AWS Greengrass allows you to build edge applications fast using familiar AWS constructs. But no matter how fast we build things, we want to build them faster. In this workshop, we show how to build and deploy a Greengrass Core manually. Then we provide hands-on experience with a new open source tool that lets you iterate on your applications faster than ever. Finally, we ask for feedback on how the tool meets your needs.

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AWS Greengrass, Containers, and
Your Dev Process for Edge Apps
Tim Mattison
Global Tech Lead, IoT Partners
Amazon Web Services
G P S W S 4 0 4
Craig Williams
Principal Solutions Architect, IoT
Amazon Web Services

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Survey time
• How many of you have deployed AWS Greengrass manually?
• How long did the first deployment take?
• How many of you wrote your own automation?

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Big goals
Be comfortable deploying, developing, and debugging AWS Greengrass
code
Never be afraid to destroy and recreate AWS Greengrass cores

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Detailed goals
AWS Greengrass "Hello, World" running on Amazon Elastic Compute
Cloud (Amazon EC2)
Redeploy updated "Hello, World"
AWS Greengrass with multiple AWS Lambda functions
Optional: AWS Greengrass running on Docker on Amazon EC2

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AWS Greengrass Core

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What do you care about as a developer?
I have collections of Lambda functions I want to run at the edge
That code
• Communicates with other functions
• Uses AWS services (when available)
• Accesses local resources

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Features (1 of 2)…
• Builds Python, NodeJS, and Java Lambda functions
• Uploads and creates aliases for Lambda functions
• Describes the AWS Greengrass core in just a few files
• Simplifies working with local resources
• Has sane defaults and examples that make getting started easier
• Allows you to build AWS Greengrass cores in minutes

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Features (2 of 2)…
• Allows modifications of groups in place
• Add/remove a device
• Add/remove a function
• Add/remove a subscription
• Deployments will overwrite these in place changes!
• Can launch AWS CloudFormation stacks for supporting infrastructure
• Builds Docker images with AWS Greengrass in them

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Highest-level concepts
• Deployment configuration
• Function configuration

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Deployment configuration
• What to deploy
• A list of zero or more functions
• Role and policy information

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Deployment configuration

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Function configuration
• Language to use, function handler name, amount of memory, pinned
or event based, timeout
• Inputs - topics the function can accept messages on
• Outputs - topics the function is expected to send messages on
• Inputs and outputs from different functions are wired together
• Cloud topics are topics that are routed to/from the cloud

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Function configuration

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A more complex function configuration

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Additional function configuration
• Local resources
• Environment variables

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Simple deployment, step 1
./deploy.sh -g ec2 -d deployments/python-hello-world.conf -a X86_64 --script
-g is the name of the group, ec2
-d is the deployment configuration file which refers to the Python Hello World function
-a is the architecture, x86_64 since we're using EC2
--script generates the bootstrap script in build/gg.ec2.sh

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Simple deployment, step 2
Copy build/gg.ec2.sh to an Ubuntu EC2 instance
On the EC2 instance run:
./gg.ec2.sh --now
This will install the dependencies for AWS Greengrass, install AWS Greengrass itself,
start AWS Greengrass, and then tail the runtime log so you can see what's going on

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What happened (deployment)?
• We built ec2-HelloWorldPython
• This avoids naming conflicts between groups running the same functions
• We wired ec2_Core/python/hello/world to the cloud
• Subscription table set up was automatic
• We created a script that can bootstrap our Amazon EC2 instance and
ran it

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Simple redeployment
./deploy.sh -g ec2 -d deployments/python-hello-world.conf
-g is the name of the group, ec2
-d is the deployment configuration file which refers to the Python Hello World function
No need to specify the architecture or get a script as output. The AWS Greengrass
service will redeploy the code to your core.

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What happened (redeployment)?
• We built ec2-HelloWorldPython and created a new version
• We created an entire new deployment
• Manual changes would be blown away at this point
• We let the AWS Greengrass service deploy our new code to the
instance

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Notes about the labs
• Any steps that have "(via console)" after them also have a
corresponding step showing how to do the task with the AWS
Greengrass Provisioner tool
• If you get through everything, let us know! There are plenty more AWS
Greengrass labs where these came from.
• If you finish quickly and think this is too easy please go back and do
the manual lab (lab 1A) to gain a full appreciation for the automation

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Your turn
reinvent.awsworkshops.com
Username: aws
Password: icebreaker

37. Thank you!
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