I got the core aggregation down fine, LAG() over the weekly partition, pretty standard.
Start by aggregating views per ISO week using DATE_TRUNC('week', timestamp) or EXTRACT(ISOYEAR FROM timestamp) and EXTRACT(WEEK FROM timestamp) to ensure correct ISO week boundaries. Then use a window function like LAG to compare each week's total to the previous week, computing absolute and percent change. Filter to the last 12 months and handle edge cases like zero previous week views.
Pro tip: Mention that ISO weeks can span year boundaries, so using EXTRACT(ISOYEAR) and EXTRACT(WEEK) together is safer than DATE_TRUNC alone. Also, consider using a calendar table or generate_series to include weeks with zero views, ensuring accurate week-over-week comparisons.
Filter the server_view table to the past 12 months based on the timestamp column. Extract the ISO year and ISO week number to group views correctly, being mindful of year boundaries.
Count the number of views (or distinct users if specified) for each ISO week. Use a CTE or subquery to produce a clean weekly summary with columns for ISO year, ISO week, and total views.
Use the LAG window function ordered by ISO year and week to get the previous week's view count. Calculate absolute change as current_views - previous_views and percent change as (current_views - previous_views) / NULLIF(previous_views, 0) * 100.
Ensure the first week has NULL or 0 for changes, and handle division by zero using NULLIF. Optionally, format the percent change to two decimal places and order results chronologically.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by acknowledging the core problem: aggregation queries drop weeks with no data because they only return rows for existing records. Then present two main solutions—a static calendar/spine table and a dynamically generated week series—and compare their trade-offs in terms of maintenance, performance, and flexibility. Finally, show how to LEFT JOIN the aggregated views to the spine and use COALESCE to replace NULLs with zeros.
Pro tip: Mention that a calendar table can be pre-populated with additional attributes like fiscal weeks or holidays, making it reusable across many queries and reducing repeated date logic. Also note that generating a series on the fly can be more flexible for ad-hoc date ranges but may have performance implications at scale.
Explain that standard GROUP BY week queries only return weeks with data, so weeks with zero views are missing. This leads to misleading trends and incomplete reports.
Decide between a persistent calendar/spine table (pre-built, indexed, reusable) and a dynamically generated week series (using recursive CTEs or generate_series). Discuss trade-offs: maintenance vs. flexibility, performance, and storage.
If using a table, query the relevant date range from it. If generating, use a recursive CTE or database-specific function to produce a complete list of weeks covering the desired period.
LEFT JOIN the week series to the aggregated views data on the week key. Use COALESCE or IFNULL to replace NULL view counts with 0, ensuring every week appears with an explicit value.
Check that the output includes all weeks, including those with zero. Consider indexing the spine table and the join key for performance, and discuss how this scales with large date ranges.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.