Introduction
Selecting the correct DBMS to use can be hard. There are qualities of each DBMS that need to be considered based on demands. There have been a lot of studies on the subject of RDBMS vs NoSQL. Many of these studies conclude that NoSQL, in general, performs better than MySQL. However, these studies are mostly using unstructured big data which is known to favor NoSQL DBMS. MongoDB and MySQL, where they do evaluate structured data.
Their results show that MongoDB outperforms MySQL when it comes to managing structured data. Their study does not use any workload scaling features that RDS can provide, which can enhance the performance of MySQL. In this article, we are going to learn MongoDB vs. MYSQL.
What is MongoDB?
MongoDB is a non-relational, document-based database that stores its data in BSON format, which is a variation of JSON. MongoDB is an open-source project that is written in C++, and it was created in 2007. It uses a flexible elastic data storage model, which allows for a highly scalable environment.
MongoDB is the most popular NoSQL database, with Redis as second. The MongoDB document allows for storing several types of data in a very flexible manner through the use of a series of key/value pairs. These documents are stored in self-contained collections from where they can be queried.
In SQL, the tables need to be predefined in a schema; in MongoDB, this is optional. A MongoDB cluster uses a replication feature that creates copies of the data; these replicas(slaves) can be used for reading operations and backups for the primary image (master), which provides MongoDB with high availability.
Sharding is a powerful feature that MongoDB uses. Sharding is a form of horizontal scaling that allows for the distribution of the collections over multiple virtual machines. This distribution is done through load balancing.
Features of MongoDB
Some features of MongoDB are as follows:
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It is a NoSQL database without schemas. Therefore, we do not need to design the database schema when using MongoDB.
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The data is kept in BSON documents because the database is document-oriented
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It includes diverse data.
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It is simple to integrate with Hadoop for Big Data.
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It is unable to perform join operations.
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It gives the fields in the documents a tremendous amount of flexibility.
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It offers scalability, availability, and high performance.
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Additionally, multiple document ACID transition is supported.
- There is no need for SQL injection
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