Applicable scenarios and advantages of MongoDB

MongoDB is a document-oriented NoSQL database known for its flexible data model, high performance, and horizontal scalability. It is particularly well-suited for handling semi-structured data and high-concurrency read/write scenarios, with widespread applications in internet services, IoT, content management, and other fields.

Typical Use Cases for MongoDB

Content Management Systems

Blog platforms, news websites, and other systems that need to store diverse content structures. Each article may contain fields like title, content, tags, and comments. Traditional relational databases require multi-table joins, while MongoDB can store the complete record in a single document:

{
  _id: ObjectId("5f8d8a7b2f4a1e3d6c9b8a7d"),
  title: "MongoDB Practical Guide",
  content: "Detailed explanation of MongoDB usage techniques...",
  tags: ["database", "NoSQL", "backend"],
  comments: [
    { user: "Zhang San", text: "Very practical", date: ISODate("2023-05-20") },
    { user: "Li Si", text: "Looking forward to more examples", date: ISODate("2023-05-21") }
  ],
  viewCount: 1024
}

Real-Time Analytics Systems

Time-series data generated by IoT devices, which may produce millions of records per second. MongoDB's sharded clusters can easily handle such write loads:

// Example sensor data document
{
  deviceId: "sensor-001",
  timestamp: ISODate("2023-06-15T08:30:45Z"),
  temperature: 23.5,
  humidity: 45,
  location: { type: "Point", coordinates: [116.404, 39.915] }
}

User Profiles and Personalized Recommendations

E-commerce platforms need to store user behavior data, where fields may change frequently. MongoDB's dynamic schema feature is a perfect fit:

{
  userId: "u1001",
  basicInfo: { name: "Wang Wu", age: 28, gender: "male" },
  preferences: {
    viewedCategories: ["electronics", "sports & outdoors"],
    purchaseHistory: [
      { productId: "p1001", date: ISODate("2023-04-12"), rating: 5 }
    ],
    searchKeywords: ["wireless earbuds", "smartwatch"]
  }
}

Core Advantages of MongoDB

Flexible Data Model

Documents can nest arrays and sub-documents without requiring a strict predefined schema. This is particularly valuable during product iterations:

// New fields can be added to documents at any time
db.products.updateOne(
  { _id: ObjectId("507f1f77bcf86cd799439011") },
  { $set: { specifications: { weight: "1.2kg", color: "black" } } }
)

High-Performance Queries

Supports rich query operators and index types, including geospatial indexes:

// Find restaurants within 5 kilometers
db.restaurants.find({
  location: {
    $near: {
      $geometry: { type: "Point", coordinates: [116.404, 39.915] },
      $maxDistance: 5000
    }
  },
  rating: { $gte: 4 }
})

Horizontal Scalability

Distributed data storage through sharding addresses single-machine storage limitations:

// Enable sharding
sh.enableSharding("ecommerce")
sh.shardCollection("ecommerce.orders", { userId: "hashed" })

High Availability Architecture

Replica sets provide automatic failover to ensure service continuity:

// 3-node replica set configuration
{
  _id: "rs0",
  members: [
    { _id: 0, host: "mongo1:27017" },
    { _id: 1, host: "mongo2:27017" },
    { _id: 2, host: "mongo3:27017", arbiterOnly: true }
  ]
}

Special Features and Applications

Aggregation Framework

Complex data analysis pipeline operations:

db.orders.aggregate([
  { $match: { date: { $gte: ISODate("2023-01-01") } } },
  { $unwind: "$items" },
  { $group: { 
    _id: "$items.category",
    totalSales: { $sum: "$items.price" },
    avgQuantity: { $avg: "$items.quantity" }
  } },
  { $sort: { totalSales: -1 } }
])

Change Streams

Real-time data synchronization:

const changeStream = db.collection('inventory').watch();
changeStream.on('change', (change) => {
  console.log('Change detected:', change);
  // Trigger cache updates or notify microservices
});

Full-Text Search

Integrated text search functionality:

db.articles.createIndex({ content: "text" })
db.articles.find({
  $text: { $search: "database performance optimization" },
  score: { $meta: "textScore" }
}).sort({ score: { $meta: "textScore" } })

Performance Optimization Practices

Indexing Strategies

Proper use of compound and partial indexes:

// Create indexes for common queries
db.customers.createIndex({ 
  region: 1, 
  lastPurchaseDate: -1 
}, { 
  partialFilterExpression: { 
    totalOrders: { $gt: 5 } 
  } 
})

Read/Write Separation

Leverage replica set read preferences:

const conn = Mongo("mongodb://replicaSet/host1,host2,host3?readPreference=secondaryPreferred")

Memory Optimization

Control working set size:

// Check collection memory usage
db.runCommand({ 
  collStats: "orders",
  scale: 1024 * 1024  // In MB
})