I am very new to Docker and have some very basic questions. I was unable to get my doubts clarified elsewhere and hence posting it here. Pardon me if the queries are very obvious. I know I lack some basic understanding regarding images but i had a hard time finding some easy to understand explanation for the whole of it.
Problem at hand:
I have my application running on an EC2 node (r4.xlarge). It is a web application which has a LOT of dependencies (system dependencies + other libraries etc). I would like to create a docker image of my machine so that i can easily run it at ease when I launch a new EC2 instance.
Questions:
Do i need to build the docker image from scratch or can I use some base image?
If i can use a base image, which one do I select? (It is hard to know the OS version on the EC2 machine and hence I am not sure which base image do i start on.
I referred this documentation-
https://docs.aws.amazon.com/AmazonECS/latest/developerguide/docker-basics.html#install_docker
But it creates from an Ubuntu base image.
The above example has instructions on installing apache (and other things needed for the application). Let's say my application needs server X to be installed + 20 system dependencies + 10 other libraries.
Ex:
yum install gcc
yum install gfortran
wget <abc>
When I create a docker file do i need to specify all the installation instructions like above? I thought creating an image is like taking a copy of your existing machine. What is the docker file supposed to have in this case?
Pointing me out to some good documentation to build a docker image on EC2 for a web app with dependencies will be very useful too.
Thanks in advance.
First, if you want to move toward docker then I will suggest using AWS ECS which specially designed for docker container and have auto-scaling and load balancing feature.
As for your question is concern so
You need a docker file which contains all the packages and application which already installed in your EC2 instance. As for base image is concern i will recommend Alpine. Docker default image is Alpine
Why Alpine?
Alpine describes itself as:
Small. Simple. Secure. Alpine Linux is a security-oriented,
lightweight Linux distribution based on musl libc and busybox.
https://nickjanetakis.com/blog/the-3-biggest-wins-when-using-alpine-as-a-base-docker-image
https://hub.docker.com/_/alpine/
Let's say my application needs server X to be installed + 20 system
dependencies + 10 other libraries.
So You need to make dockerfile which need all these you mentioned.
Again I will suggest ECS for best docker based application because that is ECS that designed for docker, not EC2.
CONTAINERIZE EVERYTHING
Amazon ECS lets you easily build all types of
containerized applications, from long-running applications and
microservices to batch jobs and machine learning applications. You can
migrate legacy Linux or Windows applications from on-premises to the
cloud and run them as containerized applications using Amazon ECS.
https://aws.amazon.com/ecs/
https://aws.amazon.com/getting-started/tutorials/deploy-docker-containers/
https://caylent.com/containers-kubernetes-docker-swarm-amazon-ecs/
You can use a base image, you specify it with the first line of
your Docker file, with FROM
The base OS of the EC2 instance doesn't matter for the container.
that's the point of containers, you can run linux on windows, arch
on debian, whatever you want.
Yes, dependencies that don't exist in your base image will need to
be specified and installed. ( Depending on the default packager
manger for the base image you are working from you might use dpkg,
or yum or apt-get. )
I have deployed the cloudera/quickstart image for a single node deployment with docker. However I would like to have a multinode cdh deployment on 4 nodes using docker. I am new to this so anyone who has done the same please let me know how can that be achieved.
Instructions and script now available:
http://blog.cloudera.com/blog/2016/08/multi-node-clusters-with-cloudera-quickstart-for-docker/
I had the same question (running a CDH cluster deployment in docker) but I didn't find tools to do that for the latest CDH releases. That is why I prepared docker images. I hope it will be useful to someone else.
To run a CDH cluster easily, you can use docker-compose. Just create a configuration file based on https://github.com/ipogudin/cloudera-cluster-docker/blob/master/docker-compose.yml Please, remember that you need to remove build sections from definitions for each service.
Note, you don't need to build images locally (unless you want to customize them). You can use built images from docker hub (https://hub.docker.com/r/ipogudin/cloudera-cluster-gateway/).
We're thinking about using mesos and mesosphere to host our docker containers. Reading the docs it says that a prerequisite is that:
Docker version 1.0.0 or later needs to be installed on each slave
node.
We don't want to manually SSH into each new machine and install the correct version of the Docker daemon. Instead we're thinking about using something like Ansible to install Docker (and perhaps other services that may be required on each slave).
Is this a good way to solve it or does Mesosphere/DCOS or any of Mesos ecosystem components have other ways of dealing with this?
I've seen the quick intro where someone from Mesosphere just use dcos resize to change the cluster size on the Google Cloud Platform. Is there a way to hook in to this process and install additional services on the (google) container when it has booted? Or is this something we should avoid and instead just use a "pre-baked image"?
In your own datacenter using your favorite configuration tool such as ansible, salt, ... is probably a good choice.
