We are deploying a Java backend and React UI application using docker-compose. Our Docker containers are running Java, Caddy, and Postgres.
What's unusual about this architecture is that we are not running the application as a cluster. Each user gets their own server with their own subdomain. Everything is working nicely, but we need a strategy for managing/updating machines as the number of users grows.
We can accept some down time in the middle of the night, so we don't need to have high availability.
We're just not sure what would be the best way to update software on all machines. And we are pretty new to Docker and have no experience with Kubernetes or Ansible, Chef, Puppet, etc. But we are quick to pick things up.
We expect to have hundreds to thousands of users. Each machine runs the same code but has environment variables that are unique to the user. Our original provisioning takes care of that, so we do not anticipate having to change those with software updates. But a solution that can also provide that ability would not be a bad thing.
So, the question is, when we make code changes and want to deploy the updated Java jar or the React application, what would be the best way to get those out there in an automated fashion?
Some things we have considered:
Docker Hub (concerns about rate limiting)
Deploying our own Docker repo
Kubernetes
Ansible
https://containrrr.dev/watchtower/
Other things that we probably need include GitHub actions to build and update the Docker images.
We are open to ideas that are not listed here, because there is a lot we don't know about managing many machines running docker-compose. So please feel free to offer suggestions. Many thanks!
In your case I advice you to use Kubernetes combination with CD tools. One of it is Buddy. I think it is the best way to make such updates in an automated fashion. Of course you can use just Kubernetes, but with Buddy or other CD tools you will make it faster and easier. In my answer I am describing Buddy but there are a lot of popular CD tools for automating workflows in Kubernetes like for example: GitLab or CodeFresh.io - you should pick which one is actually best for you. Take a look: CD-automation-tools-Kubernetes.
With Buddy you can avoid most of these steps while automating updates - (executing kubectl apply, kubectl set image commands ) by doing a simple push to Git.
Every time you updates your application code or Kubernetes configuration, you have two possibilities to update your cluster: kubectl apply or kubectl set image.
Such workflow most often looks like:
1. Edit application code or configuration .YML file
2. Push changes to your Git repository
3. Build an new Docker image
4. Push the Docker image
5. Log in to your K8s cluster
6. Run kubectl apply or kubectl set image commands to apply changes into K8s cluster
Buddy is a CD tool that you can use to automate your whole K8s release workflows like:
managing Dockerfile updates
building Docker images and pushing them to the Docker registry
applying new images on your K8s cluster
managing configuration changes of a K8s Deployment
etc.
With Buddy you will have to configure just one pipeline.
With every change in your app code or the YAML config file, this tool will apply the deployment and Kubernetes will start transforming the containers to the desired state.
Pipeline configuration for running Kubernetes pods or jobs
Assume that we have application on a K8s cluster and the its repository contains:
source code of our application
a Dockerfile with instructions on creating an image of your app
DB migration scripts
a Dockerfile with instructions on creating an image that will run the migration during the deployment (db migration runner)
In this case, we can configure a pipeline that will:
1. Build application and migrate images
2. Push them to the Docker Hub
3. Trigger the DB migration using the previously built image. We can define the image, commands and deployment and use YAML file.
4. Use either Apply K8s Deployment or Set K8s Image to update the image in your K8s application.
You can adjust above workflow properly to your environment/applications properties.
Buddy supports GitLab as a Git provider. Integration of these two tools is easy and only requires authorizing GitLab in your profile. Thanks to this integration you can create pipelines that will build, test and deploy your app code to the server. But of course if you are using GitLab there is no need to set up Buddy as an extra tool because GitLab is also CD tools tool for automating workflows in Kubernetes.
More information you can find here: buddy-workflow-kubernetes.
Read also: automating-workflows-kubernetes.
As it turns out, we found that a paid Docker Hub plan addressed all of our needs. I appreciate the excellent information from #Malgorzata.
Related
I have a lot of cronjobs I need to set on Kubernetes.
I want a file to manage them all and set them to Kubernetes on deployment. I wish that if I remove a cron from that file it will be removed from Kubernetes too.
Basically, I want to handle the corns like I'm handling them today on the machine (from a cron file that I would deploy). Add, remove and change crons.
I couldn't find a way of doing so. Does someone have an idea?
Library or framework I can use like helm? Or any other solution.
I highly recommend using gitops with argocd as a solution for Kubernetes configure management. Run crontab in deployment is a bad ideal because it hard to monitor your job result (cronjob job result can be get by kube-state-metrics exporter).
