MongoDB for SQL Developers

Documents, Not Tables

Think differently about your data.

SQL vs MongoDB

-- SQL
SELECT * FROM users WHERE age > 21;

-- MongoDB
db.users.find({ age: { $gt: 21 } })

Document Structure

// Embedded (denormalized)
{
    _id: ObjectId("..."),
    name: "John",
    orders: [
        { product: "Widget", price: 9.99 },
        { product: "Gadget", price: 19.99 }
    ]
}

// Referenced (normalized)
{
    _id: ObjectId("..."),
    name: "John",
    orderIds: [ObjectId("..."), ObjectId("...")]
}

When to Embed

• Data is always accessed together
• One-to-few relationship
• Data doesn't change often

When to Reference

• Data accessed independently
• One-to-many or many-to-many
• Data changes frequently

Common Operations

// Insert
db.users.insertOne({ name: "John", age: 30 })

// Find
db.users.find({ age: { $gte: 21 } })
db.users.findOne({ _id: ObjectId("...") })

// Update
db.users.updateOne(
    { _id: ObjectId("...") },
    { $set: { age: 31 } }
)

// Delete
db.users.deleteOne({ _id: ObjectId("...") })

Aggregation Pipeline

db.orders.aggregate([
    { $match: { status: "completed" } },
    { $group: {
        _id: "$userId",
        total: { $sum: "$amount" }
    }},
    { $sort: { total: -1 } }
])

Indexing

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

// Compound
db.orders.createIndex({ userId: 1, createdAt: -1 })

// Text search
db.posts.createIndex({ title: "text", content: "text" })