The zero-request days requirement is what got me.
Use a date spine (e.g., generate_series or recursive CTE) to create all 7 days, then LEFT JOIN aggregated request data to ensure zero-request days appear. Aggregate requests by UTC date, counting total requests and same-day accepts, then compute the rate with proper handling of division by zero.
Pro tip: Explicitly state your assumptions about the data model (e.g., one row per request with an approval timestamp) and clarify how you handle NULLs or zero denominators—this shows you think about edge cases and data quality.
Create a list of all dates in the 7-day UTC window using a date generator function or recursive CTE. This ensures days with no requests are included.
From the requests table, group by the UTC date of the request. Count total requests and count same-day accepts (where approval date equals request date).
LEFT JOIN the date spine to the aggregated metrics on date, so that days without requests get NULLs. Use COALESCE to replace NULL counts with 0.
Compute the rate as same-day accepts divided by total requests, using NULLIF or a CASE statement to avoid division by zero. Format or round as needed.
Output day, same-day accepts, total requests, and same-day accept rate, ordered by day ascending.
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Simpler than it looked but I second-guessed myself on the join direction.
First, clarify the exact UTC window for 'last week' and the definition of 'not marked as spam' (e.g., requester's spam flag is false). Then write a SQL query that filters requests within that window, counts total requests and requests where the requester is not spam, and computes the percentage rounded to two decimal places.
Pro tip: Always confirm the time window and spam definition with the interviewer before writing SQL; this shows attention to detail and avoids incorrect assumptions. Also, consider using a single query with conditional aggregation for efficiency.
Ask the interviewer to confirm the exact UTC start and end timestamps for 'last week' and how 'not marked as spam' is defined (e.g., a boolean flag on the requester).
Determine the table containing friendship requests, the timestamp column, and the column indicating whether the requester is marked as spam (likely a user table joined on requester_id).
Use conditional aggregation: COUNT(CASE WHEN requester_is_spam = false THEN 1 END) * 100.0 / COUNT(*) to get the percentage, filtering by the timestamp window.
Round the result to two decimal places using ROUND(..., 2) and verify the query logic with sample data or edge cases (e.g., no requests).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where the interview got interesting.
Start by clarifying the schema and the definition of 'unknown requesters' (e.g., missing user_id in users table). Then write a single SQL query that computes the three percentages using conditional aggregation, ensuring each denominator is clearly defined and returned alongside the percentages.
Pro tip: Explicitly state your assumptions about the data (e.g., how you identify unknown requesters) and consider edge cases like NULLs or duplicate records. This shows you think about data quality and reproducibility.
Identify the relevant tables (e.g., requests, users) and how to determine if a requester is unknown (e.g., LEFT JOIN on user_id yields NULL). Confirm the time window and the definition of 'spam'.
Use conditional aggregation (CASE WHEN) to compute counts for each scenario: excluding unknowns, treating unknowns as spam, and treating unknowns as not spam. Ensure the denominators are correctly defined for each percentage.
Construct a single query that calculates the three percentages and the corresponding counts. Use subqueries or CTEs for clarity if needed.
Check that the percentages sum to 100% within each scenario and that the counts align with expectations. Be prepared to discuss the implications of each treatment of unknowns.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I listed NULL approved_at (handled because DATE(NULL) is NULL so it never equals DATE(requested_at)), approvals outside the window (same-day accept only checks the date match, not whether approved_at is in the window, which is actually a design choice worth flagging), and the timezone thing since all boundaries are UTC so DATE() on a UTC timestamp is fine.
Start by acknowledging that edge cases are critical for robust SQL, then list 3-4 specific edge cases relevant to the query context (e.g., NULL approved_at, duplicate requests, timezone cutoffs). For each, explain the exact SQL technique used to handle it, such as COALESCE, DISTINCT, or timezone conversion functions, and briefly mention the impact on results if unhandled.
Pro tip: Tie each edge case back to a real-world scenario at Snapchat, like how timezone cutoffs affect daily active user metrics across global markets, to show business impact and technical depth.
List 3-4 edge cases that directly apply to the query context, such as NULL approved_at, approvals outside the date window, duplicate requests between the same pair, and timezone cutoffs.
For each edge case, describe the specific SQL construct used to handle it, e.g., COALESCE for NULLs, WHERE clauses for date windows, DISTINCT or GROUP BY for duplicates, and AT TIME ZONE for timezone conversion.
Mention any trade-offs, such as performance implications of DISTINCT versus GROUP BY, or why a particular timezone handling was chosen over another.
Briefly explain how each edge case, if unhandled, could skew metrics or lead to incorrect insights, tying back to Snapchat's use cases like user engagement or ad performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.