MongoDB
MongoDB is a NoSQL, document-oriented database that is designed to handle large amounts of data across many commodity servers. It is an open-source database, meaning that it is freely available to use and modify. MongoDB is known for its ability to handle unstructured data, horizontal scalability, and high availability, making it a popular choice for companies like Scrrum Labs to build applications that require flexible and scalable data management.
Example of how to connect to a MongoDB database using the MongoDB Node.js driver
const MongoClient = require('mongodb').MongoClient;
const uri = "mongodb+srv://<username>:<password>@cluster.mongodb.net/test?retryWrites=true&w=majority";
const client = new MongoClient(uri, { useNewUrlParser: true });
client.connect(err => {
const collection = client.db("test").collection("devices");
// perform actions on the collection object
client.close();
});This code snippet uses the MongoClient class from the MongoDB Node.js driver to connect to a MongoDB database. The uri variable contains the connection string to the MongoDB cluster, which includes the username and password. The useNewUrlParser option is passed to the MongoClient constructor to use the new connection string parser.
After connecting, the code retrieves a specific collection called "devices" from the "test" database. Then you can perform actions on this collection like insert, update, delete, find, etc. Finally, the code closes the connection to the database.
It's important to note that the above code is just an example and you need to replace <username> and <password> with the actual username and password of your MongoDB cluster.
What makes MongoDB different than sql as it is has document-oriented data model. Instead of using tables and rows like a traditional relational database, MongoDB stores data in a JSON-like format called BSON (Binary JSON). This allows for more powerful and expressive query language, as well as greater flexibility in the types of data that can be stored.
Some Key Difference between SQL and NoSQl Database
1)Data Model: SQL databases use a relational model, which organizes data into tables with rows and columns. NoSQL databases, on the other hand, can use a variety of data models, such as document-oriented, key-value, graph, or column-family.
2)Schema: SQL databases have a predefined schema that must be followed. NoSQL databases, on the other hand, are often schema-less, which means that they do not enforce a particular structure on the data.
3)Query Language: SQL databases use SQL as a query language, which is a rigid and less powerful language than MongoDB Query Language (MQL) or other query language used by NoSQL databases. NoSQL databases use more expressive and powerful query languages.
4)Scalability: NoSQL databases are designed to handle large amounts of data across many commodity servers and provide horizontal scalability, allowing for easy expansion by adding more servers. SQL databases are typically scaled vertically by adding more resources to a single server.
5) Flexibility: NoSQL databases are more flexible in handling unstructured data and are an ideal choice for big data analytics, content management, and other applications that require flexible and scalable data management. SQL databases are more suitable for structured data and transactional systems.
Feature of Mongodb
MongoDB also supports horizontal scalability, which means that it can easily handle increasing amounts of data by adding more servers to the database. This is accomplished through a process called sharding, where the data is split across multiple servers. This allows for better performance and increased availability, as the database can continue to operate even if one of the servers goes down.
MongoDB also provides robust security features, including built-in role-based access control, encryption at rest, and secure communication. Additionally, it supports various data backup and recovery options, such as online backup, point-in-time recovery, and replication.
Mongoose
Mongoose is an Object Document Mapper (ODM) library for MongoDB and Node.js. It is designed to work as an abstraction layer between the application and the MongoDB database, providing a more organized and easier way to interact with MongoDB.
Here's an example of how to use Mongoose to define a schema for a simple "users" collection:
const mongoose = require('mongoose');
const Schema = mongoose.Schema;
const userSchema = new Schema({
name: {
type: String,
required: true
},
email: {
type: String,
unique: true,
required: true
},
password: {
type: String,
required: true
}
});
const User = mongoose.model('User', userSchema);
module.exports = User;
In this example, we define a schema for a "users" collection that contains three fields: name, email, and password. The schema also includes validation rules, like the email field is required and unique. Once the schema is defined, we can create a Mongoose model using the mongoose.model() method. This model can be used to interact with the "users" collection in the MongoDB database.
It's worth noting that Mongoose is not the only ODM library for MongoDB, but it's one of the most popular and widely used.
Overall, MongoDB is a powerful and flexible database that is well suited for big data analytics, content management, and other applications that require scalable and flexible data management.
