There have been quite a few posts recently describing how to write custom plugins, now that the mechanism for creating and working with them has been made easier in upstream Kubernetes (as of v1.12). Here are the official plugin docs if you’re interested in learning more about how it all works.
One neat thing about the new plugin architecture is that they don’t need to be written in Go to be recognized by kubectl. There is a document in the Kubernetes repo that describes how to write your own custom plugin and even a helper library for making it easier to write plugins.
Instead of just writing another tutorial about how to make your own plugin, I decided to show how easy it is to grab and experiment with existing plugins.
Installing krew
If you haven’t heard about it yet, Krew is a new tool released by the Google Container Tools team for managing Kubernetes plugins. As far as I know this is the first plugin manager offering available, and it really scratches my itch for finding a specific tool for a specific job (while also being easy to use).
Krew basically builds on top of the kubectl plugin architecture for making it easier to deal with plugins by providing a sort of framework for keeping track of things and making sure they are doing what they are supposed to.
The following kubectl-compatible plugins are available:
/home/jmreicha/.krew/bin/kubectl-krew
/home/jmreicha/.krew/bin/kubectl-rbac_lookup
...
You can manage plugins without Krew, but if you work with a lot of plugins complexity and maintenance generally start to escalate quickly if you are managing everything manually. Below I will show you how easy it is to deal with plugins instead using Krew.
There are installation instructions in the repo, but it is really easy to get going. Run the following commands in your shell and you are ready to go.
(
set -x; cd "$(mktemp -d)" &&
curl -fsSLO "https://storage.googleapis.com/krew/v0.2.1/krew.{tar.gz,yaml}" &&
tar zxvf krew.tar.gz &&
./krew-"$(uname | tr '[:upper:]' '[:lower:]')_amd64" install \
--manifest=krew.yaml --archive=krew.tar.gz
)
# Then append the following to your .zshrc or bashrc
export PATH="${KREW_ROOT:-$HOME/.krew}/bin:$PATH"
# Then source your shell to pick up the path
source ~/.zshrc # or ~/.bashrc
You can use the kubectl plugin list command to look at all of your plugins.
Test it out to make sure it works.
kubectl krew help
If everything went smoothly you should see some help information and can start working with the plugin manager. For example, if you want to check currently available plugins you can use Krew.
kubectl krew update
kubectl krew search
Or you can browse around the plugin index on GitHub. Once you find a plugin you want to try out, just install it.
kubectl krew install view-utilization
That’s it. Krew should take care of downloading the plugin and putting it in the correct path to make it usable right away.
kubectl view-utilization
Some plugins require additional tools to be installed beforehand as dependencies but should tell you which ones are required when they are installed the first time.
Installing plugin: view-secret
CAVEATS:
\
| This plugin needs the following programs:
| * jq
/
Installed plugin: view-secret
When you are done with a plugin, you can install it just as easily as it was installed.
kubectl krew uninstall view-secret
Conclusion
I must say I am a really big fan of this new model for managing and creating plugins, and I think it will encourage the community to develop even more tools so I’m looking forward to seeing what people come up with.
Likewise I think Krew is a great fit for this because it is super easy to get installed and started with, which I think is important for gaining widespread adoption in the community. If you have an idea for a Kubectl plugin please consider adding it to the krew-index. The project maintainers are super friendly and are great about feedback and getting things merged.
Recently I have been experimenting with different ways of building multi architecture Docker images. As part of this process I wrote about Docker image manifests and the different ways you can package multi architecture builds into a single Docker image. Packaging the images is only half the problem though. You basically need to create the different Docker images for the different architectures first, before you are able to package them into manifests.
There are several ways to go about building the Docker images for various architectures. In the remainder of this post I will be showing how you can build Docker images natively against arm64 only as well as amd64/arm64 simultaneously using some slick features provided by the folks at Shippable. Having the ability to automate multi architecture builds with CI is really powerful because it avoids having to use other tools or tricks which can complicate the process.
Shippable recently announced integrated support for arm64 builds. The steps for creating these cross platform builds is fairly straight forward and is documented on their website. The only downside to using this method is that currently you must explicitly contact Shippable and requests access to use the arm64 pool of nodes for running jobs, but after that multi arch builds should be available.
