Running Spring Boot on AWS Lambda used to be a cautionary tale because of cold starts—the time it takes to initialize your function when it’s not already warm. Spring Boot’s startup time and memory footprint made it unsuitable for high-performance serverless workloads. That changes with AWS Lambda SnapStart.
In this post, we’ll explore how to run Spring Boot applications efficiently on AWS Lambda using SnapStart, and how to deploy them with AWS SAM or the Serverless Framework. We’ll cover cold start optimizations, supported runtimes, and provide real deployment examples.
1. What Is SnapStart?
SnapStart is a feature introduced by AWS that dramatically reduces cold start times for Lambda functions by pre-initializing and snapshotting the runtime state.
Here’s how it works:
- AWS initializes your Lambda function once during deployment.
- It takes a snapshot of the memory and execution environment (code loaded, classes initialized, beans wired).
- On cold starts, it simply restores from that snapshot—no more full Spring Boot initialization.
SnapStart is available for Java 11 and 17 runtimes and supports Amazon Corretto.
2. Why Spring Boot Needs SnapStart
Spring Boot apps typically have long startup times due to:
- Classpath scanning
- Dependency injection
- Bean instantiation
- Auto-configuration
This means cold starts for Spring Boot Lambda functions could range from 2 to 10 seconds, making them unsuitable for latency-sensitive use cases.
With SnapStart, this startup delay can be cut down to under 300ms, making Spring Boot serverless apps finally viable.
3. Example: Spring Boot App for AWS Lambda
Let’s create a simple REST-like Lambda using Spring Boot and the AWS Lambda adapter.
1. Add Required Dependencies
Add the AWS adapter and configure the handler:
<!-- pom.xml --> <dependency> <groupId>org.springframework.cloud</groupId> <artifactId>spring-cloud-function-adapter-aws</artifactId> </dependency>
2. Sample Function Handler
@Bean
public Function<String, String> hello() {
return name -> "Hello, " + name;
}
3. Handler Class
public class LambdaHandler extends SpringBootRequestHandler<String, String> {}
4. Enabling SnapStart
SnapStart works only on Java 11+ Lambda functions deployed using Zip package type.
To enable SnapStart, you must:
If using AWS SAM:
Resources:
MyFunction:
Type: AWS::Serverless::Function
Properties:
Runtime: java17
CodeUri: target/myapp.zip
Handler: com.example.LambdaHandler
SnapStart:
ApplyOn: PublishedVersions
AutoPublishAlias: live
If using Serverless Framework:
provider:
name: aws
runtime: java17
snapStart:
applyOn: PublishedVersions
functions:
hello:
handler: com.example.LambdaHandler
memorySize: 512
timeout: 10
snapStart: true
Then deploy using sls deploy.
5. Cold Start Performance Gains
| Without SnapStart | With SnapStart |
|---|---|
| 4–8 seconds | ~200–400 ms |
In benchmarks, enabling SnapStart for Spring Boot functions shows up to 90% reduction in cold start latency.
6. Deployment Strategies
✅ Use SnapStart with Versions & Aliases
SnapStart only works with published Lambda versions, so make sure you’re versioning your deployments and using aliases like live or prod.
✅ Tune JVM Flags
Reduce memory and optimize performance with flags like:
-javaagent:lambda-java-agent.jar -XX:+TieredCompilation -XX:InitialRAMPercentage=75.0
✅ Monitor with CloudWatch
Use CloudWatch logs and metrics to track cold start time (Duration - Init Duration), and keep an eye on SnapStart restore failures.
7. Limitations of SnapStart
- Only supported in Java 11 and 17.
- Only available for Zip-based Lambda deployments (not container images).
- Reflection-heavy frameworks might introduce non-deterministic snapshot issues.
- AWS recommends testing for SnapStart determinism bugs using
aws lambda invokeon cold versions.
8. Final Thoughts
With the introduction of SnapStart, Spring Boot on AWS Lambda is now not only possible but practical for production use. You get the power of the Spring ecosystem, combined with the scalability and cost-efficiency of serverless.
If you’re already using Spring Boot in a monolith or microservice, migrating core APIs or jobs to Lambda becomes significantly easier—with no rewrite needed.
