How to I get my Jenkins pipeline to execute my code in GCP Cloud

ghz 4hours ago ⋅ 4 views

How to I get my Jenkins pipeline to execute my code in GCP Cloud Functions?

New to Jenkins here, and I have written the following Jenkins pipeline to execute my Cloud Function, which is in GCP, but I am unsure what I need to do in order to execute my code files in GCP.

    agent any

    stages {

        stage('Build') {

            steps {

                sh 'gcloud config set account yyyy@yyyy.iam.gserviceaccount.com'

                sh '''
gcloud functions deploy helloWorld --region=us-central1 --runtime nodejs8 --trigger-http --project yyyy \

                '''

            }

        }

    }

}

Do I need to add something that defines the location of my code or what? I am unsure.

My code files are in my GCP Cloud Function, so how do I get my Jenkins pipeline to execute those files?

Please help.

I have tried to look at the Jenkins documentation, but I am unclear on what I need to add to my pipeline, so I can execute my code files.

Answer

You're on the right track with the Jenkins pipeline, but there are a few key things you'll need to adjust to ensure that your Jenkins pipeline correctly deploys and executes your Google Cloud Function.

Key Points:

  1. Source Code Location: You need to make sure that your Cloud Function's source code (JavaScript, Python, etc.) is available to the Jenkins pipeline. If the code is stored in a version control system (e.g., Git), you can use Jenkins' built-in Git plugin to pull the code.

  2. Google Cloud SDK Configuration: You are already configuring the gcloud account with gcloud config set account, which is good. But make sure your service account has the appropriate permissions to deploy Cloud Functions.

  3. Deploying the Cloud Function: You are deploying your function with gcloud functions deploy. This is correct, but you also need to point it to the directory where your Cloud Function's code resides.

Full Example of a Jenkins Pipeline for Deploying a Cloud Function:

Assuming your Cloud Function code is in a Git repository, here's how you can structure your Jenkins pipeline:

pipeline {
    agent any

    environment {
        GCP_PROJECT = 'your-gcp-project-id'
        GCP_ACCOUNT = 'yyyy@yyyy.iam.gserviceaccount.com'
        FUNCTION_NAME = 'helloWorld'
        FUNCTION_DIR = 'path/to/your/function/code' // Directory of your Cloud Function code
    }

    stages {
        stage('Checkout') {
            steps {
                // Checkout your code from a Git repository
                git 'https://github.com/your-repository/your-cloud-function.git'
            }
        }

        stage('Build') {
            steps {
                // Set the gcloud account for deployment
                sh 'gcloud config set account ${GCP_ACCOUNT}'
                sh 'gcloud config set project ${GCP_PROJECT}'

                // Deploy the Cloud Function, pointing to the directory containing your code
                sh '''
                gcloud functions deploy ${FUNCTION_NAME} \
                    --region=us-central1 \
                    --runtime=nodejs8 \
                    --trigger-http \
                    --project=${GCP_PROJECT} \
                    --entry-point=yourFunctionEntryPoint \
                    --source=${FUNCTION_DIR}
                '''
            }
        }
    }
}

Key Changes and Explanation:

  1. Checkout Stage: This stage pulls the code from your version control system (e.g., GitHub). Replace 'https://github.com/your-repository/your-cloud-function.git' with your actual repository URL.

  2. Environment Variables:

    • GCP_PROJECT: The Google Cloud project ID where your function will be deployed.
    • GCP_ACCOUNT: The service account you're using for authentication.
    • FUNCTION_NAME: The name of your Cloud Function (e.g., helloWorld).
    • FUNCTION_DIR: This is the local path (relative to the Jenkins workspace) where your Cloud Function code is located. This could be a subdirectory in your Git repository if your function's code is inside a specific folder.

  3. gcloud functions deploy: This command deploys your Cloud Function. The --source flag is used to specify the location of your Cloud Function's code. If your function code is in the root of the repository, you can leave --source as just . (current directory), or specify the relative path if it's in a subfolder.

    • --entry-point=yourFunctionEntryPoint: This is optional, and it's only necessary if your Cloud Function has a specific entry point that is different from the default (index.js for Node.js).

    • --runtime=nodejs8: Specifies the runtime for your function (you can change it based on your function's language, e.g., nodejs14 or python39).

  4. Permissions: Make sure that the service account (yyyy@yyyy.iam.gserviceaccount.com) has the Cloud Functions Admin and Service Account User roles, at a minimum, to be able to deploy Cloud Functions.

Additional Considerations:

  • Dependencies: If your function has dependencies (e.g., package.json for Node.js), you may need to install them before deploying. You can add a step before the deployment to install dependencies:

    sh 'npm install'

  • Authentication: If your Jenkins server isn't running in a GCP environment, you may need to authenticate using a service account key. You can set up authentication like this:

    sh 'gcloud auth activate-service-account ${GCP_ACCOUNT} --key-file=/path/to/your-service-account-key.json'

  • Triggering the Function: After the function is deployed, it will have a URL endpoint (because you've set --trigger-http). You can invoke the function from Jenkins or elsewhere by calling the URL. You can even add a test step to invoke the function to ensure that it works.

Final Thoughts:

Your Jenkins pipeline needs to:

  1. Checkout the Cloud Function code from the repository.
  2. Set the necessary GCP account and project.
  3. Deploy the function, specifying the location of your source code with the --source flag.

This should solve your problem and deploy the Cloud Function to GCP. Let me know if you run into any further issues!

#py