supabase-postgres-best-practices

You can audit your Postgres schema in Supabase by running an automated Python script that flags missing foreign key indexes and suboptimal types like varchar(n) or timestamp without time zone, then generates actionable remediation steps.

Common Postgres performance anti-patterns include using deprecated data types, missing indexes on foreign keys, and inefficient schema design. Automated audits can detect these issues and provide guidance for indexing, partitioning, and RLS to improve performance.

RLS configuration directly impacts Supabase database security and query performance. Auditing your RLS policies ensures conformance with best practices, preventing unauthorized access while maintaining efficient query execution through proper indexing and partitioning strategies.

Yes, you can automate Postgres partitioning and connection management recommendations using an audit script that evaluates your schema and generates actionable guidance to optimize query performance and maintainability in Supabase environments.

You need Python to run the included audit script against your SQL schema. The script surfaces performance and security issues, and you then consult the generated AGENTS.md and reference materials to apply fixes in your project.

Using varchar(n) instead of text can degrade Postgres query performance and maintainability. An automated schema audit detects this anti-pattern along with missing indexes and timestamp issues, then generates actionable remediation steps to optimize your database.