Serverless Ruby on AWS Lambda with the Jets framework

Originally published by PHIL NASH at

When AWS launched Lambda in 2014 there was no love for Ruby. Platforms like Python, Node.js, and Java started the serverless revolution for hosting and running functions in the cloud. At the end of 2018, support for Ruby was finally launched.

You can build with Ruby on Lambda using raw functions and Serverless Application Model (SAM) templates as described in the getting started guide for Ruby on Lambda, but Ruby is all about developer happiness and when the config file is longer than your program the process could be described as painful. Enter the Jets framework a framework that "leverages the power of Ruby to make serverless joyful for everyone."

From Rails to Jets

Jets combines the experience of building a Rails application with the ability to deploy to AWS Lambda and related services, including API GatewayS3, and DynamoDB. In this post we're going to see how to get started with Jets and deploy a Lambda powered Twilio application written in Ruby.

What we're building

To keep things simple we're going to build an SMS based app and to make it a bit more fun, we're going to include a little humour. When you text into the application it will respond with a bad joke. Sorry, misspelling there, I meant a dad joke, courtesy of the icanhazdadjoke API.

This application will show us how to get started with Jets, create controllers, actions and routes. We'll build a single endpoint that responds to HTTP requests from Twilio when a number receives an incoming SMS message. The endpoint will return TwiML with a random dad joke each time and hilarity will ensue.

What you'll need

To follow along with this project you'll need:

Got all that? Then let's get started.

Getting started

We'll start by installing the jets gem globally. On the command line type:

gem install jets

The Jets executable can then be used like Rails as a generator to initiate a new project and then run commands within the project. We'll create a new project now, but given our scope in this post we are going to limit it a little. First up, we're going to create the project in API mode as there's no need for HTML views in this application. That also avoids asset compilation with webpacker, which will save in our build time. We also don't need a database. Run the following command to create a new project:

jets new dad-jokes-sms --mode api --no-database

Once the generator has finished running, change into the dad-jokes-sms directory and run your application locally with the following command:

jets serve

Open up http://localhost:8888 and you will see a page that look like this:

If you see this page then your new Jets project is running successfully.

Our first Jets controller

Now we have a Jets application we can use the gem to generate parts of our application, from models and controllers to full scaffolds. For our application we're going to need a controller with one action. Generate it with the following:

jets generate controller Messages create

The generator will create and edit a number of files for us. We need to check the new routes so open config/routes.rb.

Jets generated a GET route, but Twilio webhooks make POST requests by default and I prefer to keep it that way. Set the application up to receive POST request webhooks on the /messages endpoint instead like so:

Jets.application.routes.draw do
  post 'messages', to: 'messages#create'
  root "jets/public#show"

  # The jets/public#show controller can serve static utf8 content out of the public folder.
  # Note, as part of the deploy process Jets uploads files in the public folder to s3
  # and serves them out of s3 directly. S3 is well suited to serve static assets.
  # More info here:
  any “*catchall”, to: “jets/public#show”

Now, let’s go write our controller action. Open app/controllers/messages_controller.rb and you will see one method for the create action. This is the action which will receive our Twilio webhook and respond with TwiML to send back a dad joke.

Fetching a dad joke

To send back a dad joke we need to make a call to the icanhazdadjoke API. Let’s write a quick private method we can use to achieve this.

We’ll use open-uri as it’s useful for making simple web requests (including downloading files and images). The API will respond with plain text if we ask it to, which saves us doing any parsing. Add the following to the MessagesController:

require ‘open-uri’

class MessagesController < ApplicationController
def create


def random_joke
open(‘’, { ‘Accept’ => ‘text/plain’ }).read

Now we’re ready to return our joke to Twilio as TwiML.

Returning TwiML

We’ll build up a response using the helpers from the twilio-ruby helper library. Open the Gemfile and add twilio-ruby:

source “

gem “jets”
gem “twilio-ruby”

On the command line, run bundle install to install the gem. Now in the create action instantiate a new TwiML response object, reply to the incoming message using the <Message> TwiML element and render the XML response, like so:

require ‘open-uri’

class MessagesController < ApplicationController
def create
twiml =
twiml.message body: random_joke
render xml: twiml.to_xml


def random_joke
open(‘’, { ‘Accept’ => ‘text/plain’ }).read

You can read more about how to use the twilio-ruby helper library for generating TwiML in the documentation.

