Getting Started with MongoDB: An Introduction

This guide will take you through the steps involved in setting up MongoDB on your local machine, introduce you to the MongoDB environment, basic CRUD operations (Create, Read, Update, Delete), and finally, how to write basic queries.

Section 1: Setting up MongoDB

1.1 MongoDB Download and Installation

Windows:

Download the MongoDB Community Server from the MongoDB Download Center and follow the Install instructions.

https://www.mongodb.com/try/download/community

Mac:

Use brew to install MongoDB.

brew tap mongodb/brew
brew install [email protected]

Linux (Ubuntu):

Open the terminal and execute the following commands:

sudo apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv 9DA31620334BD75D9DCB49F368818C72E52529D4
echo "deb [ arch=amd64,arm64 ] https://repo.mongodb.org/apt/ubuntu xenial/mongodb-org/4.0 multiverse" | sudo tee /etc/apt/sources.list.d/mongodb-org-4.0.list
sudo apt-get update
sudo apt-get install -y mongodb-org

1.2 Starting MongoDB

Windows:

"C:\Program Files\MongoDB\Server\<version>\bin\mongod.exe"

Mac:

brew services start mongodb/brew/mongodb-community

Linux:

sudo service mongod start

1.3 Access Local MongoDB Instance

Open MongoDB shell by typing mongo on the terminal/cmd.

mongo

Now we should be able to interact with MongoDB.

Section 2: Basic Operations in MongoDB

2.1 Creating Database

To create a database in MongoDB, use use DATABASE_NAME is used.

use myDB

2.2 Create Collection

A collection in MongoDB is similar to table in SQL databases. The command syntax is: db.createCollection(name, options)

db.createCollection('myCollection')

2.3 Inserting Data

The command to insert data is db.COLLECTION_NAME.insert(document)

db.myCollection.insert({name: "John", age: 32, email: "[email protected]"})

2.4 Query Data

The basic command to query data is db.COLLECTION_NAME.find()

db.myCollection.find()

Section 3: Writing Basic Queries

3.1 Basic Query

The basic query command is db.collection.find(query, projection)

Query by a field:

db.myCollection.find({name: "John"})

3.2 Update Query

To update data in MongoDB, you use the db.collection.updateOne() function.

db.myCollection.updateOne({name: "John"}, {$set: {email: "[email protected]"}})

3.3 Delete Query

To delete data, you use the db.collection.deleteOne() function.

db.myCollection.deleteOne({name: "John"})

With these commands, you can start exploring more about MongoDB and how it can assist you in application development.

Remember, if you'd like to exit the mongo shell and stop MongoDB, use the quit() command and stop the brew service with 'brew services stop mongodb-community'.

Sure, we'll set up the working environment assuming MongoDB is already installed on your machine.

Setting Up Your Working Environment

1. Start MongoDB Server

Once MongoDB is installed, you can start the MongoDB server by running the following command in your terminal;

mongod

A successful MongoDB server start should display a log message as follows:

[initandlisten] waiting for connections on port 27017

2. Connect MongoDB Using Shell

Open another terminal instance to interact with MongoDB using the MongoDB shell. Use the following command to start the shell:

mongo

Upon a successful connection, the MongoDB shell will indicate your MongoDB version and additional interaction options.

3. Working with Databases

You can interact with MongoDB using various commands. For instance, to display the current database you are working with, use:

db

To switch to a different database or create a new one, use:

use <database name>

4. CRUD Operations

You can perform various operations on MongoDB. Here are a few examples:

Create Data

To create a collection and insert a document, use the following command:

db.<collection name>.insert(<document>)

Read Data

To access the document you just inserted, use:

db.<collection name>.find()

Update Data

To update the document's data, use:

db.<collection name>.update({<query document>}, {<updated document>})

Delete Data

To delete the document, use:

db.<collection name>.remove({<query document>})

5. Exit MongoDB shell

To exit the shell, just type:

exit

Conclusion

Now you should have a basic understanding of how to interact with MongoDB in your local environment. Remember these commands will vary based on the platform you're using. Always consider your project requirements and permissions when setting up and interacting with your MongoDB database. Go forth and conquer the data!

Remember, practice is necessary to get comfortable with MongoDB operations. Happy data handling!

MongoDB: Understanding Databases, Collections, and Documents

For learning MongoDB, we'll implement a simple program that shows how to interact with a MongoDB database, create collections and insert and fetch documents using Python and pymongo.

1. Connecting to a MongoDB database

Before creating or using a database, we need to establish a connection with the MongoDB server. We use the pymongo client for this purpose.

