In this tutorial we show how to convert Kubernetes configuration files for a collection of microservices.
The configuration files are scrubbed and renamed versions of real-life configuration files. The files are organized in a directory hierarchy grouping related services in subdirectories. This is a common pattern. The cue tooling has been optimized for this use case.
In this tutorial we will address the following topics:
docker-compose (TODO)The data set is based on a real-life case, using different names for the services. All the inconsistencies of the real setup are replicated in the files to get a realistic impression of how a conversion to CUE would behave in practice.
The given YAML files are ordered in following directory (you can use find if you don't have tree):
$ tree ./original | head
.
└── services
├── frontend
│ ├── bartender
│ │ └── kube.yaml
│ ├── breaddispatcher
│ │ └── kube.yaml
│ ├── host
│ │ └── kube.yaml
│ ├── maitred
...
Each subdirectory contains related microservices that often share similar characteristics and configurations. The configurations include a large variety of Kubernetes objects, including services, deployments, config maps, a daemon set, a stateful set, and a cron job.
The result of the first tutorial is in the quick, for “quick and dirty” directory. A manually optimized configuration can be found int manual directory.
We first make a copy of the data directory.
$ cp -a original tmp $ cd tmp
We initialize a module so that we can treat all our configuration files in the subdirectories as part of one package. We do that later by giving all the same package name.
$ cue mod init
Creating a module also allows our packages import external packages.
Let's try to use the cue import command to convert the given YAML files into CUE.
$ cd services $ cue import ./... must specify package name with the -p flag
Since we have multiple packages and files, we need to specify the package to which they should belong.
$ cue import ./... -p kube list, flag, or files flag needed to handle multiple objects in file "./frontend/bartender/kube.yaml"
Many of the files contain more than one Kubernetes object. Moreover, we are creating a single configuration that contains all objects of all files. We need to organize all Kubernetes objects such that each is individually identifiable within a single configuration. We do so by defining a different struct for each type putting each object in this respective struct keyed by its name. This allows objects of different types to share the same name, just as is allowed by Kubernetes. To accomplish this, we tell cue to put each object in the configuration tree at the path with the “kind” as first element and “name” as second.
$ cue import ./... -p kube -l 'strings.ToCamel(kind)' -l metadata.name -f
The added -l flag defines the labels for each object, based on values from each object, using the usual CUE syntax for field labels. In this case, we use a camelcase variant of the kind field of each object and use the name field of the metadata section as the name for each object. We also added the -f flag to overwrite the few files that succeeded before.
Let's see what happened:
$ tree . | head
.
└── services
├── frontend
│ ├── bartender
│ │ ├── kube.cue
│ │ └── kube.yaml
│ ├── breaddispatcher
│ │ ├── kube.cue
│ │ └── kube.yaml
...
Each of the YAML files is converted to corresponding CUE files. Comments of the YAML files are preserved.
The result is not fully pleasing, though. Take a look at mon/prometheus/configmap.cue.
$ cat mon/prometheus/configmap.cue
package kube
apiVersion: "v1"
kind: "ConfigMap"
metadata: name: "prometheus"
data: {
"alert.rules": """
groups:
- name: rules.yaml
...
The configuration file still contains YAML embedded in a string value of one of the fields. The original YAML file might have looked like it was all structured data, but the majority of it was a string containing, hopefully, valid YAML.
The -R option attempts to detect structured YAML or JSON strings embedded in the configuration files and then converts these recursively.
<-- TODO: update import label format -->
$ cue import ./... -p kube -l 'strings.ToCamel(kind)' -l metadata.name -f -R
Now the file looks like:
$ cat mon/prometheus/configmap.cue
package kube
import "encoding/yaml"
configMap: prometheus: {
apiVersion: "v1"
kind: "ConfigMap"
metadata: name: "prometheus"
data: {
"alert.rules": yaml.Marshal(_cue_alert_rules)
_cue_alert_rules: {
groups: [{
...
That looks better! The resulting configuration file replaces the original embedded string with a call to yaml.Marshal converting a structured CUE source to a string with an equivalent YAML file. Fields starting with an underscore (_) are not included when emitting a configuration file (they are when enclosed in double quotes).
$ cue eval ./mon/prometheus -e configMap.prometheus
apiVersion: "v1"
kind: "ConfigMap"
metadata: {
name: "prometheus"
}
data: {
"alert.rules": """
groups:
- name: rules.yaml
...
