spring-cloud-function/docs/modules/ROOT/pages/adapters/aws.adoc

*{project-version}*


The https://aws.amazon.com/[AWS] adapter takes a Spring Cloud Function app and converts it to a form that can run in AWS Lambda.

[[introduction]]
== Introduction

The details of how to get stared with AWS Lambda is out of scope of this document, so the expectation is that user has some familiarity with
AWS and AWS Lambda and wants to learn what additional value spring provides.


=== Getting Started

One of the goals of Spring Cloud Function framework is to provide necessary infrastructure elements to enable a _simple function application_
to interact in a certain way in a particular environment.
A simple function application (in context or Spring) is an application that contains beans of type Supplier, Function or Consumer.
So, with AWS it means that a simple function bean should somehow be recognised and executed in AWS Lambda environment.

Let’s look at the example:

[source, java]
----
@SpringBootApplication
public class FunctionConfiguration {

	public static void main(String[] args) {
		SpringApplication.run(FunctionConfiguration.class, args);
	}

	@Bean
	public Function<String, String> uppercase() {
		return value -> value.toUpperCase();
	}
}
----

It shows a complete Spring Boot application with a function bean defined in it. What’s interesting is that on the surface this is just
another boot app, but in the context of AWS Adapter it is also a perfectly valid AWS Lambda application. No other code or configuration
is required. All you need to do is package it and deploy it, so let’s look how we can do that.

To make things simpler we’ve provided a sample project ready to be built and deployed and you can access it
https://github.com/spring-cloud/spring-cloud-function/tree/master/spring-cloud-function-samples/function-sample-aws[here].

You simply execute `./mvnw clean package` to generate JAR file. All the necessary maven plugins have already been setup to generate
appropriate AWS deployable JAR file. (You can read more details about JAR layout in <<Notes on JAR Layout>>).

Then you have to upload the JAR file (via AWS dashboard or AWS CLI) to AWS.

When ask about _handler_ you specify `org.springframework.cloud.function.adapter.aws.FunctionInvoker::handleRequest` which is a generic request handler.

image::AWS-deploy.png[width=800,scaledwidth="75%",align="center"]

That is all. Save and execute the function with some sample data which for this function is expected to be a
String which function will uppercase and return back.

While `org.springframework.cloud.function.adapter.aws.FunctionInvoker` is a general purpose AWS's `RequestHandler` implementation aimed at completely
isolating you from the specifics of AWS Lambda API, for some cases you may want to specify which specific AWS's `RequestHandler` you want
to use. The next section will explain you how you can accomplish just that.


[[aws-request-handlers]]
== AWS Request Handlers

The adapter has a couple of generic request handlers that you can use. The most generic is (and the one we used in the Getting Started section)
is `org.springframework.cloud.function.adapter.aws.FunctionInvoker` which is the implementation of AWS's `RequestStreamHandler`.
User doesn't need to do anything other then specify it as 'handler' on AWS dashboard when deploying function.
It will handle most of the case including Kinesis, streaming etc. .


If your app has more than one `@Bean` of type `Function` etc. then you can choose the one to use by configuring `spring.cloud.function.definition` 
property or environment variable. The functions are extracted from the Spring Cloud `FunctionCatalog`. In the event you don't specify `spring.cloud.function.definition`
the framework will attempt to find a default following the search order where it searches first for `Function` then `Consumer` and finally `Supplier`).

[[aws-context]]
== AWS Context

In a typical implementation of AWS Handler user has access to AWS _context_ object. With function approach you can have the same experience if you need it.
Upon each invocation the framework will add `aws-context` message header containing the AWS _context_ instance for that particular invocation. So if you need to access it 
you can simply have `Message<YourPojo>` as an input parameter to your function and then access `aws-context` from message headers. 
For convenience we provide AWSLambdaUtils.AWS_CONTEXT constant.


[[aws-function-routing]]
== AWS Function Routing

One of the core features of Spring Cloud Function is https://docs.spring.io/spring-cloud-function/docs/{project-version}/reference/html/spring-cloud-function.html#_function_routing_and_filtering[routing] 
- an ability to have one special function to delegate to other functions based on the user provided routing instructions.

In AWS Lambda environment this feature provides one additional benefit, as it allows you to bind a single function (Routing Function) 
as AWS Lambda and thus a single HTTP endpoint for API Gateway. So in the end you only manage one function and one endpoint, while benefiting 
from many function that can be part of your application.

