Serverless computing with AWS Lambda, Part 1

Get an overview of AWS Lambda's nanoservices architecture and execution model, then build your first Lambda function in Java

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The top portion of the page shows the response body, which has an id and a name. The summary section shows the amount of time that the function took to execute. In this case, you should see 296.14 ms followed by 300 ms, which is the amount of time billed. You will also see the maximum amount of memory that was used, in this case 41 MB. The log output shows any logs that you may have written through a System.out.println() statement, or by accessing the Context object's logger.

For fun you might want to test the function again and notice the change in duration. When I ran it a couple more times, the durations that I observed were, respectively, 4.4 ms and 0.64 ms. The reason is that the first time the lambda runs, AWS needs to create a container with your JAR file and deploy it to an EC2 instance. Once it has been deployed on an EC2 instance, the function will run very quickly. Note, however, that if you do not access your function for an undetermined period of time, AWS Lambda will remove your container from the EC2 instance and you'll need to absorb that initial deployment overhead again.

If you've got all of this working so far, congratulations! You've built, deployed, and tested your first Lambda function.


In this article we've answered the question, "What is serverless computing, anyway?" You've learned how serverless architectures employ nanoservices (versus microservices) to increase application scalability, while lowering the price of delivery. I introduced the serverless computing execution model and the Function-as-a-Service concept, and explained the relationship of functions, as they are used in AWS Lambda, to functional programming. Finally, you built a Lambda function in Java, then deployed the function to AWS Lambda and tested it in the AWS Lambda console.

In Part 2, we'll add support for Amazon's DynamoDB. Using DynamoDB, you'll setup your Lambda function to manage widgets on the server, rather than creating them on-the-fly. You'll also leverage the AWS SDK to create a Java client application that can invoke your Lambda function from outside of AWS. Finally, you'll use the AWS Identity and Access Management service to create an IAM user, group, and custom policy for your example application, which we'll then build and run.

This story, "Serverless computing with AWS Lambda, Part 1 " was originally published by JavaWorld.

Copyright © 2018 IDG Communications, Inc.

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