High performance Serverless Java on AWS

Is a three-second cold start killing your serverless Java app? Learn two powerful strategies that get you under 700 milliseconds on AWS Lambda.

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Why Java struggles with serverless adoption on AWS

Java's popularity as a language has not translated to serverless due to significant cold start times and a high memory footprint compared to Node.js or Python.

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Understanding the Lambda execution model and cold starts

A cold start involves creating a new microVM, downloading code, and running static initializers, whereas a warm start only executes the handler method on a reused environment.

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Benchmarking a standard Java Lambda's performance

Initial performance tests of a simple Java Lambda function show fast warm starts but unacceptable cold start latencies of over three seconds.

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Introducing AWS SnapStart for faster Java cold starts

AWS SnapStart improves cold start times by taking a snapshot of an initialized microVM during deployment and quickly restoring it for new invocations.

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Optimizing SnapStart performance with priming techniques

Priming uses CRaC hooks like `beforeCheckpoint` to pre-initialize expensive components such as HTTP clients and JSON serializers so they are included in the snapshot.

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Measuring the impact of SnapStart and priming

Activating SnapStart with priming reduces cold starts from over three seconds to one second, with further improvements to 700ms as the tiered cache warms up.

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Using the Lambda profiler for advanced priming

The AWS Lambda profiler can reveal internal SDK initialization processes, like event serialization, which can be primed to further reduce cold start times.

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Exploring GraalVM Native Image as an alternative

GraalVM's Native Image compiler creates a small, self-contained executable that achieves even faster cold starts and warm starts compared to standard JVM execution.

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Comparing the trade-offs of SnapStart vs GraalVM

While GraalVM offers the best performance, SnapStart is a fully managed solution that avoids the CI/CD complexity and dynamic invocation challenges of native image compilation.