AWS Elastic Beanstalk – How to Deploy Applications on It?

Cloud computing is a powerful technology that has seen exponential growth in recent years, making it more beneficial than ever. Platform as a Service (PaaS) is one of the key beneficiaries of this growth, and AWS Elastic Beanstalk is a leading PaaS-based service. In this article, we will explore AWS Elastic Beanstalk and its deployment process.

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Modern application development has evolved significantly with cloud-based solutions. AWS Elastic Beanstalk is a development tool based on the PaaS model, enabling easy deployment and scaling of web applications and services built with .NET, PHP, Node.js, Docker, Python, and more. It supports popular servers such as Apache, IIS, Nginx, and Passenger.

With AWS Beanstalk, developers can deploy applications without managing infrastructure provisioning, ensuring high availability and scalability.

AWS Elastic Beanstalk offers several advantages over other PaaS services:

1. Fast Deployment: Deploy applications quickly without dealing with underlying infrastructure.

2. Multi-Tenant Architecture: Supports high scalability and security, providing detailed usage reports.

3. Simplified Operations: Manages infrastructure, operations, and application stack, allowing developers to focus on code.

4. Broad Resource Control: Developers can choose AWS resources like EC2 instance types that best suit their applications.

Given these benefits, Elastic Beanstalk is a preferred choice among developers.

Understanding key concepts of Elastic Beanstalk is crucial for efficient deployment:

• Application: A group of components, including environment versions and configurations.

• App Version: A precise, deployable iteration of the web application code, stored in an S3 object.

• Environment: The current running version of an application.

• Environment Tier:

  • Web Server Environment: Handles HTTP requests from users.

  • Worker Environment: Processes time-intensive background tasks.

AWS Elastic Beanstalk comprises various architectural components:

Web-Server Environment

• Elastic Load Balancer: Distributes incoming traffic across EC2 instances.

• Auto Scaling Group: Adjusts EC2 instances dynamically based on demand.

• Host Manager: Monitors application performance and generates logs.

• Security Groups: Acts as a firewall for EC2 instances, allowing HTTP access.

Worker Environment

When the web server tier encounters resource-intensive tasks, the worker environment assists in processing them using Amazon SQS (Simple Queue Service). A daemon on the worker instance pulls requests from the SQS queue, processes them, and sends responses accordingly.

Follow these steps to deploy an application on AWS Elastic Beanstalk:

1. Go to Elastic Beanstalk Console – Click on Create New Application, enter a name and description.

2. Create an Environment – Select Create Environment under the Actions tab.

3. Choose Environment Type – Select Web Server Environment for handling HTTP requests or Worker Environment for background jobs.

4. Configure Application – Enter the domain name and description.

5. Select Platform – Choose a suitable platform for your application.

6. Deploy Application Code – Upload your application code or use a sample application provided by Elastic Beanstalk.

7. Launch Environment – Wait a few minutes for the environment to initialize.

8. Access Application – Click on the generated application URL to confirm deployment success.

Congratulations! You have successfully deployed an application on AWS Elastic Beanstalk.

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How to Launch an EC2 Instance Using a Custom AMI

Cloud computing follows the Agile software development lifecycle, enabling rapid server deployment and scalability. For instance, Netflix utilizes AWS EC2 with Autoscaling to automatically add or remove instances based on user demand. In this article, we’ll explore how to launch an EC2 instance using a custom AMI.

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Ways to Deploy Applications on an EC2 Instance

There are several ways to get your application and its settings onto an EC2 instance:

1. Configuration Management Tools: Tools like Chef and Puppet allow lifecycle management of applications (install, upgrade, rollback) on thousands of machines.
2. User Data Scripts: Pass shell scripts to the EC2 instance to install and configure applications during startup.
3. Custom AMI: An Amazon Machine Image (AMI) includes all necessary details like OS, applications, and settings. It is the quickest method to launch an EC2 instance.

Below, we detail the steps to create a custom AMI and launch an EC2 instance using it.

Steps to Launch an EC2 Instance Using a Custom AMI

Step 1: Launch an EC2 Instance Using an Existing AMI

1. Use an existing Linux or Windows AMI to launch an EC2 instance.
2. Log in to the instance (e.g., using PuTTY for Windows).

Step 2: Install the Required Application

Install your desired application and make configuration changes. For example, install Apache Tomcat on an Ubuntu EC2 instance:

sudo apt update  
sudo apt install tomcat9  
sudo systemctl start tomcat9  

• Verify the installation: Access http://ec2-public-ip:8080 in a browser.
• Ensure port 8080 is open in the Security Group’s inbound rules.

Step 3: Create a Custom AMI

1. Stop the EC2 Instance: Ensures consistency during image creation.
2. In the AWS Management Console, navigate to:
• Actions → Image → Create Image.
3. Provide the Image Name and Description.
4. Restart the instance after the image is created.
5. Navigate to the AMI tab in the left panel to monitor its status.
• The AMI starts in a pending state and changes to available.

Visibility Settings: By default, the AMI is private. You can make it public or share it with specific users:

• Actions → Manage Image Permissions.