For reference, here is the full shippable.yml file I used to test out the various types of builds and their options.
Arm64 only builds
After enabling the shippable_shared_aarch64 node pool (from the instruction above) you should have access to arm64 builds, just add the following block to your shippable.yml file.
runtime:
nodePool: shippable_shared_aarch64
The only other change that needs to be made is to point the shippable.yaml file at the newly added node pool and you should be ready to build on arm64. You can use the default “managed” build type in Shippable to create builds.
Below I have a very simple example shippable.yml file for building a Dockerfile and pushing its image to my Dockerhub account. The shippable.yml file for this build lives in the GitHub repo I configured Shippable to track.
Once you have a shippable.yml file in a repo that you would like to track and also have things set up on the Shippable side, then every time a commit/merge happens on the master branch (or whatever branch you set up Shippable to track) an arm64 Docker image gets built and pushed to the Dockerhub.
Docs for settings up this CI style job can be found here. There are many other configuration settings available to tune so I would encourage you to read the docs and also play around with the various options.
Parallel arm64 and amd64 builds
The approach for doing the simultaneous parallel builds is a little bit different and adds a little bit more complexity, but I think is worth it for the ability to automate cross platform builds. There are a few things to note about the below configuration. You can use templates in either style job. Also, notice the use of the shipctl command. This tool basically allows you to mimic some of the other functionality that exists in the default runCI jobs, including the ability to login to Docker registries via shell commands and manage other tricky parts of the build pipeline, like moving into the correct directory to build from.
Most of the rest of the config is pretty straight forward. The top level jobs directive lets you create multiple different jobs, which in turn allows you to set the runtime to use different node pools, which is how we build against amd64 and arm64. Jobs also allow for setting different environment variables among other things. The full docs for jobs shows all of the various capabilities of these jobs.
As you can see, there is a lot more manual configuration going on here than the first job.
I decided to use the top level templates directive to basically DRY the configuration so that it can be reused. I am also setting environment variables per job to ensure the correct architecture gets built and pushed for the various platforms. Otherwise the configuration is mostly straight forward. The confusion with these types of jobs if you haven’t set them up before mostly comes from figuring out where things get configured in the Shippable UI.
Conclusion
I must admit, Shippable is really easy to get started with, has good support and has good documentation. I am definitely a fan and will recommend and use their products whenever I get a chance. If you are familiar with Travis then using Shippable is easy. Shippable even supports the use of Travis compatible environment variables, which makes porting over Travis configs really easy. I hope to see more platforms and architectures supported in the future but for now arm64 is a great start.
There are some downside to using the parallel builds for multi architecture builds. Namely there is more overhead in setting up the job initially. With the runSh (and other unmanaged jobs) you don’t really have access to some of the top level yml declarations that come with managed jobs, so you will need to spend more time figuring out how to wire up the logic manually using shell commands and the shipctl tool as depicted in my above example. This ends up being more flexible in the long run but also harder to understand and get working to begin with.
Another downside of the assembly line style jobs like runSh is that they currently can’t leverage all the features that the runCI job can, including the matrix generation (though there is a feature request to add it in the future) and report parsing.
The last downside when setting up unmanaged jobs is trying to figure out how to wire up the different components on the Shippable side of things. For example you don’t just create a runCI job like the first example. You have to first create an integration with the repo that you are configuring so that shippable can make an rSync and serveral runSh jobs to connect with the repo and be able to work correctly.
Overall though, I love both of the runSh and runCI jobs. Both types of jobs lend themselves to being flexible and composable and are very easy to work with. I’d also like to mention that the support has been excellent, which is a big deal to me. The support team was super responsive and helpful trying to sort out my issues. They even opened some PRs on my test repo to fix some issues. And as far as I know, there are no other CI systems currently offering native arm64 builds which I believe will become more important as the arm architecture continues to gain momentum.
Kubernetes is complicated, as you’ve probably already discovered if you’ve used Kubernetes before. Likewise, the Kubectl command line tool can pretty much do anything but can feel cumbersome, clunky and generally overwhelming for those that are new to the Kubernetes ecosystem. In this post I want to take some time to describe a few of the CLI tools that I have discovered that help ease the pain of working with and managing Kubernetes from the command line.