Yay!
In this tutorial we show how to quickly eliminate boilerplate from a set of configurations. Manual tailoring will usually give better results, but takes considerably more thought, while taking the quick and dirty approach gets you mostly there. The result of such a quick conversion also forms a good basis for a more thoughtful manual optimization.
Now we have imported the YAML files we can start the simplification process.
Before we start the restructuring, lets save a full evaluation so that we can verify that simplifications yield the same results.
$ cue eval -c ./... > snapshot
The -c option tells cue that only concrete values, that is valid JSON, are allowed. We focus on the objects defined in the various kube.cue files. A quick inspection reveals that many of the Deployments and Services share common structure.
We copy one of the files containing both as a basis for creating our template to the root of the directory tree.
$ cp frontend/breaddispatcher/kube.cue .
Modify this file as below.
$ cat <<EOF > kube.cue
package kube
service: [ID=_]: {
apiVersion: "v1"
kind: "Service"
metadata: {
name: ID
labels: {
app: ID // by convention
domain: "prod" // always the same in the given files
component: string // varies per directory
}
}
spec: {
// Any port has the following properties.
ports: [...{
port: int
protocol: *"TCP" | "UDP" // from the Kubernetes definition
name: string | *"client"
}]
selector: metadata.labels // we want those to be the same
}
}
deployment: [ID=_]: {
apiVersion: "apps/v1"
kind: "Deployment"
metadata: name: ID
spec: {
// 1 is the default, but we allow any number
replicas: *1 | int
template: {
metadata: labels: {
app: ID
domain: "prod"
component: string
}
// we always have one namesake container
spec: containers: [{ name: ID }]
}
}
}
EOF
By replacing the service and deployment name with [ID=_] we have changed the definition into a template matching any field. CUE bind the field name to ID as a result. During importing we used metadata.name as a key for the object names, so we can now set this field to ID.
Templates are applied to (are unified with) all entries in the struct in which they are defined, so we need to either strip fields specific to the breaddispatcher definition, generalize them, or remove them.
One of the labels defined in the Kubernetes metadata seems to be always set to parent directory name. We enforce this by defining component: string, meaning that a field of name component must be set to some string value, and then define this later on. Any underspecified field results in an error when converting to, for instance, JSON. So a deployment or service will only be valid if this label is defined.
Let's compare the result of merging our new template to our original snapshot.
$ cue eval ./... -c > snapshot2
--- ./mon/alertmanager
service.alertmanager.metadata.labels.component: incomplete value (string):
./kube.cue:11:24
service.alertmanager.spec.selector.component: incomplete value (string):
./kube.cue:11:24
deployment.alertmanager.spec.template.metadata.labels.component: incomplete value (string):
./kube.cue:36:28
service."node-exporter".metadata.labels.component: incomplete value (string):
./kube.cue:11:24
...
Oops. The alert manager does not specify the component label. This demonstrates how constraints can be used to catch inconsistencies in your configurations.
As there are very few objects that do not specify this label, we will modify the configurations to include them everywhere. We do this by setting a newly defined top-level field in each directory to the directory name and modify our master template file to use it.
# set the component label to our new top-level field $ sed -i.bak 's/component:.*string/component: Component/' kube.cue && rm kube.cue.bak # add the new top-level field to our previous template definitions $ cat <<EOF >> kube.cue Component :: string EOF # add a file with the component label to each directory $ ls -d */ | sed 's/.$//' | xargs -I DIR sh -c 'cd DIR; echo "package kube Component :: \"DIR\" " > kube.cue; cd ..' # format the files $ cue fmt kube.cue */kube.cue
Let's try again to see if it is fixed:
$ cue eval -c ./... > snapshot2 $ diff snapshot snapshot2 ...
Except for having more consistent labels and some reordering, nothing changed. We are happy and save the result as the new baseline.
$ cp snapshot2 snapshot
The corresponding boilerplate can now be removed with cue trim.
$ find . | grep kube.cue | xargs wc | tail -1
1792 3616 34815 total
$ cue trim ./...
$ find . | grep kube.cue | xargs wc | tail -1
1223 2374 22903 total
cue trim removes configuration from files that is already generated by templates or comprehensions. In doing so it removed over 500 lines of configuration, or over 30%!
