home / skills / ancoleman / ai-design-components / using-document-databases

using-document-databases skill

/skills/using-document-databases

This skill helps you design and implement flexible document databases across MongoDB, DynamoDB, and Firestore to accelerate schemas, indexing, and queries.

npx playbooks add skill ancoleman/ai-design-components --skill using-document-databases

Review the files below or copy the command above to add this skill to your agents.

Files (20)
SKILL.md
8.2 KB
---
name: using-document-databases
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.
---

# 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:

| Relationship | Pattern | Example |
|--------------|---------|---------|
| One-to-Few | Embed | User addresses (2-3 max) |
| One-to-Many | Hybrid | Blog posts → comments |
| One-to-Millions | Reference | User → events (logging) |
| Many-to-Many | Reference | Products ↔ Categories |

### Embedding Example (MongoDB)

```javascript
// User with embedded addresses
{
  _id: ObjectId("..."),
  email: "[email protected]",
  name: "Jane Doe",
  addresses: [
    {
      type: "home",
      street: "123 Main St",
      city: "Boston",
      default: true
    }
  ],
  preferences: {
    theme: "dark",
    notifications: { email: true, sms: false }
  }
}
```

### Referencing Example (E-commerce)

```javascript
// Orders reference products
{
  _id: ObjectId("..."),
  userId: ObjectId("..."),
  items: [
    {
      productId: ObjectId("..."),      // Reference
      priceAtPurchase: 49.99,          // Denormalize (historical)
      quantity: 2
    }
  ],
  totalAmount: 99.98
}
```

**When to denormalize:**
- Frequently read together
- Historical snapshots (prices, names)
- Read-heavy workloads

## Indexing Strategies

### MongoDB Index Types

```javascript
// 1. Single field (unique email)
db.users.createIndex({ email: 1 }, { unique: true })

// 2. Compound index (ORDER MATTERS!)
db.orders.createIndex({ status: 1, createdAt: -1 })

// 3. Partial index (index subset)
db.orders.createIndex(
  { userId: 1 },
  { partialFilterExpression: { status: { $eq: "pending" }}}
)

// 4. TTL index (auto-delete after 30 days)
db.sessions.createIndex(
  { createdAt: 1 },
  { expireAfterSeconds: 2592000 }
)

// 5. Text index (full-text search)
db.articles.createIndex({
  title: "text",
  content: "text"
})
```

**Index Best Practices:**
- Add indexes for all query filters
- Compound index order: Equality → Range → Sort
- Use covering indexes (query + projection in index)
- Use `explain()` to verify index usage
- Monitor with Performance Advisor (Atlas)

**Validate indexes with the script:**
```bash
python scripts/validate_indexes.py
```

See `references/indexing-strategies.md` for complete guide.

## MongoDB Aggregation Pipelines

**Key Operators:** `$match` (filter), `$group` (aggregate), `$lookup` (join), `$unwind` (arrays), `$project` (reshape)

**For complete pipeline patterns and examples, see:** `references/aggregation-patterns.md`

## DynamoDB Single-Table Design

Design for access patterns using PK/SK patterns. Store multiple entity types in one table with composite keys.

**For complete single-table design patterns and GSI strategies, see:** `references/dynamodb.md`

## Firestore Real-Time Patterns

Use `onSnapshot()` for real-time listeners and Firestore security rules for access control.

**For complete real-time patterns and security rules, see:** `references/firestore.md`

## Multi-Language Examples

**Complete implementations available in `examples/` directory:**
- `examples/mongodb-fastapi/` - Python FastAPI + MongoDB
- `examples/mongodb-nextjs/` - TypeScript Next.js + MongoDB
- `examples/dynamodb-serverless/` - Python Lambda + DynamoDB
- `examples/firestore-react/` - React + Firestore real-time

## Frontend Skill Integration

- **Media Skill** - Use MongoDB GridFS for large file storage with metadata
- **AI Chat Skill** - MongoDB Atlas Vector Search for semantic conversation retrieval
- **Feedback Skill** - DynamoDB for high-throughput event logging with TTL

**For integration examples, see:** `references/skill-integrations.md`

## Performance Optimization

**Key practices:**
- Always use indexes for query filters (verify with `.explain()`)
- Use connection pooling (reuse clients across requests)
- Avoid collection scans in production

**For complete optimization guide, see:** `references/performance.md`

## Common Patterns

**Pagination:** Use cursor-based pagination for large datasets (recommended over offset)
**Soft Deletes:** Mark as deleted with timestamp instead of removing
**Audit Logs:** Store version history within documents

**For implementation details, see:** `references/common-patterns.md`

## Validation and Scripts

### Validate Index Coverage

```bash
# Run validation script
python scripts/validate_indexes.py --db myapp --collection orders

# Output:
# ✓ Query { status: "pending" } covered by index status_1
# ✗ Query { userId: "..." } missing index - add: { userId: 1 }
```

