I want to deploy an application to Airflow that accepts a config file as a parameter, pulls the git repository specified by said config, then transforms it into a Docker image, then uploads that image to GCP's Artifact Registry. What is the best practice for building a docker image inside an Airflow DAG?
I have tried orchestrating a manually-triggered cloud build run via Airflow - I have not been able to pass the necessary substitutions into the cloudbuild.yaml file using the CloudBuildCreateBuildOperator, nor have I been able to specify the workspace.
I have also created a docker image that itself can create new docker images (when the docker.sock file is mounted as a volume). However, using a KubernetesPodOperator to call this seems to go against the design philosophy of Airflow, since this task would be affecting the host machine by building new docker images directly on it.
It's not the responsability of Airflow to apply this kind of use case.
Airflow is a pipeline and tasks orchestrator based on DAGs (direct acyclic graph).
Your need corresponds to usual CI CD pipelines.
It's better to delegate this work on a tool like Cloud Build or Gitlab CI for example.
From Cloud Build, you can apply and automate all the actions specified in your question.
When you will build your image in the CI CD part, you can then in the Airflow DAG, use a Docker image if needed with KubernetesPodOperator.
This would be more coherent because each concern will be put in the right place and on the right tool.
Can a docker image access hadoop resources? Eg. submit YARN jobs and access HDFS; something like MapR's Datasci. Refinery, but for Hortonworks HDP 3.1. (May assume that the image will be launched on a hadoop cluster node).
Saw the hadoop docs for launching docker applications from hadoop nodes, but was interested in whether could go the "other way" (ie. being able to start a docker image with the conventional docker -ti ... command and have that application be able to run hadoop jars etc. (assuming that the docker image host is a hadoop node itself)). I understand that MapR hadoop has docker images for doing this, but am interested in using Hortonworks HDP 3.1. Ultimately trying to run h2o hadoop in a docker container.
Anyone know if this is possible or can confirm that this is not possible?
Yes. As long as you have the client jars and appropriate configs similar to an edge node for your container it should work.
I have an Image which i should add a dependency to it. Therefore I have tried to change the image when is running on the container and create new Image.
I have follow this article with the following commands after :
kubectl run my-app --image=gcr.io/my-project-id/my-app-image:v1 --port 8080
kubectl get pods
kubectl exec -it my-app-container-id -- /bin/bash
then in the shell of container, i have installed the dependency using "pip install NAME_OF_Dependncy".
Then I have exited from the shell of container and as it have been explained in the article, i should commit the change using this command :
sudo docker commit CONTAINER_ID nginx-template
But I can not find the corresponding command for Google Kubernetes Engine with kubectl
How should i do the commit in google container engine?
As with K8s Version 1.8. There is no way to do Hot Fix changes directly to the images.For example, Committing new image from running container. If you still change or add something by using exec it will stay until the container is running. It's not best practice in K8s eco-system.
The recommended way is to use Dockerfile and customise the images according to the necessity and requirements.After that, you can push that images to the registry(public/ private ) and deploy it with K8s manifest file.
Solution to your issue
Create a Dockerfile for your images.
Build the image by using Dockerfile.
Push the image to the registry.
write the deployment manifest file as well service manifest file.
apply the manifest file to the k8s cluster.
Now If you want to change/modify something, you just need to change/modify the Dockerfile and follow the remaining steps.
As you know that containers are a short living creature which does not have persist changed behaviour ( modified configuration, changing file system).Therefore, It's better to give new behaviour or modification at the Dockerfile.
Kubernetes Mantra
Kubernetes is Cloud Native product which means it does not matter whether you are using Google Cloud, AWS or Azure. It needs to have consistent behaviour on each cloud provider.
A colleague find out Docker and want to use it for our project. I start to use Docker for test. After reading an article about Docker swarm I want to test it.
I have installed 3 VM (ubuntu server 14.04) with docker and swarm. I followed some How To ( http://blog.remmelt.com/2014/12/07/docker-swarm-setup/ and http://devopscube.com/docker-tutorial-getting-started-with-docker-swarm/). My cluster work. I can launch for exemple a basic apache container (the image was pull in the Docker hub) but I want to use my own image (an apache server with my web site).
I tested to load an image (after save it in a .tar) but this option isn't supported by the clustering mode, same thing with the import option.
So my question is : Can I use my own image without to push it in the Docker hub and how I do this ?
If your own image is based on a Dockerfile that you build you can execute the build command on your project while targeting the swarm.
However if the image wasn't built, but created manually you need to have a registry in between that you can push to, either docker hub or some other registry solution like https://github.com/docker/docker-registry
What's the procedure for installing and running Docker on Google Compute Engine?
Until the recent GA release of Compute Engine, running Docker was not supported on GCE (due to kernel restrictions) but with the newly announced ability to deploy and use custom kernels, that restriction is no longer intact and Docker now works great on GCE.
Thanks to proppy, the instructions for running Docker on Google Compute Engine are now documented for you here: http://docs.docker.io/en/master/installation/google/. Enjoy!
They now have a VM which has docker pre-installed now.
$ gcloud compute instances create instance-name
--image projects/google-containers/global/images/container-vm-v20140522
--zone us-central1-a
--machine-type f1-micro
https://developers.google.com/compute/docs/containers/container_vms
A little late, but I wanted to add an answer with a more detailed workflow and links, since answers are still rather scattered:
Create a Docker image
a. Locally
b. Using Google Container Builder
Push local Docker image to Google Container Repository
docker tag <current name>:<current tag> gcr.io/<project name>/<new name>
gcloud docker -- push gcr.io/<project name>/<new name>
UPDATE
If you have upgraded to Docker client versions above 18.03, gcloud docker commands are no longer supported. Instead of the above push, use:
docker push gcr.io/<project name>/<new name>
If you have issues after upgrading, see more here.
