A First Look at Kubernetes Integrated Docker For Mac Platform

Docker support for Kubernetes is now in private beta. As a docker captain, I was able to be a part of the first group to get my hands on the update and can show you what to expect. Kubernetes is ONLY available to those users who are part of the private beta for Docker for Mac 17.12. To access beta builds, you must be signed in within Docker for Mac using your Docker ID. Please remember that this won’t be available to those users who missed to register for Docker Beta Program. 

[Updated: 1/7/2018] – Docker For Mac 17.12 GA is available and is the first release which includes both the orchestrators – Docker Swarm & Kubernetes under the same Docker platform. As of 1/7/2018 – Experimental Kubernetes has been released under Edge Release(still not available under D4M Stable Release). Experimental Kubernetes is still not available for Docker for Windows & Linux platform. It is slated to be available for Docker for Windows next month(mid of February) and then for Linux by March or April.

 

 

 

 

What’s new in Docker 17.12 CE Final Release?

The fresh new Docker 17.12 Community Edition Final Release  includes a standalone Kubernetes server & client, plus Docker CLI integration. The Kubernetes server runs locally within your Docker instance and is a single-node cluster. It is specifically meant for development & testing purpose only. 

Docker 17.12 CE includes the newer Docker Compose 1.18.0-rc2 release along with the below new features & further improvements like:

New Feature:

  •  VM disk size can be changed in settings. (See docker/for-mac#1037).

Bug fixes and minor changes

  • Avoid VM reboot when changing host proxy settings.
  • Don’t break HTTP traffic between containers by forwarding them via the external proxy (docker/for-mac#981)
  • Filesharing settings are now stored in settings.json
  • Daemon restart button has been moved to settings / Reset Tab
  • Display various component versions in About box
  • Better VM state handling & error messsages in case of VM crashes

Important Information:

  • The beta features are being released in a controlled manner: not all users who signed up for the beta will be able to access the features right away. You must be signed in within Docker for Mac using your Docker ID to access the beta builds.

  • The Kubernetes features are only accessible on macOS for now; Windows will follow at a later date.

  • Because this feature is still in beta, it can only be accessed using the latest Docker for Mac release, more precisely on the Edge channel. 

How to get this Beta Release?

You need to install Docker 17.12 Edge Release(it is NOT available under Stable Release yet). Once you install the latest Beta release, all you need is to log in with your Docker ID, select whale menu -> Sign in / Create Docker ID from the menu bar. 

 

 

To enable Kubernetes support and install a standalone instance of Kubernetes running as a Docker container, select Enable Kubernetes and click the Apply and restart button.

 

Once Kubernetes support is enabled, you are able to deploy your workloads, in parallel, on Kubernetes, Swarm, and as standalone containers. Enabling or disabling the Kubernetes server is not going to affect other workloads.

This new beta includes Docker CLI integration for Kubernetes too along with a standalone Kubernetes server & client and this can be verified as shown below. 

 

 

By default, Kubernetes containers are hidden from commands like docker service ls, because managing them manually is not supported. To make them visible, select Show system containers (advanced) and click Apply and restart

 

This means that now you can use the same old favourite docker ps command for displaying Kubernetes internal containers. Isn’t it cool?

 

How to get Kubectl working?

Kubectl comes out-of-the-box by default with this beta release and hence you don’t need to install it. Kubectl is a command line interface for running commands against Kubernetes clusters. It holds a lot of functionality similarity with docker CLI like docker run, docker attach, docker logs and so on. kubectl controls the Kubernetes cluster manager.

The Docker for Mac Kubernetes integration provides the Kubernetes CLI command at /usr/local/bin/kubectl. By default, the kubectl mightn’t work as expected as we still need to choose the correct context.

 

Follow the below steps to get started with kubectl:

Ajeets-MacBook-Air:~ ajeetraina$ kubectl config get-contexts

Let us pick up the docker-for-desktop context:

Ajeets-MacBook-Air:~ ajeetraina$ kubectl config use-context docker-for-desktop
Switched to context “docker-for-desktop”.

Now the kubectl should work fine and display a single node K8s cluster as shown below:

The above output shows us that we were able to connect to our Kubernetes cluster and display the status of our master node. In case you are new to Kubernetes terminology – a Kubernetes Node is a physical or virtual machine used to host application containers.  

Verifying the kubectl version:

Ajeets-MacBook-Air:~ ajeetraina$ sudo kubectl version
Client Version: version.Info{Major:”1″, Minor:”8″, GitVersion:”v1.8.2″, GitCommit:”bdaeafa71f6c7c04636251031f93464384d54963″, GitTreeState:”clean”, BuildDate:”2017-10-24T19:48:57Z”, GoVersion:”go1.8.3″, Compiler:”gc”, Platform:”darwin/amd64″}
Server Version: version.Info{Major:”1″, Minor:”8″, GitVersion:”v1.8.2″, GitCommit:”bdaeafa71f6c7c04636251031f93464384d54963″, GitTreeState:”clean”, BuildDate:”2017-10-24T19:38:10Z”, GoVersion:”go1.8.3″, Compiler:”gc”, Platform:”linux/amd64″}

Verifying the Kubernetes Containers

Displaying the Kubernetes Cluster Information

Ajeets-MacBook-Air:~ ajeetraina$ kubectl cluster-info
Kubernetes master is running at https://localhost:6443
KubeDNS is running at https://localhost:6443/api/v1/namespaces/kube-system/services/kube-dns/proxy
To further debug and diagnose cluster problems, use ‘kubectl cluster-info dump’

 

Test Drive WordPress Application on a single Node Kubernetes Cluster

With our Kubernetes cluster ready, let us now start deploying application containers. I have picked up my all time favourite WordPress application.

Let us test and deploy WordPress application on top of this single node Kubernetes cluster. We will follow the below steps:

  1. Creating a Persistent Volume
  2. Creating a Secret
  3. Deploying MySQL
  4. Deploying WordPress

Creating a Persistent Volume

We will first create two Persistent Volumes from the local-volumes.yaml file shown below:

Copy the code into a file called local-volume.yaml and then run the below command:

kubectl create -f local-volumes.yaml

You can verify the details below:

Creating a Secret

Ajeets-MacBook-Air:~ ajeetraina$ kubectl create secret generic mysql-pass --from-literal=password=collab123
secret “mysql-pass” created

Verifying the secrets:

Ajeets-MacBook-Air:~ ajeetraina$ kubectl get secrets
NAME TYPE DATA AGE
default-token-fwflq kubernetes.io/service-account-token 3 1h
mysql-pass Opaque 1 15s
Ajeets-MacBook-Air:~ ajeetraina$

Deploying MySQL

The below YAML describes a single-instance MySQL Deployment. The MySQL container mounts the PersistentVolume at /var/lib/mysql. The MYSQL_ROOT_PASSWORD environment variable sets the database password from the Secret.

Create a file called “mysql-deployment.yaml” and copy the above content. Once you save the file, run the below command to bring up MySQL container:

Ajeets-MacBook-Air:~ ajeetraina$ kubectl create -f mysql-deployment.yaml
service “wordpress-mysql” created
persistentvolumeclaim “mysql-pv-claim” created

Verifying the MySQL Pod:

Ajeets-MacBook-Air:~ ajeetraina$ kubectl get pods
NAME               READY       STATUS      RESTARTS         AGE
wordpress-mysql-7b4ffb6fb4-gfk8n 0/1 ContainerCreating 0 34s

 

 

Deploying WordPress

The below YAML describes a single-instance WordPress Deployment and Service. It uses a PVC for persistent storage & a Secret for the password as shown in the content. Did you see : type: NodePort. entry? This setting exposes WordPress to traffic from outside of the cluster.

