MongoDB Explained


MongoDB Explained

What is MongoDB?

MongoDB is a free, open-source document-oriented database that can hold a significant amount of data while also allowing you to deal with it quickly. It stores and retrieves data in the form of documents rather than tables, which is why it is termed a NoSQL database.

Role of a Website & MongoDB

A website’s job is not only to provide content to users but also to store each and every activity a user does on the site. The activities can be anything.

The data collected from the website is stored using database software. However, the data acquired from websites is typically vast, and traditional database applications will be unable to adapt to gathering and safeguarding large amounts of data. That is why MongoDB is the preferred database.

So, what exactly is MongoDB, and why is it the most popular database among businesses?

MongoDB & Languages 

MongoDB is a database developed and managed by MongoDB.Inc, which was first released in February 2009 under the SSPL (Server Side Public Licence).

It also includes official driver support for C, C++, C#, and.Net and Go, Java, Node.js, Perl, PHP, Python, Motor, Ruby, Scala, Swift, and Mongoid. As a result, any of these languages can be used to develop an application.

Many firms, like Facebook, Nokia, eBay, Adobe, Google, and others, use MongoDB to store enormous amounts of data.

Key Components of MongoDB Architecture

The following are some of the most commonly used terms that every developer should be familiar with:

  • _id 

Every record must have this field. The _id field in a MongoDB document represents a unique value, and the _id field acts as the primary key for the document. 

So, in the case of the preceding customer table, MongoDB will assign each document in the collection a 24-digit unique number.

  • Collection

A collection is a grouping of MongoDB documents. A collection is quite similar to a table in any other relational database management system (RDMS), such as Oracle. Within a single database, there is a collection. As stated in the introduction, collections do not impose any form of structure.

  • Cursor

A cursor is referred to as the pointer to a query’s result set. Through the cursor, clients can retrieve results by iterating.

  • Database

A database is analogous to a relational database management system (RDMS) in that it is a container for collections. On the file system, each database has its own collection of files.

Myriads of databases can be stored on a MongoDB server.

  • Document

A document is essentially a record in a MongoDB collection, and field names and values will be found in the document.

  • Field

The name-value pair in a document is referred to as a field. A document usually contains more than one field in relational databases, fields are similar to columns. 

Why Use MongoDB? Top 6 Reasons

Below are a few of the reasons why one should start using MongoDB.

1. Document-oriented

MySQL stores data in a relational style that is tables. MongoDB, on the other hand, is a NoSQL database that stores data in the form of JSON-like documents, which is a far superior approach to storing data in databases.

2. Ad hoc Queries

MongoDB also allows facilities to perform ad hoc queries during real-time analysis for better optimisation and performance of data.

3. Indexing

It provides a wide selection of indices to make searches easier and faster. This allows analysts to find specific data more quickly and smoothly.

4. Replication

Databases can be volatile. When firms only rely on storing their data on a single database, during unforeseen circumstances they could risk losing each and every sensitive data forever.

Thus why MongoDB has made it easier to replicate data while also providing several replication options to store the database in multiple databases, making disaster recovery and backup simpler.

5. Load Balancing

MongoDB can handle a plethora of read and write requests from clients through load balancing features like sharding and replication. Through this feature, clients can update their information without experiencing a lag in performance.


JavaScript Object Notation (JSON) is the abbreviation for JavaScript Object Notation. This is a plain text format for expressing structured data that is human-understandable. A wide range of computer languages now supports JSON.

Data Modelling in MongoDB

Unlike MySQL, which requires its databases to be organised in a table-like fashion, MongoDB avoids imposing structures on its users, instead allowing them to choose the model in which the database should be displayed.

When modelling data in MongoDB, the following things are supposed to keep in mind:

  • What Are the Needs of the Application?

Examine the application’s business requirements to determine what data and what kind of data are required. As a result, verify that the document’s structure is determined appropriately.

  • What Are the Different Types of Data Retrieval Patterns?

If you anticipate a high volume of queries, consider including indexes in your data model to boost query efficiency.

  • Is the Database Undergoing a Lot of Inserts, Updates, and Deletions?

To enhance the overall performance of your comprehensive MongoDB system, reconsider the use of indexes or, if necessary, implement sharding into your data modelling architecture.

How to Query MongoDB

The process of seeking and getting data from the MongoDB database is known as MongoDB Query. 

MongoDB queries are analogous to SQL queries in the SQL Database language, and they make retrieving data from the database easier. You can use criteria or conditions to retrieve specific data from the database while conducting a query.

To perform database query operations, MongoDB provides the function DB.collection name.find().

Let’s discuss a few of the methods to run a query on MongoDB:  

The find() Method

You must use MongoDB’s find() method to query data from a MongoDB collection.



The find() method returns a non-structured list of the documents stored.

The pretty() Method

The pretty() function can be used to display the results in a formatted manner.



The findOne() method

Aside from the find() function, the findOne() method also only returns one document.



