Observed Signal · Jun 1, 2026 · Technical Article · Source: DEV Community · Impact: 2/5 · Sentiment: Neutral
Postgres Features That Break Type-Correct Test Data
A technical blog post explains why column types alone do not guarantee that generated test data will be accepted by PostgreSQL. The author identifies six schema features commonly skipped by column-oriented generators—composite primary keys, partial unique indexes, cross-column CHECK constraints, JSONB shape expectations, GENERATED ALWAYS columns, and row-level security (RLS)—and shows how each can reject rows that are type-correct. The article gives diagnostic SQL queries to detect violations for each case, highlights pitfalls (e.g., NULL semantics in CHECKs, partial unique indexes not appearing as constraints, and Postgres 12→18 changes to generated columns), and outlines three tiers of generator tooling (simple per-column scripts, schema-aware generators like Neosync and Seedfast, and enterprise TDM platforms). It recommends running the provided queries against generated data before trusting seeding results.
Practical guidance for developers and QA: missed schema constraints cause CI/test failures and data integrity issues. Useful to engineering teams but not industry-shifting.
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Key Takeaways & Evidence Grounding
- The article lists six PostgreSQL features that can reject type-correct data: composite primary keys, partial unique indexes, cross-column CHECK constraints, JSONB shape, GENERATED ALWAYS columns, and row-level security.
- A composite primary key enforces uniqueness over the tuple and forces participating columns to NOT NULL.
- Partial unique indexes enforce uniqueness only for rows matching a WHERE predicate and are not visible via standard constraint-only introspection.
- Postgres generated columns cannot be written to directly; STORED generated columns were required syntax through Postgres 17, while Postgres 18 made the STORED keyword optional and virtual the default.
- The post provides specific diagnostic SQL queries to detect violations for each schema feature and recommends validating generated data against them.
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