On the cloud it might be easier to use virtual machine images providing docker, so for example dcos on aws uses coreOS which comes with docker out of the box. Shouldn't be too difficult with Ubuntu either...
Kubernetes seems to be all about deploying containers to a cloud of clusters. What it doesn't seem to touch is development and staging environments (or such).
During development you want to be as close as possible to production environment with some important changes:
Deployed locally (or at least somewhere where you and only you can access)
Use latest source code on page refresh (supposing its a website; ideally page auto-refresh on local file save which can be done if you mount source code and use some stuff like Yeoman).
Similarly one may want a non-public environment to do continuous integration.
Does Kubernetes support such kind of development environment or is it something one has to build, hoping that during production it'll still work?
Update (2016-07-15)
With the release of Kubernetes 1.3, Minikube is now the recommended way to run Kubernetes on your local machine for development.
You can run Kubernetes locally via Docker. Once you have a node running you can launch a pod that has a simple web server and mounts a volume from your host machine. When you hit the web server it will read from the volume and if you've changed the file on your local disk it can serve the latest version.
We've been working on a tool to do this. Basic idea is you have remote Kubernetes cluster, effectively a staging environment, and then you run code locally and it gets proxied to the remote cluster. You get transparent network access, environment variables copied over, access to volumes... as close as feasible to remote environment, but with your code running locally and under your full control.
So you can do live development, say. Docs at http://telepresence.io
The sort of "hot reload" is something we have plans to add, but is not as easy as it could be today. However, if you're feeling adventurous you can use rsync with docker exec, kubectl exec, or osc exec (all do the same thing roughly) to sync a local directory into a container whenever it changes. You can use rsync with kubectl or osc exec like so:
# rsync using osc as netcat
$ rsync -av -e 'osc exec -ip test -- /bin/bash' mylocalfolder/ /tmp/remote/folder
I've just started with Skaffold
It's really useful to apply changes in the code automatically to a local cluster.
To deploy a local cluster, the best way is Minikube or just Docker for Mac and Windows, both includes a Kubernetes interface.
EDIT 2022: By now, there are obviously dozens of way to provision k8s, unlike 2015 when we started using it. kubeadm, microk8s, k3s, kube-spray, etc.
My advice: (If your cluster can't fit on your workstation/laptop,) Rent a Hetzner server for 40 euro a month, and run WSL2 if on Windows.
Set up k8s cluster on the remote machine (with any of the above, I prefer microk8s these days). Set up Docker and Telepresence on your local Linux/Mac/WSL2 env. Install kubectl and connect it to the remote cluster.
Telepresence will let you replace a remote pod with a local docker pod, with access to local files (hopefully the same git repo that's used to build the pod you're developing/replacing), and possibly nodemon (or other language-specific auto-source-code-reload system).
Write bash functions. I cannot stress this enough, this will save you hundreds of hours of time. If replacing the pod and starting to develop isn't one line / two words, then you're doing it not-well-enough.
2016 answer below:
Another great starting point is this Vagrant setup, esp. if your host OS is Windows. The obvious advantages being
quick and painless setup
easy to destroy / recreate the machine
implicit limit on resources
ability to test horizontal scaling by creating multiple nodes
The disadvantages - you need lot of RAM, and VirtualBox is VirtualBox... for better or worse.
A mixed advantage / disadvantage is mapping files through NFS. In our setup, we created two sets of RC definitions - one that just download a docker image of our application servers; the other with 7 extra lines that set up file mapping from HostOS -> Vagrant -> VirtualBox -> CoreOS -> Kubernetes pod; overwriting the source code from the Docker image.
The downside of this is NFS file cache - with it, it's problematic, without it, it's problematically slow. Even setting mount_options: 'nolock,vers=3,udp,noac' doesn't get rid of caching problems completely, but it works most of the time. Some Gulp tasks ran in a container can take 5 minutes when they take 8 seconds on host OS. A good compromise seems to be mount_options: 'nolock,vers=3,udp,ac,hard,noatime,nodiratime,acregmin=2,acdirmin=5,acregmax=15,acdirmax=15'.
As for automatic code reload, that's language specific, but we're happy with Django's devserver for Python, and Nodemon for Node.js. For frontend projects, you can of course do a lot with something like gulp+browserSync+watch, but for many developers it's not difficult to serve from Apache and just do traditional hard refresh.
We keep 4 sets of yaml files for Kubernetes. Dev, "devstable", stage, prod. The differences between those are
env variables explicitly setting the environment (dev/stage/prod)
number of replicas
devstable, stage, prod uses docker images
dev uses docker images, and maps NFS folder with source code over them.
It's very useful to create a lot of bash aliases and autocomplete - I can just type rec users and it will do kubectl delete -f ... ; kubectl create -f .... If I want the whole set up started, I type recfo, and it recreates a dozen services, pulling the latest docker images, importing the latest db dump from Staging env and cleaning up old Docker files to save space.
