Professional Cloud Developer

Using Cloud Build and Artifact Registry to Build and Store Containers

Pipeline Integration and Optimization

Cloud Build takes source code and automatically creates container images using either a Dockerfile or buildpacks. Once created, these images must be stored in Artifact Registry, which serves as a secure, version-controlled repository. A critical first step is that you must create the target repository in Artifact Registry before you can push any images to it.

There are two main ways to store images during a build. The images field in your configuration file automatically uploads the image and displays it in your build results. Alternatively, you can use the docker push command as a specific step in your pipeline. Using both methods together provides the best visibility and tracking in the Google Cloud console.

Artifact Analysis provides automated vulnerability scanning to keep your containers secure. This "shifts left" security by catching issues early in development rather than at the end. You can configure your pipeline to block deployment if the scan detects any risks. There are two scan types: automatic scanning runs when images are pushed to a repository, and on-demand scanning lets you manually check images during the build process.

To make builds faster, use layer caching with the --cache-from argument, which reuses unchanged parts from previous builds. Adding a .gcloudignore file prevents unnecessary files from being uploaded, keeping builds lean. For additional security, Binary Authorization enforces policies that only allow signed and scanned images to be deployed, creating a secure chain of trust from build time to production.

Build Configuration and Automation

A cloudbuild.yaml file defines a multi-step build process where each step runs inside a specialized container called a builder. For example, a step might use the docker builder to run a build command with specific arguments. This configuration gives developers fine-grained control over how applications are packaged.

You can start a build using the gcloud builds submit command from your local terminal or script. There are three ways to define how the image gets created: using a Dockerfile for custom environment instructions, using Cloud Buildpacks to create images automatically without a Dockerfile, or referencing a custom cloudbuild.yaml for complex multi-stage workflows. The choice depends on your application's complexity and how much control you need.

Substitution variables like $PROJECT_ID let you reuse the same build configuration across different projects and environments. These act as placeholders that Cloud Build fills in automatically. To fully automate the workflow, set up Build Triggers that start a build whenever code is updated in a repository, ensuring the latest code is always ready for deployment.

After the build finishes, the resulting image goes to a specific location in Artifact Registry. Including the images field in your config ensures the image is tracked and displayed on the build results page. While you can also use a docker push step, the images field provides better visibility in the Google Cloud console.

Artifact Management and Repository Security

Artifact Registry stores your container images in a central, managed location. To automate a build, you use a build config file written in YAML or JSON that tells Cloud Build exactly what to do, such as using the Docker builder to create an image. Including substitutions like $PROJECT_ID makes your build files flexible across different environments, and adding the images field ensures Cloud Build knows to push the finished product to your repository.

Access control uses Identity and Access Management (IAM) to determine who can view or modify your files. When working across different projects, granting the Artifact Registry Writer role to the build service account is often necessary for successful automation.

Artifact Registry offers different repository modes to organize your work. Standard repositories store private container images and packages. Remote repositories act as a proxy to cache images from external sources. Virtual repositories group multiple repositories under a single address for easier access. Choosing the right mode helps optimize storage and maintain security compliance.

Artifact Analysis automatically performs vulnerability scanning on your images to find security risks before deployment. You can also set up cleanup policies to automatically remove old or unused images, which saves storage costs and keeps your environment organized. Together, these tools ensure your software supply chain remains safe and efficient.