class).to(Bar.class).getType())); } -}
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 |
|
| |
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).
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:
spring.cloud.function.web.export.enabled=true
-spring.cloud.function.web.export.source.url=http://${AWS_LAMBDA_RUNTIME_API:localhost}/2018-06-01/runtime/invocation/next
-spring.cloud.function.web.export.sink.url=http://${AWS_LAMBDA_RUNTIME_API:localhost}/2018-06-01/runtime/invocation/{{destination}}/response
-spring.cloud.function.web.export.sink.name=origin|uppercasewhere "uppercase" is the name of your function ("origin" is the name of the Supplier that is provided by Spring Cloud Function Web). 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.
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.
This project provides an adapter layer for a Spring Cloud Function application onto Azure. +}
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 |
|
| |
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).
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:
spring.cloud.function.web.export.enabled=true
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.
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.
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.
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.
Example:
public class FooHandler extends AzureSpringBootRequestHandler<Foo, Bar> { @FunctionName("uppercase") public Bar execute( diff --git a/single/spring-cloud-function.html b/single/spring-cloud-function.html index 6ae0d492c..d27baed54 100644 --- a/single/spring-cloud-function.html +++ b/single/spring-cloud-function.html @@ -229,10 +229,7 @@ look something like this:class).to(Bar.class).getType())); } -}
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 |
|
| |
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).
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:
spring.cloud.function.web.export.enabled=true
-spring.cloud.function.web.export.source.url=http://${AWS_LAMBDA_RUNTIME_API:localhost}/2018-06-01/runtime/invocation/next
-spring.cloud.function.web.export.sink.url=http://${AWS_LAMBDA_RUNTIME_API:localhost}/2018-06-01/runtime/invocation/{{destination}}/response
-spring.cloud.function.web.export.sink.name=origin|uppercasewhere "uppercase" is the name of your function ("origin" is the name of the Supplier that is provided by Spring Cloud Function Web). 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.
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.
This project provides an adapter layer for a Spring Cloud Function application onto Azure. +}
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 |
|
| |
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).
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:
spring.cloud.function.web.export.enabled=true
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.
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.
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.
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.
Example:
public class FooHandler extends AzureSpringBootRequestHandler<Foo, Bar> { @FunctionName("uppercase") public Bar execute( diff --git a/spring-cloud-function.xml b/spring-cloud-function.xml index bc7d841eb..e4f6c95b9 100644 --- a/spring-cloud-function.xml +++ b/spring-cloud-function.xml @@ -609,11 +609,8 @@ public class FuncApplication implements ApplicationContextInitializer<GenericCustom Runtime 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 :spring.cloud.function.web.export.enabled=true -spring.cloud.function.web.export.source.url=http://${AWS_LAMBDA_RUNTIME_API:localhost}/2018-06-01/runtime/invocation/next -spring.cloud.function.web.export.sink.url=http://${AWS_LAMBDA_RUNTIME_API:localhost}/2018-06-01/runtime/invocation/{{destination}}/response -spring.cloud.function.web.export.sink.name=origin|uppercase -where "uppercase" is the name of your function ("origin" is the name of the +Supplier that is provided by Spring Cloud Function Web). Then provide abootstrap 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 exampleFuncApplication 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.spring.cloud.function.web.export.enabled=true +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 exampleFuncApplication 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.