The following is proof that nothing changed semantically:
$ cue eval -c ./... > snapshot2
$ diff snapshot snapshot2 | wc
0 0 0
We can do better, though. A first thing to note is that DaemonSets and StatefulSets share a similar structure to Deployments. We generalize the top-level template as follows:
$ cat <<EOF >> kube.cue
daemonSet: [ID=_]: _spec & {
apiVersion: "apps/v1"
kind: "DaemonSet"
Name :: ID
}
statefulSet: [ID=_]: _spec & {
apiVersion: "apps/v1"
kind: "StatefulSet"
Name :: ID
}
deployment: [ID=_]: _spec & {
apiVersion: "apps/v1"
kind: "Deployment"
Name :: ID
spec: replicas: *1 | int
}
configMap: [ID=_]: {
metadata: name: ID
metadata: labels: component: Component
}
_spec: {
Name :: string
metadata: name: Name
metadata: labels: component: Component
spec: selector: {}
spec: template: {
metadata: labels: {
app: Name
component: Component
domain: "prod"
}
spec: containers: [{name: Name}]
}
}
EOF
$ cue fmt
The common configuration has been factored out into _spec. We introduced Name to aid both specifying and referring to the name of an object. For completeness, we added configMap as a top-level entry.
Note that we have not yet removed the old definition of deployment. This is fine. As it is equivalent to the new one, unifying them will have no effect. We leave its removal as an exercise to the reader.
Next we observe that all deployments, stateful sets and daemon sets have an accompanying service which shares many of the same fields. We add:
$ cat <<EOF >> kube.cue
// Define the _export option and set the default to true
// for all ports defined in all containers.
_spec: spec: template: spec: containers: [...{
ports: [...{
_export: *true | false // include the port in the service
}]
}]
for x in [deployment, daemonSet, statefulSet] for k, v in x {
service: "\(k)": {
spec: selector: v.spec.template.metadata.labels
spec: ports: [
for c in v.spec.template.spec.containers
for p in c.ports
if p._export {
let Port = p.containerPort // Port is an alias
port: *Port | int
targetPort: *Port | int
}
]
}
}
EOF
$ cue fmt
This example introduces a few new concepts. Open-ended lists are indicated with an ellipsis (...). The value following an ellipsis is unified with any subsequent elements and defines the “type”, or template, for additional list elements.
The Port declaration is an alias. Aliases are only visible in their lexical scope and are not part of the model. They can be used to make shadowed fields visible within nested scopes or, in this case, to reduce boilerplate without introducing new fields.
Finally, this example introduces list and field comprehensions. List comprehensions are analogous to list comprehensions found in other languages. Field comprehensions allow inserting fields in structs. In this case, the field comprehension adds a namesake service for any deployment, daemonSet, and statefulSet. Field comprehensions can also be used to add a field conditionally.
Specifying the targetPort is not necessary, but since many files define it, defining it here will allow those definitions to be removed using cue trim. We add an option _export for ports defined in containers to specify whether to include them in the service and explicitly set this to false for the respective ports in infra/events, infra/tasks, and infra/watcher.
For the purpose of this tutorial, here are some quick patches:
$ cat <<EOF >> infra/events/kube.cue
deployment: events: spec: template: spec: containers: [{ ports: [{_export: false}, _] }]
EOF
$ cat <<EOF >> infra/tasks/kube.cue
deployment: tasks: spec: template: spec: containers: [{ ports: [{_export: false}, _] }]
EOF
$ cat <<EOF >> infra/watcher/kube.cue
deployment: watcher: spec: template: spec: containers: [{ ports: [{_export: false}, _] }]
EOF
In practice it would be more proper form to add this field in the original port declaration.
We verify that all changes are acceptable and store another snapshot. Then we run trim to further reduce our configuration:
$ cue trim ./...
$ find . | grep kube.cue | xargs wc | tail -1
1129 2270 22073 total
This is after removing the rewritten and now redundant deployment definition.
We shaved off almost another 100 lines, even after adding the template. You can verify that the service definitions are now gone in most of the files. What remains is either some additional configuration, or inconsistencies that should probably be cleaned up.
