Cloud9 IDE and Command Line Clients

AWS Cloud9 IDE

AWS Cloud9 is a cloud-based integrated development environment (IDE) that lets you write, run, and debug your code with just a browser. It includes a code editor, debugger, and terminal. Cloud9 comes pre-packaged with essential tools for popular programming languages and the AWS Command Line Interface (CLI) pre-installed so you don’t need to install files or configure your laptop for this workshop. Your Cloud9 environment will have access to the same AWS resources as the user with which you logged into the AWS Management Console.

Take a moment now and setup your Cloud9 development environment.

✅ Step-by-step Instructions

  1. Go to the AWS Management Console, click Services then select Cloud9 under Developer Tools.

  2. Click Create environment.

  3. Enter Development into Name and optionally provide a Description.

  4. Click Next step.

  5. You may leave Environment settings at their defaults of launching a new t2.micro EC2 instance which will be paused after 30 minutes of inactivity.

  6. Click Next step.

  7. Review the environment settings and click Create environment. It will take several minutes for your environment to be provisioned and prepared.

  8. Once ready, your IDE will open to a welcome screen. Below that, you should see a terminal prompt similar to:

setup-cloud9-terminal.png

If you are running this workshop at an AWS event: Paste here the Credentials/CLI Snippets you copied before to configure your environment with your credentials. If you are running it in your own account, you can use the default profile Cloud9 sets up for you. You can check it via cat ~/.aws/credentials.

setup-cloud9-credentials.png

Now you can run AWS CLI commands in here just like you would on your local computer. Verify that your user is logged in by running aws sts get-caller-identity.

aws sts get-caller-identity

You’ll see output indicating your account and user information:

Admin:~/environment $ aws sts get-caller-identity

{
    "Account": "123456789012",
    "UserId": "AKIAI44QH8DHBEXAMPLE",
    "Arn": "arn:aws:iam::123456789012:user/Alice"
}

Keep your AWS Cloud9 IDE opened in a tab throughout this workshop as you’ll use it for activities like building and running a sample app in a Docker container and using the AWS CLI.

Command Line Clients

The modules utilize two command-line clients to simulate and display sensor data from the unicorns in the fleet. These are small programs written in the Go Programming Language. The below instructions in the Installation section walks through downloading pre-built binaries, but you can also download the source and build it manually:

Producer

The producer generates sensor data from a unicorn taking a passenger on a Wild Ryde. Each second, it emits the location of the unicorn as a latitude and longitude point, the distance traveled in meters in the previous second, and the unicorn’s current level of magic and health points.

Consumer

The consumer reads and displays formatted JSON messages from an Amazon Kinesis stream which allow us to monitor in real-time what’s being sent to the stream. Using the consumer, you can monitor the data the producer and your applications are sending.

Installation

  1. Switch to the tab where you have your Cloud9 environment opened.

  2. Download and unpack the command line clients by running the following command in the Cloud9 terminal:

    curl -s https://data-processing.serverlessworkshops.io/client/client.tar | tar -xv

This will unpack the consumer and producer files to your Cloud9 environment.

⭐ Tips

💡 Keep an open scratch pad in Cloud9 or a text editor on your local computer for notes. When the step-by-step directions tell you to note something such as an ID or Amazon Resource Name (ARN), copy and paste that into the scratch pad.

⭐ Recap

🔑 Use a unique personal, development AWS Account, event engine or EventBox

🔑 Use one of the US East (N. Virginia), US West (Oregon), EU* (Ireland, London, Frankfurt) Regions if using your own AWS account.

🔑 Keep your AWS Cloud9 IDE opened in a tab

Next

✅ Proceed to the first module, Streaming Data, wherein you’ll create a Kinesis stream, send unicorn data to that stream, and visualize unicorn positions on a live map.