Create a compute instance.
This process actually obfuscates a number of steps. It creates a virtual machine (VM) instance using Google Compute Engine, which uses a Google-provided, container-optimized OS image. The image includes Docker and additional software responsible for starting our docker container. Our container image is then pulled from the Container Repository, and run using docker run when the VM starts. Note: you still need to use docker attach even though the container is running. It's worth pointing out only one container can be run per VM instance. Use Kubernetes to deploy multiple containers per VM (the steps are similar). Find more details on all the options in the links at the bottom of this post.
gcloud beta compute instances create-with-container <desired instance name> \
--zone <google zone> \
--container-stdin \
--container-tty \
--container-image <google repository path>:<tag> \
--container-command <command (in quotes)> \
--service-account <e-mail>
Tip You can view available gcloud projects with gcloud projects list
SSH into the compute instance.
gcloud beta compute ssh <instance name> \
--zone <zone>
Stop or Delete the instance. If an instance is stopped, you will still be billed for resources such as static IPs and persistent disks. To avoid being billed at all, use delete the instance.
a. Stop
gcloud compute instances stop <instance name>
b. Delete
gcloud compute instances delete <instance name>
Related Links:
More on deploying containers on VMs
More on zones
More create-with-container options
As of now, for just Docker, the Container-optimized OS is certainly the way to go:
gcloud compute images list --project=cos-cloud --no-standard-images
It comes with Docker and Kubernetes preinstalled. The only thing it lacks is the Cloud SDK command-line tools. (It also lacks python3, despite Google's announce of Python 2 sunset on 2020-01-01. Well, it's still 27 days to go...)
As an additional piece of information I wanted to share, I was searching for a standard image that would offer both docker and gcloud/gsutil preinstalled (and found none, oops). I do not think I'm alone in this boat, as gcloud is the thing you could hardly go by without on GCE¹.
My best find so far was the Ubuntu 18.04 image that came with their own (non-Debian) package manager, snap. The image comes with the Cloud SDK preinstalled, and Docker installs literally in a snap, 11 seconds on an F1 instance initial test, about 6s on an n1-standard-1. The only snag I hit was the error message that the docker authorization helper was not available; an attempt to add it with gcloud components install failed because the SDK was installed as a snap, too. However, the helper is actually there, only not in the PATH. The following was what got me the both tools available in a single transient builder VM in the least amount of setup script runtime, starting off the supported Ubuntu 18.04 LTS image²:
snap install docker
ln -s /snap/google-cloud-sdk/current/bin/docker-credential-gcloud /usr/bin
gcloud -q auth configure-docker
¹ I needed both for a Daisy workflow imaging a disk with both artifacts from GS buckets and a couple huge, 2GB+ library images from the local gcr.io registry that were shared between the build (as cloud builder layers) and the runtime (where I had to create and extract containers to the newly built image). But that's besides the point; one may needs both tools for a multitude of possible reasons.
² Use gcloud compute images list --uri | grep ubuntu-1804 to get the most current one.
Google's GitHub site offers now a gce image including docker. https://github.com/GoogleCloudPlatform/cloud-sdk-docker-image
It's as easy as:
creating a Compute Engine instance
curl https://get.docker.io | bash
Using docker-machine is another way to host your google compute instance with docker.
docker-machine create \
--driver google \
--google-project $PROJECT \
--google-zone asia-east1-c \
--google-machine-type f1-micro $YOUR_INSTANCE
If you want to login this machine on google cloud compute instance, just use docker-machine ssh $YOUR_INSTANCE
Refer to docker machine driver gce
There is now improved support for containers on GCE:
Google Compute Engine is extending its support for Docker containers. This release is an Open Preview of a container-optimized OS image that includes Docker and an open source agent to manage containers. Below, you'll find links to interact with the community interested in Docker on Google, open source repositories, and examples to get started. We look forward to hearing your feedback and seeing what you build.
Note that this is currently (as of 27 May 2014) in Open Preview:
This is an Open Preview release of containers on Virtual Machines. As a result, we may make backward-incompatible changes and it is not covered by any SLA or deprecation policy. Customers should take this into account when using this Open Preview release.
Running Docker on GCE instance is not supported. The instance goes down and not able to login again.
We can use the Docker image given by the GCE, to create a instance.
If your google cloud virtual machine is based on ubuntu use the following command to install docker
sudo apt install docker.io
You may use this link: https://cloud.google.com/cloud-build/docs/quickstart-docker#top_of_page.
The said link explains how to use Cloud Build to build a Docker image and push the image to Container Registry. You will first build the image using a Dockerfile and then build the same image using the Cloud Build's build configuration file.
Its better to get it while creating compute instance
Go to the VM instances page.
Click the Create instance button to create a new instance.
Under the Container section, check Deploy container image.
Specify a container image name under Container image and configure options to run the container if desired. For example, you can specify gcr.io/cloud-marketplace/google/nginx1:1.12 for the container image.
Click Create.
Installing Docker on GCP Compute Engine VMs:
This is the link to GCP documentation on the topic:
https://cloud.google.com/compute/docs/containers#installing
In it it links to the Docker install guide, you should follow the instructions depending on what type of Linux you have running in the vm.