I'm in the need of learning how to use Kubernetes. I've read the first sentences of a couple of introductory tutorials, and never have found one which explains me, step by step, how to build a simulated real world example on a single computer.
Is Kubernetes by nature so distributed that even the 101-level tutorials can only be performed on clusters?
Or can I learn (execute important examples) the important stuff there is to know by just using my Laptop without needing to use a stack of Raspberry Pi's, AWS or GCP?
The easiest might be minikube.
Minikube is a tool that makes it easy to run Kubernetes locally.
Minikube runs a single-node Kubernetes cluster inside a VM on your
laptop for users looking to try out Kubernetes or develop with it
day-to-day.
For a resource that explains how to use this, try this getting started guide. It runs through an entire example application using a local development environment.
If you are okay with using Google Cloud Platform (I think one gets free credits initially), there is hello-node.
If you want to run the latest and greatest (not necessary stable) and you're using Linux, is also possible to spin up a local cluster on Linux from a cloned copy of the kubernetes sources, using hack/local_up_cluster.sh.
Related
I have been working with kubernetes in a staging environment for a couple of month and want to switch to production, I came across a tool called Rancher almost 2 weeks ago and since then am going through their documents.
It was recommended by the developers and also in the community not to use rancher in production kubernete and preferably create a separated cluster for that and add an agent to your main production cluster from that one.
However in the latest stable version, there is actually an option you can tick to use the rancher only for local cluster so this question came to my mind that:
If the latest stable version of rancher is modified to be deployed on production cluster itself rather than having dedicated cluster? and if there is any security or restarting issues can happen that deletes all the configurations for other components on cluster
Note: on another staging environment I installed on the local clustor an instance of wordpress and ghost and both were working fine.
I still think the best option for you would be to have fully accessible own cluster and you wont be dependent to rancher cloud solutions. I am not saying Rancher is bad - no. Just If you are talking about PRODUCTION environment - my personal opinion cluster should be own. Sure arguable topic.
What I can mention also here - you can use any of Useful Interactive Terminal and Graphical UI Tools for Kubernetes . for example Octant
Octant is a browser-based UI aimed at application developers giving
them visibility into how their application is running. I also think
this tool can really benefit anyone using K8s, especially if you
forget the various options to kubectl to inspect your K8s Cluster
and/or workloads. Octant is also a VMware Open Source project and it
is supported on Windows, Mac and Linux (including ARM) and runs
locally on a system that has access to a K8S Cluster. After installing
Octant, just type octant and it will start listening on localhost:7777
and you just launch your web browser to access the UI.
I have set up a Kubernetes cluster using Kubernetes Engine on GCP to work on some data preprocessing and modelling using Dask. I installed Dask using Helm following these instructions.
Right now, I see that there are two folders, work and examples
I was able to execute the contents of the notebooks in the example folder confirming that everything is working as expected.
My questions now are as follows
What are the suggested workflow to follow when working on a cluster? Should I just create a new notebook under work and begin prototyping my data preprocessing scripts?
How can I ensure that my work doesn't get erased whenever I upgrade my Helm deployment? Would you just manually move them to a bucket every time you upgrade (which seems tedious)? or would you create a simple vm instance, prototype there, then move everything to the cluster when running on the full dataset?
I'm new to working with data in a distributed environment in the cloud so any suggestions are welcome.
What are the suggested workflow to follow when working on a cluster?
There are many workflows that work well for different groups. There is no single blessed workflow.
Should I just create a new notebook under work and begin prototyping my data preprocessing scripts?
Sure, that would be fine.
How can I ensure that my work doesn't get erased whenever I upgrade my Helm deployment?
You might save your data to some more permanent store, like cloud storage, or a git repository hosted elsewhere.
Would you just manually move them to a bucket every time you upgrade (which seems tedious)?
Yes, that would work (and yes, it is)
or would you create a simple vm instance, prototype there, then move everything to the cluster when running on the full dataset?
Yes, that would also work.
In Summary
The Helm chart includes a Jupyter notebook server for convenience and easy testing, but it is no substitute for a full fledged long-term persistent productivity suite. For that you might consider a project like JupyterHub (which handles the problems you list above) or one of the many enterprise-targeted variants on the market today. It would be easy to use Dask alongside any of those.
