AWS-Lumberyard

 

AWS

 

Launch an instance using EC2

Use a foundation model in the Amazon Bedrock playground

Set up a cost budget using AWS Budgets

Create a web app using AWS Lambda

Create an Amazon RDS Database

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

AWS

 

AWS Lumberyard: An Overview

Amazon Web Services (AWS) Lumberyard is a free, cross-platform game engine developed by Amazon, designed to empower creators with professional-grade tools for building high-quality games and interactive experiences. Based on CryEngine technology, Lumberyard was first introduced in 2016 as Amazon’s foray into the highly competitive world of game development engines. Its creation reflects Amazon’s broader vision to integrate cloud computing, multiplayer networking, and content creation into one cohesive ecosystem for developers.

Foundation and Technology

At its core, Lumberyard is built on the architecture of CryEngine, a renowned graphics engine known for its high-fidelity visuals and rendering capabilities. Amazon licensed the CryEngine framework and expanded upon it, layering in AWS cloud services and new features aimed at both small studios and AAA developers. Lumberyard was designed to appeal to developers seeking cinematic quality graphics, real-time rendering, and powerful asset pipelines. It provides a visual scripting system (Script Canvas), a component entity system, physics simulation, and support for C++ coding, allowing teams with varying expertise to collaborate.

AWS Integration

What sets Lumberyard apart from other engines like Unity or Unreal is its deep integration with Amazon Web Services. Developers can seamlessly connect their games to AWS cloud infrastructure for scalable multiplayer servers, analytics, data storage, and live game operations. For example, with Amazon GameLift, teams can deploy and manage multiplayer servers with relative ease, ensuring low-latency experiences for players worldwide. This makes Lumberyard particularly appealing for developers who want to build large, online, persistent worlds without the overhead of building server infrastructure from scratch.

Multiplayer and Networking

Lumberyard includes a sophisticated networking layer tailored for modern online games. It allows developers to build real-time multiplayer features, matchmaking, and in-game communications. The engine’s architecture supports peer-to-peer and client-server models, with AWS cloud services enhancing scalability. This focus on online connectivity reflects Amazon’s recognition that multiplayer and social features are central to today’s gaming ecosystem.

Visual Tools and Developer Features

Lumberyard emphasizes accessibility through tools like Script Canvas, a node-based visual scripting environment, and an advanced animation editor known as EMotion FX. These features lower barriers for non-programmers, enabling artists and designers to contribute directly to gameplay logic and character development. Lumberyard also supports popular workflows such as integration with Autodesk Maya, 3ds Max, and Photoshop. Its rendering pipeline supports cutting-edge effects like physically based rendering (PBR), dynamic global illumination, and realistic vegetation and terrain systems.

Community and Open Source Transition

Initially, Lumberyard was distributed as a proprietary engine, free of royalties and licensing fees, but tied to AWS for cloud services. Over time, Amazon recognized the benefits of openness and, in 2021, transitioned Lumberyard into an open-source project called the Open 3D Engine (O3DE) under the Linux Foundation. This shift gave developers more freedom to customize and contribute to the engine’s evolution while maintaining AWS as the preferred cloud backend.

Conclusion

AWS Lumberyard represented Amazon’s bold attempt to merge world-class graphics with cloud-based scalability. By combining CryEngine’s rendering power, AWS’s cloud services, and user-friendly tools, it offered a compelling choice for developers seeking to build visually stunning, connected games. Though now succeeded by the community-driven O3DE, Lumberyard’s legacy lies in shaping a new era of open, cloud-integrated game development.

 

Launch an instance using EC2

 

Here’s a clear step-by-step explanation of the process to launch an instance using Amazon EC2 (Elastic Compute Cloud):

 

Launching an Instance in Amazon EC2

Amazon EC2 provides scalable virtual servers in the cloud. Launching an instance essentially means creating and running a virtual machine (VM) in the AWS environment. The process involves several steps, each allowing you to customize the instance to your needs.

 

1. Log in to the AWS Management Console

• Go to the AWS Management Console.
• Navigate to EC2 under the “Services” menu.
• Click Launch Instance to begin the setup process.

 

2. Choose an Amazon Machine Image (AMI)

• An AMI is a preconfigured template for your instance that includes an operating system (Linux, Windows, etc.) and optional applications.
• AWS offers:
• Quick Start AMIs (common OS templates like Ubuntu, Amazon Linux, Windows Server).
• AWS Marketplace AMIs (vendor-provided software packages).
• Custom AMIs you or your organization have created.

 

3. Select an Instance Type

• Choose the instance type, which defines the hardware of the host computer:
• vCPU (virtual CPUs)
• Memory (RAM)
• Storage type
• Networking performance
• For example, t2.micro or t3.micro are commonly used free-tier eligible types.

 

4. Configure Instance Details

• Set the number of instances.
• Choose the network (VPC) and subnet.
• Assign IAM roles (if needed) for secure access to other AWS services.
• Configure advanced features such as shutdown behavior, monitoring (CloudWatch), and tenancy (shared or dedicated hardware).

 

5. Add Storage

• Specify the size and type of storage (Elastic Block Store – EBS).
• By default, the selected AMI determines the root volume size (e.g., 8GB).
• You can add additional volumes or configure options like encryption.

 

6. Add Tags (Optional)

• Tags are key–value pairs to organize and manage resources.
• For example:
• Key: Name
• Value: WebServer1

 

7. Configure Security Group

• A security group acts as a virtual firewall.
• Define inbound and outbound rules to control traffic.
• Example: Allow SSH (port 22) for Linux, or RDP (port 3389) for Windows, and allow HTTP (port 80) for web traffic.

 

8. Review and Launch

• Review all configuration details.
• When ready, click Launch.
• You’ll be prompted to select or create a key pair:
• A key pair consists of a public key (stored by AWS) and a private key (.pem file you download).
• This private key is required for secure SSH or RDP access to your instance.

