What is MongoDB? MongoDB is the most popular NoSQL database. The term NoSQL means ‘non-relational’. MongoDB is not based on a table-like relational database structure. The format in which MongoDB stores data is called BSON. It is similar to the JSON format, allowing for more flexible data storage and retrieval.
SQL databases store the data in a tabular format in a predefined data model which is not very flexible concerning today’s real-world applications. Modern apps are more networked and interactive than ever. They are storing and processing more data than ever. SQL databases are not right for handling large data as they are not horizontally scalable.
However, NoSQL databases like MongoDB are highly scalable and have better performance. With its flexible document model, we can add more servers and increase productivity.
History of MongoDB: How It Evolved?
10gen, an American software company started developing MongoDB in 2007 as a part of their planned platform as a service product. However, in 2009 they shifted to an open-source development model and began offering commercial support and other services. In 2013, 10gen changed its name to MongoDB Inc.
MongoDB being a NoSQL database was part of a larger paradigm shift from SQL Databases to NoSQL databases. This shift was required to address problems like Scalability, Schema Flexibility, High Availability, Minimal Downtime, Large Data Sets, etc.
MongoDB’s initial version 1.0 was released in August 2009. Subsequently, as of now most recent version which is version 8.0 was released in October 2024.
Over the past decade-plus, various features were added in different versions, features as More Indexes Per Collection, Faster Index Creation, production-ready sharding, Support for IPV6, Enhanced Geospatial Support, Hashed Index, SCRAM-SHA-1 Authentication, Text Search, etc. Many more features and security enhancements have been made over the years, you can find a detailed list in MongoDB Version History.
Top 5 Facts about MongoDB
- MongoDB has been downloaded over 265 million times as of Q1 FY2023. [source]
- MongoDB was developed in C++ and released in 2009. [source]
- MongoDB provides db.stats() command returns a document containing various statistics about the database, including document counts, sizes, storage utilization, etc. [source]
- MongoDB Inc. became a publicly traded company on October 20, 2017, listed on NASDAQ as MDB with an IPO price of $24 per share. [source]
- MongoDB Community Edition is free and available for Windows, Linux, and macOS.[source]
Relational Database vs MongoDB
| Relational Database | MongoDB |
| Relational Database has a typical schema design that shows the number of tables and the relation between these tables. | MongoDB is document-oriented, there is no concept of schema or relation. |
| Relational Databases support complex transactions as complex join operations are available. | MongoDB does not support complex transactions as complex join operations are not available. |
| Relational Databases are not very flexible or scalable, making them less capable of handling big data. | Highly flexible and scalable document structures are allowed in MongoDB. One data document of a collection in MongoDB can have two fields whereas the other document in the same collection can have four. |
| Relational Databases are slow and with conventional storage techniques, this reduces efficiency. | Efficient indexing and storage techniques make MongoDB faster than Relational Databases. |
| Relational Databases have a Table-like structure where there are Rows and Columns. | MongoDB follows a different structure, similar to JSON, A Row in MongoDB is called a Document, and a Column is called a Field. |
MongoDB Features
MongoDB comes with a lot of features; we’ll list the most popular ones. For more detailed features you can visit MongoDB Features.
- Document Oriented: MongoDB stores the main subject in a minimal number of documents and not by breaking it into multiple relational structures like in a Relational Database.
- Indexing: MongoDB indexes each document for efficient searching, otherwise a query would have to scan every document of a collection which is highly inefficient in big data cases. Time to search is also reduced drastically when indexing is done.
- Scalability: MongoDB partitions data across servers which makes it highly scalable. Data is partitioned into chunks using a ‘shard key’, these data chunks are evenly distributed across shards that reside across many physical servers allowing for the ability to add new machines to a running database.
- Replication and High Availability: With MongoDB availability is highly increased due to multiple copies of data on different servers. Redundancy protects us from hardware failures. If a server goes down, data can be retrieved from other active servers virtually making the data available always.
- Aggregation: Aggregation operations process data records and return the computed results. It is like the GROUPBY clause in SQL.
Why Prefer MongoDB over Relational Databases?
Legacy applications mostly run on Relational Databases; few have made the switch to NoSQL databases, mainly MongoDB. Newer applications directly use NoSQL databases, mainly MongoDB. According to recent market share estimates, MongoDB holds the largest share among NoSQL databases, with approximately 45.68% of the market, making it the most widely used NoSQL database.
