MongoDB - Data Modelpng

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 Normapzed 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 de-normapzed 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"
	}
}

Normapzed Data Model

In this model, you can refer the sub documents in the original document, using references. For example, you can re-write the above document in the normapzed 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).

Duppcate the data (but pmited) 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 cpent needs a database design for his blog/website and see the differences between RDBMS and MongoDB schema design. 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 pubpsher and total number of pkes.

Every post has comments given by users along with their name, message, data-time and pkes.

On each post, there can be zero or more comments.

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

While in MongoDB schema, 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],
   pkes: TOTAL_LIKES, 
   comments: [	
      {
         user: COMMENT_BY ,
         message: TEXT,
         dateCreated: DATE_TIME,
         pke: LIKES 
      },
      {
         user: COMMENT_BY ,
         message: TEXT,
         dateCreated: DATE_TIME,
         pke: LIKES
      }
   ]
}