 

9. Connect to the Instance

• Once the instance status is running, you can connect:
• Linux Instance → Use SSH from your terminal:
• ssh -i your-key.pem ec2-user@public-dns-name
• Windows Instance → Use RDP with the Administrator password (retrieved from the AWS console using your key pair).

 

Summary

Launching an EC2 instance involves:

1. Logging into AWS Console
2. Choosing an AMI
3. Selecting an instance type
4. Configuring details
5. Adding storage
6. Tagging resources
7. Setting up security groups
8. Reviewing and launching with a key pair
9. Connecting to your running instance

 

here’s a practical, copy-pasteable guide to automate EC2 launches with the AWS CLI (no console needed).

0) Prereqs

• AWS CLI v2 installed and aws configure done (access key, secret, default region).
• An IAM user/role with ec2:* permissions needed below (at minimum: RunInstances, CreateTags, CreateSecurityGroup, AuthorizeSecurityGroupIngress, CreateKeyPair, etc.).

---

1) Pick an AMI programmatically (keeps things current)

Use AWS Systems Manager public parameters so you don’t hardcode AMI IDs:

REGION=us-east-1

aws ssm get-parameters \

  --names /aws/service/ami-amazon-linux-latest/al2023-ami-kernel-6.1-x86_64 \

  --query 'Parameters[0].Value' --output text --region $REGION

Save it:

AMI_ID=$(aws ssm get-parameters \

  --names /aws/service/ami-amazon-linux-latest/al2023-ami-kernel-6.1-x86_64 \

  --query 'Parameters[0].Value' --output text --region $REGION)

---

2) Create (or reuse) a key pair

KEY_NAME=my-ec2-key

aws ec2 create-key-pair --key-name $KEY_NAME \

  --query 'KeyMaterial' --output text --region $REGION > ${KEY_NAME}.pem

chmod 400 ${KEY_NAME}.pem

(If it exists already, skip and reuse.)

---

3) Create a minimal security group

VPC_ID=$(aws ec2 describe-vpcs --query 'Vpcs[0].VpcId' --output text --region $REGION)

SG_ID=$(aws ec2 create-security-group \

  --group-name web-sg --description "Web SG" --vpc-id $VPC_ID \

  --query 'GroupId' --output text --region $REGION)

 

# Allow SSH from your IP (replace 1.2.3.4/32) and HTTP for a demo

aws ec2 authorize-security-group-ingress --group-id $SG_ID \

  --ip-permissions '[

    {"IpProtocol":"tcp","FromPort":22,"ToPort":22,"IpRanges":[{"CidrIp":"1.2.3.4/32"}]},

    {"IpProtocol":"tcp","FromPort":80,"ToPort":80,"IpRanges":[{"CidrIp":"0.0.0.0/0"}]}

  ]' --region $REGION

---

4) (Optional) User-data to bootstrap the instance

Example that installs a tiny web page on Amazon Linux:

cat > user-data.sh <<'EOF'

#!/bin/bash

dnf -y install nginx

echo "<h1>Hello from AWS CLI</h1>" > /usr/share/nginx/html/index.html

systemctl enable --now nginx

EOF

---

5) Launch directly with run-instances

Quick one-off:

INSTANCE_TYPE=t3.micro

 

aws ec2 run-instances \

  --image-id $AMI_ID \

  --instance-type $INSTANCE_TYPE \

  --key-name $KEY_NAME \

  --security-group-ids $SG_ID \

  --user-data file://user-data.sh \

  --tag-specifications 'ResourceType=instance,Tags=[{Key=Name,Value=cli-web-1}]' \

  --count 1 \

  --region $REGION

Add --dry-run first to validate permissions without launching.

Wait until it’s ready and fetch its public IP:

INSTANCE_ID=$(aws ec2 describe-instances \

  --filters Name=tag:Name,Values=cli-web-1 Name=instance-state-name,Values=pending,running \

  --query 'Reservations[0].Instances[0].InstanceId' --output text --region $REGION)

 

aws ec2 wait instance-running --instance-ids $INSTANCE_ID --region $REGION

 

PUBLIC_IP=$(aws ec2 describe-instances --instance-ids $INSTANCE_ID \

  --query 'Reservations[0].Instances[0].PublicIpAddress' \

  --output text --region $REGION)

 

echo "IP: $PUBLIC_IP"

Test:

curl -I http://$PUBLIC_IP/

ssh -i ${KEY_NAME}.pem ec2-user@$PUBLIC_IP

---

6) Reusable automation with Launch Templates

Create a template once, then launch as many as you want.

Template (JSON)

cat > lt-data.json <<EOF

{

  "ImageId": "$AMI_ID",

  "InstanceType": "t3.micro",

  "KeyName": "$KEY_NAME",

  "SecurityGroupIds": ["$SG_ID"],

  "UserData": "$(base64 -w0 user-data.sh)"

}

EOF

Create the Launch Template

LT_NAME=web-lt

aws ec2 create-launch-template \

  --launch-template-name $LT_NAME \

  --version-description "v1" \

  --launch-template-data file://lt-data.json \

  --region $REGION

Launch from the template

aws ec2 run-instances \

  --launch-template LaunchTemplateName=$LT_NAME,Version=1 \

  --tag-specifications 'ResourceType=instance,Tags=[{Key=Name,Value=cli-web-from-lt}]' \

  --region $REGION

You can evolve the template via new versions (e.g., different AMI, instance type, user-data) and refer to Version=$Latest.

---

7) Optional: Spot & scaling

• Spot Instances (cheap, can be interrupted):

·         aws ec2 run-instances \

·           --launch-template LaunchTemplateName=$LT_NAME,Version=$Latest \

·           --instance-market-options 'MarketType=spot' \

·           --region $REGION

• Auto Scaling Group: pair the launch template with an ASG to keep N instances running automatically.

