To apply complex business logic on a JSON response using Python's standard library within a Workflow, invoking a Cloud Function is the most efficient and straightforward approach. Here's why option A is the best choice:
Cloud Functions:
Cloud Functions provide a lightweight, serverless execution environment for running code in response to events. They support Python and can easily integrate with Workflows.
This approach ensures simplicity and speed of execution, as Cloud Functions can be invoked directly from a Workflow and handle the complex logic required.
Flexibility and Simplicity:
Using Cloud Functions allows you to leverage Python's extensive standard library and ecosystem, making it easier to implement and maintain the complex business logic.
Cloud Functions abstract the underlying infrastructure, allowing you to focus on the application logic without worrying about server management.
Performance:
Cloud Functions are optimized for fast execution and can handle the processing of the JSON response efficiently.
They are designed to scale automatically based on demand, ensuring that your workflow remains performant.
Steps to Implement:
Write the Cloud Function:
Develop a Cloud Function in Python that processes the JSON response and applies the necessary business logic.
Deploy the function to Google Cloud.
Invoke Cloud Function from Workflow:
Modify your Workflow to call the Cloud Function using an HTTP request or Google Cloud Function connector.
steps:
- callCloudFunction:
call: http.post
args:
url: https://REGION-PROJECT_ID.cloudfunctions.net/FUNCTION_NAME
body:
key: value
Process Results:
Handle the response from the Cloud Function and proceed with the next steps in the Workflow, such as loading data into BigQuery.
Reference:
Google Cloud Functions Documentation
Using Workflows with Cloud Functions
Workflows Standard Library