One-shot containers with Serverless

Have you ever had short lived containers like the following use cases:

  • Batch and ETL (Extract, Transform & Load) Jobs.
  • Database backups and synchronisation.
  • Machine Learning algorithms for generation of learning and training models.
  • Integration & Sanity tests.
  • Web scrapers & crawlers.

And you were wondering how you can deploy your container periodically or in response to an event ? The answer is by using Lambda itself, the idea is by making a Lambda function trigger a deployment of your container from the build server. The following figure illustrates how this process can be implemented:

 

I have wrote a simple application in Go to simulate a short time process using sleep method:

As Go is a complied language, I have used Docker multi-stage build feature to build a lightweight Docker image with the following Dockerfile:

Next, I have a simple CI/CD workflow in Jenkins, the following is the Jenkinsfile used to build the pipeline:

An example of the pipeline execution is given as follows:

Now, all changes to the application will trigger a new build on Jenkins which will build the new Docker image, push the image to a private registry and deploy the new Docker image to the Swarm cluster:

If you issue the “docker service logs APP_NAME” on one of the cluster managers, your application should be working as expected:

Now our application is ready, let’s make execute everyday at 8am using a Lambda function. The following is the entrypoint (handler) that will be executed on each invocation of the function:

It uses the Jenkins API to trigger the deployment process job.

Now the function is defined, use the shell script below to create the following:

  • Build a deployment package (.zip file).
  • Create an IAM role with permissions to push logs to CloudWatch.
  • Create a Go based Lambda function from the deployment package.
  • Create a CloudWatch Event rule that will be executed everyday at 8am.
  • Make the CloudWatch Event invoke the Lambda function.

As a result, a Lambda function will be created as follows:

To test it out, you can invoke it manually either from the Lambda Console or using the following AWS CLI command:

A new deployment should be triggered in Jenkins and your application should be deployed once again:

That’s it, it was a quick example on how you can use Serverless with Containers, you can go further and use Lambda functions to scale out/scale in your services in your Swarm/Kubernetes cluster by using either CloudWatch events for expected increasing traffic (Holidays, Black Friday …) or other AWS managed services like API Gateway in response to incoming client requests.

Full code can be found on my GitHub. Make sure to drop your comments, feedback, or suggestions below — or connect with me directly on Twitter @mlabouardy.

Infrastructure Cost Optimization with Lambda

Having multiple environments is important to build a continuous integration/deployment pipeline and be able to reproduce bugs in production with ease but this comes at price. In order to reduce cost of AWS infrastructure, instances which are running 24/7 unnecessarily (sandbox & staging environments) must be shut down outside of regular business hours. 

The figure below describes an automated process to schedule, stop and start instances to help cutting costs. The solution is a perfect example of using Serverless computing.

Note: full code is available on my GitHub.

2 Lambda functions will be created, they will scan all environments looking for a specific tag. The tag we use is named Environment. Instances without an Environment tag will not be affected:

The StartEnvironment function will query the StartInstances method with the list of instance ids returned by the previous function:

Similarly, the StopEnvironment function will query the StopInstances method:

Finally, both functions will post a message to Slack channel for real-time notification:

Now our functions are defined, let’s build the deployment packages (zip files) using the following Bash script:

The functions require an IAM role to be able to interact with EC2. The StartEnvironment function has to be able to describe and start EC2 instances:

The StopEnvironment function has to be able to describe and stop EC2 instances:

Finally, create an IAM role for each function and attach the above policies:

The script will output the ARN for each IAM role:

Before jumping to deployment part, we need to create a Slack WebHook to be able to post messages to Slack channel:

Next, use the following script to deploy your functions to AWS Lambda (make sure to replace the IAM roles, Slack WebHook token & the target environment):

Once deployed, if you sign in to AWS Management Console, navigate to Lambda Console, you should see both functions has been deployed successfully:

StartEnvironment:

StopEnvironment:

To further automate the process of invoking the Lambda function at the right time. AWS CloudWatch Scheduled Events will be used.

Create a new CloudWatch rule with the below cron expression (It will be invoked everyday at 9 AM):

And another rule to stop the environment at 6 PM:

Note: All times are GMT time.

Testing:

a – Stop Environment

Result:

b – Start Environment

Result:

The solution is easy to deploy and can help reduce operational costs.

Full code can be found on my GitHub. Make sure to drop your comments, feedback, or suggestions below — or connect with me directly on Twitter @mlabouardy.

Deploy a Swarm Cluster with Alexa

Serverless and Containers changed the way we leverage public clouds and how we write, deploy and maintain applications. A great way to combine the two paradigms is to build a voice assistant with Alexa based on Lambda functions – written in Go – to deploy a Docker Swarm cluster on AWS.

The figure below shows all components needed to deploy a production-ready Swarm cluster on AWS with Alexa.

Note: Full code is available on my GitHub.

A user will ask Amazon Echo to deploy a Swarm Cluster:

Echo will intercept the user’s voice command with built-in natural language understanding and speech recognition. Convey them to the Alexa service. A custom Alexa skill will convert the voice commands to intents:

The Alexa skill will trigger a Lambda function for intent fulfilment:

The Lambda Function will use the AWS EC2 API to deploy a fleet of EC2 instances from an AMI with Docker CE preinstalled (I used Packer to bake the AMI to reduce the cold-start of the instances). Then, push the cluster IP addresses to a SQS:

Next, the function will insert a new item to a DynamoDB table with the current state of the cluster:

Once the SQS received the message, a CloudWatch alarm (it monitors the ApproximateNumberOfMessagesVisible parameter) will be triggered and as a result it will publish a message to an SNS topic:

The SNS topic triggers a subscribed Lambda function:

The Lambda function will pull the queue for a new cluster and use the AWS System Manager API to provision a Swarm cluster on the fleet of EC2 instances created earlier:

For debugging, the function will output the Swarm Token to CloudWatch:

Finally, it will update the DynamoDB item state from Pending to Done and delete the message from SQS.

