Most developers are familiar with relational SQL databases, where tables are used for defining the structure of a dataset and where relationships are established by linking tables together.
By contrast, NoSQL ("not only SQL") databases such as MongoDB store data using flexible models. But just because a NoSQL database isn't traditional doesn't mean it can't store relational data — it stores the same data differently and, in some cases, even more efficiently.
Because NoSQL databases were built to tackle the complex data problems of the internet era, many can handle relational data while offering more modern functionalities such as:
Many developers, accustomed to using RDBMS for decades, may not be aware of the advantages that NoSQL databases bring to applications built for today's internet. These advantages include:
There are primarily four types of NoSQL databases in use today:
NoSQL databases are best for modern applications where data models evolve and scalability is essential.
NoSQL databases are often used in agile projects because schema changes — which are common — don't require a lot of overhead. This allows developers to focus on business logic and algorithms instead of dealing with schema updates. If you anticipate that your application's data model needs to remain flexible to accommodate changes over time, the flexible schema approach of NoSQL databases — specifically document databases — may fit well for your needs.
Finally, scalability is a major reason to choose a NoSQL database. NoSQL databases have been designed for easy horizontal scaling without the need for specialized third-party components. MongoDB, for example, supports sharding out of the box. Whether you need to support hundreds of users or hundreds of millions of users, a NoSQL database can scale seamlessly.
The kind of NoSQL database that best fits your needs ultimately centers around a few main concerns: transactions, data models, query performance, scaling, and deployment. The following table lists the distinctive approaches for each of these concerns or when it would be advantageous to use a given type of NoSQL database over another.
| Database Type | Document database | Key-value store | Wide column store | Graph database |
|---|---|---|---|---|
| Example Offering | MongoDB | Redis | Apache HBase | Neo4J |
| Transactions | Supports ACID transactions. | Supports ACID transactions. | Does not support transactions, but guarantees atomicity at the row level. | Supports ACID transactions. |
| Data Models | Best when data is modeled by a set of interrelated objects, with its flexibility toward data structure making it a good general purpose database. Documents can contain nested structures for capturing complex data. | Excellent for frequent high-speed access to the same chunks of data, even if those chunks of data are large. | Best for extremely large sets of data, where querying patterns are predictable, often for supporting aggregation and analytics. | Suitable when there is a need to store and query data about the connections between related data, such as in social network contexts. |
| Query Performance | Fast retrieval of individual documents, much like key-value stores, but can also support complex queries with aggregation pipelines. | Best for instantaneous retrieval when queries are not complex; useful for caching use cases. | Excellent when aggregating values on particular columns, with strong compression and in-memory support. | Fast for simple queries, but also efficient when answering questions about connections between data; excels when profiling data and determining clusters of related objects. |
All four types of NoSQL databases have a similar approach: excellent horizontal scaling achieved through a combination of in-memory support and distributed systems that serve the database from a cluster of nodes. Similarly, all four NoSQL databases are deployable as self-managed systems on-premises, in a private cloud, or as a DBaaS in the public cloud.
With all types of NoSQL databases offering similar advantages over RDBMS, we see that the question of which kind of NoSQL database to choose depends mostly on your data model and querying requirements.
Each NoSQL database type has its own pros and cons and your choice should depend on your actual use cases. Yet, for most modern websites, the ideal NoSQL databases to use are the document stores, as they support most general use cases.
A document database is particularly suited for websites thanks to its ability to support complex data structures and parent-child relationships within a single document record. There is no need to incorporate object-relational mappers (ORMs) or object-document mappers (ODM) because document databases store the raw objects in JSON/BSON or similar formats.
As the most efficient NoSQL database option for websites and API endpoints, MongoDB is easy to get started with, and it can grow and evolve with your website. MongoDB already has excellent integration with popular web programming languages like Python, PHP, Node, Java, Golang, and many others. Additionally, several libraries help with using MongoDB with various web frameworks like Laravel or Ruby on Rails.
When you decide to use MongoDB for your website or web application, it may seem daunting to deal with setup and infrastructure. If you simply want to focus on building your app, you can offload the infrastructure concerns by using a DBaaS. You can let the experts at MongoDB handle the database management while your development team focuses on what they do best.
MongoDB Atlas is the fully-managed DBaaS offering of MongoDB. That means you can sign up and start using a MongoDB database without deploying and managing a cluster yourself. You can decide how to scale and grow your cluster with a few simple clicks. MongoDB Atlas also offers a free tier, allowing you to learn and experiment with the platform.
You can also choose which cloud provider to use for your cluster. So, if your application already runs on Amazon AWS, Microsoft Azure, or Google Cloud, then MongoDB Atlas can be deployed seamlessly in the same cloud and connect securely with your application.
NoSQL databases have enabled us to create applications that can scale to handle large volumes of data and a large number of users. They support a wide variety of use cases across industries. However, with so many different NoSQL databases out there, deciding which one to use is not easy.
When choosing a database for a general purpose web application, document databases are the ideal choice. They support a variety of data structures without the overhead of implementing a wide column store. In addition, they enjoy the benefits of complex querying capabilities and simple scaling.
MongoDB Atlas allows developers to build their web applications without managing database infrastructure, coupled with the flexibility of using whichever programming language they prefer.
It depends on your use case, especially when it comes to your data model and querying needs.
Consider your use case first, and learn what you most urgently need to use. After all, you’re most motivated to learn what you need to use.
If there is no present urgency, and you're looking for a general purpose database, document databases are a great option to learn first. For those with relational database backgrounds, MongoDB provides excellent documentation to map concepts and syntax from SQL to MongoDB.
Your use case — in particular, your application's data model and querying needs — should dictate which NoSQL database you ought to choose. See the table above for guidance on which would fit your application needs most suitably.
MongoDB has enjoyed massive adoption and popularity since the emergence of NoSQL databases. In 2020 — for the fourth year in a row — Stack Overflow published its survey rating MongoDB as the database most wanted by developers. Integrating with popular programming languages, MongoDB has become the one of the best NoSQL databases for Golang just as much as it is considered one of the best NoSQL databases for Python. It brings schema flexibility for evolving data models, simple scalability with its scale-out, distributed approach to its architecture, and incredible query performance for most common business application needs.
Developers find working with data in document databases like MongoDB to be incredibly intuitive. Documents map to objects in most popular programming languages, so developers don't need to split related data between tables or use an ORM. With a model that is intuitive to developers, MongoDB has been adopted as the NoSQL database of choice for web applications by millions of developers worldwide.