Ajeets-MacBook-Air:~ ajeetraina$ kubectl create -f wordpress-deployment.yaml
service “wordpress” created
persistentvolumeclaim “wp-pv-claim” created
deployment “wordpress” created

Let us verify the pods using kubectl get pods command:

 

 

Verify that the Services are up and running by running the following command:

 

 

You can fetch overall status of the pods and running containers using the below command:

 

By now, you should be able to visit http://localhost and see WordPress page as shown below:

 

 

While you access the WordPress page,you can see logs under the WordPress container as shown below:

 

Hence, this is how your WordPress page can be customized flawlessly.

 

In my next blog post, I will deep dive into how Docker Swarm and Kubernetes gonna work together under the same cluster environment.

Did you find this blog helpful?  Feel free to share your experience. Get in touch @ajeetsraina

If you are looking out for contribution/discussion, join me at Docker Community Slack Channel.

 

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Running LinuxKit locally on Oracle VirtualBox Platform Made Easy

LinuxKit GITHUB repository has already crossed 1800 commits, 3600+ stars &  been forked 420+ times since April 2017 when it was open sourced by Docker Inc for the first time. LinuxKit today support dozens of platforms which falls under Cloud, local hypervisor & Bare metal systems categories. Recently, arm64 support was added and I published a blog post which talks about building LinuxKit for the minimalist Raspberry Pi 3.  Improved Kubernetes support has been another enhancement and you can follow my blog to build Multi-Node Kubernetes  cluster using LinuxKit. In case you want to build Multi-Node Kubernetes cluster using the newly added CRI-containerd & Moby, don’t miss out this blog post.

 

 

In case you’re very new to LinuxKit , it is a toolkit for building secure, portable & lean operating systems for containers. It uses moby tooling to build system images. Everything runs in a container. LinuxKit uses the linuxkit tool for building, pushing and running Virtual Machine images. The moby tool assembles a set of containerized components into in image. The simplest type of image is just a tar file of the contents.The YAML configuration specifies the components used to build up an image . All components are downloaded at build time to create an image. The image is self-contained and immutable, so it can be tested reliably for continuous delivery.

A Look at LinuxKit Architecture

At the base of LinuxKit, there is a modern Linux kernel which specifies a kernel Docker image, containing a kernel and a filesystem tarball, eg containing modules. The minimal init is the base init process Docker image, which is unpacked as the base system, containing initcontainerd and a few tools. It is basically built from pkg/init/.  The onboot  containers are the system containers, executed sequentially in order. They should terminate quickly when done. The services is the system services, which normally run for the whole time the system is up. The .files are additional files to add to the image

 

What’s New in LinuxKit?

Below are the list of new features which has been introduced in LinuxKit recently –

 

 

Early this year, I wrote a blog post which talks about how to manually create LinuxKit ISO image and then mount it to run it under Oracle VirtualBox. The method was complicated as it required converting VMDK file into .VDI format first and then registering the VM using VBoxManage CLI.

Test-Drive LinuxKit OS on Oracle VirtualBox running on macOS Sierra

Now with the introduction of linuxkit run vbox CLI, it is just a matter of 2-3 minutes to get it  on VirtualBox up and running.

Under this blog post, we will see how LinuxKit OS can be built and run on Oracle VirtualBox in just 2 minutes.

Pre-requisites:

  • MacOS Sierra 
  • Docker for Mac installed on MacOS
  • Docker Up and Running
  • Oracle VirtualBox 

Clone the LinuxKit Repository:

git clone https://github.com/linuxkit/linuxkit

Building the LinuxKit Tool

cd linuxkit
make

Place LinuxKit under the right executable PATH:

cp bin/linuxkit /usr/local/bin/

Building ISO image for VirtulBox.

Before we go ahead and build ISO for Virtualbox, let us look at the newly introduced command line option:

 

Now you can use `LinuxKit build` option to build the ISO image. Let us look into this sub-command:

 

Let’s run the below command to build iso-bios format of docker.yml which can be found under linuxkit/examples directory under LinuxKit repository.

linuxkit build -format iso-bios --name testbox docker.yml

This builds up ISO image as shown below:

 

Running the ISO for VirtualBox

Justin Cormack, a LinuxKit maintainer did a great job in introducing a new CLI option linuxkit run box  as shown below:

Run the below command to initiate  LinuxKit OS on VirtualBox in the form of VM:

linuxkit run vbox --iso testbox.iso

This will initiate a VM called testbox under Virtualbox as shown below:

You can verify under VirtualBox Manager:

 

Open up Console to see LinuxKit running under this new VM:

 

So, you can access either through terminal or directly under the console but NOT both at the same time.

Accessing Docker Service Container

To access the Docker service container, first list out the running service containers:

ctr tasks ls

This will list out the running service containers as shown below:

 

Lets us enter into docker service container and verify Docker release version:

ctr tasks exec -t --exec-id 502 docker sh

This will allow it to enter into shell as shown. Run docker version command to verify the currently available Docker release.

 

 

Please note that networking doesn’t get enabled by default for these service container. You will need to  manually enable “Cable Connected” option under VirtualBox > Settings > Network > Advanced to get the IP address assigned to the network interface.

 

I have raised this issue with LinuxKit Team and you can track it here.

Let us go back to the terminal and try to pull few Docker images as shown below:

 

Wow ! So we have Docker service container running  inside LinuxKit OS on top of Oracle VirtualBox platform flawlessly.

Did you find this blog helpful?  Feel free to share your experience. Get in touch @ajeetsraina

Interested to read more on LinuxKit? Check this out – 

Building a secure Docker Host VM on VMware ESXi using LinuxKit & Moby

Building a Secure VM based on LinuxKit on Microsoft Azure Platform

Building Docker For Mac 17.06 Community Edition using Moby & LinuxKit

Running LinuxKit on AWS Platform made easy

If you are looking out for contribution/discussion, join me at Docker Community Slack Channel.

 

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Building a minimalistic LinuxKit OS on Raspberry Pi 3 using Moby

LinuxKit GITHUB repository recently crossed 3495 stars, forked around 410+ times and added 80+ contributors. Just 7 months old project and it has already gained lot of momentum across the Moby community. In case you’re new, LinuxKit is the first use case for the Moby Project, which is basically a toolkit used for building secure, portable, and lean operating systems for containers. It uses YAML files to describe the complete system, and Moby uses it to assemble the boot image and verify the signature. LinuxKit uses the Moby tool for image builds, and the LinuxKit tool for pushing and running VM images.

In the last Dockercon 2017 EU, Docker Inc. announced the latest additions which includes – 

  • Support for arm64 architecture
  • Improved platform support (Azure, IBM Bluemix, AWS, Google Cloud Platform, VMware, Hyper-V, Packets.net etc.)
  • Upcoming Kubernetes support (I tried it and believe me it was a great experience)
  • Linux containers on Windows (LCOW) preview support
  • Multi-arch build system
  • Fully immutable system images
  • Namespace sharing
  • Support for the latest Linux kernels

LinuxKit today supports building & booting images on a Raspberry Pi 3 using the mainline arm64 bit kernels. Under this blog post, I will show how to build Linuxkit OS on Raspberry Pi 3.

Pre-requisites:

  1. OpenSUSE 42.2 Leap 
  2. Raspberry Pi 3
  3. SD card
  4. Win32 Disk Imager 
  5. SD Formatter(Windows Client) or Etcher(for MacOS)

[PLEASE NOTE – In case you’re trying to build LinuxKit on Raspbian OS running on your Pi box, it is NOT going to work. Reason – There is no 32-bit kernel built for Linuxkit. As of today, only 64-bit based kernel is available for LinuxKit OS. If you’re planning to build Linuxkit with 32-bit kernel, feel free to share your findings.I picked up openSUSE OS as it has a arm64 flash for Raspberry Pi 3 ]

Early this year I wrote a blog on how to test-Drive Docker 1.12 on first 64-bit ARM OpenSUSE running on Raspberry Pi 3. You can refer it to get started.

