Kotless: Kotlin Serverless Framework

Kotless

Kotless stands for Kotlin serverless framework.

Its focus lies in reducing the routine of serverless deployment creation by generating it straight from the code of the application itself.

So, simply speaking, Kotless gives you one magic button to deploy your Web application as a serverless application on AWS and Azure!

Kotless consists of two main parts:

• DSL provides a way of defining serverless applications. There are three DSLs supported:
  • Kotless DSL — Kotless own DSL that provides annotations to declare routing, scheduled events, etc.
  • Ktor — Ktor engine that is introspected by Kotless. Use standard Ktor syntax and Kotless will generate deployment.
  • Spring Boot — Spring Boot serverless container that is introspected by Kotless. Use standard Spring syntax and Kotless will generate deployment.
• Kotless Gradle Plugin provides a way of deploying serverless application. For that, it:
  • performs the tasks of generating Terraform code from the application code and, subsequently, deploying it to AWS or Azure;
  • runs application locally, emulates the AWS environment (if necessary) and provides the possibility for IDE debugging.

One of the key features of Kotless is its ability to embed into existing applications. Kotless makes super easy deployment of existing Spring and Ktor applications to AWS and Microsoft Azure serverless platforms.

Getting started

Setting up project

Kotless uses Gradle to wrap around the existing building process and insert the deployment into it.

Consider using one of the latest versions of Gradle, starting with the 7.2 version.

Basically, if you already use Gradle, you only need to do two things.

Firstly, set up the Kotless Gradle plugin.

You will have to tell Gradle where to find the plugin by editing settings.gradle.kts:

pluginManagement {
    resolutionStrategy {
        this.eachPlugin {
            if (requested.id.id == "io.kotless") {
                useModule("io.kotless:gradle:${this.requested.version}")
            }
        }
    }

    repositories {
        maven(url = uri("https://packages.jetbrains.team/maven/p/ktls/maven"))
        gradlePluginPortal()
        mavenCentral()
    }
}

And apply the plugin:

//Imports are necessary, for this example
import io.kotless.plugin.gradle.dsl.Webapp.Route53
import io.kotless.plugin.gradle.dsl.kotless

//Group may be used by Kotless DSL to reduce the number of introspected classes by package
//So, don't forget to set it
group = "org.example"
version = "0.1.0"


plugins {
    //Version of Kotlin should be 1.3.72+
    kotlin("jvm") version "1.5.31" apply true

    id("io.kotless") version "0.2.0" apply true
}

Secondly, add Kotless DSL (or Ktor, or Spring Boot) as a library to your application:

repositories {
    mavenCentral()
    //Kotless repository
    maven(url = uri("https://packages.jetbrains.team/maven/p/ktls/maven"))
}

dependencies {
    implementation("io.kotless", "kotless-lang", "0.2.0")
    implementation("io.kotless", "kotless-lang-aws", "0.2.0")
//    if you want to deploy to Microsoft Azure, just replace -aws with -azure    
//    implementation("io.kotless", "ktor-lang-azure", "0.2.0")


    //or for Ktor (Note, that `ktor-lang` depends on Ktor version 1.5.0)
    //implementation("io.kotless", "ktor-lang", "0.2.0")
    //implementation("io.kotless", "ktor-lang-aws", "0.2.0")
    //implementation("io.kotless", "ktor-lang-azure", "0.2.0")

    //or for Spring Boot (Note, that `spring-boot-lang` depends on Spring Boot version 2.3.0.RELEASE)
    //implementation("io.kotless", "spring-boot-lang", "0.2.0")
    //implementation("io.kotless", "spring-boot-lang-aws", "0.2.0")
    //implementation("io.kotless", "spring-boot-lang-azure", "0.2.0")
}

Please note that if you use Ktor or Spring Boot you will need to replace existing in your project dependency with a special Kotless *-lang dependency. Also, after that you will need to align version of dependent libraries (like Spring Security) with version bundled in *-lang (see this paragraph)

This gives you access to DSL interfaces in your code and sets up a Lambda dispatcher inside your application.

Deploying to the cloud

Depending on a use case, you may want to deploy application either in an AWS or Microsoft Azure.

Note, that if you even don't have a cloud account, you can still use Kotless locally to run and debug your application -- just use local Gradle task.

Deploying to AWS

If you don't have an AWS account, you can create it following simple instruction by Hadi Hariri.

If you have an AWS account and want to perform the real deployment — let's set up everything for it! It's rather simple:

kotless {
    config {

        aws {
            storage {
                bucket = "kotless.s3.example.com"
            }

            profile = "example"
            region = "eu-west-1"
        }
    }

    webapp {
        dns("kotless", "example.com")
    }
}

Here we set up the config of Kotless itself:

• the bucket, which will be used to store lambdas and configs;
• Terraform configuration with the name of the profile to access AWS.

Then we set up a specific application to deploy:

• Route53 alias for the resulting application (you need to pre-create an ACM certificate for the DNS record).

And that's the whole setup!

Deploying to Azure

Deployment to Microsoft Azure is also pretty straightforward and simple:

kotless {
    config {
        azure {
            storage {
                storageAccount = "your-storage-account"
                container = "container-which-kotless-would-use"
            }

            terraform {
                backend {
                    resourceGroup = "your-resource-group"
                }
            }
        }
    }

    webapp {
        dns("kotless", "example.com")
    }
}

Here we set up the config of Kotless itself:

• the storage, which will be used to store lambdas and configs;
• Terraform configuration with the name of the profile to access Azure.

Then we set up a specific application to deploy:

• Azure DNS alias for the resulting application (you need to pre-create certificate for the DNS record).

