What is MongoDB and Why It is Used? [Updated For 2024]
MongoDB is an open-source and NoSQL database system used to tackle large volumes of unstructured or semi-structured hierarchical and graph-based data. It follows modern principles and you can enjoy various advanced features such as higher compatibility with file formats like JSON, BSON, and XML. Undoubtedly, this database system is one of the top ones nowadays.
Talking about some stunning statistics of MongoDB, it is on the #1 ranking with a market share of 45.13% in the NoSQL databases market. It has more than 68,000+ customers in over 100 countries and downloaded more than 210 million times. According to Workat.tech, more than 1.5 million students registered for MongoDB University courses.
Isn’t it interesting enough?
Want to learn more about what is MongoDB and why it is used? All you need to do is, stay with this article and be good to go. Without further ado, let’s dive right into this.
Benefits of Using MongoDB NoSQL Database
MongoDB is a popular NoSQL database system with quality features and capabilities to replace traditional principles of relational databases such as MySQL database system. It can benefit you in several ways, let’s have a deep look at its benefits and advantages.
Flexible Options For Schema Design
Unlike relational databases where you need to first specify the schema, here you have flexible options with MongoDB NoSQL database management system. It supports BSON file format and allows you to make different structures for each format, and add fields without impacting the overall database schema structure. Meanwhile, you can make changes in schema over time to update your existing data.
Easy Hierarchical Relationships Representation
Another appreciative benefit of using the MongoDB NoSQL database system is that you can easily represent hierarchical relationships because the database stores the data in BSON format. This is how you reduce the need for joins which were used with projects on relational databases. You can also represent nested data with it.
Compatible with Unstructured Data
MongoDB is a non-relational database that supports unstructured data processing. It's because of the data model which it follows: schema-less document-oriented data model, capabilities to store data in BSON format, and options to flexibly change the schema over time.
Relational databases such as MySQL and PostgreSQL don’t show compatibility with hierarchical and unstructured data such as images, videos, graphs, etc. But with MongoDB NoSQL database you can easily process and embed this type of data.
Horizontal Scalability
MongoDB supports horizontal scalability and that’s why it is suitable for larger projects and applications where you tackle high traffic. In this type of scaling, the horizontal partitioning is done, while the method is also called the sharding technique.
Let’s understand it.
The sharding technique allows the MongoDB NoSQL database system to efficiently distribute high volumes of data across multiple nodes or servers for better management and reduced loads.
Indexing Support For High Performance
MongoDB efficiently supports the creation and management of indexes to improve data retrieving and query performance. This database supports different types of indexes such as single-field indexes, compound indexes, multikey indexes, text indexes, and geospatial indexes. All of these types of indexes are used for different purposes in specific types of conditions.
Complex Data Transformations
Most relational databases are not compatible with complex data transformation within the database, but NoSQL databases such as MongoDB provide you with this feature. This is because of the powerful aggregation framework that it has. It’s a pipeline-based system with which you can filter, analyze, and transform your data within the database.
Balanced Load Distribution
You rarely face any speed and performance-related issues when using MongoDB. This is because the database highly promotes balanced load distribution because of the sharding technique described above.
By doing horizontal partitioning, MongoDB divides the large volumes of data loads to different servers and nodes ensuring high speed & performance.
Cross Platform Compatibility
MongoDB ensures higher cross-platform compatibility because of several reasons like including its flexible schema, document-oriented structure, horizontal scalability, and aggregation frameworks. As it stores data in JSON format, so compatibility with different applications is ensured.
Pay attention – According to the experts, MongoDB supports geographically distributed databases. This feature is adding values to its cross-platform compatibility.
Geospatial Indexing Support
Geospatial indexing allows you to organize location based data for better querying system. You can promote the use of indexes in fields using this NoSQL database system and support management and organization of geographic data.
If your application is based on geographical data in which you’re spreading awareness about distances between points, mapping and related things MongoDB is best for you. You can find nearby locations and easily calculate distances between different places using this database system.
