Learn about the key differences between MySQL and MongoDB, including features, flexibility, and applications for enterprise business.
MySQL and MongoDB are two of the most demanded and competitive database services for web applications. Both are database management systems (DBMS) that allow you to extract data and create reports from a site or application, but they are designed in different ways. MySQL is a legacy system structured in tables, while MongoDB is a document-based system.
What is MySQL?
SQL stands for “structured query language”. Developed in 1995, the MySQL database has become a default database structure and, as a result, has been widely adopted.
For more than two decades, the
structured query language has been the fundamental design model for developing relational database management systems (RDBMS) to maintain data and data storage
.
The language is built in a way that allows any type of data to be easily entered, categorized, searched, and retrieved. From a corporate big data database to a small site like a website for an on-premises business, MySQL supports data querying, storage, and data security as a standardized database design.
The “My” in “MySQL” is not a pronoun, as some may confuse it. It is a shortened name of one of the developers’ daughters. Swedish developers originally created the ubiquitous database and continue to have an impact on the database space. However, Oracle Corporation owns it today. Competitors have also subsequently adapted the relational language, as seen with the MS SQL database server and PostgreSQL.
What is
MongoDB?
Founded in 2007, MongoDB Inc. was a new approach to database design. MongoDB created a way to store the “huge” amount of data needed for scalable use cases, and the “Mongo” in the name is short for humongous. As both digital services and sites grew, the massive amount of data needed to scale this growth presented the need for more flexible database management and functionality. MongoDB is designed to address the need for agile, information-rich database performance. It stores data as MongoDB documents, which is the touchstone of its design.
MongoDB serves e-commerce and content service sites, for example, that benefit from its flexibility and scalability. Companies use the MongoDB database as a high-performance solution to update data faster in structure and information.
MongoDB vs. MySQL:
Similarities and Differences To understand their
unique differences, it helps to understand their similarities and then how their database schemas diverge
.
There
is much about these two systems that are similar. In essence, both are database management systems (DBMs) that serve as a ground-level information network for any type of site or digital 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:
- Java
- Python
- Node.js (in JavaScript support
- Server-side PHP (or hypertext preprocessor)
)
Web applications require a database as database services to run properly. Both MongoDB and MySQL allow organizations to distribute, modify, or deploy cloud-native applications, for example.
In addition, the developers of both systems originally created them as open-source databases, where the code is free for anyone to use and distribute
.
In short, both are fundamentally open source database management options, but that’s where the similarities end. The driving difference is in the way each is designed.
Differences
MongoDB is a document-based, non-relational database management system. Also called 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). Table-based design is the data query structure for search and is considered a SQL database. In addition, the data is searchable and accessible in relation to another point or dataset.
As data volume and management needs grew, enterprises began to perceive MySQL as a more rigid and not-so-flexible architecture for reformatting data structures. This sentiment is largely due to table-based design, which allows sites and applications to apply a finite set of multivariate search queries.
How MySQL and MongoDB work
It would be a mistake to assume that one system is making leaps beyond the other in performance and responsiveness. Both MongoDB and MySQL work fast, and both are powerfully designed DBMs.
MySQL is a legacy system
MySQL is designed with
SQL and designed in a B-tree table structure, which means that logarithmic interactions within the structure allow the server engine to quickly scan and search datasets
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. Language is how to access it.
In the last decade, MySQL operated primarily on a non-transactional storage base, meaning that data is defined and separated from other data, making it easier to locate 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 data search is accelerated by the tree structure, creating an efficient search, index, and query experience. MySQL uses this structure to store data in fields, or datasets, that are relational to other data.
For example, a company directory may exist as an individual information data field, and the data field may include departmental information. In terms of data, these are also identified as value pairs or “key value pairs”. Both datasets point to a department as the key attribute, and elements within the data fields further define the department, such as in its purpose, employees, and other relevant attributes. When structured in a MySQL database, this is related data.
In addition, it can run MySQL on almost any operating system, from Windows to Linux to MacOS, although historically, users note that Linux is optimal.
MongoDB
is a NoSQL system
MongoDB is known as a NoSQL database, or non-relational system. It relies on documents as a unit of data for search, making it an object-based system. It is written and uses binary JSON language; it also uses the MongoDB query language, which many see as a universal, lighter or more flexible structure to work with. In addition, MongoDB employs BSON, JSON-like documents that are binary-encoded into typically smaller files. Many developers find these easier to manipulate, making data management faster.
Like MySQL, MongoDB supports several types of storage engines. But the structure is what sets it apart and why many organizations see it as a reason to choose this type of database system. It is structured with a dynamic “schema” design, which is a way of ordering information that makes it flexible and fast.
MongoDB is a particularly useful system for structured and unstructured data. Structured data is simple: 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 is looking to better manage as this type becomes even more prevalent in big data.
MySQL Benefits
MySQL is enterprise-grade and powerful across all platforms and networks. It is a leader in the space and continues to create and launch comparable database options, such as an enhanced combination of NoSQL DBM capabilities. It is also known to be compatible with more systems due to its time on the market and mobility. Therefore, it is also seen as a scalable solution.
Due to its cross-section of API, server, tools, and programmatic and administrative options, MySQL is considered highly accessible and generally runs with no downtime.
Finally, it is created with data authentication, with Secure Sockets Layer (SSL) security. The security layer makes sensitive data more protected in transmission.
Benefits of MongoDB
MongoDB’s use of dynamic schema design fosters a more flexible environment for data search, coding, integration, and database development
.
In addition, it features easy-to-change fields, allowing users to avoid large-scale revision or recalibration to change organizational or data needs
. The document data model
also provides enterprises with a more sophisticated experience in storing, accessing, indexing and combining any type of data, for both native and code-friendly data models. Therefore, conversion mapping is not necessary for greater durability and easier scaling.
Applications for MySQL and MongoDB MySQL is suitable for the following use cases: High-traffic sites, such as
- e-commerce or social sites Sites
- security protocols, such as government-based and high-compliance industries
that require high
MongoDB is optimal for the following use cases
:
- Legacy companies looking to upgrade big data
- Content Management Systems (CMS)
- High-query sites
and applications, such as MySQL, MongoDB and IBM analysis applications IBM
currently supports the open source MySQL database on the IBM platform with a choice in IBM Cloud Kubernetes Service or VMWare vCenter Server
. Learn more about IBM
and IBM
Cloud Databases for MongoDB. IBM now offers developer support for current MongoDB features to automate time-consuming DBM tasks more easily in a secure environment.