MongoDB data modeling is no doubt the most important part of NoSQL data management. It uses a unique mechanism to manage the data and follows a special pattern for data modeling. Before anyone starts working on any project, the main concern focuses on how to model the data and how all data will be managed. In this blog, you will get to know how MongoDB is capable of data modeling and how you can manage data using MongoDB.

Before you proceed further, let’s discuss the key challenges of the MongoDB Data Modelling.

Key challenges of MongoDB data modeling

The key challenges of MongoDB data modeling are as follows:

Balancing needs of the application. The performance characteristics of the database engine. The data retrieval patterns.

Now, that you have these key challenges of MongoDB data modeling, it is the time to know the design factors for consideration before actually going for data modeling.

Various design factors to consider before MongoDB Data Modelling

Before jumping to the data modeling and designing, one must consider the following factors:

The design must be according to the user requirements. Data must be combined in a single document if it is going to be used together. Data should be duplicated in a limited manner as disk space is cheaper than the compute time. If you feel to link different collections (like joins) through references, then try to do it at the time of write and not at the time of reading. You should optimize the data as much as possible for the most frequent use cases. Consider the application usage of the data.

Important MongoDB data modeling relationships between the documents

While discussing the data modeling, it can’t be skipped to mention the data modeling document relationships. MongoDB has document and documents have following data modeling relationships:

One-to-one relationship with Embedded documents

One document can contain data of importance instead of making the normalized data and referencing each other. Referencing document would result in querying with multiple queries to resolve the reference.

{ _id: "Habile", name: "Alexander Simmons", age: 30, address: { street: "221B Bakers Street", city: "London", state: "ENG", zip: "200001" } } 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 { _id : "Habile" , name : "Alexander Simmons" , age : 30 , address : { street : "221B Bakers Street" , city : "London" , state : "ENG" , zip : "200001" } }

Here address sub-document can be managed with the references, but the better data model would be to embed the data into a single document as done here.

One-to-many relationship with Embedded documents

Similar to the one-to-one relationship embedded document, this will embed multiple sub-documents of importance inside a single document. This will help you to avoid firing multiple queries for referencing the individual documents.

{ _id: "Habile", name: "Alexander Simmons", age: 30, addresses: [ { street: "221B Bakers Street", city: "London", state: "ENG", zip: "200001" }, { street: "221C Bakers Street", city: "London", state: "ENG", zip: "200031" } ] } 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 { _id : "Habile" , name : "Alexander Simmons" , age : 30 , addresses : [ { street : "221B Bakers Street" , city : "London" , state : "ENG" , zip : "200001" } , { street : "221C Bakers Street" , city : "London" , state : "ENG" , zip : "200031" } ] }

Here multiple addresses are embedded inside a single document to get the all the addresses of an employee.

One-to-many relationship with document references

This is somewhat different from the previous two and it has its own importance. Embedding the document inside each other can sometimes be very infeasible, like in case of increasing data. Let’s consider the following example:

{ _id: "TMH", name: "Tata McGraw Hill" } { _id: bk-001, title: "All About MongoDB", author: [ "Jane Thomas", "Michael Holding" ], published_date: ISODate("2013-07-24"), pages: 200, language: "English", publisher_id: "TMH"} { _id: bk-002, title: "Guide to MongoDB Developers", author: "Jane Thomas", published_date: ISODate("2015-07-07"), pages: 100, language: "English", publisher_id: "TMH" } 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 { _id : "TMH" , name : "Tata McGraw Hill" } { _id : bk - 001 , title : "All About MongoDB" , author : [ "Jane Thomas" , "Michael Holding" ] , published_date : ISODate ( "2013-07-24" ) , pages : 200 , language : "English" , publisher_id : "TMH" } { _id : bk - 002 , title : "Guide to MongoDB Developers" , author : "Jane Thomas" , published_date : ISODate ( "2015-07-07" ) , pages : 100 , language : "English" , publisher_id : "TMH" }

Example considers the publisher and the book document where the publisher is referenced in multiple book documents so as to avoid the mutable data and to store the data in a well-arranged manner.

You may also consider reading this: A Complete MongoDB Introduction

Design decisions that you can take for MongoDB data modeling

The challenges are many, and with challenges comes the desire to find better solutions. Same as in the case of the MongoDB, it has got some good data modeling solutions. Those design solutions can opt as a decision for the flexibility of the data.

For MongoDB data modeling, always consider a good inherent structure of the data. The inherent structure of the data can be achieved through.

Flexible Schema Document Structure Write operation atomicity Data usage and performance

Flexible Schema

MongoDB has a very essential property to support the flexible schema. While designing the data models this can be used as a major solution. In conventional RDBMS, the schema is pre-defined and you need to insert the data as per the Schema, but in MongoDB, there is no such restriction.

There may be documents with different sets of fields and datatypes in each of them. Documents can be updated to a new structure whenever there is a need.

This gives a great advantage in the mapping of documents to any object or an entity.

NOTE: In best practices, documents in a collection have a similar structure.

Document Structure

Data management ultimately points to the same thing and that is the Document Structure. Without document structure, there is nothing to go with. MongoDB is designed such that it allows related data to be embedded within a document.

Following are the ways in which document structure build the data model in MongoDB:

Embedded documents can be utilized to build the relationship between the data and storing them inside a single document. A denormalized form of data helps in retrieving the related data in the single database.

The document will consist of JSON format which will store the data in key-value pairs.

For the similar case shown in the image above, the conventional RDBMS would have used multiple tables but in MongoDB, it can be managed well inside a single document that can have multiple embedded sub-documents.

MongoDB data modeling can be achieved using references of different documents inside any single document. References store the relationship between the data. One or more documents can be referenced inside one another.

The document with object ID 1 (as shown in top image) has easily been related to other documents

Write operation atomicity

MongoDB supports atomic write operations. The data model facilitates to combine all the related data in a single document. It does not normalize the data across multiple documents and collections.

One write operation for multiple documents will always be atomic in nature for each document that is modified, but important to note here is that the operation as a whole is not atomic. (eg: – db.Employee.UpdateMany({…}))

Data usage and performance

Data modeling is essential but while actually implementing it, one must consider that how the database will be used inside the application. For instance, if the most recent data is used every time and there is no requirement for old data then it can be capped. Similarly, is there are mainly read operations on the data then it can be indexed.

That is all about the MongoDB data modeling which can be utilized while any project development.

Bottom Line

MongoDB data modeling provides the best-optimized solutions for unstructured or semi-structured data. In this blog, I have tried to take you on the tour of MongoDB data modeling, its challenges, various design factors, data modeling document relationships, and design decisions that can be opted for data modeling.

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I hope this blog brings a close insight into the MongoDB data modeling that you wanted to know. In case of any queries feel free to ask in the comments section and do share your thoughts about the blog.