MongoDB Schema Design: The 2 Questions to Ask Yourself

Note: Make sure you've reviewed our preliminary discussion on MongoDB Schema Design as this post builds upon those concepts.

MongoDB's document-oriented, denormalized design means you have to consider different factors than you would with traditional relational databases. Document growth and application-level joins are key considerations when deciding how to structure collections and documents. Here's how to approach MongoDB schema design by answering two essential questions.

Key Takeaways

• Understand the implications of normalized vs. denormalized data designs.
• Determine how your application will query data to inform schema structure.
• Consider how your documents will grow over time, especially in terms of array data.
• Balance performance, complexity, and future scalability in your design.

Preface: Normalized vs. Denormalized Data

Traditional RDBMS systems are structured with normalization, organizing data into separate relational tables to minimize redundancy and ensure integrity. For example, a Products table might reference a distinct Parts table.

This model provides atomic read/write operations without data duplication. You could query the Products table and perform joins to get all associated Parts for a product efficiently.

MongoDB, using a document-oriented approach, differs significantly. It represents data as JSON documents with embedded entities that capture relationships. A Product document might contain an embedded array of Parts.

Benefits of this denormalized approach include flexible schema evolution and faster read/write operations since documents encapsulate data together. Thus, a Products query can directly access parts information without needing joins.

Although MongoDB allows a degree of normalization—by separating documents into collections and using references—whether to use this feature depends on your specific schema requirements.

1) How Will You Query the Data?

The first critical question in schema design is "How will I query the data?" This influences how you structure documents and set relationships between entities. For instance, if your data involves Products with multiple Parts, storing Parts within Product documents might optimize read operations without needing joins.

However, if querying Parts directly is frequent, separating Parts into distinct collections could be beneficial. This method requires an application-level join to gather Part details for a Product but avoids querying every Product document for Parts information.

An alternative is to include key details like Part names, along with Part references, in the Product document. While this mitigates the need for joins when fetching part names, it complicates updates—each Part name change requires updating all Product documents that reference it.

Hence, tailoring your schema to query patterns is crucial. Frequent Product reads might justify embedding Part names, while scenarios involving frequent Part updates might necessitate a cleaner decoupling of collections.

2) How Will Your Documents Grow?

Document growth is another critical factor in schema planning. Consider a User collection where each user has a followers array. While storing follower IDs in an array might seem straightforward, it's problematic if a user accumulates billions of followers. The BSON document size limit (around 16MB) restricts how much a document can grow.

This highlights the importance of understanding data cardinality. With one-to-few relationships, nesting is fine. But for one-to-many or one-to-infinite relationships, you'll need a strategy that accommodates growth without hitting limits.

Conclusion

Careful consideration of document growth and application query patterns is essential for effective MongoDB schema design. Balancing data normalization with the dynamic nature of your application will lead to efficient and scalable data models.

FAQ

What are the key differences between normalized and denormalized data structures?

Normalized structures reduce redundancy by organizing data into tables with relationships, whereas denormalized structures like those in MongoDB use embedded documents to store related data together for more direct access.

How do I decide between embedding and referencing documents?

This decision often hinges on query patterns and update frequency. Embedding is ideal for data that’s frequently read together, while referencing suits data that's updated independently and queried separately.

How does document growth impact performance?

Large documents can affect performance by increasing read complexity and risk of hitting size limits. Properly planning for growth by understanding data relationships can help avoid these issues.

Is there a limit to MongoDB document size?

Yes, the maximum BSON document size is approximately 16 megabytes, which imposes constraints on how much data a single document can store.