Note: This code assumes that you are running MongoDB server locally and listening on the default port 27017. If your configuration is different, adjust the connection string accordingly.

from pymongo import MongoClient

# creates a connection to the MongoDB
client = MongoClient('localhost', 27017)

2. Databases in MongoDB

In MongoDB, you can switch to a database (or create one, if it doesn't exist) using attribute access. Here's how you can create or switch to a database called 'mydatabase'.

# switching to 'mydatabase' database
db = client.mydatabase

3. Collections in MongoDB

Collections in MongoDB are similar to tables in SQL databases. They store the documents (the data). We can create a collection named 'mycollection' in a similar way.

# creating a collection called 'mycollection'
collection = db.mycollection

4. Documents in MongoDB

Documents are the records that are stored in a collection. They are stored in a binary representation of JSON format called BSON. Here's how you can insert and fetch documents from a collection.

Inserting documents

# inserting a document into the collection
doc = {'name': 'John Doe', 'age': 30, 'occupation': 'Engineer'}
result = collection.insert_one(doc)

Fetching documents

# fetching all documents from the collection
for doc in collection.find():
    print(doc)

This retrieves and prints all the documents from mycollection. You should see output similar to the following:

{'_id': ObjectId('5f58f9df11f1e2d0b4988f7d'), 'name': 'John Doe', 'age': 30, 'occupation': 'Engineer'}

Here, _id is a unique identifier for each document, automatically generated by MongoDB unless otherwise specified.

Conclusion

This introduction should help you understand how MongoDB databases, collections and documents work, and how to interact with them using Python and pymongo. In the next lesson, you'll learn about more complex operations like updating and deleting documents, using queries to fetch specific documents, and more.

Creating a MongoDB Database

Objectives

• Creating MongoDB Database and Collection
• Inserting documents in a collection
• Querying data from a collection

MongoDB organizes data in the form of databases, collections, and documents. In this module, we will create a MongoDB database, set up collections, insert documents to collections, and finally, query data from these collections.

Step 1: Establishing Connection with MongoDB

To interact with MongoDB through Node.js, we need a MongoDB driver. In this case, we're considering that the MongoDB driver has already been installed. Here's how we establish connection:

Insert the following script into a .js file of your preference, for this instance it will be database.js

const MongoClient = require('mongodb').MongoClient;
const url = "mongodb://localhost:27017/";
MongoClient.connect(url, function(err, db) {
    if (err) throw err;
    console.log("Connected to MongoDB!");
    db.close();
});

When script execution is successful, 'Connected to MongoDB!' will be printed onto the console.

Step 2: Creating a MongoDB Database

To create a new database, specify a new database name as a parameter in the MongoClient().connect() method:

MongoClient.connect(url, function(err, db) {
  if (err) throw err;
  var dbo = db.db("mydb");
  console.log("Database created!");
  db.close();
});

When this script is run, a new database titled 'mydb' is created.

Step 3: Creating a Collection

To create a collection:

MongoClient.connect(url, function(err, db) {
  if (err) throw err;
  var dbo = db.db("mydb");
  dbo.createCollection("users", function(err, res) {
    if (err) throw err;
    console.log("Collection created!");
    db.close();
  });
});

When this script is run, a new collection titled 'users' will be created in the 'mydb' database.

Step 4: Inserting Documents into a Collection

To insert a document:

MongoClient.connect(url, function(err, db) {
  if (err) throw err;
  var dbo = db.db("mydb");
  var myobj = { name: "John Doe", address: "123 Street" };
  dbo.collection("users").insertOne(myobj, function(err, res) {
    if (err) throw err;
    console.log("Document inserted");
    db.close();
  });
});

The method insertOne({document}), when used on the collection object, inserts a document into the collection. 'users' is our collection in this case.

Step 5: Querying Data from a Collection

Finally, to query a document from the collection:

MongoClient.connect(url, function(err, db) {
  if (err) throw err;
  var dbo = db.db("mydb");
  dbo.collection("users").findOne({}, function(err, result) {
    if (err) throw err;
    console.log(result.name);
    db.close();
   });
});

The findOne() method returns the first occurrence in the selection. The first parameter of the findOne() method is a query object. In this case we use an empty query object, which selects all documents in the collection.

We've succeeded in creating a MongoDB database and collection, inserting documents, and querying data from the collection. Remember each script should be run in the node.js environment.

Managing MongoDB Collections: From Creation to Modification

Prerequisites

Before we begin, ensure that MongoDB is installed and running on your system, and you're familiar with performing basic MongoDB operations like creating a database and understanding databases, collections, and documents.