And that's the whole setup!

Creating application

Now you can create your first serverless application with Kotless DSL:

@Get("/")
fun main() = "Hello world!"

Or with Ktor:

class Server : Kotless() {
    override fun prepare(app: Application) {
        app.routing {
            get("/") {
                call.respondText { "Hello World!" }
            }
        }
    }
}

Or with Spring Boot:

@SpringBootApplication
open class Application : Kotless() {
    override val bootKlass: KClass<*> = this::class
}

@RestController
object Pages {
    @GetMapping("/")
    fun main() = "Hello World!"
}

Local start

Kotless-based applications can start locally as an HTTP server. This functionality is supported by all DSLs.

Moreover, Kotless local start may spin up an AWS emulation (docker required). Just instantiate your AWS service client using override for Kotless local starts:

val client = AmazonDynamoDBClientBuilder.standard().withKotlessLocal(AwsResource.DynamoDB).build()

And enable it in Gradle:

kotless {
    //<...>
    extensions {
        local {
            //enables AWS emulation (disabled by default)
            useAWSEmulation = true
        }
    }
}

During the local run, LocalStack will be started and all clients will be pointed to its endpoint automatically.

Local start functionality does not require any access to cloud provider, so you may check how your application behaves without an AWS account. Also, it gives you the possibility to debug your application locally from your IDE.

Integration with existing applications

Kotless is able to deploy existing Spring Boot or Ktor application to AWS serverless platform. To do it, you'll need to set up a plugin and replace existing dependency with the appropriate Kotless DSL.

For Ktor, you should replace existing engine ( e.g. implementation("io.ktor", "ktor-server-netty", "1.5.0")) with implementation("io.kotless", "ktor-lang", "0.1.6"). Note that this dependency bundles Ktor of version 1.5.0, so you may need to upgrade other Ktor libraries (like ktor-html-builder) to this version.

For Spring Boot you should replace the starter you use ( e.g. implementation("org.springframework.boot", "spring-boot-starter-web", "2.3.0.RELASE)) with implementation("io.kotless", "spring-boot-lang", "0.1.6"). Note that this dependency bundles Spring Boot of version 2.4.2, so you also may need to upgrade other Spring Boot libraries to this version.

Once it is done, you may hit deploy task and make your application serverless. Note, that you will still be able to run application locally via local Gradle task.

Advanced features

While Kotless can be used as a framework for the rapid creation of serverless applications, it has many more features covering different areas of application.

Including, but not limited to:

• Lambdas auto-warming — Kotless creates schedulers to execute warming sequences to never leave your lambdas cold. As a result, applications under moderate load are not vulnerable to cold-start problem.
• Permissions management — you can declare which permissions to which AWS resources are required for application via annotations on Kotlin functions, classes or objects. Permissions will be granted automatically.
• Static resources — Kotless will deploy static resources to S3 and set up CDN for them. It may greatly improve the response time of your application and is supported by all DSLs.
• Scheduled events — Kotless sets up timers to execute @Scheduled jobs on schedule;
• Terraform extensions — Kotless-generated code can be extended by custom Terraform code;

Kotless is in active development, so we are currently working on extending this list with such features as:

• Support of other clouds — Kotless is based on a cloud-agnostic schema, so we are working on support of other clouds.
• Support of multiplatform applications — Kotless will not use any platform-specific libraries to give you a choice of a Lambda runtime (JVM/JS/Native).
• Versioned deployment — Kotless will be able to deploy several versions of the application and maintain one of them as active.
• Implicit permissions granting — Kotless will be able to deduce permissions from AWS SDK function calls.
• Events handlers support — Kotless will generate events subscriptions for properly annotated events handlers.

Examples

Any explanation becomes much better with a proper example.

In the repository's examples folder, you can find example projects built with Kotless DSL:

• kotless/site — a site about Kotless written with Kotless DSL (site.kotless.io). This example demonstrates @StaticGet and @Get (static and dynamic routes) usage, as well as Link API.
• kotless/shortener — a simple URL shortener written with Kotless DSL (short.kotless.io). This example demonstrates @Get ( dynamic routes), @Scheduled (scheduled lambdas), Permissions API (for DynamoDB access), and Terraform extensions.

Similar examples exist for Ktor:

• ktor/site — a site about Kotless written with Ktor (ktor.site.kotless.io). This example demonstrates static {...} and routing {...} usage.
• ktor/shortener — a simple URL shortener written with Ktor (ktor.short.kotless.io). This example demonstrates routing { ... } (dynamic routes), Permissions API (for DynamoDB access), and Terraform extensions.

And for Spring Boot:

• spring/site — a site about Kotless written with Spring Boot (spring.site.kotless.io). This example demonstrates usage of statics and @RestController.
• spring/shortener — a simple URL shortener written with Spring Boot (spring.short.kotless.io). This example demonstrates usage of @RestController (dynamic routes), Permissions API (for DynamoDB access), and Terraform extensions.

Want to know more?

You may take a look at Wiki where the client documentation on Kotless is located.

Apart from that, the Kotless code itself is widely documented, and you can take a look into its interfaces to get to know Kotless better.

You may ask questions and participate in discussions on #kotless channel in KotlinLang slack.

Acknowledgements

Special thanks to:

• Alexandra Pavlova (aka sunalex) for our beautiful logo;
• Yaroslav Golubev for help with documentation;
• Gregor Billing for help with the Gradle plugin and more.

Download Details:

Author: JetBrains
Source Code: https://github.com/JetBrains/kotless 
License: Apache-2.0 license

#serverless #kotlin #devops #lambda 

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Kotless: Kotlin Serverless Framework

Kotless

Kotless stands for Kotlin serverless framework.

