From 8d9924304b7c3c81d9356dd02ce71cf9fba8d8d1 Mon Sep 17 00:00:00 2001 From: buildmaster Date: Wed, 24 Jul 2019 11:56:30 +0000 Subject: [PATCH] Sync docs from master to gh-pages --- spring-cloud-function.html | 684 +------------------------------------ 1 file changed, 1 insertion(+), 683 deletions(-) diff --git a/spring-cloud-function.html b/spring-cloud-function.html index c925c9789..ed58b8c31 100644 --- a/spring-cloud-function.html +++ b/spring-cloud-function.html @@ -111,13 +111,7 @@ $(addBlockSwitches);
  • Dynamic Compilation
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  • Serverless Platform Adapters - -
    • +
  • Serverless Platform Adapters
    • @@ -957,682 +951,6 @@ has its own Spring Cloud Function adapter. And Java Function Invoker acts natively is an adapter for Spring Cloud Function jars.

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    AWS Lambda

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    3.0.0.BUILD-SNAPSHOT

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    The AWS adapter takes a Spring Cloud Function app and converts it to a form that can run in AWS Lambda.

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    Introduction

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    The adapter has a couple of generic request handlers that you can use. The most generic is SpringBootStreamHandler, which uses a Jackson ObjectMapper provided by Spring Boot to serialize and deserialize the objects in the function. There is also a SpringBootRequestHandler which you can extend, and provide the input and output types as type parameters (enabling AWS to inspect the class and do the JSON conversions itself).

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    If your app has more than one @Bean of type Function etc. then you can choose the one to use by configuring function.name (e.g. as FUNCTION_NAME environment variable in AWS). The functions are extracted from the Spring Cloud FunctionCatalog (searching first for Function then Consumer and finally Supplier).

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    Notes on JAR Layout

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    You don’t need the Spring Cloud Function Web or Stream adapter at runtime in Lambda, so you might -need to exclude those before you create the JAR you send to AWS. A Lambda application has to be -shaded, but a Spring Boot standalone application does not, so you can run the same app using 2 -separate jars (as per the sample). The sample app creates 2 jar files, one with an aws -classifier for deploying in Lambda, and one executable (thin) jar that includes spring-cloud-function-web -at runtime. Spring Cloud Function will try and locate a "main class" for you from the JAR file -manifest, using the Start-Class attribute (which will be added for you by the Spring Boot -tooling if you use the starter parent). If there is no Start-Class in your manifest you can -use an environment variable or system property MAIN_CLASS when you deploy the function to AWS.

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    If you are not using the functional bean definitions but relying on Spring Boot’s auto-configuration, -then additional transformers must be configured as part of the maven-shade-plugin execution.

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    <plugin>
-	<groupId>org.apache.maven.plugins</groupId>
-	<artifactId>maven-shade-plugin</artifactId>
-	<dependencies>
-		<dependency>
-			<groupId>org.springframework.boot</groupId>
-			<artifactId>spring-boot-maven-plugin</artifactId>
-		</dependency>
-	</dependencies>
-	<configuration>
-		<createDependencyReducedPom>false</createDependencyReducedPom>
-		<shadedArtifactAttached>true</shadedArtifactAttached>
-		<shadedClassifierName>aws</shadedClassifierName>
-		<transformers>
-			<transformer implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer">
-				<resource>META-INF/spring.handlers</resource>
-			</transformer>
-			<transformer implementation="org.springframework.boot.maven.PropertiesMergingResourceTransformer">
-				<resource>META-INF/spring.factories</resource>
-			</transformer>
-			<transformer implementation="org.apache.maven.plugins.shade.resource.AppendingTransformer">
-				<resource>META-INF/spring.schemas</resource>
-			</transformer>
-		</transformers>
-	</configuration>
-</plugin>
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    Build file setup

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    In order to run Spring Cloud Function applications on AWS Lambda, you can leverage Maven or Gradle - plugins offered by the cloud platform provider.

