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Kubernetes Helm Charts

You can use the Helm charts to deploy services on Kubernetes for development, test, and proof of concept environments.

Important

The Helm charts are in developer preview and are not supported for production use.

Introduction

Helm is an open-source packaging tool that helps you install applications and services on Kubernetes.

Helm uses a packaging format called charts. Charts are a collection of YAML templates that describe a related set of Kubernetes resources.

This repository provides Helm charts for the following Confluent Platform services:

  • Kafka brokers
  • ZooKeeper
  • Kafka Connect
  • Confluent Schema Registry
  • Confluent REST Proxy

Environment Preparation

You must have a Kubernetes cluster that has Helm configured.

Tested Software

These Helm charts have been tested with the following software versions:

For local Kubernetes installation with Minikube, see create-local-minikube.

Install Helm on Kubernetes

Follow the directions to install and deploy Helm to the Kubernetes cluster.

View a list of all deployed releases in releases in the local installation.

helm init
helm repo update
helm list

Important

For Helm versions prior to 2.9.1, you may see "connect: connection refused", and will need to fix up the deployment before proceeding.

kubectl delete --namespace kube-system svc tiller-deploy
kubectl delete --namespace kube-system deploy tiller-deploy
kubectl create serviceaccount --namespace kube-system tiller
kubectl create clusterrolebinding tiller-cluster-rule --clusterrole=cluster-admin --serviceaccount=kube-system:tiller
kubectl patch deploy --namespace kube-system tiller-deploy -p '{"spec":{"template":{"spec":{"serviceAccount":"tiller"}}}}'
helm init --service-account tiller --upgrade

Run

Follow these steps to run .

Persistence

The ZooKeeper and Kafka cluster are deployed with StatefulSets that have a volumeClaimTemplate which provides the persistent volume for each replica. You can define the size of the volumes by changing dataDirSize and dataLogDirSize under cp-zookeeper and size under cp-kafka in values.yaml.

You also could use the cloud provider's volumes by specifying StorageClass.. For example, if you are on AWS your storage class will look like this:

apiVersion: storage.k8s.io/v1beta1
kind: StorageClass
metadata:
  name: ssd
provisioner: kubernetes.io/aws-ebs
parameters:
  type: gp2

Tip

To adapt this example to your needs, read the Kubernetes StorageClass documentation.

The StorageClass that was created can be specified in dataLogDirStorageClass and dataDirStorageClass under cp-zookeeper and in storageClass under cp-kafka in values.yaml.

To deploy non-persistent Kafka and ZooKeeper clusters, you must change the value of persistence.enabled under cp-kafka and cp-zookeeper in values.yaml . These type of clusters are suitable for development and testing purposes. The StatefulSets are going to use emptyDir volumes, this means that its content is strictly related to the pod life cycle and is deleted when the pod goes down.

Install cp-helm-charts

Clone the Confluent Helm Chart repo

> helm repo add confluentinc https://confluentinc.github.io/cp-helm-charts/

"confluentinc" has been added to your repositories

> helm repo update

Hang tight while we grab the latest from your chart repositories... ...Skip local chart repository ...Successfully got an update from the "confluentinc" chart repository ...Successfully got an update from the "stable" chart repository Update Complete. ⎈ Happy Helming!⎈

Install a 3 node ensemble, a Kafka cluster of 3 brokers, 1 Confluent Schema Registry instance, 1 REST Proxy instance, and 1 Kafka Connect worker in your Kubernetes environment. Naming the chart --name my-confluent-oss is optional, but we assume this is the name in the remainder of the documentation.

helm install confluentinc/cp-helm-charts --name my-confluent-oss

If you want to install without the Confluent Schema Registry instance, the REST Proxy instance, and the Kafka Connect worker:

helm install --set cp-schema-registry.enabled=false,cp-kafka-rest.enabled=false,cp-kafka-connect.enabled=false confluentinc/cp-helm-charts

View the installed Helm releases:

helm list

NAME REVISION UPDATED STATUS CHART NAMESPACE my-confluent-oss 1 Tue Jun 12 16:56:39 2018 DEPLOYED cp-helm-charts-0.1.0 default

Verify Installation

Helm

This step is optional: run the embedded test pod in each sub-chart to verify installation:

helm test my-confluent-oss

Kafka cluster

This step is optional: to verify that Kafka is working as expected, connect to one of the Kafka pods and produce some messages to a Kafka topic.

