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MongoDB introduces a NoSQL solution for data storage and management, consisting of documents represented in JSON style. Like other database systems, MongoDB can also use ORM.

In this article, we will explain the concepts of ORM and general concepts in MongoDB.

Object Relational Mapping (ORM)

Object-relational mapping is a technique that makes it easier to work with data in a relational database system. It acts as a connecting bridge between your business model and the storage database.

ORM works by mapping data stored as relations in the database to objects in the application language. This provides the advantage of ease of programming to the person writing the code, as the data can now be accessed naturally and fluently using the same programming language.

Let us further explain how this is possible.

When using an object-oriented language, the programmer must store data as objects in the business logic layer. This data is also stored in a relational database in the form of tables or relations.

Converting this relational data to objects when coding can result in an ugly application. It is also more error-prone because programmers have to use two different languages ​​and write raw queries in one application.

Therefore, ORM solves this problem by simplifying this process.

ORM in MongoDB

Now the question arises, since MongoDB is a NoSQL solution and does not use relations. Does ORM still apply to it? The answer is yes, ORM can also be used in MongoDB, but it is more colloquially called ODM here.

ODM stands for Object Document Mapping, and since MongoDB stores data in documents, relations are replaced with documents.

For NoSQL databases like MongoDB, using an ODM is not a big advantage since they are already simple. But it comes with the added benefit of increased developer productivity, which is crucial for the overall performance of the application.

However, ODM is still widely used in MongoDB. This is because ODM helps in using logical relationships between data which cannot be easily represented otherwise as such relationships are not identifiable in NoSQL.

Without ODM, programmers have to manually implement these code relationships. Since MongoDB is a document-oriented architecture, it is very suitable for integration with ODM because documents can also be called objects.

Therefore, ODM frameworks are used with MongoDB to provide an abstraction over the data model layer.

MongoDB ORM Example

There are many ORM libraries available for MongoDB written in various languages.

Some of these are also open source. We will look at a few examples in this article.

Mongoid

Mongoid is suitable for applications using Ruby on Rails framework. Using Mongoid ODM as MongoDB ORM for Ruby-based applications, programmers can use database resources more easily because they are already familiar with the language, which is also the benefit of using ODM.

For example, here is a code snippet that inserts an instance into a database using Mongoid.

Person.create!(
first_name: "Ali",
last_name: "Raza"
) # => Person instance

The following is an example of a MongoDB query written using Mongoid.

Person.
where(:dob.gte => "1990-01-01").
in(first_name: [ "Ali", "Raza" ]).
union.
in(first_name: [ "Ahmad" ])

Spring Data MongoDB

The most popular choice among Java-based ORM developers for MongoDB is Spring Data MongoDB. It provides a consistent Spring-based model for programming against new databases.

You can integrate Spring Data MongoDB into your Maven project by <dependencyManagement>using the following dependency in your pom.xml file inside the <dependency> tag .

<dependencyManagement>
<dependencies>
<dependency>
<groupId>org.springframework.data</groupId>
<artifactId>spring-data-bom</artifactId>
<version>2022.0.0</version>
<scope>import</scope>
<type>pom</type>
</dependency>
</dependencies>
</dependencyManagement>

MongoEngine

MongoEngine is an ODM for MongoDB in Python. It uses a simple high-level API and is built on top of PyMongo, the official MongoDB driver for Python.

Below is an example of how to create a database schema in Python using MongoEngine by defining a class that inherits from Document.

from mongoengine import *
import datetime

class Page(Document):
title = StringField(max_length=200, required=True)
date_modified = DateTimeField(default=datetime.datetime.utcnow)

Here are some simple queries to access the database:

for page in Page.objects:
print page.title
myPage = Page.objects(title='Title')

Summarize

In this article, we have described in detail the usage of ORM in MongoDB, which helps to minimize the complexity of the development process. ORM acts as a translator, converting your database documents into objects of the language you are coding, thus making the programmer's task easy.

We also discussed the working of some MongoDB ORM examples. It is not possible to cover each of them in detail within the scope of this article.

We hope you have understood the concept of ORM in MongoDB. Keep learning!