See https://github.com/kubernetes/kubernetes/issues/12278 for how to mount a volume from the host machine, the equivalent of:
docker run -v hostPath:ContainerPath
Having a nice local development feedback loop is a topic of rapid development in the Kubernetes ecosystem.
Breaking this question down, there are a few tools that I believe support this goal well.
Docker for Mac Kubernetes
Docker for Mac Kubernetes (Docker Desktop is the generic cross platform name) provides an excellent option for local development. For virtualization, it uses HyperKit which is built on the native Hypervisor framework in macOS instead of VirtualBox.
The Kubernetes feature was first released as beta on the edge channel in January 2018 and has come a long way since, becoming a certified Kubernetes in April 2018, and graduating to the stable channel in July 2018.
In my experience, it's much easier to work with than Minikube, particularly on macOS, and especially when it comes to issues like RBAC, Helm, hypervisor, private registry, etc.
Helm
As far as distributing your code and pulling updates locally, Helm is one of the most popular options. You can publish your applications via CI/CD as Helm charts (and also the underlying Docker images which they reference). Then you can pull these charts from your Helm chart registry locally and upgrade on your local cluster.
Azure Draft
You can also use a tool like Azure Draft to do simple local deploys and generate basic Helm charts from common language templates, sort of like buildpacks, to automate that piece of the puzzle.
Skaffold
Skaffold is like Azure Draft but more mature, much broader in scope, and made by Google. It has a very pluggable architecture. I think in the future more people will use it for local app development for Kubernetes.
If you have used React, I think of Skaffold as "Create React App for Kubernetes".
Kompose or Compose on Kubernetes
Docker Compose, while unrelated to Kubernetes, is one alternative that some companies use to provide a simple, easy, and portable local development environment analogous to the Kubernetes environment that they run in production. However, going this route means diverging your production and local development setups.
Kompose is a Docker Compose to Kubernetes converter. This could be a useful path for someone already running their applications as collections of containers locally.
Compose on Kubernetes is a recently open sourced (December 2018) offering from Docker which allows deploying Docker Compose files directly to a Kubernetes cluster via a custom controller.
Kubespary is helpful setting up local clusters. Mostly, I used vagrant based cluster on local machine.
Kubespray configuration
You could tweak these variables to have the desired kubernetes version.
The disadvantage of using minkube is that it spawns another virtual machine over your machine. Also, with latest minikube version it minimum requires to have 2 CPU and 2GB of RAM from your system, which makes it pretty heavy If you do not have the system with enough resources.
This is the reason I switched to microk8s for development on kubernetes and I love it. microk8s supports the DNS, local-storage, dashboard, istio, ingress and many more, everything you need to test your microservices.
It is designed to be a fast and lightweight upstream Kubernetes installation isolated from your local environment. This isolation is achieved by packaging all the binaries for Kubernetes, Docker.io, iptables, and CNI in a single snap package.
A single node kubernetes cluster can be installed within a minute with a single command:
snap install microk8s --classic
Make sure your system doesn't have any docker or kubelet service running. Microk8s will install all the required services automatically.
Please have a look at the following link to enable other add ons in microk8s.
https://github.com/ubuntu/microk8s
You can check the status using:
velotio#velotio-ThinkPad-E470:~/PycharmProjects/k8sClient$ microk8s.status
microk8s is running
addons:
ingress: disabled
dns: disabled
metrics-server: disabled
istio: disabled
gpu: disabled
storage: disabled
dashboard: disabled
registry: disabled
Have a look at https://github.com/okteto/okteto and Okteto Cloud.
The value proposition is to have the classical development experience than working locally, prior to docker, where you can have hot-reloads, incremental builds, debuggers... but all your local changes are immediately synchronized to a remote container. Remote containers give you access to the speed of cloud, allow a new level of collaboration, and integrates development in a production-like environment. Also, it eliminates the burden of local installations.
As specified before by Robert, minikube is the way to go.
Here is a quick guide to get started with minikube. The general steps are:
Install minikube
Create minikube cluster (in a Virtual Machine which can be VirtualBox or Docker for Mac or HyperV in case of Windows)
Create Docker image of your application file (by using Dockerfile)
Run the image by creating a Deployment
Create a service which exposes your application so that you can access it.
Here is the way I did a local set up for Kubernetes in Windows 10: -
Use Docker Desktop
Enable Kubernetes in the settings option of Docker Desktop
In Docker Desktop by default resource allocated for Memory is 2GB so to use Kubernetes
with Docker Desktop increase the memory.
Install kubectl as a client to talk to Kubernetes cluster
Run command kubectl config get-contexts to get the available cluster
Run command kubectl config use-context docker-desktop to use the docker desktop
Build a docker image of your application
Write a YAML file (descriptive method to create your deployment in Kubernetes) pointing
to the image created in above step cluster
Expose a service of type node port for each of your deployment to make it available to
the outside world