As storage costs rapidly decreased, the amount of data that applications needed to store and query increased. This data came in all shapes and sizes — structured, semi-structured, and polymorphic — and defining the schema in advance became nearly impossible. NoSQL databases allow developers to store huge amounts of unstructured data, giving them a lot of flexibility.
Additionally, the Agile Manifesto was rising in popularity, and software engineers were rethinking the way they developed software. They were recognizing the need to rapidly adapt to changing requirements. They needed the ability to iterate quickly and make changes throughout their software stack — all the way down to the database. NoSQL databases gave them this flexibility.
Cloud computing also rose in popularity, and developers began using public clouds to host their applications and data. They wanted the ability to distribute data across multiple servers and regions to make their applications resilient, to scale out instead of scale up, and to intelligently geo-place their data. Some NoSQL databases like MongoDB provide these capabilities.
NoSQL database features
Each NoSQL database has its own unique features. At a high level, many NoSQL databases have the following features:
Check out What are the Benefits of NoSQL Databases? to learn more about each of the features listed above.
Types of NoSQL databases
Over time, four major types of NoSQL databases emerged: document databases, key-value databases, wide-column stores, and graph databases.
- Key-value databases are a simpler type of database where each item contains keys and values.
- Wide-column stores store data in tables, rows, and dynamic columns.
- Graph databases store data in nodes and edges. Nodes typically store information about people, places, and things, while edges store information about the relationships between the nodes.
To learn more, visit Understanding the Different Types of NoSQL Databases.
Difference between RDBMS and NoSQL databases
While a variety of differences exist between relational database management systems (RDBMS) and NoSQL databases, one of the key differences is the way the data is modeled in the database. In this section, we'll work through an example of modeling the same data in a relational database and a NoSQL database. Then, we'll highlight some of the other key differences between relational databases and NoSQL databases.
RDBMS vs NoSQL: Data Modeling Example
Let's consider an example of storing information about a user and their hobbies. We need to store a user's first name, last name, cell phone number, city, and hobbies.
In a relational database, we'd likely create two tables: one for Users and one for Hobbies.
In order to retrieve all of the information about a user and their hobbies, information from the Users table and Hobbies table will need to be joined together.
The data model we design for a NoSQL database will depend on the type of NoSQL database we choose. Let's consider how to store the same information about a user and their hobbies in a document database like MongoDB.
In order to retrieve all of the information about a user and their hobbies, a single document can be retrieved from the database. No joins are required, resulting in faster queries.
To see a more detailed version of this data modeling example, read Mapping Terms and Concepts from SQL to MongoDB.
Other differences between RDBMS and relational databases
While the example above highlights the differences in data models between relational databases and NoSQL databases, many other important differences exist, including:
- Flexibility of the schema
- Scaling technique
- Support for transactions
- Reliance on data to object mapping
To learn more about the differences between relational databases and NoSQL databases, visit NoSQL vs SQL Databases, or watch From RDBMS to NoSQL presentation from AWs re:Invent 2022.
NoSQL databases are used in nearly every industry. Use cases range from the highly critical (e.g., storing financial data and healthcare records) to the more fun abrnd frivolous (e.g., storing IoT readings from a smart kitty litter box).
In the following sections, we'll explore when you should choose to use a NoSQL database and common misconceptions about NoSQL databases.
When should NoSQL be used?
When deciding which database to use, decision-makers typically find one or more of the following factors lead them to selecting a NoSQL database:
- Fast-paced Agile development
- Storage of structured and semi-structured data
- Huge volumes of data
- Requirements for scale-out architecture
- Modern application paradigms like microservices and real-time streaming
See When to Use NoSQL Databases and Exploring NoSQL Database Examples for more detailed information on the reasons listed above.
NoSQL database misconceptions
Over the years, many misconceptions about NoSQL databases have spread throughout the developer community. In this section, we'll discuss two of the most common misconceptions:
- Relationship data is best suited for relational databases.
- NoSQL databases don't support ACID transactions.
To learn more about common misconceptions, read Everything You Know About MongoDB is Wrong.
Misconception: relationship data is best suited for relational databases
A common misconception is that NoSQL databases or non-relational databases don’t store relationship data well. NoSQL databases can store relationship data — they just store it differently than relational databases do.
In fact, when compared with relational databases, many find modeling relationship data in NoSQL databases to be easier than in relational databases, because related data doesn’t have to be split between tables. NoSQL data models allow related data to be nested within a single data structure.
Misconception: NoSQL databases don't support ACID transactions
Another common misconception is that NoSQL databases don't support ACID transactions. Some NoSQL databases like MongoDB do, in fact, support ACID transactions.
Note that the way data is modeled in NoSQL databases can eliminate the need for multi-record transactions in many use cases. Consider the earlier example where we stored information about a user and their hobbies in both a relational database and a document database. In order to ensure information about a user and their hobbies was updated together in a relational database, we'd need to use a transaction to update records in two tables. In order to do the same in a document database, we could update a single document — no multi-record transaction required.
NoSQL query tutorial
A variety of NoSQL databases exist. Today, we'll be trying MongoDB, the world's most popular NoSQL database according to DB-Engines.
In this tutorial, you'll load a sample database and learn to query it — all without installing anything on your computer or paying anything.
Authenticate to MongoDB Atlas
The easiest way to get started with MongoDB is MongoDB Atlas. Atlas is MongoDB's fully managed database-as-a-service. Atlas has a forever free tier, which is what you'll be using today.
- Navigate to Atlas.
- Create an account if you haven't already.
- Log into Atlas.
- Create an Atlas organization and project.
For more information on how to complete the steps above, visit the official MongoDB documentation on creating an Atlas account.
Create a cluster and a database
A cluster is a place where you can store your MongoDB databases. In this section, you'll create a free cluster.
Once you have a cluster, you can begin storing data in Atlas. You could choose to manually create a database in the Atlas Data Explorer, in the MongoDB Shell, in MongoDB Compass, or using your favorite programming language. Instead, in this example, you will import Atlas's sample dataset.
- Create a free cluster by following the steps in the official MongoDB documentation.
- Load the sample dataset by following the instructions in the official MongoDB documentation.
Loading the sample dataset will take several minutes.
While we don't need to think about database design for this tutorial, note that database design and data modeling are major factors in MongoDB performance. Learn more about best practices for modeling data in MongoDB:
Query the database
Now that you have data in your cluster, let's query it! Just like you had multiple ways to create a database, you have multiple options for querying a database: in the Atlas Data Explorer, in the MongoDB Shell, in MongoDB Compass, or using your favorite programming language.
In this section, you’ll query the database using the Atlas Data Explorer. This is a good way to get started querying, as it requires zero setup.
- 1. Navigate to the Data Explorer (the Collections tab), if you are not already there. See the official MongoDB documentation for information on how to navigate to the Data Explorer.
The left panel of the Data Explorer displays a list of databases and collections in the current cluster. The right panel of the Data Explorer displays a list of documents in the current collection.