---

8) Clean up

aws ec2 terminate-instances --instance-ids $INSTANCE_ID --region $REGION

aws ec2 delete-launch-template --launch-template-name $LT_NAME --region $REGION

aws ec2 delete-security-group --group-id $SG_ID --region $REGION

aws ec2 delete-key-pair --key-name $KEY_NAME --region $REGION

rm -f ${KEY_NAME}.pem user-data.sh lt-data.json

---

Tips

• Use profiles: add --profile myprofile.
• Pin a region consistently to avoid “AMI not found”.
• Prefer launch templates for repeatability and ASG integration.
• Always start with --dry-run in production environments.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Use a foundation model in the Amazon Bedrock playground

 

Here’s a clear explanation of the process to use a foundation model in the Amazon Bedrock playground:

 

Using a Foundation Model in the Amazon Bedrock Playground

Amazon Bedrock is a fully managed service that lets you build and scale generative AI applications without needing to manage infrastructure or train models from scratch. It provides access to multiple foundation models (FMs) from providers like Anthropic, Cohere, Meta, Mistral, and Stability AI through an API. The Bedrock playground is an interactive web-based environment in the AWS Console where you can experiment with these models before integrating them into your applications.

 

1. Log in to the AWS Management Console

• Sign in to your AWS account.
• From the services menu, navigate to Amazon Bedrock.
• Open the Bedrock Playground section.

 

2. Choose a Foundation Model

• The playground lists several foundation models across categories such as:
• Text generation (chatbots, summarization, Q&A).
• Embeddings (semantic search, recommendations).
• Image generation (create visuals from text prompts).
• Select a model provider (e.g., Anthropic Claude, AI21 Jurassic, Cohere, Stability AI).
• Each model has different strengths—some are optimized for reasoning and conversation, others for creative writing or image synthesis.

 

3. Set Up Your Prompt

• In the prompt editor, you can type your input (for example, “Write a summary of cloud computing in simple terms”).
• For text models:
• You can structure prompts as instructions, questions, or even simulate chat dialogues.
• For image models:
• You provide a descriptive text prompt (e.g., “A futuristic city skyline at sunset in cyberpunk style”).

 

4. Adjust Parameters

• The playground allows you to tweak generation settings:
• Temperature – Controls creativity vs. precision (higher values = more creative, lower = more deterministic).
• Maximum tokens – Sets the length of the output.
• Top-p / Top-k – Influence randomness and diversity of responses.
• These settings let you experiment with how the model behaves.

 

5. Run and Review Output

• Click Submit / Generate to run the model.
• The foundation model processes your prompt and displays the output directly in the playground.
• For conversational models, you can continue the dialogue interactively by entering follow-up prompts.

 

6. Compare Models (Optional)

• The playground supports side-by-side comparisons.
• You can select multiple models and run the same prompt through them to see differences in style, accuracy, and tone.
• This helps you evaluate which foundation model best fits your use case.

 

7. Save and Export

• You can save successful prompts and outputs for reference.
• The playground often provides code snippets (Python, JavaScript, etc.) to show how to call the same model via the Bedrock API in your applications.

 

8. Move Toward Deployment

• Once you’re satisfied with model behavior in the playground:
• You can integrate the chosen foundation model into your app using the Amazon Bedrock API.
• This involves creating a Bedrock endpoint, calling the model programmatically, and embedding it in your workflows.

 

Summary

Using a foundation model in the Amazon Bedrock playground involves:

1. Logging into AWS Console and opening Bedrock.
2. Selecting a foundation model (text, embedding, or image).
3. Writing prompts in the editor.
4. Adjusting parameters like temperature and token limits.
5. Generating and reviewing outputs.
6. Optionally comparing multiple models.
7. Saving prompts and outputs.
8. Transitioning from testing to application integration via APIs.

---

Here’s a guided, click-by-click demo (with text “screenshots”) for using a foundation model in the Amazon Bedrock Playground.

 

Step-by-step: Use a model in Amazon Bedrock Playground

0) Prereqs (one-time)

• You have an AWS account and model access enabled for at least one Bedrock model (you can request/modify access in the console). (AWS Documentation)

 

1) Open the Playground

1. In the AWS Console, search “Bedrock” and open Amazon Bedrock.
2. In the left nav, under Playgrounds, choose Chat/text (for text/chat) or Image (for image generation). (AWS Documentation)

[Screenshot (text)]

• Left nav: “Home · Model catalog · Knowledge bases · Guardrails · Playgrounds ▸ Chat/text | Image”
• Page header: “Playgrounds” with tabs “Chat” and “Single prompt.”

 

2) Pick a foundation model

• At the top of the playground, open the Model dropdown and select a model (e.g., Claude, Cohere, Mistral, Meta, Amazon Nova for text/chat; Stability for images). If you don’t see a model, click Manage model access and enable it for your account. (AWS Documentation)

[Screenshot (text)]

• Top toolbar: “Model: [Select…] ▾ Mode: [Chat | Single prompt]”
• Banner hint: “You don’t have access to this model. Manage model access.”

 

3) Compose your prompt (Chat/text)

1. In Chat mode, type your instruction (e.g., “Summarize zero-trust networking in 120 words”).
2. (Optional) Click Attach to include an image or document as extra context (multimodal chat).
3. Click Generate. Subsequent turns keep the conversation context. (AWS Documentation)

[Screenshot (text)]

• Prompt box at bottom: “Message the model…” with a paperclip icon (Attach).
• Right pane: “Response” area streams model output.

