MySQL is a popular Oracle-developed, free-to-use, and open-source relational database management system (RDBMS). MySQL stores data using tables and rows enforces referential integrity and uses structured query language (SQL) for data access, as with other relational structures when users need to access data from a MySQL database, they need to create a SQL query that brings together several tables to create a view of the data they need.
Database schemas and data models need to be defined ahead of time, and data must match this schema to be stored in the database. This rigid information storage approach provides some degree of security but trades this for flexibility. If it is necessary to store a new type or format of data in the database, schema migration must occur, which can become difficult and costly as the database size increases.
MongoDB is also open source and free to use; its design concepts, however, vary from conventional relational frameworks. MongoDB also styled as a non-relational (or NoSQL) method, takes a radically different approach to data storage, representing data as a collection of JSON-like documents (actually stored as binary JSON or BSON) as opposed to the relational system table and row format.
MongoDB documents consist of a set of key/value pairs of various types, including arrays and nested documents; the main difference, however, is that the arrangement of the key/value pairs will vary from document to document in a given collection. Since records are self-descriptive, this more versatile approach is possible.
There are important core distinctions between these two database systems. It is a matter of strategy rather than purely a technical decision to choose which one to use.
MySQL is a mature relational database system, offering seasoned IT professionals a familiar database environment.
MongoDB is a well-established, non-relational database system that offers enhanced versatility and horizontal scalability, but at the expense of some relational database, security features, such as referential integrity.
In the following pages, when deciding between MongoDB and MySQL, we'll look at some of the various considerations.
MongoDB vs MySQL- User-Friendliness
For developers, MongoDB is an attractive choice. To someone with programming experience, the data storage theory is clear and instantly understandable.
With no implemented schema, MongoDB stores data in collections. This versatile data storage approach makes it especially ideal for developers who may not be database experts, but who want to use a database to help their application growth.
This versatility is an essential benefit compared to MySQL: You must first consider the concepts of normalization, referential integrity, and relational database architecture to get the best out of a relational database.
MongoDB offers a versatile developer interface for teams that create applications that do not need all the safety features provided by relational systems with the ability to store documents from various schemes, including unstructured data sets.
A web application that doesn't rely on structured schemas is a typical example of such an application; it can easily serve unstructured, semi-structured, or structured data, all from the same set of MongoDB.
For users with extensive experience using conventional SQL scripting, developing relational database strategies, or changing or upgrading existing applications that already operate with a relational framework, MySQL is a popular option. For applications that need very complicated yet rigid data structures and database schemas over a large number of tables, relational databases may also be a safer option.
A banking application that needs very strict referential integrity and transactional guarantees to be implemented to protect precise point-in-time data integrity may be a typical example of such a system.
It is important to clarify, however, that MongoDB also supports transaction ACID properties (atomicity, consistency, isolation, and durability). This allows for greater flexibility in designing a transactional data model that can scale horizontally in a distributed environment and does not affect multi-document transaction efficiency.
A main advantage of the MongoDB layout is that the database is extremely simple to scale. A sharded cluster configuration enables a part of the database, called a shard, to be configured as a replica set as well. Data is spread over several servers in a shared cluster. This highly versatile approach allows MongoDB to scale both read and write output horizontally to cater to any scale of applications.
A replica set is the replication of a group of servers from MongoDB that carry the same data, ensuring high availability and recovery from disasters.
With a MySQL database framework, scalability choices are far more limited. You usually have two options: vertical scalability, or adding replicas to read. Vertical scaling means adding more resources to the current database server, but there is an implicit upper limit to this.
Read replication means connecting to other server's read-only versions of the database. This is, however, usually limited to a total of five replicas, which can only be used for reading operations. This can cause problems for either write-heavy applications, or write and read frequently for the database, as it is usual for replicas to lag behind the written master. MySQL has been introduced with multi-master replication support, but its implementation is more constrained than the features present in MongoDB.
It is very difficult to compare the performance of two entirely different database systems since both management systems approach the role of data storage and retrieval in radically different ways. Although two SQL databases can be directly compared with a collection of standard SQL benchmarks, it is much more difficult and subjective to do the same for non-relational and relational databases.
MySQL, for example, is designed for high-performance joins across multiple properly indexed tables. Joins are provided by the $lookup operation in MongoDB, but they are less important because of the way MongoDB documents appear to be used; they adopt a hierarchical data model and hold most of the data in one document, thereby removing the need for joins across multiple documentation
MongoDB is also optimized for write efficiency and provides a particular insert many() API to quickly insert data, prioritizing speed over transaction protection where row by row MySQL data needs to be inserted.
Observing some of the high-level query behaviors of the two systems, we can see that when choosing a large number of records, MySQL is faster, while MongoDB inserts or updates a large number of records significantly faster
MongoDB vs MySQL Flexibility
This is a clear one, and for MongoDB, a hands-down win. The schematic nature of MongoDB documents makes it incredibly simple overtime to develop and upgrade applications without the need for complicated and costly schema migration processes to be run as you would for a relational database.
There are more dynamic options with MongoDB for updating a collection's schema, such as creating new fields based on an aggregation pipeline or updating nested array fields. As databases increase in size, this advantage is especially important.
On the other hand, the migration of schemas and stored procedures that can be dependent on the modified schemas is slower for larger MySQL databases. The modular architecture of MongoDB makes this a lot less of a problem.
It is worth pointing out that there is a lot in common with both databases. Both are free to get started, both are easy to install on Linux and Windows, and both have broad support for common languages like Java, node.js, and Python is the programming language.
Furthermore, MongoDB provides MongoDB Atlas, a managed cloud solution that is also forever free to use for exploratory purposes, whereas you will need to have an account with one of the major public cloud providers for a MySQL managed cloud version and fall under their free tier terms in order not to pay.
With a versatile collection of permissions, MongoDB leverages the traditional role-based access control model. Users are assigned to a function, and specific permissions over data sets and database operations are given by that role. All correspondence is TLS encrypted, and encrypted documents can be written to MongoDB data collections using a master key that is never open to MongoDB, achieving data encryption at rest.
The same encryption features as MongoDB are provided by MySQL; its authentication model is also similar. Users may be given tasks, but also rights, granting them permission for specific operations of the database and specific data sets.
The key differences between MongoDB and MySQL, a schematic non-relational database system and a relational database system, respectively, were discussed in this article. When it is easier to use one than the other, we have clarified.
For each device, we have addressed scalability, consistency, and user-friendliness. Finally, from a comparative point of view, we have also clarified the flexibility and security features for both database systems.
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