MongoDB Schema Design: Denormalization

Design a Denormalized Schema for an E-commerce Order

Create a MongoDB document schema for an order that embeds product details and customer information to optimize read performance.

Expected output: A JSON schema with embedded product and customer data inside the order document.

Hint: Consider which data is frequently accessed together and which changes rarely.

Compare Embedding vs Referencing

List pros and cons of embedding and referencing for a social media post and comments scenario.

Expected output: A comparison table outlining benefits and drawbacks of both approaches.

Hint: Think about read/write frequency and data size.

Denormalization in MongoDB means embedding related data within documents to reduce the number of queries and improve read performance. It is used to optimize read-heavy workloads by avoiding costly joins.

You should avoid denormalization when the embedded data changes frequently, is very large, or when maintaining data consistency across duplicates is complex.

MongoDB uses embedded documents and referencing instead of joins. Embedding stores related data in one document, while referencing uses ObjectIds to link documents, reducing the need for joins.