  1. Download OpenSUSE 42.2 Leap from this link and follow the blog thoroughly to get it ready on SD card.

 

  

    2. Install Docker on OpenSUSE 42.2 

By default, OpenSUSE Leap comes with Docker 1.12 edition. Though it is pretty old, we can still build LinuxKit on the operating system. Ensure that Docker is up and running on your Raspberry Pi 3 box:

systemctl restart docker 

 

      3.  Building Moby tool on Raspberry Pi

You will need Go version 1.8 atleast to get Moby and Linuxkit built on OpenSUSE 11.3.

go get -u github.com/moby/tool/cmd/moby

 

      4.  Building LinuxKit 

 

linux:~/linuxkit # go get -u github.com/linuxkit/linuxkit/src/cmd/linuxkit
github.com/linuxkit/linuxkit/src/cmd/linuxkit
/usr/lib64/go/1.8/pkg/tool/linux_arm64/link: running gcc failed: fork/exec /usr/bin/gcc: cannot allocate memory

In case you encounter the above error message, you need to fix this issue by adding more space to swap partition.

Step:1 – Create a 1GB File

We use the DD command to create the 1G file basically writing “0″ into the file swap_1 in the “/” file system.

linux:~/linuxkit # dd if=/dev/zero of=/swap_1 bs=1024 count=1000000
1000000+0 records in
1000000+0 records out
1024000000 bytes (1.0 GB, 977 MiB) copied, 64.4194 s, 15.9 MB/s

Step-2 – Setting up File as a swap area.

linux:~/linuxkit # mkswap /swap_1
mkswap: /swap_1: insecure permissions 0644, 0600 suggested.
Setting up swapspace version 1, size = 976.6 MiB (1023995904 bytes)
no label, UUID=e2f577a5-ff87-40ee-adfc-78e8feef3a36

Step-3 – Enabling Swap File for system to start paging physical memory pages onto it.

linux:~/linuxkit # swapon /swap_1
swapon: /swap_1: insecure permissions 0644, 0600 suggested.

Step-4: Verify the new free memory:

linux:~/linuxkit # free -tom
             total       used       free     shared    buffers     cached
Mem:           785        745         40          3          1        688
Swap:         1462         65       1396
Total:        2247        810       1437

 

Now, you can build up LinuxKit tool flawlessly. Once installed, you can verify it with the below command:

 

Let us build minimal LinuxKit OS using the below command:

moby build -format rpi3 minimal.yml

 

 

Now it’s time to convert minimal.tar into ISO format. You can leverage the following tool:

bash-3.2# hdiutil makehybrid -o /Users/ajeetraina/Desktop/linuxkit.iso minimal -iso -joliet
Creating hybrid image…

By now, you should be able to boot the minimal LinuxKit ISO on your Raspberry Pi system via uboot tool. It can also be directly be extracted onto a FAT32 formatted SD card to boot your Raspberry Pi. 

Did you find this blog helpful?  Feel free to share your experience. Get in touch @ajeetsraina

If you are looking out for contribution/discussion, join me at Docker Community Slack Channel.

 

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How to Build Kubernetes Cluster using CRI-containerd & Moby

Let’s talk about CRI Vs CRI-Containerd…

Container Runtime Interface(a.ka. CRI) is a standard way to integrate Container Runtime with Kubernetes. It is new plugin interface for container runtimes. It is a plugin interface which enables kubelet to use a wide variety of container runtimes, without the need to recompile.Prior to the existence of CRI, container runtimes (e.g., docker, rkt ) were integrated with kubelet through implementing an internal, high-level interface in kubelet. Formerly known as OCID, CRI-O is strictly focused on OCI-compliant runtimes and container images. 

Last month, the new CRI-O version 1.2 got announced. This is a minor release of the v1.0.x cycle supporting Kubernetes 1.7.With CRI, Kubernetes can be container runtime-agnostic. Now this provides flexibility to the providers of container runtimes who don’t need to implement features that Kubernetes already provides. CRI-O allows you to run containers directly from Kubernetes – without any unnecessary code or tooling.

For those viewers who compare Docker Runtime Engine Vs CRI-O, here is an important note – 

CRI-O is not really a competition to the docker project – in fact it shares the same OCI runC container runtime used by docker engine, the same image format, and allows for the use of docker build and related tooling. Through this new runtime, it is expected to bring developers more flexibility by adding other image builders and tooling in the future.Please remember that CRI is not an interface for full-fledge, all inclusive container runtime.

How does CRI-O workflow look like ?

When Kubernetes needs to run a container, it talks to CRI-O and the CRI-O daemon works with container runtime  to start the container. When Kubernetes needs to stop the container, CRI-O handles that. Everything  just works behind the scenes to manage Linux containers.

 

 

Kubelet is a node agent which has gRPC client which talks to gRPC server rightly called as shim. The shim then talks to container runtime. Today the default implementation is Docker Shim.Docker shim then talks to Docker daemon using the classical APIs. This works really well.

CRI consists of a protocol buffers and gRPC API, and libraries, with additional specifications and tools under active development.

Introducing CRI-containerd

CRI-containerd is containerd based implementation of CRI.  This project started in April 2017. In order to have Kubernetes consume containerd for its container runtime, containerd team implemented the CRI interface.  CRI is responsible for distribution and the lifecycle of pods and containers running on a cluster.The scope of containerd 1.0 aligns with the requirement of CRI. In case you want to deep-dive into it, don’t miss out this link.

Below is how CRI-containerd architecture look like:

                                                                                                             

In my last blog post, I talked about how to setup Multi-Node Kubernetes cluster using LinuxKit. It basically used Docker Engine to build up minimal & immutable Kubernetes OS images with LinuxKit. Under this blog post, we will see how to build it using CRI-containerd.

Infrastructure Setup:

  • OS – Ubuntu 17.04 
  • System – ESXi VM
  • Memory – 8 GB
  • SSH Key generated using ssh-keygen -t rsa and put under $HOME/.ssh/ folder

Caution – Please note that this is still under experimental version. It is currently under active development and hence don’t expect it to work as full-fledge K8s cluster.

Copy the below script to your Linux System and execute it flawlessly –

 

 

Did you notice the parameter –  KUBE_RUNTIME=cri-containerd ?

The above parameter is used to specify that we want to build minimal and immutable K8s ISO image using CRI-containerd. If you don’t specify the parameter, it will use Docker Engine to build this ISO image.

The above script is going to take sometime to finish.

It’s always a good idea to see what CRI-containerd specific files are present. 

Let us look into CRI-Containerd directory – 

Looking at the content of cri-containerd.yml where it defines a service called as critical-containerd.

The below list of files gets created with the output files like Kube-master-efi.iso right under Kubernetes directory – 

 

 

 

By the end of this script you should be able to see Kubernetes LinuxKit OS booting up – 

 

 

CRI-containerd let the user containers in the same sandbox share the network namespace and hence you will see the message ” This system is namespaced”.

You can find the overall screen logs to watch how it boots up –

 

Next, let us try to see what container services are running:

 

You will notice that cri-containerd service is up and running. 

