MongoDB is a powerful NoSQL database that's highly scalable and flexible, perfect for modern applications. However, as your data grows, performance issues can arise if you're not careful. Whether you're building with MERN, MEVN, or using MongoDB with a different stack, knowing how to optimise your database is essential.

In this guide, we’ll explore key MongoDB performance optimisation techniques every developer should know in 2025.

Use Indexes Wisely

Indexes are the #1 way to boost query performance. Without them, MongoDB scans every document in a collection, which is a slow process.

✅ Best Practices:

Always index fields used in queries (find(), sort(), aggregate())
Use compound indexes for queries involving multiple fields
Use covered queries to avoid fetching full documents
Avoid unnecessary indexes — they take up RAM and slow down writes

// Example: create a compound index on 'status' and 'createdAt'
db.orders.createIndex({ status: 1, createdAt: -1 });

Schema Design Matters

Even though MongoDB is schema-less, poor document structure can kill performance.

✅ Tips:

Keep documents between 1KB to 16MB – avoid bloated documents
Use embedding for related data you always fetch together
Use referencing (manual joins) for data shared across collections

Rule of thumb: Embed for "contains", reference for "relates to".

Use Projection to Limit Returned Data

Always fetch only the fields you need. Returning the full document ({}) increases payload and memory usage.

// Good
db.users.find({}, { name: 1, email: 1 });
// Bad
db.users.find({});

Use the Aggregation Pipeline Efficiently

MongoDB's aggregation framework is powerful, but can become slow with large datasets.

✅ Optimise your pipeline:

Use $match and $project as early as possible
Avoid unnecessary $lookup and $unwind
Use $facet for parallel processing, where possible

Clean Up Unused or Large Collections

Over time, logs, history, and temp data pile up.

✅ Do this:

Set TTL (Time-to-Live) indexes for auto-expiry
Regularly archive or delete old data
Avoid huge collections with millions of unused entries

// Example: documents older than 30 days will auto-delete
db.sessions.createIndex({ createdAt: 1 }, { expireAfterSeconds: 2592000 });

Monitor with MongoDB Atlas or Compass

Use built-in tools like:

MongoDB Atlas Performance Advisor
Compass Explain Plan
MongoDB Profiler

These help you identify slow queries, missing indexes, and CPU/memory bottlenecks.

Connection Pooling and Caching

Frequent connections kill performance. Use:

Connection pooling in your driver settings
Redis or in-memory caching for frequent read operations

Use Explain() to Analyse Queries

Use .explain("executionStats") to see how MongoDB handles a query — whether it uses an index, scans documents, or hits memory limits.

db.users.find({ email: "test@example.com" }).explain("executionStats");

Shard Large Datasets

If your collection is massive (millions of documents), consider sharding.

Sharding splits data across multiple servers, improving read/write throughput.

Use a good shard key — one with high cardinality and even distribution.

Scale with Replica Sets and Horizontal Scaling

Use replica sets to:

Improve read scalability
Increase availability
Ensure fault tolerance

Use horizontal scaling (sharding) when a single server can't handle your load.

MongoDB is fast by design, but performance depends on you. Indexing, schema structure, query design, and monitoring are your best friends.

By following these techniques, you'll avoid slow queries, bloated collections, and sleepless nights debugging production issues.

MongoDB Performance Optimization Techniques