They gave some scaffolding upfront: a video table with user_id, video_id, call_duration, number_joins, number_rejoins, and timestamp, plus a user table with user_id and country.
Start by clarifying the requirements and scope of the video call logs, then propose a normalized schema that separates client-side and server-side data into distinct tables linked by a common call session ID. Explain how the tables support key queries like call quality analysis, troubleshooting, and user history, and discuss trade-offs between normalization and denormalization for performance.
Pro tip: Demonstrate awareness of data volume and retention policies by suggesting partitioning or time-series optimizations for the server-side logs, and mention how you'd handle privacy and compliance (e.g., GDPR) when storing client-side data.
Ask questions to understand what data needs to be captured, who will query it, and what the expected scale is. This ensures your design addresses real needs rather than assumptions.
Determine the main entities such as CallSession, ClientLog, and ServerLog, and define how they relate (e.g., one call session has many client and server log entries).
Propose specific tables with columns, data types, and keys. For example, a calls table for session metadata, a client_logs table for client-side events, and a server_logs table for server-side metrics.
Discuss how the schema handles large volumes of data, including indexing, partitioning, and potential denormalization for read-heavy workloads.
Explain how you would handle sensitive data, data retention policies, and compliance requirements like GDPR or CCPA.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Straightforward join between the video and user tables, group by country and date.
Start by clarifying the schema: identify the tables for calls, users, and countries, and the timestamp column. Then write a SQL query that joins these tables, groups by country and date, and counts distinct call IDs. Finally, consider edge cases like time zones and null values.
Pro tip: Mention that you'd confirm the definition of a 'video call' (e.g., duration > 0) and whether to count distinct calls or participants, as this affects the query. Also, discuss how to handle time zones to ensure daily counts align with business definitions.
Ask about the tables involved (e.g., calls, users, countries) and the relevant columns (call_id, user_id, country, call_timestamp). Confirm the granularity of the data.
Determine what constitutes a video call (e.g., call_type = 'video', duration > 0) and whether to count distinct calls or participants. Clarify if all calls should be included or only completed ones.
Decide how to convert timestamps to the appropriate time zone for daily grouping. Use DATE_TRUNC or equivalent to group by day.
Join the necessary tables, apply filters, group by country and date, and count distinct call IDs. Use appropriate aggregation functions.
Check for nulls, duplicates, and performance considerations (e.g., indexing). Discuss how to handle edge cases like calls spanning midnight.
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 (e.g., what constitutes a video call, how to handle missing dates). Then outline a SQL solution using window functions with ROWS BETWEEN to compute rolling sums for each window size, ensuring you partition by country and order by date. Finally, discuss performance considerations and potential optimizations for large datasets.
Pro tip: Mention that you would pre-aggregate daily counts in a CTE or subquery before applying window functions to avoid redundant computation and improve performance. Also, consider using a calendar table to ensure all dates are present, even if there were no calls.
Confirm the definition of a video call (e.g., call type, duration threshold) and whether the rolling windows should include the current day. Ask about the expected data volume and performance constraints.
Outline a multi-step approach: first, aggregate daily video call counts per country; second, use window functions to compute rolling sums for 7, 14, and 30 days. Consider using a calendar table to fill missing dates.
Use SUM() OVER (PARTITION BY country ORDER BY date ROWS BETWEEN N-1 PRECEDING AND CURRENT ROW) for each window size. Ensure the date range is handled correctly, especially for the first few days where the window is incomplete.
Discuss handling of missing dates (e.g., left join with calendar), and performance optimizations like indexing on (country, date) and pre-aggregation. Mention potential use of materialized views for large-scale data.
Explain how you would validate the query (e.g., check sums for a specific country/date manually) and interpret the rolling metrics to derive insights, such as trends in video call adoption.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.