Its focus lies in reducing the routine of serverless deployment creation by generating it straight from the code of the application itself.

So, simply speaking, Kotless gives you one magic button to deploy your Web application as a serverless application on AWS and Azure!

Kotless consists of two main parts:

• DSL provides a way of defining serverless applications. There are three DSLs supported:
  • Kotless DSL — Kotless own DSL that provides annotations to declare routing, scheduled events, etc.
  • Ktor — Ktor engine that is introspected by Kotless. Use standard Ktor syntax and Kotless will generate deployment.
  • Spring Boot — Spring Boot serverless container that is introspected by Kotless. Use standard Spring syntax and Kotless will generate deployment.
• Kotless Gradle Plugin provides a way of deploying serverless application. For that, it:
  • performs the tasks of generating Terraform code from the application code and, subsequently, deploying it to AWS or Azure;
  • runs application locally, emulates the AWS environment (if necessary) and provides the possibility for IDE debugging.

One of the key features of Kotless is its ability to embed into existing applications. Kotless makes super easy deployment of existing Spring and Ktor applications to AWS and Microsoft Azure serverless platforms.

Getting started

Setting up project

Kotless uses Gradle to wrap around the existing building process and insert the deployment into it.

Consider using one of the latest versions of Gradle, starting with the 7.2 version.

Basically, if you already use Gradle, you only need to do two things.

Firstly, set up the Kotless Gradle plugin.

You will have to tell Gradle where to find the plugin by editing settings.gradle.kts:

pluginManagement {
    resolutionStrategy {
        this.eachPlugin {
            if (requested.id.id == "io.kotless") {
                useModule("io.kotless:gradle:${this.requested.version}")
            }
        }
    }

    repositories {
        maven(url = uri("https://packages.jetbrains.team/maven/p/ktls/maven"))
        gradlePluginPortal()
        mavenCentral()
    }
}

And apply the plugin:

//Imports are necessary, for this example
import io.kotless.plugin.gradle.dsl.Webapp.Route53
import io.kotless.plugin.gradle.dsl.kotless

//Group may be used by Kotless DSL to reduce the number of introspected classes by package
//So, don't forget to set it
group = "org.example"
version = "0.1.0"


plugins {
    //Version of Kotlin should be 1.3.72+
    kotlin("jvm") version "1.5.31" apply true

    id("io.kotless") version "0.2.0" apply true
}

Secondly, add Kotless DSL (or Ktor, or Spring Boot) as a library to your application:

repositories {
    mavenCentral()
    //Kotless repository
    maven(url = uri("https://packages.jetbrains.team/maven/p/ktls/maven"))
}

dependencies {
    implementation("io.kotless", "kotless-lang", "0.2.0")
    implementation("io.kotless", "kotless-lang-aws", "0.2.0")
//    if you want to deploy to Microsoft Azure, just replace -aws with -azure    
//    implementation("io.kotless", "ktor-lang-azure", "0.2.0")


    //or for Ktor (Note, that `ktor-lang` depends on Ktor version 1.5.0)
    //implementation("io.kotless", "ktor-lang", "0.2.0")
    //implementation("io.kotless", "ktor-lang-aws", "0.2.0")
    //implementation("io.kotless", "ktor-lang-azure", "0.2.0")

    //or for Spring Boot (Note, that `spring-boot-lang` depends on Spring Boot version 2.3.0.RELEASE)
    //implementation("io.kotless", "spring-boot-lang", "0.2.0")
    //implementation("io.kotless", "spring-boot-lang-aws", "0.2.0")
    //implementation("io.kotless", "spring-boot-lang-azure", "0.2.0")
}

Please note that if you use Ktor or Spring Boot you will need to replace existing in your project dependency with a special Kotless *-lang dependency. Also, after that you will need to align version of dependent libraries (like Spring Security) with version bundled in *-lang (see this paragraph)

This gives you access to DSL interfaces in your code and sets up a Lambda dispatcher inside your application.

Deploying to the cloud

Depending on a use case, you may want to deploy application either in an AWS or Microsoft Azure.

Note, that if you even don't have a cloud account, you can still use Kotless locally to run and debug your application -- just use local Gradle task.

Deploying to AWS

If you don't have an AWS account, you can create it following simple instruction by Hadi Hariri.

If you have an AWS account and want to perform the real deployment — let's set up everything for it! It's rather simple:

kotless {
    config {

        aws {
            storage {
                bucket = "kotless.s3.example.com"
            }

            profile = "example"
            region = "eu-west-1"
        }
    }

    webapp {
        dns("kotless", "example.com")
    }
}

Here we set up the config of Kotless itself:

• the bucket, which will be used to store lambdas and configs;
• Terraform configuration with the name of the profile to access AWS.

Then we set up a specific application to deploy:

• Route53 alias for the resulting application (you need to pre-create an ACM certificate for the DNS record).

And that's the whole setup!

Deploying to Azure

Deployment to Microsoft Azure is also pretty straightforward and simple:

kotless {
    config {
        azure {
            storage {
                storageAccount = "your-storage-account"
                container = "container-which-kotless-would-use"
            }

            terraform {
                backend {
                    resourceGroup = "your-resource-group"
                }
            }
        }
    }

    webapp {
        dns("kotless", "example.com")
    }
}

Here we set up the config of Kotless itself:

• the storage, which will be used to store lambdas and configs;
• Terraform configuration with the name of the profile to access Azure.

Then we set up a specific application to deploy:

• Azure DNS alias for the resulting application (you need to pre-create certificate for the DNS record).