We can run this locally to test we are getting the expected response. If you stopped the application, start it again with jets serve. Make a POST request to localhost:8888/messages using curl and you’ll see your joke, provided by icanhazdadjoke, in the TwiML response:

curl --data “” http://localhost:8888/messages
<?xml version=“1.0” encoding=“UTF-8”?>
<Message>What’s the advantage of living in Switzerland? Well, the flag is a big plus.</Message>

Great, our Jets application is working! Now to deploy it to AWS Lambda.

Are you ready to deploy? IAM!

To deploy our Jets application to AWS we first need to set up our project with credentials to allow it to access AWS services. A good practice here is to create a user that has the minimum number of permissions required to do everything it needs to. The Jets documentation describes the minimum permissions that our user will need. Within our AWS account we’re going to create a policy that contains these permissions and a new user that will be assigned the policy. We can then use that user’s credentials to deploy our application.

In your AWS console find the IAM service (or head straight to the IAM section).

Go to the Policies section and create a new policy.

Choose the JSON tab and enter the following JSON from the Jets documentation:

“Version”: “2012-10-17”,
“Statement”: [
“Effect”: “Allow”,
“Action”: [
“Resource”: [


Click through to review the policy and give it a name.

Save the policy. Now we need to create a new user and attach the policy to it, giving it the permissions to create the resources Jets needs to deploy. Open the Users section in the IAM console and create a new user.

Give the user a name and select Programmatic Access for the Access Type.

Click Next to choose the permissions for your new user. Choose Attach existing policies directly and filter for the name of the policy you just created. Select that policy and click Next.

Click Next until you reach the success page.

Save the Access key ID and Secret access key from the last screen. We’ll need them to deploy with. Now we’re ready to deploy.

Deploying to Lambda

On the command line enter:


Jets will use the credentials as environment variables to set up all the resources in your AWS account to run the application. It takes a little while, but when it is complete you will have a URL where your application is running.

You’ll notice this deployed to a “dev” environment. You can read more about how Jets handles environments in the documentation.

We can now test this URL using curl. Remember we use the route /messages so add that to the end of your API Gateway endpoint and make a POST request.

$ curl --data “” https://YOUR_API_GATEWAY_ENDPOINT/messages
<?xml version=“1.0” encoding=“UTF-8”?>
<Message>I knew a guy who collected candy canes, they were all in mint condition</Message>

Now to have dad jokes on hand at all times let’s hook this up to a Twilio number.

Dad jokes by SMS

Head into your Twilio console to your active phone numbers. If you already have a number you want to work with, edit it, otherwise buy a new number that can receive incoming SMS messages. In the field for A message comes in enter your application URL.

Save your number and send it a message. You should get a dad joke in response. Now, in celebration, go and tell someone nearby that joke and let me know whether they laugh or groan.

Serverless Comedy

In this post we’ve seen how to get started with Jets to write Ruby applications that you can deploy to AWS Lambda. You can see the full project on GitHub.

There’s a lot more to what Jets can help you accomplish, including responding to eventsstoring data in databases and even run your existing Rails application. Check out these articles from the Jets documentation for more on what you can do with Jets.

Originally published by PHIL NASH at


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What is GEEK

Buddha Community

Serverless Ruby on AWS Lambda with the Jets framework
Hermann  Frami

Hermann Frami


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)


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 --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 --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

  - 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


#sm main functions

#serverless file generated by build

#main serverless directories generated for sls deploy

#feature serverless directories generated sls deploy

#serverless logs file generated for main sls deploy

#serverless logs file generated for feature sls deploy

#Webpack config copied in each feature

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

optionsshortcutrequiredvaluesdefault value
--remove-rtrue, falsefalse
--basePath-pstringsame 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

optionsshortcutrequiredvaluesdefault value
--path-pstringsame as name

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

optionsshortcutrequiredvaluesdefault value

Saving build Config in serverless.yml

You can also save config in serverless.yml file

      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)

optionsshortcutrequiredvaluesdefault value
--sm-paralleltrue, falsetrue
--sm-scopelocal, globallocal

Saving deploy Config in serverless.yml

You can also save config in serverless.yml file

      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: 
License: MIT license

#serverless #aws #node #lambda 

Christa  Stehr

Christa Stehr


How To Unite AWS KMS with Serverless Application Model (SAM)

The Basics

AWS KMS is a Key Management Service that let you create Cryptographic keys that you can use to encrypt and decrypt data and also other keys. You can read more about it here.