1. Creating a Collection

In MongoDB, if you want to insert a document into a collection that doesn't exist, MongoDB automatically creates the collection for you. However, you can also explicitly create a collection using the db.createCollection() method.

use sampleDB
db.createCollection("sampleCollection")

First, we use the use command to create or switch to a database named sampleDB. Then we create a collection named sampleCollection.

2. Viewing Collections

View the collections in your current database with the show collections command.

show collections

3. Inserting Documents

To insert a document into your collection, use db.collection.insertOne(). For example:

db.sampleCollection.insertOne({
  title: "My First Document",
  description: "This is the first document in our collection",
  tags: ["first", "document"],
  views: 0
})

4. Updating Documents

The db.collection.updateOne() function updates the first document that matches your filter criteria.

db.sampleCollection.updateOne(
  { title: "My First Document"},
  {
    $set: { description: "Updated Description"},
    $inc: { views: 1 }
  }
)

This command changes the description value to "Updated Description" and increments views by 1 for the first document where title equals "My First Document".

5. Deleting Documents

In MongoDB, you can delete a document using the db.collection.deleteOne() method.

db.sampleCollection.deleteOne({ title: "My First Document" })

This command deletes the first document where title equals "My First Document".

6. Deleting a Collection

To drop a collection (i.e., delete the collection itself and all documents within), use the db.collection.drop() command.

db.sampleCollection.drop()

This command deletes the sampleCollection collection entirely, including all documents within.

Conclusion

And that wraps up our summary of managing MongoDB collections, from creation to modification. With this knowledge, you're now equipped to create collections, insert documents into them, update those documents, and delete both documents and entire collections as needed.

Handling Documents in MongoDB: Insertion, Query and Update

In this hands-on section, we will be dealing with documents in MongoDB – how to handle them, especially inserting, querying, and updating documents.

Insertion

Inserting documents in MongoDB can either be done one at a time using the insertOne() function or many at a time using the insertMany() function. The following JavaScript code provides a practical implementation:

const MongoClient = require('mongodb').MongoClient;

MongoClient.connect("mongodb://localhost:27017", function(err, db) {
  if (err) throw err;
  let dbo = db.db("mydb");

  let myObj = { name: "John", address: "Highway 37" };
  dbo.collection("customers").insertOne(myObj, function(err, res) {
    if (err) throw err;
    console.log("1 document inserted");
    db.close();
  });
});

Or using insertMany() to insert multiple documents:

const MongoClient = require('mongodb').MongoClient;

MongoClient.connect("mongodb://localhost:27017", function(err, db) {
  if (err) throw err;
  let dbo = db.db("mydb");

  let myObj = [
    { name: 'John', address: 'Highway 66'},
    { name: 'Peter', address: 'Lowstreet 27'},
    { name: 'Amy', address: 'Apple st 652'},
  ];
  dbo.collection("customers").insertMany(myObj, function(err, res) {
    if (err) throw err;
    console.log("documents inserted");
    db.close();
  });
});

Query

To query documents in a MongoDB collection, we use the find() function.

An example of a query for a single document with a specific attribute:

const MongoClient = require('mongodb').MongoClient;

MongoClient.connect("mongodb://localhost:27017", function(err, db) {
  if (err) throw err;
  let dbo = db.db("mydb");

  dbo.collection("customers").findOne({}, function(err, result) {
    if (err) throw err;
    console.log(result.name);
    db.close();
  });
});

Query for multiple documents:

const MongoClient = require('mongodb').MongoClient;

MongoClient.connect("mongodb://localhost:27017", function(err, db) {
  if (err) throw err;
  let dbo = db.db("mydb");

  dbo.collection("customers").find({}).toArray(function(err, result) {
    if (err) throw err;
    console.log(result);
    db.close();
  });
});

Update

To update a document, we need to use the updateOne() function:

const MongoClient = require('mongodb').MongoClient;

MongoClient.connect("mongodb://localhost:27017", function(err, db) {
  if (err) throw err;
  let dbo = db.db("mydb");
  var myquery = { address: "Valley 345" };
  var newvalues = {$set: {name: "Mickey", address: "Canyon 123" }};

  dbo.collection("customers").updateOne(myquery, newvalues, function(err, res) {
    if (err) throw err;
    console.log("1 document updated");
    db.close();
  });
});

In this example, we’re querying a document that has an address "Valley 345" before updating both its name as well as its address.

It's important to note that these functions are asynchronous and return promises in Node.js driver.

Delving Into Indexing: Enhancing Your MongoDB Performance

Effective use of indexing in MongoDB can significantly improve the performance of your queries. Let's dive in and demonstrate how to implement indexes to fetch data faster from MongoDB.

Here, we assume you already have a MongoDB database with a collection and you are familiar with the basic commands of MongoDB. We will be using a users collection in our examples below.

