Apr 17, 2023 By Team YoungWonks *
Introduction to MongoDB and MySQL
MongoDB is an open-source database developed by MongoDB, Inc. whereas MySQL is a popular open-source relational database management system (RDBMS) that is developed and distributed by Oracle Corporation.
MongoDB and MySQL are widely used in the development of mobile applications and web applications like e-commerce websites and content management systems. Both have programming support for most of the languages like Java, NodeJS, and Python. While these database management systems (DBMS) have similarities, they also have differences that make them better suited for different projects and use cases.
We will cover MongoDB vs MySQL in the following areas:
- Data Storage Model: SQL or NoSQL
- Efficiency: Scalability and Performance
- Query Language
- ACID Compliance
- Aggregation and Stored Procedures
Data Storage Model: SQL or NoSQL
One of the main differences between MongoDB and MySQL is the data storage model that they use. MongoDB is a NoSQL database, which means that MongoDB stores data in flexible, JSON-like documents called BSON (Binary JSON). This allows it to store complex data structures and handle large volumes of data more efficiently than traditional relational databases like MySQL and PostgreSQL.
Data in MongoDB is stored in collections, which contain documents. Each document has key-value pairs and can have a different structure, allowing for flexible and dynamic schema design. For example, two documents in the same collection can have different keys or even different number of key-value pairs. This means that it is possible to store semi-structured or unstructured data in MongoDB.
On the other hand, MySQL, is one of the relational database management systems (RDBMS) that store data in tables with rows and columns. It is designed to support structured data and uses SQL (Structured Query Language) to manage and manipulate the data.
Data in MySQL is stored in tables with a defined schema. Each table has a set of columns with specific data types, and each row in the table represents a single record. In MySQL, the data structure needs to be defined beforehand and is less flexible compared to other databases. MySQL is more suitable for managing structured data like financial transactions and product information.
Efficiency: Scalability and Performance
MongoDB and MySQL differ significantly in scalability and performance. MongoDB is designed for high-performance horizontal scalability, meaning it can scale out by adding more servers to a cluster and can handle big data and high levels of concurrency more efficiently than MySQL. It is often used for applications that require real-time analytics, as well as for applications that require the ability to scale up or down quickly. MongoDB offers a highly efficient and reliable replication system that allows for the automatic synchronization of data across multiple servers. This ensures high availability of data even in the event of server failure or downtime.
Although MySQL can still handle a large amount of data, it usually scales vertically by adding more resources to a single server. It is more suitable for applications with limited concurrency and smaller data sizes.
By observing how the two systems handle queries, we find that MySQL database performance is faster when selecting many records, while MongoDB excels at inserting or updating a large number of records.
Drawbacks to using MongoDB and MySQL
There are also a couple of drawbacks to using either of the databases. In MongoDB documents, nesting at a depth of more than 100 layers is not allowed. Their document is restricted in size to 16 MB. Also, as MongoDB does not support join operations, the programmer must manually include it which leads to memory being utilized more frequently than necessary.
Although MySQL is a highly efficient and reliable relational database management system for handling structured data, it does have some drawbacks. One major limitation is its lack of support for flexible or unstructured data, which means that it cannot handle semi-structured or unstructured data sets as efficiently as MongoDB. Additionally, while MySQL supports scalability, it typically achieves this by increasing the resources of a single server rather than horizontal scaling, making it less well-suited to applications with large amounts of data or high levels of concurrency. Another drawback of MySQL is its relatively limited replication and synchronization capabilities.
Both MongoDB and MySQL make use of SSL and offer secure reliable authentication mechanisms that can be customized to meet the specific needs of an application or organization. One key difference between MongoDB and MySQL's authentication mechanisms is that MongoDB allows users to define access controls at the database, collection, and document levels. This means that users can define different authentication rules for different parts of the database, providing more fine-grained control over access. In MySQL, authentication rules are defined at the user level, and users can only be granted or revoked permission to perform specific actions on the database as a whole.
The query language of a DBMS allows users to retrieve, update, and manipulate data stored in the database.
MongoDB uses a query language that is more flexible and expressive. It allows users to specify not just the data to retrieve, but also the structure and shape of the data they want. MongoDB query language uses JSON-like syntax and provides a wide range of operators and expressions for filtering, aggregating, and transforming data.
SQL (Structured Query Language) is a declarative language used in relational databases to manipulate data in a structured manner. SQL statements specify what data to retrieve, but not how to retrieve it. The database management system figures out the best way to execute the query based on the indexes, data distribution, and other factors.
Being a non-relational database, MongoDB does not have built-in relationships like SQL does (read next paragraph). Instead, relationships are represented as references between documents. To model relationships in MongoDB, you can store a reference to another document in one document, or you can store an array of references in a document. This approach provides more flexibility, as it allows you to represent complex hierarchical relationships and embed related data in a single document, but it also requires you to write more code to enforce relationships and maintain consistency.
In MySQL databases, relationships are explicitly defined and are a fundamental part of the data model. Relationships can be established between tables using foreign keys, which are columns in one table that refer to the primary key of another table. There are three main types of relationships in SQL: one-to-one, one-to-many, and many-to-many.