And that's the whole setup!

Creating application

Now you can create your first serverless application with Kotless DSL:

@Get("/")
fun main() = "Hello world!"

Or with Ktor:

class Server : Kotless() {
    override fun prepare(app: Application) {
        app.routing {
            get("/") {
                call.respondText { "Hello World!" }
            }
        }
    }
}

Or with Spring Boot:

@SpringBootApplication
open class Application : Kotless() {
    override val bootKlass: KClass<*> = this::class
}

@RestController
object Pages {
    @GetMapping("/")
    fun main() = "Hello World!"
}

Local start

Kotless-based applications can start locally as an HTTP server. This functionality is supported by all DSLs.

Moreover, Kotless local start may spin up an AWS emulation (docker required). Just instantiate your AWS service client using override for Kotless local starts:

val client = AmazonDynamoDBClientBuilder.standard().withKotlessLocal(AwsResource.DynamoDB).build()

And enable it in Gradle:

kotless {
    //<...>
    extensions {
        local {
            //enables AWS emulation (disabled by default)
            useAWSEmulation = true
        }
    }
}

During the local run, LocalStack will be started and all clients will be pointed to its endpoint automatically.

Local start functionality does not require any access to cloud provider, so you may check how your application behaves without an AWS account. Also, it gives you the possibility to debug your application locally from your IDE.

Integration with existing applications

Kotless is able to deploy existing Spring Boot or Ktor application to AWS serverless platform. To do it, you'll need to set up a plugin and replace existing dependency with the appropriate Kotless DSL.

For Ktor, you should replace existing engine ( e.g. implementation("io.ktor", "ktor-server-netty", "1.5.0")) with implementation("io.kotless", "ktor-lang", "0.1.6"). Note that this dependency bundles Ktor of version 1.5.0, so you may need to upgrade other Ktor libraries (like ktor-html-builder) to this version.

For Spring Boot you should replace the starter you use ( e.g. implementation("org.springframework.boot", "spring-boot-starter-web", "2.3.0.RELASE)) with implementation("io.kotless", "spring-boot-lang", "0.1.6"). Note that this dependency bundles Spring Boot of version 2.4.2, so you also may need to upgrade other Spring Boot libraries to this version.

Once it is done, you may hit deploy task and make your application serverless. Note, that you will still be able to run application locally via local Gradle task.

Advanced features

While Kotless can be used as a framework for the rapid creation of serverless applications, it has many more features covering different areas of application.

Including, but not limited to:

• Lambdas auto-warming — Kotless creates schedulers to execute warming sequences to never leave your lambdas cold. As a result, applications under moderate load are not vulnerable to cold-start problem.
• Permissions management — you can declare which permissions to which AWS resources are required for application via annotations on Kotlin functions, classes or objects. Permissions will be granted automatically.
• Static resources — Kotless will deploy static resources to S3 and set up CDN for them. It may greatly improve the response time of your application and is supported by all DSLs.
• Scheduled events — Kotless sets up timers to execute @Scheduled jobs on schedule;
• Terraform extensions — Kotless-generated code can be extended by custom Terraform code;

Kotless is in active development, so we are currently working on extending this list with such features as:

• Support of other clouds — Kotless is based on a cloud-agnostic schema, so we are working on support of other clouds.
• Support of multiplatform applications — Kotless will not use any platform-specific libraries to give you a choice of a Lambda runtime (JVM/JS/Native).
• Versioned deployment — Kotless will be able to deploy several versions of the application and maintain one of them as active.
• Implicit permissions granting — Kotless will be able to deduce permissions from AWS SDK function calls.
• Events handlers support — Kotless will generate events subscriptions for properly annotated events handlers.

Examples

Any explanation becomes much better with a proper example.

In the repository's examples folder, you can find example projects built with Kotless DSL:

• kotless/site — a site about Kotless written with Kotless DSL (site.kotless.io). This example demonstrates @StaticGet and @Get (static and dynamic routes) usage, as well as Link API.
• kotless/shortener — a simple URL shortener written with Kotless DSL (short.kotless.io). This example demonstrates @Get ( dynamic routes), @Scheduled (scheduled lambdas), Permissions API (for DynamoDB access), and Terraform extensions.

Similar examples exist for Ktor:

• ktor/site — a site about Kotless written with Ktor (ktor.site.kotless.io). This example demonstrates static {...} and routing {...} usage.
• ktor/shortener — a simple URL shortener written with Ktor (ktor.short.kotless.io). This example demonstrates routing { ... } (dynamic routes), Permissions API (for DynamoDB access), and Terraform extensions.

And for Spring Boot:

• spring/site — a site about Kotless written with Spring Boot (spring.site.kotless.io). This example demonstrates usage of statics and @RestController.
• spring/shortener — a simple URL shortener written with Spring Boot (spring.short.kotless.io). This example demonstrates usage of @RestController (dynamic routes), Permissions API (for DynamoDB access), and Terraform extensions.

Want to know more?

You may take a look at Wiki where the client documentation on Kotless is located.

Apart from that, the Kotless code itself is widely documented, and you can take a look into its interfaces to get to know Kotless better.

You may ask questions and participate in discussions on #kotless channel in KotlinLang slack.

Acknowledgements

Special thanks to:

• Alexandra Pavlova (aka sunalex) for our beautiful logo;
• Yaroslav Golubev for help with documentation;
• Gregor Billing for help with the Gradle plugin and more.

