Start by clarifying the table schema and whether 'total users' means all rows or only active/valid users. Then write a simple COUNT(*) query, but also discuss edge cases like NULLs, duplicates, and performance considerations for large tables.
Pro tip: Mention that COUNT(*) is generally faster than COUNT(column) and that for very large tables, you might use an approximate count or a pre-aggregated metric to avoid full table scans.
Ask whether 'total users' includes all rows or only distinct/active users, and confirm the table schema (e.g., primary key, status column).
Use SELECT COUNT(*) FROM users; for total rows, or SELECT COUNT(DISTINCT user_id) FROM users; if duplicates are possible.
Consider NULLs (COUNT(column) ignores NULLs), duplicates, and whether to filter by a condition like status = 'active'.
For large tables, COUNT(*) can be slow; mention indexes, approximate counts, or using a summary table if real-time accuracy isn't required.
Suggest running the query on a sample or using EXPLAIN to check performance, and verify results against known metrics if available.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Straightforward GROUP BY on a truncated timestamp, then ORDER BY and LIMIT 1.
Clarify the definition of 'new user signups' and the relevant date column (e.g., created_at). Then write a SQL query that truncates the signup date to the month, counts the number of signups per month, and orders by count descending to find the top month.
Pro tip: Mention that you would check for data quality issues like NULL signup dates or duplicate users, and consider whether to use COUNT(DISTINCT user_id) to avoid double-counting.
Confirm the definition of 'new user signup' and identify the correct date column (e.g., created_at) in the users table.
Use DATE_TRUNC or equivalent to group signups by calendar month, and count the number of signups per month.
Order the aggregated results by the count in descending order and limit to 1 to get the month with the highest signups.
Check for ties, missing data, or timezone considerations that could affect the result.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
COUNT + GROUP BY on followed_user_id, then join back to users for the name.
Start by clarifying the schema of the follows table and the definition of 'largest follower count' (e.g., total followers vs. net followers). Then write a SQL query that groups by the followed user ID, counts distinct followers, and orders descending with a limit 1, while considering edge cases like ties and inactive users.
Pro tip: Discuss how you would handle ties (e.g., using RANK() or DENSE_RANK() to return all top users) and mention that for large-scale systems, pre-aggregated counts or a materialized view might be more efficient than a raw GROUP BY.
Ask about the table structure (e.g., columns like follower_id, followee_id, timestamp) and define what 'largest follower count' means (e.g., total followers, distinct followers, or net followers after unfollows).
Use a GROUP BY on the followee_id, COUNT(DISTINCT follower_id) to get follower counts, ORDER BY count DESC, and LIMIT 1 to find the top user.
Consider if multiple users have the same max count; use window functions like RANK() or DENSE_RANK() to return all tied users, and handle cases with no follows or inactive users.
Mention indexing on followee_id, and for large datasets, discuss using pre-aggregated tables, materialized views, or approximate algorithms if exact counts are not required.
Explain how you would sanity-check the result (e.g., compare with known metrics) and interpret it in the context of Nextdoor's neighborhood graph, such as identifying influential users.
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 metric definitions, then design a modular SQL pipeline that computes each metric separately before joining into a daily aggregate table. Use timezone conversion early in the pipeline and ensure 7-day comparisons are handled via window functions or self-joins.
Pro tip: Mention that you would materialize intermediate results as CTEs or temp tables to avoid recomputation and ensure consistency, and that you'd validate the timezone conversion with edge cases like DST transitions.
Ask about table structures, metric definitions (e.g., what counts as a new user, DAU), and how 7-day comparisons should be presented (e.g., day-over-day or rolling average).
Write separate CTEs for new users, DAU, new photos, follow edges, and follower buckets, each aggregating raw data by day in the target timezone.
Calculate new photos per DAU and use window functions (e.g., LAG) to compute 7-day comparisons for each metric.
Combine all metrics into a single daily aggregate table, ensuring proper handling of missing dates and null values.
Add indexes, consider partitioning, and validate results against known benchmarks or sample data to ensure correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The definition sounds clean but the timezone boundary stuff makes it annoying.
First, clarify the definitions: signup day D is the date of the signup event in the reporting timezone, and week-1 retention requires at least one event on days D+7 through D+13 inclusive. Then write a SQL query that joins signups to events within that window, aggregates per user, and computes the retention rate as the share of users with any event.
Pro tip: Explicitly state that you're using the reporting timezone for all date calculations and that the window is inclusive of both D+7 and D+13; this avoids off-by-one errors and shows attention to detail.
Identify users who signed up on day D by selecting user_id and the signup date converted to the reporting timezone.
For each user in the cohort, check if they have at least one event between D+7 and D+13 inclusive, using the reporting timezone for event timestamps.
Count the number of users in the cohort and the number of retained users, then divide to get the week-1 retention rate.
Consider users with no events, multiple events, and ensure the date arithmetic correctly handles timezone conversions and inclusive bounds.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.