mongodb

name	mongodb
description	MongoDB patterns: schema design, aggregation pipeline, indexes, transactions, Atlas Search, Mongoose vs native driver

MongoDB Skill

When to activate

• Designing a MongoDB schema (embedding vs referencing)
• Writing aggregation pipelines for complex queries
• Setting up indexes for query performance
• Using MongoDB transactions across multiple documents
• Integrating MongoDB Atlas Search (full-text)
• Choosing between Mongoose and the native driver

When NOT to use

• Relational data with many joins — use PostgreSQL
• When you need ACID transactions at scale — consider PostgreSQL
• Simple key-value caching — use Redis
• Analytics on large datasets — use a data warehouse

Instructions

When to embed vs reference

Embed when:

• Data is always accessed together (post + its comments if comments are few)
• Child data has no independent lifecycle
• Array size stays bounded (< 100 items, rarely grows to thousands)

Reference when:

• Data is accessed independently
• Array could grow unboundedly (all orders for a user)
• Many-to-many relationships

// Embedded — good for address (always with user, doesn't change often)
{
  _id: ObjectId("..."),
  email: "[e-mail]",
  address: {
    street: "123 Main St",
    city: "San Francisco",
    zip: "94105"
  }
}

// Referenced — good for orders (many, independent lifecycle)
// User document
{ _id: ObjectId("user1"), email: "[e-mail]" }

// Order document (references user)
{ _id: ObjectId("order1"), userId: ObjectId("user1"), total: 99.99 }

Native driver (Node.js)

import { MongoClient, ObjectId } from 'mongodb'

const client = new MongoClient(process.env.MONGODB_URI!)
const db = client.db('myapp')
const users = db.collection('users')

// Connect (call once at startup)
await client.connect()

// CRUD
const user = await users.findOne({ _id: new ObjectId(id) })
const result = await users.insertOne({ email, name, createdAt: new Date() })
await users.updateOne(
  { _id: new ObjectId(id) },
  { $set: { name }, $currentDate: { updatedAt: true } }
)
await users.deleteOne({ _id: new ObjectId(id) })

// Find with projection and sort
const recent = await users
  .find({ active: true })
  .({ : , : , :  })
  .({ : - })
  .()
  .()

Mongoose (schema-based ODM)

import mongoose, { Schema, model, type InferSchemaType } from 'mongoose'

const userSchema = new Schema({
  email:     { type: String, required: true, unique: true, lowercase: true },
  name:      { type: String, required: true },
  role:      { type: String, enum: ['user', 'admin'], default: 'user' },
  tags:      [String],
  metadata:  { type: Map, of: String },
  createdAt: { type: Date, default: Date.now },
})

// Indexes
userSchema.index({ email: 1 }, { unique: true })
userSchema.index({ createdAt: -1 })
userSchema.index({ tags:  })


userSchema.().(() {
   ..()[]
})


userSchema.(,  () {
   (.()) {
    . =  bcrypt.(., )
  }
  ()
})

  = < userSchema>
   = model<>(, userSchema)


 user =  .(id).()  
 users =  .({ :  }).().()
 user =  .(
  { email },
  { : { name } },
  { : , :  }
)

Aggregation pipeline — the most powerful MongoDB feature

// Sales report: revenue by category for the last 30 days
db.orders.aggregate([
  // Stage 1: filter
  { $match: {
    status: 'completed',
    createdAt: { $gte: new Date(Date.now() - 30 * 24 * 60 * 60 * 1000) }
  }},

  // Stage 2: unwind array (one doc per item)
  { $unwind: '$items' },

  // Stage 3: group by category
  { $group: {
    _id: '$items.category',
    totalRevenue: { $sum: { $multiply: ['$items.price', '$items.qty'] } },
    orderCount: { $addToSet: '$_id' },  // count distinct orders
    avgOrderValue: { $avg: '$total' },
  }},

