Top 10 MongoDB Best Practices Every Developer Should Follow

MongoDB is a widely used NoSQL database that provides high flexibility, scalability, and performance. However, using MongoDB efficiently requires following best practices to ensure optimal performance, security, and maintainability.

In this article, we will explore the top 10 MongoDB best practices that will help you design efficient and scalable applications.

1. Choose the Right Data Model

MongoDB offers schemaless design flexibility, but choosing the right data model is crucial for performance and scalability.

✅ Good Practice (Embedded Documents for One-to-Few Relationships)

{
  "_id": 1,
  "name": "John Doe",
  "orders": [
    { "order_id": 101, "amount": 50 },
    { "order_id": 102, "amount": 30 }
  ]
}

❌ Bad Practice (Using Separate Collections for One-to-Few Relationships)

{
  "_id": 1,
  "name": "John Doe"
}

{
  "_id": 101,
  "user_id": 1,
  "amount": 50
}

🔹 Why?

• Embedding is better when there are few related documents.
• Reduces extra queries and improves read performance.

📌 Tip: Use referencing instead of embedding for one-to-many relationships.

2. Indexing for Faster Queries

Indexes improve query performance by reducing the amount of data scanned.

✅ Good Practice (Creating Indexes)

db.users.createIndex({ "email": 1 })

db.orders.createIndex({ "user_id": 1, "amount": -1 })

❌ Bad Practice (Querying Without Index)

db.users.find({ "email": "johndoe@example.com" })

• This results in a full collection scan, slowing down queries.

🔹 Why?

• Indexing speeds up queries significantly.
• Improves read efficiency, especially for large datasets.

📌 Tip: Use explain("executionStats") to analyze query performance.

3. Use the Aggregation Pipeline for Complex Queries

MongoDB’s Aggregation Framework helps process data efficiently.

✅ Good Practice (Aggregation Pipeline)

db.orders.aggregate([
  { "$match": { "status": "completed" } },
  { "$group": { "_id": "$user_id", "totalSpent": { "$sum": "$amount" } } }
])

❌ Bad Practice (Multiple Queries & Loops)

users = db.users.find()
for user in users:
    orders = db.orders.find({"user_id": user["_id"]})
    total = sum(order["amount"] for order in orders)

🔹 Why?

• Aggregation reduces multiple queries into a single optimized pipeline.
• Improves performance and efficiency.

📌 Tip: Use allowDiskUse: true for large aggregation queries.

4. Avoid Large Documents (Document Size ≤ 16MB)

MongoDB has a document size limit of 16MB, but keeping documents smaller improves performance.

✅ Good Practice (Normalized Data)

{
  "_id": 1,
  "name": "John Doe",
  "profile_id": 101
}

{
  "_id": 101,
  "bio": "Software Engineer...",
  "social_links": ["LinkedIn", "GitHub"]
}

❌ Bad Practice (Storing Large JSON in One Document)

{
  "_id": 1,
  "name": "John Doe",
  "bio": "Software Engineer...",
  "social_links": ["LinkedIn", "GitHub"],
  "blog_posts": [ /* 1000+ posts */ ]
}

🔹 Why?

• Large documents slow down queries and increase memory usage.
• Split large datasets into multiple collections.

📌 Tip: Use GridFS for storing large files.

5. Use Projections to Reduce Data Transfer

Fetching only required fields improves query performance.

✅ Good Practice (Projection)

db.users.find({}, { "name": 1, "email": 1, "_id": 0 })

❌ Bad Practice (Fetching Entire Document)

db.users.find({})

🔹 Why?

• Reduces network load.
• Improves query response time.

📌 Tip: Use projection to return only the necessary fields.

6. Optimize Write Operations with Bulk Inserts

For high-volume inserts, use bulk writes to improve efficiency.

✅ Good Practice (Bulk Insert)

db.orders.insertMany([
  { "user_id": 1, "amount": 100 },
  { "user_id": 2, "amount": 200 }
])

❌ Bad Practice (Multiple Insert Calls)

db.orders.insertOne({ "user_id": 1, "amount": 100 })
db.orders.insertOne({ "user_id": 2, "amount": 200 })

🔹 Why?

• Bulk inserts reduce network overhead.
• Improves write performance.

📌 Tip: Use ordered: false to continue inserting even if some fail.

7. Implement TTL (Time-To-Live) for Expiring Data

TTL indexes automatically delete old or temporary data.

✅ Good Practice (Setting a TTL Index)

db.logs.createIndex({ "createdAt": 1 }, { expireAfterSeconds: 3600 })

❌ Bad Practice (Manually Deleting Old Records)

db.logs.deleteMany({ "createdAt": { "$lt": new Date(Date.now() - 3600000) } })

🔹 Why?

• Saves storage space automatically.
• Eliminates manual cleanup jobs.

📌 Tip: Use TTL indexes for session data, logs, and cache.

8. Secure Your MongoDB Deployment

MongoDB should be properly secured to prevent unauthorized access.

✅ Good Practice (Enable Authentication)

mongod --auth

db.createUser({
  user: "admin",
  pwd: "strongpassword",
  roles: ["root"]
})

❌ Bad Practice (Running Without Authentication)

mongod --noauth

🔹 Why?

• Prevents unauthorized access.
• Protects sensitive data.

📌 Tip: Always bind MongoDB to a secure IP (bind_ip setting).

9. Monitor Performance with MongoDB Tools

Use MongoDB built-in tools to monitor performance.

✅ Good Practice:

db.serverStatus()

db.currentOp()

🔹 Why?

• Helps identify slow queries.
• Monitors CPU, memory, and disk usage.

📌 Tip: Use MongoDB Atlas or Prometheus-Grafana for monitoring.

10. Backup Your Database Regularly

Regular backups prevent data loss.

✅ Good Practice:

mongodump --out /backups/mongo

🔹 Why?

• Protects against data corruption or accidental deletion.
• Ensures disaster recovery.

📌 Tip: Automate backups using MongoDB Atlas or cron jobs.

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Final Thoughts

Following these MongoDB best practices ensures that your database is scalable, secure, and performant. Whether you’re working on small projects or large-scale applications, applying these tips will optimize your database performance.

📢 Which MongoDB best practices do you follow? Share your thoughts in the comments! 🚀

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