Download Details:

Author: JetBrains
Source Code: https://github.com/JetBrains/kotless 
License: Apache-2.0 license

#serverless #kotlin #devops #lambda 

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Serverless Plugin for Microservice Code Management and Deployment

Serverless M

Serverless M (or Serverless Modular) is a plugin for the serverless framework. This plugins helps you in managing multiple serverless projects with a single serverless.yml file. This plugin gives you a super charged CLI options that you can use to create new features, build them in a single file and deploy them all in parallel

Currently this plugin is tested for the below stack only

• AWS
• NodeJS λ
• Rest API (You can use other events as well)

Prerequisites

Make sure you have the serverless CLI installed

# Install serverless globally
$ npm install serverless -g

Getting Started

To start the serverless modular project locally you can either start with es5 or es6 templates or add it as a plugin

ES6 Template install

# Step 1. Download the template
$ sls create --template-url https://github.com/aa2kb/serverless-modular/tree/master/template/modular-es6 --path myModularService

# Step 2. Change directory
$ cd myModularService

# Step 3. Create a package.json file
$ npm init

# Step 3. Install dependencies
$ npm i serverless-modular serverless-webpack webpack --save-dev

ES5 Template install

# Step 1. Download the template
$ sls create --template-url https://github.com/aa2kb/serverless-modular/tree/master/template/modular-es5 --path myModularService

# Step 2. Change directory
$ cd myModularService

# Step 3. Create a package.json file
$ npm init

# Step 3. Install dependencies
$ npm i serverless-modular --save-dev

If you dont want to use the templates above you can just add in your existing project

Adding it as plugin

plugins:
  - serverless-modular

Now you are all done to start building your serverless modular functions

API Reference

The serverless CLI can be accessed by

# Serverless Modular CLI
$ serverless modular

# shorthand
$ sls m

Serverless Modular CLI is based on 4 main commands

• sls m init
• sls m feature
• sls m function
• sls m build
• sls m deploy

init command

sls m init

The serverless init command helps in creating a basic .gitignore that is useful for serverless modular.

The basic .gitignore for serverless modular looks like this

#node_modules
node_modules

#sm main functions
sm.functions.yml

#serverless file generated by build
src/**/serverless.yml

#main serverless directories generated for sls deploy
.serverless

#feature serverless directories generated sls deploy
src/**/.serverless

#serverless logs file generated for main sls deploy
.sm.log

#serverless logs file generated for feature sls deploy
src/**/.sm.log

#Webpack config copied in each feature
src/**/webpack.config.js

---

feature command

The feature command helps in building new features for your project

options (feature Command)

This command comes with three options

--name: Specify the name you want for your feature

--remove: set value to true if you want to remove the feature

--basePath: Specify the basepath you want for your feature, this base path should be unique for all features. helps in running offline with offline plugin and for API Gateway

options	shortcut	required	values	default value
--name	-n	✅	string	N/A
--remove	-r	❎	true, false	false
--basePath	-p	❎	string	same as name

Examples (feature Command)

Creating a basic feature

# Creating a jedi feature
$ sls m feature -n jedi

Creating a feature with different base path

# A feature with different base path
$ sls m feature -n jedi -p tatooine

Deleting a feature

# Anakin is going to delete the jedi feature
$ sls m feature -n jedi -r true

---

function command

The function command helps in adding new function to a feature

options (function Command)

This command comes with four options

--name: Specify the name you want for your function

--feature: Specify the name of the existing feature

--path: Specify the path for HTTP endpoint helps in running offline with offline plugin and for API Gateway

--method: Specify the path for HTTP method helps in running offline with offline plugin and for API Gateway

options	shortcut	required	values	default value
--name	-n	✅	string	N/A
--feature	-f	✅	string	N/A
--path	-p	❎	string	same as name
--method	-m	❎	string	'GET'

Examples (function Command)

Creating a basic function

# Creating a cloak function for jedi feature
$ sls m function -n cloak -f jedi

Creating a basic function with different path and method

# Creating a cloak function for jedi feature with custom path and HTTP method
$ sls m function -n cloak -f jedi -p powers -m POST

---

build command

The build command helps in building the project for local or global scope

options (build Command)

This command comes with four options

--scope: Specify the scope of the build, use this with "--feature" tag

--feature: Specify the name of the existing feature you want to build

options	shortcut	required	values	default value
--scope	-s	❎	string	local
--feature	-f	❎	string	N/A

Saving build Config in serverless.yml

You can also save config in serverless.yml file

custom:
  smConfig:
    build:
      scope: local

Examples (build Command)

all feature build (local scope)

# Building all local features
$ sls m build

Single feature build (local scope)

# Building a single feature
$ sls m build -f jedi -s local

All features build global scope

# Building all features with global scope
$ sls m build -s global

deploy command

The deploy command helps in deploying serverless projects to AWS (it uses sls deploy command)

options (deploy Command)

This command comes with four options

--sm-parallel: Specify if you want to deploy parallel (will only run in parallel when doing multiple deployments)

--sm-scope: Specify if you want to deploy local features or global

--sm-features: Specify the local features you want to deploy (comma separated if multiple)

options	shortcut	required	values	default value
--sm-parallel	❎	❎	true, false	true
--sm-scope	❎	❎	local, global	local
--sm-features	❎	❎	string	N/A
--sm-ignore-build	❎	❎	string	false

Saving deploy Config in serverless.yml

You can also save config in serverless.yml file

custom:
  smConfig:
    deploy:
      scope: local
      parallel: true
      ignoreBuild: true

Examples (deploy Command)

Deploy all features locally

# deploy all local features
$ sls m deploy

Deploy all features globally

# deploy all global features
$ sls m deploy --sm-scope global

Deploy single feature

# deploy all global features
$ sls m deploy --sm-features jedi

Deploy Multiple features

# deploy all global features
$ sls m deploy --sm-features jedi,sith,dark_side

Deploy Multiple features in sequence

# deploy all global features
$ sls m deploy  --sm-features jedi,sith,dark_side --sm-parallel false

---

Author: aa2kb
Source Code: https://github.com/aa2kb/serverless-modular 
License: MIT license

#serverless #aws #node #lambda 

Serverless Applications - Pros and Cons to Help Businesses Decide - Prismetric

In the past few years, especially after Amazon Web Services (AWS) introduced its Lambda platform, serverless architecture became the business realm’s buzzword. The increasing popularity of serverless applications saw market leaders like Netflix, Airbnb, Nike, etc., adopting the serverless architecture to handle their backend functions better. Moreover, serverless architecture’s market size is expected to reach a whopping $9.17 billion by the year 2023.