But we have another trick up our sleeve. With the -s or --simplify option we can tell trim or fmt to collapse structs with a single element onto a single line. For instance:
$ head frontend/breaddispatcher/kube.cue
package kube
deployment: breaddispatcher: {
spec: {
template: {
metadata: {
annotations: {
"prometheus.io.scrape": "true"
"prometheus.io.port": "7080"
}
$ cue trim ./... -s
$ head -7 frontend/breaddispatcher/kube.cue
package kube
deployment: breaddispatcher: spec: template: {
metadata: annotations: {
"prometheus.io.scrape": "true"
"prometheus.io.port": "7080"
}
$ find . | grep kube.cue | xargs wc | tail -1
975 2116 20264 total
Another 150 lines lost! Collapsing lines like this can improve the readability of a configuration by removing considerable amounts of punctuation.
In the previous section we defined templates for services and deployments in the root of our directory structure to capture the common traits of all services and deployments. In addition, we defined a directory-specific label. In this section we will look into generalizing the objects per directory.
frontendWe observe that all deployments in subdirectories of frontend have a single container with one port, which is usually 7080, but sometimes 8080. Also, most have two prometheus-related annotations, while some have one. We leave the inconsistencies in ports, but add both annotations unconditionally.
$ cat <<EOF >> frontend/kube.cue
deployment: [string]: spec: template: {
metadata: annotations: {
"prometheus.io.scrape": "true"
"prometheus.io.port": "\(spec.containers[0].ports[0].containerPort)"
}
spec: containers: [{
ports: [{containerPort: *7080 | int}] // 7080 is the default
}]
}
EOF
$ cue fmt ./frontend
# check differences
$ cue eval -c ./... > snapshot2
$ diff snapshot snapshot2
368a369
> prometheus.io.port: "7080"
577a579
> prometheus.io.port: "8080"
$ cp snapshot2 snapshot
Two lines with annotations added, improving consistency.
$ cue trim -s ./frontend/...
$ find . | grep kube.cue | xargs wc | tail -1
931 2052 19624 total
Another 40 lines removed. We may have gotten used to larger reductions, but at this point there is just not much left to remove: in some of the frontend files there are only 4 lines of configuration left.
kitchenIn this directory we observe that all deployments have without exception one container with port 8080, all have the same liveness probe, a single line of prometheus annotation, and most have two or three disks with similar patterns.
Let's add everything but the disks for now:
$ cat <<EOF >> kitchen/kube.cue
deployment: [string]: spec: template: {
metadata: annotations: "prometheus.io.scrape": "true"
spec: containers: [{
ports: [{
containerPort: 8080
}]
livenessProbe: {
httpGet: {
path: "/debug/health"
port: 8080
}
initialDelaySeconds: 40
periodSeconds: 3
}
}]
}
EOF
$ cue fmt ./kitchen
A diff reveals that one prometheus annotation was added to a service. We assume this to be an accidental omission and accept the differences
Disks need to be defined in both the template spec section as well as in the container where they are used. We prefer to keep these two definitions together. We take the volumes definition from expiditer (the first config in that directory with two disks), and generalize it:
$ cat <<EOF >> kitchen/kube.cue
deployment: [ID=_]: spec: template: spec: {
hasDisks :: *true | bool
// field comprehension using just "if"
if hasDisks {
volumes: [{
name: *"\(ID)-disk" | string
gcePersistentDisk: pdName: *"\(ID)-disk" | string
gcePersistentDisk: fsType: "ext4"
}, {
name: *"secret-\(ID)" | string
secret: secretName: *"\(ID)-secrets" | string
}, ...]
containers: [{
volumeMounts: [{
name: *"\(ID)-disk" | string
mountPath: *"/logs" | string
}, {
mountPath: *"/etc/certs" | string
name: *"secret-\(ID)" | string
readOnly: true
}, ...]
}]
}
}
EOF
$ cat <<EOF >> kitchen/souschef/kube.cue
deployment: souschef: spec: template: spec: {
hasDisks :: false
}
EOF
$ cue fmt ./kitchen/...
This template definition is not ideal: the definitions are positional, so if configurations were to define the disks in a different order, there would be no reuse or even conflicts. Also note that in order to deal with this restriction, almost all field values are just default values and can be overridden by instances. A better way would be define a map of volumes, similarly to how we organized the top-level Kubernetes objects, and then generate these two sections from this map. This requires some design, though, and does not belong in a “quick-and-dirty” tutorial. Later in this document we introduce a manually optimized configuration.