Section 1: The Basic of Indexing

Indexes in MongoDB work similar to the indexes in a book. An index in MongoDB is a special data structure that holds data of a few fields of documents on which the index is created. Indexes can help your queries to become more efficient.

To create an index, we use createIndex() function.

// Connect to your MongoDB database
use my_database

// create an index on username field
db.users.createIndex({username: 1})

The 1 indicates that we're creating an ascending index on the username field. A descending index would be indicated by -1.

Section 2: Using Indexes to Speed Up Queries

Let's see how an index can speed up queries. Suppose you frequently query for users based on their username.

// find user without index
db.users.find({username: 'codeguy'})

If the username field is not indexed, MongoDB must perform a collection scan, i.e. scan every document in the collection, to select these documents. If an index exists for the username, MongoDB can use the index to limit the number of documents it must inspect.

Section 3: Compound Indexes

Compound indexes are indexes that are built using more than one field of the documents in a MongoDB collection.

// create a compound index on username and email field
db.users.createIndex({username: 1, email: 1})

Compound indexes can support queries that match on multiple fields.

Section 4: Analyzing Query Performance: explain()

We can use the explain() method to get statistics about the performance of a query using the following command:

db.users.find({username: 'codeguy'}).explain("executionStats")

This will return a document that describes the process and gives you details about the query performance.

Note: Over-indexing can affect the write operation negatively as the index also needs to be updated every time a document is inserted/updated.

Section 5: Removing Indexes

To remove an index that you no longer need, you can use dropIndex() function.

// remove index on username field
db.users.dropIndex({username: 1})

This example would drop the index on the username field.

Remember: Don't index every field, as index creation is not free. It consumes disk space and memory, choose the fields to index wisely.

Conclusion

Indexing is a powerful feature in MongoDB, utilized to make data retrieval more efficient. It's an essential part of dealing with large amounts of data and optimising for read performance. But careful and sensible application of indexing is necessary to avoid negatively impacting write performance.

Implementing Aggregation: Understanding Data Processing Pipelines

Aggregation operations in MongoDB group values from multiple documents together, performing a variety of operations on the grouped data to return a single result. The aggregation pipeline framework is a powerful and flexible tool provided by MongoDB, making the processing of data more streamlined.

This section will guide you on how to construct and execute aggregation pipelines, using examples to demonstrate every step.

1. Preliminary Actions

Before we start to explore aggregation, make sure you have a database and collections to work with. We continue with the "Test" database and "students" collection. Note that it is assumed that you have documents inserted in your collections.

use Test

2. The Aggregation Pipeline

The aggregation pipeline is a framework for data aggregation modelled on the concept of data processing pipelines. It is a multi-stage pipeline where each stage transforms the aggregated data.

An aggregation pipeline works as follow:

db.collection.aggregate([
   { $stage1 },
   { $stage2 },
   // ...
])

Each $stage represents a particular operation to be performed. There are many operations available, such as $match, $group, $sum, $avg, etc.

3. $match Stage

The $match stage filters the documents to pass only documents that match the specified condition(s) to the next pipeline stage.

Let's count the number of students for each age.

db.students.aggregate([
   { $match: { age: { $gt: 18 } } }
])

This will return a list of students who are older than 18.

4. $group Stage

The $group stage groups the documents by some specified expression and outputs a document for each distinct grouping. The output documents contain an _id field which contains the distinct group by key. The output documents can also contain computed fields that hold the values of some accumulator expression grouped by the _id.

db.students.aggregate([
   { $group : { _id : '$age', count: { $sum: 1 } } }
])

This will return a list of distinct student ages and the count for each.

5. Combining $match and $group

Let's combine $match and $group in one pipeline to get the count of students older than 18 for each age.

db.students.aggregate([
   { $match: { age: { $gt: 18 } } },
   { $group : { _id : '$age', count: { $sum: 1 } } }
])

This will return a list of distinct ages with the count of students older than 18 for each age.

Conclusion

In this section, we have learned how to create an aggregation pipeline in MongoDB using the $match and $group stages. This is a simple example, but aggregation pipelines can get very complex, depending on the nature of the data and the type of query. The order of stages matters and can significantly impact the results and performance of the pipeline. As you get more comfortable with MongoDB, you can experiment with additional stages and combination to fit your specific needs.

Data Modeling in MongoDB: A Practical Perspective

In this part, we will focus on practical implementation of data modeling in MongoDB. Instead of using relational data modeling, MongoDB uses a document model which is far more flexible and more natural for developers in many applications.

This data model allows you to nest documents in a way that is similar to nesting objects and arrays in programming languages. The data can be represented in a way that is very close to the underlying concepts, leading to less friction between how the data is used in the application versus how it is stored in the database.

