MySQL is an open-source RDBMS, which stands for a Relational Database Management System. More specifically, an RDBMS is a program used to update, manage and formulate relational databases. A relational database is a type of database (usually arranged into tables) that allows for recognizing data in relation to another piece of data within the same database.
MySQL is the most popular database system in the world. Released in 1995, it has decades of high-valued reputation and reliability. Moreover, it is fairly easy to use. Since the database schema is pre-defined according to specific conditions and rules, the data is organized in rows and columns, demonstrating the relationships between the various tables' fields.
Along with MySQL, PostgreSQL and SQL are RDBMSs that utilize their own variation of SQL (Structured Query Language).
MongoDB is also an open-source database but works as a document datastore, unlike MySQL’s RDBMS features. It stores documents within collections instead of tables with relations among them.
When using MongoDB, the data schema is not fixed. It is possible to remove or modify document properties within a collection, which allows for superior flexibility. In fact, documents can even be in the same collection and yet have completely different structures among themselves.
When comparing both open-source databases, the main difference is that MySQL is relational, while MongoDB is a document datastore.
Let’s examine other differences between MySQL and MongoDB regarding the following attributes:
In MongoDB, data is displayed in key-value pairs like JSON documents, allowing the database to have less constraints considering the schema design. This can be particularly advantageous for data with potential for fast growth or other changes. Plus, MongoDB does provide a pre-defined structured which can be adopted if preferred.
Regarding data schema, the same does not happen in MySQL. Even though it is possible to change the schema, modifications are not as flexible and dynamic as in document databases.
Before storing any data, MySQL mandatorily requires a pre-establishment of how the tables and columns will be organized. Altering the data schema requires carefully rethinking the database's DDL (Data Definition Language) and DML (Data Modeling Language).
Both databases, relational and document, use DDL and DML concepts. However, in relational databases, establishing the DDL and DML is vital. Contrarily, MongoDB has a more malleable data schema, thus, not being as concerned as MySQL about how data is structured.
Even though it may seem like a big con, this consistency is actually one of MySQL's greatest strengths because it keeps the data structured and clean.
Each MongoDB database contains collections, which in turn, are filled with documents. These documents can include various fields and types of information, allowing for data storage of documents that vary in content and size.
In MySQL, since the data schema is more constrained, every row within a table requires the same columns, which can be particularly hard to manage when working with high-volume databases. Hence, MySQL does not handle large and complex databases as easily as MongoDB.
In other words, MongoDB document database is superior to MySQL relational database when dealing with diverse and large quantities of complex data.
One of the most common questions when comparing MySQL vs MongoDB is which is faster.
MongoDB can accept more extensive amounts of structured or unstructured data faster than MySQL. However, imagine a business working with fairly small and less diverse amounts of data: speed is not necessarily something to be concerned for since other features (like reliability and data consistency) have the priority.
More important than comparing them in terms of speed, understanding the businesses' or projects' data requirements will determine which one is more suitable for your project and its potential to provide better results and performance.
MySQL is a mature and reasoned solution to ensure data privacy and integrity. Due to its explicit schema, MySQL creates reliable database structures by using tables that systematize data types, making the respective values queried adequately and easy to search. Since it requires data to be structured beforehand, this results in less technical debt.
Nonetheless, it can be a disadvantage in some cases, as it might be hard to design a suitable schema for complex data. Definitely, not an option for unstructured data.
On the other hand, MongoDB has a more flexible and faster performance for unstructured data. Document datastores are good when the data schema is hard to design beforehand. However, if the data is diverse, then creating indexes on the data's attributes becomes challenging, which means MongoDB requires frequent optimization of the data schema. Otherwise, it might be risking problems related to data consistency.
MySQL utilizes a privilege-based security model, which requires user authentication and can also provide or deny user privileges on a particular database. Plus, transferring data from the database to the server MySQL necessarily employs encrypted connections between clients and the server, using the Secure Sockets Layer (SSL) - a security protocol.
MongoDB's security consists of role-based access control that includes authentication, authorization, and auditing. Additionally, if encryption is desired, it is also possible to apply Transport Layer Security (TLS) and SSL.
Even though MongoDB and MySQL provide safe security models, if reliability and data consistency are a business priority, MySQL is the safest choice.
In computer science, ACID refers to a set of database transactions' properties that ensure data validity. It stands for atomicity, consistency, isolation, and durability.
