https://github.com/jeffbryner/gcp-cloud-function-kickstarter

bootstrap your way into a gcp cloud function
https://github.com/jeffbryner/gcp-cloud-function-kickstarter

Last synced: 11 days ago
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bootstrap your way into a gcp cloud function

• Host: GitHub
• URL: https://github.com/jeffbryner/gcp-cloud-function-kickstarter
• Owner: jeffbryner
• License: mpl-2.0
• Created: 2021-11-07T01:32:57.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2022-04-11T00:08:29.000Z (almost 3 years ago)
• Last Synced: 2024-11-08T21:19:02.341Z (2 months ago)
• Language: HCL
• Size: 14.6 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Why?

While serverless.com is a great way to boot up lambas and cloud functions, it's not always compatible with terraform ci/cd pipelines.

This project is meant to be a terraform ci/cd friendly way to bootstrap your way into a gcp cloud function with ease!

## Assumptions

- You have an existing GCP project

- You'd like to add a python based cloud function

- You'd like a simple way to add it that's compatible with a terraform-based ci/cd pipeline

## How?

Copy the terraform.tfvars.example as terraform.tfvars, retrieve the project ID for your target GCP project/environment and add it to the file.

In the directory where you stored main.tf:

- terraform init

- Import the target project into terraform ala: terraform import google_project.target prj-sample-project-id-goes-here

- Edit terraform.tfvars to match the basic project elements (name, billing accoujnt, id)

- Make a copy of backend.tf.example as backend.tf with a proper bucket name, etc if you'd like to store terraform state in GCP and run terraform init --force-copy to copy state to the bucket.

- A terraform apply should now create the zip file for the main.py function, upload it to a bucket, create a cloud function and emit the url for you to call it.

- Voila! A basic kickstart.

## Next steps

This pattern is a simple 'zip and deploy' and includes sample dependencies on numpy and pandas just to show how to include libraries in your requirements.txt.