You can test your skill on your Amazon Echo, Echo Dot, or any Alexa device by saying, “Alexa, open Docker

At the end of the workflow described above, a Swarm cluster will be created:

At this point you can see your Swarm status by firing the following command as shown below:

Improvements & Limitations:

  • Lambda execution timeout if the cluster size is huge. You can use a Master Lambda function to spawn child Lambda.
  • CloudWatch & SNS parts can be deleted if SQS is supported as Lambda event source (AWS PLEAAASE !). DynamoDB streams or Kinesis streams cannot be used to notify Lambda as I wanted to create some kind of delay for the instances to be fully created before setting up the Swarm cluster. (maybe Simple Workflow Service ?)
  • Inject SNS before SQS. SNS can add the message to SQS and trigger the Lambda function. We won’t need CloudWatch Alarm.
  • You can improve the Skill by adding new custom intents to deploy Docker containers on the cluster or ask Alexa to deploy the cluster on a VPC

In-depth details about the skill can be found on my GitHub. Make sure to drop your comments, feedback, or suggestions below — or connect with me directly on Twitter @mlabouardy.

Serverless Golang API with AWS Lambda

AWS has announced few days ago, Go as supported language for AWS Lambda. So, I got my hand dirty and I made a Serverless Golang Lambda Function to discover new Movies by genres, I went even further and created a Frontend in top of my API with Angular 5.

Note: The full source code for this application can be found on GitHub 

To get started, install the dependencies below:

Create a main.go file with the following code:

The handler function takes as a parameter the movie genre ID then query the TMDb API  – Awesome free API for Movies and TV Shows – to get list of movies. I registred the handler using the lambda.Start() method.

To test our handler before deploying it, we can create a basic Unit Test:

Issue the following command to run the test:

Next, build an executable binary for Linux:

Zip it up into a deployment package:

Use the AWS CLI to create a new Lambda Function:

Note: substitute role flag with your own IAM role.

Sign in to the AWS Management Console, and navigate to Lambda Dashboard, you should see your lambda function has been created:

Set TMDb API KEY (Sign up for an account) as environment variable:

Create a new test event:

 Upon successful execution, view results in the console:

To provide the HTTPS frontend for our API, let’s add API Gateway as a trigger to the function:

Deployment:

Now, if you point your favorite browser to the Invoke URL:

Congratulations   you have created your first Lambda function in Go.

Let’s build a quick UI in top of the API with Angular 5. Create an Angular project from scratch using Angular CLI. Then, generate a new Service to calls the API Gateway URL:

In the main component iterate over the API response:

Note: the full code is in GitHub.

Generate production grade artifacts:

The build artifacts will be stored in the dist/ directory

Next, create an S3 bucket with AWS CLI:

Upload the build artifacts to the bucket:

Finally, turns website hosting on for your bucket:

If you point your browser to the S3 Bucket URL, you should be happy:

Amazon Alexa GitHub Followers Counter

This post is part of “Alexa” series. I will walk you through how to build an Amazon Alexa Skill with Node.JS and Lambda to get numbers of followers & repositories in GitHub in real-time.

Note: all the code is available in my GitHub.

Amazon Echo will captures voice commands and send them to the Alexa Skill to convert them into structured text commands. A recognized command is sent to an AWS Lambda function that will call GitHub API to get response.

To get started, sign up to Amazon Developer Console,  and create a new Alexa Skill:

The invocation name is what user will say to trigger the skill. In our case it will be “github“.

Click on “Next” to bring up the Interaction Model page, use the intent schema below:

Intents will map user’s voice command to services that our Alexa skill can address. For instance, here I defined an intent called GetGithubFollowerCount, which will line up with a portion of code in my Lambda funtion that I leverage in a bit.

The programming languages are defined as a Custom Slot Type, with the following possible values:

Now our intents are defined, we need to link them to a human request that will trigger this linkage. To do this multiple sentences (utterances) are listed to make the interaction as natural as possible.

Result:

Click on “Next” and you will move onto a page that allows us to use an ARN (Amazon Resource Name) to link to AWS Lambda.

Before that, let’s create our lambda function, login to AWS Management Console, then navigate to Lambda Dashboard and create a new function from scratch:

Select Alexa Skills Kit as trigger:

I wrote the Lambda functions in Node.JS, although that code isn’t actually that interesting so I won’t go into it in much detail.

This function is fired when there is an incoming request from Alexa. The function will:

  • Process the request
  • Call GitHub API
  • Send the response back to Alexa

Create a zip file consisting of the function above and any dependencies (node_modules). Then, specify the .zip file name as your deployment package at the time you create the Lambda function. Don’t forget to set your GitHub Username as an environment variable:

Back in the Alexa Skill we need to link our Lambda function as our endpoint for the Alexa Skill:

That’s it, let’s test it out using a Service Simulation by clicking on “Next“.

GetFollowerCount Intent : 

GetRepositoryCount Intent:

GetGithubRepositoryCountByLanguage Intent:

You can see that the Lambda responds as expected !

Test it now with Amazon Echo, by saying “Alexa, ask GitHub for …” :