MongoDB is a kind of database that enables us to manage a lot of data. As opposed to traditional databases, MongoDB stores data in "documents" rather than tables. It's a NoSQL database, so it differs from other databases in that regard. Anyone can use MongoDB for free because it is open-source. It was created in 2017, making it also fairly recent. MongoDB might be the answer if you need to manage and store a lot of data! So, what exactly is “NO SQL?”
“Not Only SQL,” referred to as "NoSQL," describes a particular kind of database that doesn't make use of SQL [Structured Query Language] as its main data access language. We occasionally have a lot of data, such as long texts or large images, that won't fit neatly into a table. In this case, a NoSQL database might be useful. It effectively organizes and stores all types of data, and if we have too much, we can spread it across several servers.
Although NoSQL databases frequently respond quickly, they might not always be as reliable or able to support transactions as other databases. This NoSQL database has large amounts of semi-structured or unstructured data that are frequently stored because of its ability to scale horizontally across multiple servers. Although NoSQL databases are frequently faster and more adaptable than SQL databases, they may not provide the same level of consistency and transaction support.
The “NoSQL” databases include:
Key-value store
They are designed to be quick and adaptable and store data as key-value pairs which be compared to a robust and quick data search engine. They excel at managing large amounts of data and traffic; two well-known examples are Redis and Amazon DynamoDB.
Wide column-oriented database
These databases are great for storing and organizing large amounts of potentially changing data. Columns are used to store the data rather than rows as in a traditional database. As a result, they become extremely adaptable and capable of dealing with a diverse range of information. Examples of this type of database include HBase and Cassandra.
Multimodal Data Base
These databases are helpful for storing all sorts of data – like documents. They perform similar tasks to search engines, handle data in a manner akin to documents, and have a structure similar to a map or diagram. They are very flexible and capable of handling a variety of data. A couple of well-known examples of this kind of database are ArangoDB and OrientDB.
Graph Database
These databases are ideal if there is a lot of data connected in various ways; they function like a super-powered filing cabinet and use "nodes" and "edges" to store and organize the data, making it simple to find and comprehend. Neo4j and TigerGraph are a couple of examples of this kind of database.
Document Database
These databases are ideal if you have a lot of data that doesn't fit into standard rows and columns. Semi-structured databases store data in the form of documents that resemble JSON. These documents are very adaptable and simple to use because they resemble lists or dictionaries in appearance.
Search Engines
Multiple texts are stored and retrieved with great ease using these databases because the search-optimized databases are designed to store and query large amounts of text data. You can type a few keywords in the search function, and it will return all relevant results. The two best-known examples of this kind of database are Elasticsearch and Apache Solr.
Since MongoDB is a document database, tables, rows, and columns are not necessary. Instead, data is stored in a document using the JSON (JavaScript Object Notation) format, which is object-based and structured as an object with key-value pairs.
{
“key”: “value”
}
This is what JSON data would look like:
{
“_id”: 1,
“firstName”: ”Jess”,
“lastName”: ”Agorye”,
“age”: 28,
“phone”: [“080”,”090”]
}
A single document can store multiple pieces of data for a single record using the JSON format. Instead of being divided up, all the user- or client-related information is bound together. Although documents are stored on the server in BISON (Binary JSON), an extension of JSON, they are worked with in MongoDB in JSON format.
Features of MongoDB
The outstanding scalability, availability, and performance of MongoDB are well known, and some of the key features are;
Components of MongoDB
There are several parts that makeup MongoDB, including
MongoDB Atlas: This database service runs in the cloud which makes it easy to set up, manage, and scale MongoDB clusters.
MongoDB Server: This main MongoDB engine handles operations like data storage, indexing, and querying as well as responding to data requests.
MongoDB Stitch: With the aid of this backend-as-a-service (BaaS) platform, applications using MongoDB as the database can be easily created and deployed.
MongoDB Drivers: These libraries can be used by your application to perform tasks like creating, reading, updating, and deleting documents on the MongoDB server.
MongoDB Compass: Compass is a querying, optimization, and analysis tool with an interactive graphical user interface (GUI) for MongoDB data. It can also be used to examine and work with data in Mongo databases.
MongoDB Shell: This is an interactive JavaScript interface to MongoDB, you can communicate with the MongoDB server and execute queries using this command-line interface (CLI).
MongoDB Advantages
MongoDB is intended to make data more accessible. Using this document database has many benefits over using traditional relational databases because it is designed to make data easy to access. A few advantages of the database management system include
Scalability: MongoDB is not relationally coupled, which makes it very scalable. Documents can be easily distributed across multiple nodes via horizontal scaling.
Flexible query model: MongoDB supports dynamic querying, using document-based query language based on JavaScript.
Great user experience for developers: MongoDB has extensive documentation to get started, and there’s also a community forum for developers to ask questions.
Built for small and large data loads, it can be used for different applications, both small and complex. The document-oriented data structure makes it possible to store data that are complex and also supports efficient querying.
High Performance: MongoDB uses Document Oriented Model which supports efficient querying and indexing.
MongoDB Disadvantages
Data access is made simple by MongoDB's design, but there are some drawbacks as well:
High memory usage and It may require more resources than other databases, particularly for large, complex applications.
Limited Nesting can be more difficult to set up and maintain than other databases.
ACID (atomicity, consistency, isolation, and durability) transactions are not supported.
Has a significant learning curve, particularly if you are unfamiliar with NoSQL databases.
The fact that it is a NoSQL database, however, means that it uses a different data model than conventional relational databases, which is a surprising disadvantage.
MySQL Defined