We add the two disk by default and define a _hasDisks option to opt out. The souschef configuration is the only one that defines no disks.
$ cue trim -s ./kitchen/...
# check differences
$ cue eval ./... > snapshot2
$ diff snapshot snapshot2
...
$ cp snapshot2 snapshot
$ find . | grep kube.cue | xargs wc | tail -1
807 1862 17190 total
The diff shows that we added the _hadDisks option, but otherwise reveals no differences. We also reduced the configuration by a sizeable amount once more.
However, on closer inspection of the remaining files we see a lot of remaining fields in the disk specifications as a result of inconsistent naming. Reducing configurations like we did in this exercise exposes inconsistencies. The inconsistencies can be removed by simply deleting the overrides in the specific configuration. Leaving them as is gives a clear signal that a configuration is inconsistent.
There is still some gain to be made with the other directories. At nearly a 1000-line, or 55%, reduction, we leave the rest as an exercise to the reader.
We have shown how CUE can be used to reduce boilerplate, enforce consistencies, and detect inconsistencies. Being able to deal with consistencies and inconsistencies is a consequence of the constraint-based model and harder to do with inheritance-based languages.
We have indirectly also shown how CUE is well-suited for machine manipulation. This is a factor of syntax and the order independence that follows from its semantics. The trim command is one of many possible automated refactor tools made possible by this property. Also this would be harder to do with inheritance-based configuration languages.
The cue export command can be used to convert the created configuration back to JSON. In our case, this requires a top-level “emit value” to convert our mapped Kubernetes objects back to a list. Typically, this output is piped to tools like kubectl or etcdctl.
In practice this means typing the same commands ad nauseam. The next step is often to write wrapper tools. But as there is often no one-size-fits-all solution, this lead to the proliferation of marginally useful tools. The cue tool provides an alternative by allowing the declaration of frequently used commands in CUE itself. Advantages:
Commands are defined in files ending with _tool.cue in the same package as where the configuration files are defined on which the commands should operate. Top-level values in the configuration are visible by the tool files as long as they are not shadowed by top-level fields in the tool files. Top-level fields in the tool files are not visible in the configuration files and are not part of any model.
The tool definitions also have access to additional builtin packages. A CUE configuration is fully hermetic, disallowing any outside influence. This property enables automated analysis and manipulation such as the trim command. The tool definitions, however, have access to such things as command line flags and environment variables, random generators, file listings, and so on.
We define the following tools for our example:
kubectl for creationTo work with Kubernetes we need to convert our map of Kubernetes objects back to a simple list. We create the tool file to do just that.
$ cat <<EOF > kube_tool.cue
package kube
objects: [ for v in objectSets for x in v { x } ]
objectSets: [
service,
deployment,
statefulSet,
daemonSet,
configMap,
]
EOF
Commands are defined in the command section at the top-level of a tool file. A cue command defines command line flags, environment variables, as well as a set of tasks. Examples tasks are load or write a file, dump something to the console, download a web page, or execute a command.
We start by defining the ls command which dumps all our objects
$ cat <<EOF > ls_tool.cue
package kube
import (
"text/tabwriter"
"tool/cli"
"tool/file"
)
command: ls: {
task: print: cli.Print & {
text: tabwriter.Write([
for x in objects {
"\(x.kind) \t\(x.metadata.labels.component) \t\(x.metadata.name)"
}
])
}
task: write: file.Create & {
filename: "foo.txt"
contents: task.print.text
}
}
EOF
NOTE: THE API OF THE TASK DEFINITIONS WILL CHANGE. Although we may keep supporting this form if needed.
The command is now available in the cue tool:
$ cue cmd ls ./frontend/maitred Service frontend maitred Deployment frontend maitred
As long as the name does not conflict with an existing command it can be used as a top-level command as well:
$ cue ls ./frontend/maitred ...
If more than one instance is selected the cue tool may either operate on them one by one or merge them. The default is to merge them. Different instances of a package are typically not compatible: different subdirectories may have different specializations. A merge pre-expands templates of each instance and then merges their root values. The result may contain conflicts, such as our top-level Component field, but our per-type maps of Kubernetes objects should be free of conflict (if there is, we have a problem with Kubernetes down the line). A merge thus gives us a unified view of all objects.
