Start by clarifying the schema and definitions (e.g., what constitutes a call, how to identify initiators and recipients, and the time window). Then write a query that groups by caller and counts distinct recipients within the last 7 days, filtering for those with more than 3 distinct recipients, and finally counts the number of such callers.
Pro tip: Mention that you would confirm whether 'past seven days' means the last 7*24 hours or calendar days, and whether the call must have been initiated within that period or just occurred. Also, consider if there are any edge cases like calls to oneself or null recipients.
Ask about the table structure, column names, and definitions: what identifies a user, a call, the initiator, the recipient, and the timestamp. Confirm the exact meaning of 'past seven days' and 'unique recipients'.
Use a WHERE clause to restrict to calls initiated in the last 7 days, based on the call timestamp. Ensure the timestamp is in the correct timezone if relevant.
Group the filtered calls by the initiator (caller) and count the distinct recipients for each caller. Use COUNT(DISTINCT recipient_id) to avoid double-counting.
Apply a HAVING clause to keep only those callers whose distinct recipient count exceeds 3.
Wrap the previous result in a subquery or use a COUNT over the filtered groups to return the total number of distinct users who meet the criteria.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Needed to join Calls and Users, filter dau_flag = 1 and country = 'fr' and ds = yesterday, then figure out who appeared as either caller or recipient in a call that day.
Clarify the definitions of 'daily active user', 'participated in at least one video call', and 'yesterday' to ensure alignment. Then outline the data sources and steps to compute the numerator (DAU from France who had ≥1 video call) and denominator (total DAU from France), and finally calculate the percentage. Consider edge cases like time zones and data completeness.
Pro tip: Proactively mention that you would validate the metric by checking for data anomalies (e.g., spikes or drops) and consider segmenting by device or call type to provide deeper insights, showing you think beyond the basic calculation.
Define 'daily active user' (e.g., logged-in user who performed any activity), 'participated in a video call' (e.g., initiated or joined a call), and 'yesterday' (e.g., UTC date). Confirm with stakeholders if ambiguous.
Determine which tables or logs contain user activity and video call events, such as a DAU table and a video call events table. Ensure they can be joined on user ID and date.
Write SQL queries to count distinct users from France who were active yesterday (denominator) and those among them who had at least one video call (numerator). Use appropriate filters for country and date.
Divide the numerator by the denominator and multiply by 100 to get the percentage. Handle potential division by zero (e.g., if no DAU from France).
Check for data quality issues (e.g., missing data, time zone mismatches) and consider if the result makes sense. Optionally, segment by dimensions like age or device to provide context.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Use three self-joins on the Calls table to find paths A→B→C→A, ensuring all three callers are distinct. Filter for the cycle condition and return each cycle once by enforcing an ordering (e.g., A < B < C) to avoid duplicates.
Pro tip: Clarify the schema first (e.g., caller, callee, timestamp) and mention that if the table has multiple rows per pair, you may need to deduplicate or consider time ordering. Also, discuss how to scale the query for large datasets, as self-joins can be expensive.
Identify the relevant columns (e.g., caller, callee) and confirm that the table may have multiple calls between the same pair. Clarify that a cycle requires three distinct people and directed edges A→B, B→C, C→A.
Plan to join the Calls table to itself three times: first for A→B, second for B→C, third for C→A. Use aliases to distinguish the instances.
Construct the query: SELECT ... FROM Calls c1 JOIN Calls c2 ON c1.callee = c2.caller JOIN Calls c3 ON c2.callee = c3.caller WHERE c3.callee = c1.caller AND c1.caller < c1.callee AND c1.callee < c2.callee (or similar) to ensure distinctness and avoid duplicates.
Use DISTINCT or a canonical ordering (e.g., A < B < C) to return each cycle only once. Decide whether to output the three names or just a count, based on the question.
Mentally test with sample data to ensure correctness. Discuss potential performance improvements, such as indexing on caller and callee, or using a graph database for large-scale cycle detection.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.