GROUP BY and HAVING

Aggregation turns detailed rows into summarized metrics, so correctness depends on grouping dimensions and measure definitions. In PostgreSQL, every selected column must either be grouped or aggregated, which forces you to make dimension choices explicit. That is helpful because many reporting bugs come from accidental dimensions, like grouping by plan and month when you only wanted month. Start by writing the smallest valid grouped query: dimensions in GROUP BY, one measure such as SUM(amount), and no optional fields. Then add additional measures one by one and validate each against known totals. For counts, decide whether you need raw row count, distinct entity count, or conditional count. COUNT(*) answers different business questions than COUNT(DISTINCT user_id). Analysts who state this choice in plain language before coding avoid many downstream disputes about why numbers differ between dashboards.

WHERE and HAVING operate at different stages in PostgreSQL query processing. WHERE filters base rows before grouping, while HAVING filters grouped rows after aggregates are computed. Use WHERE for row level constraints like date range or paid status, and HAVING for aggregate thresholds like revenue above 5000. Mixing these up either throws errors or silently changes metric meaning. A robust QA approach is to test a query on a single month and one plan where you can manually verify results. You can also compare the grouped output sum back to a trusted base query to ensure no rows were dropped unexpectedly. Keep row grain visible in your output labels, for example one row per month_start, plan_id. When teams document both grain and measure formula, reports become easier to maintain and audits become much faster.