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    Maven
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    In order to use the adapter plugin for Maven, add the plugin dependency to your pom.xml -file:

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    <dependencies>
-	<dependency>
-		<groupId>org.springframework.cloud</groupId>
-		<artifactId>spring-cloud-function-adapter-aws</artifactId>
-	</dependency>
-</dependencies>
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    As pointed out in the Notes on JAR Layout, you wil need a shaded jar in order to upload it -to AWS Lambda. You can use the Maven Shade Plugin for that. -The example of the setup can be found above.

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    You can use theSpring Boot Maven Plugin to generate the thin jar.

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    <plugin>
-	<groupId>org.springframework.boot</groupId>
-	<artifactId>spring-boot-maven-plugin</artifactId>
-	<dependencies>
-		<dependency>
-			<groupId>org.springframework.boot.experimental</groupId>
-			<artifactId>spring-boot-thin-layout</artifactId>
-			<version>${wrapper.version}</version>
-		</dependency>
-	</dependencies>
-</plugin>
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    You can find the entire sample pom.xml file for deploying Spring Cloud Function -applications to AWS Lambda with Maven here.

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    Gradle
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    In order to use the adapter plugin for Gradle, add the dependency to your build.gradle file:

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    dependencies {
-	compile("org.springframework.cloud:spring-cloud-function-adapter-aws:${version}")
-}
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    As pointed out in Notes on JAR Layout, you wil need a shaded jar in order to upload it -to AWS Lambda. You can use the Gradle Shadow Plugin for that:

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    buildscript {
-	dependencies {
-		classpath "com.github.jengelman.gradle.plugins:shadow:${shadowPluginVersion}"
-	}
-}
-apply plugin: 'com.github.johnrengelman.shadow'
-
-assemble.dependsOn = [shadowJar]
-
-import com.github.jengelman.gradle.plugins.shadow.transformers.*
-
-shadowJar {
-	classifier = 'aws'
-	dependencies {
-		exclude(
-			dependency("org.springframework.cloud:spring-cloud-function-web:${springCloudFunctionVersion}"))
-	}
-	// Required for Spring
-	mergeServiceFiles()
-	append 'META-INF/spring.handlers'
-	append 'META-INF/spring.schemas'
-	append 'META-INF/spring.tooling'
-	transform(PropertiesFileTransformer) {
-		paths = ['META-INF/spring.factories']
-		mergeStrategy = "append"
-	}
-}
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    You can use the Spring Boot Gradle Plugin and Spring Boot Thin Gradle Plugin to generate -the thin jar.

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    buildscript {
-	dependencies {
-		classpath("org.springframework.boot.experimental:spring-boot-thin-gradle-plugin:${wrapperVersion}")
-		classpath("org.springframework.boot:spring-boot-gradle-plugin:${springBootVersion}")
-	}
-}
-apply plugin: 'org.springframework.boot'
-apply plugin: 'org.springframework.boot.experimental.thin-launcher'
-assemble.dependsOn = [thinJar]
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    You can find the entire sample build.gradle file for deploying Spring Cloud Function -applications to AWS Lambda with Gradle here.

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    Upload

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    Build the sample under spring-cloud-function-samples/function-sample-aws and upload the -aws jar file to Lambda. The handler can be example.Handler or org.springframework.cloud.function.adapter.aws.SpringBootStreamHandler (FQN of the class, not a method reference, although Lambda does accept method references).

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    ./mvnw -U clean package
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    Using the AWS command line tools it looks like this:

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    aws lambda create-function --function-name Uppercase --role arn:aws:iam::[USERID]:role/service-role/[ROLE] --zip-file fileb://function-sample-aws/target/function-sample-aws-2.0.0.BUILD-SNAPSHOT-aws.jar --handler org.springframework.cloud.function.adapter.aws.SpringBootStreamHandler --description "Spring Cloud Function Adapter Example" --runtime java8 --region us-east-1 --timeout 30 --memory-size 1024 --publish
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    The input type for the function in the AWS sample is a Foo with a single property called "value". So you would need this to test it:

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    {
-  "value": "test"
-}
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    - - - - - -
    - - -The AWS sample app is written in the "functional" style (as an ApplicationContextInitializer). This is much faster on startup in Lambda than the traditional @Bean style, so if you don’t need @Beans (or @EnableAutoConfiguration) it’s a good choice. Warm starts are not affected. -
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    Type Conversion

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    Spring Cloud Function will attempt to transparently handle type conversion between the raw -input stream and types declared by your function.