ACID compliance is a set of properties (Atomicity, Consistency, Isolation, and Durability) that ensure reliable and consistent processing of transactions in databases. SQL databases like MySQL and PostgreSQL are typically ACID-compliant, while MongoDB provides limited ACID guarantees.
In MySQL databases, each transaction is processed completely or not at all, and the database remains in a consistent state at all times. If a transaction fails, it is automatically rolled back, and the database remains unchanged.
MongoDB, on the other hand, provides atomic transactions at the document level, meaning that all operations within a single document are treated as a single transaction. However, MongoDB does not provide full ACID compliance across multiple documents, meaning that some data may be left in an inconsistent state if a transaction fails.
Aggregation and Stored Procedures
What is MongoDB used for? What is MySQL used for?
When deciding which database to use for a project, it is important to consider the specific needs and requirements of the application. If you need a database that can handle large volumes of data, handle high levels of concurrency, and scale quickly, MongoDB may be a good choice. On the other hand, if you need a database that supports structured database schema, transactions, and complex queries, MySQL may be a better fit.
Both MongoDB databases and MySQL databases have cloud services available. Some popular cloud services for MongoDB include MongoDB Atlas, Amazon DocumentDB, and Google Cloud MongoDB. Some popular cloud services for SQL databases include Amazon RDS, Microsoft Azure SQL Database, and Google Cloud SQL. These cloud services allow you to store, manage, and scale your database easily and efficiently in the cloud.
You can also have a look at the cheatsheet for MongoDB vs MySQL through this link: https://www.youngwonks.com/resources/mongodb-vs-mysql-cheatsheet
Frequently asked questions
This section answers some frequently asked questions related to MongoDB and MySQL.
What is Structured Query Language with an example?
Structured Query Language (SQL) is a standard programming language used to manage and manipulate data in relational databases like MySQL. SQL provides a standardized way to interact with the database, making it easy for developers to insert, update, and retrieve data. An example of SQL syntax is as follows:
SELECT * FROM companies WHERE name = 'YoungWonks';
This SQL query selects all columns from the companies table where the name column is equal to 'YoungWonks'. The asterisk (*) represents all columns in the table.
What is the difference between SQL and MySQL?
The main difference between SQL and MySQL is that SQL is a language used to interact with databases, while MySQL is a relational database management system (RDBMS) used to store and manage data. While SQL can be used on any RDBMS, including MySQL, it cannot do all the things that an RDBMS like MySQL can do without input from other languages such as PHP or Java. With MySQL, however, you can use its built-in functions and programming interfaces to create an application program or website quickly and efficiently. Furthermore, MySQL has built-in security features that ensure the integrity of your data stored in the database.
Is MySQL used anymore?
Yes, MySQL is still widely used in many industries and continues to be a popular database structure choice for managing data. It is one of the most widely used relational databases today, with a user base that includes everything from small start-ups to large enterprises. It is also used in storage engines like Archive, Merge, CSV, and Blackhole.
Is MySQL free to use?
Yes, MySQL is free to use. It is released under the GNU General Public License and can be used for both commercial and non-commercial purposes on Windows, Linux, and MacOS platforms. In addition, some distributions of MySQL offer additional features or support options for a fee.
Is MongoDB better than SQL?
Neither MongoDB nor SQL can be said to be better than the other. Both databases have their strengths and weaknesses, and the choice depends on the specific requirements of the application. Developers must assess their needs in scaling, data type management, data models, data security and query behavior before deciding which database to use.
What is the difference between SQL and MongoDB?
SQL and MongoDB are two vastly different database management systems that are designed to cater to different needs. SQL is a language used to interact with relational databases, while MongoDB is a NoSQL, document-oriented database. The most significant difference between the two lies in the way they store data. For more information on MongoDB, please read our following blog: https://www.youngwonks.com/blog/What-is-MongoDB
Is JSON a programming language? What is JSON used for?
JSON is used for data exchange between client-side and server-side applications, as well as between different applications and services. It is used to transmit data in a format that is easy to parse and can be used in a variety of programming languages. JSON is also used for storing and querying data in NoSQL databases, such as MongoDB, which uses document-oriented data models. JSON is a preferred format for APIs because it is easy to understand and work with for developers. Many major web services such as Twitter, Facebook, and Google use JSON as a standard format for API responses. In summary, while JSON is not a programming language, it is a valuable tool for transmitting and storing data and plays a crucial role in modern web development.
What is the difference between sharding and partitioning?
Sharding is a method of horizontally partitioning data across multiple servers or databases, while partitioning is a method of logically grouping related records and separating them from other records. Sharding helps with scalability and performance by spreading out the load across multiple machines, thus increasing throughput. Partitioning helps with query optimization by providing an efficient way to separate data into smaller chunks for each query operation. Both sharding and partitioning can improve the performance of database operations, but they have different implementations and use cases.
*Contributors: Written by Shreya Rajendra; Edited by Rohit Budania; Lead image by Shivendra Singh