- Big Data: If we have a huge amount of data to be stored in tables, MongoDB is better at storing and retrieving big data due to indexing.
- Unstable Schema: Adding a new record/column in a Relational Database is hard whereas MongoDB is schema-less. Adding a new field does not affect old documents and will be very easy.
- Data Availability: As data is stored across different servers by sharding, data has high availability and safety in case of hardware failures.
- Widely Used: MongoDB has become very popular with developers across the spectrum due to its significant advantages over Relational Databases. Due to its open-source nature, it is secure and widely supported.
What are the Language MongoDB Supports?
MongoDB currently provides official support for all popular programming languages like Node.js, C, C++, PHP, Python, Ruby, Java, C#, Perl, Scala, Go Lang and Erlang.
Cloud Database vs Local
MongoDB Atlas is a cloud database platform that is much easier to use than hosting your local database.
However, MongoDB can also be installed locally which allows you to host your own MongoDB server on your hardware (which may be better in certain cases).
In this case, you will have to manage the server, upgrades, and other maintenance.
MongoDB Atlas gives you certain advantages like a free shared cluster, lets you choose a cloud provider (AWS, Azure, Google Cloud, etc.), and also lets you choose a region depending on availability.
MongoDB Atlas manages all the physical servers meaning that upgrades, maintenance, and availability are handled by MongoDB Atlas. This ensures high-level availability.
Available MongoDB Atlas Plans
| Free | Dedicated | Serverless |
| For learning and exploring MongoDB in a cloud environment. | For production applications with sophisticated workload requirements. | For application development and testing, or workloads with variable traffic. |
| 512MB Storage | 10GB Storage | Up to 1TB Storage |
| Shared Ram | 2GB Ram | Auto-Scale Ram |
| Shared vCPU | 2vCPUs | Auto-Scale vCPU |
For Big Data, MongoDB also offers Enterprise plans for which you will have to contact their sales team at MongoDB Sales.
Example Usage of MongoDB
We will demonstrate basic examples of accessing and updating MongoDB documents. For this, we will use Prisma ORM, a popular ORM that will serve as an interface between our web app and MongoDB database. It abstracts the complexities of communicating with the server and allows you to focus on building your application.
When using Prisma, we must first add Prisma to our project:
yarn add prisma prisma/client | npm install Prisma Prisma/client When we have successfully added it, we must initiate it:
yarn prisma init | npx prisma init After we have initiated Prisma, a folder named Prisma will be automatically created at the root of the project with a file inside it named Prisma. Schema. For our example here is how the file should look:
generator client {
provider = "Prisma-client-js"
}
datasource db {
provider = "MongoDB"
url = env("DATABASE_URL")
}
model User {
id String @id @default(auto()) @map("_id") @db.ObjectId
username String
email String @unique
age Int
createdAt DateTime @default(now())
} Once we have completed our schema, we have to run the following command to generate the model:
yarn prisma generate | npx prisma generate Once we have generated the model, we can create functions to create/update users like this:
import { PrismaClient } from '@prisma/client'
const prisma = new PrismaClient()
async function createUser() {
try {
const newUser = await prisma.user.create({
data: {
username: 'john_doe',
email: 'john@example.com',
age: 25,
},
})
return newUser;
} catch (error) {
console.error('Error creating user:', error)
throw error
}
}
async function updates(userId: string) {
try {
const updatedUser = await prisma. user.update({
where: {
Id: userId,
},
data: {
email: 'john.doe@updated.com',
age: 26,
},
})
} catch (error) {
console.error('Error updating user:', error)
throw error
}
}The above code block is a basic example of how you can create and update a user in MongoDB with the help of Prisma. For more information on Prisma, you can visit Prisma’s Website to get started on integrating Prisma with your project!
Wrapping Up: What is MongoDB?
Having used MongoDB on multiple projects currently in production, we can safely say that among NoSQL databases, MongoDB has so far been the best one due to its scalability, safety, and robust structure.
Pairing it with an ORM like Prisma saves a lot of time and energy. MongoDB’s ability to handle and process Big Data is excellent making it a go-to choice for many large corporations like Google, Forbes, Netflix, Facebook, eBay, Adobe, Cisco, etc.