lambda-authorizer-basic-auth

This is a sample template for lambda-authorizer-basic-auth - Below is a brief explanation of what we have generated for you:

.
├── README.md                   <-- This instructions file
├── lambda_authorizer_basic_auth                 <-- Source code for a lambda function
│   ├── __init__.py
│   └── app.py                  <-- Lambda function code
├── requirements.txt            <-- Python dependencies
├── template.yaml               <-- SAM template
└── tests                       <-- Unit tests
    └── unit
        ├── __init__.py
        └── test_handler.py

Requirements

• AWS CLI already configured with at least PowerUser permission
• Python 2.7 installed
• Docker installed
• Python Virtual Environment

Setup process

Installing dependencies

AWS Lambda requires a flat folder with the application as well as its dependencies. Therefore, we need to have a 2 step process in order to enable local testing as well as packaging/deployment later on - This consist of two commands you can run as follows:

pip install -r requirements.txt -t lambda_authorizer_basic_auth/build/
cp lambda_authorizer_basic_auth/*.py lambda_authorizer_basic_auth/build/

1. Step 1 install our dependencies into build folder
2. Step 2 copies our application into build folder

NOTE: As you change your application code as well as dependencies during development you'll need to make sure these steps are repeated in order to execute your Lambda and/or API Gateway locally.

Local development

Packaging and deployment

AWS Lambda Python runtime requires a flat folder with all dependencies including the application. SAM will use CodeUri property to know where to look up for both application and dependencies:

...
    HelloWorldFunction:
        Type: AWS::Serverless::Function
        Properties:
            CodeUri: lambda_authorizer_basic_auth/
            ...

Firstly, we need a S3 bucket where we can upload our Lambda functions packaged as ZIP before we deploy anything - If you don't have a S3 bucket to store code artifacts then this is a good time to create one:

aws s3 mb s3://BUCKET_NAME

Next, run the following command to package our Lambda function to S3:

sam package \
    --template-file template.yaml \
    --output-template-file packaged.yaml \
    --s3-bucket REPLACE_THIS_WITH_YOUR_S3_BUCKET_NAME

Next, the following command will create a Cloudformation Stack and deploy your SAM resources.

sam deploy \
    --template-file packaged.yaml \
    --stack-name lambda-authorizer-basic-auth \
    --capabilities CAPABILITY_IAM

See Serverless Application Model (SAM) HOWTO Guide for more details in how to get started.

After deployment is complete you can run the following command to retrieve the API Gateway Endpoint URL:

aws cloudformation describe-stacks \
    --stack-name lambda-authorizer-basic-auth \
    --query 'Stacks[].Outputs'

Testing

We use Pytest for testing our code and you can install it using pip: pip install pytest

Next, we run pytest against our tests folder to run our initial unit tests:

python -m pytest tests/ -v

NOTE: It is recommended to use a Python Virtual environment to separate your application development from your system Python installation.

Appendix

Python Virtual environment

In case you're new to this, python2 virtualenv module is not available in the standard library so we need to install it and then we can install our dependencies:

1. Create a new virtual environment
2. Install dependencies in the new virtual environment

pip install virtualenv
virtualenv .venv
. .venv/bin/activate
pip install -r requirements.txt

NOTE: You can find more information about Virtual Environment at Python Official Docs here. Alternatively, you may want to look at Pipenv as the new way of setting up development workflows

AWS CLI commands

AWS CLI commands to package, deploy and describe outputs defined within the cloudformation stack:

sam package \
    --template-file template.yaml \
    --output-template-file packaged.yaml \
    --s3-bucket REPLACE_THIS_WITH_YOUR_S3_BUCKET_NAME

sam deploy \
    --template-file packaged.yaml \
    --stack-name lambda-authorizer-basic-auth \
    --capabilities CAPABILITY_IAM \
    --parameter-overrides MyParameterSample=MySampleValue

aws cloudformation describe-stacks \
    --stack-name lambda-authorizer-basic-auth --query 'Stacks[].Outputs'

Bringing to the next level

Here are a few ideas that you can use to get more acquainted as to how this overall process works:

• Create an additional API resource (e.g. /hello/{proxy+}) and return the name requested through this new path
• Update unit test to capture that
• Package & Deploy

Next, you can use the following resources to know more about beyond hello world samples and how others structure their Serverless applications:

• AWS Serverless Application Repository
• Chalice Python Serverless framework
• Sample Python with 3rd party dependencies, pipenv and Makefile: sam init --location https://github.com/aws-samples/cookiecutter-aws-sam-python