MongoDB vs MySQL Comparison: Which Database Is Better?

Nowadays, in times of technology, new companies and companies are thinking about storing and managing data better to get a great customer perspective. And we can also meet new user expectations or beat the competitive market with new business models and applications.

Considering the best DBMS is always a difficult task in this technological world, as there are many options to choose from. DBMS is divided into two types: relational and non-relational. In recent years, the use of MySQL has increased significantly in relational database systems such as PostgreSQL.

On the other hand, not relational database management systems like MongoDB. These can store and manage large amounts of data.

Let us discuss which DBMS is better suited considering relational and non-relational DBMS, namely MongoDB and MySQL.

So let's take a closer look at comparing MongoDB and MySQL

What is MySQL?

MySQL is an advanced relational open source DBMS. It is developed, supported and distributed by Oracle. MySQL stores and manages data using tables and structured query language (SQL) to access the database. The SQL language is used on a server called SQL Server Databases.

Use simple commands such as "INSERT", "DELETE", "UPDATE", "SELECT" to access and manage data. With MySQL, you can predefine the database schema based on your configuration and requirements to monitor the connections between fields and tables.

What is MongoDB?

MongoDB is popularly known as a non-relational database system. In MongoDB, the data is saved in the form of a document format in a binary representation model called BSON. Related information is stored collectively by MongoDB for quick access to queries.
In MongoDB, a document is called a large JSON object. It does not contain a specific scheme or format. For each type of query access, associated data is saved in the query language MongoDB. This process is different for different field types.

If a new field type has to be combined with a document, the field can be generated without having to change all other documents in the entry process, without having to separate the system and update a catalog of the central system. Essentially, schema validation is used to support data governance handlers for each collection.

Data Structure and Storage

As we know, MySQL follows the relational model database system. In what data are stored in the form of tables. Together with you, you have to predefine the schema, depending on the requirements between fields and tables.

In MongoDB, on the other hand, the data is saved in the form of documents in a capture format. This big difference is great support for developers. Define the schema based on the code and there is no need to continue the schema migrations in the future.

Concepts and Terminology Comparison

Many terms in MongoDB have close analogies in MySQL. The following table describes the general connections between MongoDB and MySQL.

MongoDB                                               MySQL

Collection                                                                           Table
Field                                                                                     Column
Aggregation Pipeline                                                        Group_BY
Secondary Index                                                                 Secondary Index
ACID Transactions                                                             ACID Transactions

Syntax Comparison and Query Language

The query languages MongoDB and MySQL are strong. An unstructured query language used by MongoDB. And documents that are saved in the form of large JSON file formats. At the time of running MongoDB, various types of operators are used that are similar to the JSON document file. MongoDB supports Boolean queries.

On the other hand, MySQL works with the structured query language when accessing the database. Maybe it's easy. This language is very strong and consists of two parts.

DDL (data definition language)
DML (data processing language)

Select records from the required table for the example:

MySQL:

SELECT * FROM table_name

MongoDB:

db.table_name.find()


Insert records into the required table and it is mentioned below:

MySQL:

INSERT INTO table_name (cust_id, branch, status) VALUES ('app1', 'sub', 'C')

MongoDB:

$db.table_name.insert({ cust_id: 'app1', branch: 'sub', status: 'C'})

Some of the concepts are mentioned below.

SQL Concepts                               MongoDB Concepts

SELECT                                                                   $project
LIMIT                                                                      $limit
join                                                                          $lookup
ORDER BY                                                              $sort
COUNT                                                                    $sum

Speed and Performance

In MySQL, data is spread across multiple tables, so multiple tables must be able to write and read data at the same time. While in MongoDB all documents are stored in a single entity called a JSON file, this is the reason why access to data is very fast. This means that all data is written and read in a single property document. Although MySQL is compatible with JSON, you may not get the same benefits that MongoDB offers.

MySQL is slow compared to MongoDB because it uses large amounts of data. If the volume of data is larger, it cannot deal with unstructured language.

Developer Productivity

Die Erstellung von Anwendungen in MySQL ist ein langsamer Prozess, da das strukturierte Modell starrer Tabellen verwendet wird. Im gleichen Fall hat das Arbeiten mit JSON-Dokumenten in MongoDB große Entwicklungszyklen, die bis zu friday fünfmal dauern. MongoDB-Dokumente werden direkt Oops zugewiesen, sodass Entwickler leichter sehen und verstehen können, wie Anwendungsdaten in die Datenbank eingefügt werden.

Security Model

MongoDB can create its control with a number of variable requirements. It contains important security functions such as verification, authorization and authentication etc. SSL (Secure Sockets Layer) and TLS (Transport Layer Security) are also supported for encryption of the server endpoint. This ensures that the only customer required to encrypt can choose to access the documents.

As Business Application When MongoDB used

1. Require cloud-based services
2. You need to reduce the cost of schema migration
3. The requirement of the database administrator is less
4. Shading solutions are required.

Adobe, Electronic Arts, Ebay, Cisco, Google, Facebook etc. These are the big companies that use MongoDB as the programming language for databases.

As Business Application When MySQL used

1. Very small budget
2. Fixed schema for databases
3. Privacy priority
4. Request high transaction rates

NASA, U.S. Navy, YouTube, Netflix, Spotify, Uber, Bank of America, etc. These are the big companies around the world that use MySQL as the programming language for databases.

Conclusion

MongoDB and MySQL have their weaknesses and strengths. If you need data to support older applications or multi-line transactions, the relational database is the right choice for your company or company. If you need more flexibility and schema-free options, both of them may be able to work with unstructured data. In this case, MongoDB is the best and best option. MongoDB is also known as a NoSQL database. This database is more unusual and is suitable for processing further data.

Author Bio

Anjaneyulu Naini loves writing excellence and is passionate about technology. He believes that a skill or talent is worth more than just a degree. He currently works as a content writer on MindMajix.com.