Kubernetes allows one to orchestrate cloud resources in an elegant fashion, specifically… by allowing one to interact with a single point of entry to the cluster and instructing through this interface how to allocate the different resource within. Basically, talk to your cloud and have it manage your compute containers.
Things like ensuring that 5 instances of a worker remain operational at all times are some of the things that Kubernetes makes easy. This post serves as a Note-to-Self to the future me fiddling to get a nice setup of my services online.
At the time of writing (Februari 2015) the Google Cloud container API is still in alpha. So will most likely change, but I have a hunch that underlying principles will remain largely the same.
#Introduction Kubernetes introduces a layer or more of abstraction to devops hackers. The idea is to address the entire infrastructure as a single unit. Kubernetes helps us in orchestrating our cloud.
There is quite a bit of housekeeping required to get a cluster up and running. When working with Docker , just getting different containers to play ball together demands the configuration of discovery services among other things. Within the docker scenario this is already a bit of a hassle between containers on the same (virtual) machine. Setting up an application over a cluster of multiple (virtual) machines will require a bit more work obviously.
Every machine added to the cluster needs to know how to deal with discovery and if at all possible we rather would not spend our energy on focussing on the individual (virtual) machines, because you don’t want to have to remember what every machine does. Let the cluster take care of its own housekeeping .
With a master, Kubernetes offers us a entry point into the black box we call our cloud.
The master runs the interface service (
kube-apiserver) which accepts all
commands we issue. The discovery service,
specifically, enables the services to fetch necessary configurations from
a central location.
The scheduling service assigns work to different nodes in the cluster (e.g.:
determining on which machines which workload is to be executed based on current
workload and other properties).
The management service (which is in fact the Kubernetes replication
controller) enforces that the specified requirements for workunits are honored
(.e.g.: when one have requested N instances of a worker and some become
unhealthy, the replication-controller sees to it that these workers are killed
and new instances are spawned to keep the requested count of services up).
The registration service deals with registering Kubelets with the Kubernetes
In the traditional CoreOS setup, for instance, one sets up `etcd` to replicate the store content among the different machines in the cluster, but the Kubernetes case simply uses a single box for the store. Yes, this does mean that the Kubernetes benefits disappear with the master going offline.
I could dive into the ins and outs of every service in the diagram above, but the good fellas and gals at DigitalOcean have a clear writeup on the different Kubernetes components.
The nodes run a management service (
kubelet) which stays in touch with the
master and maintains the workload to be executed on the node. The networking
service provides the necessary plumbing to handle network in a
Kubernetes-friendly manner (basically every node on a Kubernetes network
requires its own subnet,
flannel sees to it that Kubernetes has the impression
that this is the case). The proxy service takes care of ensuring that
requests are mapped to corresponding container endpoints. Last, but definitely
not least the containerization service takes care of facilitating the running
of containers on our node(s).
Scattered across the nodes, the actual (virtual) machines, Kubernetes spawns, monitors and kills the specified pods which are the atoms in the Kubernetes universe. Pods, being the smallest deployable units in the Kubernetosphere, are collections of containers so there may be multiple containers operating in a pod. All the containers within a pod share the same volumes, the networking namespace, ip- and port space (to simplify communication between containers within the pod). In the section on pods, I discuss a use-case which describes how one may utilize such pods.
For this example we will set up a master and different minions. Instead of asking Google Cloud to just spawn a cluster for us, we will add these machines one by one (this should also make it a bit easier to expand the nodepool later on).
Spawn a Master
Create a Kubernetes master using the
file contributed Kelsey Hightower. This will setup the
Kubernetes API service on port 8080. Just to make things a bit easier, tag the
machine to allow more flexibility in defining the firewall rules later on.