Tip: Switch to Single prompt mode for one-off prompts without chat history. (AWS Documentation)

 

4) Tune output with parameters

• In the Response settings/parameters panel, adjust: Temperature, Max tokens, Top-p, Top-k (names may vary by model). Then re-run to see differences. (AWS Documentation)

[Screenshot (text)]

• Side panel: “Generation settings”
• Temperature [0.7]
• Max tokens [512]
• Top-p [0.9]
• Top-k [50]

 

5) Compare models side-by-side (optional)

• Use Compare to run the same prompt across multiple models and review outputs in columns. (Note: speech-to-speech models aren’t supported in compare.) (AWS Documentation)

[Screenshot (text)]

• Toolbar: “Compare ▸ Select models (up to 3)”
• Grid with columns labeled by model names; identical prompt shown above each result.

 

6) Image generation (optional)

• In Playgrounds ▸ Image, enter a descriptive prompt (e.g., “futuristic city at dusk, wide angle”).
• (Optional) Upload a reference image to edit or generate variations. Click Generate. (AWS Documentation)

[Screenshot (text)]

• Left: prompt box + advanced options (resolution, steps, guidance).
• Right: tiled image results with Download and Variations buttons.

 

7) After you generate

• Use the playground to iterate quickly. When satisfied, note that what you do here corresponds to the Bedrock runtime APIs (e.g., InvokeModel, Converse/streaming). This makes it straightforward to reproduce in code outside the console. (AWS Documentation)

[Screenshot (text)]

• Info banner: “Running a prompt in a playground is equivalent to calling the Bedrock runtime APIs.”
• Link: “Learn more about Bedrock runtime.”

 

8) Troubleshooting & tips

• Model not listed? You likely need to request access for that provider/model. (AWS Documentation)
• Region mismatch? Ensure you’re in a Bedrock-supported region (e.g., us-east-1 / us-west-2). (AWS Documentation)
• Costs: You’re billed for inference usage per model’s pricing; use the playground sparingly when exploring. (See Bedrock docs/pricing from the service homepage.) (Amazon Web Services, Inc.)

 

Quick recap

1. Console → Amazon Bedrock → Playgrounds.
2. Choose Chat/text or Image.
3. Select a model, write a prompt, tweak parameters, and Generate.
4. (Optional) Compare models.
5. Transition to code using the corresponding runtime APIs. (AWS Documentation)

 

Create a one-page printable checklist or add a mini lab (e.g., “evaluate the same prompt on 3 models and record differences”).

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Set up a cost budget using AWS Budgets

 

Here’s a clear step-by-step explanation of the process to set up a cost budget using AWS Budgets:

 

Setting Up a Cost Budget in AWS Budgets

AWS Budgets helps you plan and monitor your cloud spending. By creating a cost budget, you can track how much you’re spending in AWS against a threshold you define. You’ll also receive alerts (via email or SNS) when your actual or forecasted costs exceed that threshold.

 

1. Log in to the AWS Management Console

• Sign in to your AWS account.
• In the Services menu, search for and open AWS Budgets.

 

2. Start Creating a Budget

• On the AWS Budgets dashboard, click Create budget.
• You’ll be prompted to choose a budget type. Select Cost budget, then click Next.

 

3. Set Budget Details

• Enter a name for your budget (e.g., “Monthly EC2 Budget”).
• Choose the period for tracking:
• Monthly (most common)
• Quarterly or annually
• Define the budget renewal type:
• Recurring budget (resets each month, quarter, or year).
• Expiring budget (runs for a specific time frame only).

 

4. Define Budget Scope

• Choose whether to track:
• Linked accounts (if you’re in an AWS Organization).
• Specific services (e.g., EC2, S3, RDS).
• Tags (to monitor specific projects or environments).
• This helps narrow your budget to the resources that matter most.

 

5. Set the Budgeted Amount

• Enter the budget limit (e.g., $100 for monthly costs).
• Choose how to measure costs:
• Unblended costs (default, shows list prices).
• Amortized costs (spreads upfront RI or Savings Plan costs).
• Net unblended costs (after discounts, credits, and refunds).

 

6. Configure Alerts (Optional but Recommended)

• Add an alert threshold to notify you when costs exceed a percentage of the budget:
• Example: Send an alert at 80% of budgeted amount and again at 100%.
• Select actual costs or forecasted costs:
• Actual costs alert you when you’ve already spent the amount.
• Forecasted costs alert you when AWS predicts you’ll exceed the budget by the end of the period.
• Enter an email address (or an Amazon SNS topic) to receive alerts.

 

7. Review and Create

• Review all settings (budget name, amount, scope, alerts).
• Click Create budget to finalize.
• Your budget will now appear in the AWS Budgets dashboard.

 

8. Monitor and Take Action

• After setup, AWS will track your costs in near real-time.
• If your usage crosses the defined threshold, you’ll get an alert.
• Use these insights to optimize resources (e.g., stop unused EC2 instances, move data to cheaper S3 storage tiers, or buy Savings Plans).

 

Summary

Setting up a cost budget in AWS Budgets involves:

1. Logging into AWS Budgets.
2. Choosing Cost budget.
3. Setting name, period, and renewal type.
4. Defining scope (accounts, services, or tags).
5. Entering the budget amount.
6. Adding alert thresholds and recipients.
7. Reviewing and creating the budget.
8. Monitoring costs and responding to alerts.

---

AWS Budgets by itself only sends alerts (emails or SNS notifications).
If you want to go beyond alerts and automate actions (e.g., stop EC2 instances when a cost threshold is crossed), you need to combine Budgets with AWS Budgets Actions, SNS, and sometimes AWS Lambda or SSM.

Here’s the breakdown:

---

1. Alerts Only (default Budgets feature)

• When creating a budget, you can add alerts.
• Alerts are sent to email addresses or SNS topics when actual or forecasted spend crosses your threshold.
• These alerts don’t take direct action—they’re notifications only.
• Good for: Teams that just need visibility into overspending.

---

2. Automated Actions via AWS Budgets Actions

Since 2020, AWS Budgets supports actions tied to budget thresholds. With this, you can:

• Stop, start, or terminate EC2 and RDS instances automatically.
• Update IAM policies to restrict usage.

