MongoDB Performance Optimization

40 min

MongoDB performance optimization requires understanding query execution, indexing strategies, and monitoring. Performance issues can arise from missing indexes, inefficient queries, large document sizes, or resource constraints. Identifying and addressing performance bottlenecks is essential for maintaining responsive applications. Understanding optimization techniques enables you to build high-performance MongoDB applications.

Query optimization starts with understanding execution plans. The explain() method shows how MongoDB executes queries, including which indexes are used, whether collection scans occur, and execution statistics. Understanding execution plans helps identify missing indexes, inefficient query patterns, and optimization opportunities. Regular query analysis is essential for maintaining performance as data grows.

Indexing is fundamental to query performance. Proper indexes enable MongoDB to quickly locate documents without scanning entire collections. Indexes should be created on frequently queried fields, fields used in sort operations, and fields used in compound queries. However, indexes add overhead to write operations, so balance is important. Understanding indexing enables optimal query performance.

Query patterns significantly impact performance. Using projection to limit returned fields reduces data transfer. Using $match early in aggregation pipelines filters documents before expensive operations. Avoiding unnecessary sorting and limiting result sets improves performance. Understanding query patterns enables efficient data retrieval.

Monitoring tools help track database performance and identify issues. MongoDB provides built-in monitoring through serverStatus, currentOp, and various statistics commands. MongoDB Atlas and other monitoring tools provide dashboards, alerts, and performance metrics. Regular monitoring helps identify performance degradation early. Understanding monitoring enables proactive performance management.

Additional optimization techniques include connection pooling (reusing connections), read preferences (distributing reads across replica set members), write concerns (balancing durability with performance), and sharding (distributing data across multiple servers). Understanding these techniques enables you to optimize MongoDB for your specific use case and scale requirements.

Key Concepts

Query optimization requires understanding execution plans and indexing.
Indexes are essential for fast query performance.
Query patterns significantly impact performance.
Monitoring tools help identify performance issues.
Multiple optimization techniques work together for best performance.

Learning Objectives

Master

Analyzing query execution plans with explain()
Creating and managing indexes for optimal performance
Optimizing query patterns and aggregation pipelines
Monitoring MongoDB performance

Develop

Understanding database performance optimization
Designing efficient query patterns
Proactively managing database performance

Tips

Use explain() to analyze queries: db.collection.find({}).explain('executionStats').
Create indexes on frequently queried fields for better performance.
Use projection to limit returned fields: db.collection.find({}, { field1: 1, field2: 1 }).
Use $match early in aggregation pipelines to filter documents.

Common Pitfalls

Not analyzing queries, missing optimization opportunities.
Creating too many indexes, slowing down write operations.
Not using projection, transferring unnecessary data.
Not monitoring performance, missing degradation issues.

Summary

Performance optimization requires understanding queries, indexes, and monitoring.
Proper indexing is fundamental to query performance.
Query patterns and execution plans reveal optimization opportunities.
Regular monitoring helps maintain optimal performance.

Exercise

Optimize MongoDB performance using various techniques.

// Analyze query performance
db.users.find({ email: "alice@example.com" }).explain("executionStats")

// Check if query uses index
db.users.find({ age: { $gte: 25 } }).explain("queryPlanner")

// Create compound index for better performance
db.users.createIndex({ email: 1, age: 1 })

// Use covered queries (query can be satisfied entirely from index)
db.users.find(
  { email: "alice@example.com" },
  { _id: 0, email: 1, age: 1 }
).explain("executionStats")

// Optimize aggregation pipeline
db.sales.aggregate([
  { $match: { amount: { $gte: 100 } }}, // Early filtering
  { $group: { 
    _id: "$product", 
    totalSales: { $sum: "$amount" }
  }},
  { $sort: { totalSales: -1 }},
  { $limit: 10 }
], { allowDiskUse: true }) // For large datasets

// Use projection to limit data transfer
db.users.find(
  { age: { $gte: 25 } },
  { name: 1, email: 1, _id: 0 }
)

// Use batch operations for better performance
db.users.bulkWrite([
  { insertOne: { document: { name: "Alice", email: "alice@example.com" }}},
  { updateOne: { 
    filter: { name: "Bob" }, 
    update: { $set: { age: 31 }}
  }},
  { deleteOne: { filter: { name: "Carol" }}}
])

// Monitor database performance
// Check current operations
db.currentOp()

// Check database stats
db.stats()

// Check collection stats
db.users.stats()

// Check index usage
db.users.aggregate([
  { $indexStats: {} }
])

// Set read preferences for replica sets
db.users.find({ age: { $gte: 25 }}).readPref("secondary")

// Use write concerns for durability
db.users.insertOne(
  { name: "New User", email: "new@example.com" },
  { writeConcern: { w: "majority", j: true }}
)

Exercise Tips

Use covered queries when possible: queries satisfied entirely from indexes.
Use bulkWrite for batch operations: more efficient than individual operations.
Monitor index usage: db.collection.aggregate([{ $indexStats: {} }]).
Use read preferences for replica sets: distribute reads across secondaries.

MongoDB Performance Optimization