I believe it is needless to say that
CLUSTER need to be
filled it by you, but there I said it. For a list of available zones query
gcloud compute zones list. Do yourself a favor while you read through this
article and keep all the machines you create within the same zone (it makes
things easier for now, but you can spread your clusters over multiple zones
once you understand how things work ). For a list of
available machine types within your selected zone query
gcloud compute machine-types list --zone ZONE and replace
MECH. Yet again,
do yourself a favor and pick a mech that has more guts then the
mech. Be creative with
CLUSTER. I called mine Abell 2744.
If all is well the previous command should spawn a Kubernetes master. Confirm
that the master machine is up by reviewing the list of running instances
gcloud compute instances list --zone ZONE is queried.
Assuming that the actual ip address of the master machine is substituted
IP is mentioned, one may attempt to execute a call to the Kubernetes
wget hangs on this request, it most likely means that there are no firewall
rules in place to allow your call to hit the actual master server.
Enable traffic to master machines within the infrastructure over port 8080 by creating a firewall rules.
Note that the target tag enables one to describe firewall rules that apply to multiple machines without having to specify each machine explicitly. This is one of the sweet cons of machine tagging .
After adding the necessary firewall rule one should be able to perform the
wget call without any problems.
Now that the master is accessible from the internet, one may try to use the Kubernetes Controller interface to query the amount of nodes on the cluster.
Some may not have the
kubectl command available on their machines which may
be used as a replacement for
clusters/kubectl.sh in the previous example. It
is shipped as part of the google-cloud-sdk bundle. If for some reason, this
does not apply use the kubectl.sh which is located in the clusters directory of
the kubernetes project you may have to modify your path a
bit depending on your working directory.
Spawn a Minion
A Kubernetes cluster becomes very interesting once we start adding nodes (formerly known as minions) to it.
Before creating these nodes we need to replace every occurence of the string
<master-private-ip> for the real private ip address of the master.
Using the following command one may simply perform the replacement provided
IP is substituted for the ip address.
sed to replace the token for the IP address in mind, we ensure that
we have every occurence of the phrase substituted.
In order to allow our cluster machines to communicate through etcd (all minions need to communicate with the master in order) one can set up a firewall rule that applies to machines bearing the given target tag (this is why I love tagging my machines).
Now it is time to create the nodes (or minions).
NAME one may enter one or multple machine names (I chose the names of
three Minion resulting to
NAME being substituted with
dave kevin stuart).
For testing purposes one may select a
g1-small mech, while
picking a zone close to home .
Kubernetes performs work in pods. Pods are collections of containers may be bundled together for several reasons.
Imagine a web worker, that receives content and performs some operation on it. You could seperate that into a web worker, which focusses on merely handling the incoming request, and a processor which focusses on performing that operation that we want to execute.
- The web worker receives text while the processor scans the text to determine the sentiment of the submittor or whether the text has been plagiarized.
- The web worker receives an audio sample while the processor filters the sample to flag it whether the secret phrase (“open sesame”) is used, or simply just filter to eliminate non-vocal sound.
- The web worker receives a video while the processor filter the fragment to blur out faces, license plates and nudity.
For several reasons we could decide to run both on the same computation unit as the web worker could fetch and store the data for processing upon which the processor picks up the data and does its magic. Gosh, would it be great if these services could share their storage volumes? Within a pod they do .
Service with A Smile
Now that we have the plumbing in place we need to start pumping some fluids through the pipelines. Kubernetes allows us to define pods which execute the work we need done.
Kubernetes introduces the notion of pods as a unit for describing services that may require replication over the cluster. One can easily start a pod in Kubernetes
In order to create a nginx server one could run:
Basically this spawns a replication controller, which manages the docker service(s) it is instructed to run. In the current example a simple nginx server is setup. The replication controller just keeps an eye out over the containers and ensures that the two services run. Upon failure necessary steps will be taken.
The cool thing about Kubernetes is that we can specify the amount of replicas to keep alive. A replication controller is setup by Kubernetes to ensure that the two replicas are running at all times. If any service suffers from a health failure, the replication controller will see to it that another instance is spawned.
Google has made exposed some
kubectl features to be accessible through other
calls such as
gcloud preview container pods create --help.