managing-database-partitions

Process use when you need to work with database partitioning. This skill provides table partitioning strategies with comprehensive guidance and automation. Trigger with phrases like "partition tables", "implement partitioning", or "optimize large tables".

Ir a la instalación

Repositorio: jeremylongshore/claude-code-plugins-plus-skills
Última actividad en el origen: 17 de julio de 2026 a las 22:18
Idioma detectado de SKILL.md: inglés
Estrellas: 2604
Forks: 378

Opciones de instalación

De forma predeterminada está seleccionado el prompt que primero revisa el origen. Puedes cambiar a un comando directo o descargar una copia local.

Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.

Un comando directo omite el prompt de revisión. Revisa el origen antes de ejecutarlo.

npx skills add https://github.com/jeremylongshore/claude-code-plugins-plus-skills --skill managing-database-partitions

El comando permanece en una sola línea. Desplázate horizontalmente para revisarlo antes de copiarlo.

¿Prefieres una copia local? Descarga los archivos que SkillsMP tiene disponibles ahora.

Revisa los archivos de origen

Lee SKILL.md y los archivos complementarios que muestra SkillsMP antes de decidir si quieres instalarlo.

Explorador de archivos

4 archivos

SKILL.md

Instrucciones de origen · Vista previa de solo lectura

Volver a la búsqueda Volver a la instalación

clerk-core-workflow-a

Implement user sign-up and sign-in flows with Clerk. Use when building authentication UI, customizing sign-in experience, or implementing OAuth social login. Trigger with phrases like "clerk sign-in", "clerk sign-up", "clerk login flow", "clerk OAuth", "clerk social login".

2026-07-23

clerk-core-workflow-b

Implement session management and middleware with Clerk. Use when managing user sessions, configuring route protection, or implementing token refresh and custom JWT templates. Trigger with phrases like "clerk session", "clerk middleware", "clerk route protection", "clerk token", "clerk JWT".

2026-07-23

clerk-enterprise-rbac

Configure enterprise SSO, role-based access control, and organization management. Use when implementing SSO integration, configuring role-based permissions, or setting up organization-level controls. Trigger with phrases like "clerk SSO", "clerk RBAC", "clerk enterprise", "clerk roles", "clerk permissions", "clerk organizations".

2026-07-23

Ver todos los de este repositorio

Database Partition Manager

Overview

Implement and manage table partitioning for PostgreSQL and MySQL to improve query performance and simplify data lifecycle management on large tables. This skill covers range partitioning (by date or ID), list partitioning (by category or region), hash partitioning (for even distribution), and composite partitioning.

Prerequisites

PostgreSQL 10+ (declarative partitioning) or MySQL 5.7+ (native partitioning)

Database admin credentials with CREATE TABLE and ALTER TABLE permissions

psql or mysql CLI for executing partition DDL

Table size metrics: SELECT pg_size_pretty(pg_total_relation_size('table_name')) or SELECT data_length FROM information_schema.TABLES

Query patterns on the target table (especially WHERE clause columns used for filtering)

Maintenance window availability for initial partition migration on existing tables

Instructions

Identify partitioning candidates by finding tables that exceed 10GB or 100M rows, have time-based query patterns, or require periodic data purging. Query pg_stat_user_tables to find tables with high sequential scan counts on large row sets.

Select the partition key based on the most common query filter column. For time-series data, use the timestamp column. For multi-tenant data, use tenant_id. The partition key must appear in most WHERE clauses to enable partition pruning.

Choose the partitioning strategy:

Range: Best for time-series data. Create monthly or daily partitions. Queries filtering by date range scan only relevant partitions.
List: Best for categorical data. Create one partition per category, region, or status value.
Hash: Best for even distribution when no natural range exists. Distribute rows across N partitions using hash of the partition key.
Composite: Combine range + list for multi-dimensional partitioning (e.g., range by date, then list by region).

For PostgreSQL, create the partitioned parent table: CREATE TABLE orders (id bigint, created_at timestamptz, ...) PARTITION BY RANGE (created_at). Then create child partitions: CREATE TABLE orders_2024_01 PARTITION OF orders FOR VALUES FROM ('2024-01-01') TO ('2024-02-01').

For MySQL, define partitions inline: ALTER TABLE orders PARTITION BY RANGE (YEAR(created_at) * 100 + MONTH(created_at)) (PARTITION p202401 VALUES LESS THAN (202402), ...).

