postgres-patterns

To optimize PostgreSQL queries with slow WHERE clauses, apply indexing strategies like composite or partial indexes based on equality and range conditions. This reduces scan overhead and accelerates data retrieval.

Implement Row Level Security (RLS) in PostgreSQL by defining policies that restrict data access per user role. This Skill provides RLS policy examples following Supabase best practices for secure database operations.

Common PostgreSQL anti-patterns causing table bloat and slow queries include poor data type selection and inefficient schema design. This Skill helps detect these anti-patterns to troubleshoot and resolve performance issues.

You need a partial index in PostgreSQL when querying a specific subset of rows frequently. Partial indexes optimize query performance by indexing only the rows matching a conditional WHERE clause, saving storage space.

Data type selection impacts PostgreSQL schema design by determining storage efficiency and query speed. Choosing appropriate data types and applying composite indexes prevents table bloat and enhances overall database optimization.

Yes, these PostgreSQL optimization patterns are based on Supabase best practices. They directly apply to Supabase database operations for efficient schema design, RLS implementation, and query optimization.