I have been experimenting with Linux on a recently acquired Lenovo X1 Carbon and as part of the process I have been exploring and testing out various productivity tools, including terminal emulators.
The first thing I discovered in this process is that there’s a lot of terminals out there. And they’re all slightly different, and in my experience, almost none of them were able to do all of the tweaks I like.
Here is the list of terminals I have tested out (so far).
As you can see, that is a bunch of terminals. To keep things short, in my exploration and testing, the best 3 I found were as follows.
Alacritty – 3rd
It is fast, defaults are great, works perfectly with the tiling window managers. This one would would probably have been first on the list if it was able to provide a blinking cursor but as I found with many of the options there seemed to almost always be a gotcha.
A few high points for this terminal, written in Rust, it uses the GPU to offload rendering, cross platform, and maybe my favorite feature, the configuration file is yaml based. Docs are also good and the community is really taking off so I have a strong feeling this one will continue improving.
urxvt – 2nd
The biggest problem with this terminal for me is that it is painful to configure. There are no preferences configured out of the box, which to some is preferred, but also, digging through old blog posts and perl scripts is not how I like to spend my time.
This terminal is second on the list because it was the only other terminal that was able to do all of the small little tweaks and adjustments that suit my preferences. And it is fast and light weight, which are good things.
This terminal was a clear winner. It was able to do all of my custom tweaks and settings and because it has nice defaults I only needed to add about 10 lines of extra configuration.
This terminal has a lot of other stuff going for it. It is written in Rust, which makes it fast, it uses the GPU to offload rendering, which also makes it fast, easy to configure, and for the most part just works. Only gotcha I found was that I needed to explicitly turn on copy to selection in my configuration, but that was easy.
I plan on keeping this list updated to some extent as I find more Terminals to try out. As you can see, there are many different options and seem to be more and more all the time.
Your experience may differ so obviously take these musings with a pinch of salt, and please do look through the various options and try things out to see if they will work for you. That said, I do think I have a specific enough use case that these recommendations should be helpful in guiding most users.
Here is the repo with all of my various configurations if you want to check something out. There were a few terminal configs that didn’t end up there just because they were too minimal or I didn’t like them.
One challenge when dealing with Terraform is keeping things clean and repeatable. My current favorite approach to accomplish this task using shell scripts is by using a combination of null_resources and triggers to control when scripts should be updated. These controls combined with Terraform provisioners and template_files provide a nice flexible way to deal with otherwise potentially messy scripts.
I like to provide more than one trigger so that they can be recomputed when either a variable that gets passed into the script changes, or, the script itself changes. This trick is handy when you need to bend Terraform into doing something that it usually doesn’t handle, like some startup or bootstrap process.
Below is the full example with templated scripts and triggers in Terraform v0.11. The same logic should also work with Terraform v0.12 with minimal changes.
data "template_file" "cool_script" {
template = "${file("${path.module}/script.sh")}"
vars {
my_cool_var = "${var.my_cool_var}"
}
}
resource "null_resource" "script" {
# Trigger when the script when variables change
triggers = {
my_trigger = "${var.my_cool_var}"
script_sha = "${sha256(file("${path.module}/script.sh"))}"
}
provisioner "local-exec" {
command = "${data.template_file.cool_script.rendered}"
interpreter = ["/bin/bash", "-c"]
}
}
You can provide as many variables to the template_file as the script needs and any time those get updated, the null_resource will pick these changes up and update/rerun your script for you. Notice that the provisioner in the null_resource is basically set up to call bash against the rendered script that we created, using the values in the vars.
If you call the script again, nothing should change because we already computed the SHA of the rendered script and told Terraform to keep track of that state. The below example is a simple way for telling Terraform to keep track of changes to the script/template.
Once the script is updated, the next time the script gets called, its values and output should update accordingly. Likewise, since we are also using a var as a trigger, when that changes the script will also be updated.
Terraform is flexible enough to allow us to do things like this because often times there are situations and edge cases where Terraform can’t really perform some actions. The above example is a common workaround for provisioning resources that either don’t have an API that Terraform can tap into, or are just tasks that are only handled via some startup or bootstrap script/process.
One thing that I love about bash is that there is never a shortage of new tips and tricks to learn. I have been using bash for over 10 years now and just stumbled on this little trick.
This one (as the title implies) allows you to quickly substitute a string into the previous command and rerun the command with the substitution.
Quick substitution is officially part of the Bash Event Designators mechanism and is a great way to fix a typo from a previous command. Below is an example.
# Simple example to highlight substitutions
echo foo
# This will replace the string "foo" with "bar" and rerun the last command
^foo^bar
This shorthand notation is great for most use cases, with the exception of needing to replace multiple instances of a given string. Luckily that is easily addressed with the Event Designators expanded substitution syntax, shown below.
