Database Sharding

This handles horizontal database partitioning when you've outgrown a single server or need geographic distribution. It covers the main sharding strategies: range-based (split by ID ranges), hash-based (distribute via consistent hashing), and directory-based (lookup table routing). You get practical SQL examples for defining shards with constraint checks and routing logic. The approach is solid for multi-tenant isolation or when read/write throughput becomes your bottleneck. One thing to note is that cross-shard queries get messy fast, so pick your shard key carefully upfront. The reference guides break down each strategy separately, which helps when you're trying to decide between them based on your access patterns.

skills.sh installs: 446
GitHub stars: 379
last updated: Aug 21, 2026

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Install

$ npx -y skills add aj-geddes/useful-ai-prompts --skill database-sharding --agent claude-code

Installs into .claude/skills of the current project.

Files

Overview

Implement horizontal data partitioning across multiple database servers. Covers sharding strategies, consistent hashing, shard key selection, and cross-shard querying patterns.

When to Use

Database size exceeds single server capacity
Read/write throughput needs horizontal scaling
Geographic data distribution requirements
Multi-tenant data isolation
Cost optimization through distributed architecture
Load balancing across database instances

Quick Start

Minimal working example:

-- Define shard ranges
-- Shard 0: user_id 0-999999
-- Shard 1: user_id 1000000-1999999
-- Shard 2: user_id 2000000-2999999

CREATE TABLE users_shard_0 (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  user_id BIGINT NOT NULL,
  email VARCHAR(255) NOT NULL,
  created_at TIMESTAMP DEFAULT NOW(),
  CONSTRAINT shard_0_range CHECK (user_id BETWEEN 0 AND 999999)
);

SKILL.md on GitHub