Step 1: Create schema and Data models

The mongoose library is very popular and handy when it comes to creating data schemas in MongoDB. Mongoose provides an easy and concise way of creating MongoDB schemas.

Mongoose allows us to have access to the MongoDB commands for CRUD simply and easily.

First, we are going to install and include mongoose in our project.

npm install mongoose

Once installed, we include mongoose in our project

const mongoose = require('mongoose'); 
mongoose.connect('mongodb://localhost/test', {useNewUrlParser: true, useUnifiedTopology: true});

This line connects to the test database that we created earlier.

Now create a schema for a blog post

const Schema = mongoose.Schema;

const BlogPostSchema = new Schema ({
  title: String,
  body: String,
  date: {
    type: String,
    default: Date.now()
  }
});

const BlogPost = mongoose.model('BlogPost', BlogPostSchema);
module.exports = BlogPost;

This establishes the database schema for a BlogPost, which includes a title, a body, and a date, which defaults to the current date. The mongoose.model line is what actually produces the model.

Step 2: Using models to create documents in MongoDB

Now, with our model defined, we can create a new document.

// Include the BlogPost model :
var BlogPost = require('./models/BlogPost.js');

// Using the BlogPost model, let's create our first blog post.

var firstPost = new BlogPost({ 
  title: 'A Beginner’s Guide to MongoDB', 
  body: 'MongoDB is a NoSQL database that offers a high performance, high availability, and easy scalability....' 
});

firstPost.save((error)=>{
  if(error){
    console.log(error);
  }else{
    console.log('Saved successfully!')
  }
});

The save method is what actually sends our new blog post from the application memory to the mongoDB server.

That's it! We've successfully implemented data modeling in MongoDB.

MongoDB Replica Set: Ensuring Availability & Redundancy

A replica set in MongoDB is a group of mongod processes that maintain the same data set. It provides redundancy and high availability, and is the basis for all production deployments. This section will demonstrate the implementation of a replica set.

Pre-Requisites

This activity assumes you have MongoDB installed and are familiar with starting and interacting with MongoDB servers and shell. We will not go into the installation steps of MongoDB as it has already been covered in previous sections.

1. Creating The Replica Set

To start with, we will create three mongod instances representing the nodes of our replica set:

1. Start the primary node:

mongod --port 27017 --dbpath /data/rs1 --replSet rs

2. Start the secondary nodes:

mongod --port 27018 --dbpath /data/rs2 --replSet rs
mongod --port 27019 --dbpath /data/rs3 --replSet rs

NOTE: Ensure that paths /data/rs1, /data/rs2 and /data/rs3 exist. These will store the MongoDB data files for each node.

2. Initiating The Replica Set

Now we need to initialize the replica set. Open a new terminal and start the MongoDB client:

mongo

Inside the MongoDB client, initiate the replica set:

config = {
    "_id" : "rs",
    "members" : [
        {
            "_id" : 0,
            "host" : "localhost:27017"
        },
        {
            "_id" : 1,
            "host" : "localhost:27018"
        },
        {
            "_id" : 2,
            "host" : "localhost:27019"
        }
    ]
};

rs.initiate(config)

After executing the command, your replica set should be up and running. You can confirm this via the rs.status() command.

3. Working with The Replica Set

After setting up the replica set, use the MongoDB shell connected to your primary node (running at localhost:27017) as usual. When you add data here, it will automatically be replicated to the secondary nodes.

Let's create a test database and collection and add some sample documents:

use builddb
db.mycol.insert({ "building" : "Taj Mahal", "city" : "Agra" })
db.mycol.insert({ "building" : "Eiffel Tower", "city" : "Paris" })

You can check the data from secondary nodes as well using the MongoDB shell. However, before running a find() or any other read operation, you need to allow secondary reads by running rs.slaveOk() after connecting to a secondary node.

Note on Redundancy & Availability

In the event of primary node failure, an election will decide a secondary to become the new primary. No data loss occurs as all nodes contain the same set of data. Once the former primary comes back online, it joins the replica set as a secondary.

A minimum of three nodes is recommended for a production environment to ensure redundancy and high availability. If you can't afford three, at least consider one primary and one secondary node, along with an arbiter node.

This guide ends our practical implementation of a MongoDB Replica Set. Now you have a functioning replica set that ensures data availability and redundancy by keeping multiple copies of the data.

MongoDB Sharding: Managing Data Distribution

Sharding is a method of storing data records across multiple machines. MongoDB uses sharding to support deployments with very large data sets and high throughput operations. To shard a MongoDB deployment, you create a sharded cluster.

