Welcome to another tutorial on MongoDB. Here we will see the advantages of MongoDB over other databases.
In the previous tutorial, you learned about the interesting features offered by MongoDB. All developers understand the reason why it is better to use NoSQL based database for big data transactions and for implementing a scalable model.
Now let’s see the advantage of using MongoDB.
Advantages
It is very easy to install and setup the MongoDB.
The common feature of MongoDB is that it is a schema-less database. That is to say that no schema migrations are needed again. Since MongoDB is schema-free, your code defines your schema.
An attractive feature of MongoDB is the ability to derive a document-based data model. It can do this Because it stores the data in the form of BSON (Binary JSON), ruby hashes, and so on, and helps to store the data in a very rich way while being capable of holding arrays and other documents at the same time.
The document query language supported by MongoDB plays a vital role in supporting dynamic queries in a database.
It is very easy to scale.
Also, because of the structuring (i.e. BSON format - key-value pair) way of the data in MongoDB, no complex joins are required.
In addition, Performance tuning is extremely easy compared to any relational databases.
Here there is no need of mapping the application objects to the data objects.
MongoDB enables faster access to the data due to its nature of using internal memory for storage.
Since it is a NoSQL database, it is secure because no SQL injection can be made.
Also, it can be used as a file system, which is an easier way of load balancing.
MongoDB supports the search by regex and fields.
It can run as a windows service.
Allows a good amount of documentation.
It does not require a VM to be run.
Also, it follows the regular release cycle of its newer versions.
The support for Sharding is one among many key features. As you already know Sharding is the process of storing the data in different machines and MongoDB's ability to process the data, as and when the size of the data grows. This process results in horizontal scaling. With sharding, a larger amount of data can be written and read back when there is an increase in data growth.