Installing Cloudflow

This guide shows how to install Cloudflow, using the Helm and kubectl command-line tools. It also shows how to install Kafka, Spark, and Flink operators that integrate with Cloudflow.

Installing Cloudflow

In this guide, we will use Helm to install Cloudflow. Please see Preparing a development environment for how to download and install the Cloudflow CLI.

The first step is to create the namespace, if it does not exist yet, to install Cloudflow into:

kubectl create ns cloudflow
Many subsequent commands will assume that the namespace is cloudflow.

First, we add the Cloudflow Helm repository and update the local index:

helm repo add cloudflow-helm-charts https://lightbend.github.io/cloudflow-helm-charts/
helm repo update

Installing

The kafkaClusters.default.bootstrapServers parameter sets the address and port of the default Kafka cluster that Cloudflow will use. In this example, we have used the address of a Strimzi created Kafka cluster located in the cloudflow namespace.

kafkaClusters.default.bootstrapServers=cloudflow-strimzi-kafka-bootstrap.cloudflow:9092

The following command installs Cloudflow using the Helm chart:

helm install cloudflow cloudflow-helm-charts/cloudflow --namespace cloudflow \
  --version "2.0.14" \
  --set kafkaClusters.default.bootstrapServers=cloudflow-strimzi-kafka-bootstrap.cloudflow:9092

Default and named Kafka clusters

At least one Kafka cluster is required to connect streamlets together in a blueprint. If you install Cloudflow and provide a kafkaClusters.default.bootstrapServers parameter then this cluster will be used by default for all streamlet connections.

You can optionally customize the number of partitions and replicas that Cloudflow will use as parameters when creating Cloudflow managed topics on the Kafka cluster. To override the default settings you can define the number of partitions with kafkaClusters.default.partitions and the number of replicas with kafkaClusters.default.replicas.

You can optionally provide default properties for Kafka Producers, Consumers, and shared connection properties that can be used for both clients used in streamlets. To provide the properties it’s recommended to create a custom YAML file that includes the list of connection properties to include, and then pass the YAML file to Helm using the -f flag during a Helm install or upgrade.

To customize everything for the default Kafka cluster create a YAML file with your all your configuration values. For example:

kafka-clusters.conf
kafkaClusters:
  default:
    bootstrapServers: "cloudflow-strimzi-kafka-bootstrap.cloudflow:9092"
    partitions: 3
    replicas: 2
    connectionConfig:
        bootstrap.servers: "my-server.europe-west1.gcp.cloud:9092"
        security.protocol: "SASL_SSL"
        sasl.jaas.config: "org.apache.kafka.common.security.plain.PlainLoginModule   required username=\"MYAPIKEY\"   password=\"MyPassWord\";"
        ssl.endpoint.identification.algorithm: "https"
        sasl.mechanism: "PLAIN"
    producerConfig:
      batch.size: "1024"
    consumerConfig:
      fetch.max.bytes: "52428800"

Then use these values when you install or upgrade Cloudflow:

helm install cloudflow cloudflow-helm-charts/cloudflow --namespace cloudflow \
  --version "2.0.14" \
  -f ./kafka-clusters.yaml

You can provide as many additional named Kafka clusters as you like that can be referenced within the topic configuration of a blueprint (see the Named Kafka Clusters section of the Using Blueprint docs for more details). To add additional clusters define a new property for each under the kafkaClusters Helm value and provide the same configuration information as can be done for the default cluster. The property name will be the name that is referenced by blueprints. For example:

kafka-clusters.conf
  kafkaClusters:
    default:
      ...
    us-east:
      bootstrapServers: "us-east-0.kafka.xyzcorp:9092,us-east-1.kafka.xyzcorp:9092,us-east-2.kafka.xyzcorp:9092"
      partitions: 10
      replicas: 3
      connectionConfig: {}
      producerConfig: {}
      consumerConfig: {}

Can be referenced within a blueprint:

my-topic-name {
  producers = [streamlet-a1.out, streamlet-a2.out]
  consumers = [streamlet-b.in]
  cluster = us-east
}

All named Kafka clusters (including the default) are created as Kubernetes Secrets within the Cloudflow installation namespace.

Named Kafka clusters can be defined at install or upgrade time.

Verifying the installation

Check the status of the installation process using kubectl. When the Cloudflow operator pod is in Running status, the installation is complete.

$ kubectl get pods -n cloudflow
NAME                                                READY   STATUS    RESTARTS   AGE
cloudflow-operator-6b7d7cbdfc-xb6w5                 1/1     Running   0          10s

You can now deploy an Akka-based Cloudflow application into the cluster as it only requires Kafka to be set up. More on this in the development section of the documentation.

Adding Spark support

If you plan to write applications that utilize Spark, you will need to install the Spark operator before deploying your Cloudflow application.

Continue with Adding Spark Support.

If you plan to write applications that utilize Flink you will need to install the Flink operator before deploying your Cloudflow application.

Continue with Adding Flink Support.

Next: Installing Enterprise components