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    For example, if your function signature is as such Function<Foo, Bar> we will attempt to convert -incoming stream event to an instance of Foo.

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    In the event type is not known or can not be determined (e.g., Function<?, ?>) we will attempt to -convert an incoming stream event to a generic Map.

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    Raw Input
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    There are times when you may want to have access to a raw input. In this case all you need is to declare your -function signature to accept InputStream. For example, Function<InputStream, ?>. In this case -we will not attempt any conversion and will pass the raw input directly to a function.

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    Functional Bean Definitions

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    Your functions will start much quicker if you can use functional bean definitions instead of @Bean. To do this make your main class -an ApplicationContextInitializer<GenericApplicationContext> and use the registerBean() methods in GenericApplicationContext to -create all the beans you need. You function need sto be registered as a bean of type FunctionRegistration so that the input and -output types can be accessed by the framework. There is an example in github (the AWS sample is written in this style). It would -look something like this:

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    @SpringBootApplication
-public class FuncApplication implements ApplicationContextInitializer<GenericApplicationContext> {
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-	public static void main(String[] args) throws Exception {
-		FunctionalSpringApplication.run(FuncApplication.class, args);
-	}
-
-	public Function<Foo, Bar> function() {
-		return value -> new Bar(value.uppercase()));
-	}
-
-	@Override
-	public void initialize(GenericApplicationContext context) {
-		context.registerBean("function", FunctionRegistration.class,
-			() -> new FunctionRegistration<Function<Foo, Bar>>(function())
-				.type(FunctionType.from(Foo.class).to(Bar.class).getType()));
-	}
-
-}
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    Platform Specific Features

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    HTTP and API Gateway
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    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).

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

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    ServiceAWS TypesGeneric Handler

    API Gateway

    APIGatewayProxyRequestEvent, APIGatewayProxyResponseEvent

    org.springframework.cloud.function.adapter.aws.SpringBootApiGatewayRequestHandler

    Kinesis

    KinesisEvent

    org.springframework.cloud.function.adapter.aws.SpringBootKinesisEventHandler

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    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).

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    Custom Runtime

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    An AWS Lambda custom runtime can be created really easily using the HTTP export features in Spring Cloud Function Web. To make this work just add Spring Cloud Function AWS and Spring Cloud Function Web as dependencies in your project and set the following in your application.properties:

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    spring.cloud.function.web.export.enabled=true
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    Set the handler name in AWS to the name of your function. Then provide a bootstrap script in the root of your zip/jar that runs the Spring Boot application. The functional bean definition style works for custom runtimes too, and is faster than the @Bean style, so the example FuncApplication above would work. 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.

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    Microsoft Azure

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    3.0.0.BUILD-SNAPSHOT

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    The Azure adapter bootstraps a Spring Cloud Function context and channels function calls from the Azure framework into the user functions, using Spring Boot configuration where necessary. Azure Functions has quite a unique, but invasive programming model, involving annotations in user code that are specific to the platform. The easiest way to use it with Spring Cloud is to extend a base class and write a method in it with the @FunctionName annotation which delegates to a base class method.

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    This project provides an adapter layer for a Spring Cloud Function application onto Azure. -You can write an app with a single @Bean of type Function and it will be deployable in Azure if you get the JAR file laid out right.

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    There is an AzureSpringBootRequestHandler which you must extend, and provide the input and output types as annotated method parameters (enabling Azure to inspect the class and create JSON bindings). The base class has two useful methods (handleRequest and handleOutput) to which you can delegate the actual function call, so mostly the function will only ever have one line.

