I love docker and especially for complex CI environments it is just amazing.
The one thing that I really miss when working with Docker compared to a Virtual Machine is the ability to
save and restore snapshots of the container and I was wondering if Docker offers anything similar?
docker checkpoint may answer your needs.
Note that checkpoints is experimental feature and you may need to rerun your docker engine in experimental mode.
Related
I am new to docker and I have done some ground work on docker like how to build a image, how to create a container, what is dockerfile.yml, docker-compose.yml file does etc. I have done my practice in my local machine. I am having following questions when it is coming to real time development using docker.
1) Does each developer in a team has to do development on docker and create images in their local machine and push it to docker registry ? or developers work without docker and one person will be creating final image from the committed code?
2) For each release, do we maintain the container or an image for that release?
3) what is the best practice to maintain the database means do we incorporate the database in image and build the container or we include only application related things and build image and container, and this container will communicate to database which is in outside container or physical database server ?
Thanks in advance.
Questions like these generally do not have a definitive answer. It depends heavily on your company, your team, the tooling that is being used, the software stack, etc. The best thing to do would be to lean on the senior development resources and senior technical leadership in your organization to help shape the answers to questions like these.
Take the following answers with a silo of salt as there is no way to answer these kinds of questions definitively.
1) Depends on what is most convenient for the developers and what language you are using. Some developers find an all container workflow to be convenient, some developers find they can iterate faster with their existing IDE/CLI workflow and test against running container images last.
In most cases you will want to let CI/CD tooling take care of the builds that are intended for production.
2) Yes. You can use container tagging for this purpose.
3) Running databases in containers is possible, but unless your team is experienced with containers and container orchestration I would leave the databases on traditional bare-metal or VMs.
Containers are a fancy wrapper around a single linux process. Generally the rule of thumb is one container for one process. You should not be combining multiple things in a single container. (This story gets slightly more complicated when you go to something like Red Hat OpenShift or Kubernetes as the discussion revolves around how many containers per pod).
The setup I'm used to is that developers mostly ignore Docker, until they need it to deploy. Tools like Node's node_modules directory or Python's virtual environments can help isolate subproject from each other. Any individual developer should be able to run docker build to build an image, but won't usually need to until the final stages of testing a particular change. You should deploy a continuous integration system and that will take responsibility for testing and building a final Docker image of each commit.
You never "maintain containers". If a container goes wrong, delete it and start a new one. Your CI system should build an image for each release, and you should deploy a registry to hold these.
You should never keep the database in the same container as the application. (See the previous point about routinely deleting containers.) My experience has generally been that production databases are important and special enough to merit their own dedicated non-Docker hosts, but there's nothing actually wrong with running a database in Docker; just make sure you know how to do backups and restores and migrations and whatever else on it.
There's no technical reason you can't use Docker Compose for production, but if you wind up needing to deploy your application on more than one physical server you might find it limiting. Kubernetes is more complex but seems to be the current winner in this space; Docker Swarm has some momentum; Hashicorp Nomad is out there; or you can build a manual deployment system by hand. (Note that at least Kubernetes and Nomad are both very big on the "something changed so I'm going to delete and recreate a container" concept, and both make it extremely tricky to do live development in a quasi-production setup.)
Also note that where I say "deploy" there are public-cloud versions of all of these things (Docker Hub, CircleCI, end-to-end solutions including a registry and Kubernetes built on top of AWS or Azure or GCP) and if you're comfortable with the cost-to-effort tradeoff and the implications of using an external service in your build/deploy chain then these can help you get started faster
We are looking to make use of Docker to run integration tests within CI builds (with Bazel).
We need to support Debian as well as MacOS.
In order to guarantee build correctness, and ensure determinism and portability, we cannot rely on the host having a running docker daemon. The build needs to come with its own docker daemon.
What is the best way to achieve this? Is there a standard “portable” docker binary?
If not, what do you think would be the right approach to implement this?
In linux systems, I imagine this would be relatively simple, as we would just need to download the binaries and run.
In MacOS, I guess we would need to bundle it with hyperkit.
Would love to hear your thoughts on this.
In terms of building Docker images, you should look at bazelbuild/rules_docker (disclaimer: I wrote/own them). They implement the only properly deterministic Docker builds of which I'm aware (at least to Bazel's standard).
They do this by avoiding Dockerfile and the Docker daemon (which most other approaches use), as it is unclear these can produce deterministic artifacts. This avoids the root requirement too, which is nice.
However, you specifically asked about testing, which tl;dr we have not solved.
#ittaiz is also interested in this and started this Github issue for discussing it. Would you mind moving the discussion there?
I'm currently into Docker and I'm asking myself why containers in general weren't hyped before Docker? I mean it's not like containers were something new. The technology has been around for quite some time now. But Docker gained like it's success overnight.
Is there something I didn't keep in mind?
It's a very broad question but I will try to answer you.
Docker was at first build on LXC, they switched to libcontainer later.
LXC is actually pretty hard to use compared to Docker, you don't have all the Docker related stuff like Dockerfile, Compose and all.
So I would say that container wasn't really a thing because of the difficulty of LXC.
As Wassim, I would say the main reason was that it needed motivated sysadmins, specific kernels (with OpenVZ and AUFS),...
Creating the same thing as a docker image was a complicated process.
Today it is a straightforward process, create a Dockerfile, just do
docker build -t mytag .
and you have created an image.
In 2004, you could not do that so easily.
I've found quite a few blogs on how to run your Jenkins in Docker but none really explain the advantages of doing it.
These are the only reasons I found:reasons to use Docker.
