If you’re in the initial stages of building an application, it is very important to choose the right database. Going ahead with the wrong one can hurt your database’s scalability, performance, and flexibility. This article on MySQL vs NoSQL aims to differentiate between one of the top relational databases (MySQL) and other non-relational (NoSQL) databases. We’ll explore some of the key differences in terms of data structure, scalability, performance, flexibility, and more, while understanding the key characteristics of each of them.
A relational database is a structured database that uses tables to store data. Each table stores an entity, with every row containing one instance of that entity. For example, for a student table, each row would uniquely identify a student, and the different columns would specify different attributes about the student. All relational databases use SQL (Structured Query Language) to interact with the database. If you know SQL, you can easily understand the minor variations that other relational databases offer. To get started with MySQL in the cloud, you can use PlanetScale’s free Hobby plan.
A non-relational database, on the other hand, doesn’t have a rigid structure. It can store data in the form of key-value pairs, documents, graphs, or column families. Due to the lack of a strict structure, it offers more flexibility, and you can store a wide variety of unstructured data. These databases don’t have a universal language, and it differs with each offering.
MySQL is one of the most popular open-source relational database management systems. Since it is a relational database, reliability and scalability are at the forefront of MySQL. It can handle large amounts of data and can scale well, as long as the data is structured. MySQL also offers great security features, transaction support following the ACID properties, and reliable backup and recovery options. It has also been around for much longer than NoSQL databases, so the ecosystem is rich and mature.
Here are some of the unique characteristics of MySQL vs NoSQL databases, and what MySQL has to offer:
- MySQL is easier to get started with.
- Structured data is simpler to visualize and prototype.
- Can handle large amounts of structured data.
- Supports a wide variety of programming languages.
- More mature than NoSQL databases.
- Robust transaction support.
- Good backup and recovery options.
There’s a small debate on whether NoSQL stands for NO SQL or Not Only SQL. Whatever that may be, NoSQL databases are popular for their ability to handle any kind of unstructured data. This stems from the need for databases that can handle real-world data in certain scenarios that are highly unstructured and not suitable for relational databases.
Industries that rely on big data most often use NoSQL databases, as they scale well and also efficiently process volatile data. Some of the most popular NoSQL databases include MongoDB, Cassandra, Apache CouchDB, and DynamoDB. Most NoSQL databases are inherently distributed and can ramp up or down the number of nodes based on the traffic, offering unmatched scalability and availability.
Here are some of the unique characteristics of NoSQL vs MySQL, and the benefits of NoSQL:
- NoSQL handles unstructured data very well.
- Extremely flexible.
- Inherently distributed and scalable.
- Highly available and fault tolerant.
- Schema design is not rigid.
- Supports a wide variety of unstructured data (documents like JSON, graphs, key-value pairs, etc.)
In this section, we’re going to differentiate between MySQL and NoSQL databases in terms of their data structure, schema, scalability, performance, querying, flexibility, data storage, and data modeling capabilities. In the end, we’ll cover a few use cases for SQL databases and NoSQL databases and see which one is the winner.
MySQL: MySQL is a relational database and data is stored in tables. There may be hundreds of tables in a real-world system. To use them effectively, we make use of keys to form relationships. If you have complex relationships in your data, you’re better off with a MySQL database. MySQL throws an error if you try to add data to the table that doesn’t conform to the predefined data structure. Schema design is much more of a straightforward process and is easier to visualize and prototype thanks to the structured nature of the data. But on the other hand, you must know what kind of data is expected in the system to design the schema efficiently.
NoSQL: NoSQL databases are best suited for data that doesn’t fit into a table. These can be documents, key-value pairs, graphs, or other unstructured data. Although they may not enforce a strict schema like relational databases, NoSQL still incorporates mechanisms for maintaining data integrity. The error handling and validation process in NoSQL may differ based on the specific implementation and configuration of the chosen NoSQL database. Schema design in a NoSQL database is quite challenging, mainly due to the unstructured nature of the data.
MySQL: Relational databases are horizontally and vertically scalable. There are several ways you can approach this: Vertically: By increasing server power by adding more storage, memory or processing power. Horizontally: By distributing the workload across multiple servers. The data itself can be horizontally scaled with sharding to deal with massive data sets.
NoSQL: NoSQL databases are also scalable vertically and horizontally, just like SQL databases.
- Performance is dependable on the nature of the data in the application.
- It prioritizes data integrity and consistency.
- Performs great for applications with unstructured data.
- Consistency and data integrity may be compromised compared to MySQL.
- Excels in horizontal scaling (due to flexible data models, distributes architecture, and automatic load balancing).
MySQL: MySQL uses SQL to query and interact with the database. There may be minor variations of SQL with offerings like PostgreSQL, MySQL, or Oracle SQL. But the underlying language is the same and can be used across any offering.
MySQL: MySQL, being a relational database, offers limited flexibility. This is because relational databases are inherently rigid in terms of their structure, which results in better data integrity and consistency.
NoSQL: These databases don’t follow a rigid structure and offer better flexibility in changing the way the data is formatted, based on how they are changing in your application. If you have rapidly changing data requirements, NoSQL databases are the way to go.
MySQL: MySQL follows a strict tabular structure for storing all kinds of data. Any data that you store must be modeled as a table. You form relationships between other tables to query and retrieve the required data. MySQL is more efficient in handling structured data.
NoSQL: NoSQL databases don’t have a particular structure to follow. Even if you are having a specific set of fields for a document, you can simply add a new document that has an additional field, and it won’t result in an error. NoSQL offers much more flexibility in terms of both the kind of data you store and in its structure.
MySQL: Transactional applications where data integrity and consistency are of utmost importance can use relational databases like MySQL. Think of applications in finance, or other mission-critical applications. Twitter is one company that uses MySQL. Please note that any domain which has structured data would use relational databases, even if they’re using NoSQL databases to manage unstructured data.
NoSQL: Best use cases for NoSQL databases involve large amounts of unstructured data. Video and music streaming platforms, as well as platforms dealing with a lot of images, are great examples. Think Spotify, etc. Other examples are some social media networks and applications in big data. Just like before, domains that use NoSQL databases, such as Forbes and Toyota, also use relational databases like MySQL to store their structured data (for example, user info on social networks).
In this battle of MySQL vs NoSQL, there is obviously no clear winner. The main reason for that is there shouldn’t be a winner. Each of these offers advantages and performance gains in specific scenarios. You, as a developer, must be equipped with the knowledge to understand how these work and when to use the appropriate database. Unique business needs must define the kind of database you need, rather than what’s under the hood.