Alfred AWS Lambda Python

Template repository documentation for serverless projects with AWS Lambda in Python. Use this template for any serverless project on AWS Lambda

Directory tree 🌲

.
├── .github
│   └── workflows
│       └── black.yml
├── htmlcov
│   └── .keep
├── src
│   ├── __init__.py
│   └── lambda_function.py
├── tests
│   └── __init__.py
├── .coveragerc
├── .dockerignore
├── .editorconfig
├── .gitignore
├── Dockerfile
├── Dockerfile-local
├── Makefile
├── README.md
├── docker-compose.yml
├── example.env
├── requirements.txt
└── requirements-test.txt

Summary 📚

Considerations 📋

Cloud services ☁️

How to test 🧪

Before launching the project you must create your environment variables file (.env).

Using docker 🐳

The project use docker, so just run:

docker compose up

Makefile 🎮

Makefiles are used to help decide which parts of a large program need to be recompiled. The project has a Makefile with abbreviations for useful project commands, if you have make installed you can use it.

Command	Purpose
build-and-start	Build the image with which the container is created based on the Dockerfile and upload a container with the project's source code ready to run
start	Start the stopped container
clean	Stop and delete the running containers along with the resources created for the service (volumes, networks, etc)
bash	Start an interactive container terminal (bash)
test	Run the unit tests
test-and-report	Run the unit tests and generate a coverage report
coverage-report	Generate a coverage report
coverage-html	Generate a coverage report in html files

Remember that to run the above commands you must use the make binary

How to deploy 🚀

This repository has GitHub actions configuration files. When a new commit is pushed to the main branch, a new version of the Lambda function will automatically be deployed to the development environment.

The event that fires the deployment flow is a push. Which means that Pull Requests pointing to the main branch will also trigger the event.

If you want to deploy a new version of the Lambda function in the production environment you can do it by going to the GitHub repository and within the actions tab you can trigger the deployment flow to production.

Manually

Source code as a zip file 📦

Your AWS Lambda function's code consists of scripts or compiled programs and their dependencies. You use a deployment package to deploy your function code to Lambda. Lambda supports two types of deployment packages: container images and .zip file archives.

To create the deployment package for a .zip file archive, you can use a built-in .zip file archive utility or any other .zip file utility (such as 7zip) for your command line tool. Note the following requirements for using a .zip file as your deployment package:

• The .zip file contains your function's code and any dependencies used to run your function's code (if applicable) on Lambda. If your function depends only on standard libraries, or AWS SDK libraries, you don't need to include these libraries in your .zip file. These libraries are included with the supported Lambda runtime environments.

• If the .zip file is larger than 50 MB, we recommend uploading it to your function from an Amazon Simple Storage Service (Amazon S3) bucket.

• You need to build the deployment package to be compatible with this instruction set architecture of the function.

• Lambda uses POSIX file permissions, so you may need to set permissions for the deployment package folder before you create the .zip file archive.

You can integrate some GitHub actions flows that are responsible for deploying the .zip file and creating a new version of the lambda with the file content:

git merge origin/zip_deploy

Docker image based Lambda function 🐋

You can push your container images to an Amazon ECR repository with the docker push command. To push a Docker image to an Amazon ECR repository, the Amazon ECR repository must exist before you push the image.

1. Authenticate your Docker client to the Amazon ECR registry to which you intend to push your image. Authentication tokens must be obtained for each registry used, and the tokens are valid for 12 hours:

aws ecr get-login-password --region <region> | docker login --username AWS --password-stdin <aws_account_id>.dkr.ecr.<region>.amazonaws.com

2. Tag your image with the Amazon ECR registry, repository, and optional image tag name combination to use. The registry format is <aws_account_id>.dkr.ecr.<region>.amazonaws.com. The repository name should match the repository that you created for your image. If you omit the image tag, we assume that the tag is latest:

docker tag e9ae3c220b23 <aws_account_id>.dkr.ecr.<region>.amazonaws.com/<my-repository>:<tag>

3. Push the image using the docker push command:

docker push <aws_account_id>.dkr.ecr.<region>.amazonaws.com/<my-repository>:<tag>

4. Create the Lambda function or update the function code to deploy the image to an existing function.

You can integrate some GitHub actions flows that are responsible for deploying the docker image and creating a new version of the lambda with the file content:

git merge origin/docker_deploy

Authors ✍️

• Alfred's Tech Team