  1. List your pods and wait until they are all in Running state.

    kubectl get pods

  2. Connect to the container cp-kafka-broker in a Kafka broker pod to produce messages to a Kafka topic. If you specified a different release name, substitute my-confluent-oss with whatever you named your release.

    kubectl exec -c cp-kafka-broker -it my-confluent-oss-cp-kafka-0 -- /bin/bash /usr/bin/kafka-console-producer --broker-list localhost:9092 --topic test

    Wait for a > prompt, and enter some text.

    m1
m2

    Press Control-d to close the producer session.

  3. Consume the messages from the same Kafka topic as above.

    kubectl exec -c cp-kafka-broker -it my-confluent-oss-cp-kafka-0 -- /bin/bash  /usr/bin/kafka-console-consumer --bootstrap-server localhost:9092 --topic test --from-beginning

    You should see the messages which were published from the console producer. Press Control-c to stop consuming.

Manual Test

  1. Clone Helm Chars git repository

    git clone https://github.com/confluentinc/cp-helm-charts.git

  2. Deploy a client pod.

    kubectl apply -f cp-helm-charts/examples/zookeeper-client.yaml

  3. Connect to the client pod and use the zookeeper-shell command to explore brokers, topics, etc:

    kubectl exec -it zookeeper-client -- /bin/bash zookeeper-shell <zookeeper service>:<port> ls /brokers/ids
kubectl exec -it zookeeper-client -- /bin/bash zookeeper-shell <zookeeper service>:<port> get /brokers/ids/0
kubectl exec -it zookeeper-client -- /bin/bash zookeeper-shell <zookeeper service>:<port> ls /brokers/topics
Kafka

  1. Deploy a Kafka client pod.

    kubectl apply -f cp-helm-charts/examples/kafka-client.yaml

  2. Log into the Pod

    kubectl exec -it kafka-client -- /bin/bash

  3. From within the kafka-client pod, explore with kafka commands:

    ## Setup
export RELEASE_NAME=<release name>
export ZOOKEEPERS=${RELEASE_NAME}-cp-zookeeper:2181
export KAFKAS=${RELEASE_NAME}-cp-kafka-headless:9092

## Create Topic
kafka-topics --zookeeper $ZOOKEEPERS --create --topic test-rep-one --partitions 6 --replication-factor 1

## Producer
kafka-run-class org.apache.kafka.tools.ProducerPerformance --print-metrics --topic test-rep-one --num-records 6000000 --throughput 100000 --record-size 100 --producer-props bootstrap.servers=$KAFKAS buffer.memory=67108864 batch.size=8196

## Consumer
kafka-consumer-perf-test --broker-list $KAFKAS --messages 6000000 --threads 1 --topic test-rep-one --print-metrics

Run A Streams Application

Now that you have running in your Kubernetes cluster, you may run a KSQL example. KSQL is the streaming SQL engine that enables real-time data processing against Apache Kafka.

Operations

Scaling

Tip

All scaling operations should be done offline with no producer or consumer connection.

Tip

The number of nodes should always be odd number.

Install cp-helm-charts with default 3 node ensemble

helm install cp-helm-charts

Scale nodes up to 5, change servers under cp-zookeeper to 5 in values.yaml

helm upgrade <release name> cp-helm-charts

Scale nodes down to 3, change servers under cp-zookeeper to 3 in values.yaml

helm upgrade <release name> cp-helm-charts

Kafka

Important

Scaling Kafka brokers without doing Partition Reassignment will cause data loss. You must reassign partitions correctly before scaling the Kafka cluster.

