Deployment and Debugging of Google Cloud Functions

2 min read

📅 2024-03-16 — Session: Deployment and Debugging of Google Cloud Functions

🕒 00:20–23:35
🏷️ Labels: Google Cloud, Cloud Functions, Deployment, Debugging, Flask, Firestore
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to ensure the successful deployment and debugging of Google Cloud Functions, focusing on environment variables, HTTP triggers, error handling, and integration with Cloud Scheduler.

Key Activities

  • Updated environment variables in Google App Engine, specifically the OpenAI API key, and redeployed the application.
  • Implemented a Flask function for weekly ticket distribution using Firestore and prepared it for deployment on Google Cloud Functions.
  • Adapted Flask functions for deployment on Google Cloud Functions, addressing differences in HTTP request handling.
  • Troubleshot deployment issues, including ‘EntityTooLarge’ errors and Cloud Scheduler job creation.
  • Deployed a Flask app as a Google Cloud Function, resolving package size issues and setting up the scheduler.
  • Updated and refined deployed Google Cloud Functions, focusing on code editing, versioning, and monitoring.
  • Diagnosed errors in Google Cloud Functions related to Firestore queries, including deprecated filter arguments and HTTP 500 errors.
  • Set the GOOGLE_CLOUD_PROJECT environment variable for Cloud Functions using both console and command-line methods.
  • Addressed ‘NOT_FOUND’ errors in Cloud Scheduler, identifying causes such as deployment delays and permissions issues.
  • Implemented logging in Cloud Functions to trace execution flow and debug issues.

Achievements

  • Successfully updated environment variables and redeployed applications on Google App Engine.
  • Implemented and prepared Flask functions for deployment on Google Cloud Functions.
  • Resolved multiple deployment issues and improved error handling in Cloud Functions.
  • Enhanced debugging capabilities through logging and error diagnosis.

Pending Tasks

  • Further refine Firestore query syntax to prevent deprecated warnings.
  • Continuously monitor deployed functions for performance and error logging.
  • Explore additional optimizations for Cloud Scheduler integration.

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