![]() Since the plan is to create a native image from the Java application, and run even the Runtime API implementation as part of the native image, I needed a Java-based implementation of the runtime. In the root of the ZIP, there has to be the bootstrap file, and that’s it. This is what AWS will call upon invocation.Ī custom runtime has to be provided to Lambda in the form of a ZIP archive. ![]() This is the entrypoint for any custom runtime function. In addition to the Runtime API, AWS looks for a file called bootstrap. If there’s any language you want to use in your Lambda, you can very well just write your own Runtime API implementation and package it in a way AWS can invoke it, and that’s it. It’s a set of HTTP APIs to call, and that’s it. The Runtime API docs defines these interactions. What is a custom runtime in the first place? There are well-defined APIs by AWS in any Lambda environment, and how any of the functions are interacting with the AWS environment. So what if the same technology is used in a lambda function? Well, in 2018, AWS announced their support for a feature called Custom runtimes. The resulting executable is bound to the operating system it’s been compiled to, but it’s much faster and memory efficient than it’s brother running on the JVM. JDK classes, garbage collector, application classes, everything. From a practical standpoint, it just means whatever your application code needs, it will be packaged into a single binary, e.g. The concept behind GraalVM’s native image is to compile Java code ahead-of-time (AoT) into a standalone executable without the need to run an actual JVM. Okay, let’s jump into native images and GraalVM. On average the DynamoDB example takes 175ms to execute which is a pretty big difference compared to the cold response time. When you spice it up with a little DynamoDB writing, the cold-start response time becomes unacceptable. In normal circumstances, with a simple Java application (and I mean simple, like a Hello World example), Lambda is slow in case of cold-start. Then it hit me, how great it would be if GraalVM is used in the serverless world with AWS Lambda. But I didn’t really get why startup time is so crucial for a normal Spring Boot application. I’ve tried it myself with a simple Spring Boot app, and well. I’ve read several articles how great it is, and how unbeliavable it speeds up Java applications. I was always wondering what GraalVM is capable of. I’d like to take you to a merely different journey to fight cold startup with GraalVM and native images. You can try to tackle it by having a CloudWatch trigger that periodically calls your Lambda, and thus keeping it warm. ![]() You can use Provisioned Concurrency to tell AWS how many Lambda environments you want to be ready at all times. There are a number of different ways how to mitigate cold startup times. Although after some time, a warm Lambda environment will be killed by AWS in case there’s no invocation, and the same cold-start will occur. When the environment is already prepared, the JVM is ready to go and only your application code needs to be invoked. AWS has to prepare a runtime environment for your application when it executes the first time. And it’s not some Java specific limitation, but other runtimes like. Have you ever tried running a Java application on AWS Lambda? Well, even the simplest Java application takes significant time to start up at first.
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