Step 4: Launch a New EC2 Instance Using the Custom AMI

1. Go to the EC2 Management Console → Launch Instance.
2. Under My AMIs, select the custom AMI created earlier.
3. Follow the standard EC2 instance launch process.
4. Verify your application (e.g., Tomcat) by accessing the instance’s public 
   
   

   http://ec2-public-ip:8080
   

   
   
   Important Notes

1. Deregister the AMI: Once the EC2 instance is terminated, deregister the AMI to avoid unnecessary storage costs.
2. Terminate Unused Instances: If an EC2 instance remains active, it incurs costs even if unused.

Why Use a Custom AMI?

Creating a custom AMI simplifies deployment:

• Faster Launch: Applications and configurations are pre-installed.
• Consistency: Launch identical instances every time.
• Scalability: Ideal for Autoscaling environments.

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AWS Lambda Tutorial

AWS Lambda is a compute service offered by Amazon Web Services (AWS). Unlike EC2, Elastic Beanstalk, or OpsWorks, Lambda is designed to simplify the development and execution of code without the need to manage servers or underlying infrastructure. Below, we’ll explore what AWS Lambda is, how it works, and its use cases.

What is AWS Lambda?

AWS Lambda is a serverless compute service that allows developers to run code without provisioning or managing servers. With Lambda, you only need to provide the code, and AWS handles the infrastructure, scaling, and resource management automatically.

Key Benefits of AWS Lambda:

• Focus solely on writing and deploying application code.
• AWS handles launching, scaling, patching, and updating resources.
• Ideal for event-driven applications or tasks with variable workloads

Where to Use AWS Lambda

AWS Lambda is best suited for event-driven tasks, where specific triggers initiate the execution of code. For example:

• Backend processing for web and mobile applications.
• Image processing, such as resizing, filtering, or compression.
• Automated data processing tasks triggered by changes in a database or object storage (e.g., S3 buckets).

How AWS Lambda Works

Lambda functions are triggered by event sources such as file uploads, database updates, or HTTP requests. Let’s walk through an example where an image upload to an S3 bucket triggers a Lambda function:

1. Event Trigger: A user uploads an image to an S3 bucket.
2. Notification Setup: The S3 bucket has a notification configured to trigger a Lambda function.
3. Lambda Execution Role: IAM (Identity and Access Management) roles provide Lambda the necessary permissions to access resources (e.g., S3).
4. Code Execution: The Lambda function executes tasks such as resizing or filtering the uploaded image.

Why Choose AWS Lambda?

When architecting solutions, you have several compute options:

1. AWS EC2:
   Requires manual setup and management of virtual machines, software, and infrastructure.
2. AWS Elastic Beanstalk:
   Best for deploying full applications, not suitable for backend-only tasks.
3. AWS OpsWorks:
   Focuses on application lifecycle management, which may not be needed for simple backend tasks.
4. AWS Lambda:
   A lightweight, serverless option where you only upload your code, and AWS handles everything else.

Example Use Case:

• News Feed Processing:
• During high traffic (e.g., mornings), Lambda scales to handle spikes.
• During low traffic (e.g., nights), Lambda incurs no cost due to inactivity.
• However, for continuously running workloads, EC2 might be a better choice.

Limitations of AWS Lambda

While Lambda is powerful, it has some architectural and hardware limitations:

• Disk Size: Limited to 512 MB.
• Memory: Configurable between 128 MB and 1536 MB.
• Execution Timeout: Maximum of 15 minutes.
• Request Payload: Maximum size of 6 MB for synchronous requests and 128 KB for event payloads.

Pricing

AWS Lambda follows a pay-as-you-go model:

1. Number of Requests:
• First 1 million requests are free.
• $0.20 per 1 million requests thereafter.
2. Execution Time:
• Calculated based on memory allocation and execution duration.
• $0.00001667 per GB-second of usage.

Creating a Lambda Function

Here’s a step-by-step guide to creating a Lambda function that logs a message when an object is added to an S3 bucket:

1. Navigate to AWS Lambda Console:
   Under the "Compute" section in the AWS Management Console, select AWS Lambda.
2. Create a New Lambda Function:
   Click "Create Function" and select a blueprint (e.g., blank function).
3. Set the Trigger:
   Choose S3 as the trigger source and specify the bucket and event (e.g., object creation).
4. Configure the Lambda Function:
• Provide a name and description for the function.
• Select a runtime (e.g., Node.js, Python).
• Use the inline editor to add the following code:javascript
  Copy code
  
  
  exports.handler = (event, context, callback) => {
•     console.log("An object added to S3");
• };
• 
  
  
  
• Set up an execution role with appropriate permissions (e.g., S3 object read-only access).
5. Deploy and Test:
• Upload a file to the specified S3 bucket.
• Verify that the Lambda function logs the event in CloudWatch.

Conclusion

AWS Lambda simplifies application development by eliminating the need to manage infrastructure. It’s ideal for event-driven applications, variable workloads, and automating backend processes. However, for long-running or continuous workloads, EC2 might be a better fit.

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