MongoDB is a general-purpose, distributed, document-based NoSQL database.
SQL databases are still the vast majority. But with the exponential increase in consumption and data creation we need to prepare for what is to come. And NoSQL databases are proving to be an excellent alternative for that.
In this post we will understand better what are NoSQL databases and what are their advantages compared to traditional relational databases.
But before we go ahead and talk about MongoDB, let’s give an introduction about NoSQL databases.
A comparison between relational and non-relational databases, you can check in this other post.
NoSQL databases (also known as “not only SQL”) are not tabular and store data differently from relational tables.
In relational databases you need to create tables, define schemas, define the types of data fields, define the relationships between the tables, etc, before you can actually insert the data.
In NoSQL databases you don’t have to worry about that. The process of creating the documents, in the case of MongoDB is much simpler and more dynamic.
NoSQL databases come in a variety of types based on the data models. The main types are documents, key-value, wide-column, and graph. They provide flexible and scalable schemas and support high user demands.
One of the advantages of the NoSQL database is that they are really easy to scale and are much faster in most types of operations we perform on the database.
There are certain situations where using the relational database may be a better choice than NoSQL, but when you are dealing with a large amount of data, the NoSQL database should be the best choice.
Today, the advantages of NoSQL databases are no secret, especially when cloud computing has gained widespread adoption.
NoSQL databases were created in response to the limitations of traditional relational database technology. When compared to relational databases, NoSQL databases are more scalable and offer superior performance, and their data model solves several deficiencies of the relational model.
NoSQL’s advantages include, but are not limited to, the:
MongoDB is a document-oriented NoSQL database used for storing large volumes of data. Instead of using tables and rows as in traditional relational databases, MongoDB makes use of collections and documents.
Documents consist of pairs of key-values that are the basic unit of data in MongoDB. Collections contain sets of documents and functions that are almost equivalent to relational database tables.
If you come from a relational database, you can think of documents as relational database rows and collections as tables.
{
"_id": "5",
"isActive": false,
"age": 24,
"eyeColor": "brown",
"name": "Ada Compton"
}
This is a similar structure to JSON. Where data is stored in the form of pairs of keys and values.
And a collection, is nothing more than a set of documents. that do not necessarily have to have the same fields, as in the example of the collection below:
{
"_id": "5",
"isActive": false,
"age": 24,
"eyeColor": "brown",
"name": "Ada Compton"
}
{
"_id": "61",
"username": "debug",
"password": "4#$%ˆ&1233",
"admin": true,
}
The biggest difference between MongoDB and SQL databases is the way they handle data. In SQL databases, data is stored in the form of a traditional two-dimensional row-column structure, while MongoDB uses documents to store information, which allows the storage of any type of data.
Let’s put face to face some of the main differences between MongoDB and other SQL databases:
| The database uses Tables with rows and columns. | Base | Database uses Documents that consist of key-value pairs. |
| RDBMS is a relational database management system and works with relational database. | Concept | MongoDB is a document-oriented, non-relational database management system that works with document-based databases. |
| RDBMS is vertically scalable. Performance increases with increasing RAM. | Scalability | MongoDB supports horizontal scaling through Sharding , distributing data across multiple machines. |
| Difficulty to store hierarchical data. | Hierarchical | It has built-in support to store hierarchical data. |
| RDBMS supports complex joins. | Joins | Databases inSQL like MongoDB use denormalized data, so ‘relational’ joins are impossible. |
| RDBMS is slower in processing large hierarchical data. | Performance | MongoDB is extremely fast in processing large hierarchical data. |
| It follows the principles ACID, Atomicity, Consistency, Insulation and Durability. | Principle | It follows the CAP principle, Consistency, Availability and Partition Tolerance. |
| RDBMS uses SQL to query database. | Query Language | MongoDB uses BSON to query database. |
| Vulnerable to SQL Injection attacks. | SQL Injection | SQL injection is not possible. |
| The schema must be defined in RDBMS before you can use a database. | Schema | The schema can be created and accessed dynamically on MongoDB. |
MongoDB has no defined schemas, i.e. each document can have its own set of unique fields within a collection. In addition, it is distributed and easily scalable geographically/horizontally for better performance.
When considering moving to a document-oriented database, you may consider the following points:
Most databases force you to use heavy mappings, such as ORMs (Object Relational Mappers), to get the object shaped data to use in the programs. Like Java Hibernate and C# Entity Framework.
MongoDB’s decision to store and represent data in a document format using JSON means that you can access it from any language, in data structures native to that language (e.g. dictionaries in Python, associative arrays in JavaScript, Maps in Java, etc.).
If you’re used to having to take down your site or application to change the structure of your data, you’re in luck: MongoDB is designed to accept changes much easier.
No downtime is required to change schemes, and you can start writing new data to MongoDB at any time without interrupting your operations.
What good is a database if you can’t find things in it? MongoDB is designed to facilitate access to data.
MongoDB Query Language (MQL) is a complete and powerful language that allows you to do deep document queries and even run complex analytical pipelines with just a few lines of JSON-like MQL.
MongoDB is designed from the ground up to be a distributed database. Create replication clusters in real-time, and make large or high-performance collections across multiple clusters to sustain performance and scale-up horizontally.
Document oriented database has certain advantages: flexibility (lack of rigid structure), good adaptation to modern JavaScript frameworks (direct use of JSON), large data processing, and real-time statistics/data analysis.
MongoDB is one of the most widely used NoSQL databases. It is easy to understand. The document query language provides many options and is as powerful as SQL. Unlike relational databases, MongoDB is easy to scale. MongoDB is widely used along with NodeJS and AngularJS and ReactJS frameworks. It is a central part of the MEAN and MERN stack.
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