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MongoDB – Data Modelling

Data in MongoDB has a flexible schema. documents in the same collection. They do not need to have the same set of fields or structure Common fields in a collection’s documents may hold different types of data.

Data Model Design

MongoDB provides two types of data models: — Embedded data model and Normalized data model. Based on the requirement, you can use either of the models while preparing your document.

Embedded Data Model

In this model, you can have (embed) all the related data in a single document, it is also known as a de-normalized data model.

For example, assume we are getting the details of employees in three different documents namely, Personal_details, Contact and, Address, you can embed all the three documents in a single one as shown below −

{
       _id: ,
       Emp_ID: "10025AE336"
       Personal_details:{
              First_Name: "Radhika",
              Last_Name: "Sharma",
              Date_Of_Birth: "1995-09-26"
       },
       Contact: {
              e-mail: "radhika_sharma.123@gmail.com",
              phone: "9848022338"
       },
       Address: {
              city: "Hyderabad",
              Area: "Madapur",
              State: "Telangana"
       }
}

Normalized Data Model

In this model, you can refer to the subdocuments in the original document, using references. For example, you can re-write the above document in the normalized model as:

Employee:

{
       _id: <ObjectId101>,
       Emp_ID: "10025AE336"
}

Personal_details:

{
       _id: <ObjectId102>,
       empDocID: " ObjectId101",
       First_Name: "Radhika",
       Last_Name: "Sharma",
       Date_Of_Birth: "1995-09-26"
}

Contact:

{
       _id: <ObjectId103>,
       empDocID: " ObjectId101",
       e-mail: "radhika_sharma.123@gmail.com",
       phone: "9848022338"
}

Address:

{
       _id: <ObjectId104>,
       empDocID: " ObjectId101",
       city: "Hyderabad",
       Area: "Madapur",
       State: "Telangana"
}

Considerations while designing Schema in MongoDB

  • Design your schema according to user requirements.
  • Combine objects into one document if you will use them together. Otherwise separate them (but make sure there should not be need of joins).
  • Duplicate the data (but limited) because disk space is cheap as compare to compute time.
  • Do joins while write, not on read.
  • Optimize your schema for most frequent use cases.
  • Do complex aggregation in the schema.

Example

Suppose a client needs a database design for his blog/website and sees the differences between RDBMS and MongoDB schema design. The website has the following requirements.

  • Every post has the unique title, description and url.
  • Every post can have one or more tags.
  • Every post has the name of its publisher and total number of likes.
  • Every post has comments given by users along with their name, message, data-time and likes.
  • On each post, there can be zero or more comments.

In the RDBMS schema, the design for the above requirements will have a minimum of three tables.

While in MongoDB schema, the design will have one collection post and the following structure −

{
   _id: POST_ID
   title: TITLE_OF_POST, 
   description: POST_DESCRIPTION,
   by: POST_BY,
   url: URL_OF_POST,
   tags: [TAG1, TAG2, TAG3],
   likes: TOTAL_LIKES, 
   comments: [      
      {
         user:'COMMENT_BY',
         message: TEXT,
         dateCreated: DATE_TIME,
         like: LIKES 
      },
      {
         user:'COMMENT_BY',
         message: TEXT,
         dateCreated: DATE_TIME,
         like: LIKES
      }
   ]
}

So while showing the data, in RDBMS you need to join three tables and in MongoDB, data will be shown from one collection only.

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