MongoDB in 2025-2026: New Features, Atlas, and Evolution

// Benchmark comparison (conceptual)
// MongoDB 7.0: 10,000 queries/sec
// MongoDB 8.0: 14,500 queries/sec (45% improvement)

// New aggregation stages in 8.0

// $unionWith - merge results from collections
db.orders.aggregate([
  { $match: { status: 'completed' } },
  { $unionWith: 'returns' }
])

// $setWindowFields - advanced window functions
db.sales.aggregate([
  {
    $setWindowFields: {
      sortBy: { date: 1 },
      output: {
        cumulativeTotal: {
          $sum: "$amount",
          window: { documents: ["unbounded", "current"] }
        }
      }
    }
  }
])

// Enhanced change streams with filtering
const changeStream = db.collection.watch([
  {
    $match: {
      operationType: { $in: ['insert', 'update'] },
      'fullDocument.category': 'Electronics'
    }
  }
]);

// Full document lookup with increase
const streamWithFullDoc = db.collection.watch([
  { $match: { operationType: 'update' } }
], {
  fullDocument: 'updateLookup',
  fullDocumentBeforeChange: 'whenAvailable'
});

// Atlas Serverless benefits:
// - Pay only for what you use
// - Auto-scaling from zero to any workload
// - No server management
// - Built-in high availability

// Connection string format
const uri = 'mongodb+srv://<project>.<instance>.mongodb.net/test';

// Using MongoDB Driver with serverless
const client = new MongoClient(uri, {
  // Serverless connections may have different behavior
  serverSelectionTimeoutMS: 30000,
  retryWrites: true,
  retryReads: true
});

// Tips for reducing serverless costs:
// 1. Use appropriate read/write concerns
// 2. Implement connection pooling
// 3. Cache frequently accessed data
// 4. Use projection to limit data transfer

// Example: Efficient query
const product = await db.products.findOne(
  { sku: 'LAPTOP-001' },
  { projection: { name: 1, price: 1 } }  // Only needed fields
);

// Create vector index
db.collection.createIndex(
  { embedding: 'knnVector' },
  {
    knnVector: {
      dimension: 1536,
      m: 2,
      efConstruction: 100
    }
  }
)

// Insert document with vector
await db.products.insertOne({
  name: 'Laptop Pro',
  description: 'High-performance laptop',
  embedding: [0.123, -0.456, ...],  // 1536-dimensional vector
  category: 'Electronics'
})

// Perform vector search
const semanticResults = await db.products.aggregate([
  {
    $vectorSearch: {
      index: 'embedding_index',
      path: 'embedding',
      queryVector: queryEmbedding,
      numCandidates: 100,
      limit: 10
    }
  },
  {
    $project: {
      name: 1,
      description: 1,
      score: { $meta: 'vectorSearchScore' }
    }
  }
])

// Hybrid search with filtering
const hybridResults = await db.products.aggregate([
  {
    $vectorSearch: {
      index: 'embedding_index',
      path: 'embedding',
      queryVector: queryEmbedding,
      numCandidates: 100,
      limit: 20,
      filter: { category: 'Electronics', price: { $lt: 1000 } }
    }
  }
])

// Atlas multi-cloud configuration
// - AWS, Azure, Google Cloud
// - Cross-region replication
// - Global clusters

// Create global cluster (via Atlas CLI or UI)
// Configuration:
// - Americas (read preference: nearest)
// - Europe (read preference: nearest)
// - Asia Pacific (read preference: nearest)

// Connection with custom read preference
const client = new MongoClient(uri, {
  readPreference: 'secondaryPreferred',
  // Or per-operation
});
db.products.find({}).readPref('nearest')

// Configure data residency
// - At-rest encryption with customer keys
// - Specific regions for compliance
// - Locality constraints

// Improved mongosh features
// - Better autocomplete
// - Syntax highlighting
// - Multi-line editing
// - History search

// New operators
db.users.find({
  $dateDiff: {
    startDate: '$created_at',
    endDate: '$$NOW',
    unit: 'day'
  }
})

