Introduction The relational databases held the leadership for decades and at that time the choice was quite obvious, either MySQL, Oracle, or MS SQL, just to name a few. They’ve served as a basis for tons of enterprise applications, while modern apps require more diversity and scalability. Non-relational databases, like MongoDB, have appeared to meet the existing requirements and replace current relational environment. This post originally appeared on DA-14 website. Read the original post here . What is MySQL? is a full-featured open-source relational database management system (RDBMS) that was originally built by MySQL AB and currently owned by Oracle Corporation. It stores data in tables that are grouped into a database, uses Structured Query Language (SQL) to access data and such commands as ‘SELECT’, ‘UPDATE’, ‘INSERT’ and ‘DELETE’ to manage it. Related information can be stored in different tables, but the usage of JOIN operation allows you to correlate it, perform queries across various tables and minimize the chance of data duplication. MySQL MySQL is compatible with nearly all operating systems, namely Windows, Linux, Unix, Apple, FreeBSD and many others. It supports various storage engines, like InnoDB (it is the default one), Federated, MyISAM, Memory, CSV, Archive, Blackhole and Merge. What is MongoDB? is a popular open-source document-oriented database developed by 10gen, later called the MongoDB Inc. In this case, documents are created and stored in BSON files, Binary JSON (JavaScript Object Notation) format, so all JS types of data are supported. That being the case, MongoDB is often applied for Node.js projects. Besides of that, JSON enables transferring data between servers and web apps with the use of the human-readable format. It is also a better option, when it comes to storage capacity and speed, as it offers greater efficiency and reliability. MongoDB One of the top benefits offered by MongoDB is the use of dynamic schemas that eliminates the need to pre-define the structure, like fields or value types. Such model allows hierarchical relationships representation, array storage, and ability to change the records structure by simply adding or deleting fields. This NoSQL solution comes with embedding, auto-sharding, and on-board replication for better scalability and high availability. Interesting to read: Databases in Details: How to Choose the Right One? MySQL vs MongoDB 2017: side-by-side comparison MySQL vs MongoDB: pros and cons Comparing MongoDB vs MySQL performance is difficult, since both management systems are extremely useful and the core differences underlie their basic operations and initial approach. However, MongoDB vs MySQL is a hot argument that is going on for a while now: mature relational database against a young non-relational system. Both are open-source and easily available, as well as both systems offer commercial versions with tons of additional features. Which database to choose? MongoDB attracts users with its open and simple philosophy, as well as the collaborative and helpful community, while users report the exact opposite regarding MySQL, after Oracle’s acquisition. Another issue with the latter one is owner’s focus on MariaDB development along with refuse to accept community patches and to provide sustainability plan. These factors have resulted in a standstill, though MySQL is still the go-to solution for multiple companies worldwide. Comparing MongoDB speed vs MySQL, developers note that the latter one lacks speed and experience difficulties with large data volumes, so it’ll be a better choice for companies with smaller databases and looking for a more general solution. While this is one of the advantages of MongoDB over MySQL: the ability to cope with large and unstructured amounts of data. To answer the main question: “when to use MongoDB instead of MySQL?” you need to take into account your project requirements and further goals. MySQL is well-recognized for its high performance, flexibility, reliable data protection, high availability, and management ease. Proper data indexing can solve the issue with performance, facilitate interaction and ensure robustness. But if your data is unstructured and complex, or if you can’t pre-define your schema, you’d better opt for MongoDB. And what is more, if you need to handle a large volume of data and store it as documents — MongoDB will help you to meet the challenges.
