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MySQL and MongoDB are two of the most in-demand and competitive database services for web applications. Both are database management systems (DBMS) that enable you to extract data and create reports from a site or application, but they are designed in different ways. MySQL is a legacy table-structured system, whereas MongoDB is a document-based system.
SQL stands for “structured query language.” Developed in 1995, the MySQL database has become a default database structure and widely adopted as a result.
For over two decades, structured query language has been the mainstay design-model for developing relational database management systems (RDBMS) so as to maintain data and data storage.
The language is constructed in a way that enables any data type to be entered, categorized, searched and retrieved easily. From a big data corporate database to a small site like a website for a local business, MySQL supports data query, storage and data security as a standardized database design.
The “My” in “MySQL” is not a pronoun, as some may mistake it to be. It is a shortened name of one of the developers’ daughters. Swedish developers originally created the ubiquitous database and continue to have an impact in the database space. However, the Oracle corporation owns it today. Competitors have also subsequently adapted the relational language, as seen with the MS SQL database server and PostgreSQL.
Founded in 2007, MongoDB Inc. was a new approach to databases design. MongoDB created a way to store the “humongous” amount of data necessary for scalable use-cases, and the “Mongo” in the name is short for humongous. As both digital services and sites grew, the enormous amount of data required to scale this growth presented a need for more flexible database management and functionality. MongoDB’s design is in response to a need for nimble, information-rich database performance. It stores data as MongoDB documents, which is the touchstone of its design.
MongoDB serves e-commerce and content serving sites, for example, that benefit from its flexibility and scalability. Businesses use the MongoDB database as a high-performance solution to update data more quickly in structure and information.
To understand their unique differences, it’s helpful to understand their similarities, and then how their database schemas diverge.
There’s much about these two systems that are alike. At their core, both are database management systems (DBMs) that serve as the ground-level information network for any type of digital site or space. They store data on a computer system or network as part of the foundation of web applications.
With drivers (or interfaces), both MySQL and MongoDB support the following programming languages:
Web apps require a data foundation as database services to run successfully. Both MongoDB and MySQL enable organizations to distribute, modify or deploy cloud-native applications, for example.
Moreover, developers for both systems originally created these as open-source databases, where code is free for anyone to use and to distribute.
In sum, these are both fundamentally open-source database management options, but that is where similarities end. The driving difference is in the way each one is designed.
MongoDB is a document-based non-relational database management system. It’s also called an object-based system. It was designed to supplant the MySQL structure as an easier way to work with data.
On the other hand, MySQL is a table-based system (or open-source relational database). The table-based design is the data query structure for search and is considered an SQL database. Also, data is searchable and accessible in relation to another data point or set.
As data management and data volume needs grew, businesses began to perceive MySQL as a more rigid architecture and not as flexible for reformatting data structures. This sentiment is largely due to the table-based design, which enables sites and apps to apply a finite set of multivariate search queries.
It would be a mistake to assume that one system is leaps beyond the other in performance and responsiveness. Both MongoDB and MySQL perform fast, and both are powerfully designed DBMs.
MySQL is designed with SQL and designed on a B-tree table structure, meaning that logarithmic interactions within the structure enable the server engine to quickly scan and search data sets for related data.
MySQL has two main components: A type of storage engine and the language used to work with data. The storage engine is where data is created, retrieved, sent and stored. The language is how to access it.
In the last decade, MySQL operated mostly with a non-transactional storage base, meaning that data is defined and separate apart from other data, making it easier to locate for updates. Currently, the system employs a transactional storage engine, but works with many other types of storage formats, such as CSV (comma-separated-values) or gzip (compression-based engine format).
MySQL is also node-based, so that search for data is hastened by the tree structure, creating an efficient search, index and query experience. MySQL employs this structure to store data in fields, or data sets, that are relational to other data.
For example, a company directory may exist as a data field of individual information, and the data field may include departmental information. In data terms, these are also identified as value pairs, or “key value pairs.” Both data sets point to a department as the key attribute, and the elements within the data fields further define the department, as in its purpose, employees and other relevant attributes. When structured in a MySQL database, these are related data.
Furthermore, you can run MySQL on almost any operating system, from Windows to Linux and MacOS — though historically, users note that Linux is optimal.
MongoDB is known as a NoSQL database, or non-relational system. It’s founded upon documents as the unit of data for search, thus making it an object-based system. It is written with and employs binary JSON language; it also uses MongoDB query language, which many view as a universal, lighter or more flexible structure with which to work. Additionally, MongoDB employs BSON — JSON-like documents that are binary coded into typically smaller files. Many developers find these easier to manipulate, making data management faster.
Like MySQL, MongoDB supports various storage engine types. But the structure is what sets it apart and for what many organizations view as a reason to choose this type of database system. It is structured with a dynamic “schema” design, which is a way information is ordered that makes it flexible and fast.
MongoDB is a particularly useful system for both structured and unstructured data. Structured data is straightforward — written content is an example. Unstructured data is more difficult to store and organize. Rich media or facial recognition are just a few types that MongoDB seeks to better manage as this type becomes even more prevalent in big data.
MySQL is enterprise-grade and powerful across platforms and networks. It is a leader in the space and continues to create and release comparable database options, such as an upgraded blend of NoSQL DBM capabilities. It is also known to be compatible with more systems because of its time on market and mobility. Therefore, it is also viewed as a scalable solution.
Because of its cross-section of API, server, programmatic and administrative tools and options, MySQL is considered highly accessible and typically runs with no downtime.
Finally, it is created with data authentication, with secure socket layer (SSL) security. The security layer makes sensitive data more protected in transmission.
Additionally, it features easy-to-change fields, which enables users to avoid large-scale overhaul or re-calibration for changing organizational or data needs.
The document data model also provides businesses with a more sophisticated experience in storing, accessing, indexing and combining any type of data, for both code-friendly and native data models. Thus, conversion mapping is not necessary for greater durability and easier scaling.
MySQL is well-suited for the following use cases:
MongoDB is optimal for the following use cases:
Source: IBM
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