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# High performance Serverless Java on AWS

Are cold start latencies keeping your Java apps off AWS Lambda? Discover how SnapStart and GraalVM can slash boot times by 70% to optimize your serverless performance.

- **Speakers:** [Vadym Kazulkin](https://www.wearedevelopers.com/@vadym-kazulkin)
- **Event:** WeAreDevelopers LIVE
- **Published:** September 11, 2024
- **Duration:** 44:20
- **URL:** https://www.wearedevelopers.com/videos/1205-high-performance-serverless-java-on-aws

## Summary

Java remains a top programming language globally, yet its adoption within AWS Serverless environments hovers around 10% due to challenges with cold start latencies and memory footprint overhead. In AWS Lambda's execution lifecycle, cold starts involve booting the Firecracker MicroVM, initializing the Java runtime, running static initializer blocks, and establishing downstream connections. For latency-sensitive APIs, these steps can add seconds of delay, undermining the fast-response promise of serverless architecture.

To mitigate these issues, AWS SnapStart offers a fully managed solution that creates an encrypted snapshot of the initialized environment prior to invocation. By heavily applying "priming" techniques—such as preloading the AWS CRT HTTP client, warming DynamoDB database connection pools, and instantiating JSON serializers during the snapshot phase—developers can reliably reduce P90 cold starts by up to 70%. Further performance gains can be unlocked by tuning the Corretto runtime, such as using tiered compilation (e.g., stopping at level 1) to conserve CPU cycles, sticking to the G1GC garbage collector over less optimized alternatives, prioritizing memory sizes around 1024MB to ensure adequate fractional CPU allocation.

For absolute peak execution speeds, deploying a GraalVM Native Image via a custom runtime sidesteps the JVM overhead altogether, pushing cold starts under 500ms and reducing memory consumption sharply. However, deploying an ahead-of-time compiled binary assumes complete control over reflection dependencies and often introduces steep CI/CD resource requirements, trading operational simplicity for uncompromised runtime speed. 

Ultimately, while GraalVM is unrivaled for intensive environments prioritizing minimal duration costs and maximum throughput, AWS SnapStart represents the most frictionless trade-off for typical workloads, seamlessly bridging Java's maturity with the elastic scale of modern serverless infrastructure.

**Keywords:** aws lambda cold starts, serverless java performance, aws snapstart, graalvm native image, firecracker microvm, lambda priming techniques, aws crt http client, tiered compilation settings, g1gc garbage collection, aws corretto runtime, serverless memory profiling, ahead-of-time compilation, dynamodb connection latency, spring boot serverless migration

## Chapters

1. **State of Java on AWS serverless applications** (00:02) — How managed Amazon Corretto distributions grant Java engineers stable long-term framework support.
1. **Understanding AWS Lambda cold and warm starts** (03:40) — The lifecycle phases of serverless container creation dictate application request latency constraints.
1. **Measuring baseline latency in serverless stateless applications** (08:17) — A simple API architecture highlights the precise initialization overheads of dynamic class loading.
1. **Reducing cold start duration with AWS snap start** (11:55) — Managed snapshot creation circumvents runtime static initialization delays during the standard deployment phase.
1. **Implementing priming techniques with checkpoint snapshot hooks** (15:11) — Pre-loading HTTP clients and deserializers into lifecycle snapshots minimizes manual restoration overhead margins.
1. **Evaluating invocation performance with snapshot dependency priming** (18:18) — Synthetic concurrency benchmarks display massive latency drops when coupling configurations with class initializations.
1. **Tuning Lambda memory allocations for snapshot performance** (20:28) — Assigning strict memory boundaries bounds infrastructure cost creep without significantly hindering functional execution scale.
1. **Evaluating deployment bundle sizes and Lambda layers** (21:38) — Keeping dependency inclusions aggressively minimal resolves severe latency spikes associated with external layer loading.
1. **Optimizing Java tiered compilation in short-lived environments** (24:03) — Restricting runtime background compilation entirely to initial baseline profiling improves overall responsiveness across ephemeral requests.
1. **Selecting garbage collectors for serverless memory constraints** (26:14) — Default garbage collectors fundamentally outstrip parallel operations when navigating heavily bound execution allocation parameters.
1. **Minimizing connection latency with native HTTP clients** (26:57) — Selecting strictly synchronous network client packages restricts the initialization timeouts native to standard web adapters.
1. **Comparing serverless ARM and x86 silicon for snapshots** (29:14) — Leveraging ARM architectures implements scalable computing hardware efficiencies while presenting minimal cold start overhead adjustments.
1. **Navigating tiered cache limitations for encrypted snapshot distributions** (31:08) — Multi-zone geographic encryption restricts cache speeds strictly during initial serverless environment processing cycles.
1. **Accelerating initialization times using GraalVM native images** (35:31) — Ahead-of-time source compilations support customized backend logic that severely shrinks required platform execution intervals.
1. **Addressing reflection limitations in ahead-of-time binary compilation** (37:58) — Maintaining precise framework mapping dictionaries exponentially expands necessary programmatic build times while circumventing reflection errors.
1. **Balancing complexity tradeoffs in serverless Java transitions** (40:01) — Choosing natively managed environment setups over customized binary generations depends entirely on internal operational maintenance bandwidth.

## Related Moments

- [Why Java lags in serverless adoption on AWS](https://www.wearedevelopers.com/videos/1555-high-performance-serverless-java-on-aws) (from "High performance Serverless Java on AWS")
- [Optimizing Java startup processes for fast serverless workloads](https://www.wearedevelopers.com/videos/1008-serverless-java-in-action-cloud-agnostic-design-patterns-and-tips) (from "Serverless Java in Action: Cloud Agnostic Design Patterns and Tips")
- [Evolving serverless compute with Firecracker and SnapStart](https://www.wearedevelopers.com/videos/100214-20-years-of-solving-unsolvable-problems) (from "20 Years of Solving Unsolvable Problems")
- [Optimizing application performance via GraalVM native images](https://www.wearedevelopers.com/videos/537-bootiful-spring-boot-3) (from "Bootiful Spring Boot 3")
- [Overcoming Java cold starts using the Quarkus framework](https://www.wearedevelopers.com/videos/243-serverless-native-java-with-quarkus) (from "Serverless-Native Java with Quarkus")
- [Demystifying AWS Lambda cold start mechanics](https://www.wearedevelopers.com/videos/1555-high-performance-serverless-java-on-aws) (from "High performance Serverless Java on AWS")

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