Analytics Bugs Usually Start in Query Design
PostgreSQL gives strong guarantees, but those guarantees require explicit query structure. Most feedback analytics bugs come from loosely written aggregate queries.
Typical Pitfalls
Non-Grouped Selection
Selecting columns that are neither grouped nor aggregated leads to errors or undefined expectations.
Mixed Raw Expressions
Raw SQL fragments without clear aliases make downstream transformation fragile.
Date Boundary Drift
Inconsistent timezone handling causes daily/weekly cards to misalign with expected reporting windows.
Query Design Rules We Apply
- keep each aggregate query focused on one question,
- separate distribution and trend queries,
- use explicit aliases for computed fields,
- normalize time boundaries before grouping,
- avoid implicit
select *in grouped contexts.
Index Strategy for Feedback Workloads
Baseline indexes worth having:
project_id,status,type,created_at.
Composite indexes can improve hot paths depending on filter patterns.
Practical Pattern: Split and Compose
Instead of one complex query for entire dashboard, run multiple narrow queries and compose response in application layer.
Benefits:
- easier validation,
- safer optimization,
- clearer ownership when debugging.
Debugging Checklist
- compare aggregate output against raw sample rows,
- verify timezone assumptions end-to-end,
- test edge windows (month end, DST transitions),
- profile heavy queries with realistic cardinality.
Closing
Reliable product analytics is not about clever SQL tricks. It is about explicit, testable query design.