Clarify the schema and definitions (e.g., what 'received' means, how to handle multiple messages per day), then write a SQL query that filters messages from the past 7 days, groups by user and date, counts messages, and finally counts distinct users with more than 10 messages on any day. Walk through the logic step by step, considering edge cases like time zones and date boundaries.
Pro tip: Mention that you would validate the query with a small sample or by checking edge cases (e.g., users with exactly 10 messages) to ensure correctness, and discuss how the metric might be used to inform product decisions.
Ask clarifying questions about the table structure (e.g., columns for user_id, message_id, sent_date, read_date) and define what 'received' means (e.g., sent_date or read_date). Confirm the time window (past 7 days from today) and whether to consider calendar days or rolling 24-hour periods.
Write a subquery or CTE to select only messages where the relevant date (e.g., sent_date) falls within the last 7 days. Use appropriate date functions (e.g., DATE_SUB(CURRENT_DATE, INTERVAL 7 DAY)) and consider time zone adjustments if necessary.
Group the filtered messages by user_id and date (truncated to day) and count the number of messages. This yields a table with columns: user_id, date, message_count.
From the aggregated table, filter for rows where message_count > 10. Then count the distinct user_ids that appear in this filtered set.
Combine the steps into a single SQL query (using CTEs for readability). Discuss potential edge cases: users with multiple messages on the same day but different timestamps, handling of NULLs, and whether to include users with exactly 10 messages (no, since 'more than 10').
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the definitions of 'spammer', 'read', and 'last week' to ensure alignment with the interviewer. Then, outline the SQL query structure: filter messages from spammers in the last week, compute read-rate per spammer, and average those rates. Finally, discuss potential edge cases and validation.
Pro tip: When defining 'spammer', consider using a threshold on the violating_scores table (e.g., score > 0.8) and mention that you would confirm this with the interviewer. Also, specify that you would use a LEFT JOIN to include messages with no read events, treating them as unread.
Define 'spammer' (e.g., users with violating_score above a threshold), 'read' (e.g., read_at is not null), and 'last week' (e.g., last 7 days from today). Confirm these with the interviewer.
Use the violating_scores table to select spammer user IDs, then filter the messages table to include only messages sent by these users in the last week.
For each spammer, calculate the number of read messages divided by total messages sent. Use a LEFT JOIN to messages_read or check read_at to count reads.
Average the per-spammer read-rates to get the overall average read-rate for spammers. Be clear whether to weight by message volume or not.
Consider spammers with zero messages, messages with multiple read events, and time zone issues. Suggest sanity checks like comparing to overall read-rate.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.