Java is for many years one of the most popular programming languages, but it is known for its high cold start times and high memory footprint, compared to other programming languages like Node.js and Python. In this talk I'll look at the general best practices and techniques we can use to decrease memory consumption and cold start times for Java Serverless applications on AWS Lambda including GraalVM (Native Image) and AWS own offering SnapStart based on Firecracker microVM snapshot and restore and CRaC (Coordinated Restore at Checkpoint) runtime hooks. We'll also look into the SnapStart priming techniques which help us increase the Lambda performance and discuss the impact of the SnapStart snapshot tiered low-latency cache on the Lambda cold start times. We'll use Java 25 and 2 different serverless databases for our measurements : relational one - Amazon Aurora DSQL and NoSQL one - Amazon DynamoDB. We'll also explore how Project Leyden, its AOT cache and AWS Lambda Managed Instances fit into the picture.

Java is for many years one of the most popular programming languages, but it is known for its high cold start times and high memory footprint, compared to other programming languages like Node.js and Python. In this talk I'll look at the general best practices and techniques we can use to decrease memory consumption and cold start times for Java Serverless applications on AWS Lambda including GraalVM (Native Image) and AWS own offering SnapStart based on Firecracker microVM snapshot and restore and CRaC (Coordinated Restore at Checkpoint) runtime hooks. We'll also look into the SnapStart priming techniques which help us increase the Lambda performance and discuss the impact of the SnapStart snapshot tiered low-latency cache on the Lambda cold start times. We'll use Java 25 and 2 different serverless databases for our measurements : relational one - Amazon Aurora DSQL and NoSQL one - Amazon DynamoDB. We'll also explore how Project Leyden, its AOT cache and AWS Lambda Managed Instances fit into the picture.