postgres-patterns

Query optimization uses proven patterns like composite indexes, efficient JOIN strategies, and targeted WHERE filters to reduce latency. Apply pattern-based guidance to your workload—composite indexes on frequently filtered columns and appropriate filter strategies typically deliver the fastest results.

PostgreSQL indexing strategies include B-tree for standard columns, BRIN for large sequential data, and GIN for text and JSON fields. Partial and composite indexes further reduce query cost by indexing only relevant data subsets, aligned with your access patterns.

Row Level Security (RLS) enforces data access control at the row level by applying policies that filter query results based on user context. RLS combined with safe security defaults prevents unauthorized data exposure and aligns with production governance best practices.

Yes. Schema design patterns guide effective table structure, indexing decisions, and migration work across backend services and analytics environments. Run a pattern audit against your current schema to identify candidate indexes and RLS policies matching your workload.

GIN indexes optimize text search and JSON queries by indexing individual elements, enabling fast containment and equality checks. B-tree and BRIN serve different workloads—choose GIN for unstructured search, B-tree for range queries, and BRIN for large sequential datasets.

Partial indexes reduce index size and maintenance cost by indexing only rows matching a WHERE condition, speeding up queries on filtered subsets. They're ideal when most queries target a specific data slice, delivering faster execution and lower storage overhead.