What Is MongoDB? Working, Architecture, Features, and Use Cases

MongoDB is an open-source database, compatible across platforms and able to work with data using JSON and similar formats. This article explains how MongoDB works, its architecture, key features, and six real-world examples of MongoDB applications. 

What Is MongoDB? 

MongoDB is defined as an open-source database with a document-oriented architecture, which is compatible across platforms and can help work with data using JSON and similar formats. 

MongoDB is a type of NoSQL database that is used for managing large volumes of data. The name “Mongo” comes from “Humongous,” describing the data size the software is meant for. While MongoDB may not be for everyone, it is a type of database system that organizations handling big data want to work with. 

MongoDB is an open-source database management system that is document oriented and designed to store and manipulate large-scale data. It uses different terminologies and structures as compared to SQL databases. For example, MongoDB uses collections and documents instead of traditional relational databases’ tables and rows. 

MongoDB was developed and released In February 2009. The company hosting it is mongoDB.inc, using the SSPL (server-side public license). The company created MongoDB to support multiple drivers for multiple programming languages like C, the C#, C++, .Net, Java, Swift, Python, PHP, etc. MongoDB is used by numerous companies that store big data, such as Nokia, eBay, Aadhar, Shutterfly, EA, etc

MongoDB is unique even among NoSQL databases because it applies all the NoSQL technology while maintaining some critical functionality in the relational database systems.

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Understanding the Working of MongoDB 

MongoDB is a highly flexible database characteristic of most NoSQL databases. It allows you to store and work on different data types in one document. MongoDB also stores data far larger than relational databases. These core characteristics of MongoDB are due to the document storage format employed by the database system called JSON. 

JSON (JavaScript Object Notation) is an open standard file and data exchange format that stores and transmits data objects using readable text. It is a widespread data format used in several electronic data interchanges. Although originally derived from JavaScript, it is now language independent because many currently used programming languages have codes that allow for data in JSON format, stored with the .json extension. 

Some parts of MongoDB are vital to its function: 

• Drivers: These are tools and software available on servers that can interact with MongoDB. MongoDB supports C, C++, .Net, Go, C#, Python, etc.
• Storage engine: This is part of MongoDB that deals with the amount of data stored on the disk and memory. It also has to do with search engines used for looking up data. The default MongoDB storage engine is the WiredTiger storage engine. However, there are other storage engines, such as the in-memory storage engine and MMAPv1 storage engine, and the encrypted storage engine that MongoDB also uses.
• MongoDB Shell: Mongo Shell comes as an accessible and interactive platform for MongoDB. The platform is used to update data, query data, and manage data for different uses. 

Data storage in MongoDB 

MongoDB uses collections to store a bulk load of data. Within the collections are documents analogous to rows in a table in the relational database system. Documents (which can have multiple and different fields and data types) are made up of keys, the most basic data unit in MongoDB. 

One can alter the structure of already saved data in a document under the NoSQL MongoDB by adding or removing new and current fields. Different data types can also be used with the same document. This gives MongoDB a flexible structure. 

MongoDB works in two layers – the application layer and the data layer.

1. Application layer: The application layer can also be called the final abstraction layer. It is made up of the front end and the back end. The front end or user interface is the platform where the user interacts with the database through a mobile app (Android or iOS) or directly from the web. On the other hand, the backend is the server, which performs logical operations and uses queries to interact with MongoDB with drivers.

2. Data layer: The data layer consists directly of the MongoDB server. When queries are sent from the backend of the application layer, MongoDB receives the queries and sends them on to the storage engine. This is what reads or writes the data in the memory.

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MongoDB Architecture

MongoDB’s overall architecture transmits data from the application to the drivers and, together with the mongo shell, down to the mongo server and storage engines.

MongoDB was built to represent the ideal database system, which combines the critical functionality of the SQL relational database and reflects the NoSQL database’s core innovations. MongoDB picks off from where SQL databases like Oracle stop and adds to it.