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    Example:

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    public class FooHandler extends AzureSpringBootRequestHandler<Foo, Bar> {
-	@FunctionName("uppercase")
-	public Bar execute(
-			@HttpTrigger(name = "req", methods = { HttpMethod.GET,
-					HttpMethod.POST }, authLevel = AuthorizationLevel.ANONYMOUS)
-                    Foo foo,
-			ExecutionContext context) {
-		return handleRequest(foo, context);
-	}
-}
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    This Azure handler will delegate to a Function<Foo,Bar> bean (or a Function<Publisher<Foo>,Publisher<Bar>>). Some Azure triggers (e.g. @CosmosDBTrigger) result in a input type of List and in that case you can bind to List in the Azure handler, or String (the raw JSON). The List input delegates to a Function with input type Map<String,Object>, or Publisher or List of the same type. The output of the Function can be a List (one-for-one) or a single value (aggregation), and the output binding in the Azure declaration should match.

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    If your app has more than one @Bean of type Function etc. then you can choose the one to use by configuring function.name. Or if you make the @FunctionName in the Azure handler method match the function name it should work that way (also for function apps with multiple functions). The functions are extracted from the Spring Cloud FunctionCatalog so the default function names are the same as the bean names.

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    Accessing Azure ExecutionContext
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    Some time there is a need to access the target execution context provided by Azure runtime in the form of com.microsoft.azure.functions.ExecutionContext. -For example one of such needs is logging, so it can appear in the Azure console.

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    For that purpose Spring Cloud Function will register ExecutionContext as bean in the Application context, so it could be injected into your function. -For example

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    @Bean
-public Function<Foo, Bar> uppercase(ExecutionContext targetContext) {
-	return foo -> {
-		targetContext.getLogger().info("Invoking 'uppercase' on " + foo.getValue());
-		return new Bar(foo.getValue().toUpperCase());
-	};
-}
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    Normally type-based injection should suffice, however if need to you can also utilise the bean name under which it is registered which is targetExecutionContext.

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    Notes on JAR Layout
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    You don’t need the Spring Cloud Function Web at runtime in Azure, so you can exclude this -before you create the JAR you deploy to Azure, but it won’t be used if you include it, so -it doesn’t hurt to leave it in. A function application on Azure is an archive generated by - the Maven plugin. The function lives in the JAR file generated by this project. - The sample creates it as an executable jar, using the thin layout, so that Azure can find - the handler classes. If you prefer you can just use a regular flat JAR file. - The dependencies should not be included.

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    Build file setup

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    In order to run Spring Cloud Function applications on Microsoft Azure, you can leverage the Maven -plugin offered by the cloud platform provider.

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    In order to use the adapter plugin for Maven, add the plugin dependency to your pom.xml -file:

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    <dependencies>
-	<dependency>
-		<groupId>org.springframework.cloud</groupId>
-		<artifactId>spring-cloud-function-adapter-azure</artifactId>
-	</dependency>
-</dependencies>
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    Then, configure the plugin. You will need to provide Azure-specific configuration for your -application, specifying the resourceGroup, appName and other optional properties, and - add the package goal execution so that the function.json file required by Azure is - generated for you. Full plugin documentation can be found in the plugin repository.

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    <plugin>
-	<groupId>com.microsoft.azure</groupId>
-	<artifactId>azure-functions-maven-plugin</artifactId>
-	<configuration>
-		<resourceGroup>${functionResourceGroup}</resourceGroup>
-		<appName>${functionAppName}</appName>
-	</configuration>
-	<executions>
-		<execution>
-			<id>package-functions</id>
-			<goals>
-				<goal>package</goal>
-			</goals>
-		</execution>
-	</executions>
-</plugin>
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    You will also have to ensure that the files to be scanned by the plugin can be found in the -Azure functions staging directory (see the plugin repository - for more details on the staging directory and it’s default location).