Global_Serverless_Architecture_Market_2019-2023

Why use serverless computing?
As a business it is best to approach a professional mobile app development company to build apps that are deployed on various servers; nevertheless, businesses should understand that the benefits of the serverless applications lie in the possibility it promises ideal business implementations and not in the hype created by cloud vendors. With the serverless architecture, the developers can easily code arbitrary codes on-demand without worrying about the underlying hardware.

But as is the case with all game-changing trends, many businesses opt for serverless applications just for the sake of being up-to-date with their peers without thinking about the actual need of their business.

The serverless applications work well with stateless use cases, the cases which execute cleanly and give the next operation in a sequence. On the other hand, the serverless architecture is not fit for predictable applications where there is a lot of reading and writing in the backend system.

Another benefit of working with the serverless software architecture is that the third-party service provider will charge based on the total number of requests. As the number of requests increases, the charge is bound to increase, but then it will cost significantly less than a dedicated IT infrastructure.

Defining serverless software architecture
In serverless software architecture, the application logic is implemented in an environment where operating systems, servers, or virtual machines are not visible. Although where the application logic is executed is running on any operating system which uses physical servers. But the difference here is that managing the infrastructure is the soul of the service provider and the mobile app developer focuses only on writing the codes.

There are two different approaches when it comes to serverless applications. They are

Backend as a service (BaaS)
Function as a service (FaaS)

1. Backend as a service (BaaS)
   The basic required functionality of the growing number of third party services is to provide server-side logic and maintain their internal state. This requirement has led to applications that do not have server-side logic or any application-specific logic. Thus they depend on third-party services for everything.

Moreover, other examples of third-party services are Autho, AWS Cognito (authentication as a service), Amazon Kinesis, Keen IO (analytics as a service), and many more.

2. Function as a Service (FaaS)
   FaaS is the modern alternative to traditional architecture when the application still requires server-side logic. With Function as a Service, the developer can focus on implementing stateless functions triggered by events and can communicate efficiently with the external world.

FaaS serverless architecture is majorly used with microservices architecture as it renders everything to the organization. AWS Lambda, Google Cloud functions, etc., are some of the examples of FaaS implementation.

Pros of Serverless applications
There are specific ways in which serverless applications can redefine the way business is done in the modern age and has some distinct advantages over the traditional could platforms. Here are a few –

🔹 Highly Scalable
The flexible nature of the serverless architecture makes it ideal for scaling the applications. The serverless application’s benefit is that it allows the vendor to run each of the functions in separate containers, allowing optimizing them automatically and effectively. Moreover, unlike in the traditional cloud, one doesn’t need to purchase a certain number of resources in serverless applications and can be as flexible as possible.

🔹 Cost-Effective
As the organizations don’t need to spend hundreds and thousands of dollars on hardware, they don’t need to pay anything to the engineers to maintain the hardware. The serverless application’s pricing model is execution based as the organization is charged according to the executions they have made.

The company that uses the serverless applications is allotted a specific amount of time, and the pricing of the execution depends on the memory required. Different types of costs like presence detection, access authorization, image processing, etc., associated with a physical or virtual server is completely eliminated with the serverless applications.

🔹 Focuses on user experience
As the companies don’t always think about maintaining the servers, it allows them to focus on more productive things like developing and improving customer service features. A recent survey says that about 56% of the users are either using or planning to use the serverless applications in the coming six months.

Moreover, as the companies would save money with serverless apps as they don’t have to maintain any hardware system, it can be then utilized to enhance the level of customer service and features of the apps.

🔹 Ease of migration
It is easy to get started with serverless applications by porting individual features and operate them as on-demand events. For example, in a CMS, a video plugin requires transcoding video for different formats and bitrates. If the organization wished to do this with a WordPress server, it might not be a good fit as it would require resources dedicated to serving pages rather than encoding the video.

Moreover, the benefits of serverless applications can be used optimally to handle metadata encoding and creation. Similarly, serverless apps can be used in other plugins that are often prone to critical vulnerabilities.

Cons of serverless applications
Despite having some clear benefits, serverless applications are not specific for every single use case. We have listed the top things that an organization should keep in mind while opting for serverless applications.

🔹 Complete dependence on third-party vendor
In the realm of serverless applications, the third-party vendor is the king, and the organizations have no options but to play according to their rules. For example, if an application is set in Lambda, it is not easy to port it into Azure. The same is the case for coding languages. In present times, only Python developers and Node.js developers have the luxury to choose between existing serverless options.

Therefore, if you are planning to consider serverless applications for your next project, make sure that your vendor has everything needed to complete the project.

🔹 Challenges in debugging with traditional tools
It isn’t easy to perform debugging, especially for large enterprise applications that include various individual functions. Serverless applications use traditional tools and thus provide no option to attach a debugger in the public cloud. The organization can either do the debugging process locally or use logging for the same purpose. In addition to this, the DevOps tools in the serverless application do not support the idea of quickly deploying small bits of codes into running applications.