How it works:

1. When setting up your budget, go to the “Configure actions” step.
2. Choose an action type:
• EC2 or RDS instance control (stop, start, terminate).
• IAM permissions control (apply a policy to restrict usage).
3. Select the resources to act on.
4. Link an SNS topic for notifications (optional but recommended).
5. Confirm with an IAM role that allows Budgets to perform these actions on your behalf.

Example:

• If your monthly spend exceeds $50, AWS Budgets can automatically stop all t3.micro instances in a chosen region.

---

3. More Complex Automation with Lambda + SNS

If you need more flexibility than built-in Budgets Actions:

1. Create an SNS topic.
2. Subscribe an AWS Lambda function to it.
3. Configure your Budget to send alerts to that SNS topic.
4. In Lambda, write custom logic—for example:
• Stop or downsize all running EC2 instances.
• Move data from expensive storage classes to cheaper ones (S3 IA/Glacier).
• Notify Slack/Teams with a webhook.

Sample Lambda pseudocode (Python):

import boto3

 

def lambda_handler(event, context):

    ec2 = boto3.client('ec2')

    # Get all running instances

    response = ec2.describe_instances(Filters=[{'Name':'instance-state-name','Values':['running']}])

    instance_ids = [i['InstanceId'] for r in response['Reservations'] for i in r['Instances']]

    if instance_ids:

        ec2.stop_instances(InstanceIds=instance_ids)

        print(f"Stopped instances: {instance_ids}")

---

4. Which Should You Use?

• Alerts only → If you just want cost visibility.
• Budgets Actions → If you need quick, built-in automation (stop/start/terminate EC2/RDS, update IAM).
• SNS + Lambda → If you want full control and can code custom responses.

---

✅ Summary:
By default, AWS Budgets provides alerts only. If you want automatic cost control, enable Budgets Actions to stop/start/terminate EC2 or RDS or adjust IAM permissions. For advanced workflows, integrate alerts with SNS + Lambda for customized automation.

---

Create a step-by-step tutorial (with click-path descriptions like the console demo earlier) showing exactly how to set up a Budget Action that stops EC2 instances when a threshold is crossed.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Create a web app using AWS Lambda

 

Here’s a structured explanation of the process to create a web app using AWS Lambda:

 

Creating a Web App Using AWS Lambda

AWS Lambda is a serverless compute service that lets you run code without managing servers. Instead of provisioning EC2 instances or containers, you write functions that execute in response to events such as HTTP requests, file uploads, or database changes. To build a simple web app, you combine Lambda with Amazon API Gateway (to handle HTTP requests) and optionally DynamoDB or S3 (for data storage).

 

1. Set Up Your AWS Environment

• Sign in to the AWS Management Console.
• Ensure you have the necessary permissions for Lambda, API Gateway, IAM, and S3/DynamoDB (if used).

 

2. Create a Lambda Function

1. Go to the AWS Lambda Console.
2. Click Create function.
3. Choose:
• Author from scratch
• Name: e.g., MyWebAppFunction
• Runtime: Select a language (Python, Node.js, Java, etc.).
• Execution role: Assign an IAM role with basic Lambda permissions (and permissions for other services if needed).
4. Click Create function.

 

3. Write the Lambda Code

• In the Lambda editor, write your application logic.
• Example: A simple Node.js handler returning a web response:

exports.handler = async (event) => {

    const response = {

        statusCode: 200,

        headers: { "Content-Type": "text/html" },

        body: "<h1>Hello from My Web App on AWS Lambda!</h1>",

    };

    return response;

};

• Save and Deploy the function.

 

4. Create an API Gateway Endpoint

1. Go to the Amazon API Gateway Console.
2. Choose Create API → HTTP API (simpler) or REST API (more customizable).
3. Define a new API (e.g., MyWebAppAPI).
4. Create a route (e.g., /) with method GET.
5. Connect the route to your Lambda function (MyWebAppFunction).
6. Deploy the API to a stage (e.g., prod).

 

5. Test the Web App

• API Gateway generates an endpoint URL like:
• https://abc123.execute-api.us-east-1.amazonaws.com/prod/
• Open this URL in your browser.
• You should see:
• Hello from My Web App on AWS Lambda!

 

6. (Optional) Add Storage or Database

• Amazon DynamoDB – Store user data (e.g., profiles, posts, messages).
• Amazon S3 – Store static assets like images, CSS, or JavaScript files.
• Update your Lambda function to interact with these services.

 

7. Secure and Scale

• Use IAM roles and resource policies to secure access.
• Enable CORS in API Gateway if your web app is accessed from browsers.
• Lambda scales automatically with requests, so no server management is needed.

 

8. (Optional) Host a Front-End

• If you’re building a full-stack app, host your front-end (HTML, CSS, JS) in an S3 bucket with static website hosting or AWS Amplify Hosting, and connect it to your Lambda-powered API.

 

Summary

The process of creating a web app with AWS Lambda involves:

1. Creating a Lambda function with your backend logic.
2. Using Amazon API Gateway to expose it as a web endpoint.
3. Writing and deploying code to return web responses.
4. Testing the API in a browser.
5. Optionally integrating storage (S3, DynamoDB).
6. Securing and scaling automatically.

---

Create a simple CRUD web app using API Gateway + Lambda (Node.js) + DynamoDB, with copy-pasteable bits you can deploy fast.

---

What you’ll build

• DynamoDB table: Todos (partition key id).
• Single Lambda handling all CRUD via HTTP methods.
• API Gateway (HTTP API) routes:
• POST /items → Create
• GET /items/{id} → Read one
• GET /items → Read all
• PUT /items/{id} → Update
• DELETE /items/{id} → Delete
• CORS enabled so a browser front-end can call it.