Migrate data from an existing unpartitioned table to a partitioned table:

Create the new partitioned table with identical schema
Copy data in batches: INSERT INTO orders_partitioned SELECT * FROM orders_old WHERE created_at BETWEEN ... AND ...
Verify row counts match between old and new tables
Rename tables atomically: ALTER TABLE orders RENAME TO orders_old; ALTER TABLE orders_partitioned RENAME TO orders;

Create indexes on each partition. In PostgreSQL, indexes on the parent table automatically propagate to child partitions. Create the primary key and any secondary indexes on the partitioned table.

Automate future partition creation with a scheduled script or cron job. For monthly range partitions, create the next 3 months of partitions in advance to prevent INSERT failures when a new month begins.

Implement partition maintenance: drop or detach old partitions for data retention (ALTER TABLE orders DETACH PARTITION orders_2022_01), then archive or delete the detached partition. This is vastly faster than DELETE FROM orders WHERE created_at < '2023-01-01'.

Verify partition pruning works by running EXPLAIN on typical queries and confirming only relevant partitions are scanned. Look for "Partitions: 1/24" in the plan output indicating effective pruning.

Output

Partition DDL scripts for creating partitioned tables and child partitions

Data migration scripts for moving data from unpartitioned to partitioned tables

Partition maintenance scripts for automated creation, detachment, and archival

Partition pruning verification queries confirming optimizer uses partition elimination

Cron job configurations for scheduled partition creation and cleanup

Error Handling

Error

Cause

Solution

no partition of relation "table" found for row

INSERT targets a range with no matching partition

Create the missing partition; implement automated partition pre-creation for future ranges

Partition pruning not occurring

Query filter does not use the partition key, or uses a function on the key column

Rewrite query to filter directly on the partition key column; avoid wrapping partition key in functions

Slow data migration from unpartitioned table

Single large INSERT/SELECT locks the table and fills WAL

Migrate in batches by partition range; use pg_repack for online migration; increase maintenance_work_mem and max_wal_size

Foreign key references prevent partitioning

PostgreSQL does not support foreign keys referencing partitioned tables (pre-v12)

Upgrade to PostgreSQL 12+; or remove FK constraints and enforce referential integrity at application level

Too many partitions causing planner slowdown

Hundreds or thousands of child partitions degrade query planning time

Use wider partition ranges (monthly instead of daily); enable enable_partition_pruning; consider sub-partitioning instead of flat partitioning

Examples

Monthly range partitioning for an events table: A 500GB events table with 2B rows partitioned by created_at into monthly partitions. Queries filtering by date range (last 7 days, last month) now scan only 1-2 partitions instead of the full table. Partition drop replaces a 4-hour DELETE operation with a sub-second DDL command for monthly data purges.

Hash partitioning for a sessions table: A sessions table with random UUID primary keys and no natural range column. Hash partition by session_id across 16 partitions to distribute I/O evenly. Parallel sequential scans across partitions improve full-table analytic queries by 8x on an 8-core server.

Composite partitioning for multi-region SaaS: Orders table partitioned first by range on created_at (monthly), then by list on region (us-east, us-west, eu, asia). Queries for "all US orders this month" prune to just 2 of 48 total partitions, reducing scan volume by 96%.

Resources

name	managing-database-partitions
description	Process use when you need to work with database partitioning. This skill provides table partitioning strategies with comprehensive guidance and automation. Trigger with phrases like "partition tables", "implement partitioning", or "optimize large tables".
allowed-tools	Read, Write, Edit, Grep, Glob, Bash(psql:), Bash(mysql:), Bash(mongosh:*)
version	1.25.0
author	Jeremy Longshore <jeremy@intentsolutions.io>
license	MIT
tags	["database","database-partitions"]
compatibility	Designed for Claude Code, also compatible with Codex and OpenClaw

name	managing-database-partitions
description	Process use when you need to work with database partitioning. This skill provides table partitioning strategies with comprehensive guidance and automation. Trigger with phrases like "partition tables", "implement partitioning", or "optimize large tables".
allowed-tools	Read, Write, Edit, Grep, Glob, Bash(psql:), Bash(mysql:), Bash(mongosh:*)
version	1.25.0
author	Jeremy Longshore <jeremy@intentsolutions.io>
license	MIT
tags	["database","database-partitions"]
compatibility	Designed for Claude Code, also compatible with Codex and OpenClaw

managing-database-partitions

Datos de origen

Opciones de instalación

Revisa los archivos de origen

Más de este repositorio

Más de este repositorio

Database Partition Manager

Overview

Prerequisites

Instructions

Output

Error Handling

Examples

Resources