While MySQL is considered ACID, being ACID compliant for MongoDB is not a priority since it would require sacrificing speed and high availability. In 2018, MongoDB made it possible to sustain ACID multi-document transactions. However, this option is off by default. On the other hand, MySQL's transactions are ACID, which ensures data validity considering transactions properties.
MySQL uses structured query language (SQL) when requesting information from a database table or combination of tables. SQL is the most popular and utilized query language requiring only a data definition language (DDL) and a data manipulation language (DML) to communicate with the database.
On the other hand, MongoDB uses an unstructured query language.
Requesting data or information from the JSON documents database implies that the query should specify the properties of the documents to obtain matching results. MongoDB supports multiple languages (such as Python, Java, C##, Perl, PHP, Ruby, and JavaScript) in which queries can be built.
To perform a query in MongoDB, the following function should be applied: DB.collection.find(). A compound query can establish specific conditions for various fields within the collection's documents using query operators. Query operations (e.g. $and, $or, $type, $eq, etc.) specify the conditions and enable query filter documents. Once the conditions are defined, it identifies which information or record to select accordingly and, further, update, read or delete it.
Nevertheless, MongoDB does not perform JOIN operations nor has an equivalent. With MySQL, JOIN operations (inner, outer, left, right, and cross) are applied to retrieve data from two or more database tables. Simply put, these operations allow relational data to relate by using a single SQL statement.
Check our article on Queries on Rails to find out more about query languages and JOIN operations.
It is hard to say which database is better when it all depends on the context they are being explored. Both MySQL and MongoDB are powerful database management systems that operate differently from one another. Therefore, even if one of the databases is the most suitable option for a specific business or project, it may not be the best solution for a different purpose. And some companies even rely on both systems to tackle distinct tasks.
One of the very few things MongoDB and MySQL have in common is that they are open source and easy to access. Moreover, both systems provide commercial versions with additional features. Apart from these similarities, at the core of their performance is their relational and non-relational nature.
As a document database, MongoDB is the most suitable solution for high-volume environments, considering it does not limit the amount and types of data one wishes to store. It is particularly beneficial for cloud-based services since MongoDB's horizontal scalability aligns perfectly with the cloud's agility. Plus, it reduces workload, eases scaling within a business or project, and provides high availability and fast data recoveries.
Despite the many advantages this system might have, MySQL surpasses MongoDB in reliability and data consistency. And if security is also a priority, then MySQL is widely recognized as one of the most secure DBMS.
Relational databases are the most appropriate option when the type of application demands multi-row transactions (for instance, in accounting and banking systems). In addition to providing safety, MySQL also enables a high transaction rate. In fact, MongoDB focuses on allowing a high insert rate, whereas MySQL supports ACID transactions and concentrates on delivering transaction safety.
Overall, MySQL is highly recommended for businesses, institutions, or projects with a fixed data schema and does not intend to scale much in data diversity, thus requiring easy and low maintenance while ensuring data integrity and reliability.
On the other hand, MongoDB is the most suitable choice for growing businesses or projects with an unstable data schema. This system's non-relational data nature allows documents to be freely used and stored without a structure, making it easy to update and retrieve. MongoDB is often used in projects that require content management, handle IoT (Internet-of-Things), perform real-time analytics, and so on.
MySQL is an open-source relational database, meaning that its data is organized into tables allowing you to relate a piece of data with other parts of it. MongoDB is also open-source but works as a document database. Hence, it does not associate records, and its data schema is unfixed, allowing for a more dynamic and flexible database with a higher capacity to insert information.
Before deciding upon the best database system, the specific business or project's priorities should be clear and well-established.
Since MongoDB handles large amounts of data better than MySQL, it is the fittest option for cloud-based services, for applications prone to grow and change, and for environments characterized by high data volume.
With MySQL, its fixed and structured data schema provides greater consistency and reliability than most databases. Another great benefit of using MySQL is its superior data security due to ACID-compliant transactions, being the most suitable choice for applications that value this feature.
In short, both databases will deliver a very satisfying performance if applied to a context that matches both the applications' needs and desires with the system's characteristics and features.
Marketing intern with a particular interest in technology and research. In my free time, I play volleyball and spoil my dog as much as possible.
CEO @ Imaginary Cloud and co-author of the Product Design Process book. I enjoy food, wine, and Krav Maga (not necessarily in this order).
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