Building a Sharded Cluster

The sharded cluster consists of shards, config servers, and mongos instances.

1. Shard: Each shard is a separate MongoDB instance that stores a subset of the collection's data.

2. Config Server: The config servers store the cluster's metadata, which includes the configuration settings and the distribution of the data in the cluster.

3. Mongos: The mongos provide the interface between the client applications and the sharded cluster.

We will create a sharded cluster with one shard, one config server, and one mongos.

Setting Up Config Server

On your terminal, run this command:

mongod --configsvr --replSet configReplSet --bind_ip localhost --port 27019 --dbpath /data/configdb

Once the server is running in your terminal, you should then initiate the replica set. Open your MongoDB shell, and run:

rs.initiate({_id: "configReplSet", configsvr: true, members: [{ _id : 0, host : "localhost:27019" }]})

Setting Up Shard

Open a new terminal window and start a mongod instance:

mongod --shardsvr --replSet shardReplSet --bind_ip localhost --port 27018 --dbpath /data/sharddb

Just like before, you should then initiate the replica set for the shard. Open your MongoDB shell, and run:

rs.initiate({_id : "shardReplSet", members: [{ _id : 0, host : "localhost:27018" }]})

Setting Up mongos

In a new terminal, run the following command:

mongos --configdb configReplSet/localhost:27019 --bind_ip localhost --port 27017

Adding Shard to Cluster

Finally, connect to mongos with a MongoDB shell (in a new terminal window) and add the Shard:

mongo --port 27017

In the MongoDB shell, run the following commands:

sh.addShard( "shardReplSet/localhost:27018" )

Now, you have a sharded cluster set up.

Sharding a Database

1. Enable Sharding for a Database

sh.enableSharding("<database>")

Replace "<database>" with the name of your database.

2. Shard a Collection

Before you shard a collection, you must create an index on the shard key:

db.collection.createIndex( { "<field>": 1 } )

Then, you can shard the collection:

sh.shardCollection("<database>.<collection>", { "<field>": 1 } )

Replace "<database>" with the name of your database, "<collection>" with the name of your collection, and "<field>" with the field you want to use for sharding.

Remember to choose the sharding key wisely, as changes in the shard key are quite complex. The optimal shard key can allow MongoDB to distribute data evenly throughout the cluster.

Securing MongoDB: Implementing Authorization and Authentication

To add security to your MongoDB databases, the most basic steps involve the implementation of authentication and authorization. Authentication verifies the identity of a user, and authorization determines what an authenticated user is allowed to do. This is section 12 of the in-depth MongoDB course for beginners.

MongoDB uses the authentication mechanism to identify the user that is connected to the MongoDB instance, and the authorization mechanism to verify what actions the user can perform against the MongoDB server.

Prerequisites

Ensure MongoDB is up and running on your system. If MongoDB is installed as a service, stop the service before proceeding to the next step.

You will need:

• A running MongoDB instance
• MongoDB shell or a MongoDB GUI

Enable Authentication

Firstly, we will enable the authentication in MongoDB. If your MongoDB is running as a service, you need to stop it before proceeding.

In your MongoDB configuration file, usually located at /etc/mongod.conf, add the following lines under the security node:

security:
  authorization: "enabled"

Save the changes and start the MongoDB service again.

Create an Administrative User

Now, you will create a user administrator that has permission to create other users.

Connect to your MongoDB instance:

mongo

Now switch to the admin database:

use admin

Then, create the user:

db.createUser(
  {
    user: "adminUser",
    pwd: "adminPassword",
    roles: [ { role: "userAdminAnyDatabase", db: "admin" } ]
  }
)

Exit the MongoDB shell with quit().

connect to your MongoDB instance with the user you created:

mongo -u "adminUser" -p "adminPassword" --authenticationDatabase "admin"

Create a Database User

While the administrative user has the permission to create other users, it's not advisable to perform regular operations using this account. Therefore, you will create a user specifically for your database operations.

Switch to the specific database for which you want to create a user. Let's say the database is myDatabase:

use myDatabase

Then, create your user:

db.createUser(
  {
    user: "myUser",
    pwd: "myPassword",
    roles: [ { role: "readWrite", db: "myDatabase" } ]
  }
)

Exit the shell.

You should now connect to your MongoDB instance with the user specified for your database:

mongo -u "myUser" -p "myPassword" --authenticationDatabase "myDatabase"

Conclusion

Now, your MongoDB instance is secured with authentication. Only the users with their respective credentials can perform actions on the databases to which they have access.

This is the basic level of security for MongoDB. For more advanced security measures consider enabling encryption, auditing, or configuring MongoDB in a network that limits interfaces and ports to those that are required.