Let us enter into one of tasks containers and initialize the master node with kubeadm-init script which comes by default –

(ns: getty) linuxkit-ee342f3aebd6:~# ctr tasks exec -t --exec-id 654 kubelet sh
/ # ls

Execute the below script –

/ # kubeadm-init.sh
/ # kubeadm-init.sh
[kubeadm] WARNING: kubeadm is in beta, please do not use it for production clusters.
[init] Using Kubernetes version: v1.8.2
[init] Using Authorization modes: [Node RBAC]
[preflight] Skipping pre-flight checks
[kubeadm] WARNING: starting in 1.8, tokens expire after 24 hours by default (if you require a non-expiring token use --token-ttl 0)
[certificates] Generated ca certificate and key.
[certificates] Generated apiserver certificate and key.
[certificates] apiserver serving cert is signed for DNS names [linuxkit-ee342f3aebd6 kubernetes kubernetes.default kubernetes.default.svc kubernetes.default.svc.cluster.local] and IPs [10.96.0.1 10.0.2.15]
[certificates] Generated apiserver-kubelet-client certificate and key.
[certificates] Generated sa key and public key.
[certificates] Generated front-proxy-ca certificate and key.
[certificates] Generated front-proxy-client certificate and key.
[certificates] Valid certificates and keys now exist in “/etc/kubernetes/pki”
[kubeconfig] Wrote KubeConfig file to disk: “admin.conf”
[kubeconfig] Wrote KubeConfig file to disk: “kubelet.conf”
[kubeconfig] Wrote KubeConfig file to disk: “controller-manager.conf”
[kubeconfig] Wrote KubeConfig file to disk: “scheduler.conf”
[controlplane] Wrote Static Pod manifest for component kube-apiserver to “/etc/kubernetes/manifests/kube-apiserver.yaml”
[controlplane] Wrote Static Pod manifest for component kube-controller-manager to “/etc/kubernetes/manifests/kube-controller-manager.yaml”
[controlplane] Wrote Static Pod manifest for component kube-scheduler to “/etc/kubernetes/manifests/kube-scheduler.yaml”
[etcd] Wrote Static Pod manifest for a local etcd instance to “/etc/kubernetes/manifests/etcd.yaml”
[init] Waiting for the kubelet to boot up the control plane as Static Pods from directory “/etc/kubernetes/manifests”
…..

Now you should be able to join the other worker nodes( I have discussed the further steps under  this link ).

Did you find this blog helpful?  Feel free to share your experience. Get in touch @ajeetsraina

If you are looking out for contribution/discussion, join me at Docker Community Slack Channel.

 

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Getting Started with Multi-Node Kubernetes Cluster using LinuxKit

Here’s a BIG news for the entire Container Community – “Kubernetes Support is coming to Docker Platform“. What does this mean? This means that developers and operators can now build apps with Docker and seamlessly test and deploy them using both Docker Swarm and Kubernetes. This announcement was made on the first day of Dockercon EU 2017 Copenhagen where Solomon Hykes, Founder of Docker invited Kubernetes Co-Founder  Tim Hockin on the stage for the first time. Tim welcomed the Docker family to the vibrant Kubernetes world. 

In case you missed out the news, here are the important takeaways from Docker-Kubernetes announcement –

  • Kubernetes support will be added to Docker Enterprise Edition for the first time.
  • Kubernetes support will be added optionally to Docker Community Edition for Mac and Windows.
  • Kubernetes and Docker Swarm both orchestrators will be available under Docker Platform.
  • Kubernetes integration will be made available under Moby projects too.

Docker Inc firmly believes that bringing Kubernetes to Docker will simplify and advance the management of Kubernetes for developers, enterprise IT and hackers to deliver the advanced capabilities of Docker to a broader set of applications.

 

 

In case you want to be first to test drive, click here to sign up and be notified when the beta is ready.

Please be aware that the beta program is expected to be ready at the end of 2017.

Can’t wait for beta program? Here’s a quick guide on how to get Multi-Node Kubernetes cluster up and running using LinuxKit.

Under this blog post, we will see how to build 5-Node Kubernetes Cluster using LinuxKit. Here’s the bonus – We will try to deploy WordPress application on top of Kubernetes Cluster. I will be building it on my macOS Sierra 10.12.6 system running the latest Docker for Mac 17.09.0 Community Edition up and running.

Pre-requisite:

  1. Ensure that the latest Docker release is up and running.

       2. Clone the LinuxKit repository:

sudo git clone https://github.com/ajeetraina/linuxkit
cd projects/kubernetes/

3. Execute the below script to build Kubernetes OS images using LinuxKit & Moby:

sudo chmod +x script4k8s.sh

All I have done is put all the manual steps under a script as shown below:

 

 

This should build up Kubernetes OS image.

Run the below command to know the IP address of the kubelet container:

ip adds show dev eth0

 

You can check the list of tasks running as shown below:

Once this kubelet is ready, it’s time to login into it directly from a new terminal:

sudo ./ssh_into_kubelet.sh 192.168.65.3

So you have already SSH into the kubelet container rightly named “LinuxKit Kubernetes Project”.

We are going to mark it as “Master Node”.

Initializing the Kubernetes Master Node

Now it’s time to manually initialize master node with Kubeadm command as shown below:

sudo kubeadm-init.sh

Wait for few minutes till kubeadm command completes. Once done, you can use kubeadm join argument as shown below:

 

Your Kubernetes master node gets initialized successfully as shown below:

 

 

It’s time to run the below list of commands command to join the list of worker nodes:

pwd
/Users/ajeetraina/linuxkit/projects/kubernetes
sudo ./boot.sh 1 --token 4cec03.1a7ccb44115f427a 192.168.65.3:6443
sudo ./boot.sh 2 --token 4cec03.1a7ccb44115f427a 192.168.65.3:6443
sudo ./boot.sh 3 --token 4cec03.1a7ccb44115f427a 192.168.65.3:6443
sudo ./boot.sh 4 --token 4cec03.1a7ccb44115f427a 192.168.65.3:6443

 

It brings up 5-Node Kubernetes cluster as shown below:

 

Deploying WordPress Application on Kubernetes Cluster:

Let us try to deploy a WordPress application on top of this Kubernetes Cluster.

Head over to the below directory:

cd linuxkit/projects/kubernetes/wordpress

 

Creating a Persistent Volume

kubectl create -f local-volumes.yaml

The content of local-volumes.yml look like:


This creates 2 persistent volumes:

Verifying the volumes:

 

Creating a Secret for MySQL Password

kubectl create secret generic mysql-pass --from-literal=password=mysql123

 
Verifying the secrets using the below command:
kubectl get secrets

 

Deploying MySQL:

kubectl create -f mysql-deployment.yaml

 

Verify that the Pod is running by running the following command:

kubectl get pods

 

Deploying WordPress:

kubectl create -f wordpress-deployment.yaml

By now, you should be able to access WordPress UI using the web browser.

Did you find this blog helpful?  Feel free to share your experience. Get in touch @ajeetsraina

If you are looking out for contribution/discussion, join me at Docker Community Slack Channel.

 

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Docker, Prometheus & Pushgateway for NVIDIA GPU Metrics & Monitoring

In my last blog post, I talked about how to get started with NVIDIA docker & interaction with NVIDIA GPU system. I demonstrated NVIDIA Deep Learning GPU Training System, a.k.a DIGITS by running it inside Docker container. ICYMI –  DIGITS is essentially a webapp for training deep learning models and is used to rapidly train the highly accurate deep neural network (DNNs) for image classification, segmentation and object detection tasks.The currently supported frameworks are: Caffe, Torch, and Tensorflow. It simplifies common deep learning tasks such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real time with advanced visualizations, and selecting the best performing model from the results browser for deployment. 

 

In a typical HPC environment where you run 100 and 100s of NVIDIA GPU equipped cluster of nodes, it becomes important to monitor those systems to gain insight of the performance metrics, memory usage, temperature and utilization. . Tools like Ganglia & Nagios etc. are very popular due to their scalable  & distributed monitoring architecture for high-performance computing systems such as clusters and Grids. It leverages widely used technologies such as XML for data representation, XDR for compact, portable data transport, and RRDtool for data storage and visualization. But with the advent of container technology, there is a need of modern monitoring tools and solutions which works well with Docker & Microservices. 

It’s all modern world of Prometheus Stack…

Prometheus is 100% open-source service monitoring system and time series database written in Go.It is a full monitoring and trending system that includes built-in and active scraping, storing, querying, graphing, and alerting based on time series data. It has knowledge about what the world should look like (which endpoints should exist, what time series patterns mean trouble, etc.), and actively tries to find faults.