Important points about Keys

Please note that the customer master keys(CMK) generated can only be used to encrypt small amount of data like passwords, RSA key. You can use AWS KMS CMKs to generate, encrypt, and decrypt data keys. However, AWS KMS does not store, manage, or track your data keys, or perform cryptographic operations with data keys.

You must use and manage data keys outside of AWS KMS. KMS API uses AWS KMS CMK in the encryption operations and they cannot accept more than 4 KB (4096 bytes) of data. To encrypt application data, use the server-side encryption features of an AWS service, or a client-side encryption library, such as the AWS Encryption SDK or the Amazon S3 encryption client.


We want to create signup and login forms for a website.

Passwords should be encrypted and stored in DynamoDB database.

What do we need?

  1. KMS key to encrypt and decrypt data
  2. DynamoDB table to store password.
  3. Lambda functions & APIs to process Login and Sign up forms.
  4. Sign up/ Login forms in HTML.

Lets Implement it as Serverless Application Model (SAM)!

Lets first create the Key that we will use to encrypt and decrypt password.

    Type: AWS::KMS::Key
      Description: CMK for encrypting and decrypting
        Version: '2012-10-17'
        Id: key-default-1
        - Sid: Enable IAM User Permissions
          Effect: Allow
            AWS: !Sub arn:aws:iam::${AWS::AccountId}:root
          Action: kms:*
          Resource: '*'
        - Sid: Allow administration of the key
          Effect: Allow
            AWS: !Sub arn:aws:iam::${AWS::AccountId}:user/${KeyAdmin}
          - kms:Create*
          - kms:Describe*
          - kms:Enable*
          - kms:List*
          - kms:Put*
          - kms:Update*
          - kms:Revoke*
          - kms:Disable*
          - kms:Get*
          - kms:Delete*
          - kms:ScheduleKeyDeletion
          - kms:CancelKeyDeletion
          Resource: '*'
        - Sid: Allow use of the key
          Effect: Allow
            AWS: !Sub arn:aws:iam::${AWS::AccountId}:user/${KeyUser}
          - kms:DescribeKey
          - kms:Encrypt
          - kms:Decrypt
          - kms:ReEncrypt*
          - kms:GenerateDataKey
          - kms:GenerateDataKeyWithoutPlaintext
          Resource: '*'

The important thing in above snippet is the KeyPolicy. KMS requires a Key Administrator and Key User. As a best practice your Key Administrator and Key User should be 2 separate user in your Organisation. We are allowing all permissions to the root users.

So if your key Administrator leaves the organisation, the root user will be able to delete this key. As you can see **KeyAdmin **can manage the key but not use it and KeyUser can only use the key. ${KeyAdmin} and **${KeyUser} **are parameters in the SAM template.

You would be asked to provide values for these parameters during SAM Deploy.

#aws #serverless #aws-sam #aws-key-management-service #aws-certification #aws-api-gateway #tutorial-for-beginners #aws-blogs

Hermann  Frami

Hermann Frami


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.


  • 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 --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

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

  - serverless-scaleway-functions
    - '!node_modules/**'
    - '!.gitignore'
    - '!.git/**'

    # Local environment variables - used only in given function
      local: local

Note: 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

  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 :


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)


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:

  # ...
  runtime: node16
    handler: src/handlers/firstHandler.myFirstHandler
    handler: src/handlers/secondHandler.mySecondHandler


Similar to node, path to handler file src/testing/

- src
  - handlers
    -  => def my_first_handler
    - => def my_second_handler
- serverless.yml

In serverless.yml:

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


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:

  # ...
  runtime: go118
    handler: "."
    handler: src/testing
    handler: src/second


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:

  handler: myHandler.handle
    # "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
          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):

      directory: my-directory
      # Events key
        - schedule:
            rate: '1 * * * *'
              key: value
              key2: value2

You may refer to the follow examples:

Custom domains

Custom domains allows users to use their own domains.

For domain configuration please Refer to Scaleway Documentation

Integration with serverless framework example :

    handler: handler.handle
    # Local environment variables - used only in given function
      local: local

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):

      directory: my-container-directory
      # port: 8080
      # Environment only available in this container 
        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
│   ├──
│   └── (...)
├── 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.