There are many more tools out there and the list keeps growing, so I will probably revisit this post in the future to add more cool stuff as the community continues to grow and evolve.
Where to find projects?
As a side note, there are a few places to check for tools and projects. The first is the CNCF Cloud Native Landscape. This site aims to keep track of all the various different projects in the Cloud/Kubernetes world. An entire post could be written about all of the features and and filters but at the highest level it is useful for exploring and discovering all the different evolving projects. Make sure to check out the filtering capabilities.
The other project I have found to be extremely useful for finding different projects is the awesome-kubernetes repo on Github. I found a number of tools mentioned in this post because of the awesome-kubernetes project. There is some overlap between the Cloud Native Landscape and awesome-kubernetes but they mostly compliment each other very nicely. For example, awesome-kubernetes has a lot more resources for working with Kubernetes and a lot of the smalller projects and utilities that haven’t made it into the Cloud Native Landscape. Definitely check this project out if you’re looking to explore more of the Kubernetes ecosystem.
Kubectl tricks
These are various little tidbits that I have found to help boost my productivity from the CLI.
Tab completion – The first thing you will probably want to get working when starting. There are just too many options to memorize and tab completion provides a nice way to look through all of the various commands when learning how Kubernetes works. To install (on OS X) run the following command.
brew install bash-completion
In zsh, adding the completion is as simple as running source <(kubectl completion bash). The same behavior can be accomplished in zsh using source <(kubectl completion zsh).
Aliases and shortcuts – One distinct flavor of Kubernetes is how cumbersome the CLI can be. If you use Zsh and something like oh-my-zsh, there is a default set of aliases that work pretty well, which you can find here. There are a many posts about aliases out there already so I won’t go into too much detail about them. I will say though that aliasing k to kubectl is one of the best time savers I have found so far. Just add the following snippet to your bash/zsh profile for maximum glory.
alias k=kubectl
kubectl –export – This is a nice hidden feature that basically allows users to switch Kubernetes from imperative (create) to declarative (apply). The --export flag will basically take an existing object and strip out unwanted/unneeded metadata like statuses and timestamps and present a clear version of what’s running, which can then be exported to a file and applied to the cluster. The biggest advantage of using declarative configs is the ability to mange and maintain them in git repos.
kubectl top – In newer versions, there is the top command, which gives a high level overview of CPU and memory utilization in the cluster. Utilization can be filtered at the node level as well as the pod level to give a very quick and dirty view into potential bottlenecks in the cluster. In older versions, Heapster needs to be installed for this functionaliity to work correctly, and in newer versions needs metrics-server to be running.
kubectl explain – This is a utility built in to Kubectl that basically provides a man page for what each Kubernetes resource does. It is a simple way to explore Kubernetes without leaving the terminal
This is an amazing little utility for quickly moving between Kubernetes contexts and namespaces. Once you start working with multiple different Kubernetes clusters, you notice how cumbersome it is to switch between environments and namespaces. Kubectx solves this problem by providing a quick and easy way to see what environments and namespaces a user is currently in and also quickly switch between them. I haven’t had any issues with this tool and it is quickly becoming one of my favorites.
Dealing with log output using Kubectl is a bit of a chore. Stern (and similarly kail) offer a much nicer user experience when dealing with logs. These tools allow users the ability to do things like show logs for multiple containers in pod, use regex matching to tail logs for specific containers, give nice colored output for distinguishing between logs, filter logs by namespaces and a bunch of other nice features.
Obviously for a full setup, using an aggregated/centralized logging solution with something like Fluenctd or Logstash would be more ideal, but for examining logs in a pinch, these tools do a great job and are among my favorites. As an added bonus, I don’t have to copy/paste log names any more.
yq is a nice little command line tool for parsing yaml files, which works in a similar way to the venerable jq. Parsing, reading, updating yaml can sometimes be tricky and this tool is a great and lightweight way to manipulate configurations. This tool is especially useful for things like CI/CD where a tag or version might change that is nested deep inside yaml.
