Operating Elasticsearch in Kubernetes

Demo

[Slides PDF]

This demo is part of the talk 'Operating Elasticsearch in Kubernetes' by Mikkel Larsen and Oliver Trosien. It goes step-by-stop through the deployment of the ES Operator into Kubernetes, and creation and scaling of an Elasticsearch cluster.

Prerequisites

Have a Kubernetes cluster at hand (e.g. kind or minikube), and kubectl configured to point to it.

Step 1 - Set up Roles

The ES Operator needs special permissions to access Kubernetes APIs, and Elasticsearch needs privileged access to increase the operating system limits for memory-mapped files.

Therefore as the first step we deploy a serviceAccount es-operator with the necessary RBAC roles attached.

kubectl apply -f manifests/cluster-roles.yaml

Step 2 - Register Custom Resource Definitions

The ES Operator manages two custom resources. These need to be registered in your cluster.

kubectl apply -f manifests/custom-resource-definitions.yaml

Step 3 - Deploy ES Operator

Next, we'll deploy our operator. It will be created from a deployment manifest in the namespace demo, and pull the latest image.

kubectl apply -f manifests/es-operator.yaml

You can check if it was successfully launched:

kubectl -n demo get pods

Step 4 - Bootstrap Your Elasticsearch Cluster

The Elasticsearch will be boot-strapped from a set of master nodes. For the purpose of this demo, a single master is sufficient. For production a set of three masters is recommended.

kubectl apply -f manifests/elasticsearch-cluster.yaml

The manifest also creates services for the transport and HTTP protocols. If you tunnel to port 9200 on the master, you should be able to communicate with your Elasticsearch cluster.

kubectl -n demo port-forward svc/es-http 9200

Step 5 - Add Elasticsearch Data Sets

Finally, let's add data nodes. For the purpose of this demo we have a simple stack will launch one data node, and scales up based on the number of shards and CPU usage.

kubectl apply -f manifests/es-data1.yaml

To check the results, first look for the custom resources.

kubectl -n demo get eds

The ES Operator creates a StatefulSet, which will spawn the Pod. This can take a few minutes depending on your network and cluster performance.

kubectl -n demo get sts
kubectl -n demo get pods

Step 6: Index Creation

We differentiated the stacks using an Elasticsearch node tag called group. It is advised to use this tag to bind indices with the same scaling requirements to nodes with the same group tag, by using the shard allocation setting like this:

curl -XPUT localhost:9200/demo-index1 -HContent-type:application/json \
-d '{"settings": {"index": { "number_of_shards":5, "number_of_replicas":0, "routing.allocation.include.group": "data1"}}}'

Step 7: Scaling Up

Create another index, and watch es-operator scale up to meet the maxShardsPerNode requirement.

curl -XPUT localhost:9200/demo-index2 -HContent-type:application/json  -d '{"settings": {"index": { "number_of_shards":6, "number_of_replicas":0, "routing.allocation.include.group": "data1"}}}'