mongodb-patterns

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SKILL.md (first lines)

# MongoDB Patterns

Document database design and query optimization for MongoDB.

## Document Modeling Strategies

```typescript
// EMBED when: 1:1 or 1:few, data read together, child has no independent lifecycle
interface Order {
  _id: ObjectId
  customerId: ObjectId
  status: 'pending' | 'paid' | 'shipped'
  items: OrderItem[]        // Embedded - always read with order
  shippingAddress: Address  // Embedded - 1:1
  createdAt: Date
}

interface OrderItem {
  productId: ObjectId
  name: string              // Denormalized - avoid join at read time
  price: number             // Snapshot at purchase time
  quantity: number
}

// REFERENCE when: 1:many (unbounded), independent queries, shared across documents
interface Product {
  _id: ObjectId
  name: string
  price: number
  categoryId: ObjectId     // Reference - category queried independently
  reviews: never           // DON'T embed - unbounded array
}

// Bucket pattern: group time-series data into fixed-size documents
interface SensorBucket {
  _id: ObjectId
  sensorId: string
  startTime: Date
  endTime: Date
  count: number            // Track bucket fullness
  measurements: {          // Embed up to 200 per bucket
    timestamp: Date
    value: number
  }[]
}
```

## Indexing Strategy

```typescript
// Compound index: field order matters (ESR rule)
// Equality → Sort → Range
db.orders.createIndex({
  status: 1,       // Equality: exact match filter
  createdAt: -1,   // Sort: avoid in-memory sort
  total: 1         // Range: price > 100
})

// Partial index: only index documents matching filter (smaller index)
db.orders.createIndex(
  { customerId: 1, createdAt: -1 },
  { partialFilterExpression: { status: 'pending' } }
)

// Text index for search
db.products.createIndex({ name: 'text', description: 'text' })

// TTL index for auto-expiration
db.sessions.createIndex(
  { createdAt: 1 },
  { expireAfterSeconds: 86400 }  // Auto-delete after 24h
)

// Wildcard index for dynamic schemas
db.events.createIndex({ 'metadata.$**': 1 })
```

## Aggregation Pipeline

```typescript
// Sales analytics: top products by revenue per category
const pipeline = [
  // Stage 1: Filter date range
  { $match: {
    createdAt: { $gte: new Date('2025-01-01'), $lt: new Date('2025-02-01') },
    status: 'paid'
  }},

  // Stage 2: Unwind embedded items array
  { $unwind: '$items' },

  // Stage 3: Group by product
  { $group: {
    _id: '$items.productId',
    productName: { $first: '$items.name' },
    totalRevenue: { $sum: { $multiply: ['$items.price', '$items.quantity'] } },
    totalSold: { $sum: '$items.quantity' },
    orderCount: { $addToSet: '$_id' }
  }},

  // Stage 4: Add computed fields
  { $addFields: {
    orderCount: { $size: '$orderCount' },
    avgOrderValue: { $divide: ['$totalRevenue', { $size: '$orderCount' }] }
  }},

  // Stage 5: Sort by revenue descending
  { $sort: { totalRevenue: -1 } },

  // Stage 6: Limit to top 20
  { $limit: 20 },

  // Stage 7: Lookup category details
  { $lookup: {
    from: 'products',
    localField: '_id',
    foreignField: '_id',
    pipeline: [{ $project: { categoryId: 1 } }],
    as: 'product'

From vibeeval/vibecosystem, MIT.

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