$ cue ls ./... Service infra tasks Service frontend bartender Service frontend breaddispatcher Service frontend host Service frontend maitred Service frontend valeter Service frontend waiter Service frontend waterdispatcher Service infra download Service infra etcd Service infra events ... Deployment proxy nginx StatefulSet infra etcd DaemonSet mon node-exporter ConfigMap mon alertmanager ConfigMap mon prometheus ConfigMap proxy authproxy ConfigMap proxy nginx
The following adds a command to dump the selected objects as a YAML stream.
$ cat <<EOF > dump_tool.cue
package kube
import (
"encoding/yaml"
"tool/cli"
)
command: dump: {
task: print: cli.Print & {
text: yaml.MarshalStream(objects)
}
}
EOF
The MarshalStream command converts the list of objects to a ‘---’-separated stream of YAML values.
The create command sends a list of objects to kubectl create.
$ cat <<EOF > create_tool.cue
package kube
import (
"encoding/yaml"
"tool/exec"
"tool/cli"
)
command: create: {
task: kube: exec.Run & {
cmd: "kubectl create --dry-run -f -"
stdin: yaml.MarshalStream(objects)
stdout: string
}
task: display: cli.Print & {
text: task.kube.stdout
}
}
EOF
This command has two tasks, named kube and display. The display task depends on the output of the kube task. The cue tool does a static analysis of the dependencies and runs all tasks which dependencies are satisfied in parallel while blocking tasks for which an input is missing.
$ cue create ./frontend/... service "bartender" created (dry run) service "breaddispatcher" created (dry run) service "host" created (dry run) service "maitred" created (dry run) service "valeter" created (dry run) service "waiter" created (dry run) service "waterdispatcher" created (dry run) deployment.apps "bartender" created (dry run) deployment.apps "breaddispatcher" created (dry run) deployment.apps "host" created (dry run) deployment.apps "maitred" created (dry run) deployment.apps "valeter" created (dry run) deployment.apps "waiter" created (dry run) deployment.apps "waterdispatcher" created (dry run)
A production real-life version of this could should omit the --dry-run flag of course.
In order for cue get go to generate the CUE templates from Go sources, you first need to have the sources locally:
$ go get k8s.io/api/apps/v1
$ cue get go k8s.io/api/core/v1 $ cue get go k8s.io/api/apps/v1
Now that we have the Kubernetes definitions in our module, we can import and use them:
$ cat <<EOF > k8s_defs.cue package kube import ( "k8s.io/api/core/v1" apps_v1 "k8s.io/api/apps/v1" ) service: [string]: v1.Service deployment: [string]: apps_v1.Deployment daemonSet: [string]: apps_v1.DaemonSet statefulSet: [string]: apps_v1.StatefulSet EOF
And, finally, we'll format again:
cue fmt
In Section “Quick 'n Dirty” we showed how to quickly get going with CUE. With a bit more deliberation, one can reduce configurations even further. Also, we would like to define a configuration that is more generic and less tied to Kubernetes.
We will rely heavily on CUEs order independence, which makes it easy to combine two configurations of the same object in a well-defined way. This makes it easy, for instance, to put frequently used fields in one file and more esoteric one in another and then combine them without fear that one will override the other. We will take this approach in this section.
The end result of this tutorial is in the manual directory. In the next sections we will show how to get there.
The basic premise of our configuration is to maintain two configurations, a simple and abstract one, and one compatible with Kubernetes. The Kubernetes version is automatically generated from the simple configuration. Each simplified object has a kubernetes section that get gets merged into the Kubernetes object upon conversion.
We define one top-level file with our generic definitions.
// file cloud.cue
package cloud
service: [Name=_]: {
name: *Name | string // the name of the service
...
// Kubernetes-specific options that get mixed in when converting
// to Kubernetes.
kubernetes: {
}
}
deployment: [Name=_]: {
name: *Name | string
...
}
A Kubernetes-specific file then contains the definitions to convert the generic objects to Kubernetes.
Overall, the code modeling our services and the code generating the kubernetes code is separated, while still allowing to inject Kubernetes-specific data into our general model. At the same time, we can add additional information to our model without it ending up in the Kubernetes definitions causing it to barf.
For our design we assume that all Kubernetes Pod derivatives only define one container. This is clearly not the case in general, but often it does and it is good practice. Conveniently, it simplifies our model as well.
