Does it support vector databases for AI?

Yes, it provides guidance on using the pgvector extension and vector types for similarity search embeddings.

How does it handle performance for large tables?

It recommends declarative partitioning (Range, List, or Hash) and specialized indexes like BRIN for very large, naturally ordered datasets.

Does it help with database security?

Yes, it provides implementation patterns for Row-Level Security (RLS) policies to control data access at the row level.

What primary key type does this skill recommend?

It prefers BIGINT GENERATED ALWAYS AS IDENTITY for standard tables and UUID v7 for distributed or opaque identifiers.

Why does it suggest TEXT over VARCHAR(n)?

In PostgreSQL, there is no performance difference between the two; using TEXT with CHECK constraints is more flexible than hardcoded length limits.

PostgreSQL Table Design

Name: PostgreSQL Table Design
Author: HermeticOrmus

byHermeticOrmus

Gestión de Bases de Datos

Architects high-performance, production-ready PostgreSQL schemas using modern best practices and advanced indexing patterns.

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The PostgreSQL Table Design skill provides comprehensive guidance for creating robust database schemas tailored specifically for PostgreSQL's unique architecture. It enforces modern standards like 3NF normalization, identity columns over legacy serial types, and precise data type selection—such as TIMESTAMPTZ for global consistency and NUMERIC for exact decimals. Beyond basic modeling, the skill offers expert-level insights into indexing strategies (B-tree, GIN, GiST), MVCC-aware performance tuning, Row-Level Security, and advanced features like partitioning and vector search. It is an essential tool for developers building scalable backends who need to ensure data integrity and query efficiency from day one.

Características Principales

Optimized data type selection including JSONB, Vector, and Range types
0 GitHub stars
Performance-tuned patterns for insert-heavy and update-heavy workloads
Comprehensive indexing strategies including Partial, Covering, and Expression indexes
Declarative partitioning and schema evolution best practices
Implementation of Row-Level Security (RLS) and advanced constraints

Casos de Uso

Architecting a scalable multi-tenant SaaS database with Row-Level Security
Migrating legacy database schemas to modern PostgreSQL standards
Optimizing high-traffic tables to handle MVCC bloat and index maintenance

Acerca de

Características Principales

Optimized data type selection including JSONB, Vector, and Range types
0 GitHub stars
Performance-tuned patterns for insert-heavy and update-heavy workloads
Comprehensive indexing strategies including Partial, Covering, and Expression indexes
Declarative partitioning and schema evolution best practices
Implementation of Row-Level Security (RLS) and advanced constraints

Casos de Uso

Architecting a scalable multi-tenant SaaS database with Row-Level Security
Migrating legacy database schemas to modern PostgreSQL standards
Optimizing high-traffic tables to handle MVCC bloat and index maintenance