rate DESC, total listings DESC. Finally, discuss potential edge cases and performance considerations.
Pro tip: Mention that using AVG(is_visible) directly works for 0/1 flags, but if is_visible is stored as a string or boolean, you may need to cast it. Also, consider using a window function or subquery to rank shops if you need to return additional shop details.
Confirm the table structures, data types, and definitions (e.g., is_visible as 0/1, shop_id in listings). Ask if shops with zero listings should be included (they shouldn't due to the 5-listing filter).
Use GROUP BY shop_id to compute AVG(is_visible) AS visibility_rate and COUNT(*) AS total_listings. Apply HAVING COUNT(*) >= 5 to filter shops.
Order the results by visibility_rate DESC, then by total_listings DESC to break ties. Optionally, include shop_id for deterministic ordering.
Discuss indexing on shop_id, handling NULLs in is_visible (e.g., COALESCE or ignore), and whether to use a subquery or CTE for readability.
Walk through the query logic, test with sample data if possible, and explain how it meets the requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the schema and definitions: confirm the shop_events table columns (shop_id, event_type, event_timestamp), the shops table with creation date, and the exact date window for activity. Then outline a SQL query that aggregates events per shop, computes weighted counts, and joins with shop metadata to derive is_new, visibility_rate, and activity_score, handling edge cases with NULLIF and COALESCE.
Pro tip: Explicitly state your assumptions about the date window and event weights, and mention that you would validate the query with sample data or edge cases (e.g., shops with no events or listings) to ensure correctness before scaling.
Ask about the exact date window, event types and their weights, and the definition of visibility_rate (e.g., listings per shop). Confirm table structures and join keys.
Aggregate shop_events within the date window, applying weights to each event type, and sum to get activity_score. Use a LEFT JOIN from shops to ensure all shops are included, with COALESCE to default to 0.
Calculate is_new by comparing shop creation date to current date (or reference date) within 30 days. Compute visibility_rate as listings count divided by something (e.g., total possible listings), using NULLIF to avoid division by zero and return NULL if fewer than 1 listing.
Join the aggregated activity data with shop metadata, ensuring one row per shop. Use COALESCE for activity_score and CASE for visibility_rate to handle NULLs appropriately.
Test with edge cases (new shops, no events, no listings) and consider indexing on shop_id and event_timestamp for performance. Explain any trade-offs in the query design.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Median via analytic functions is always a bit awkward in SQL.
Start by building a CTE that computes per-shop metrics (mean, median via analytic function, and active flag) from the previous query's output. Then aggregate these metrics at the shop type level (new vs existing) using conditional aggregation, ensuring the median is correctly computed with an analytic function before grouping. Finally, format the result as a 3-row summary table (new, existing, overall) with the required columns.
Pro tip: When using an analytic function for median, compute it at the shop level first, then aggregate—avoid trying to compute median of medians. Also, explicitly handle ties and nulls in activity scores to ensure accurate active shop counts.
Create a CTE that calculates each shop's mean activity score, median activity score using an analytic function (e.g., PERCENTILE_CONT or MEDIAN), and a flag indicating if the shop is active (activity score > 0).
Add a column that labels each shop as 'new' or 'existing' based on the business definition (e.g., shop creation date within a recent period).
Group by the shop type label and compute the average of the per-shop means, the average of the per-shop medians, and the fraction of active shops (sum of active flags divided by count).
Use UNION ALL to add a third row that aggregates across all shops (without grouping by shop type) to provide an overall comparison.
Ensure the final table has three rows (new, existing, overall) and the required columns, and validate that the median calculation is correct by checking a few shops manually.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The SQL itself is a simple filter and ORDER BY, the interesting part is the conceptual question at the end.
Start by clarifying the definitions of 'new shops', 'activity score', and 'visibility rate', then outline the SQL query with appropriate filters and ordering. Explicitly discuss how you handle NULLs versus zeros and how you would mitigate survivorship bias by considering shops that may have been removed or never appeared.
Pro tip: Mention that you would validate the results by checking the distribution of activity scores and visibility rates, and consider segmenting by shop category or region to ensure the insights are actionable.
Define what 'new shop' means (e.g., created within last 30 days), how 'activity score' is calculated, and what 'visibility rate' represents (e.g., impressions per user or per session). Confirm whether NULLs should be treated as 0 or excluded.
Construct a query that filters for new shops, visibility rate < 0.5, orders by activity score descending, and limits to top 5. Use COALESCE or CASE to handle NULLs appropriately.
Explain that NULL indicates missing data, while 0 indicates a true zero value. For visibility rate, if the denominator is zero, the rate is undefined; decide whether to exclude or set to 0 based on business context.
Consider shops that may have been deleted or never appeared in the dataset. Use left joins to include all shops, and analyze historical data to see if underexposed shops later became successful.
Check for outliers, ensure the top 5 are truly underexposed, and provide recommendations for surfacing these shops (e.g., boosting visibility in recommendations).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.