---

1) Create the DynamoDB table

aws dynamodb create-table \

  --table-name Todos \

  --attribute-definitions AttributeName=id,AttributeType=S \

  --key-schema AttributeName=id,KeyType=HASH \

  --billing-mode PAY_PER_REQUEST

---

2) Lambda code (Node.js 20)

Create index.mjs:

import { DynamoDBClient } from "@aws-sdk/client-dynamodb";

import {

  PutCommand, GetCommand, ScanCommand, UpdateCommand, DeleteCommand,

  DynamoDBDocumentClient

} from "@aws-sdk/lib-dynamodb";

import { randomUUID } from "crypto";

 

const client = DynamoDBDocumentClient.from(new DynamoDBClient({}));

const TABLE = process.env.TABLE_NAME || "Todos";

 

const json = (statusCode, body) => ({

  statusCode,

  headers: {

    "Content-Type": "application/json",

    "Access-Control-Allow-Origin": "*",

    "Access-Control-Allow-Headers": "*",

    "Access-Control-Allow-Methods": "GET,POST,PUT,DELETE,OPTIONS"

  },

  body: JSON.stringify(body)

});

 

export const handler = async (event) => {

  try {

    const { routeKey, body, pathParameters, queryStringParameters } = event;

    const parsed = body ? JSON.parse(body) : {};

 

    switch (routeKey) {

 

      case "POST /items": {

        const item = {

          id: parsed.id || randomUUID(),

          title: parsed.title ?? "",

          done: !!parsed.done,

          createdAt: new Date().toISOString()

        };

        await client.send(new PutCommand({ TableName: TABLE, Item: item }));

        return json(201, item);

      }

 

      case "GET /items/{id}": {

        const id = pathParameters?.id;

        const res = await client.send(new GetCommand({ TableName: TABLE, Key: { id } }));

        return res.Item ? json(200, res.Item) : json(404, { message: "Not found" });

      }

 

      case "GET /items": {

        // Optional basic pagination with limit & start key

        const limit = Number(queryStringParameters?.limit || 25);

        const startKey = queryStringParameters?.lastKey ? { id: queryStringParameters.lastKey } : undefined;

        const res = await client.send(new ScanCommand({ TableName: TABLE, Limit: limit, ExclusiveStartKey: startKey }));

        return json(200, { items: res.Items ?? [], lastKey: res.LastEvaluatedKey?.id });

      }

 

      case "PUT /items/{id}": {

        const id = pathParameters?.id;

        const { title, done } = parsed;

        const res = await client.send(new UpdateCommand({

          TableName: TABLE,

          Key: { id },

          UpdateExpression: "SET #t = :t, #d = :d",

          ExpressionAttributeNames: { "#t": "title", "#d": "done" },

          ExpressionAttributeValues: { ":t": title, ":d": !!done },

          ReturnValues: "ALL_NEW"

        }));

        return json(200, res.Attributes);

      }

 

      case "DELETE /items/{id}": {

        const id = pathParameters?.id;

        await client.send(new DeleteCommand({ TableName: TABLE, Key: { id } }));

        return json(204, {});

      }

 

      case "OPTIONS /{proxy+}":

      case "OPTIONS /items":

      case "OPTIONS /items/{id}":

        return json(204, {});

 

      default:

        return json(400, { message: `Unsupported routeKey: ${routeKey}` });

    }

 

  } catch (err) {

    console.error(err);

    return json(500, { message: "Server error", error: String(err) });

  }

};

Create package.json:

{

  "type": "module",

  "dependencies": {

    "@aws-sdk/client-dynamodb": "^3.614.0",

    "@aws-sdk/lib-dynamodb": "^3.614.0"

  }

}

Install deps and zip:

npm i

zip -r function.zip index.mjs node_modules

---

3) Create the Lambda + permissions

REGION=us-east-1

ROLE_NAME=crud-lambda-role

aws iam create-role --role-name $ROLE_NAME \

  --assume-role-policy-document '{

    "Version":"2012-10-17",

    "Statement":[{"Effect":"Allow","Principal":{"Service":"lambda.amazonaws.com"},"Action":"sts:AssumeRole"}]

  }'

 

aws iam attach-role-policy --role-name $ROLE_NAME \

  --policy-arn arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole

 

aws iam put-role-policy --role-name $ROLE_NAME --policy-name DDBAccess --policy-document "{

  \"Version\":\"2012-10-17\",

  \"Statement\":[

    {\"Effect\":\"Allow\",\"Action\":[\"dynamodb:PutItem\",\"dynamodb:GetItem\",\"dynamodb:Scan\",\"dynamodb:UpdateItem\",\"dynamodb:DeleteItem\"],\"Resource\":\"*\"}

  ]

}"

 

# Wait a few seconds for role propagation

sleep 10

 

ROLE_ARN=$(aws iam get-role --role-name $ROLE_NAME --query 'Role.Arn' --output text)

 

aws lambda create-function \

  --function-name crud-todos-fn \

  --runtime nodejs20.x \

  --role $ROLE_ARN \

  --handler index.handler \

  --zip-file fileb://function.zip \

  --environment "Variables={TABLE_NAME=Todos}" \

  --region $REGION

---

4) Create the HTTP API + routes

API_ID=$(aws apigatewayv2 create-api \

  --name crud-todos-api \

  --protocol-type HTTP \

  --target arn:aws:lambda:$REGION:$(aws sts get-caller-identity --query Account --output text):function:crud-todos-fn \

  --cors-configuration 'AllowOrigins=["*"],AllowMethods=["GET","POST","PUT","DELETE","OPTIONS"],AllowHeaders=["*"]' \

  --query 'ApiId' --output text --region $REGION)

 

