Welcome to the MongoDB tutorial! This tutorial will guide you through MongoDB's basic operations and concepts.
MongoDB is a NoSQL database that stores data in flexible, JSON-like documents. In this section, we will cover the installation process.
The MongoDB Query API provides methods to query the database, find documents, and update or delete them based on criteria.
Learn how to create a database in MongoDB using the `use` command or by inserting data into a collection.
db = connect("mongodb://localhost:27017/mydatabase");
MongoDB stores data in collections, which are analogous to tables in relational databases. Here's how to create a collection:
db.createCollection("users");
Learn how to insert data into a collection using the `insertOne()` or `insertMany()` methods.
db.users.insertOne({ name: "John Doe", age: 30 });
Use the `find()` method to query a collection and retrieve documents based on specific conditions.
db.users.find({ age: { $gt: 25 } });
Update documents in a collection with the `updateOne()`, `updateMany()`, or `replaceOne()` methods.
db.users.updateOne({ name: "John Doe" }, { $set: { age: 31 } });
Learn how to delete documents using the `deleteOne()` and `deleteMany()` methods.
db.users.deleteOne({ name: "John Doe" });
MongoDB provides several query operators to filter documents. These include `$gt`, `$lt`, `$eq`, `$in`, and more.
db.users.find({ age: { $gt: 20, $lt: 30 } });
Learn how to perform advanced updates in MongoDB, including using the `$set`, `$unset`, `$inc` operators, and more.
db.users.updateOne({ name: "John Doe" }, { $inc: { age: 1 } });
MongoDB provides various operators like comparison operators ($gt, $lt, etc.), logical operators ($and, $or), and array operators to filter data.
MongoDB aggregation framework is used to process data and return computed results. It is commonly used for data analysis and transformation.
The `$group` operator is used to group documents by a specified expression and perform aggregation operations on each group.
db.orders.aggregate([
{ $group: { _id: "$customer_id", totalAmount: { $sum: "$amount" } } }
]);
The `$limit` operator is used to restrict the number of documents returned in the aggregation pipeline.
db.orders.aggregate([
{ $limit: 5 }
]);
The `$project` operator is used to include, exclude, or add new fields to the documents in the aggregation pipeline.
db.orders.aggregate([
{ $project: { _id: 0, customer_id: 1, amount: 1 } }
]);
The `$sort` operator is used to sort documents in the aggregation pipeline based on one or more fields.
db.orders.aggregate([
{ $sort: { amount: -1 } }
]);
The `$match` operator is used to filter documents that meet a specific condition.
db.orders.aggregate([
{ $match: { status: "completed" } }
]);
The `$addFields` operator is used to add new fields to documents in the aggregation pipeline.
db.orders.aggregate([
{ $addFields: { discount: { $multiply: ["$amount", 0.1] } } }
]);
The `$count` operator is used to count the number of documents that match the specified condition.
db.orders.aggregate([
{ $count: "totalOrders" }
]);
The `$lookup` operator allows you to join documents from another collection.
db.orders.aggregate([
{ $lookup: { from: "customers", localField: "customer_id", foreignField: "_id", as: "customer_info" } }
]);
The `$out` operator is used to write the results of an aggregation pipeline to a new collection.
db.orders.aggregate([
{ $match: { status: "completed" } },
{ $out: "completed_orders" }
]);
Indexes improve the efficiency of searches in MongoDB. You can create indexes on fields that are frequently used in queries.
db.orders.createIndex({ customer_id: 1 });
MongoDB supports schema validation, allowing you to enforce rules on your documents when inserting or updating data.
db.createCollection("orders", {
validator: {
$jsonSchema: {
bsonType: "object",
required: ["customer_id", "amount"],
properties: {
customer_id: { bsonType: "int" },
amount: { bsonType: "double" }
}
}
}
});
The MongoDB Data API allows you to interact with MongoDB through HTTP endpoints for ease of integration with web apps and services.
MongoDB provides official drivers for various programming languages, enabling easy integration into your application.
The MongoDB Node.js driver is a native JavaScript driver that provides a simple API for interacting with MongoDB from Node.js applications.
const { MongoClient } = require('mongodb');
MongoClient.connect('mongodb://localhost:27017')
.then(client => {
const db = client.db('mydatabase');
// Further operations
});
MongoDB Charts is a fully managed data visualization tool that allows you to create visualizations and dashboards based on your MongoDB data. You can create charts to explore and analyze your data in a graphical way without the need for complex queries or external tools.
With MongoDB Charts, you can visualize your MongoDB data in various chart types, including bar charts, line graphs, scatter plots, pie charts, and more. It is integrated directly with MongoDB Atlas, which allows for seamless data access and integration.
To create a chart in MongoDB Charts, follow these basic steps:
Here are some common chart types in MongoDB Charts:
MongoDB Exercises offer hands-on practice for learners to apply their knowledge of MongoDB in real-world scenarios. By completing exercises, you can strengthen your understanding of MongoDB concepts such as CRUD operations, aggregation, indexing, and more.
These exercises are designed to test your ability to work with MongoDB databases, collections, and documents, while also challenging you to solve problems using MongoDB’s powerful features.
To solve MongoDB exercises:
Upon completing MongoDB-related courses and exercises, you can earn a MongoDB certificate. The certificate validates your knowledge and skills in working with MongoDB and is a valuable addition to your resume.
To earn your MongoDB certification: