Kotlin Native Runtime for AWS Lambda

A runtime for executing AWS Lambda Functions written in Kotlin/Native, designed to reduce cold start issues common with the JVM platform.

Project Structure

• lambda-runtime: Library providing the Lambda runtime.
• lambda-events: Library with strongly-typed Lambda event models like APIGatewayRequest, DynamoDBEvent, S3Event, KafkaEvent, SQSEvent, etc.
• sample: Sample project demonstrating examples of lambda functions.

Supported OS-only runtime machines

• Amazon Linux 2023 (provided.al2023) with x86_64 architecture
• Amazon Linux 2 (provided.al2) with x86_64 architecture

Performance

Benchmarks of Kotlin Native's "Hello World" Lambda function on Amazon Linux 2023 (x86_64) configured with different amounts of provisioned memory. For more detailed benchmarks, read the article.

• Lambda 128mb provisioned memory. Kotlin/Native outperforms JVM-based runtimes like GraalVM and Java 17 and Java 21 SnapStart.
• Lambda 256mb provisioned memory. Competitive with Python in execution time and memory usage, while surpassing JVM-based runtimes.
• Lambda 512mb provisioned memory. Kotlin/Native ranks among the top 5 fastest runtimes, ahead of Python, C#, and JVM, only trailing behind C++, Rust, LLRT, and Go.
• Lambda 1024mb provisioned memory

Getting started

1. Create a Kotlin Multiplatform Project

See Get started with Kotlin/Native for more details.

2. Add Dependencies

Add the following to your build.gradle file:

plugins {
    id("io.github.trueangle.plugin.lambda") version "0.0.1"
}

kotlin {
    sourceSets {
        nativeMain.dependencies {
            implementation("io.github.trueangle:lambda-runtime:0.0.2")
            implementation("io.github.trueangle:lambda-events:0.0.2")
        }
    }
}

3. Specify Entry Point and Targets

kotlin {
    listOf(
        macosArm64(),
        macosX64(),
        linuxX64(),
    ).forEach {
        it.binaries {
            executable {
                entryPoint = "com.github.trueangle.knative.lambda.runtime.sample.main" // Link this to your main function entry point
                freeCompilerArgs += listOf("-Xallocator=mimalloc") // to understand how the choice of allocator impacts the performance, refer https://medium.com/aws-tip/leveraging-kotlin-native-for-efficient-serverless-applications-on-aws-lambda-66d992c074cc
            }
        }
    }
}

4. Choose Lambda Function Type

Buffered

Buffered Lambda functions collect all data before sending a response. This is a default behavior of Lambda function. Response payload max size: 6 MB.

class HelloWorldLambdaHandler : LambdaBufferedHandler<APIGatewayV2Request, APIGatewayV2Response> {
    override suspend fun handleRequest(
        input: APIGatewayV2Request,
        context: Context
    ): APIGatewayV2Response {
        return APIGatewayV2Response(
            statusCode = 200,
            body = "Hello world",
            cookies = null,
            headers = null,
            isBase64Encoded = false
        )
    }
}

LambdaBufferedHandler<I, O> is designed to work with any Kotlin class that is supported by the Kotlin serialization library as both the input and output types. This allows you to define your own custom request and response models annotated with @Serializable or utilize existing ones provided by the lambda-events module.

Streaming

A streaming function sends data as soon as it's available, instead of waiting for all the data. It processes and returns the response in chunks, which is useful for large or ongoing tasks. This allows for faster responses and can handle data as it comes in. More details here. For example, SampleStreamingHandler reads a large json file and streams it in chunks.

class SampleStreamingHandler : LambdaStreamHandler<ByteArray, ByteWriteChannel> {
    override suspend fun handleRequest(
        input: ByteArray,
        output: ByteWriteChannel,
        context: Context
    ) {
        ByteReadChannel(SystemFileSystem.source(Path("hello.json")).buffered()).copyTo(output)
    }
}

LambdaStreamHandler<I, ByteWriteChannel> accepts any serializable input and outputs to ByteWriteChannel which is then streamed to the client.

Both LambdaBufferedHandler and LambdaStreamHandler are suspend functions, so you can use Kotlin coroutines to handle the request in non-blocking way.

Each handler accepts Context object which can be used to get information about the function execution environment, such as the request ID, resource limits, and other details.