The ideal is packaging your manifest (it may be kubernetes manifest, kustomize, helm...etc...) -> put them to git -> argocd makes sure your configure deployed correctly
The advantages of gitops are include:
centralize your configuration
versioning your configuration
git authentication & authorization
traceable
multi-cluster deployment with argocd
automation deployment & sync
...
Gitops is not a difficult and is the mordern way for kubernetes configure management. Let's try
I used Helm to do so. I built a template to go over all crons, which I inserted as values to the helm template (Very similar to crontab but more structured) - see in the example.
Then, all I need to do is run a helm upgrade with a new corn (values) file and it updates everything accordingly. If I updated, removed, or added a new corn everything is happening automatically and with versioning. You can also add a namespace to your cronjobs to make it more encapsulated.
Here is a very good and easy-to-understand example I used. And its git repo
In my organization we use IBM Urban code to deploy docker images to Kubernetes. Deploying using Urban code is not easy and process is not transparent. Sometimes output of UC is confusing to release management. Are there any better tools used by the industry to deploy docker application in kubernetes, docker EE platform?
I can share how we are doing it in our start-up.
We've built our own pipeline around Jenkins and Google Kubernetes Engine. There are not that many steps involved:
Create a tag of your built image(s): docker tag <source_image> <target_image>
Push image(s) to the Google Container Registry: gcloud docker -- push <target_image>
Change yaml file definitions to select new <target_image>
Update K8s configuration: kubectl apply -f <yaml_file>
Of course in real life this is a little more complex and automatically updates tons of microservices but you get the gist.
Because you asked for tools, there are lots of solutions out there to help you, please have a look at this list to get an overview. It all pretty much depends what kind of environment you want to use it in. Some prominent examples are:
Werker
Codefresh
Spinnaker
KubeCI
You can use below tools for deploying docker apps to kubernetes
Jenkins with kubernetes CD plugin
https://github.com/jenkinsci/kubernetes-cd-plugin
Spinnaker
I'm building a cluster visualization tool for Kubernetes that runs inside users' clusters.
My goal is to make this tool freely available. The most obvious way to distribute it is to tell people to kubectl apply -f www.ourgithub/our-configs.yaml, which pulls our images and voila.
That's all fine. Now the problem is how do we push updates?
I've considered these options but none seem very good:
Using something like https://github.com/chartmuseum/helm-push
Having the apps themselves check for updates and "restart" themselves (i.e imagePullPolicy=always scale to 0)
Having users download an executable on their machines that periodically checks for updates
I want to be able to push updates reliably so I want to make sure I'm using the most robust method there is.
What is the best practice for this?
Separate CI/CD pipeline for building and testing docker images and separate pipeline for deploying.
Your pipeline should deploy an application in a version that's is already running on the environment, deploy a new one, run e2e tests to verify everything is correct and then push a new version to the desired cluster.
I've checked out helm.sh of course, but at first glance the entire setup seems a little complicated (helm-client & tiller-server). It seems to me like I can get away by just having a helm-client in most cases.
This is what I currently do
Let's say I have a project composed of 3 services viz. postgres, express, nginx.
I create a directory called product-release that is as follows:
product-release/
.git/
k8s/
postgres/
Deployment.yaml
Service.yaml
Secret.mustache.yaml # Needs to be rendered by the dev before use
express/
Deployment.yaml
Service.yaml
nginx/
Deployment.yaml
Service.yaml
updates/
0.1__0.2/
Job.yaml # postgres schema migration
update.sh # k8s API server scritps to patch/replace existing k8s objects, and runs the state change job
The usual git stuff can apply now. Everytime I make a change, I make changes to the spec files, test them, write the update scripts to help move from the last version to this current version and then commit it and tag it.
Questions:
This works for me so far, but is this "the right way"?
Why does helm have the tiller server? Isn't it simpler to do the templating on the client-side? Of course, if you want to separate the activity of the deployment from the knowledge of the application (like secrets) the templating would have to happen on the server, but otherwise why?
Seems that https://redspread.com/ (open source) addresses this particular issue, but needs more development before it'll be production ready - at least from my team quick glance at it.
We'll stick with keeping yaml files in git together with the deployed application for now I guess.
We are using kubernetes/helm (the latest/incubated version) and a central repository for Helm charts (with references container images built for our component releases).
In other words, the Helm package definitions and its dependencies are separate from the source code and image definitions that make up the several components of our web applications.