How is it different from Nagios?

Though both serves a purpose of monitoring, Prometheus wins this debate with the below major points – 

  • Nagios is host-based. Each host can have one or more services, which has one check.There is no notion of labels or a query language. But Prometheus comes with its robust query language called “PromQL”. Prometheus provides a functional expression language that lets the user select and aggregate time series data in real time. The result of an expression can either be shown as a graph, viewed as tabular data in Prometheus’s expression browser, or consumed by external systems via the HTTP API.
  • Nagios is suitable for basic monitoring of small and/or static systems where blackbox probing is sufficient. But if you want to do whitebox monitoring, or have a dynamic or cloud based environment then Prometheus is a good choice.
  • Nagios is primarily just about alerting based on the exit codes of scripts. These are called “checks”. There is silencing of individual alerts, however no grouping, routing or deduplication.

Let’s talk about Prometheus Pushgateway..

Occasionally you will need to monitor components which cannot be scraped. They might live behind a firewall, or they might be too short-lived to expose data reliably via the pull model. The Prometheus Pushgateway allows you to push time series from these components to an intermediary job which Prometheus can scrape. Combined with Prometheus’s simple text-based exposition format, this makes it easy to instrument even shell scripts without a client library.

The Prometheus Pushgateway allow ephemeral and batch jobs to expose their metrics to Prometheus. Since these kinds of jobs may not exist long enough to be scraped, they can instead push their metrics to a Pushgateway. The Pushgateway then exposes these metrics to Prometheus. It is important to understand that the Pushgateway is explicitly not an aggregator or distributed counter but rather a metrics cache. It does not have statsd-like semantics. The metrics pushed are exactly the same as you would present for scraping in a permanently running program.For machine-level metrics, the textfile collector of the Node exporter is usually more appropriate. The Pushgateway is intended for service-level metrics. It is not an event store

Under this blog post, I will showcase how NVIDIA Docker, Prometheus & Pushgateway come together to  push NVIDIA GPU metrics to Prometheus Stack.

Infrastructure Setup:

  • Docker Version: 17.06
  • OS: Ubuntu 16.04 LTS
  • Environment : Managed Server Instance with GPU
  • GPU: GeForce GTX 1080 Graphics card

Cloning the GITHUB Repository

Run the below command to clone the below repository to your Ubuntu 16.04 system equipped with GPU card:

git clone https://github.com/ajeetraina/nvidia-prometheus-stats

Script to bring up Prometheus Stack(Includes Grafana)

Change to nvidia-prometheus-stats directory with proper execute permission & then execute the ‘start_containers.sh’ script as shown below:

cd nvidia-prometheus-stats
sudo chmod +x start_containers.sh
sudo sh start_containers.sh

This script will bring up 3 containers in sequence – Pushgateway, Prometheus & Grafana

Executing GPU Metrics Script:

NVIDIA provides a python module for monitoring NVIDIA GPUs using the newly released Python bindings for NVML (NVIDIA Management Library). These bindings are under BSD license and allow simplified access to GPU metrics like temperature, memory usage, and utilization.

Next, under the same directory, you will find a python script called “test.py”.

Execute the script (after IP under line number – 124 as per your host machine) as shown below:

sudo python test.py

That’s it. It is time to open up Prometheus & Grafana UI under http://<IP-address>:9090

Just type gpu under the Expression section and you will see the list of GPU metrics automatically turned up as shown below:

Accessing the targets

Go to Status > Targets to see what targets are accessible. The Status should show up UP.

Click on Push gateway Endpoint to access the GPU metrics in details as shown:

Accessing Grafana

 You can access Grafana through the below link:

                                          http://<IP-address>:3000

 

Did you find this blog helpful?  Feel free to share your experience. Get in touch @ajeetsraina

If you are looking out for contribution/discussion, join me at Docker Community Slack Channel.

 

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Building a secure Docker Host VM on VMware ESXi using LinuxKit & Moby

Post Dockercon 2017 @ Austin TX,  I raised a feature request titled “LinuxKit command to push vmware.vmdk to remote ESXi datastore”. Within few weeks time, the feature was introduced by LinuxKit team. A Special thanks goes to Daniel Finneran who worked hard to get this feature merged into the LinuxKit main branch.

 

LinuxKit project is 5 month old now. It has already bagged 3100+ stars, added up 69 contributors and 350+ forks till date. If you are pretty new, LinuxKit is not a full host operating system, as it primarily has two jobs: run containerd containers, and be secure. It uses modern kernels, and updates frequently following new releases. As such, the system does not contain extraneous packages or drivers by default. Because LinuxKit is customizable, it is up to individual operators to include any additional bits they may require.

LinuxKit is undoubtedly Secure

The core system components included in LinuxKit userspace are key to security, and written in type safe languages, such as RustGo and OCaml, and run with maximum privilege separation and isolation. The project is currently leveraging MirageOS to construct unikernels to achieve this, and that progress can be tracked here: as of this writing, dhcp is the first such type safe program. There is ongoing work to remove more C components, and to improve, fuzz test and isolate the base daemons. Further rationale about the decision to rewrite system daemons in MirageOS is explained at length in this document. I am planning to come up with blog post to brief on “LinuxKit Security” aspect. Keep an eye on this space in future..

Let’s talk about building a secure Docker Host VM…

I am a great fan of VMware PowerCLI. I have been using it since the time I was working full time in VMware Inc.(during 2010-2011 timeframe). Today the most quickest way to get VMware PowerCLI up and running is by using PhotonOS based Docker Image. Just one Docker CLI and you are already inside Photon OS running PowerShell & PowerCLI to connect to remote ESXi to build up VMware Infrastructure. Still this mightn’t give you a secure Docker host environment. If you are really interested to build a secure, portable and lean Docker Host operating system, LinuxKit is the right tool. But how?

Under this blog post, I am going to show how Moby & LinuxKit can help you in building a secure Docker 17.07 Host VM on top of VMware ESXi.

Pre-requisites:

  • VMware vSphere ESXi 6.x
  • Linux or MacOS with Go packages installed
  • Docker 17.06/17.07 installed on the system

The below commands has been executed on one of my local Ubuntu 16.04 LTS system which can reach out to ESXi system flawlessly.

Cloning the LinuxKit Repository:

git clone https://github.com/linuxkit/linuxkit

Building Moby & LinuxKit:

cd linuxkit
make

Configuring the right PATH for Moby & LinuxKit

cp bin/moby /usr/local/bin
cd bin/linuxkit /usr/local/bin

A Peep into vmware.yml File

The first 3 lines shows modern, securely configured kernel. The init section spins up containerd to run services. The onboot section allows dhcpd for networking. The services includes getty service container for shell, runs nginx service container. The trust section indicates all images signed and verified.

Building VMware ISO Image using Moby

moby build -output iso-bios -name vmware vmware.yml

 

Pushing VMware ISO Image to remote ESXi datastore

linuxkit push vcenter -datastore=datastore1 -hostname=myesxi.dell.com -url https://root:xxx@100.98.x.x/sdk -folder=linuxkit vmware.iso

Usage of linuxkit push vcenter :-

 

Running a secure VM directly from the ESXi datastore using LinuxKit

dell@redfish-ubuntu:~/linuxkit/examples$ sudo linuxkit run vcenter -cpus 8 -datastore datastore1 -mem 2048 -network ‘VM Network’ -hostname myesxi.dell.com -powerOn -url  https://root:xxx@100.98.x.x/sdk vmware.iso
Creating new LinuxKit Virtual Machine
Adding ISO to the Virtual Machine
Adding VM Networking
Powering on LinuxKit VM

Now let us verify that VM is up and running using either VMware vSphere Client or SDK URL.

You will find that VM is already booted up with the latest Docker 17.07 platform up and running.

