MongoDB

Document database development with aggregation pipelines and schema design.

When to Use

• Working with document-oriented data
• Flexible schema requirements
• Real-time analytics with aggregation
• Geospatial queries

Quick Start

// Insert document
db.users.insertOne({
  email: "john@example.com",
  name: "John Doe",
  profile: { age: 30, city: "NYC" },
  tags: ["developer", "nodejs"],
  createdAt: new Date(),
});

// Query with projection
db.users.find({ "profile.city": "NYC" }, { name: 1, email: 1 });

Core Concepts

Aggregation Pipeline

db.orders.aggregate([
  {
    $match: { status: "completed", createdAt: { $gte: ISODate("2024-01-01") } },
  },
  {
    $group: {
      _id: "$customerId",
      totalAmount: { $sum: "$amount" },
      orderCount: { $count: {} },
      avgOrder: { $avg: "$amount" },
    },
  },
  {
    $lookup: {
      from: "customers",
      localField: "_id",
      foreignField: "_id",
      as: "customer",
    },
  },
  { $unwind: "$customer" },
  {
    $project: {
      customerName: "$customer.name",
      totalAmount: 1,
      orderCount: 1,
    },
  },
  { $sort: { totalAmount: -1 } },
  { $limit: 10 },
]);

Indexing

// Single field index
db.users.createIndex({ email: 1 }, { unique: true });

// Compound index
db.orders.createIndex({ customerId: 1, createdAt: -1 });

// Text index for search
db.products.createIndex({ name: "text", description: "text" });

// TTL index for auto-expiry
db.sessions.createIndex({ createdAt: 1 }, { expireAfterSeconds: 3600 });

Common Patterns

Schema Design

// Embedding for 1:few
{
  _id: ObjectId("..."),
  name: "John",
  addresses: [
    { type: "home", city: "NYC" },
    { type: "work", city: "Boston" }
  ]
}

// References for 1:many
{
  _id: ObjectId("..."),
  name: "John",
  orderIds: [ObjectId("..."), ObjectId("...")]
}

// Bucket pattern for time-series
{
  sensorId: "temp-001",
  date: ISODate("2024-01-15"),
  readings: [
    { time: ISODate("..."), value: 22.5 },
    { time: ISODate("..."), value: 23.1 }
  ]
}

Best Practices

Do:

• Design schema based on query patterns
• Use compound indexes for common queries
• Enable retryable writes
• Use Atlas for managed hosting

Don't:

• Create unbounded arrays
• Use $where with user input
• Over-normalize (denormalize for reads)
• Ignore write concern for important data

Troubleshooting

Issue	Cause	Solution
Slow query	Missing index	Use explain(), add index
Document too large	Exceeds 16MB	Use GridFS or restructure
High memory usage	Large working set	Scale up or shard

References

• MongoDB Documentation
• MongoDB University