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    You can find the entire sample pom.xml file for deploying Spring Cloud Function -applications to Microsoft Azure with Maven here.

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    - - -As of yet, only Maven plugin is available. Gradle plugin has not been created by -the cloud platform provider. -
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    Build

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    ./mvnw -U clean package
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    Running the sample

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    You can run the sample locally, just like the other Spring Cloud Function samples:

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    and curl -H "Content-Type: text/plain" localhost:8080/function -d '{"value": "hello foobar"}'.

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    You will need the az CLI app (see https://docs.microsoft.com/en-us/azure/azure-functions/functions-create-first-java-maven for more detail). To deploy the function on Azure runtime:

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    $ az login
-$ mvn azure-functions:deploy
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    On another terminal try this: curl https://<azure-function-url-from-the-log>/api/uppercase -d '{"value": "hello foobar!"}'. Please ensure that you use the right URL for the function above. Alternatively you can test the function in the Azure Dashboard UI (click on the function name, go to the right hand side and click "Test" and to the bottom right, "Run").

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    The input type for the function in the Azure sample is a Foo with a single property called "value". So you need this to test it with something like below:

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    {
-  "value": "foobar"
-}
    -
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    -
    - - - - - -
    - - -The Azure sample app is written in the "non-functional" style (using @Bean). The functional style (with just Function or ApplicationContextInitializer) is much faster on startup in Azure than the traditional @Bean style, so if you don’t need @Beans (or @EnableAutoConfiguration) it’s a good choice. Warm starts are not affected. -
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    Apache OpenWhisk

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    3.0.0.BUILD-SNAPSHOT

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    The OpenWhisk adapter is in the form of an executable jar that can be used in a a docker image to be deployed to Openwhisk. The platform works in request-response mode, listening on port 8080 on a specific endpoint, so the adapter is a simple Spring MVC application.

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    Quick Start

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    Implement a POF (be sure to use the functions package):

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    package functions;
-
-import java.util.function.Function;
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-public class Uppercase implements Function<String, String> {
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-	public String apply(String input) {
-		return input.toUpperCase();
-	}
-}
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    Install it into your local Maven repository:

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    ./mvnw clean install
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    Create a function.properties file that provides its Maven coordinates. For example:

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    dependencies.function: com.example:pof:0.0.1-SNAPSHOT
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    Copy the openwhisk runner JAR to the working directory (same directory as the properties file):

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    cp spring-cloud-function-adapters/spring-cloud-function-adapter-openwhisk/target/spring-cloud-function-adapter-openwhisk-2.0.0.BUILD-SNAPSHOT.jar runner.jar
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    Generate a m2 repo from the --thin.dryrun of the runner JAR with the above properties file:

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    java -jar -Dthin.root=m2 runner.jar --thin.name=function --thin.dryrun
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    Use the following Dockerfile:

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    FROM openjdk:8-jdk-alpine
-VOLUME /tmp
-COPY m2 /m2
-ADD runner.jar .
-ADD function.properties .
-ENV JAVA_OPTS=""
-ENTRYPOINT [ "java", "-Djava.security.egd=file:/dev/./urandom", "-jar", "runner.jar", "--thin.root=/m2", "--thin.name=function", "--function.name=uppercase"]
-EXPOSE 8080
    -
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    - - - - - -
    - - -you could use a Spring Cloud Function app, instead of just a jar with a POF in it, in which case you would have to change the way the app runs in the container so that it picks up the main class as a source file. For example, you could change the ENTRYPOINT above and add --spring.main.sources=com.example.SampleApplication. -
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    Build the Docker image:

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    docker build -t [username/appname] .
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    Push the Docker image:

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    docker push [username/appname]
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    Use the OpenWhisk CLI (e.g. after vagrant ssh) to create the action:

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    wsk action create example --docker [username/appname]
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    Invoke the action:

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    wsk action invoke example --result --param payload foo
-{
-    "result": "FOO"
-}
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    -