# Using Document Databases

Document database implementation for flexible schema applications.

- Repository: https://github.com/ancoleman/ai-design-components (526 stars)
- Category: Design
- License: MIT
- Last updated: 2025-12-11
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add ancoleman/ai-design-components --skill using-document-databases -g -a claude-code -y`
Codex: `npx skills add ancoleman/ai-design-components --skill using-document-databases -g -a codex -y`
Cursor: `npx skills add ancoleman/ai-design-components --skill using-document-databases -g -a cursor -y`
Gemini CLI: `npx skills add ancoleman/ai-design-components --skill using-document-databases -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add ancoleman/ai-design-components`

## Description

Document database implementation for flexible schema applications. Use when building content management, user profiles, catalogs, or event logging. Covers MongoDB (primary), DynamoDB, Firestore, schema design patterns, indexing strategies, and aggregation pipelines.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Document Database Implementation

Guide NoSQL document database selection and implementation for flexible schema applications across Python, TypeScript, Rust, and Go.

## When to Use This Skill

Use document databases when applications need:
- **Flexible schemas** - Data models evolve rapidly without migrations
- **Nested structures** - JSON-like hierarchical data
- **Horizontal scaling** - Built-in sharding and replication
- **Developer velocity** - Object-to-database mapping without ORM complexity

## Database Selection

### Quick Decision Framework

```
DEPLOYMENT ENVIRONMENT?
├── AWS-Native Application → DynamoDB
│   ✓ Serverless, auto-scaling, single-digit ms latency
│   ✗ Limited query flexibility
│
├── Firebase/GCP Ecosystem → Firestore
│   ✓ Real-time sync, offline support, mobile-first
│   ✗ More expensive for heavy reads
│
└── General-Purpose/Complex Queries → MongoDB
    ✓ Rich aggregation, full-text search, vector search
    ✓ ACID transactions, self-hosted or managed
```

### Database Comparison

| Database | Best For | Latency | Max Item | Query Language |
|----------|----------|---------|----------|----------------|
| **MongoDB** | General-purpose, complex queries | 1-5ms | 16MB | MQL (rich) |
| **DynamoDB** | AWS serverless, predictable performance | <10ms | 400KB | PartiQL (limited) |
| **Firestore** | Real-time apps, mobile-first | 50-200ms | 1MB | Firebase queries |

See `references/mongodb.md` for MongoDB details
See `references/dynamodb.md` for DynamoDB single-table design
See `references/firestore.md` for Firestore real-time patterns

## Schema Design Patterns

### Embedding vs Referencing

**Use the decision matrix in `references/schema-design-patterns.md`**

Quick guide:

The rest: https://github.com/ancoleman/ai-design-components/blob/HEAD/skills/using-document-databases/SKILL.md