There is also the lesser known jsonpath option that allows you to interact with the json version of a Kubernetes object, baked into kubectl. This feature is definitely less powerful than jq/yq but works well when you don’t want to overcomplicate things. Below you can see we can use it to quickly grab the name of an object.
kubectl get pods -o=jsonpath='{.items[0].metadata.name}'
Working with yaml and json for configuration in general seems to be an emerging pattern for almost all of the new projects. It is definitely worth learning a few tools like yq and jq to get better at parsing and manipulating data using these tools.
Similar to the above, ksonnet and jsonnet are basically templating tools for working with Kubernetes and json objects. These two tools work nicely for managing Kubernetes manifests and make a great fit for automating deployments, etc. with CI/CD.
ksonnet and jsonnet are gaining popularity because of their ease of use and simplicity compared to a tool like Helm, which also does templating but needs a system level permission pod running in the Kubernetes cluster. Jsonnet is all client side, which removes the added attack vector but still provides users with a lot of flexibility for creating and managing configs that a templating language provides.
More random Kubernetes tricks
Since 1.10, kubectl has the ability to port forward to resource name rather than just a pod. So instead of looking up pods that are running and connecting to one all the time, you can just grab the service name or deployment and just port forward to it.
port-forward TYPE/NAME [LOCAL_PORT:]REMOTE_PORT
k port-forward deployment/mydeployment 5000:6000
New in 1.11, which will be dropping soonish, there is a top level command called api-resource, which allows users to view and interact with API objects. This will be a nice troubleshooting tool to have if for example you are wanting to see what kinds of objects are in a namespace. The following command will show you these objects.
k api-resources --verbs=list --namespace -o name | xargs -n 1 kubectl get -o name -n foo
Another handy trick is the ability to grab a base64 string and decode it on the fly. This is useful when you are working with secrets and need to quickly look at what’s in the secret. You can adapt the following command to accomplish this (make sure you have jq installed).
k get secret my-secret --namespace default -o json | jq -r '.data | .["secret-field"]' | base64 --decode
Just replace .["secret-field"] to use your own field.
UPDATE: I just recently discovered a simple command line tool for decoding base64 on the fly called Kubernetes Secret Decode (ksd for short). This tool looks for base64 and renders it out for you automatically so you don’t have to worry about screwing around with jq and base64 to extract data out when you want to look at a secret.
k get secret my-secret --namespace default -o json | ksd
That command is much cleaner and easier to use. This utility is a Go app and there are binaries for it on the releases page, just download it and put it in your path and you are good to go.
Conclusion
The Kubernetes ecosystem is a vast world, and it only continues to grow and evolve. There are many more kubectl use cases and community to tools that I haven’t discovered yet. Feel free to let me know any other kubectl tricks you know of, and I will update them here.
I would love to grow this list over time as I get more acquainted with Kubernetes and its different tools.
Bash is great. As I have discovered over the years, Bash contains many different layers, like a good movie or a fine wine. It is fun to explore and expose these different layers and find uses for them. As my experience level has increased, I have (slowly) uncovered a number of these features of Bash that make life easier and worked to incorporate them in different ways into my own workflows and use them within my own style.
The great thing about fine arts, Bash included, is that there are so many nuances and for Bash, a huge number of features and uses, which makes the learning process that much more fun.
It does take a lot of time and practice to get used to the syntax and to become effective with these shortcuts. I use this page as a reference whenever I think of something that sounds like it would be useful and could save time in a script or a command. At first, it may take more time to look up how to use these shortcuts, but eventually, with practice and drilling will become second nature and become real time savers.
Shell shortcuts
Navigating the Bash shell is easy to do. But it takes time to learn how to do well. Below are a number of shortcuts that make the navigation process much more efficient. I use nearly all of the shortcuts daily (except Ctrl + t and Ctrl + xx, which I only recently discovered). In a similar vein, I wrote a separate post long ago about setting up CLI shortcuts on iterm that can further augment the capabilities of the CLI.
Ctrl + a => Return to the start of the command you’re typing
Ctrl + e => Go to the end of the command you’re typing
Ctrl + u => Cut everything before the cursor to a special clipboard
Ctrl + k => Cut everything after the cursor to a special clipboard
Ctrl + y => Paste from the special clipboard that Ctrl + u and Ctrl + k save their data to
Ctrl + t => Swap the two characters before the cursor (you can actually use this to transport a character from the left to the right, try it!)