# This will substitute ALL occurrences of foo in the previous command
!!:gs/foo/bar/
# Slightly different syntax allows you to do the same thing in ZSH
^foo^bar^:G
The syntax is slightly more complicated in the first example but should be familiar enough to anyone that has used sed and/or vim substitutions, and the second example is almost identical to the shorthand substitution.
fc
Taking things one step further, we can actually edit the previous command to fix anything more than a typo of different argument. fc is actually a bash builtin function so it is available almost everywhere.
fc is especially useful for dealing with very long, complicated commands.
# Oops, we messed this up
echo fobarr | grep bar
# To fix it, just open the above in your default editor
fc
# when you write and quit the file it will put the contents into your current command
There are many great tutorials available so I would recommend looking around to see all the options and get more ideas.
The landscape of command line driver text manipulation and processing tools is somewhat large and confusing, with more and more tools emerging all the time. Because I am having trouble keeping them all in my head, I decided to make a little reference guide to help remember which tool to choose for the correct task at hand.
Reach for jq first when you need to do any kind of processing with JSON files.
From the website, “jq is like sed for JSON data – you can use it to slice and filter and map and transform structured data with the same ease that sed, awk, grep and friends let you play with text.”
sudo apt-get install jq
# or
brew install jq
# consume json and output as unchanged json
curl 'https://api.github.com/repos/stedolan/jq/commits?per_page=5' | jq '.'
This project looks very interesting and is a refreshing thing to see in the Linux world. JC is basically a way to create structured objects (ala PowerShell) as JSON output from running various Linux commands. And from the GitHub repo, ” This tool serializes the output of popular gnu linux command line tools and file types to structured JSON output. This allows piping of output to tools like jq”.
Transforming data into structured objects can massively simplify interacting with them by pairing the output with jq to interact with.
This tool can be confusing because there is both a Python version and a Go version. On top of that, the Python version includes its own version of xq, which is different than the standalone xq tool.
The main differences between the Python and Go version is that the Python version can deal with both yaml and xml while the Go version is meant to be used as a command line tool to deal with only yaml.
From the website, oq is “A performant, portable jq wrapper thats facilitates the consumption and output of formats other than JSON; using jq filters to transform the data”.
The claim to fame that oq has is that it is very similar to jq but works better with other data formats, including xml and yaml. For example, you can read in some xml (and others), apply some filters, and output to yaml (and others). This flexibility makes oq a good option if you need to deal with different data formats oustide of JSON.
snap install oq
# or
brew tap blacksmoke16/tap && brew install oq
# consume json and output xml
echo '{"name": "Jim"}' | oq -o xml .
From the Github page, “Apply XPath expressions to XML, like jq does for JSONPath and JSON”.
The coolest use case I have found for xq so far is taking in an xml file and outputting it into a json file, which surprising I haven’t found another tool that can do this (oc authors say there are plans to do this in the future). The simplest example is to curl a page, pipe it through xq to change it to json and then pipe it again and use jq to manipulate the data.
This is a newer tool, with a slightly different approach aimed at helping to automate configurations, especially for things like Kubernetes but should work with most structured data.
This tool works with json, yaml and hcl and can be used in conjunction with Javascript, making it an interesting option.
curl -Lo jk https://github.com/jkcfg/jk/releases/download/0.3.1/jk-darwin-amd64
chmod +x jk
sudo mv jk /usr/local/bin/
// alice.js
const alice = {
name: 'Alice',
beverage: 'Club-Mate',
monitors: 2,
languages: [
'python',
'haskell',
'c++',
'68k assembly', // Alice is cool like that!
],
};
// Instruct to write the alice object as a YAML file.
export default [
{ value: alice, file: `developers/${alice.name.toLowerCase()}.yaml` },
];
jk generate -v alice.js
This is basically a simplified templating language that only intends to deal with yaml. The approach the authors took was to create yaml templates and sanbdox/embed Python into the templating engine, allowing users to call on the power of Python inside of their templates.
The easiest way to play around with ytt if you don’t want to clone the repo is to try out the online playground.
From the GitHub page, ” Configula is a configuration generation language and processor. It’s goal is to make the programmatic definition of declarative configuration easy and intuitive”.
Similar in some ways to ytt, but instead of embedding Python into the yaml template file, you create a .py file and then render the py file into yaml using the Configula command line tool.
git clone https://github.com/brendandburns/configula
cd configula
# tiny.py
# Define a YAML object where the 'foo' field has the value of evaluating 1 + 2 (e.g. 3)
my_obj = foo: !~ 1 + 2
my_obj.render()
./configula examples/tiny.py
Jsonnet bills itself as a “data templating language for app and tool developers”. This tool was originally created by folks working at Google, and has been around for quite some time now. For some reason always seems to fly underneath the radar but it is super powerful.
This tool is a superset of JSON and allows you to add conditionals, loops and other functions available as part of its standard library. Jsonnet can render itself into json and yaml output.