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>


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

Documentation and useful Links


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: 
License: MIT license

#serverless #function #aws #lambda 

Desmond  Gerber

Desmond Gerber


How to Fix GLIBCXX Errors From Serverless Framework and AWS Lambda

While I haven’t done too much with Serverless Framework and Functions as a Service (Faas) recently, I did in the past and it isn’t something that I’ve forgotten. In the past I demonstrated how to deploy Node.js functions to Amazon Web Services (AWS) Lambda that contain native dependencies. While not a necessity for all Lambda functions, it is for functions that use libraries for specific operating systems and architectures. For example, my previous article titled, Use AWS Lambda and API Gateway with Node.js and Couchbase NoSQL, fell into this situation. Making use of an EC2 instance or a Docker container with Amazon Linux will help most of the time, but there are scenarios where a little bit extra must be done to accomplish the task.

In certain circumstances everything may package and deploy correctly, but still throw errors. For example, a common error is around libstdc++ and a version of GLIBCXX not being found.

In this tutorial we’re going to see how to resolve library errors that might not be caught in a typical packaging and deployment scenario with Serverless Framework and AWS Lambda.

For this example we’re going to reference my previous Couchbase example, but we’re not going to think too hard on the code. Instead, lets imagine that we’ve packaged the functions and deployed them as I recommended previously.

When we try to run our functions, either from an API Gateway endpoint or directly as an invocation, we might come across this error in our CloudWatch logs:

module initialization error: Error
    at Object.Module._extensions..node (module.js:681:18)
    at Module.load (module.js:565:32)
    at tryModuleLoad (module.js:505:12)
    at Function.Module._load (module.js:497:3)
    at Module.require (module.js:596:17)
    at require (internal/module.js:11:18)
    at bindings (/var/task/node_modules/bindings/bindings.js:84:48)
    at Object.<anonymous> (/var/task/node_modules/couchbase/lib/binding.js:213:36)
    at Module._compile (module.js:652:30)
    at Object.Module._extensions..js (module.js:663:10)

Great, the above error is not at all helpful to us. For whatever reason a module didn’t initialize and all we can see is that Couchbase was involved somehow. To be fair, I was getting this error when I looked at my CloudWatch dashboard, not when I tried to invoke the functions directly.

If you search the web for the error in question, you get a lot of results that indicate that you probably have native dependencies and you just need to build them using the same flavor and architecture of Linux that AWS Lambda uses. Well, that is great, but if you followed my previous tutorials you’ll know that we downloaded the amazonlinux image on Docker and downloaded our Node.js modules through that container deployment.

So we have to dig a bit deeper into the problem.

If we invoke the function directly, without API Gateway or some other trigger, we get a new error:

    "errorMessage": "/var/lang/bin/../lib/ version `GLIBCXX_3.4.21' not found (required by /var/task/node_modules/couchbase/build/Release/couchbase_impl.node)",
    "errorType": "Error",
    "stackTrace": [
        "Module.load (module.js:565:32)",
        "tryModuleLoad (module.js:505:12)",
        "Function.Module._load (module.js:497:3)",
        "Module.require (module.js:596:17)",
        "require (internal/module.js:11:18)",
        "bindings (/var/task/node_modules/bindings/bindings.js:84:48)",
        "Object.<anonymous> (/var/task/node_modules/couchbase/lib/binding.js:213:36)",
        "Module._compile (module.js:652:30)",
        "Object.Module._extensions..js (module.js:663:10)"

To invoke your function you can either use serverless invoke -f <function-name> with the Serverless CLI, or through the AWS Lambda dashboard. In this new error, we have information regarding libstdc++ and GLIBCXX. If you’re like me, you have absolutely no idea what that is. While I develop using numerous technologies, C and C++ is not one of them.

After doing some searching around the web, people are saying numerous things about what the problem could be. Here is an aggregate of some of the things that came back in my search:

  • The dependencies were not compiled using the correct Amazon Linux flavor.
  • The version of libstdc++ in the Docker image didn’t match the actual version on AWS Lambda.
  • The libstdc++ application or library wasn’t truly installed.

So what do I do with this information?

In the first scenario, I’m pretty sure my dependencies were installed using the correct Amazon Linux flavor. I had mapped my project directory with Docker and installed my Node.js modules from within my Docker container. Life should be good because it worked the last time I tried about a year ago. In the second scenario, I have no idea how to check what version of libstdc++ I’m using in my Docker container or on AWS Lambda. I’m only hoping that the versions are matched up. In the third scenario, I did find this file in the container, so I was hoping that was enough.

