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• Artifact Registry

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Migrate container images from a third-party registry

Artifact Registry doesn't monitor third-party registries for updates to images you copy to Artifact Registry. If you want to incorporate a newer version of an image into your pipeline, then you must push it to Artifact Registry.

Migration overview

Migration of your container images includes the following steps:

1. Set up prerequisites.
2. Identify images to migrate.
   • Search your Dockerfile files and deployment manifests for references to third-party registries
   • Determine pull frequency of images from third-party registries using Cloud Logging and BigQuery.
3. Copy identified images to Artifact Registry.
4. Verify that permissions to the registry are correctly configured, particularly if Artifact Registry and your Google Cloud deployment environment are in different projects.
5. Update manifests for your deployments.
6. Re-deploy your workloads.

Before you begin

gcloud init

gcloud services enable artifactregistry.googleapis.com

gcloud init

gcloud services enable artifactregistry.googleapis.com

export PROJECT=$(gcloud config get-value project)

go version

Costs

This guide uses the following billable components of Google Cloud:

• GKE
• Artifact Registry
• BigQuery
• Logging

Identify images to migrate

Search the files you use to build and deploy your container images for references to third-party registries, then check how often you pull the images.

Identify references in Dockerfiles

In the directory with your Dockerfiles, run the command:

grep -inr -H --include Dockerfile\* "FROM" . | grep -i -v -E 'docker.pkg.dev|gcr.io'

The output looks like the following example:

./code/build/baseimage/Dockerfile:1:FROM debian:stretch
./code/build/ubuntubase/Dockerfile:1:FROM ubuntu:latest
./code/build/pythonbase/Dockerfile:1:FROM python:3.5-buster

This command searches all the Dockerfiles in your directory and identifies the "FROM" line. Adjust the command as needed to match the way you store your Dockerfiles.

Identify references in manifests

1. In the directory with your GKE or Cloud Run manifests run the following command:

   grep -inr -H --include \*.yaml "image:" . | grep -i -v -E 'docker.pkg.dev|gcr.io'

   The output resembles the following:

       ./code/deploy/k8s/ubuntu16-04.yaml:63: image: busybox:1.31.1-uclibc
       ./code/deploy/k8s/master.yaml:26:      image: kubernetes/redis:v1
       

   This command looks at all YAML files in your directory and identifies the image: line. Adjust the command as needed to work with how your manifests are stored
2. To list images running on a cluster run the following command:

   kubectl get all --all-namespaces -o yaml | grep image: | grep -i -v -E 'docker.pkg.dev|gcr.io'

   This command returns all objects running in the selected Kubernetes cluster and gets their image names. The output resembles the following:

       - image: nginx
         image: nginx:latest
           - image: nginx
           - image: nginx
       

Run the previous commands for all GKE clusters across all Google Cloud projects for total coverage.

Identify pull frequency from a third-party registry

In projects that pull from third-party registries, use information about image pull frequency to determine if you usage is near or over any rate limits that the third-party registry enforces.

Collect log data

Create a log sink to export data to BigQuery. A log sink includes a destination and a query that selects the log entries to export. You can create a sink by querying individual projects, or you can use a script to collect data across projects.

To create a sink for a single project:

1. In the Google Cloud console, go to the segment Logs Explorer page:

   Go to Logs Explorer

   If you use the search bar to find this page, then select the result whose subheading is Logging.

2. Choose a Google Cloud project.

3. On the Query builder tab, enter the following query:

     resource.type="k8s_pod"
     jsonPayload.reason="Pulling"
   

4. Change history filter from Last 1 hour to Last 7 Days.

5. Click Run Query.

6. After verifying that results show up correctly, click Actions > Create Sink.

7. In the Sink details dialog, complete the following:

   1. In the Sink Name field, enter image_pull_logs.
   2. In the Sink description, enter a description of the sink.

8. Click Next.

9. In the Sink destination dialog, select the following values:

   1. In the Select Sink service field, select BigQuery dataset.
   2. In the Select BigQuery dataset field, select Create a new BigQuery dataset and complete the required information in the dialog that opens. For more information on how to create a BigQuery dataset, see Create datasets.
   3. Click Create dataset.

10. Click Next.

    In the Choose logs to include in sink section, the query matches the query you ran in the Query builder tab.

11. Click Next.

12. Optional: Choose logs to filter out of the sink. For more information on how to query and filter Cloud Logging data, see Logging query language.