More details are available in the provided https://github.com/spring-cloud/spring-cloud-function/tree/main/spring-cloud-function-samples/function-sample-aws-routing[sample],
yet few general things worth mentioning.

Routing capabilities will be enabled by default whenever there is more then one function in your application as `org.springframework.cloud.function.adapter.aws.FunctionInvoker`
can not determine which function to bind as AWS Lambda, so it defaults to `RoutingFunction`.
This means that all you need to do is provide routing instructions which you can do https://docs.spring.io/spring-cloud-function/docs/{project-version}/reference/html/spring-cloud-function.html#_function_routing_and_filtering[using several mechanisms] 
(see https://github.com/spring-cloud/spring-cloud-function/tree/main/spring-cloud-function-samples/function-sample-aws-routing[sample] for more details).

Also, note that since AWS does not allow dots `.` and/or hyphens`-` in the name of the environment variable, you can benefit from boot support and simply substitute
dots with underscores and hyphens with camel case.  So for example `spring.cloud.function.definition` becomes `spring_cloud_function_definition`
and `spring.cloud.function.routing-expression` becomes `spring_cloud_function_routingExpression`. 


[[http-and-api-gateway]]
== HTTP and API Gateway

AWS has some platform-specific data types, including batching of messages, which is much more efficient than processing each one individually. To make use of these types you can write a function that depends on those types. Or you can rely on Spring to extract the data from the AWS types and convert it to a Spring `Message`. To do this you tell AWS that the function is of a specific generic handler type (depending on the AWS service) and provide a bean of type `Function<Message<S>,Message<T>>`, where `S` and `T` are your business data types. If there is more than one bean of type `Function` you may also need to configure the Spring Boot property `function.name` to be the name of the target bean (e.g. use `FUNCTION_NAME` as an environment variable).

The supported AWS services and generic handler types are listed below:

|===
| Service     | AWS Types | Generic Handler |

| API Gateway | `APIGatewayProxyRequestEvent`, `APIGatewayProxyResponseEvent` | `org.springframework.cloud.function.adapter.aws.SpringBootApiGatewayRequestHandler` |
| Kinesis     | KinesisEvent | org.springframework.cloud.function.adapter.aws.SpringBootKinesisEventHandler |
|===


For example, to deploy behind an API Gateway, use `--handler org.springframework.cloud.function.adapter.aws.SpringBootApiGatewayRequestHandler` in your AWS command line (in via the UI) and define a `@Bean` of type `Function<Message<Foo>,Message<Bar>>` where `Foo` and `Bar` are POJO types (the data will be marshalled and unmarshalled by AWS using Jackson).

[[custom-runtime]]
== Custom Runtime

You can also benefit from https://docs.aws.amazon.com/lambda/latest/dg/runtimes-custom.html[AWS Lambda custom runtime] feature of AWS Lambda 
and Spring Cloud Function provides all the necessary components to make it easy.

From the code perspective the application should look no different then any other Spring Cloud Function application.
The only thing you need to do is to provide a `bootstrap` script in the root of your zip/jar that runs the Spring Boot application.
and select "Custom Runtime" when creating a function in AWS.
Here is an example 'bootstrap' file:
```text
#!/bin/sh

cd ${LAMBDA_TASK_ROOT:-.}

java -Dspring.main.web-application-type=none -Dspring.jmx.enabled=false \
  -noverify -XX:TieredStopAtLevel=1 -Xss256K -XX:MaxMetaspaceSize=128M \
  -Djava.security.egd=file:/dev/./urandom \
  -cp .:`echo lib/*.jar | tr ' ' :` com.example.LambdaApplication
```
The `com.example.LambdaApplication` represents your application which contains function beans.

Set the handler name in AWS to the name of your function. You can use function composition here as well (e.g., `uppecrase|reverse`).
That is pretty much all. Once you upload your zip/jar to AWS your function will run in custom runtime.
We provide a https://github.com/spring-cloud/spring-cloud-function/tree/master/spring-cloud-function-samples/function-sample-aws-custom-new[sample project] 
where you can also see how to configure yoru POM to properly generate the zip file.

The functional bean definition style works for custom runtimes as well, and is 
faster than the `@Bean` style. A custom runtime can start up much quicker even than a functional bean implementation 
of a Java lambda - it depends mostly on the number of classes you need to load at runtime. 
Spring doesn't do very much here, so you can reduce the cold start time by only using primitive types in your function, for instance, 
and not doing any work in custom `@PostConstruct` initializers.