Building a Docker Host VM using Moby & LinuxKit

In case you want to build a Docker Host VM, you can refer to the below vmware.yml file:

Just re-run the below command to get the new VM image:

moby build -output iso-bios -name vmware docker-vmware.yml

Follow the above steps to push it to remote datastore and run it using LinuxKit. Hence, you have a secured Docker 17.07 Host ready to build Docker Images and build up application stack.

How about building Photon OS based Docker Image using Moby & LinuxKit? Once you build it and push it to VM , its all ready to build Virtual Infrastructure. Interesting, Isn’t it?

Did you find this blog helpful?  Feel free to share your experience. Get in touch @ajeetsraina

If you are looking out for contribution/discussion, join me at Docker Community Slack Channel.

 

 

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Running NVIDIA Docker in the GPU-Accelerated Data Center

It’s time to look at GPUs inside Docker container..

Docker is the leading container platform which provides both hardware and software encapsulation by allowing multiple containers to run on the same system at the same time each with their own set of resources (CPU, memory, etc) and their own dedicated set of dependencies (library version, environment variables, etc.). Docker  can now be used to containerize GPU-accelerated applications. In case you’re new to GPU-accelerated computing, it is basically the use of graphics processing unit  to accelerates high performance computing workloads and applications. This means you can easily containerize and isolate accelerated application without any modifications and deploy it on any supported GPU-enabled infrastructure. 

Docker does not natively support NVIDIA GPUs within containers. Though there are available workaround like fully installing the NVIDIA drivers inside the container and map in the character devices corresponding to the NVIDIA GPUs (e.g. /dev/nvidia0) on launch but still it is not recommended.

Here comes nvidia-docker plugin for a rescue…

The nvidia-docker is an open source project hosted on GITHUB and it provides driver-agnostic CUDA images  & docker command line wrapper that mounts the user mode components of the driver and the GPUs (character devices) into the container at launch. With this enablement, the NVIDIA Docker plugin enables deployment of GPU-accelerated applications across any Linux GPU server with NVIDIA Docker support. What does this mean? – Using Docker, we can develop and prototype GPU applications on a workstation, and then ship and run those applications anywhere that supports GPU containers. Earlier this year, the nvidia-docker 1.0.1 release announced the support for Docker 17.03 Community & Enterprise Edition both.

Some of the key notable benefits includes –

  • Legacy accelerated compute apps can be containerized and deployed on newer systems, on premise, or in the cloud.
  • Ease of Deployment
  • Isolation of Resource
  • Bare Metal Performance
  • Facilitate Collaboration
  • Run access heterogeneous CUDA toolkit environments (sharing the host driver)
  • Specific GPU resources can be allocated to container for better isolation and performance.
  • You can easily share, collaborate, and test applications across different environments.
  • Portable and reproducible builds

                                                                                                                                                               ~source: Nvidia

 

Let’s talk about libnvidia-container a bit..

libnvidia is NVIDIA container runtime library. The repository  provides a library and a simple CLI utility to automatically configure GNU/Linux containers leveraging NVIDIA hardware.The implementation relies on kernel primitives and is designed to be agnostic of the container runtime. Basic features includes –

  • Integrates with the container internals
  • Agnostic of the container runtime
  • Drop-in GPU support for runtime developers
  • Better stability, follows driver releases
  • Brings features seamlessly (Graphics, Display, Exclusive mode, VM, etc.)

                                                                                                                                                                             ~ source: NVIDIA

Under this blog post, I will show you how to get started with nvidia-docker to interact with NVIDIA GPU system and then look at few of interesting applications which can be build for GPU-accelerated data center. Let us get started – 

Infrastructure Setup:

Docker Version: 17.06

OS: Ubuntu 16.04 LTS

Environment : Manager Server Instance with GPU

GPU: GeForce GTX 1080 Graphics card

 

  • Verify that GPU card is equipped in your hardware:

 

  • Install nvidia-docker & nvidia-docker-plugin under Ubuntu 16.04 using wget as shown below:
sudo wget -P /tmp https://github.com/NVIDIA/nvidia-docker/releases/download/v1.0.1/nvidia-docker_1.0.1-1_amd64.deb
sudo dpkg -i /tmp/nvidia-docker*.deb && rm /tmp/nvidia-docker*.deb

Initializing nvidia-docker service:

ajit@Ubuntu-1604-xenial-64-minimal:~$ systemctl status nvidia-docker
 nvidia-docker.service -- NVIDIA Docker
plugin Loaded: loaded (/lib/systemd/system/nvidia-docker.service; enabled; vendor preset: enabled)
Active: active (running) since Sun 2017-08-20 10:52:43 CEST; 6 days ago
Docs: https://github.com/NVIDIA/nvidia-docker/wiki Main
PID: 19921 (nvidia-docker-p)
Tasks: 13
Memory: 12.3M
CPU: 5.046s
CGroup: /system.slice/nvidia-docker.service
                └─19921 /usr/bin/nvidia-docker-plugin -s /var/lib/nvidia-docker

Whenever nvidia-docker is installed, it creates a Docker volume and mounts the devices into a docker container automatically.

Did you know?

It is possible to avoid replying on nvidia-wrapper to launch GPU containers using ONLY docker and that can be done by using the REST API directly as shown below:

docker run -ti --rm `curl -s http://localhost:3476/docker/cli` nvidia/cuda nvidia-smi

NVIDIA’s System Management Interface

If you want to know the status of your NVIDIA GPU, then nvidia-smi is the handy command which can be run using nvidia-cuda container. This is generally useful when  you’re having trouble getting your NVIDIA GPUs to run GPGPU code.

nvidia-docker run --rm nvidia/cuda nvidia-smi

 

Listing all NVIDIA Devices:

nvidia-docker run --rm nvidia/cuda nvidia-smi -L
GPU 0: GeForce GTX 1080 (UUID: GPU-70ecf884-c4fb-159b-a67e-26b4ce96681d)

Listing all available data on the particular GPU:

nvidia-docker run --rm nvidia/cuda nvidia-smi -i 0 -q

Listing details for each GPU:

nvidia-docker run --rm nvidia/cuda nvidia-smi --query-gpu=index,name,uuid,serial --format=csv
index, name, uuid, serial0, GeForce GTX 1080, GPU-70ecf884-c4fb-159b-a67e-26b4ce96681d, [Not Supported]

Listing the available clock speeds:

nvidia-docker run --rm nvidia/cuda nvidia-smi -q -d SUPPORTED_CLOCKS

Building & Testing NVIDIA-Docker Images

If you look at samples/ folder under the nvidia-docker repository , there are couple of  images that can be used to quickly test nvidia-docker on your machine. Unfortunately, the samples are not available on the Docker Hub, hence you will need to build the images locally. I have built few of them which I am going to showcase:

cd /nvidia-docker/samples/ubuntu-16.04/deviceQuery/
docker build -t ajeetraina/nvidia-devicequery .

 

Running the DeviceQuery container

You can leverage ajeetraina/nvidia-devicequery container directly as shown below:

 

 

Listing the current GPU clock speed, default clock speed & maximum possible clock speed:

nvidia-docker run --rm nvidia/cuda nvidia-smi -q -d CLOCK

Retrieving the System Topology:

The topology refers to how the PCI-Express devices (GPUs, InfiniBand HCAs, storage controllers, etc.) connect to each other and to the system’s CPUs.  This can be retrieved as follow:

 

A Quick Look at NVIDIA Deep Learning..

The NVIDIA Deep Learning GPU Training System, a.k.a DIGITS is a webapp for training deep learning models. It puts  the power of deep learning into the hands of engineers & data scientists. It can be used to rapidly train the highly accurate deep neural network (DNNs) for image classification, segmentation and object detection tasks.The currently supported frameworks are: Caffe, Torch, and Tensorflow.

DIGITS simplifies common deep learning tasks such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real time with advanced visualizations, and selecting the best performing model from the results browser for deployment. DIGITS is completely interactive so that data scientists can focus on designing and training networks rather than programming and debugging.

