MongoDB Performance Interview Questions

Introduction

This guide covers essential MongoDB performance optimization concepts commonly asked in technical interviews. Each question includes detailed answers and practical examples.

Medium

1. What are the key factors affecting MongoDB performance?

Several factors influence MongoDB performance:

  • Query patterns and optimization
  • Index usage and design
  • Hardware resources (CPU, RAM, Disk)
  • Network latency and bandwidth
  • Data modeling and schema design
Performance Impact Areas:
  • Query execution time
  • Write throughput
  • Memory usage
  • Disk I/O operations
  • Network utilization
Hard

2. How do you optimize query performance in MongoDB?

Query optimization involves several strategies:

1. Query Analysis and Optimization
// Analyze query performance
db.orders.find({
    status: "completed",
    customerId: "123",
    orderDate: { $gte: new Date("2024-01-01") }
}).explain("executionStats")

// Optimize query with proper indexes
db.orders.createIndex({
    status: 1,
    customerId: 1,
    orderDate: 1
})

// Use projection to limit fields
db.orders.find(
    { status: "completed" },
    { orderId: 1, total: 1, _id: 0 }
)
2. Query Patterns
// Use covered queries
db.orders.find(
    { status: "completed" },
    { status: 1, orderId: 1, _id: 0 }
)

// Implement pagination
db.orders.find()
    .sort({ orderDate: -1 })
    .skip(20)
    .limit(10)

// Use aggregation for complex queries
db.orders.aggregate([
    { $match: { status: "completed" } },
    { $group: {
        _id: "$customerId",
        totalOrders: { $sum: 1 },
        totalAmount: { $sum: "$total" }
    }},
    { $sort: { totalAmount: -1 } }
])
Hard

3. How do you monitor and analyze MongoDB performance?

Performance monitoring involves various tools and techniques:

1. Built-in Monitoring
// Check server status
db.serverStatus()

// Monitor current operations
db.currentOp()

// Check database stats
db.stats()

// Analyze collection stats
db.orders.stats()
2. Performance Metrics
// Monitor index usage
db.orders.aggregate([
    { $indexStats: {} }
])

// Check query performance
db.orders.find({
    status: "completed"
}).explain("allPlansExecution")

// Monitor system metrics
db.adminCommand({
    serverStatus: 1,
    metrics: 1
})
Hard

4. What are the best practices for MongoDB performance optimization?

Follow these best practices for optimal performance:

Performance Best Practices:
  • Design efficient data models
  • Create appropriate indexes
  • Optimize query patterns
  • Implement proper monitoring
  • Use connection pooling
  • Configure write concern appropriately
Implementation Examples
// 1. Connection pooling
const client = new MongoClient(uri, {
    maxPoolSize: 50,
    minPoolSize: 10,
    maxIdleTimeMS: 30000
})

// 2. Write concern configuration
db.orders.insertOne({
    orderId: "123",
    total: 100
}, {
    writeConcern: { w: "majority", wtimeout: 5000 }
})

// 3. Batch operations
db.orders.bulkWrite([
    { insertOne: { document: { orderId: "1" } } },
    { insertOne: { document: { orderId: "2" } } }
])
Hard

5. How do you handle performance issues in production?

Production performance troubleshooting involves several steps:

1. Performance Analysis
// Identify slow queries
db.system.profile.find({
    millis: { $gt: 100 }
}).sort({ millis: -1 })

// Check resource usage
db.serverStatus().mem
db.serverStatus().opcounters

// Analyze index usage
db.orders.aggregate([
    { $indexStats: {} }
])
2. Performance Tuning
// Optimize memory usage
db.adminCommand({
    setParameter: 1,
    internalQueryExecMaxBlockingSortBytes: 33554432
})

// Configure read preferences
db.orders.find().readPref("secondary")

// Implement caching
db.orders.find({
    status: "completed"
}).hint({ status: 1 })

Next Steps

Continue your MongoDB interview preparation with:

MongoDB Interview Questions