It does not discard the entire technology as these have proven to be a sustainable structure for decades. MongoDB then acknowledges the changing data framework and demand of modern applications and merges the old and the new to bring about a hybrid nexus architecture. 

The MongoDB nexus architecture consists of six ideas or components. The first three reflect part of the SQL databases, while the other three meet the current demand that modern applications and big data technology have placed on database systems.

The initial three components are: 

• Expressive query language & secondary indexes: This allows users to find and manipulate data easily and efficiently. Indexes are supported by the Mongo database system itself and not by individual applications using the database.

• Strong consistency: This part of the MongoDB nexus architecture allows applications to read data written to the database immediately. 
• Enterprise integration and management: Sometimes, it is easy to look at databases as solitary performers in the entire IT stack, but they are just a part of any application’s infrastructure and should be able to seamlessly fit into the rest of the IT infrastructure as a whole. 

The three components of MongoDB architecture that meet the new demands placed by modern applications are:

• Flexible data model: In MongoDB, you can store and combine data of different types, from graphs to documents, without affecting the app’s performance or causing any downtime. 
• Scalability and performance: MongoDB, like all NoSQL databases, was built using the sharding principle that allows for easy hardware or on-cloud scalability. With MongoDB, there is almost no boundary to the extent by which you can scale the database, and all these can be done easier and with lower latency than the relational database. 
• Always-on deployment: MongoDB was designed to run across several nodes, automatically replicating and synchronizing data to consistently provide the best experience for all users all over the globe. 

What is MongoDB flexible storage architecture?

MongoDB uses a flexible storage architecture to meet different application needs and deployment designs with just one database technology. This is because of the multiple storage engines, which allow users to combine as needed for every deployment. Formerly, applications would need to use more than one database technology to meet specific requirements, and integrating these technologies was a highly complex endeavor. MongoDB automatically moves data between the storage engines depending on the current need. These storage elements include:

• Default WiredTiger storage engine: This provides all-around performance and storage for most applications and is the default storage engine used in MongoDB. 
• Encrypted storage engine: It is used to protect sensitive information via encryption. 
• In-memory storage engine: It is used by sensitive and demanding applications that require minimal latency periods. 
• The MMAPv1 engine: This is an improved engine from previous versions of MongoDB.

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6 Key Features of MongoDB

The essential features of MongoDB include: 

1. Document-oriented model

MongoDB was created to suit the productive and flexible mind of a developer. One way of achieving this is by making it document-oriented. The document model allows data to be stored as documents grouped into collections. Documents are self-contained, making it easy for developers to focus on a particular data set without splitting it across tables. 

MongoDB also uses the BSON format to store documents. BSON is a binary encoded JSON format that allows for storing images, videos, text, etc. It is easy to interact using a MongoDB driver specific to the programming language in use. 

2. Replication

With other databases, especially relational databases, there is always the problem of data loss. When data is stored on a single server, it creates multiple points of failure. The server could crash, hardware can malfunction, the computer network service may be interrupted, and other similar events can make it difficult for an individual to access data. 

With MongoDB replication, you can mitigate potential data failure by replicating data across multiple servers. These servers are used to scale up the database, data recovery, and as a backup. With replication, there is also the potential for the database storage engine to spread the read load across the various replications. 

Out of a group of replica sets, one, the primary server or node, is the server that accepts all incoming write applications. In case of failure, the system will elect one of the secondary servers to be the primary node within a short time. 

3. Load balancing 

Proper load balancing is fundamental to database management for expansion in large-scale organizations. As client traffic and requests come in thousands and millions, they must be well distributed across the different servers to maximize performance and reduce over congestion. MongoDB efficiently handles read and write requests, balancing the incoming load across its multiple servers and ensuring data consistency. This means that with MongoDB, there is no need for additional load balancers.

4. Schema-less database

MongoDB is a schema-less database meaning that it can consist of different types of documents in a single collection. This is in broad contrast to the relational database with a fixed schema structure, ensuring similar data is entered into rows within a table. 

