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postgres-patterns
Patrones de base de datos PostgreSQL para optimización de consultas, diseño de esquemas, indexación y seguridad. Basado en las buenas prácticas de Supabase.
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Patrones de base de datos PostgreSQL para optimización de consultas, diseño de esquemas, indexación y seguridad. Basado en las buenas prácticas de Supabase.
Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents. v2.1 adds project-scoped instincts to prevent cross-project contamination.
Orchestrate building a brand-new feature end to end — research, plan, TDD implementation, review, and gated commit — by delegating each phase to the matching ECC agent. Use when adding a capability that does not exist yet.
Orchestrate bootstrapping a working MVP from a design or spec document — ingest the doc, plan thin vertical slices, scaffold the first end-to-end slice, then TDD-implement, review, and gated commit. Use to turn an SDD/PRD into a running starting point.
Orchestrate altering an existing, working feature to new desired behavior — update its tests to the new spec, change the implementation to match, review, and gated commit. Use when behavior is not broken but should be different.
Orchestrate fixing a bug — reproduce it as a failing regression test, fix to green, review, and gated commit — by delegating each phase to the matching ECC agent. Use when existing behavior is broken or wrong.
Shared orchestration engine for the orch-* skill family. Defines the gated Research-Plan-TDD-Review-Commit pipeline, the size classifier, the agent map, and the two human gates that the orch-* operation skills delegate to. Not usually invoked directly.
| name | postgres-patterns |
| description | Patrones de base de datos PostgreSQL para optimización de consultas, diseño de esquemas, indexación y seguridad. Basado en las buenas prácticas de Supabase. |
| origin | ECC |
Referencia rápida de las buenas prácticas de PostgreSQL. Para orientación detallada, usa el agente database-reviewer.
| Patrón de Consulta | Tipo de Índice | Ejemplo |
|---|---|---|
WHERE col = value | B-tree (por defecto) | CREATE INDEX idx ON t (col) |
WHERE col > value | B-tree | CREATE INDEX idx ON t (col) |
WHERE a = x AND b > y | Compuesto | CREATE INDEX idx ON t (a, b) |
WHERE jsonb @> '{}' | GIN | CREATE INDEX idx ON t USING gin (col) |
WHERE tsv @@ query | GIN | CREATE INDEX idx ON t USING gin (col) |
| Rangos de series temporales | BRIN | CREATE INDEX idx ON t USING brin (col) |
| Caso de Uso | Tipo Correcto | Evitar |
|---|---|---|
| IDs | bigint | int, UUID aleatorio |
| Cadenas | text | varchar(255) |
| Timestamps | timestamptz | timestamp |
| Dinero | numeric(10,2) | float |
| Flags | boolean | varchar, int |
Orden del Índice Compuesto:
-- Columnas de igualdad primero, luego columnas de rango
CREATE INDEX idx ON orders (status, created_at);
-- Funciona para: WHERE status = 'pending' AND created_at > '2024-01-01'
Índice de Cobertura:
CREATE INDEX idx ON users (email) INCLUDE (name, created_at);
-- Evita la búsqueda en tabla para SELECT email, name, created_at
Índice Parcial:
CREATE INDEX idx ON users (email) WHERE deleted_at IS NULL;
-- Índice más pequeño, solo incluye usuarios activos
Política RLS (Optimizada):
CREATE POLICY policy ON orders
USING ((SELECT auth.uid()) = user_id); -- ¡Envolver en SELECT!
UPSERT:
INSERT INTO settings (user_id, key, value)
VALUES (123, 'theme', 'dark')
ON CONFLICT (user_id, key)
DO UPDATE SET value = EXCLUDED.value;
Paginación por Cursor:
SELECT * FROM products WHERE id > $last_id ORDER BY id LIMIT 20;
-- O(1) vs OFFSET que es O(n)
Procesamiento de Cola:
UPDATE jobs SET status = 'processing'
WHERE id = (
SELECT id FROM jobs WHERE status = 'pending'
ORDER BY created_at LIMIT 1
FOR UPDATE SKIP LOCKED
) RETURNING *;
-- Encontrar claves foráneas sin índice
SELECT conrelid::regclass, a.attname
FROM pg_constraint c
JOIN pg_attribute a ON a.attrelid = c.conrelid AND a.attnum = ANY(c.conkey)
WHERE c.contype = 'f'
AND NOT EXISTS (
SELECT 1 FROM pg_index i
WHERE i.indrelid = c.conrelid AND a.attnum = ANY(i.indkey)
);
-- Encontrar consultas lentas
SELECT query, mean_exec_time, calls
FROM pg_stat_statements
WHERE mean_exec_time > 100
ORDER BY mean_exec_time DESC;
-- Verificar bloat de tablas
SELECT relname, n_dead_tup, last_vacuum
FROM pg_stat_user_tables
WHERE n_dead_tup > 1000
ORDER BY n_dead_tup DESC;
-- Límites de conexión (ajustar según RAM)
ALTER SYSTEM SET max_connections = 100;
ALTER SYSTEM SET work_mem = '8MB';
-- Timeouts
ALTER SYSTEM SET idle_in_transaction_session_timeout = '30s';
ALTER SYSTEM SET statement_timeout = '30s';
-- Monitoreo
CREATE EXTENSION IF NOT EXISTS pg_stat_statements;
-- Valores predeterminados de seguridad
REVOKE ALL ON SCHEMA public FROM public;
SELECT pg_reload_conf();
database-reviewer - Flujo de trabajo completo de revisión de base de datosclickhouse-io - Patrones de analítica en ClickHousebackend-patterns - Patrones de API y backendBasado en Agent Skills de Supabase (crédito: equipo de Supabase) (Licencia MIT)