For (pretty much) everything else, there is Sed. Sed, short for stream editor, has been around forever and is basically a Swiss army knife for manipulating text, and if you have been using *nix for any length of time you have more than likely come across this tool before. From their docs, Sed is “a stream editor is used to perform basic text transformations on an input stream”.
The odds are good that Sed will likely do what you’re looking for if you can’t use one of the aforementioned tools.
Kubernetes API changes are coming up and I wanted to make a quick blog post to highlight what this means and show a few of things I have discovered to deal with the changes.
First, there have been some relevant announcements regarding the changes and deprecations recently. The first being the API Depractions in 1.16 announcement, which describes the changes to the API and some of the things to look at and do to fix problems
The next post is the Kubernetes 1.16 release announcement, which contains a section “Significant Changes to the Kubernetes API” that references the deprecation post.
Another excellent resource for learning about how Kubernetes deprecations work is the API deprecation documentation, highlighted in the deprecation post, but not widely shared.
In my opinion, the Kubernetes community really dropped the ball in terms of communicating these changes and missed an opportunity to describe and discuss the problems that these changes will create. I understand that the community is gigantic and it would be impossible to cover every case, but to me, the few blog posts describing the changes and not much other official communication or guides for how to handle and fix the impending problems is a little bit underwhelming.
The average user probably doesn’t pay attention to these blog posts, and there are a lot of old Helm charts out in the wild still, so I’m confident that the incoming changes will create headaches and table flips when people start upgrading. As an example, if you have an old API defined and running in a pre 1.16 cluster, and upgrade without fixing the API version first, APPS IN YOUR CLUSTER WILL BREAK. The good news is that new clusters won’t allow the old API versions, making errors easier to see and deal with.
Testing for and fixing deprecated APIs
With that mini rant out of the way, there is a simple but effective way to test your existing configurations for API compatibility.
Conftest is a nice little tool that helps write tests against structured configuration data, using the Rego language using Open Policy Agent (OPA). Conftest works with many file types including JSON, TOML and HCL, which makes it a great choice for testing a variety of different configurations, but is especially useful for testing Kubernetes YAML configurations.
wget https://github.com/instrumenta/conftest/releases/download/v0.15.0/conftest_0.15.0_Linux_x86_64.tar.gz
tar xzf conftest_0.15.0_Linux_x86_64.tar.gz
sudo mv conftest /usr/local/bin
Then we can use the handy policy provided by the deprek8 repo to validate the API versions.
curl https://raw.githubusercontent.com/naquada/deprek8/master/policy/deprek8.rego > deprek8.rego
conftest test -p deprek8.rego sample/manifest.yaml
Here’s what a FAIL condition might look like according to what is defined in the rego policy file for an outdated API version.
FAIL - sample/manifest.yaml - Deployment/my-deployment: API extensions/v1beta1 for Deployment is no longer served by default, use apps/v1 instead.
The Rego policy is what actually defines the behavior that Conftest will display and as you can see, it found an issue with the Deployment object defined in the test manifest.
Below is the Rego policy that causes Conftest to spit out the FAILure message. The syntax is clean and easy to follow, so writing and adjusting policies is easy.
_deny = msg {
resources := ["DaemonSet", "Deployment", "ReplicaSet"]
input.apiVersion == "extensions/v1beta1"
input.kind == resources[_]
msg := sprintf("%s/%s: API extensions/v1beta1 for %s is no longer served by default, use apps/v1 instead.", [input.kind, input.metadata.name, input.kind])
}
Once you know what is wrong with the configuration, you can use the kubectl convert subcommand to fix up the existing deprecated API objects. Again, attempting to create objects using deprecated APIs in 1.16 will be rejected automatically by Kubernetes, so you will only need to deal with converting existing objects in old clusters being upgraded.
From the above error, we know the object type (Deployment) and the version (extensions/v1beta1). With this information we can run the convert command to fix the object.
# General syntax
kubectl convert -f <file> --output-version <group>/<version>
# The --output-version flag allows specifying the API version to upgrade to
kubectl convert -f sample/manifest.yaml --output-version apps/v1
# Omitting the --output-version flag will convert to the latest version
kubectl convert -f sample/manifest.yaml
After the existing objects have been converted and any manifest files have been updated you should be safe to upgrade Kubernetes.
Bonus
There was a fantastic episode of TGIK awhile back called Kubernetes API Removal and You that describes in great detail what all of the deprections mean and how to fix them – definitely worth a watch if you have the time.
Conclusion
OPA and testing configurations using tools like conftest and Rego policies is a great way to harden and help standardize configurations. Taken a step further, these configuration testing tools can be extended to test all sorts of other things.
Conftest looks especially promising because of the number of file types that it understands. There is a lot of potential here for doing things like unit testing Kubernetes configuration files and other things like Terraform configs.
I haven’t written any Rego policies yet but the language looks pretty straight forward and easy to deal with. I think that as configurations continue to evolve, tools like Conftest (OPA), Kubeval and Kustomize will gain more traction and help simplify some of the complexities of Kubernetes.