#serverless-application #serverless #serverless-computing #serverless-architeture #serverless-application-prosand-cons

Serverless Framework: Deploy on Scaleway Functions

Serverless Framework: Deploy on Scaleway Functions

The Scaleway functions plugin for Serverless Framework allows users to deploy their functions and containers to Scaleway Functions with a simple serverless deploy.

Serverless Framework handles everything from creating namespaces to function/code deployment by calling APIs endpoint under the hood.

Requirements

• Install node.js
• Install Serverless CLI (npm install serverless -g)

Let's work into ~/my-srvless-projects

# mkdir ~/my-srvless-projects
# cd ~/my-srvless-projects

Create a Project

The easiest way to create a project is to use one of our templates. The list of templates is here

Let's use python3

serverless create --template-url https://github.com/scaleway/serverless-scaleway-functions/tree/master/examples/python3 --path myService

Once it's done, we can install mandatory node packages used by serverless

cd mypython3functions
npm i

Note: these packages are only used by serverless, they are not shipped with your functions.

Configure your functions

Your functions are defined in the serverless.yml file created:

service: scaleway-python3
configValidationMode: off

useDotenv: true

provider:
  name: scaleway
  runtime: python310
  # Global Environment variables - used in every functions
  env:
    test: test
  # Storing credentials in this file is strongly not recommanded for security concerns, please refer to README.md about best practices
  scwToken: <scw-token>
  scwProject: <scw-project-id>
  # region in which the deployment will happen (default: fr-par)
  scwRegion: <scw-region>

plugins:
  - serverless-scaleway-functions
  
package:
  patterns:
    - '!node_modules/**'
    - '!.gitignore'
    - '!.git/**'

functions:
  first:
    handler: handler.py
    # Local environment variables - used only in given function
    env:
      local: local

Note: provider.name and plugins MUST NOT be changed, they enable us to use the scaleway provider

This file contains the configuration of one namespace containing one or more functions (in this example, only one) of the same runtime (here python3)

The different parameters are:

• service: your namespace name
• useDotenv: Load environment variables from .env files (default: false), read Security and secret management
• configValidationMode: Configuration validation: 'error' (fatal error), 'warn' (logged to the output) or 'off' (default: warn)
• provider.runtime: the runtime of your functions (check the supported runtimes above)
• provider.env: environment variables attached to your namespace are injected to all your namespace functions
• provider.secret: secret environment variables attached to your namespace are injected to all your namespace functions, see this example project
• scwToken: Scaleway token you got in prerequisites
• scwProject: Scaleway org id you got in prerequisites
• scwRegion: Scaleway region in which the deployment will take place (default: fr-par)
• package.patterns: usually, you don't need to configure it. Enable to include/exclude directories to/from the deployment
• functions: Configure of your fonctions. It's a yml dictionary, with the key being the function name
  • handler (Required): file or function which will be executed. See the next section for runtime specific handlers
  • env (Optional): environment variables specific for the current function
  • secret (Optional): secret environment variables specific for the current function, see this example project
  • minScale (Optional): how many function instances we keep running (default: 0)
  • maxScale (Optional): maximum number of instances this function can scale to (default: 20)
  • memoryLimit: ram allocated to the function instances. See the introduction for the list of supported values
  • timeout: is the maximum duration in seconds that the request will wait to be served before it times out (default: 300 seconds)
  • runtime: (Optional) runtime of the function, if you need to deploy multiple functions with different runtimes in your Serverless Project. If absent, provider.runtime will be used to deploy the function, see this example project.
  • events (Optional): List of events to trigger your functions (e.g, trigger a function based on a schedule with CRONJobs). See events section below
  • custom_domains (Optional): List of custom domains, refer to Custom Domain Documentation

Security and secret management

You configuration file may contains sensitive data, your project ID and your Token must not be shared and must not be commited in VCS.

To keep your informations safe and be able to share or commit your serverles.yml file you should remove your credentials from the file. Then you can :

• use global environment variables
• use .env file and keep it secret

To use .env file you can modify your serverless.yml file as following :

# This will alow the plugin to read your .env file
useDotenv: true

provider:
  name: scaleway
  runtime: node16

  scwToken: ${env:SCW_SECRET_KEY}
  scwProject: ${env:SCW_DEFAULT_PROJECT_ID}
  scwRegion: ${env:SCW_REGION}

And then create a .env file next to your serverless.yml file, containing following values :

SCW_SECRET_KEY=XXX
SCW_DEFAULT_PROJECT_ID=XXX
SCW_REGION=fr-par

You can use this pattern to hide your secrets (for example a connexion string to a database or a S3 bucket).

Functions Handler

Based on the chosen runtime, the handler variable on function might vary.

Using ES Modules

Node has two module systems: CommonJS modules and ECMAScript (ES) modules. By default, Node treats your code files as CommonJS modules, however ES modules have also been available since the release of node16 runtime on Scaleway Serverless Functions. ES modules give you a more modern way to re-use your code.

According to the official documentation, to use ES modules you can specify the module type in package.json, as in the following example:

  ...
  "type": "module",
  ...

This then enables you to write your code for ES modules:

export {handle};

function handle (event, context, cb) {
    return {
        body: process.version,
        statusCode: 200,
    };
};

The use of ES modules is encouraged, since they are more efficient and make setup and debugging much easier.