  // Stage 4: project clean output
  { $project: {
    category: '$_id',
    totalRevenue: { $round: ['$totalRevenue', 2] },
    orderCount: { $size: '$orderCount' },
    avgOrderValue: { $round: ['$avgOrderValue', ] },
    : ,
  }},

  
  { : { : - } },
])

Common pipeline stages:

Stage	Use for
$match	Filter documents (put early to use indexes)
$group	Aggregate: sum, count, avg, min, max, push
$project	Reshape documents, add computed fields
$unwind	Flatten array into multiple documents
$lookup	Left join with another collection
$sort	Sort results
$limit / $skip	Pagination
$facet	Multiple aggregations in one pass
$bucket	Group into ranges (histogram)
$addFields	Add/modify fields without changing the rest

Lookup (join):

db.orders.aggregate([
  { $lookup: {
    from: 'users',
    localField: 'userId',
    foreignField: '_id',
    as: 'user',
    pipeline: [
      { $project: { email: 1, name: 1 } }  // only fetch needed fields
    ]
  }},
  { $unwind: '$user' },  // convert [user] array to user object
])

Indexes

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

// Compound (query most selective field first)
db.orders.createIndex({ userId: 1, createdAt: -1 })

// Text index for basic search
db.posts.createIndex({ title: 'text', content: 'text' }, {
  weights: { title: 10, content: 1 },  // title matches rank higher
  default_language: 'english'
})
db.posts.find({ $text: { $search: 'typescript patterns' } },
              { score: { $meta: 'textScore' } })
       .sort({ score: { $meta: 'textScore' } })

// Partial index (only index a subset)
db.orders.createIndex(
  { createdAt: 1 },
  { partialFilterExpression: { status: 'pending' } }
)

// TTL index (auto-delete documents)
db.sessions.createIndex({ :  }, { :  })

Transactions

// Multi-document transactions (requires replica set)
const session = client.startSession()

try {
  await session.withTransaction(async () => {
    await accounts.updateOne(
      { _id: fromId },
      { $inc: { balance: -amount } },
      { session }
    )
    await accounts.updateOne(
      { _id: toId },
      { $inc: { balance: amount } },
      { session }
    )
    await ledger.insertOne(
      { from: fromId, to: toId, amount, createdAt: new Date() },
      { session }
    )
  })
} finally {
  await session.endSession()
}

Update operators

// $set — update specific fields
{ $set: { name: 'Alice', 'address.city': 'NYC' } }

// $inc — increment/decrement
{ $inc: { views: 1, stock: -1 } }

// $push — append to array
{ $push: { tags: 'typescript' } }

// $addToSet — add to array only if not present
{ $addToSet: { tags: 'typescript' } }

// $pull — remove from array by value
{ $pull: { tags: 'draft' } }

// $pop — remove first or last element
{ $pop: { history: -1 } }  // -1 = first, 1 = last

// $unset — remove a field
{ $unset: { legacyField: '' } }

// $currentDate — set to current date
{ $currentDate: { updatedAt: true } }

Example

User: Build a MongoDB aggregation to find the top 5 content creators by total post views in the last 7 days, with their user info, broken down by post category.

Expected pipeline:

db.posts.aggregate([
  { $match: { publishedAt: { $gte: new Date(Date.now() - 7 * 86400000) } }},
  { $group: {
    _id: { authorId: '$authorId', category: '$category' },
    totalViews: { $sum: '$views' },
    postCount: { $sum: 1 }
  }},
  { $group: {
    _id: '$_id.authorId',
    totalViews: { $sum: '$totalViews' },
    breakdown: { $push: { category: '$_id.category', views: '$totalViews' } }
  }},
  { $sort: { totalViews: -1 } },
  { $limit: 5 },
  { $lookup: { from: 'users', localField: '_id', foreignField: '_id', as: 'user',
    pipeline: [{ $project: { email: 1, name: 1 } }] }},
  { :  },
])

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