Notice: Tiller has been removed in Helm v3. Checkout this answer to see details on why it needs tiller in Helm v2 and why it's removed in Helm v3: https://v3.helm.sh/docs/faq/#removal-of-tiller
According to the idea of GitOps, what you did is a right way (to perform release from a git repo). However, if you want to push it further to make it more common, you can plan more goals including:
Choose a configuration management system beyond k8s app declarative definition only. E.g., Helm (like above answer https://stackoverflow.com/a/42053983/914967), Kustomize. They're pure client-side only.
avoid custom release process by altering update.sh with popular tools like kubectl apply or helm install.
drive change delivery from git tags/branches by using a CI/CD engine like argocd, Travis CI or GitHub Actions.
Uses branching strategy so that you can try changes on test/staging/production/ environment before delivering it directly.
I am hoping to find a good way to automate the process of going from code to a deployed application on my kubernetes cluster.
In order to build and deploy my app I need to first build the docker image, tag it, and then push it to ECR. I then need to update my deployment.yaml with the new tag for the docker image and run the deployment with kubectl apply -f deployment.yaml.
This will go and perform a rolling deployment on the kubernetes cluster updating the pods to the new version of the container image, once this deployment has completed I may need to do other application specific things such as running database migrations, or cache clear/warming which may or may not need to run for a given deployment.
I suppose I could just write a shell script that runs all of these commands, and run it whenever I want to start up a new deployment, but I am hoping there is a better/industry standard way to solve these problems that I have missed.
As I was writing this question I noticed stackoverflow recommend this question: Kubernetes Deployments. One of the answers to it seems to imply at least some of what I am looking for is coming soon to kubernetes, but I want to make sure that if there is a better solution I could be using now that I at least know about it.
My colleague has a good blog post about this topic:
http://blog.jonparrott.com/building-a-paas-on-kubernetes/
Basically, Kubernetes is not a Platform-as-a-Service, it's a toolkit on which you can build your own Platform-a-as-Service. It's not very opinionated by design, instead it focuses on solving some tricky problems with scheduling, networking, and coordinating containers, and lets you layer in your opinions on top of it.
One of the simplest ways to automate the workflows you're describing is using a Makefile.
A step up from that, you can design your own miniature PaaS, which the author of the first blog post did here:
https://github.com/jonparrott/noel
Or, you could get involved in more sophisticated efforts to build an open source PaaS on Kubernetes, like OpenShift:
https://www.openshift.com/
or Deis, which is building a Heroku-like platform on Kubernetes:
https://deis.com/
or Redspread, which is building "Git for Kubernetes cluster":
https://redspread.com/
and there are many other examples of people building PaaS on top of Kubernetes. But I think it will be a long time, if ever, that there is an "industry standard" way to deploy to Kubernetes, since half the purpose is to enable multiple deployment workflows for different use cases.
I do want to note that as far as building container images, Google Cloud Container Builder can be a useful tool, since you can do things like use it to automatically build an image any time you push to a repository which could then get deployed. Alternatively, Jenkins is a popular way to automate CI/CD flows with Kubernetes.
I suppose I could just write a shell script that runs all of these commands, and run it whenever I want to start up a new deployment, but I am hoping there is a better/industry standard way to solve these problems that I have missed.
The company I work for (Weaveworks) and other folks in the space had been advocating for an approach that we call GitOps, please take a look at our series of blog posts covering the topic:
GitOps - Operations by Pull Request
The GitOps Pipeline - Part 2
GitOps Part 3 - Observability
Storing Secure Sealed Secrets using GitOps
The gist of it is that you push images from CI, your checked YAML manifests in git (usually different repo from app code). This repo with manifests is then applied to each of your clusters (dev/prod) by a reconciliation operator. You can automate it all yourself quite easily, but also do take a look at what we have built.
Disclaimer: I am a Kubernetes contributor and Weaveworks employee. We build open-source and commercial tools that help people to get to production with Kubernetes sooner.
We're working on an open source project called Jenkins X which is a proposed sub project of the Jenkins foundation aimed at automating CI/CD on Kubernetes using Jenkins and GitOps for promotion.
When you merge a change to the master branch, Jenkins X creates a new semantically versioned distribution of your app (pom.xml, jar, docker image, helm chart). The pipeline then automates the generation of Pull Requests to promote your application through all of the Environments via GitOps.
Here's a demo of how to automate CI/CD with multiple environments on Kubernetes using GitOps for promotion between environments and Preview Environments on Pull Requests - using Spring Boot and nodejs apps (but we support many languages + frameworks).