DENORMALIZATION IN MONGODB -BENAZIR NADAF

BENAZIR NADAF | BCA2302295

Denormalization in MongoDB : explain with examples 

🔍 Introduction

MongoDB is a document-based NoSQL database known for its flexibility, horizontal scalability, and dynamic schema. When working with relational data in traditional databases, we normalize data to reduce redundancy. But in MongoDB, denormalization is a common and often recommended practice to improve performance, especially for read-heavy applications.

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📚 What is Denormalization?

> Denormalization in MongoDB means embedding related data into a single document instead of storing it in separate collections and linking them. This improves query speed, reduces joins, and offers a better data access pattern for certain use cases.

🆚 Normalization vs. Denormalization

Feature Normalization (SQL style) Denormalization (MongoDB style)

Structure Split into multiple tables Embedded into fewer collections

Data Redundancy Minimal Often higher

Query Speed Slower due to joins Faster as everything is in one place

Write Complexity Easier Harder to maintain consistency

Ideal For Write-heavy apps Read-heavy apps

💡 Example: Normalized vs. Denormalized in MongoDB

🔹 1. Normalized Structure (Referencing)

// users collection

{

  "_id": 1,

  "name": "Alice",

  "order_ids": [101, 102]

}

// orders collection

{

  "_id": 101,

  "item": "Laptop",

  "price": 45000

45000

To get Alice’s orders, you'd need to query both users and oorders

🔹 2. Denormalized Structure (Embedded)

// users collection

{

  "_id": 1,

  "name": "Alice",

  "orders": [

    {

      "item": "Laptop",

      "price": 45000

    },

    {

      "item": "Mouse",

      "price": 800

    }

  ]

}

👉 Now, all of Alice’s order info is in one document — faster reads with just one query.

✅ When to Use Denormalization

You want to optimize read performance.

Your data is mostly read-heavy.

Related data is always queried together.

You want to avoid costly joins.

⚠️ When to Avoid Denormalization

When data changes frequently (duplication = harder updates).

When storage space is limited (denormalized docs are bigger).

When you have strict data consistency needs.

🛠️ Best Practices

Denormalize only when needed — don’t overdo it.

Use array of subdocuments wisely.

Make use of MongoDB’s aggregation framework for powerful queries on denormalized data.

Always index fields used in read queries.

🔚 Conclusion 

Denormalization in MongoDB helps boost performance by reducing the need for multiple queries and joins. However, it comes with trade-offs like duplication and maintenance complexity. Choose denormalization wisely depending on your application’s use case — especially when speed matters more than strict normalization rules

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Benazir Nadaf

University: Shree Balaji University, Pune

School: School of Computer Studies

Course: BCA (Bachelor of Computer Applications)

Interests: NoSQL, MongoDB, and related technologies

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