mongodb

What this skill does

# MongoDB - Document Database Patterns

## Overview

MongoDB is a document-oriented NoSQL database that stores data in flexible, JSON-like documents. It excels at handling unstructured or semi-structured data, hierarchical relationships, and scenarios requiring horizontal scaling.

**Key Features**:
- Flexible schema (schemaless documents)
- Rich query language with secondary indexes
- Aggregation framework for analytics
- Horizontal scaling (sharding)
- Replica sets for high availability
- Change streams for real-time data
- Geospatial and full-text search

**When to Use MongoDB**:
- Rapidly evolving schemas
- Hierarchical/nested data (embedded documents)
- Real-time analytics with aggregation
- Geospatial applications
- Content management systems
- IoT data ingestion
- Catalog/inventory systems

**When NOT to Use MongoDB**:
- Complex multi-table joins (use RDBMS)
- ACID transactions across many documents (improved in 4.0+, but limited)
- Strict schema enforcement requirements

## Schema Design Fundamentals

### Document Structure

```javascript
// MongoDB document (BSON format)
{
  "_id": ObjectId("507f1f77bcf86cd799439011"),  // Primary key (auto-generated)
  "email": "[email protected]",
  "name": "Alice Johnson",
  "profile": {                                   // Embedded document
    "bio": "Software developer",
    "avatar": "https://example.com/avatar.jpg",
    "social": {
      "twitter": "@alice",
      "github": "alice-dev"
    }
  },
  "tags": ["developer", "python", "mongodb"],    // Array field
  "createdAt": ISODate("2024-01-15T10:30:00Z"),
  "updatedAt": ISODate("2024-01-20T14:22:00Z")
}
```

### Embedded vs Referenced Documents

**Embedded (Denormalized)** - Store related data in same document:

```javascript
// Embedded: Good for 1:1 or 1:Few relationships
// User with embedded address
{
  "_id": ObjectId("..."),
  "name": "Alice",
  "address": {
    "street": "123 Main St",
    "city": "San Francisco",
    "zipCode": "94102"
  }
}

// Embedded: Order with line items (1:Many bounded)
{
  "_id": ObjectId("..."),
  "orderNumber": "ORD-2024-001",
  "customer": { "name": "Alice", "email": "[email protected]" },
  "items": [
    { "productId": "SKU001", "name": "Widget", "quantity": 2, "price": 29.99 },
    { "productId": "SKU002", "name": "Gadget", "quantity": 1, "price": 49.99 }
  ],
  "total": 109.97
}
```

**When to Embed**:
- Data is queried together frequently
- Child data doesn't make sense without parent
- 1:1 or 1:Few relationships
- Child data is bounded (won't grow unbounded)
- Data doesn't need to be accessed independently

**Referenced (Normalized)** - Store references to other documents:

```javascript
// Referenced: Good for 1:Many unbounded or Many:Many
// User document
{
  "_id": ObjectId("user123"),
  "name": "Alice",
  "email": "[email protected]"
}

// Posts collection (references user)
{
  "_id": ObjectId("post456"),
  "authorId": ObjectId("user123"),   // Reference to user
  "title": "MongoDB Schema Design",
  "content": "...",
  "commentCount": 42
}

// Comments collection (references post)
{
  "_id": ObjectId("comment789"),
  "postId": ObjectId("post456"),     // Reference to post
  "authorId": ObjectId("user999"),   // Reference to commenter
  "text": "Great article!",
  "createdAt": ISODate("2024-01-20T10:00:00Z")
}
```

**When to Reference**:
- Many:Many relationships
- 1:Many with unbounded growth (comments, logs)
- Data is accessed independently
- Document size would exceed 16MB limit
- Need atomic updates on referenced document

### Hybrid Pattern (Extended Reference)

```javascript
// Post with denormalized author info + reference
{
  "_id": ObjectId("post456"),
  "title": "MongoDB Best Practices",
  "content": "...",
  "author": {
    "_id": ObjectId("user123"),      // Reference for lookups
    "name": "Alice",                 // Denormalized for display
    "avatar": "https://..."          // Frequently needed fields
  },
  "commentCount": 42,
  "lastCommentAt": ISODate("...")
}
```