Ctrl + w => Delete the word / argument left of the cursor
Ctrl + l => Clear the screen
Ctrl + _ => Undo previous key press
Ctrl + xx => Toggle between current position and the start of the line
There are some nice Alt key shortcuts in Linux as well. You can map the alt key in OSX pretty easily to unlock these shortcuts.
Alt + l => Uncapitalize the next word that the cursor is under (If the cursor is in the middle of the the word it will capitalize the last half of the word).
Alt + u => Capitalize the word that the cursor is under
Alt + t => Swap words or arguments that the cursor is under with the previous
Alt + . => Paste the last word of the previous command
Alt + b => Move backward one word
Alt + f => Move forward one word
Alt + r => Undo any changes that have been done to the current command
Argument tricks
Argument tricks can help to grow the navigation capabilities that Bash shortcuts provide and can even further speed up your effectiveness in the terminal. Below is a list of special arguments that can be passed to any command that can be expanded into various commands.
Repeating
!! => Repeat the previous (full) command
!foo => Repeat the most recent command that starts with ‘foo‘ (e.g. !ls)
!^ => Repeat the first argument of the previous command
!$ => Repeat the last argument of the previous command
!* => Repeat all arguments of last command
!:<number> => Repeat a specifically positioned argument
!:1-2 => Repeat a range of arguments
Printing
!$:p => Print out the word that !$ would substitute
!*:p => Print out the previous command except for the last word
!foo:p =>Print out the command that !foo would run
Special parameters
When writing scripts , there are a number of special parameters you can feed into the shell. This can be convenient for doing lots of different things in scripts. Part of the fun of writing scripts and automating things is discovering creative ways to fit together the various pieces of the puzzle in elegant ways. The “special” parameters listed below can be seen as pieces of the puzzle, and can be very powerful building blocks in your scripts.
$* => Expand parameters. Expands to a single word for each parameter separated by IFS delimeter – think spaces
$@ => Expand parameters. Each parameter expand to a separate word, enclosed by “” – think arrays
$# => Expand the number of parameters of a command
$? => Expand the exit status of the previous command
$$ => Expand the pid of the shell
$! => Expand the pid of the most recent command
$0 => Expand the name of the shell or script
$_ => Expand the last previous argument
Conclusion
There are some many crevices and cracks of Bash to explore, I keep finding new and interesting things about Bash that lead down new paths and help my skills grow. I hope some of these tricks give you some ideas that can help and improve your own Bash style and workflows in the future.
Having the ability to call Jenkins jobs remotely is pretty slick and adds some extra flexibility and allows for some interesting applications. For example, you could use remote builds to call a script from a chat app or from some other web application. I have chosen to write a quick bash script as a proof of concept, but this could easily be extended or written in a different language via one of its language specific libraries.
The instructions for the method I am using assume that you are using the Jenkins freestyle build as I haven’t experimented much yet with pipelines for remote builds yet.
The first step is to enable remote builds for the Jenkins job that will be triggered. There is an option in the job for “Trigger builds remotely” which allows the job to be called from a script.
The authentication token can be any arbitrary string you choose. Also note the URL below, you will need that later as part of the script to call this job.
With the authentication token configured and the Jenkins URL recorded, you can begin writing the script. The first step is to populate some variables for kicking off the job. Below is an example of how you might do this.
Be sure to fill in these variables with the correctly corresponding values. job_token should correspond to the string you entered above in the Jenkins job, auth should correspond to your github username/token combination. If you are not familiar with Github tokens you can find more information about setting them up here.
As part of the script, you will want to create a Jenkins “crumb” using your Github credentials that will be used to prevent cross-site scripting attacks. Here’s what the creation of the crumb looks like (borrowed from this Stackoverflow post).
In the example job above, I am using several Jenkins parameters as part of the build. The json name values correspond to the parameters. Notice that I am using variables for a few of the values above, make sure those variables are wrapped in singe quotes to correctly escape the json. The syntax for variables is slightly different but allows for some additional flexibility in the job configuration and also allows the script to be called with dynamic values.
If you call this script now, it should kick off a Jenkins job for you with all of the values you have provided.
