Running Flatcar Container Linux on Google Compute Engine

    Before proceeding, you will need a GCE account ( GCE free trial ) and install gcloud on your machine. In each command below, be sure to insert your project name in place of <project-id>.

    After installation, log into your account with gcloud auth login and enter your project ID when prompted.

    Choosing a channel

    Flatcar Container Linux is designed to be updated automatically with different schedules per channel. You can disable this feature , although we don’t recommend it. Read the release notes for specific features and bug fixes.

    Create 3 instances from the image above using our Ignition from example.ign:

    The Stable channel should be used by production clusters. Versions of Flatcar Container Linux are battle-tested within the Beta and Alpha channels before being promoted. The current version is Flatcar Container Linux 3227.2.2.

    gcloud compute instances create flatcar1 flatcar2 flatcar3 --image-project kinvolk-public --image-family flatcar-stable --zone us-central1-a --machine-type n1-standard-1 --metadata-from-file user-data=config.ign

    The Beta channel consists of promoted Alpha releases. The current version is Flatcar Container Linux 3277.1.2.

    gcloud compute instances create flatcar1 flatcar2 flatcar3 --image-project kinvolk-public --image-family flatcar-beta --zone us-central1-a --machine-type n1-standard-1 --metadata-from-file user-data=config.ign

    The Alpha channel closely tracks master and is released frequently. The newest versions of system libraries and utilities will be available for testing. The current version is Flatcar Container Linux 3346.0.0.

    gcloud compute instances create flatcar1 flatcar2 flatcar3 --image-project kinvolk-public --image-family flatcar-alpha --zone us-central1-a --machine-type n1-standard-1 --metadata-from-file user-data=config.ign

    Uploading an Image

    If you prefer, you can also run Flatcar Container Linux by uploading a custom image to your account.

    To do so, run the following command:

    docker run -it quay.io/kinvolk/google-cloud-flatcar-image-upload \
      --bucket-name <bucket name> \
      --project-id <project id>
    

    Where:

    • <bucket name> should be a valid bucket name.
    • <project id> should be your project ID.

    During execution, the script will ask you to log into your Google account and then create all necessary resources for uploading an image. It will then download the requested Flatcar Container Linux image and upload it to the Google Cloud.

    To see all available options, run:

    docker run -it quay.io/kinvolk/google-cloud-flatcar-image-upload --help
    
    Usage: /usr/local/bin/upload_images.sh [OPTION...]
    
     Required arguments:
      -b, --bucket-name Name of GCP bucket for storing images.
      -p, --project-id  ID of the project for creating bucket.
    
     Optional arguments:
      -c, --channel     Flatcar Container Linux release channel. Defaults to 'stable'.
      -v, --version     Flatcar Container Linux version. Defaults to 'current'.
      -i, --image-name  Image name, which will be used later in Lokomotive configuration. Defaults to 'flatcar-<channel>'.
    
     Optional flags:
       -f, --force-reupload If used, image will be uploaded even if it already exist in the bucket.
       -F, --force-recreate If user, if compute image already exist, it will be removed and recreated.
    

    The Dockerfile for the quay.io/kinvolk/google-cloud-flatcar-image-upload image is managed here .

    Upgrade from CoreOS Container Linux

    You can also upgrade from an existing CoreOS Container Linux system .

    Container Linux Config

    Flatcar Container Linux allows you to configure machine parameters, configure networking, launch systemd units on startup, and more via Container Linux Configs (CLC). These configs are then transpiled into Ignition configs and given to booting machines. Head over to the docs to learn about the supported features .

    You can provide a raw Ignition JSON config to Flatcar Container Linux via the Google Cloud console’s metadata field user-data or via a flag using gcloud.

    As an example, this CLC YAML config will start an NGINX Docker container:

    systemd:
      units:
        - name: nginx.service
          enabled: true
          contents: |
            [Unit]
            Description=NGINX example
            After=docker.service
            Requires=docker.service
            [Service]
            TimeoutStartSec=0
            ExecStartPre=-/usr/bin/docker rm --force nginx1
            ExecStart=/usr/bin/docker run --name nginx1 --pull always --net host docker.io/nginx:1
            ExecStop=/usr/bin/docker stop nginx1
            Restart=always
            RestartSec=5s
            [Install]
            WantedBy=multi-user.target        
    

    Transpile it to Ignition JSON:

    cat cl.yaml | docker run --rm -i ghcr.io/flatcar/ct:latest -platform gce > ignition.json
    

    Additional storage

    Additional disks attached to instances can be mounted with a .mount unit. Each disk can be accessed via /dev/disk/by-id/google-<disk-name>. Here’s the Container Linux Config to format and mount a disk called database-backup:

    storage:
      filesystems:
        - mount:
            device: /dev/disk/by-id/scsi-0Google_PersistentDisk_database-backup
            format: ext4
    
    systemd:
      units:
        - name: media-backup.mount
          enable: true
          contents: |
            [Mount]
            What=/dev/disk/by-id/scsi-0Google_PersistentDisk_database-backup
            Where=/media/backup
            Type=ext4
    
            [Install]
            RequiredBy=local-fs.target        
    

    For more information about mounting storage, Google’s own documentation is the best source. You can also read about mounting storage on Flatcar Container Linux .