13. Click Create Sink.

    Your log sink is created.

To create a sink for multiple projects:

1. Open Cloud Shell.

2. Run the following commands in Cloud Shell:

   PROJECTS="PROJECT-LIST"
   DESTINATION_PROJECT="DATASET-PROJECT"
   DATASET="DATASET-NAME"
   
   for source_project in $PROJECTS
   do
     gcloud logging --project="${source_project}" sinks create image_pull_logs bigquery.googleapis.com/projects/${DESTINATION_PROJECT}/datasets/${DATASET} --log-filter='resource.type="k8s_pod" jsonPayload.reason="Pulling"'
   done
   

   where

   • PROJECT-LIST is a list of Google Cloud project IDs, separated with spaces. For example project1 project2 project3.
   • DATASET-PROJECT is the project where you want to store your dataset.
   • DATASET-NAME is the name for the dataset, for example image_pull_logs.

After you create a sink, it takes time for data to flow to BigQuery tables, depending on how frequently images are pulled.

Query for pull frequency

Once you have a representative sample of image pulls that your builds make, run a query for pull frequency.

1. Go to the BigQuery console.

2. Run the following query:

   SELECT
     REGEXP_EXTRACT(jsonPayload.message, r'"(.*?)"') AS imageName,
     COUNT(*) AS numberOfPulls
   FROM
         `DATASET-PROJECT.DATASET-NAME.events_*`
   GROUP BY
         imageName
   ORDER BY
         numberOfPulls DESC
   

   where

   • DATASET-PROJECT is the project that contains your dataset.
   • DATASET-NAME is the name of the dataset.

Copy images to Artifact Registry

After you have identified images from third-party registries, you are ready to copy them to Artifact Registry. The gcrane tool helps you with the copying process.

1. Create a text file images.txt with the names of the images you identified. For example:

   ubuntu:18.04
   debian:buster
   hello-world:latest
   redis:buster
   jupyter/tensorflow-notebook
   

2. Download gcrane.

     GO111MODULE=on go get github.com/google/go-containerregistry/cmd/gcrane
   

3. Create a script named copy_images.sh to copy your list of files.

   #!/bin/bash
   
   images=$(cat images.txt)
   
   if [ -z "${AR_PROJECT}" ]
   then
       echo ERROR: AR_PROJECT must be set before running this
       exit 1
   fi
   
   for img in ${images}
   do
       gcrane cp ${img} LOCATION-docker.pkg.dev/${AR_PROJECT}/${img}
   done
   

   Replace LOCATION with the regional or multi-regional location of the repository.

   Make the script executable:

     chmod +x copy_images.sh
   

4. Run the script to copy the files:

   AR_PROJECT=${PROJECT}
   ./copy_images.sh
   

Verify permissions

Make sure that permissions are configured correctly before you update and re-deploy your workloads.

For more information, see the access control documentation.

Update manifests to reference Artifact Registry

Update your Dockerfiles and your manifests to refer to Artifact Registry instead of the third-party registry.

The following example shows manifest referencing a third-party registry:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx-deployment
spec:
  selector:
    matchLabels:
      app: nginx
  replicas: 2
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: nginx:1.14.2
        ports:
        - containerPort: 80

This updated version of the manifest points to an image on us-docker.pkg.dev.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: nginx-deployment
spec:
  selector:
    matchLabels:
      app: nginx
  replicas: 2
  template:
    metadata:
      labels:
        app: nginx
    spec:
      containers:
      - name: nginx
        image: us-docker.pkg.dev/<AR_PROJECT>/nginx:1.14.2
        ports:
        - containerPort: 80

For a large number of manifests, use sed or another tool that can handle updates across many text files.

Re-deploy workloads

Re-deploy workloads with your updated manifests.

Keep track of new image pulls by running the following query in the BigQuery console:

SELECT`

FORMAT_TIMESTAMP("%D %R", timestamp) as timeOfImagePull,
REGEXP_EXTRACT(jsonPayload.message, r'"(.*?)"') AS imageName,
COUNT(*) AS numberOfPulls
FROM
  `image_pull_logs.events_*`
GROUP BY
  timeOfImagePull,
  imageName
ORDER BY
  timeOfImagePull DESC,
  numberOfPulls DESC

All new image pulls should be from Artifact Registry and contain the string docker.pkg.dev.