AND in MongoDB

The $and keyword is used to query documents based on the AND condition. The syntax of AND is:


>db.mycol.find({ $and: [ {<key1>:<value1>}, { <key2>:<value2>} ] })

OR in MongoDB

The $or keyword is used to query documents based on an OR condition. The syntax of OR is:


      $or: [
         {key1: value1}, {key2:value2}

Now let’s look at some examples involving MongoDB. Here, we are working with:

Database: MainExample

Collection: Article 

Field selection

The find() method returns a Non-Structured form of the database collection ({<Key>: <value>}), which includes MongoDB’s auto-created key ” id ” and collection data inserted by the user or admin.





Finding a single document

The findOne() method in MongoDB can be used to locate a single document. This method returns the first document that meets the supplied filter query phrase.


db.collection_name.findOne ()



Displaying documents in a formatted way

The pretty() method in MongoDB allows us to show documents from a specified collection in a well-formatted manner.





Check the existence filter query

If a collection document exists on a specified key, the $exists operator returns i


db.collection_name.find({< key > : {$exists : < boolean >}})



Sort the fields

The finding result in MongoDB is returned in sorted order, which might be ascending or descending. A parameter, such as determining the order

  • 1 (Positive One): It denotes the record’s Ascending Order of Attribute Values.
  • -1 (Negative One): It denotes the record’s Descending Order of Attribute Values.


 db.collection_name.find(). sort({< key > : 1}) 


db.article.find({author : "devil"}).sort({example :  1}).pretty() 

MongoDB Features

MongoDB has a few notable aspects that are worth learning about in order to understand why it is such an effective database solution:

  • Schema-less Database 

A schema-less database means that different types of documents can be stored in the same collection. In other words, a single collection in the MongoDB database can hold numerous documents, each of which may have a different amount of fields, content, and size.

In contrast to relational databases, one document doesn’t need to be similar to another. MongoDB gives databases a lot of flexibility thanks to this fantastic feature.

  • Document Oriented

Unlike RDBMS, MongoDB stores all data in documents rather than tables. In these documents, data is stored in fields (key-value pairs) rather than rows and columns, making the data far more flexible than in a relational database management system. Each document also has its own unique object id.

  • Indexing

Every field in the documents in the MongoDB database is indexed with primary and secondary indices, making it easier and faster to get or search data from the pool of data. If the data isn’t indexed, the database will have to search each document individually for the query, which takes a long time and is inefficient.

  • Scalability

Sharding refers to the distribution of data across multiple servers; in this case, a significant amount of data is partitioned into portions using the shard key. These data pieces are evenly distributed across multiple physical servers. It can also be used to add additional machines to an existing database.

  • Replication

MongoDB delivers high availability and redundancy through replication, which produces multiple copies of data and transmits them to a different server so that if one fails, the data may be retrieved from another.

  • Aggregation

It enables you to conduct operations on grouped data and obtain a single or computed result. It’s analogous to the GROUPBY clause in SQL. Aggregation pipelines, map-reduce functions, and single-purpose aggregation methods are among the three types of aggregations available.

  • High Performance

Due to characteristics such as scalability, indexing, replication, and others, MongoDB has a very high speed and data persistence when compared to other databases.

MongoDB vs. RDBMS

RDBMS stands for Relation Database Management System, and it is one of the most widely used database systems. However, MongoDB remains the favoured database. The following are the reasons for this:


  • It’s a document-oriented, non-relational database.
  • It’s ideal for storing hierarchical data.
  • It has a dynamic schema.
  • The CAP theorem lies at its heart (Consistency, Availability, and Partition tolerance).
  • It is substantially faster than RDBMS in terms of performance.


  • It is a relational database.
  • It is unsuitable for storing hierarchical data.
  • It has a predetermined structure.
  • It is centred on ACID characteristics (Atomicity, Consistency, Isolation, and Durability). 
  • It is slower than MongoDB in terms of performance.

Pros and Cons of MongoDB

Let’s take a look at some of its advantages and disadvantages before we wrap up:

Pros of MongoDB 

  • It’s a NoSQL database with no schema. When working with MongoDB, you don’t have to worry about designing the database schema.
  • It does not support JOIN operation.
  • It gives the fields in the documents a lot more flexibility.
  • It contains a wide range of information.
  • It has a high level of performance, availability, and scalability.
  • It effectively supports geospatial.
  • The data is stored in BSON documents in this document-oriented database.
  • It also allows ACID transitions between multiple documents.
  • It does not necessitate the use of SQL injection.
  • It’s simple to integrate with Hadoop Big Data.

Cons of MongoDB 

  • It stores data in a large amount of memory.
  • In the documents, you are not allowed to keep more than 16MB of data.
  • In BSON, data layering is similarly restricted; data cannot be nested more than 100 levels deep.


MongoDB is a database system that allows software and apps to work together smoothly by storing data simply, efficiently, and securely.

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