Note that using "type": "module" or "type": "commonjs" in your package.json will enable/disable some features in Node runtime. For a comprehensive list of differences, please refer to the official documentation, the following is a summary only:

• commonjs is used as default value
• commonjs allows you to use require/module.exports (synchronous code loading, it basically copies all file contents)
• module allows you to use import/export ES6 instructions (asynchronous loading, more optimized as it imports only the pieces of code you need)

Node

Path to your handler file (from serverless.yml), omit ./, ../, and add the exported function to use as a handler :

- src
  - handlers
    - firstHandler.js  => module.exports.myFirstHandler = ...
    - secondHandler.js => module.exports.mySecondHandler = ...
- serverless.yml

In serverless.yml:

provider:
  # ...
  runtime: node16
functions:
  first:
    handler: src/handlers/firstHandler.myFirstHandler
  second:
    handler: src/handlers/secondHandler.mySecondHandler

Python

Similar to node, path to handler file src/testing/handler.py:

- src
  - handlers
    - firstHandler.py  => def my_first_handler
    - secondHandler.py => def my_second_handler
- serverless.yml

In serverless.yml:

provider:
  # ...
  runtime: python310 # or python37, python38, python39
functions:
  first:
    handler: src/handlers/firstHandler.my_first_handler
  second:
    handler: src/handlers/secondHandler.my_second_handler

Golang

Path to your handler's package, for example if I have the following structure:

- src
  - testing
    - handler.go -> package main in src/testing subdirectory
  - second
    - handler.go -> package main in src/second subdirectory
- serverless.yml
- handler.go -> package main at the root of project

Your serverless.yml functions should look something like this:

provider:
  # ...
  runtime: go118
functions:
  main:
    handler: "."
  testing:
    handler: src/testing
  second:
    handler: src/second

Events

With events, you may link your functions with specific triggers, which might include CRON Schedule (Time based), MQTT Queues (Publish on a topic will trigger the function), S3 Object update (Upload an object will trigger the function).

Note that we do not include HTTP triggers in our event types, as a HTTP endpoint is created for every function. Triggers are just a new way to trigger your Function, but you will always be able to execute your code via HTTP.

Here is a list of supported triggers on Scaleway Serverless, and the configuration parameters required to deploy them:

• schedule: Trigger your function based on CRON schedules
  • rate: CRON Schedule (UNIX Format) on which your function will be executed
  • input: key-value mapping to define arguments that will be passed into your function's event object during execution.

To link a Trigger to your function, you may define a key events in your function:

functions:
  handler: myHandler.handle
  events:
    # "events" is a list of triggers, the first key being the type of trigger.
    - schedule:
        # CRON Job Schedule (UNIX Format)
        rate: '1 * * * *'
        # Input variable are passed in your function's event during execution
        input:
          key: value
          key2: value2

You may link Events to your Containers too (See section Managing containers below for more informations on how to deploy containers):

custom:
  containers:
    mycontainer:
      directory: my-directory
      # Events key
      events:
        - schedule:
            rate: '1 * * * *'
            input:
              key: value
              key2: value2

You may refer to the follow examples:

• NodeJS with schedule trigger
• Container with Schedule Trigger

Custom domains

Custom domains allows users to use their own domains.

For domain configuration please Refer to Scaleway Documentation

Integration with serverless framework example :

functions:
  first:
    handler: handler.handle
    # Local environment variables - used only in given function
    env:
      local: local
    custom_domains:
      - func1.scaleway.com
      - func2.scaleway.com

Note As your domain must have a record to your function hostname, you should deploy your function once to read its hostname. Custom Domains configurations will be available after the first deploy.

Note: Serverless Framework will consider the configuration file as the source of truth.

If you create a domain with other tools (Scaleway's Console, CLI or API) you must refer created domain into your serverless configuration file. Otherwise it will be deleted as Serverless Framework will give the priority to its configuration.

Managing containers

Requirements: You need to have Docker installed to be able to build and push your image to your Scaleway registry.

You must define your containers inside the custom.containers field in your serverless.yml manifest. Each container must specify the relative path of its application directory (containing the Dockerfile, and all files related to the application to deploy):

custom:
  containers:
    mycontainer:
      directory: my-container-directory
      # port: 8080
      # Environment only available in this container 
      env:
        MY_VARIABLE: "my-value"

Here is an example of the files you should have, the directory containing your Dockerfile and scripts is my-container-directory.

.
├── my-container-directory
│   ├── Dockerfile
│   ├── requirements.txt
│   ├── server.py
│   └── (...)
├── node_modules
│   ├── serverless-scaleway-functions
│   └── (...)
├── package-lock.json
├── package.json
└── serverless.yml

Scaleway's platform will automatically inject a PORT environment variable on which your server should be listening for incoming traffic. By default, this PORT is 8080. You may change the port in your serverless.yml.

You may use the container example to getting started.

Logs

The serverless logs command lets you watch the logs of a specific function or container.

Pass the function or container name you want to fetch the logs for with --function:

serverless logs --function <function_or_container_name>

Info

serverless info command gives you informations your current deployement state in JSON format.

Documentation and useful Links

• Official Scaleway Serverless Functions Documentation
• Official Scaleway Serverless Containers Documentation
• Scaleway Functions Golang Runtime (you MUST use this library if you plan to develop with Golang).
• Serverless Framework documentation
• Scaleway Cloud Provider
• Examples/Templates for different runtimes on Scaleway Functions

Contributing

This plugin is mainly developed and maintained by Scaleway Serverless Team but you are free to open issues or discuss with us on our Community Slack Channels #serverless-containers and #serverless-functions.

Author: Scaleway
Source Code: https://github.com/scaleway/serverless-scaleway-functions 
License: MIT license

#serverless #function #aws #lambda