![]() While this offers a less complex (and potentially much less expensive) way to build systems, it presents a new challenge to operators and developers who are interested in lambda monitoring: how do you build fast and resilient functions when many traditional system and application metrics are either unavailable or no longer relevant? AWS Lambda and competitors like Google Cloud Functions or Microsoft Azure Functions are designed to let developers write scalable code without having to think about the details of the container, operating system, or infrastructure that actually runs the program. Public class HelloLambdaHandler implements RequestHandler else if (input.getPath().One of the most discussed components of serverless compute architecture is Function-as-a-Service (FaaS) products like Amazon Web Services Lambda. Using AWS’s guide to deploying a Spring Boot application as a Lambda, I wrote a simple application containing a single controller: I’ll walk you through the comparison I made because the process itself is generally applicable if you want to:ĭetermine which memory setting provides the lowest cost and/or best performance for your functionĭecide whether you want to take on a large (in terms of package size) dependency to gain better performance or ease the development processĬompare the cost and performance of two different runtimes for solving the same problem if you work on a polyglot team To understand this better, I created two functions that serve the same purpose: one that leverages Spring Boot and follows the quick start guide, and one that doesn’t use either. Knowing this, it was surprising to me that AWS even offered the AWS Serverless Java Container library because I would have expected cold start times for a Spring Boot application to be unreasonable for a serverless API. The next time the function is invoked, AWS needs to go through the process of creating a container again, incurring another cold start. If, for that period of time, your Lambda function is not invoked at all, the container is destroyed. The container then stays around for a period of time that isn’t clearly documented by AWS but I’ve read that, in others’ experience, depending on the runtime this could be as long as 45 minutes. This includes copying down the binaries, starting up the JVM for Java Lambdas, and any initialization done in the Lambda itself (which we know for a Spring Boot Application isn’t uncommon to take several seconds). Behind the scenes, the first time a Lambda is invoked, AWS creates a container to service that initial and subsequent requests routed to that instance. ![]() You simply package your application as a fat jar, exclude the embedded Tomcat container, and provide a handler that loads your application and proxies events to it.Ī common reason you’ll hear not to use a framework like Spring in the context of a Lambda Function is because of execution time, and more specifically what’s known as the cold start time. AWS provides the AWS Serverless Java Container library and a quickstart guide to making that transition. If you’re migrating an existing application that is relatively small and isn’t heavily trafficked, you may choose to run your application in a Lambda rather than as a container. For a variety of reasons, they’re looking to adopt a serverless architecture using this starting context. ![]() Many of our clients have existing Spring Boot applications and teams who have built up a lot of expertise using Spring over the years.
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