// Recent driver improvements:
// - Improved connection pooling
// - Better error messages
// - TypeScript support
// - Promise-based API

// Using TypeScript
interface User {
  _id: ObjectId;
  username: string;
  email: string;
  age?: number;
}

const users = await db
  .collection<User>('users')
  .find({ username: 'john' })
  .toArray();

// MongoDB Realm (Atlas Device Sync)
// - Offline-first mobile apps
// - Real-time sync
// - Local database

// Realm configuration
const realm = await Realm.open({
  schema: [UserSchema, ProductSchema],
  sync: {
    user: app.currentUser,
    partitionValue: 'myapp'
  }
});

// Encryption at rest with customer keys (CMK)
// AWS KMS, Azure Key Vault, Google Cloud KMS

// Configure in Atlas:
// Project Settings -> Security -> Encryption at Rest
// Select customer-managed key

// Configure comprehensive audit logging
db.adminCommand({
  auditLog: {
    filter: {
      atype: { $in: ['createCollection', 'dropCollection'] },
      'param.ns': 'myapp.sensitive'
    },
    format: 'JSON',
    path: '/var/log/mongodb/audit.json'
  }
})

// Secure network configuration
// - AWS PrivateLink
// - Azure Private Link
// - Google Cloud Private Service Connect

// Configure in Atlas:
// Network Access -> Peering -> Add Peering Connection

// Time series collections (general availability)
// Optimized for IoT, metrics, events

db.createCollection('metrics', {
  timeseries: {
    timeField: 'timestamp',
    metaField: 'sensor_id',
    granularity: 'hours'
  },
  storageEngine: {
    wiredTiger: {
      configString: 'block_compressor=zstd'
    }
  }
})

// Insert time series data
await db.metrics.insertMany([
  { sensor_id: 'sensor-1', timestamp: new Date(), temperature: 22.5 },
  { sensor_id: 'sensor-1', timestamp: new Date(), temperature: 23.0 }
])

// Atlas Search enhancements
// - Autocomplete
// - Fuzzy matching
// - Highlighting
// - Custom scoring

// Create autocomplete index
db.products.createIndex(
  { name: 'autocomplete' },
  {
    autocomplete: {
      query: 'laptop',
      path: 'name',
      fuzzy: { maxEdits: 1 },
      score: { value: 1 }
    }
  }
)

// MongoDB Data Lake
// Query data across Atlas and external sources

// Connect to S3
db.adminCommand({
  datasources: {
    name: 's3_backup',
    type: 's3',
    bucketName: 'analytics-backup',
    region: 'us-east-1'
  }
})

// Query external data
db.getSiblingDB('$external').runCommand({
  find: 's3_backup.logs'
})

// Atlas Data API with GraphQL
// Generate GraphQL from schema

// Example GraphQL query
const query = `
  query GetProducts($category: String!) {
    products(filter: { category: $category }) {
      name
      price
      description
    }
  }
`;

// MongoDB Kafka Connector
// Source: MongoDB -> Kafka
// Sink: Kafka -> MongoDB

// Kafka Connect configuration (source)
{
  "name": "mongo-source",
  "config": {
    "connector.class": "com.mongodb.kafka.MongoSourceConnector",
    "connection.uri": "mongodb://mongo:27017",
    "database": "orders",
    "collection": "orders",
    "topic.prefix": "orders",
    "publish.full.document": "true"
  }
}

// Recommended patterns for 2026:
// 1. Use Atlas for managed deployments
// 2. Implement vector search for AI features
// 3. Leverage serverless for variable workloads
// 4. Use multi-cloud for compliance

// Migration considerations:
// - Schema migration tools
// - Backward compatibility
// - Performance testing

// Performance best practices:
// 1. Use appropriate indexes
// 2. Monitor query performance
// 3. Leverage caching
// 4. Use connection pooling
// 5. Optimize aggregation pipelines

// Cost optimization strategies:
// 1. Choose right tier (Serverless vs Dedicated)
// 2. Use provisioned capacity wisely
// 3. Implement data lifecycle policies
// 4. Archive old data
// 5. Monitor usage patterns