DLiveBlade

Social Blade for Dlive TV

This is official repo for DliveBlade, a population stat page for the Dlive platform in python. In version 0.4 we finally implemented the Serverless-Application-Model which comes with some neat testing features, that speed up local development and deployment:

• SAM Deploy
• Cloudformation Stack
• Sam local invocation
• Reduced the deployment packge
• Lambda Layers

File Structure

.
├── README.md                   <-- This instructions file
├── event.json                  <-- API Gateway Proxy Integration event payload
├── scripts                     <-- Application scripts
├── ├── compile.sh                      
├── ├── chromedriver.sh                 
├── ├── convert.sh                                    
├── src                             <-- Source code for a lambda function
│   ├── __init__.py                     <-- This instructions file
│   ├── app.py                          <-- Lambda function code
│   ├── event.json                      <-- Event payload with the Base64 encoded selenium script                
│   ├── requirements.txt                <-- Lambda function code
├── selenium-lambda-layer                   <-- Lambda layer       
├── ├── bin                                     <-- Chromedriver and chrome binary
├── ├── ├── chromedriver                            
├── ├── ├── headless-chromium                       
├── ├── python                                  <-- This instructions file
├── ├── ├── lib                                     
├── ├── ├── ├── python3.7
├── ├── ├── ├── ├── ssitepackages
├── template.yaml               <-- SAM Template
└── tests                       <-- Unit tests
    └── unit
        ├── __init__.py
        └── test_handler.py

Requirements

• AWS CLI already configured with Administrator permission
• Python 3 installed
• Docker installed

Setup process

Local development

Invoking function locally using a local sample payload

sam local invoke lambdium --event .src/event.json

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:

...
    Lambdium:
        Type: AWS::Serverless::Function
        Properties:
            CodeUri: src/
            ...

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

Alternatively you can use the compile.sh script to package the Lambda function to S3

./scripts/compile.sh

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

sam deploy \
    --template-file packaged.yaml \
    --stack-name sam-app \

TODO

• Complete