Two Popular Editions
This non-relational database system come up with two different editions: Community and Atlas. Community based edition is free and open-source provides basic management and organizing tools for small and medium projects. Using this edition is good if you don’t need advanced tools and features.
In contrast, the second edition named MongoDB Atlas is the cloud-based and fully managed database service which provides you with several features and tools. You can easily scale your project, manage data and promote automated backups. Choosing any edition depends on your project size, needs and personalized requirements.
These were all the benefits of the MongoDB NoSQL database management system. Now, let’s discuss its limitations or disadvantages in detail below.
Limitations of MongoDB NoSQL Database
Where NoSQL databases such as MongoDB provide you with flexibility and benefits, they also have some disadvantages. Explore them.
Hard Efforts to Manage Schema
Flexible schema is often the great advantage of the MongoDB database system, but sometimes it might be challenging. For example, if you have an application whose data is evolving day by day, it might become difficult for you to manage schema changes and avoid data integrity issues.
Increased Memory Storage
MongoDB relies on in-memory usage which is good to maintain data consistency and performance. But when you are working on a large project, the feature might become challenging to you. Meanwhile, it can lead to increased memory storage disturbing the overall performance.
No SQL-style Joins
MongoDB does not support SQL-style traditional joins due to which NoSQL developers might need to structure the whole data differently or adopt a manual denormalization process. It might be irritating or lengthy, especially for those developers who are more familiar with relational database structures.
Indexes and Overhead
MongoDB allows you to create indexes and ensure high query performance. But at the same moment, it introduces overhead during write operations. It has an impact on database speed and performance.
Not a Storage Efficient Database
Yes, MongoDB is not a storage-efficient database. Although, it manages your unstructured data in a mannered way but uses object-oriented and BSON format which generally contributes to large data sizes. That’s how it’s not an efficient database type, especially in terms of data storage.
MongoDB vs. MySQL: Which is Right For Your Project?
There are a few factors you need to keep in mind while choosing any database system between MySQL and MongoDB. If your data is structured, choose MySQL. In contrast, if your data is unstructured or semi-structured, choose MongoDB.
Apart from this, if you need to change data schemas, want to scale your project horizontally, geospatial indexing and data storage in JSON format, MongoDB is the right choice to you. In contrast, if you need to tackle complex data and show relationships between data along with well-defined schema, ACID compliance, transaction support, tools and frameworks, go with MySQL database system.
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Conclusion
In the above article, we have shared everything about what is MongoDB and why it is used. Along with this, we also shared the benefits, limitations, and other important points regarding this particular NoSQL database system.
We also explained how RemotePlatz can help you connect with the right MongoDB developer at costs you can comfortably afford and enhance your project success rate.
FAQs
What is the Purpose of MongoDB?
The purpose of the MongoDB NoSQL database management system is to store, manage, and retrieve unstructured data. In fact, the database is also used when you have large volumes of semi-structured data to be processed such as images, graphs, and hierarchical data, and files in formats like JSON, BSON, and XML.
What is MongoDB Best Used For?
MongoDB NoSQL database system is best used for making applications and to store, retrieving, and managing large volumes of data. As it’s a non-relational database, so it's also best when you have unstructured data to be processed and you’re looking for higher horizontal scalability of projects.
Why Use MongoDB Instead of SQL?
MongoDB is preferred over SQL because you can deal with evolving and dynamic data structures with it. While SQL doesn’t allow this and works on traditional relational principles. Moreover, MongoDB also allows you to modify the data over time through various methods and promotes horizontal scalability.
What Are the Disadvantages of MongoDB?
MongoDB is not a suitable choice when your application highly requires ACID compliance and relies on complex transactions. Also, the joins and transactions features of this database are not efficient as compared to the relational databases. Lastly, the indexing strategies of this NoSQL database system were observed to be consuming more disk space.
Why Should I Use MongoDB Over MySQL?
You should use MongoDB over MySQL if you have large volumes of unstructured data to be processed. Plus, you also have semi-structured data like graphs and other multimedia like images and videos to embed in the database. Apart from this, the decision to choose which database purely depends on the needs and requirements of your project.