We base the model loosely on the master templates we derived in Section “Quick 'n Dirty”. The first step we took is to eliminate statefulSet and daemonSet and rather just have a deployment allowing different kinds.
deployment: [Name=_]: _base & {
name: *Name | string
...
The kind only needs to be specified if the deployment is a stateful set or daemonset. This also eliminates the need for _spec.
The next step is to pull common fields, such as image to the top level.
Arguments can be specified as a map.
arg: [string]: string
args: [ for k, v in arg { "-\(k)=\(v)" } ] | [...string]
If order matters, users could explicitly specify the list as well.
For ports we define two simple maps from name to port number:
// expose port defines named ports that is exposed in the service
expose: port: [string]: int
// port defines a named port that is not exposed in the service.
port: [string]: int
Both maps get defined in the container definition, but only port gets included in the service definition. This may not be the best model, and does not support all features, but it shows how one can chose a different representation.
A similar story holds for environment variables. In most cases mapping strings to string suffices. The testdata uses other options though. We define a simple env map and an envSpec for more elaborate cases:
env: [string]: string
envSpec: [string]: {}
envSpec: {
for k, v in env {
"\(k)" value: v
}
}
The simple map automatically gets mapped into the more elaborate map which then presents the full picture.
Finally, our assumption that there is one container per deployment allows us to create a single definition for volumes, combining the information for volume spec and volume mount.
volume: [Name=_]: {
name: *Name | string
mountPath: string
subPath: null | string
readOnly: bool
kubernetes: {}
}
All other fields that we way want to define can go into a generic kubernetes struct that gets merged in with all other generated kubernetes data. This even allows us to augment generated data, such as adding additional fields to the container.
The service definition is straightforward. As we eliminated stateful and daemon sets, the field comprehension to automatically derive a service is now a bit simpler:
// define services implied by deployments
service: {
for k, spec in deployment {
"\(k)": {
// Copy over all ports exposed from containers.
for Name, Port in spec.expose.port {
port: "\(Name)": {
port: *Port | int
targetPort: *Port | int
}
}
// Copy over the labels
label: spec.label
}
}
}
The complete top-level model definitions can be found at doc/tutorial/kubernetes/manual/services/cloud.cue.
The tailorings for this specific project (the labels) are defined here.
Converting services is fairly straightforward.
kubernetes: services: {
for k, x in service {
"\(k)": x.kubernetes & {
apiVersion: "v1"
kind: "Service"
metadata: name: x.name
metadata: labels: x.label
spec: selector: x.label
spec: ports: [ for p in x.port { p } ]
}
}
}
We add the Kubernetes boilerplate, map the top-level fields and mix in the raw kubernetes fields for each service.
Mapping deployments is a bit more involved, though analogous. The complete definitions for Kubernetes conversions can be found at doc/tutorial/kubernetes/manual/services/k8s.cue.
Converting the top-level definitions to concrete Kubernetes code is the hardest part of this exercise. That said, most CUE users will never have to resort to this level of CUE to write configurations. For instance, none of the files in the subdirectories contain comprehensions, not even the template files in these directories (such as kitchen/kube.cue). Furthermore, none of the configuration files in any of the leaf directories contain string interpolations.
The fully written out manual configuration can be found in the manual subdirectory. Running our usual count yields
$ find . | grep kube.cue | xargs wc | tail -1
542 1190 11520 total
This does not count our conversion templates. Assuming that the top-level templates are reusable, and if we don't count them for both approaches, the manual approach shaves off about another 150 lines. If we count the templates as well, the two approaches are roughly equal.
We have shown that we can further compact a configuration by manually optimizing template files. However, we have also shown that the manual optimization only gives a marginal benefit with respect to the quick-and-dirty semi-automatic reduction. The benefits for the manual definition largely lies in the organizational flexibility one gets.
Manually tailoring your configurations allows creating an abstraction layer between logical definitions and Kubernetes-specific definitions. At the same time, CUE's order independence makes it easy to mix in low-level Kubernetes configuration wherever it is convenient and applicable.
Manual tailoring also allows us to add our own definitions without breaking Kubernetes. This is crucial in defining information relevant to definitions, but unrelated to Kubernetes, where they belong.
Separating abstract from concrete configuration also allows us to create difference adaptors for the same configuration.