# Scaling And Sharding

> Database Scaling & Sharding

- Skill: `j4flmao/scaling-and-sharding` (Agent Skill)
- Install (CLI): `npx skillmds@latest add j4flmao/scaling-and-sharding`
- Raw SKILL.md: https://api.skillmd.com/api/skills/j4flmao/scaling-and-sharding/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: j4flmao (https://skillmd.com/u/j4flmao)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/j4flmao/scaling-and-sharding

---

# Database Scaling & Sharding

## Core Concepts

As data grows beyond the capacity of a single machine, databases must scale.

### 1. Vertical vs Horizontal Scaling
- **Vertical (Scale-Up):** Adding more CPU/RAM to a single server. Easy, but has a hard physical limit.
- **Horizontal (Scale-Out):** Adding more servers. Requires partitioning data (Sharding).

### 2. Sharding & Consistent Hashing
Sharding splits a large table across multiple databases based on a Shard Key (e.g., `user_id`).
- **Modulo Hashing:** `hash(user_id) % N`. Fails catastrophically if `N` (number of servers) changes, requiring massive data reshuffling.
- **Consistent Hashing:** Maps data and servers onto a logical ring. If a server is added/removed, only the data adjacent to that server is remapped, minimizing disruption.

### Consistent Hashing Ring Map

```mermaid
%%{init: {"theme": "default", "flowchart": {"useMaxWidth": true}}}%%
flowchart TD
    subgraph Ring ["Consistent Hash Ring"]
        A["Node A (Hash 0-100)"]
        B["Node B (Hash 101-200)"]
        C["Node C (Hash 201-300)"]
    end
    
    subgraph Router ["API Gateway / DB Router"]
        D["Request: user_id = 45"]
        E["Request: user_id = 150"]
    end
    
    D -->|"Hash(45) -> Route to A"| A
    E -->|"Hash(150) -> Route to B"| B
    
    A -.->|"Replication"| C
```