## Query Patterns

### Basic CRUD Operations

```javascript
// Find documents
db.users.find({ email: "[email protected]" })
db.users.find({ age: { $gte: 18, $lte: 65 } })
db.users.find({ tags: { $in: ["developer", "designer"] } })

// Find with projection (select specific fields)
db.users.find(
  { status: "active" },
  { name: 1, email: 1, _id: 0 }  // Include name, email; exclude _id
)

// Find one
db.users.findOne({ email: "[email protected]" })

// Insert
db.users.insertOne({ name: "Bob", email: "[email protected]" })
db.users.insertMany([
  { name: "Charlie", email: "[email protected]" },
  { name: "Diana", email: "[email protected]" }
])

// Update
db.users.updateOne(
  { email: "[email protected]" },
  { $set: { name: "Alice Updated", updatedAt: new Date() } }
)

db.users.updateMany(
  { status: "inactive" },
  { $set: { archived: true } }
)

// Upsert (update or insert)
db.users.updateOne(
  { email: "[email protected]" },
  { $set: { name: "New User", createdAt: new Date() } },
  { upsert: true }
)

// Delete
db.users.deleteOne({ email: "[email protected]" })
db.users.deleteMany({ status: "deleted" })
```

### Query Operators

```javascript
// Comparison
db.products.find({ price: { $gt: 100 } })        // Greater than
db.products.find({ price: { $gte: 100 } })       // Greater than or equal
db.products.find({ price: { $lt: 50 } })         // Less than
db.products.find({ price: { $lte: 50 } })        // Less than or equal
db.products.find({ price: { $ne: 0 } })          // Not equal
db.products.find({ category: { $in: ["A", "B"] } })   // In array
db.products.find({ category: { $nin: ["C", "D"] } })  // Not in array

// Logical
db.users.find({ $and: [{ age: { $gte: 18 } }, { status: "active" }] })
db.users.find({ $or: [{ role: "admin" }, { role: "moderator" }] })
db.users.find({ age: { $not: { $lt: 18 } } })

// Element
db.users.find({ middleName: { $exists: true } })  // Field exists
db.users.find({ age: { $type: "number" } })       // Field type

// Array
db.posts.find({ tags: "mongodb" })                // Contains element
db.posts.find({ tags: { $all: ["mongodb", "database"] } })  // Contains all
db.posts.find({ tags: { $size: 3 } })             // Array size
db.posts.find({ "tags.0": "featured" })           // First element

// Embedded documents
db.users.find({ "address.city": "San Francisco" })
db.users.find({ "profile.social.twitter": { $exists: true } })

// Regex
db.users.find({ name: { $regex: /^alice/i } })
db.users.find({ email: { $regex: /@example\.com$/ } })
```

### Update Operators

```javascript
// Field updates
db.users.updateOne(
  { _id: userId },
  {
    $set: { name: "New Name" },           // Set field value
    $unset: { temporaryField: "" },       // Remove field
    $rename: { oldName: "newName" },      // Rename field
    $inc: { loginCount: 1 },              // Increment
    $mul: { price: 1.1 },                 // Multiply
    $min: { lowestScore: 50 },            // Set if less than current
    $max: { highestScore: 100 },          // Set if greater than current
    $currentDate: { updatedAt: true }     // Set to current date
  }
)

// Array updates
db.posts.updateOne(
  { _id: postId },
  {
    $push: { tags: "new-tag" },           // Add to array
    $addToSet: { tags: "unique-tag" },    // Add if not exists
    $pop: { tags: 1 },                    // Remove last (-1 for first)
    $pull: { tags: "old-tag" },           // Remove specific value
    $pullAll: { tags: ["a", "b"] }        // Remove multiple values
  }
)

// Array with modifiers
db.posts.updateOne(
  { _id: postId },
  {
    $push: {
      comments: {
        $each: [comment1, comment2],      // Add multiple
        $sort: { createdAt: -1 },         // Sort after push
        $slice: -100                      // Keep only last 100
      }
    }
  }
)

// Positional update (update matched array element)
db.posts.updateOne(
  { _id: postId, "comments._id": commentId },
  { $set: { "comments.$.text": "Updated comment" } }
)