Serverless computing is changing how developers build and deploy applications. With AWS Lambda, you can run code without managing servers, scaling infrastructure, or provisioning capacity — and only pay for what you use.

While Java is traditionally viewed as heavyweight for serverless, frameworks like Spring Boot, when combined with the AWS Serverless Java Container, allow you to bring the power of Spring’s ecosystem to the serverless world.

This guide will help you create, optimize, and deploy a Spring Boot application to AWS Lambda effectively.

Why Use Spring Boot with AWS Lambda?

  • Rich ecosystem (Spring Data, Security, Validation)
  • Familiar developer experience
  • Dependency injection and modular architecture
  • Integration with AWS services (SQS, SNS, DynamoDB, S3)

Use Spring Boot for APIs, background jobs, event-driven processing, or microservices in serverless form.

Project Setup and Dependencies

Start with a regular Spring Boot application. Add the following dependencies to your pom.xml:

<dependency>
<groupId>com.amazonaws.serverless</groupId>
<artifactId>aws-serverless-java-container-springboot2</artifactId>
<version>1.8.2</version>
</dependency>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency>

This enables you to run a Spring Boot app as a Lambda function that handles API Gateway requests.

Creating the Handler Class

Create a handler class that bridges API Gateway and your Spring Boot app:

public class StreamLambdaHandler
extends SpringBootProxyHandlerBuilder<StreamLambdaHandler>
implements RequestStreamHandler {

    public StreamLambdaHandler() {
        super(SpringBootApp.class);
    }
}

This initializes the Spring context once and handles all incoming HTTP requests.

Controller Example

Use @RestController like any typical Spring Boot app:

@RestController
@RequestMapping("/hello")
public class HelloController {

    @GetMapping
    public String sayHello() {
        return "Hello from Lambda with Spring Boot!";
    }
}

Map endpoints to Lambda’s API Gateway using path-based routing.

Building and Packaging

Use the Spring Boot Maven Plugin and the AWS SAM CLI or raw zip packaging:

mvn clean package

Package the fat JAR for deployment:

target/demo-lambda-1.0-SNAPSHOT.jar

Upload this JAR directly to AWS Lambda or deploy with the AWS SAM CLI or Serverless Framework.

Deployment Using AWS Console

  1. Go to AWS Lambda
  2. Create a new Lambda function
  3. Choose “Author from scratch”
  4. Select runtime: Java 17 (or 11/8)
  5. Upload your JAR
  6. Set the handler to: `com.example.StreamLambdaHandler::handleRequest`

Test the function using a mock API Gateway request or trigger.

Optimizing Cold Starts

Java functions on AWS Lambda suffer from cold start latency due to JVM initialization. Strategies to reduce cold starts:

  • Minimize dependencies (avoid unused Spring starters)
  • Use GraalVM with Spring Native for AOT compilation
  • Choose Java 17 or AWS SnapStart (if supported)
  • Use Provisioned Concurrency to pre-warm Lambda containers

Integrating with AWS Services

Spring Cloud AWS makes it easy to work with other AWS services:

<dependency>
<groupId>io.awspring.cloud</groupId>
<artifactId>spring-cloud-aws-starter</artifactId>
</dependency>

Example: reading from S3

@Autowired
private AmazonS3 amazonS3;

public void readFromBucket(String key) {
S3Object object = amazonS3.getObject("my-bucket", key);
}

Spring profiles can be used to switch between Lambda and local environments.

Logging and Monitoring

Use SLF4J with logback or log4j for structured logs. AWS CloudWatch will automatically capture log output from Lambda.

private static final Logger log = LoggerFactory.getLogger(MyHandler.class);
log.info("Function started...");

For tracing and observability, integrate with AWS X-Ray or OpenTelemetry for distributed tracing.

Best Practices

  • Use lightweight starter modules (exclude actuator if not needed)
  • Keep function code small and focused
  • Use environment variables for configuration
  • Handle retries and timeouts gracefully
  • Profile memory and tune JVM size to avoid OOMs

Conclusion

Spring Boot is no longer limited to monoliths and containers — it can power scalable, serverless functions on AWS Lambda with the same elegance and productivity developers love.

By leveraging tools like AWS Serverless Java Container, Spring Cloud AWS, and careful tuning, you can confidently build cost-effective, event-driven, and cloud-native Java applications without managing servers.