Startups' Practical Postgres Survival Guide
Original: The startup's Postgres survival guide
Why This Matters
Practical Postgres scaling guidance is critical as more startups rely on Postgres as their primary production database.
Hatchet co-founder Alexander Belanger published an internal engineering guide distilling two years of production Postgres experience. The guide covers schema design, query optimization, connection management, autovacuum, partitioning, and large table migration strategies for startups scaling their databases.
Hatchet co-founder Alexander Belanger released a production-focused Postgres guide originally written as an internal document for the company's engineers. The guide assumes basic SQL familiarity and is structured around the lifecycle of a growing startup's database needs.
Key schema recommendations include using identity columns or built-in UUIDs for primary keys, always using timestamptz for timestamps, and applying foreign keys with cascading deletes for low-volume tables where consistency matters. The author cautions against over-normalizing schemas, noting that jsonb columns can offer practical advantages when moving quickly.
For read queries, Belanger outlines how Postgres either locates rows quickly via indexes, unique constraints, or primary keys, or falls back to a sequential scan. The guide covers compound indexes, aligning ORDER BY with index structure, and writing performant joins. For writes, it addresses migrations, connection pooling, and bulk update patterns.
More advanced sections cover the query planner, autovacuum tuning (noting default settings can destabilize a database under load), FOR UPDATE SKIP LOCKED for queue-like patterns, table partitioning, and strategies for large-table schema migrations. The author notes that ORM abstractions can limit optimization options at scale and recommends tools like Prisma TypedSQL or sqlc for Go stacks to break through those layers when needed.