    Adding more machines

    To add more instances to the cluster, just launch more with the same Ignition config inside of the project.

    SSH and users

    Users are added to Container Linux on GCE by the user provided configuration (i.e. Ignition, cloudinit) and by either the GCE account manager or GCE OS Login . OS Login is used if it is enabled for the instance, otherwise the GCE account manager is used.

    Using the GCE account manager

    You can log in your Flatcar Container Linux instances using:

    gcloud compute ssh --zone us-central1-a [email protected]<instance-name>
    

    Users other than core, which are set up by the GCE account manager, may not be a member of required groups. If you have issues, try running commands such as journalctl with sudo.

    Using OS Login

    You can log in using your Google account on instances with OS Login enabled. OS Login needs to be enabled in the GCE console and on the instance. It is enabled by default on instances provisioned with Container Linux 1898.0.0 or later. Once enabled, you can log into your Container Linux instances using:

    gcloud compute ssh --zone us-central1-a <instance-name>
    

    This will use your GCE user to log in.

    Disabling OS Login on newly provisioned nodes

    You can disable the OS Login functionality by masking the oem-gce-enable-oslogin.service unit:

    systemd:
      units:
        - name: oem-gce-enable-oslogin.service
          mask: true
    

    When disabling OS Login functionality on the instance, it is also recommended to disable it in the GCE console.

    Monitoring

    Flatcar isn’t a supported distro for the Google Ops Agent , as it’s designed for traditional operating systems and monitoring the processes running on them.

    It’s likely however that there will be metrics within Flatcar that will be useful additions to VM metrics in Google Cloud Monitoring.

    GCP Custom Metrics

    Google provide an API and SDKs to ingest custom metrics. For example this Python script will send CPU load average and root volume utilisation every minute:

    gcp_custom_metrics.py

    #!/usr/bin/env python3
    from google.cloud import monitoring_v3
    
    import time
    import os
    import shutil
    import requests
    
    metadata_server = "http://metadata/computeMetadata/v1/"
    metadata_flavor = {'Metadata-Flavor' : 'Google'}
    
    gce_name = requests.get(metadata_server + 'instance/hostname', headers = metadata_flavor).text
    gce_project = requests.get(metadata_server + 'project/project-id', headers = metadata_flavor).text
    split_gce_name=gce_name.split(".",2)
    
    client = monitoring_v3.MetricServiceClient()
    project_id = gce_project
    project_name = f"projects/{project_id}"
    
    load_series = monitoring_v3.TimeSeries()
    load_series.metric.type = "custom.googleapis.com/node_load"
    load_series.resource.type = "gce_instance"
    load_series.resource.labels["instance_id"] = split_gce_name[0]
    load_series.resource.labels["zone"] = split_gce_name[1]
    
    du_series = monitoring_v3.TimeSeries()
    du_series.metric.type = "custom.googleapis.com/root_volume_usage"
    du_series.resource.type = "gce_instance"
    du_series.resource.labels["instance_id"] = split_gce_name[0]
    du_series.resource.labels["zone"] = split_gce_name[1]
    
    while True:
        load1, load5, load15 = os.getloadavg()
        root_total, root_used, root_free = shutil.disk_usage("/")
    
        now = time.time()
        seconds = int(now)
        nanos = int((now - seconds) * 10 ** 9)
        interval = monitoring_v3.TimeInterval(
            {"end_time": {"seconds": seconds, "nanos": nanos}}
        )
        load_point = monitoring_v3.Point({"interval": interval, "value": {"double_value": load5}})
        load_series.points = [load_point]
        client.create_time_series(request={"name": project_name, "time_series": [load_series]})
    
        du_point = monitoring_v3.Point({"interval": interval, "value": {"double_value": root_used/root_total}})
        du_series.points = [du_point]
        client.create_time_series(request={"name": project_name, "time_series": [du_series]})
    
        time.sleep(60)
    

    The script can then be packaged up into a Dockerfile:

    Dockerfile

    FROM python:3-slim
    
    WORKDIR /usr/src/app
    
    RUN pip3 install --no-cache-dir google-cloud-monitoring
    
    COPY gcp_custom_metrics.py .
    
    CMD [ "python3", "./gcp_custom_metrics.py" ]
    

    The resulting image can then be deployed to a container on each Flatcar node.

    Using Flatcar Container Linux

    Now that you have a machine booted it is time to play around. Check out the Flatcar Container Linux Quickstart guide or dig into more specific topics .