If you can believe it, my Couchbase Node.js SDK was not compiled using the correct Amazon Linux flavor. When I downloaded this dependency from within my Docker container, it was using a pre-built version of the SDK which didn’t match what I truly needed. To resolve this, I needed to build the SDK from source.

To build my Node.js module from source so that the GLIBCXX errors went away, I needed to first download the build essentials to my Amazon Linux container. From within my container, I could execute the following:

yum -y install git
yum -y groupinstall "Development Tools"

The source to the Couchbase Node.js SDK was on GitHub so I needed to install the NPM dependency from GitHub rather than the standard NPM repository. For this to work git needed to be available. Also, since the Amazon Linux instance is very basic, the appropriate compilers and build tools needed to be available. These tools were in the “Developer Tools” group.

After installing the proper OS tools, we could run the following:

npm install git+ --save

The above command will install the 2.6.2 release of the SDK, but it will also build it from source. By building it from source we can be sure that the proper operating system and architecture were used. After deploying this to AWS Lambda and AWS API Gateway, everything worked fine.

In case you’d like to create your own Docker image and save some of the hassle, you could use the following Dockerfile file:

FROM amazonlinux:1

RUN yum -y install tar
RUN yum -y install gzip
RUN yum -y install git
RUN yum -y groupinstall "Development Tools"

RUN curl -o- | bash
RUN /bin/bash -c "source /root/.nvm/; nvm install 8.10.0"

CMD /bin/bash -c "source /root/.nvm/; nvm use 8.10.0; bash"

The above blueprint will install the necessary applications, and choose the correct version of Node.js to match what AWS Lambda uses. Using Docker, you could run something like the following:

docker build -t custom-amazon-linux /path/to/Dockerfile

With the custom image available, you could deploy it as a container and map your project directory on the host, similar to how we did it in the previous tutorial.


You just saw how to solve GLIBCXX version errors related to the libstdc++ package, something that is a surprisingly common issue when it comes to function deployment on AWS Lambda. To be clear, these issues are not specific to the Couchbase Node.js SDK. These issues come up due to native dependencies being deployed after having been built using the incorrect operating system or architecture. Some Node.js and Python dependencies use native libraries and these native libraries need to match what Amazon Linux uses on AWS Lambda.

Original article source at:

#serverless #framework #aws #lambda 

Hermann  Frami

Hermann Frami


Serverless Framework: Use AWS S3 Object Lambda to Resize Images on The Fly


In this post, I will show you how to use Amazon S3 Object Lambda to resize images on the fly. The  Serverless Framework will be used to define the Infrastructure as Code and to simplify the deployment. Sharp will be used to resize the images. Lambda will be written using the Node.js 14x  Lambda runtime

What’s Amazon S3 Object Lambda

One of the most common Lambda patterns is to transform data stored inside Amazon S3. Generally, a lambda function is invoked after a file has been uploaded. Lambda would retrieve that file, apply any needed transformation (e.g. converting type of file) and store the result in S3.

That pattern was working well, however, it would require some work done onto a file despite that being accessed in the future or not.

If you needed to convert a file on the fly you should have created a Lambda function, invoke it via Amazon API GW and wait for the lambda to perform the transformation.

AWS has recently introduced  Amazon S3 Object Lambda in a good post by Danilo Poccia. S3 Object Lambda allows creating a Lambda directly connected to the S3 bucket (using  S3 Access Points) that is automatically invoked when retrieving the object from S3!

That means that our application needs only to send an S3 Get Object request to retrieve the original or transformed data

Also, a very important peculiarity of using Amazon S3 Object Lambda it’s that the file you want to retrieve doesn’t need to exist on S3! We will make use of this for our scenario

_Note: High-level AWS CLI S3 commands (e.g. _aws s3 cp_) don’t currently support S3 Object Lambda, instead we need to use low-level S3 API commands (e.g. __aws s3api get-object)_

Common Use Cases

In his post, Danilo highlighted the most common use cases for Amazon S3 Object Lambda:

  • Converting across data formats (e.g. XML to JSON)
  • Compressing or decompressing files on the fly
  • Resizing images on the fly (our use case!)
  • Many more (left to the devs’ creativity)

#aws-lambda #serverless #aws-s3 #aws