Install cp-helm-charts with default 3 brokers kafka cluster

helm install cp-helm-charts

Scale kafka brokers up to 5, change brokers under cp-kafka to 5 in values.yaml

helm upgrade <release name> cp-helm-charts

Scale kafka brokers down to 3, change brokers under cp-kafka to 3 in values.yaml

helm upgrade <release name> cp-helm-charts

Monitoring

JMX Metrics are enabled by default for all components, Prometheus JMX Exporter is installed as a sidecar container along with all Pods.

  1. Install Prometheus and Grafana in same Kubernetes cluster using helm

    helm install stable/prometheus
helm install stable/grafana
  2. Add Prometheus as Data Source in Grafana, url should be something like: http://illmannered-marmot-prometheus-server:9090

  3. Import dashboard under grafana-dashboard into Grafana Kafka Dashboard

    ZooKeeper DashboardZooKeeper Dashboard

Teardown

To remove the pods, list the pods with kubectl get pods and then delete the pods by name.

kubectl get pods
kubectl delete pod <podname>

To delete the Helm release, find the Helm release name with helm list and delete it with helm delete. You may also need to clean up leftover StatefulSets, since helm delete can leave them behind. Finally, clean up all persisted volume claims (pvc) created by this release.

helm list
helm delete <release name>
kubectl delete statefulset <release name>-cp-kafka <release name>-cp-zookeeper
kubectl delete pvc --selector=release=<release name>

To stop or delete Minikube:

minikube stop
minikube delete

Appendix: Create a Local Kubernetes Cluster

There are many deployment options to get set up with a Kubernetes cluster, and this document provides instructions for using Minikube to set up a local Kubernetes cluster. Minikube runs a single-node Kubernetes cluster inside a VM on your laptop.

You may alternatively set up a Kubernetes cluster in the cloud using other providers such as Google Kubernetes Engine (GKE).

Install Minikube and Drivers

Minikube version 0.23.0 or higher is required for docker server https://github.com/moby/moby/pull/31352[17.05], which adds support for using ARG in FROM in your Dockerfile.

First follow the basic Minikube installation instructions.

Then install the Minikube drivers. Minikube uses Docker Machine to manage the Kubernetes VM so it benefits from the driver plugin architecture that Docker Machine uses to provide a consistent way to manage various VM providers. Minikube embeds VirtualBox and VMware Fusion drivers so there are no additional steps to use them. However, other drivers require an extra binary to be present in the host PATH.

If you are running on macOS, in particular make sure to install the xhyve drivers for the native OS X hypervisor:

brew install docker-machine-driver-xhyve
sudo chown root:wheel $(brew --prefix)/opt/docker-machine-driver-xhyve/bin/docker-machine-driver-xhyve
sudo chmod u+s $(brew --prefix)/opt/docker-machine-driver-xhyve/bin/docker-machine-driver-xhyve

Start Minikube

Tip

The following command increases the memory to 6096 MB and uses the xhyve driver for the native macOS Hypervisor.

  1. Start Minikube. The following command increases the memory to 6096 MB and uses the xhyve driver for the native macOS Hypervisor.

    minikube start --kubernetes-version v1.9.4 --cpus 4 --memory 6096 --vm-driver=xhyve --v=8

  2. Continue to check status of your local Kubernetes cluster until both minikube and cluster are in Running state

    minikube status

    minikube: Running cluster: Running kubectl: Correctly Configured: pointing to minikube-vm at 192.168.99.106

  3. Work around Minikube issue #1568.

    minikube ssh -- sudo ip link set docker0 promisc on

  4. Set the context.

    eval $(minikube docker-env)

kubectl config set-context minikube.internal --cluster=minikube --user=minikube

    Context "minikube.internal" modified.

    kubectl config use-context minikube.internal

    Switched to context "minikube.internal".

Verify Minikube Local Kubernetes Environment

kubectl config current-context

minikube.internal

kubectl cluster-info

Kubernetes master is running at https://192.168.99.106:8443 KubeDNS is running at https://192.168.99.106:8443/api/v1/namespaces/kube-system/services/kube-dns:dns/proxy