As referenced in the workshop introduction, we’re expecting a great deal of traffic in the near of future. By leveraging Amazon EC2 Auto Scaling Groups, we can configure them to react to our traffic. An Auto Scaling group contains a collection of EC2 instances that share similar characteristics and are treated as a logical grouping for the purposes of instance scaling and management. For example, if a single application operates across multiple instances, you might want to increase the number of instances in that group to improve the performance of the application, or decrease the number of instances to reduce costs when demand is low. You can use the Auto Scaling group to scale the number of instances automatically based on criteria that you specify, or maintain a fixed number of instances even if an instance becomes unhealthy.
We can also use Amazon CloudWatch to define custom scaling metrics to reduce the amount of time it takes to respond or even schedule these scaling tasks. Amazon CloudWatch is a monitoring and management service built for developers, system operators, site reliability engineers (SRE), and IT managers. CloudWatch provides you with data and actionable insights to monitor your applications, understand and respond to system-wide performance changes, optimize resource utilization, and get a unified view of operational health. CloudWatch collects monitoring and operational data in the form of logs, metrics, and events, providing you with a unified view of AWS resources, applications and services that run on AWS, and on-premises servers.
AWS Lambda does this for you and scales your workload automatically based on demand. With Lambda, you can run code for virtually any type of application or backend service - all with zero administration. Just upload your code and Lambda takes care of everything required to run and scale your code with high availability. You can set up your code to automatically trigger from other AWS services or call it directly from any web or mobile app.
We can run our existing service in AWS Lambda using the AWS Serverless Java Container available on GitHub. It supports Spring Boot, which means that we can use it to run our service without changing our business logic.
In order to expose the backend Java Lambda function via HTTP, we’ll also need to leverage API Gateway. Amazon API Gateway is an AWS service that enables developers to create, publish, maintain, monitor, and secure APIs at any scale. You can create APIs that access AWS or other web services, as well as data stored in the AWS Cloud.
We are going to deploy the same code base we used in Exercise #1 with only a small change to the way we are building the code.
Let’s see what we have right now
Before we start, make sure you’re in the Cloud9 Terminal and not still logged into your Bastion. You can do this by hitting Enter/Return in the terminal a couple of times. If the prompt is in colour, then you’re set! If not, type logout and press Enter to get back to it.
If you find that the tab disappears if you do that… Use Window -> New Terminal.
- In the AWS Cloud9 Terminal get the value for API by using the following commands and then copy it into your clipboard using ⌘-C (Ctrl-C on Windows)
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cd cd environment ./tools get_value ShopBackendRestApiUrl
- Copy the value outputted into your clipboard
- Let’s SSH into the Bastion and see what the API returns
curl <url from above>/ping
You should see DUMMY /ping come back as a response.
This proves that the placeholder Lambda is deployed rather than our proper backend service. We’ll be deploying that next.
- Type logout and press Enter/Return to get back to Cloud9.
Now let’s build the Backend for AWS Lambda. It’s a slightly different build process to prepare it for deployment to AWS Lambda, but no code changes are required.
4. Run the following commands to build the Java application JAR for AWS Lambda, and to initiate the upload of the JAR for use by our function:
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cd ~/environment/backend ./gradlew shadowJar cd ~/environment ./tools upload_backend_lambda backend/build/libs/backend-0.0.1-SNAPSHOT-all.jar v1
- Now that we’ve built the application for AWS Lambda, let’s deploy our function:
./tools deploy_backend_lambda v1
- Excellent! We’ve deployed the code to a Lambda function, fronted by API Gateway. Let’s test it!
./tools get_value ShopBackendRestApiUrl
- Copy the value outputted into your clipboard
- Now let’s test the API again:
./tools ssh_to_bastion curl <url from above>/ping
You should see just PONG instead of DUMMY.
If you get Empty reply from server, just wait a few seconds and try again.
- Log out from the Bastion using logout or pressing CTRL + D
Now that we’ve deployed our backend code to a AWS Lambda, we need to point our ShopFrontend at this new API! You can do this via the Console or via the CLI. The CLI version of the instructions below will fix any mistakes you made in the console, so why not give this a go?
- The tools script has a helper to get the parameters you need:
Leave the output there in this tab so you can use it in the following instructions.
- Open a new tab and go to the AWS Elastic Beanstalk console.
- Click on the large ShopFrontend box (hopefully it’s green! If it isn’t put your hand up for a Solutions Architect to give you a hand)
- Click Configuration and then head to Modify on the Software box.
- Scroll down to Environment properties
- You’ll need to update BACKEND_DOMAIN with the output from step 1
- Create two new variables for BACKEND_PROTOCOL and BACKEND_URI_PREFIX using the empty boxes at the bottom; as you enter the first one, another box will appear. Use values from the output of Step 1 to fill in the respective new entries.
- Click Apply and you’ll be told it’s updating. If you head to the Dashboard you can wait for it to go back to Health Ok.
You tried, or didn’t try the Console approach and want the script to do it for you? No problem!
When the script says Checking for status Ready followed by ‘Environment reached status required’. This means it completed.
Expect to wait 3-5 minutes during these Checking steps.
And wait! It’ll tell you when it’s done.
Exercise #2 is complete, click Next below to continue and to test our work