To test-drive DIGITS, you can get it up and running in a single Docker container:

ajit@Ubuntu-1604-xenial-64-minimal:~$ NV_GPU=0 nvidia-docker run --name digits -d -p 5000:5000 nvidia/digits
f0e5d1f78b810037a039b34420ee4848e5809effc1c73752eb5d0ced89b1835f

In the above command, NV_GPU is a method of assigning GPU resources to a container which is critical for leveraging DOCKER in a Multi GPU System. This passes GPU ID 0 from the host system to the container as resources. Note that if you passed GPU ID 2,3 for example, the container would still see the GPUs as ID 0,1 inside the container, with the PCI ID of 2,3 from the host system. As I have a single GPU card, I just passed it as NV_GPU=0.

You can open up web browser and verify if its running on the below address:

w3m http://<dockerhostip>:5000

The below is the snippet from my w3m text browser:

 

How about Docker Compose? Is it supported?

Yes, of course.  

Let us see how Docker compose works for nvidia-docker. 

  • First we need to figure out the nvidia driver version 

 

As shown above, the nvidia driver version displays 375.66.

  • Create a docker volume that uses the nvidia-docker plugin.
docker volume create --name=nvidia_driver_375.66 -d nvidia-dockernvidia_driver_375.66

Verify it with the below command:

sudo docker volume ls
DRIVER VOLUME NAMElocal 15dd59ba1017ca5b822473fb3ed8a341b4e29e59f3725565c00810ddd5619878local
local nvidia_driver_375.66

Now let us look at docker-compose YAML file shown below:

If you have ever worked with docker-compose, you can easily understand what each line specifies. I specified /dev/nvidia0 as I had a single GPU card, capture the correct volume driver name which we specified in the last step.

Just initiate the docker-compose as shown below:

docker-compose up

This will start a container which ran nvidia-smi and then exited immediately. To keep it running, one can add tty: true inside Docker-compose file.

Let us see another interesting topic…TensorFlow

I have a sample TensorFlow based docker-compose file as shown below:

Verify if the container is up and running –

 

In the next blog post, I will showcase how to push the GPU metrics to prometheus & cAdvisor.

Did you find this blog helpful?  Feel free to share your experience. Get in touch @ajeetsraina

If you are looking out for contribution/discussion, join me at Docker Community Slack Channel

 

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Hybrid Docker Swarm Mode Cluster with Multi-OS Application Deployment

Here comes the most awaited feature of 2017 – “Building Docker Swarm cluster which includes all Windows cluster, or a hybrid cluster of Linux and Windows machines, or all Linux systems”.  With the inclusion of the recent Docker 17.03 overlay networking feature for Windows Server 2016, it’s now possible to create swarm clusters that include both Windows and Linux environment across the both public and private cloud infrastructure.

Today most of the enterprises manage a diverse set of applications or workloads that might includes both traditional applications & microservices, built on either or both of Linux and Windows platform. Docker now provides a way to modernize all these different applications by packaging them in a standard format which does not require software development teams to change their code. Organizations can containerize traditional apps and microservices and deploy them in the same cluster, which includes both Linux & Windows environment.

Building Hybrid Docker Swarm Mode Cluster Environment

Under this blog post, I will showcase how to build the hybrid Docker Swarm Mode cluster. I will be leveraging Google Cloud Platform where I have 2 Windows Server 2016 & 1 Ubuntu 16.04 instance up and running.

Setting up Windows Server 2016 with Docker 17.03 Enterprise Editio

  • Bring up Windows Server 2016 instance on Google Cloud Platform 
  • Setup Windows password using the below command and then proceed to download the RDP file
gcloud beta compute --project “psychic-cascade-175904” reset-windows-password “windows2016” --zone “asia-east1-a”
ip_address: 35.194.X.X
password: XXXXXXX
username: <yourusername>
  • Open up the RDP file downloaded under step-2 and connect to the instance remotely.
  • Just 3 commands and docker should be up and running on Windows Server 2016
Install-Module -Name DockerMsftProvider -Force
Install-Package -Name docker -ProviderName DockerMsftProvider -Force
Restart-Computer -Force

 

This should restart the Windows instance and you should be able to see Docker up and running once it comes back.

One can easily verify if Docker 17.03.2 EE  is up and running using the below command:

 

Configuring Windows Server 2016 as Docker Swarm Manager Node:

Run the below command to make this node as a Swarm master node:

docker swarm init --listen-addr 10.140.0.2:2377 --advertise-addr 10.140.0.2

 

Listing the Swarm Mode cluster nodes:

As of now, there is only 1 manager node which gets listed.

Joining Windows Server 2016 as the first Worker Node

Install Docker 17.03 EE on the 2nd Windows Server 2016 instance and issue the below command to join it as worker node:

docker swarm join --token SWMTKN-1-4ia5wbutzfoimx5xm7eujadpa6vuivksmgijk4dm56ppw5u3ib-6hvdrvee3vlnlg8oftnnj80dw 10.140.0.2:2377

 

Listing out the Swarm Mode cluster:

 

Adding Linux System to the Cluster

Login to Ubuntu 16.04 instance, install Docker 17.06  and then issue the below command to join it as 3rd node to the existing cluster:

worker@ubuntu1604:~$ sudo docker swarm join --token SWMTKN-1-4ia5wbutzfoimx5xm7eujadpa6vuivksmgijk4dm56ppw5u3ib-6hvdrvee3vlnlg8oftnnj80dw 10.140.0.2:2377
This node joined a swarm as a worker.

Wow ! This builds up our first hybrid Swarm Mode cluster which includes 2 Windows nodes & 1 Linux Nodes.

A Quick way of verifying the OS type:

 

Create an Overlay Network For Swarm Cluster

Before we start deploying application, we need to create an overlay network which spans across the cluster nodes. By default, it shows up the below listed network drivers:

As shown above, it displays 3 different networks – nat, none and ingress. Let us create a new overlay network ‘collabnet’ using the below command:

docker network create -d overlay collabnet

 

Creating our First Windows-based Service container

It’s time to create our first service. I will pick up MSSQL container which will use ‘collabnet’ overlay network and should get deployed only on Windows platform based on applied constraint as shown below:

docker service create \
--network collabnet --endpoint-mode dnsrr \
--constraint ‘node.platform.os == windows’ \
--env ACCEPT_EULA=Y \
--env-file db-credentials.env \
--name db \
microsoft/mssql-server-windows

 

Ensure that you have db-credentials.env under the same directory with the below content:

sa_password=collabnix123
SA_PASSWORD=collabnix123
DB_CONNECTION_STRING=Server=db;Database=SignUp;User Id=sa;Password=collabnix123

Once you create service, you can verify it up and running with the below command:

docker service ps db

Scaling the DB service 

Let us scale the DB service and see if it still applies only to Windows Platform:

As shown above, there are now 3 instance of the same application up and running. The MS SQL is now running on only Windows system based on the constraint specified.

Creating Linux specific applications 

Let us create a Web application having frontend as WordPress and backend as MySQL container with a constraint that it should get deployed only on Linux specific platform.

docker service create \
--replicas 1 \
--name wordpressdb1 \
--network collabnet \
--constraint ‘node.platform.os == linux’ \
--env MYSQL_ROOT_PASSWORD=collab123 \
--env MYSQL_DATABASE=wordpress
mysql:latest

docker service create \
--env WORDPRESS_DB_HOST=wordpressdb1 \
--env WORDPRESS_DB_PASSWORD=collab123 \
--network collabnet --constraint ‘node.platform.os == linux’ \
--replicas 4 \
--name wordpressapp \
--publish 80:80/tcp \
wordpress:latest

 

Hence, we saw that MS SQL service is running on Windows host instance while WordPress application specific to Linux is successfully up and running on Linux nodes which proves that the newer Swarm Mode comes with an ability to intelligently orchestrate across mixed clusters of Windows & Linux worker nodes.