MongoDB Performance Tuning and Optimization

Optimizing MongoDB is a crucial step in working with any database system. It involves a set of activities whose primary objective is to improve application performance. We will focus on two main sections in this tutorial:

1. Performance Tuning Practices in MongoDB
2. Profiling MongoDB Database

Performance Tuning Practices in MongoDB

1. Hardware Considerations

Disk I/O

Use high-performance storage that can deliver low latency and high throughput.

iostat -x

This command evaluates your Disk I/O, with %util column indicating disk utilization.

WiredTiger Storage Engine

WiredTiger is the default storage engine starting in MongoDB 3.2. It uses MultiVersion Concurrency Control (MVCC) to present a consistent view of data, improving performance.

2. Indexing

In addition to indexing described in the previous steps, ensure that the working set fits in the RAM. Use .explain('executionStats') to investigate queries and ensure they use indexed access where possible.

db.collection.find(query).explain('executionStats')

3. Sharding

Ensure that your sharded cluster is correctly balanced. Use the sh.status() command to check the distribution of chunks across your sharded cluster.

sh.status()

Profiling MongoDB Database

Profiling helps identify slow queries. MongoDB includes a database profiler that shows performance characteristics of each operation against the database.

1. Enable Profiling

By default, MongoDB has the profiler turned off (-1). You can set it to two other levels: 0, which is off; and 1 or 2, which turns it on.

Level 1 logs operations that take longer than the value of slowms_MS, and level 2 logs all operations.

db.setProfilingLevel(1)

This command activates profiling for queries that take longer than the default slowms_MS, which is 100ms.

2. Read Profiling Data

Once profiling is enabled, access the profiling data with the system.profile collection.

db.system.profile.find( { millis : { $gt : 1 } } ).sort( { ts : -1 } )

This command displays all operations that took longer than 1ms to run, sorted by the timestamp ts in descending order.

3. Analyze Profiling Data

Once you have the profiling data, study the output, pay special attention to the millis, nreturned, and responseLength fields. Use this information together with the explain() output to identify bottlenecks and potential optimization steps.

Remember to set Profiling Level back to 0 when you finished analyzing to avoid the performance costs of profiling.

db.setProfilingLevel(0)

Remember, tuning MongoDB for performance is an iterative process that involves profiling the database, identifying slow operations, refining schema design or queries, and observing the improvements.

These are practical steps you can follow to optimize MongoDB and improve the performance of your database applications. It involves a mix of techniques that range from hardware considerations, effective use of indexing, sharding, as well as profiling.

Integrating MongoDB with Node.js: Real-world Application Development

1. Setting Up Express JS Project

• First, let's start by creating a new directory:

mkdir node-mongo-app && cd node-mongo-app

• Initialize a new Node.js project:

npm init -y

• Installing necessary packages (Express JS, MongoDB Driver, and Nodemon):

npm install express mongodb nodemon

Create the following two files: index.js and db.js. Your project structure should look like this:

- node-mongo-app
  - index.js
  - db.js
  - package.json
  - package-lock.json
  - node_modules

2. Connecting to MongoDB Server

In db.js we are going to set up the connection to our MongoDB.

const { MongoClient } = require('mongodb');

let db = null;

async function connect() {
    const url = 'mongodb://localhost:27017';
    const dbName = 'myproject'; // change this to your dbname

    const client = new MongoClient(url, {
        useUnifiedTopology: true
    });

    await client.connect();
    
    console.log("Connected to server");

    db = client.db(dbName);
}

function getDb() {
    return db;
}

module.exports = { 
    connect, 
    getDb 
};

3. Setting Up the Express JS Server

In your index.js set up your express server:

const express = require('express');
const app = express();

// Body Parser Middleware
app.use(express.json());

const { connect } = require('./db');

app.listen(5000, async () => {
    console.log(`Server is running. Listening on port 5000`);

    conectionSuccessful = await connect();
    if (conectionSuccessful) {
        console.log("Successfully connected to the database");
    }
});

4. Defining Routes and Handlers

We will manipulate a Users collection, so we are going to define the following routes:

• GET /users - Get all users
• POST /users - Create a new user
• GET /users/:id - Get a user by id
• DELETE /users/:id - Delete a user
• PUT /users/:id - Update user data

In order to do so, we will create a users.js file

- node-mongo-app
  - index.js
  - db.js
  - users.js
  - package.json
  - package-lock.json
  - node_modules