# Grant API Gateway permission to invoke Lambda

aws lambda add-permission \

  --function-name crud-todos-fn \

  --statement-id apigw \

  --action lambda:InvokeFunction \

  --principal apigateway.amazonaws.com \

  --source-arn "arn:aws:execute-api:$REGION:$(aws sts get-caller-identity --query Account --output text):$API_ID/*/*/{proxy+}"

 

# Define routes

for r in "POST /items" "GET /items" "GET /items/{id}" "PUT /items/{id}" "DELETE /items/{id}" "OPTIONS /{proxy+}"; do

  METHOD=$(echo $r | cut -d' ' -f1); PATH=$(echo $r | cut -d' ' -f2-)

  aws apigatewayv2 create-route --api-id $API_ID --route-key "$METHOD $PATH" \

    --target "integrations/$(aws apigatewayv2 create-integration \

      --api-id $API_ID \

      --integration-type AWS_PROXY \

      --integration-uri arn:aws:lambda:$REGION:$(aws sts get-caller-identity --query Account --output text):function:crud-todos-fn \

      --payload-format-version 2.0 \

      --query 'IntegrationId' --output text --region $REGION)" \

    --region $REGION

done

 

# Deploy stage

aws apigatewayv2 create-stage --api-id $API_ID --stage-name prod --auto-deploy --region $REGION

 

API_URL=$(aws apigatewayv2 get-apis --query "Items[?ApiId=='$API_ID'].ApiEndpoint" --output text --region $REGION)

echo "API: $API_URL/prod"

---

5) Test with curl

BASE="$API_URL/prod"

 

# Create

curl -s -X POST "$BASE/items" -H "Content-Type: application/json" \

  -d '{"title":"Buy milk","done":false}'

 

# List

curl -s "$BASE/items"

 

# Read one (replace ID)

curl -s "$BASE/items/{id}"

 

# Update

curl -s -X PUT "$BASE/items/{id}" -H "Content-Type: application/json" \

  -d '{"title":"Buy oat milk","done":true}'

 

# Delete

curl -s -X DELETE "$BASE/items/{id}"

---

6) Front-end (optional)

Host a static SPA (HTML/JS) on S3 + CloudFront or Amplify Hosting; call the API with fetch. CORS is already open to * for quick demos—tighten in production.

---

7) Clean up

aws apigatewayv2 delete-api --api-id $API_ID --region $REGION

aws lambda delete-function --function-name crud-todos-fn --region $REGION

aws dynamodb delete-table --table-name Todos

aws iam delete-role-policy --role-name $ROLE_NAME --policy-name DDBAccess

aws iam detach-role-policy --role-name $ROLE_NAME --policy-arn arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole

aws iam delete-role --role-name $ROLE_NAME

---

Notes & production tips

• Scope IAM to the specific table ARN (least privilege).
• Add request validation and auth (e.g., Cognito authorizer) in API Gateway.
• Replace full-table Scan with Query when you design keyed access patterns.
• Use SAM / CDK to codify this stack for repeatable deploys.

 

Turn this into a SAM or CDK template so you can deploy the whole stack with one command.

 

 

 

 

 

 

 

 

 

 

 

 

Create an Amazon RDS Database

 

Here’s a clear, step-by-step explanation of the process to create an Amazon RDS database:

 

Creating an Amazon RDS Database

Amazon Relational Database Service (RDS) is a managed database service that simplifies the setup, operation, and scaling of relational databases in the cloud. It supports popular engines like MySQL, PostgreSQL, MariaDB, Oracle, and Microsoft SQL Server. With RDS, AWS handles tasks such as backups, patching, monitoring, and scaling so you can focus on applications instead of database administration.

 

1. Log in to the AWS Management Console

• Open the AWS Management Console.
• From the Services menu, select RDS.

 

2. Start the Database Creation Wizard

• In the RDS dashboard, click Create database.
• You’ll be prompted to choose between:
• Standard create (gives you full configuration control).
• Easy create (automatically selects recommended defaults).
• For more flexibility, select Standard create.

 

3. Choose a Database Engine

• Select the engine you want to use:
• Amazon Aurora (MySQL and PostgreSQL compatible, highly scalable).
• MySQL
• PostgreSQL
• MariaDB
• Oracle
• Microsoft SQL Server

 

4. Select a Database Template

• Choose a template depending on your use case:
• Production (multi-AZ deployment, backups, monitoring enabled).
• Dev/Test (lower cost, fewer high-availability features).
• Free Tier (limited resources, great for practice).

 

5. Configure Settings

• DB Instance Identifier: A unique name for your database.
• Master Username: The admin account for the database.
• Master Password: Choose a secure password (and confirm it).

 

6. Choose DB Instance Size

• Select the instance type (compute and memory resources).
• For example, db.t3.micro is free-tier eligible.
• Pick a storage type (General Purpose SSD, Provisioned IOPS, or Magnetic).
• Define allocated storage size (e.g., 20 GB).

 

7. Configure Connectivity

• Choose the Virtual Private Cloud (VPC) where the DB will run.
• Decide if the database should be publicly accessible (open to internet) or private (internal only).
• Assign a VPC security group to control inbound and outbound traffic.
• Example: Allow connections on port 3306 for MySQL.

 

8. Set Advanced Options (Optional)

• Enable automated backups (recommended).
• Set backup retention period (1–35 days).
• Enable Multi-AZ deployment for high availability.
• Enable monitoring and logging (CloudWatch).

 

9. Review and Launch

• Review all chosen settings.
• Click Create database.
• RDS will start provisioning the instance (this may take several minutes).

