| name | database-sharding |
| description | Implement database sharding for horizontal scalability. Use when scaling large databases, distributing data across multiple servers, or designing sharded architectures.
|
Database Sharding
Table of Contents
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:
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)
);
CREATE TABLE users_shard_1 (
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_1_range CHECK (user_id BETWEEN 1000000 AND 1999999)
);
CREATE OR REPLACE FUNCTION get_shard_id(p_user_id BIGINT)
RETURNS INT AS $$
BEGIN
// ... (see reference guides for full implementation)
Reference Guides
Detailed implementations in the references/ directory:
Best Practices
✅ DO
- Follow established patterns and conventions
- Write clean, maintainable code
- Add appropriate documentation
- Test thoroughly before deploying
❌ DON'T
- Skip testing or validation
- Ignore error handling
- Hard-code configuration values