1) I want most of the configuration for the server to be under version control.
2) I want the ability to run the build server locally on my machine when I’m experimenting with new features or configurations
3) I want to easily be able to set up a build server in a new environment (e.g. on a local server, or in a cloud environment such as AWS)
Luckily I have people who take care of my Jenkins server for me so these points don't matter as much.
Are these the only reasons or are there better arguments I'm overlooking, like automated scaling and load balancing when many builds are triggered at once (I assume this would be possible with Docker)?
This answer for Docker, what is it and what is the purpose
covered What is docker? and Why docker?
Docker official site also provides an explanation.
The simple guide here is:
Faster delivery of your applications
Deploy and scale more easily
Get higher density and run more workloads
Faster deployment makes for easier management
For Jenkins usage, it's faster and easier to deploy/install in the docker way.
Maybe you don't need the scale more easily feature right now. And since the docker is quite lightweight, so you can run more workloads.
However
The docker way would also bring some other problem. Generally speaking, it's the accessing privilege.
Like when you need to run Docker inside the Jenkins(in Docker), it would become complicated somehow. This blog would provide you with some knowledge of that situation.
So there is no silver bullet as always. There is no single development, in either technology or in management technique, that by itself promises even one order-of-magnitude improvement in productivity, in reliability, in simplicity.
The choice should be made based on the specific scenario.
Jenkins as Code
You list mainly the advantages of having "Jenkins as Code". Which is a very powerfull setup indeed, but does not necessary requires Docker.
So why is Docker the best choice for a Jenkins as Code setup?
Docker
The main reason is that Jenkins pipelines work really well with Docker. Without Docker you need to install additional tools and add different agents to Jenkins. With Docker,
there is no need to install additional tools, you just use images of these tools. Jenkins will download them from internet for you (Docker Hub).
For each stage in the pipeline you can use a different image (i.e. tool). Essentially you get "micro Jenkins agents" which only exists temporary. Hence you do not need fixed agents anymore. This makes your Jenkins setup much more clean.
Getting started
A while ago I have written an small blog on how to get started with Jenkins and Docker, i.e. create a Jenkins image for development which you can launch and destroy in seconds.
I'm not au fait with any of these technologies (embarrassing really), but at my present gig, the company badly needs to automate.
So as I begin to read-up on Puppet and Chef and PowerShell DSC, I then remember that Docker and containerisation is coming to Windows.
Does Docker do away with the need for these tools, or do they work together?
I understand that Docker uses virtualisation technology in the OS, so I get the feeling that Docker solves a different problem, and a configuration tool is still needed but I've no certain, practical knowledge.
Does Docker do away with the need for these tools, or do they work together?
They work together: provisioning and containerization solve different issues, and you actually can provision docker containers themselves with a provisioning tool.
See for instance "Docker: Using Puppet"
Tools like Chef & Puppet are important for configuration, but they do have one weakness that Docker helps to shore up. They are not always fully idempotent (hype notwithstanding). In other words, running Chef twice on the same virtual machine may cause unexpected and hard-to-find changes on that machine, and you'd be restoring a backup to get to a known good state.
By contrast, a Docker deployment involves building an entirely new image and swapping it out with your old image. Rollback involves simply unswapping them and comparing them to diagnose the problems in the new image.
Note that you still might very well use Chef to build your Docker container. But you might very well not. Since containers are supposed to run just one process in a particular way, I've found that a series of simple shell commands is way preferable to the overhead entailed by Chef.
In short no, you don't need anything like Chef or Puppet. Of course you can use if like to but it's not required.
If you build your system in such way that everything in containerized then what you need is only a tiny OS like CoreOS or Atomic.
So you just configure your VM via Cloud-Config if needed and deploy your container either with cloud config or Docker cli itself. The idea is your machines should have a static state and they can be created whenever you want new one and destroyed when you don't need.
There are other tools that can help with Docker orchestration which another story by itself.
Tools like Swarm, Kubernetes and Mesosphere.
docker-machine is also very helpful for development purpose. (maybe deployment too).
Here is CoreOS example:
https://coreos.com/os/docs/latest/cloud-config.html
Resource: I do it in production for different apps.
UPDATE:
BTW, Docker is not only a visualization technology. It does some sort of containerization (you can call it virtualization too) and that's only a small part of the what Docker can do. Docker can configure, build, ship and run application whit eliminating its dependencies on host machine. And that's why you don't need those classic configuration tools.
Puppet and Chef are configuration management tools, where as Docker is a virtualization tool such as LXC.
Usually you'd be using Chef or puppet to manage Docker containers. For example take a look at Chef docs.
EDIT as per #ptierno comment.
Docker is three things: a cool way to run a process, a decent image-based deploy system, and a mediocre system image builder.
The first is not related to config management as those tools aren't involved in running a process, at least not directly. The second takes the place of some amount of config management in production by doing it ahead of time when you build the image. There is still often some need for last-mile config for stuff like service discovery and secrets but this can be handled by lighter tools like consul-templates or confd. The last is where the rub lies. docker build is simple, easy to get started with, and mostly unhelpful for complex situations. You get, at most, a single inheritance tree between dockerfiles which makes stuff like multi-axis matrix builds ({app1 app2 app3} x {prod qa dev}) more difficult than it could be. Also building composable abstraction for other groups to use is difficult, though again it isn't impossible. Using something like Packer to drive image builds can produce simpler code sometimes, and supports the full suite of CAPS (Chef, Ansible, Puppet, Salt) tools. This is mostly aimed at the use case where you are treating Docker images like tiny VMs, which I wish fewer people would do, but it's a thing so here we are.