5. Specify Main Function

Create application entry point using standard main function. Call LambdaRuntime.run to execute Lambda by passing handler to it.

fun main() = LambdaRuntime.run { HelloWorldLambdaHandler() }

Or for SampleStreamingHandler

fun main() = LambdaRuntime.run { SampleStreamingHandler() }

For more examples refer to project's sample.

Testing Runtime locally

Use the AWS runtime emulator to run the runtime locally.

1. ./gradlew build to build the Lambda executable.
2. Modify runtime-emulator/Dockerfile to set the path to the generated executable (.kexe) file in build/bin/linuxX64/releaseExecutable.
3. Run docker build -t sample:latest .
4. Start server docker run -p 9000:8080 sample:latest
5. Execute the function using curl -XPOST "http://localhost:9000/2015-03-31/functions/function/invocations" -d '{}'

Build and deploy to AWS

1. Make sure you have applied the plugin id("io.github.trueangle.plugin.lambda") version "0.0.1"
2. Execute ./gradlew buildLambdaRelease. The command will output the path to the archive containing lambda executable (YOUR_MODULE_NAME.kexe) located in (YOUR_MODULE_NAME/build/bin/lambda/release/YOUR_MODULE_NAME.zip)
3. Deploy .zip archive to AWS. If you have never used AWS Lambda before, learn how to deploy Lambda function as zip archive manually or using AWS CLI:

$ aws lambda create-function --function-name LAMBDA_FUNCTION_NAME \
  --handler YOUR_MODULE_NAME.kexe \ # Important to specify the name of the Lambda executable. 
  --zip-file YOUR_MODULE_NAME.zip \
  --runtime provided.al2023 \ # Change this to provided.al2 if you would like to use Amazon Linux 2
  --role arn:aws:iam::XXXXXXXXXXXXX:role/YOUR_LAMBDA_EXECUTION_ROLE \
  --tracing-config Mode=Active

Test the function using the AWS CLI:

$ aws lambda invoke
--cli-binary-format raw-in-base64-out \
--function-name LAMBDA_FUNCTION_NAME \
--payload '{"command": "Say Hi!"}' \
output.json
$ cat output.json 

Logging

The Runtime uses AWS logging conventions for enhanced log capture, supporting String and JSON log output format. It also allows to dynamically control log levels without altering your code, simplifying the debugging process. Additionally, you can direct logs to specific Amazon CloudWatch log groups, making log management and aggregation more efficient at scale. More details on how to set log format and level refer to the article. https://aws.amazon.com/blogs/compute/introducing-advanced-logging-controls-for-aws-lambda-functions/

Use the global Log object with extension functions. The log message accepts any object / primitive type.

Log.trace(message: T?) // The most fine-grained information used to trace the path of your code's execution

Log.debug(message: T?) // Detailed information for system debugging

Log.info(message: T?) // Messages that record the normal operation of your function

Log.warn(message: T?) // Messages about potential errors that may lead to unexpected behavior if unaddressed

Log.error(message: T?) // Messages about problems that prevent the code from performing as expected

Log.fatal(message: T?) // Messages about serious errors that cause the application to stop functioning

Troubleshooting

• For Amazon Linux 2023, create a Lambda layer with the libcrypt.so dependency. This library can be taken from your Linux machine or via the Github Action workflow.
• Currently, only x86-64 architecture is supported by the runtime. LinuxArm64 is not supported by Kotlin Native yet, For more details, see:
  1. The list of supported targets for Kotlin Native ( 2.0.20-RC2) https://repo.maven.apache.org/maven2/org/jetbrains/kotlin/kotlin-native-prebuilt/2.0.20-RC2/
  2. https://youtrack.jetbrains.com/issue/KT-36871/Support-Aarch64-Linux-as-a-host-for-the-Kotlin-Native
• If you are running the project build on MacOS you might come across a set of errors connected with curl linking e.g. ld.lld: error: undefined symbol: curl_global_init. This means that your local machine uses different curl version from what is requested by the runtime. To solve that either use Gihub Actions workflow or local docker container with ubuntu 22 under the hood. Example Dockerfile and build command:

docker build -t sample .
docker run --rm -v $(pwd):/sample -w /sample sample ./gradlew build