Terraform Tutorial

---

Note: This content applies only to Cloud Run functions—formerly Cloud Functions (2nd gen).

This tutorial demonstrates how to deploy an HTTP function by uploading a function source code zip file to a Cloud Storage bucket, using Terraform to provision the resources. Terraform is an open source tool that lets you provision Google Cloud resources with declarative configuration files.

This tutorial uses a Node.js HTTP function as an example, but it also works with Python, Go, and Java HTTP functions. The instructions are the same regardless of which of these runtimes you are using.

Objectives

• Learn how to use Terraform to deploy an HTTP function.

Costs

In this document, you use the following billable components of Google Cloud:

• Cloud Run functions
• Cloud Build
• Cloud Storage
• Artifact Registry

For details, see Cloud Run functions pricing.

To generate a cost estimate based on your projected usage, use the pricing calculator. New Google Cloud users might be eligible for a free trial.

Before you begin

1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

3. Make sure that billing is enabled for your Google Cloud project.

4. Enable the Cloud Functions, Cloud Run, Cloud Build, Artifact Registry, and Cloud Storage APIs.

   Enable the APIs

5. Install the Google Cloud CLI.

6. To initialize the gcloud CLI, run the following command:

   gcloud init

7. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

8. Make sure that billing is enabled for your Google Cloud project.

9. Enable the Cloud Functions, Cloud Run, Cloud Build, Artifact Registry, and Cloud Storage APIs.

   Enable the APIs

10. Install the Google Cloud CLI.

11. To initialize the gcloud CLI, run the following command:

    gcloud init

If you already have the gcloud CLI installed, update it by running the following command:

gcloud components update

12. Prepare your development environment.
    

    Go to the Node.js setup guide

Setting up your environment

In this tutorial, you run commands in Cloud Shell. Cloud Shell is a shell environment with the Google Cloud CLI already installed, including the Google Cloud CLI, and with values already set for your current project. Cloud Shell can take several minutes to initialize:

Open Cloud Shell

Preparing the application

In Cloud Shell, perform the following steps:

1. Clone the sample app repository to your Cloud Shell instance:

   git clone https://github.com/GoogleCloudPlatform/nodejs-docs-samples.git

2. Change to the directory that contains the Cloud Run functions sample code examples:

   cd nodejs-docs-samples/functions

   The Node.js sample used in this tutorial is a basic "Hello World" HTTP function.

Create your main.tf file

1. In the nodejs-docs-samples/functions/ directory, create a main.tf file for the Terraform configuration:

   touch main.tf
   

2. Copy this Terraform configuration into your main.tf file:

   terraform {
     required_providers {
       google = {
         source  = "hashicorp/google"
         version = ">= 4.34.0"
       }
     }
   }
   
   resource "random_id" "default" {
     byte_length = 8
   }
   
   resource "google_storage_bucket" "default" {
     name                        = "${random_id.default.hex}-gcf-source" # Every bucket name must be globally unique
     location                    = "US"
     uniform_bucket_level_access = true
   }
   
   data "archive_file" "default" {
     type        = "zip"
     output_path = "/tmp/function-source.zip"
     source_dir  = "functions/hello-world/"
   }
   resource "google_storage_bucket_object" "object" {
     name   = "function-source.zip"
     bucket = google_storage_bucket.default.name
     source = data.archive_file.default.output_path # Add path to the zipped function source code
   }
   
   resource "google_cloudfunctions2_function" "default" {
     name        = "function-v2"
     location    = "us-central1"
     description = "a new function"
   
     build_config {
       runtime     = "nodejs16"
       entry_point = "helloHttp" # Set the entry point
       source {
         storage_source {
           bucket = google_storage_bucket.default.name
           object = google_storage_bucket_object.object.name
         }
       }
     }
   
     service_config {
       max_instance_count = 1
       available_memory   = "256M"
       timeout_seconds    = 60
     }
   }
   
   resource "google_cloud_run_service_iam_member" "member" {
     location = google_cloudfunctions2_function.default.location
     service  = google_cloudfunctions2_function.default.name
     role     = "roles/run.invoker"
     member   = "allUsers"
   }
   
   output "function_uri" {
     value = google_cloudfunctions2_function.default.service_config[0].uri
   }

3. Edit the main.tf file to make sure it has the correct values for the following items. You need to edit this file whenever your configuration changes (for example, to use a different runtime or deploy a different function):

   • Runtime: In this example, replace nodejs16 with the latest Node.js runtime nodejs20.
   • Function entry point: In this example, the function entry point is helloHttp.
   • Path to source directory: In this example change your source_dir to helloworld/helloworldHttp.
   • IAM member="allUsers" configuration won't succeed if your project is under a domain restriction organization policy that restricts granting IAM roles to the allUsers principal. Use with caution in production and consider a more restricted member list if possible.

Note: The main.tf file should reliably generate a unique bucket name whenever you apply the configuration. However, if you get the error "Resource already exists," you need to edit main.tf to supply a different bucket name that will be globally unique.

Initialize Terraform

In the nodejs-docs-samples/functions/ directory containing your main.tf file, run this command to add the necessary plugins and build the .terraform directory:

terraform init

Apply the Terraform configuration

In the nodejs-docs-samples/functions/ directory containing your main.tf file, deploy the function by applying the configuration. When prompted, enter yes:

terraform apply

Test the function

1. When the function finishes deploying, take note of the URI property or find it using the following command:

   gcloud functions describe function-v2 --gen2 --region=us-central1 --format="value(serviceConfig.uri)"
   

2. Make a request to this URL to see your function's "Hello World" message. Note that the function is deployed requiring authentication. Therefore you must provide credentials in your request:

   curl -H "Authorization: Bearer $(gcloud auth print-identity-token)" YOUR_FUNCTION_URL

Note: For security reasons, it is best practice to require authentication for HTTP function invocation. If desired, you can enable unauthenticated invocation after the function has been deployed.

Clean up

After completing the tutorial, you can delete everything that you created so that you don't incur any further costs.

Terraform lets you remove all the resources defined in the configuration file by running the terraform destroy command in the nodejs-docs-samples/functions/ directory containing your main.tf file:

terraform destroy

Enter yes to allow Terraform to delete your resources.