### Schema Analysis

```bash
# Analyze schema patterns
python scripts/analyze_schema.py --db myapp

# Output:
# Collection: users
# - Average document size: 2.4 KB
# - Embedding ratio: 87% (addresses, preferences)
# - Reference ratio: 13% (orderIds)
# Recommendation: Good balance
```

## Anti-Patterns to Avoid

**Unbounded Arrays:** Limit embedded arrays (use references for large collections)
**Over-Indexing:** Only index queried fields (indexes slow writes)
**DynamoDB Scans:** Always use Query with partition key (avoid Scan)

**For detailed anti-patterns, see:** `references/anti-patterns.md`

## Dependencies

### Python
```bash
# MongoDB
pip install motor pymongo

# DynamoDB
pip install boto3

# Firestore
pip install firebase-admin
```

### TypeScript
```bash
# MongoDB
npm install mongodb

# DynamoDB
npm install @aws-sdk/client-dynamodb @aws-sdk/util-dynamodb

# Firestore
npm install firebase firebase-admin
```

### Rust
```toml
# MongoDB
mongodb = "2.8"

# DynamoDB
aws-sdk-dynamodb = "1.0"
```

### Go
```bash
# MongoDB
go get go.mongodb.org/mongo-driver

# DynamoDB
go get github.com/aws/aws-sdk-go-v2/service/dynamodb
```

## Additional Resources

**Database-Specific Guides:**
- `references/mongodb.md` - Complete MongoDB documentation
- `references/dynamodb.md` - DynamoDB single-table patterns
- `references/firestore.md` - Firestore real-time guide

**Pattern Guides:**
- `references/schema-design-patterns.md` - Embedding vs referencing decisions
- `references/indexing-strategies.md` - Index optimization
- `references/aggregation-patterns.md` - MongoDB pipeline cookbook
- `references/common-patterns.md` - Pagination, soft deletes, audit logs
- `references/anti-patterns.md` - Mistakes to avoid
- `references/performance.md` - Query optimization
- `references/skill-integrations.md` - Frontend skill integration

**Examples:** `examples/mongodb-fastapi/`, `examples/mongodb-nextjs/`, `examples/dynamodb-serverless/`, `examples/firestore-react/`

Overview

This skill provides a practical guide to implementing document databases for flexible-schema applications, with MongoDB as the primary focus and complementary coverage of DynamoDB and Firestore. It explains schema design patterns, indexing strategies, aggregation pipelines, and operational best practices to build scalable content management, user profile, catalog, and event-logging systems. Examples and multi-language snippets (Python, TypeScript, Rust, Go) are included for rapid integration.

How this skill works

The skill inspects application access patterns and recommends a database choice (MongoDB, DynamoDB, or Firestore) based on deployment environment, query complexity, and real-time requirements. It outlines embedding vs referencing decisions, denormalization cues, index types and ordering, aggregation pipeline operators, and single-table design for DynamoDB. Implementation notes include connection pooling, index validation scripts, and performance checks using explain plans.

When to use it

  • When data models evolve frequently and you need flexible JSON-like documents.
  • When storing nested or hierarchical data such as user profiles, preferences, or product specs.
  • When you need horizontal scaling, replication, and low-latency reads/writes.
  • When rapid developer iteration is prioritized over rigid relational schemas.
  • When building real-time mobile apps (Firestore) or serverless AWS-native systems (DynamoDB).

Best practices

  • Choose DB by access patterns: MongoDB for rich queries, DynamoDB for AWS serverless, Firestore for real-time sync.
  • Follow embedding vs referencing rules: embed for one-to-few, reference for one-to-many-large, hybrid for moderate sizes.
  • Index for all query filters; use compound indexes with equality → range → sort ordering and verify with explain().
  • Prefer cursor-based pagination, use TTL for ephemeral data, and prefer soft deletes for auditability.
  • Avoid unbounded arrays and over-indexing; reuse connections through pooling to reduce latency.

Example use cases

  • Content management system storing articles with embedded metadata and text indexes for full-text search.
  • User profile service with embedded preferences and referenced large activity logs stored in a separate collection/table.
  • E-commerce orders where items reference product IDs and denormalized priceAtPurchase for historical accuracy.
  • High-throughput event logging on DynamoDB using single-table design and TTL for short-lived events.
  • Mobile chat or collaboration app using Firestore onSnapshot() listeners for real-time updates and offline support.

FAQ

When should I denormalize fields into documents?

Denormalize when data is frequently read together, when you need historical snapshots (e.g., priceAtPurchase), or to optimize read-heavy workloads at the cost of write complexity.

How do I choose between MongoDB, DynamoDB, and Firestore?

Choose MongoDB for complex queries and aggregation, DynamoDB for AWS-native serverless with predictable performance, and Firestore for mobile-first real-time sync and offline support.