Spring Cloud Data Flow Chart

Spring Cloud Data Flow is a toolkit for microservices-based Streaming and Batch data processing pipelines in Cloud Foundry and Kubernetes

Data processing pipelines consist of Spring Boot apps, built using the Spring Cloud Stream or Spring Cloud Task microservice frameworks. This makes Spring Cloud Data Flow suitable for a range of data processing use cases, from import/export to event streaming and predictive analytics.

This Helm chart is deprecated

Given the stable deprecation timeline, the Bitnami maintained Spring Cloud Data Flow Helm chart is now located at bitnami/charts.

The Bitnami repository is already included in the Hubs and we will continue providing the same cadence of updates, support, etc that we've been keeping here these years. Installation instructions are very similar, just adding the bitnami repo and using it during the installation (bitnami/<chart> instead of stable/<chart>)

$ helm repo add bitnami https://charts.bitnami.com/bitnami
$ helm install my-release bitnami/<chart>           # Helm 3
$ helm install --name my-release bitnami/<chart>    # Helm 2

To update an exisiting stable deployment with a chart hosted in the bitnami repository you can execute

$ helm repo add bitnami https://charts.bitnami.com/bitnami
$ helm upgrade my-release bitnami/<chart>

Issues and PRs related to the chart itself will be redirected to bitnami/charts GitHub repository. In the same way, we'll be happy to answer questions related to this migration process in this issue created as a common place for discussion.

Chart Details

This chart will provision a fully functional and fully featured Spring Cloud Data Flow installation that can deploy and manage data processing pipelines in the cluster that it is deployed to.

Either the default MySQL deployment or an external database can be used as the data store for Spring Cloud Data Flow state and either RabbitMQ or Kafka can be used as the messaging layer for streaming apps to communicate with one another.

For more information on Spring Cloud Data Flow and its capabilities, see it's documentation.

Prerequisites

Assumes that serviceAccount credentials are available so the deployed Data Flow server can access the API server (Works on GKE and Minikube by default). See Configure Service Accounts for Pods

Installing the Chart

To install the chart with the release name my-release:

$ helm install --name my-release stable/spring-cloud-data-flow

If you are using a cluster that does not have a load balancer (like Minikube) then you can install using a NodePort:

$ helm install --name my-release --set server.service.type=NodePort stable/spring-cloud-data-flow

To restrict the load balancer to an IP address range:

$ helm install --name my-release  --set server.service.loadBalancerSourceRanges='[10.0.0.0/8]' stable/spring-cloud-data-flow

Data Store

By default, MySQL is deployed with this chart. However, if you wish to use an external database, please use the following set flags to the helm command to disable MySQL deployment, for example:

--set mysql.enabled=false

In addition, you are required to set all fields listed in External Database Configuration.

Messaging Layer

There are three messaging layers available in this chart:

• RabbitMQ (default)
• RabbitMQ HA
• Kafka

To change the messaging layer to a highly available (HA) version of RabbitMQ, use the following set flags to the helm command, for example:

--set rabbitmq-ha.enabled=true,rabbitmq.enabled=false

Alternatively, to change the messaging layer to Kafka, use the following set flags to the helm command, for example:

--set kafka.enabled=true,rabbitmq.enabled=false

Only one messaging layer can be used at a given time. If RabbitMQ and Kafka are enabled, both charts will be installed with RabbitMQ being used in the deployment.

Note that this chart pulls in many different Docker images so can take a while to fully install.

Feature Toggles

If you only need to deploy tasks and schedules, streams can be disabled:

--set features.streaming.enabled=false --set rabbitmq.enabled=false

If you only need to deploy streams, tasks and schedules can be disabled:

--set features.batch.enabled=false

NOTE: Both features.streaming.enabled and features.batch.enabled should not be set to false at the same time.

Streaming and batch applications can be monitored through Prometheus and Grafana. To deploy these components and enable monitoring, set the following:

--set features.monitoring.enabled=true

When using Minikube, the Grafana URL can be obtained for example, via:

minikube service my-release-grafana --url

On a platform that provides a LoadBalancer such as GKE, the following can be checked against until the EXTERNAL-IP field is populated with the assigned load balancer IP address:

kubectl get svc my-release-grafana

See the Grafana table below for default credentials and override parameters.

Using an Ingress

If you would like to use an Ingress instead of having the services use the LoadBalancer type there are a few things to consider.

First you need to have an Ingress Controller installed in your cluster. If you don't already have one instaled, you can use the following helm command to install an NGINX Ingress Controller:

kubectl create namespace nginx-ingress
helm install --name nginx-ingress --namespace nginx-ingress stable/nginx-ingress

You can look up the IP address used by the NGINX Ingress Controller with:

ingress=$(kubectl get svc nginx-ingress-controller -n nginx-ingress -ojsonpath='{.status.loadBalancer.ingress[0].ip}')

This is useful if you would like to use xip.io instead of your own DNS resolution. The folowing options assume that you will use xip.io but you can replace the host values below with your own DNS hosts if you prefer.

To enable the creation of an Ingress resource and configure the services to use ClusterIP type use the following set options in your helm install command:

  --set server.service.type=ClusterIP \
  --set ingress.enabled=true \
  --set ingress.protocol=http \
  --set ingress.server.host=scdf.${ingress}.xip.io \

If you want to use an Ingress with the monitoring feature enabled, then use thes options instead:

  --set features.monitoring.enabled=true \
  --set server.service.type=ClusterIP \
  --set grafana.service.type=ClusterIP \
  --set prometheus.proxy.service.type=ClusterIP \
  --set ingress.enabled=true \
  --set ingress.protocol=http \
  --set ingress.server.host=scdf.${ingress}.xip.io \
  --set ingress.grafana.host=grafana.${ingress}.xip.io \

Configuration

The following tables list the configurable parameters and their default values.

RBAC Configuration

ServiceAccount Configuration

Data Flow Server Configuration

Skipper Server Configuration

Spring Cloud Deployer for Kubernetes Configuration

RabbitMQ Configuration

RabbitMQ HA Configuration

Kafka Configuration

MySQL Configuration

External Database Configuration

Feature Toggles

Ingress

Grafana

Prometheus