I'm looking to build an api on a application that is going to run its own docker container. It needs to work with some applications via its REST apis. I'm new to development and dont understand the process very well. Can you share the broad steps necessary to build and release the APIs so that my application runs safely within the docker but externally whatever communication needs to happen they work out well.
For context: I'm going to be working on a Google Compute VM instance and the application I'm building is a HyperLedger Fabric program written in GoLang.
Links to reference material and code would also be appreciated.
REST API implementation is very easy in Go. You can use the inbuilt net/http package. Here's a tutorial which will help you understand its usage. https://tutorialedge.net/golang/creating-restful-api-with-golang/
Note : If you are planning on developing a production server, the default HTTP client is not recommended. It will knock down the server on heavy frequency calls. In that case, you have to use a custom HTTP client as described here, https://medium.com/#nate510/don-t-use-go-s-default-http-client-4804cb19f779
For learning docker I would recommend the docker docs they're very good and cover a handful of stuff. Docker swarm and orchestration are useful things to learn but most people aren't using docker swarm anymore and use things like kubernetes instead. Same principles, but different tech. I would definitely go through this website: https://docs.docker.com/ and implemented on your own computer. Then just practice by looking at other peoples dockerfiles and building your own. A good understanding a linux will definitely help with installing packages and so on.
I haven't used go myself but I suspect it shouldn't be too hard to deploy into a docker container.
The last production step of deployment will be similar for whatever your using if it's docker or no docker. The VM will need an webserver like apache or nginx to expose the ports you wish to use to the public and then you will run the docker container or the go server independently and then you'll have your system!
Hope this helps!
Stupid question, but right now I'm deploying my Kubernetes cluster inside a VM. Is there a way to deploy it directly onto my machine?
I'm sure there has to be a easy fix but many of the docs I've read have been focused on deploying it inside VM.
I am assuming you are using some flavor of Linux; otherwise the information below won't be useful to you.
The easiest way of bare metal deployment ("onto your machine") is by using kubeadm. The documentation for that is excellent.
(If you need help with then reply with your exact OS flavor and version and I can edit this answer to reflect that specific situation.)
I'm wondering which options are there for docker container deployment in production. Given I have separate APP and DB server containers and data-only containers holding deployables and other holding database files.
I just have one server for now, which I would like to "docker enable", but what is the best way to deploy there(remotely will be the best option)
I just want to hit a button and some tool will take care of stopping, starting, exchanging all needed docker containers.
There is myriad of tools(Fleet, Flocker, Docker Compose etc.), I'm overwhelmed by the choices.
Only thing I'm clear is, I don't want to build images with codes from git repo. I would like to have docker images as wrappers for my releases. Have I grasped the docker ideas from wrong end?
My team recently built a Docker continuous deployment system and I thought I'd share it here since you seem to have the same questions we had. It pretty much does what you asked:
"hit a button and some tool will take care of stopping, starting, exchanging all needed docker containers"
We had the challenge that our Docker deployment scripts were getting too complex. Our containers depend on each other in various ways to make the full system so when we deployed, we'd often have dependency issues crop up.
We built a system called "Skopos" to resolve these issues. Skopos detects the current state of your running system and detects any changes being made and then automatically plans out and deploys the update into production. It creates deployment plans dynamically for each deployment based on a comparison of current state and desired state.
It can help you continuously deploy your application or service to production using tags in your repository to automatically roll out the right version to the right platform while removing the need for manual procedures or scripts.
It's free, check it out: http://datagridsys.com/getstarted/
You can import your system in 3 ways:
1. if you have a Docker Compose, we can suck that in and start working iwth it.
2. If your app is running, we can scan it and then start working with it.
3. If you have neither, you can create a quick descriptor file in YAML and then we can understand your current state.
I think most people start their container journey using tools from Docker Toolbox. Those tools provide a good start and work as promised, but you'll end up wanting more. With these tools, you are missing for example integrated overlay networking, DNS, load balancing, aggregated logging, VPN access and private image repository which are crucial for most container workloads.
To solve these problems we started to develop Kontena - Docker Container Orchestration Platform. While Kontena works great for all types of businesses and may be used to run containerized workloads at any scale, it's best suited for start-ups and small to medium sized business who require worry-free and simple to use platform to run containerized workloads.
Kontena is an open source project and you can view it on GitHub.