Using the MongoDB Node.JS Driver, the code implementation of the above routes will look like this:

const { Router } = require('express');
const { getDb } = require('./db');
const { ObjectId } = require('mongodb');

const router = new Router();

router.get('/users', async (req, res) => {
    const db = getDb();
    const users = await db.collection('Users').find({}).toArray();
    res.send(users);
});

router.post('/users', async (req, res) => {
    const db = getDb();
    const user = await db.collection('Users').insertOne(req.body);
    res.send(user);
});

router.get('/users/:id', async (req, res) => {
    const db = getDb();
    const user = await db.collection('Users').findOne({ _id: new ObjectId(req.params.id) });
    res.send(user);
});

router.delete('/users/:id', async (req, res) => {
    const db = getDb();
    const result = await db.collection('Users').deleteOne({ _id: new ObjectId(req.params.id) });
    res.send(result);
});

router.put('/users/:id', async (req, res) => {
    const db = getDb();
    const result = await db.collection('Users').updateOne(
        { _id: new ObjectId(req.params.id) },
        { $set: req.body }
    );
    res.send(result);
});

module.exports = router;

Don't forget to import the router into your index.js and use it:

const userRoutes = require('./users.js');

app.use('/', userRoutes);

5. Running the Application

To run your application, since we've installed nodemon, we can use it to listen for changes in our files and restart the server automatically. Open the package.json and modify the scripts section like this:

"scripts": {
    "start": "nodemon index.js"
}

Now you can run your server:

npm start

You have successfully integrated MongoDB with Node.js. You can use this boilerplate code to continue the development of your application.

In this final component of your MongoDB in-depth course, we will put together all of the pieces we have learned and build a robust Node.js application using MongoDB as our database. This tutorial assumes that MongoDB has been installed and configured properly. We will be using Mongoose, a MongoDB object modeling tool designed to work in an asynchronous environment.

Our application will be a simple book management system where users can add, view, edit, delete books and each book will have basic details like title, author, and ISBN.

Section 1: Creating a Node.js project

First, you have to create a new directory for your project and initiate a node application in that directory. You have to install express for HTTP functionalities, Mongoose to interact with the database and body-parser to parse incoming requests.

mkdir BooksApi
cd BooksApi
npm init -y
npm install express mongoose body-parser

Create a new file named 'app.js' in your project root directory and include express, Mongoose and body-parser to your project.

const express = require('express');
const mongoose = require('mongoose');
const bodyParser = require('body-parser');

const app = express();

app.use(bodyParser.json());

mongose.connect(
    'mongodb://localhost:27017/bookmanagement',
    {useNewUrlParser: true, useUnifiedTopology: true},
    () => console.log('connected to DB!')
);

app.listen(3000);

Section 2: Building the Book Model

Create a new file named 'Book.js' in your project directory to define the schema for a book.

const mongoose = require('mongoose');

const BookSchema = mongoose.Schema({
    title: {
        type: String,
        required: true
    },
    author: {
        type: String,
        required: true
    },
    isbn: {
        type: String,
        required: true
    }
});

module.exports = mongoose.model('books', BookSchema);

Section 3: Building the Book Routes

In this section, we will write the routes that take care of our CRUD operations.

Create a new file named 'routes.js' in your project directory:

const express = require('express');
const router = express.Router();
const Book = require('./Book');

//POST: Create a new book
router.post('/book', async (req, res) => {
    const book = new Book({
        title: req.body.title,
        author: req.body.author,
        isbn: req.body.isbn
    });

    try{
        const savedBook = await book.save();
        res.json(savedBook);
    }catch(err){
        res.json({message: err});
    }
});

//GET: Get all books
router.get('/books', async (req, res) => {
    try{
        const books = await Book.find();
        res.json(books);
    }catch(err){
        res.json({message: err});
    }
});

//GET: Get a specific book
router.get('/book/:bookId', async (req, res) => {
    try{
        const book = await Book.findById(req.params.bookId);
        res.json(book);
    }catch(err){
        res.json({message: err});
    }
});

//DELETE: Delete a book
router.delete('/book/:bookId', async (req, res) => {
    try{
        const removedBook = await Book.remove({_id: req.params.bookId});
        res.json(removedBook);
    }catch(err){
        res.json({message: err});
    }
});

//UPDATE: Update a book
router.patch('/book/:bookId', async (req, res) => {
    try{
        const updatedBook = await Book.updateOne(
            {_id: req.params.bookId}, 
            {$set: {title: req.body.title}}
        );
        res.json(updatedBook);
    }catch(err){
        res.json({message: err});
    }

});
module.exports = router;

Now, include the book routes to your 'app.js'.

const bookRoutes = require('./routes');

app.use('/api', bookRoutes);

Now, your application is ready. Start your server with node app.js and use a tool like Postman to test your HTTP endpoints.

This application can be further improved by adding error handlers, implementing data validation & sanitization, authorizing and authenticating users and applying various performance improvement techniques.