 

10. Connect to the Database

• Once the DB status is available, click on the DB instance to view details.
• Copy the endpoint (hostname) and port.
• Use a SQL client (e.g., MySQL Workbench, pgAdmin, SQL Server Management Studio) or application code to connect:

Example (MySQL from terminal):

mysql -h mydbinstance.xxxxxxx.us-east-1.rds.amazonaws.com -P 3306 -u admin -p

 

 

Summary

Creating an Amazon RDS database involves:

1. Logging into AWS RDS.
2. Starting the database creation wizard.
3. Choosing an engine (MySQL, PostgreSQL, etc.).
4. Selecting a template (production, dev/test, free tier).
5. Configuring DB name, admin credentials, and instance size.
6. Setting up connectivity (VPC, security groups, public/private).
7. Configuring backups, monitoring, and high availability.
8. Launching the DB instance.
9. Connecting to the DB using the provided endpoint.

---

initial setup and walk through the main ways to scale Amazon RDS—what each option does, trade-offs, and exactly how to do it (Console + CLI).

---

1) Scale compute (change instance class)

What it is: Move to a larger/smaller DB instance class (more vCPU/RAM; e.g., db.t3.micro → db.m7g.large).
Impact: Usually requires a restart; brief downtime unless you use Multi-AZ (failover minimizes it).

Console

1. RDS → Databases → select your instance.
2. Modify → DB instance class → choose new class.
3. (Optional) Apply immediately or during the next maintenance window.
4. Continue → Modify DB instance.

CLI

aws rds modify-db-instance \

  --db-instance-identifier mydb \

  --db-instance-class db.m7g.large \

  --apply-immediately

---

2) Scale storage capacity (increase size / autoscaling)

What it is: Grow storage (e.g., 20→200 GB) and optionally enable Storage Autoscaling with a Max allocated storage cap.
Impact: Online operation; RDS grows storage without downtime (cannot shrink).

Console

1. DB → Modify.
2. Allocated storage: raise GB.
3. Enable storage autoscaling and set Max allocated storage.
4. Continue → Modify.

CLI

# Increase storage and enable autoscaling up to 1000 GB

aws rds modify-db-instance \

  --db-instance-identifier mydb \

  --allocated-storage 200 \

  --max-allocated-storage 1000 \

  --apply-immediately

---

3) Scale storage performance (type & IOPS)

What it is: Move between gp3 / io2 and adjust provisioned IOPS for steady throughput/latency.
Impact: May cause a brief interruption when switching types.

Console

1. DB → Modify.
2. Storage type: pick gp3 or io2.
3. Set IOPS (for io2) or Throughput/IOPS (for gp3).
4. Modify.

CLI

# Switch to io2 with 20k IOPS

aws rds modify-db-instance \

  --db-instance-identifier mydb \

  --storage-type io2 \

  --iops 20000 \

  --apply-immediately

---

4) High availability vs read scaling (Multi-AZ vs Read Replicas)

• Multi-AZ = HA/failover (synchronous standby). It does not add read capacity (except the Multi-AZ DB instance with two readable standbys option in some engines/configs—distinct feature; check your engine).
• Read Replicas (asynchronous) do add read capacity. Supported for MySQL, MariaDB, PostgreSQL, and Aurora (engine specifics vary). You point read traffic to the replicas; you can promote a replica to standalone if needed.

4a) Enable Multi-AZ

Console: DB → Modify → Availability & durability → choose Multi-AZ option → Modify.
CLI:

aws rds modify-db-instance \

  --db-instance-identifier mydb \

  --multi-az \

  --apply-immediately

4b) Create Read Replicas

Console

1. DB → Actions → Create read replica.
2. Choose instance class/size, AZ/Region, storage type/IOPS.
3. Create read replica.

CLI (same region):

aws rds create-db-instance-read-replica \

  --db-instance-identifier mydb-replica-1 \

  --source-db-instance-identifier mydb \

  --db-instance-class db.m7g.large

CLI (cross-region):

aws rds create-db-instance-read-replica \

  --db-instance-identifier mydb-replica-usw2 \

  --source-db-instance-identifier arn:aws:rds:us-east-1:123456789012:db:mydb \

  --destination-region us-west-2 \

  --db-instance-class db.m7g.large

Promote a replica (break replication):

aws rds promote-read-replica --db-instance-identifier mydb-replica-1

---

5) Aurora-specific notes (if you use Aurora)

• Scale reads by adding Aurora Replicas; apps use the reader endpoint to load balance.
• Scale writes by changing the writer instance class.
• Aurora Serverless v2 scales capacity units automatically with near-instant adjustments—great for spiky/variable loads.

---

6) Connection & query scaling (cheap wins)

• Add connection pooling (e.g., RDS Proxy for MySQL/Postgres) to reduce connection storm overhead.
• Use proper indexes, avoid table scans, and adopt Query patterns instead of full-table Scan (especially with ORMs).

---

7) Observability before/after scaling

• CloudWatch metrics: CPUUtilization, FreeableMemory, ReadIOPS/WriteIOPS, Read/WriteLatency, FreeStorageSpace, DatabaseConnections, ReplicaLag.
• Enhanced Monitoring and Performance Insights to find bottlenecks (CPU vs I/O vs SQL).

---

8) Typical upgrade playbook

1. Turn on Performance Insights and review slow SQL.
2. If CPU-bound → bigger instance; if I/O-bound → IOPS / storage type; if read-heavy → Read Replicas.
3. Add RDS Proxy for bursty connections.
4. Enable Multi-AZ for HA.
5. Configure storage autoscaling to avoid surprises.

---

Quick examples (one-liners)

Bump instance class + apply later (maintenance window):

aws rds modify-db-instance \

  --db-instance-identifier mydb \

  --db-instance-class db.r7g.xlarge

Turn on Performance Insights:

aws rds modify-db-instance \

  --db-instance-identifier mydb \

  --enable-performance-insights \

  --performance-insights-retention-period 7 \

  --apply-immediately

---

Final tip

Plan changes during low-traffic windows, and if you need near-zero downtime, pair Multi-AZ with connection draining on the app side (retry logic), then modify with Apply immediately to fail over quickly.

 

 

Suggest a targeted scaling path with exact console clicks and CLI lines for your case.