In MongoDB, one collection can have documents with different sizes, numbers of fields, and different content. This brings about a very flexible database schema. Developers can benefit from this flexibility as data can be moved easily between schemas without downtime or affecting existing data. In addition, MongoDB also can enable a flexible schema to be fixed. So when necessary, you can apply validation rules to collections. 

5. Sharding

Sharing is the process of sharing large data and spreading it across several units or machines called shards. Sharding is most beneficial to a database system when it handles cumbersome and difficult queries. This is typical of web applications with millions of users logging into and using the platform daily. In MongoDB, sharding also determines more effortless horizontal scalability. Adding more shards gives a more significant avenue to spread out data, thus effectively scaling the database. 

Each shard contains a part of the dataset that makes up the whole and functions as a database that can more efficiently carry out multiple requests without downtime. In a sharding environment, queries are sent to different shards using a shard key. This handling process is called Mongos. When done correctly, sharding leads to better load balancing. 

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6. Indexing

In MongoDB, every field in a document is indexed with primary and secondary indices. This reduces the time used in searching for data from the database. The database engine can use the index to sieve out information rather than searching each document, one after the other for a particular entry. This indexing feature of MongoDB proves to be among the best features as it reduces the time in solving queries. 

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MongoDB Use Cases

MongoDB has widespread applications as a database system. Some of the real-life use cases include:

1. MetLife 

MetLife is a global company that provides employee benefits programs including insurance, annuities, etc. MetLife caters to over 90 million individuals in about 60 countries worldwide. MetLife has a customer service application known as The Wall that runs on the Mongo database software. 

Users can access their program policies, payments, and transactions, among other details. The Wall is spread across six servers located in two different data centers. Together, they have a storage capacity of 24 Terabytes. Currently, MetLife is working on some big data projects and integrates MongoDB as part of the project. 

2. Forbes

Forbes is another practical MongoDB use case. Forbes is a global media company that publishes widely-accepted information on business trends, entrepreneurship, investment, tech, and leadership. This places a high demand on the publishers to provide shareable information on current trends and news that people can easily share. With MongoDB, Forbes developed their mobile app within two months. 

They also redesigned their websites, making them MongoDB compatible. The change in database infrastructure is also reflected in the editors, who can now use Mongo’s flexible infrastructure to provide timely and dynamic content to viewers. 

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3. Otto

Otto is one of the world’s largest e-commerce companies, with a vast customer base in over 20 countries. To meet customer expectations, they must provide real-time analytics, always be available online and have current, updated apps. Otto needed to increase their response time as this was a challenge, catering to the numerous companies and millions of users visiting the site. With MongoDB, the company could reduce response time to 1-2 seconds. The flexible schema, scalability, and reliability of MongoDB aided this change. 

4. Shutterfly 

Shutterfly is a digital picture exchange and image sharing company. With more than 6 billion images, with a processing speed of over 10,000 per second, the relational databases proved unable to handle the data load adequately. Shutterfly went from using Oracle to investigating different NoSQL databases and settling in MongoDB. It currently uses MongoDB to manage the data about uploaded pictures while retaining the relational database for other operations such as payment. 

5. Unique Identification Authority of India (UIDAI)

Unique Identification Authority of India (UIDAI or Aadhaar) operates India’s biometric system, an extensive database system launched in 2009. The program stores biometric information of more than 1 billion Indians. This massive amount of data requires a well-suited database system. Aadhaar initially used MongoDB to query and search for data but has also expanded to other database systems. 

6. eBay

eBay is another international e-commerce company that allows customers direct access to retailers of different items. Some of eBay’s projects, such as cloud computing management, search suggestions, and categorization of items, are done using MongoDB.

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Takeaway 

MongoDB’s 2022 Report on Data and Innovation reveals that working with high volumes of data in different formats remains a top challenge today. 73% of developers and IT decision-makers struggle to manage data when building applications. Solutions like MongoDB make it easier to organize and mobilize data from different sources, regardless of volume or complexity. It also helps manage costs and integrate multiple platforms. 

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