Adding Linux Worker Node and promoting to Manager

You should be able to view the list of nodes from Ubuntu manager node too:

In case you are looking out for application which uses frontend application running on Windows platform whereas the backend application uses Linux, here is a quick example.

Did you find this blog helpful?  Feel free to share your experience. Get in touch @ajeetsraina

If you are looking out for contribution/discussion, join me at Docker Community Slack Channel.

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Walkthrough: Enabling IPv6 Functionality for Docker & Docker Compose

By default, Docker assigns IPv4 addresses to containers. Does Docker support IPv6 protocol too? If yes, how complicated is to get it enabled? Can I use docker-compose to build micro services which uses IPv6 addresses? What if I work for a company where our services run natively under IPv6 only environment? How shall I build Multi-Node Cluster setup using IPv6? Does Docker 17.06 Swarm Mode support IPv6?

I have been reading numerous queries, GITHUB issues around breaking IPv6 configuration while upgrading Docker version, issues related to IPv6 changes with host configuration etc. and just thought to share few of the findings around IPv6 effort ongoing in Docker upcoming releases.

Does Docker support IPv6 Protocol?

Yes. Support for IPv6 address has been there since Docker Engine 1.5 release.As of Docker 17.06 version (which is the latest stable release as of August 2017) by default, the Docker server configures the container network for IPv4 only. You can enable IPv4/IPv6 dualstack support by adding the below entry under daemon.json file as shown below:

 

File: /etc/docker/daemon.json

{
“ipv6”: true,
“fixed-cidr-v6”: “2001:db8:1::/64”
}

This is very similar to old way of running the Docker daemon with the --ipv6 flag. Docker will set up the bridge docker0 with the IPv6 link-local address fe80::1.

Why did we add “fixed-cidr-v6”: “2001:db8:1::/64” entry?

By default, containers that are created will only get a link-local IPv6 address. To assign globally routable IPv6 addresses to your containers you have to specify an IPv6 subnet to pick the addresses from. Setting the IPv6 subnet via the --fixed-cidr-v6 parameter when starting Docker daemon will help us achieve globally routable IPv6 address.

The subnet for Docker containers should at least have a size of /80. This way an IPv6 address can end with the container’s MAC address and you prevent NDP neighbor cache invalidation issues in the Docker layer.

With the --fixed-cidr-v6 parameter set Docker will add a new route to the routing table. Further IPv6 routing will be enabled (you may prevent this by starting dockerd with --ip-forward=false).

Let us closely examine the changes which Docker Host undergoes before & after IPv6 Enablement:

A Typical Host Network Configuration – Before IPv6 Enablement 

 

As shown above, before IPv6 protocol is enabled, the docker0 bridge network shows IPv4 address only.

Let us enable IPv6 on the Host system. In case you find daemon.json already created under /etc/docker directory, don’t delete the old entries, rather just add these two below entries into the file as shown:

{
“ipv6”: true,
“fixed-cidr-v6”: “2001:db8:1::/64”
}

 

 

Restarting the docker daemon to reflect the changes:

sudo systemctl restart docker

 

A Typical Host Network Configuration – After IPv6 Enablement 

Did you see anything new? Yes, the docker0 now gets populated with IPV6 configuration.(shown below)

docker0: <NO-CARRIER,BROADCAST,MULTICAST,UP> mtu 1500 qdisc noqueue state DOWN group default
link/ether 02:42:06:62:82:4d brd ff:ff:ff:ff:ff:ff inet 172.17.0.1/16 scope global docker0 valid_lft forever preferred_lft forever
inet6 2001:db8:1::1/64 scope global tentative
valid_lft forever preferred_lft forever
inet6 fe80::1/64 scope link tentative valid_lft forever preferred_lft forever

Not only this, the docker_gwbridge network interface too received IPV6 changes:

docker_gwbridge: <NO-CARRIER,BROADCAST,MULTICAST,UP> mtu 1500 qdisc noqueue state DOWN group default
link/ether 02:42:bc:0b:2a:84 brd ff:ff:ff:ff:ff:ff
inet 172.18.0.1/16 scope global docker_gwbridge
valid_lft forever preferred_lft forever
inet6 fe80::42:bcff:fe0b:2a84/64 scope link
valid_lft forever preferred_lft forever

 

PING TEST – Verifying IPv6 Functionalities For Docker Host containers

Let us try bringing up two containers on the same host and see if they can ping each using IPV6 address:

 

Setting up Ubuntu Container:

mymanager1==>sudo docker run -itd ajeetraina/ubuntu-iproute bash

Setting up CentOS Container:

mymanager1==>sudo docker run -itd ajeetraina/centos-iproute bash

[Please Note: If you are using default Ubuntu or CentOS Docker Image, you will be surprised to find that ip or ifconfig command doesn’t work. You might need to install iproute package for ip command to work & net-tools package for ifconfig to work. If you want to save time, use ajeetraina/ubuntu-iproute for Ubuntu OR ajeetraina/centos-iproute for CentOS directly.]

Now let us initiate the quick ping test:

 

In this example the Docker container is assigned a link-local address with the network suffix /64 (here: fe80::42:acff:fe11:3/64) and a globally routable IPv6 address (here: 2001:db8:1:0:0:242:ac11:3/64). The container will create connections to addresses outside of the 2001:db8:1::/64 network via the link-local gateway at fe80::1 on eth0.

mymanager1==>sudo docker exec -it 907 ping6 fe80::42:acff:fe11:2
PING fe80::42:acff:fe11:2(fe80::42:acff:fe11:2) 56 data bytes
64 bytes from fe80::42:acff:fe11:2%eth0: icmp_seq=1 ttl=64 time=0.153 ms
64 bytes from fe80::42:acff:fe11:2%eth0: icmp_seq=2 ttl=64 time=0.100 ms
^C
--- fe80::42:acff:fe11:2 ping statistics ---2 packets transmitted, 2 received, 0% packet loss, time 999
msrtt min/avg/max/mdev = 0.100/0.126/0.153/0.028 ms

So the two containers are able to reach out to each other using IPv6 address.

Does Docker Compose support IPv6 protocol?

The answer is Yes. Let us verify it using docker-compose version 1.15.0 and compose file format 2.1. I faced an issue while I use the latest 3.3 file format. As Docker Swarm Mode doesn’t support IPv6, hence it is not included under 3.3 file format. Till then, let us try to bring up container using IPv6 address using 2.1 file format:

docker-compose version
version 1.15.0, build e12f3b9
docker-py version: 2.4.2
CPython version: 2.7.13
OpenSSL version: OpenSSL 1.0.1t 3 May 2016

Let us first verify the network available in the host machine:

 

File: docker-compose.yml

version: ‘2.1’
services:
  app:
    image: busybox
    command: ping www.collabnix.com
    networks:
      app_net:
        ipv6_address: 2001:3200:3200::20
networks:
  app_net:
    enable_ipv6: true
    driver: bridge
    ipam:
      driver: default
      config:
        -- subnet: 2001:3200:3200::/64
          gateway: 2001:3200:3200::1

 

The above docker-compose file will create a new network called testping_app_net based on IPv6 network under the subnet 2001:3200:3200::/64 and container should get IPv6 address automatically assigned.

Let us bring up services using docker-compose up and see if the services communicates over IPv6 protocol:

 

Verifying the IPv6 address for each container:

As shown above, this new container gets IPv6 address – 2001:3200:3200::20 and hence they are able to reach other flawlessly.

What’s Next? Under the next blog post, I am going to showcase how does IPv6 works across the multiple host machine and will talk about ongoing effort to bring IPv6 support in terms of Swarm Mode. 

Did you find this blog helpful?  Feel free to share your experience. Get in touch @ajeetsraina

If you are looking out for contribution/discussion, join me at Docker Community Slack Channel.

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