Create custom tasks (AWS lambdas) and execute using Carrier Platform

Overview

This guide provides step-by-step instructions on how to create custom tasks (AWS lambdas) and execute using Carrier Platform. Using custom tasks you can add additional functionality for processing and reporting test results. Additional info about lambdas you can find here

Entrypoint and parameters for task

The Lambda function handler is the method in your function code that processes events. When your function is invoked, Lambda runs the handler method. Your function runs until the handler returns a response, exits, or times out. You can use the following general syntax when creating a function handler in Python:

  
def handler_name(event, context):
    ...
    return some_value

The Lambda function handler name specified at the time that you create a Task in Carrier is derived from:

The name of the file in which the Lambda handler function is located.
The name of the Python handler function.

A function handler can be any name; however, the default name in the Lambda console is lambda_function.lambda_handler. This function handler name reflects the function name (lambda_handler) and the file where the handler code is stored (lambda_function.py).

The first argument for lambda handler is the event object. It contains all the parameters that you have specified for the task during task creation. An event is a JSON-formatted document that contains data for a Lambda function to process. The Lambda runtime converts the event to an object and passes it to your function code. It is usually of the Python dict type. It can also be list, str, int, float, or the NoneType type.

In your function code you can read parameters from event object like this:

  
report_id = event.get("report_id")
project_id = event.get("project_id")
token = event.get("token")
some_parameter = event.get("some_parameter")

Additional requirements and packaging

The official documentation from AWS you can find here

If your function requires some not standard python libraries you need to specify them in requirements.txt file. Example you can find here. After that you can build your lambda function as zip archive using instruction from official documentation. Also, you can create a bash script to build lambda function using docker container + zip utility. An example below:

  
#!/bin/bash

mkdir lambda
# build all dependencies from requirements.txt
docker run --rm -v "$PWD":/var/task lambci/lambda:build-python3.7 pip install -r requirements.txt -t /var/task/lambda
# copy some additional code required by lambda function
cp -r perfreporter lambda/perfreporter
# copy template for email notification
cp -r templates lambda/templates
# copy lambda handler
cp post_processing.py lambda/post_processing.py
cd lambda
# create zip archive
zip jtl_to_excel.zip -r .
cp jtl_to_excel.zip ../
cd ..
rm -rf lambda