2024-08-29
MongoDB NoSQL Database: Document-Based Data Management
Master MongoDB for scalable NoSQL applications, including schema design, aggregation, indexing, and replication.
MongoDB is a leading NoSQL database that stores data in flexible, JSON-like documents. This guide covers MongoDB fundamentals, schema design patterns, aggregation pipelines, performance optimization, and scaling strategies.
MongoDB Basics
// Connect to MongoDB
const { MongoClient } = require('mongodb');
const uri = 'mongodb://localhost:27017';
const client = new MongoClient(uri);
// Basic CRUD operations
async function runOperations() {
await client.connect();
const db = client.db('myapp');
const users = db.collection('users');
// Insert
await users.insertOne({
name: 'John Doe',
email: 'john@example.com',
age: 30,
interests: ['coding', 'reading'],
createdAt: new Date()
});
// Find
const user = await users.findOne({ email: 'john@example.com' });
// Update
await users.updateOne(
{ email: 'john@example.com' },
{ $set: { age: 31 }, $push: { interests: 'gaming' } }
);
// Delete
await users.deleteOne({ email: 'old@example.com' });
}
Schema Design Patterns
Embedded Documents
// One-to-Few: Embed directly
{
_id: ObjectId("..."),
name: "Blog Post",
author: "John Doe",
comments: [
{ user: "Alice", text: "Great post!", date: ISODate(...) },
{ user: "Bob", text: "Thanks!", date: ISODate(...) }
]
}
// One-to-Many: Hybrid approach
{
_id: ObjectId("..."),
name: "Product",
price: 99.99,
// Embed frequently accessed data
topReviews: [
{ rating: 5, summary: "Excellent!" }
],
// Reference for full list
reviewIds: [ObjectId("..."), ObjectId("...")]
}
Reference Pattern
// Many-to-Many: Use references
// Users collection
{
_id: ObjectId("user1"),
name: "John Doe",
courseIds: [ObjectId("course1"), ObjectId("course2")]
}
// Courses collection
{
_id: ObjectId("course1"),
title: "MongoDB Basics",
studentIds: [ObjectId("user1"), ObjectId("user2")]
}
// Lookup with aggregation
db.users.aggregate([
{ $match: { _id: ObjectId("user1") } },
{ $lookup: {
from: "courses",
localField: "courseIds",
foreignField: "_id",
as: "courses"
} }
])
Aggregation Pipeline
Powerful data processing with aggregation:
db.orders.aggregate([
// Stage 1: Filter
{ $match: {
status: "completed",
date: { $gte: ISODate("2024-01-01") }
} },
// Stage 2: Join with customers
{ $lookup: {
from: "customers",
localField: "customerId",
foreignField: "_id",
as: "customer"
} },
// Stage 3: Unwind array
{ $unwind: "$customer" },
// Stage 4: Group and calculate
{ $group: {
_id: "$customer.country",
totalSales: { $sum: "$amount" },
avgOrderValue: { $avg: "$amount" },
orderCount: { $sum: 1 }
} },
// Stage 5: Sort results
{ $sort: { totalSales: -1 } },
// Stage 6: Limit results
{ $limit: 10 },
// Stage 7: Project final shape
{ $project: {
country: "$_id",
totalSales: { $round: ["$totalSales", 2] },
avgOrderValue: { $round: ["$avgOrderValue", 2] },
orderCount: 1,
_id: 0
} }
])
Indexing Strategies
// Single field index
db.users.createIndex({ email: 1 })
// Compound index
db.posts.createIndex({ author: 1, createdAt: -1 })
// Text index for search
db.articles.createIndex({ title: "text", content: "text" })
// Geospatial index
db.locations.createIndex({ location: "2dsphere" })
// Partial index (conditional)
db.orders.createIndex(
{ customerId: 1 },
{ partialFilterExpression: { status: "active" } }
)
// TTL index for automatic deletion
db.sessions.createIndex(
{ expireAt: 1 },
{ expireAfterSeconds: 0 }
)
// Analyze index usage
db.users.find({ email: "test@example.com" }).explain("executionStats")
Replication and Sharding
Replica Set Configuration
# Initialize replica set
rs.initiate({
_id: "myReplicaSet",
members: [
{ _id: 0, host: "mongo1:27017", priority: 2 },
{ _id: 1, host: "mongo2:27017", priority: 1 },
{ _id: 2, host: "mongo3:27017", priority: 1 }
]
})
# Check replica set status
rs.status()
# Read preference in application
const options = {
readPreference: 'secondaryPreferred',
readConcern: { level: 'majority' },
writeConcern: { w: 'majority', j: true }
}
Sharding for Scale
Horizontal scaling with sharding:
• Choose shard key carefully (high cardinality, even distribution)
• Enable sharding on database and collection
• Monitor chunk distribution and balancer activity
• Use hashed sharding for even distribution
• Consider zone sharding for geo-distribution
Best Practices
Schema Design
Design schemas based on query patterns. Denormalize for read performance when appropriate.
Index Management
Create indexes for frequent queries but avoid over-indexing. Monitor index usage and remove unused indexes.
Connection Pooling
Use connection pooling to reduce overhead. Set appropriate pool size based on application load.
Published on 2024-08-29 • Category: Database
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