Helm Installation

The Helm project has ended support for Helm 2 in November of 2020. As of Spring Cloud Data Flow 2.7.0 the chart will be based on Helm 3, dropping support for Helm 2.

Migration steps from Helm 2 to Helm 3 are required. In preparation for the migration, it is advised to read the Helm v2 to v3 Migration Guide for more information. Additionally, some helpful tips on data migration and upgrades can be found in the post migration issues article.

As of Spring Cloud Data Flow 2.6.1, the Bitnami team maintains the Helm chart. To report bugs and/or feature requests please do so using the Bitnami Issue Tracker.

Spring Cloud Data Flow offers a Helm Chart for deploying the Spring Cloud Data Flow server and its required services to a Kubernetes Cluster.

The following sections cover how to initialize Helm and install Spring Cloud Data Flow on a Kubernetes cluster.

If using Minikube, see Setting Minikube Resources for details on CPU and RAM resource requirements.

Installing the Spring Cloud Data Flow Server and Required Services

It is important to review the following documentation and adjust any parameter customizations that have been made for your environment or how they may differ from the legacy official Helm chart. Value names, defaults, and so on may have changed during the Bitnami chart migration. More information can be found in the Parameter tables, Upgrading, and Notable Changes sections.

Spring Cloud Data Flow

Spring Cloud Data Flow is a microservices-based Streaming and Batch data processing pipeline in Cloud Foundry and Kubernetes.

TL;DR

helm repo add bitnami https://charts.bitnami.com/bitnami
helm install my-release bitnami/spring-cloud-dataflow

Introduction

This chart bootstraps a Spring Cloud Data Flow deployment on a Kubernetes cluster using the Helm package manager.

Bitnami charts can be used with Kubeapps for deployment and management of Helm Charts in clusters.

Prerequisites

  • Kubernetes 1.12+
  • Helm 3.0-beta3+
  • PV provisioner support in the underlying infrastructure

Installing the Chart

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

helm repo add bitnami https://charts.bitnami.com/bitnami
helm install my-release bitnami/spring-cloud-dataflow

These commands deploy Spring Cloud Data Flow on the Kubernetes cluster with the default configuration. The parameters section lists the parameters that can be configured during installation.

Tip: List all releases using helm list

Uninstalling the Chart

To uninstall/delete the my-release chart:

helm uninstall my-release

Parameters

The following tables lists the configurable parameters of the Spring Cloud Data Flow chart and their default values per section/component:

Global parameters

Parameter Description Default
global.imageRegistry Global Docker image registry nil
global.imagePullSecrets Global Docker registry secret names as an array [] (does not add image pull secrets to deployed pods)
global.storageClass Global storage class for dynamic provisioning nil

Common parameters

Parameter Description Default
nameOverride String to partially override scdf.fullname nil
fullnameOverride String to fully override scdf.fullname nil
clusterDomain Default Kubernetes cluster domain cluster.local
deployer.resources.limits Streaming applications resource limits { cpu: "500m", memory: "1024Mi" }
deployer.resources.requests Streaming applications resource requests {}
deployer.resources.readinessProbe Streaming applications readiness probes requests Check values.yaml file
deployer.resources.livenessProbe Streaming applications liveness probes requests Check values.yaml file
deployer.nodeSelector Streaming applications nodeSelector ""
deployer.tolerations Streaming applications tolerations {}
deployer.volumeMounts Streaming applications extra volume mounts {}
deployer.volumes Streaming applications extra volumes {}
deployer.environmentVariables Streaming applications environment variables ""
deployer.podSecurityContext Streaming applications Security Context. {runAsUser: 1001}

Dataflow Server parameters

Parameter Description Default
server.image.registry Spring Cloud Dataflow image registry docker.io
server.image.repository Spring Cloud Dataflow image name bitnami/spring-cloud-dataflow
server.image.tag Spring Cloud Dataflow image tag {TAG_NAME}
server.image.pullPolicy Spring Cloud Dataflow image pull policy IfNotPresent
server.image.pullSecrets Specify docker-registry secret names as an array [] (does not add image pull secrets to deployed pods)
server.composedTaskRunner.image.registry Spring Cloud Dataflow Composed Task Runner image registry docker.io
server.composedTaskRunner.image.repository Spring Cloud Dataflow Composed Task Runner image name bitnami/spring-cloud-dataflow-composed-task-runner
server.composedTaskRunner.image.tag Spring Cloud Dataflow Composed Task Runner image tag {TAG_NAME}
server.configuration.streamingEnabled Enables or disables streaming data processing true
server.configuration.batchEnabled Enables or disables bath data (tasks and schedules) processing true
server.configuration.accountName The name of the account to configure for the Kubernetes platform default
server.configuration.trustK8sCerts Trust K8s certificates when querying the Kubernetes API false
server.configuration.containerRegistries Container registries configuration {} (check values.yaml for more information)
server.existingConfigmap Name of existing ConfigMap with Dataflow server configuration nil
server.extraEnvVars Extra environment variables to be set on Dataflow server container {}
server.extraEnvVarsCM Name of existing ConfigMap containing extra env vars nil
server.extraEnvVarsSecret Name of existing Secret containing extra env vars nil
server.replicaCount Number of Dataflow server replicas to deploy 1
server.strategyType Deployment Strategy Type RollingUpdate
server.affinity Affinity for pod assignment {} (evaluated as a template)
server.nodeSelector Node labels for pod assignment {} (evaluated as a template)
server.tolerations Tolerations for pod assignment [] (evaluated as a template)
server.priorityClassName Controller priorityClassName nil
server.podSecurityContext Dataflow server pods' Security Context { fsGroup: "1001" }
server.containerSecurityContext Dataflow server containers' Security Context { runAsUser: "1001" }
server.resources.limits The resources limits for the Dataflow server container {}
server.resources.requests The requested resources for the Dataflow server container {}
server.podAnnotations Annotations for Dataflow server pods {}
server.livenessProbe Liveness probe configuration for Dataflow server Check values.yaml file
server.readinessProbe Readiness probe configuration for Dataflow server Check values.yaml file
server.customLivenessProbe Override default liveness probe nil
server.customReadinessProbe Override default readiness probe nil
server.service.type Kubernetes service type ClusterIP
server.service.port Service HTTP port 8080
server.service.nodePort Service HTTP node port nil
server.service.clusterIP Dataflow server service clusterIP IP None
server.service.externalTrafficPolicy Enable client source IP preservation Cluster
server.service.loadBalancerIP loadBalancerIP if service type is LoadBalancer nil
server.service.loadBalancerSourceRanges Address that are allowed when service is LoadBalancer []
server.service.annotations Annotations for Dataflow server service {}
server.ingress.enabled Enable ingress controller resource false
server.ingress.certManager Add annotations for cert-manager false
server.ingress.hostname Default host for the ingress resource dataflow.local
server.ingress.annotations Ingress annotations []
server.ingress.extraHosts[0].name Additional hostnames to be covered nil
server.ingress.extraHosts[0].path Additional hostnames to be covered nil
server.ingress.extraTls[0].hosts[0] TLS configuration for additional hostnames to be covered nil
server.ingress.extraTls[0].secretName TLS configuration for additional hostnames to be covered nil
server.ingress.secrets[0].name TLS Secret Name nil
server.ingress.secrets[0].certificate TLS Secret Certificate nil
server.ingress.secrets[0].key TLS Secret Key nil
server.initContainers Add additional init containers to the Dataflow server pods {} (evaluated as a template)
server.sidecars Add additional sidecar containers to the Dataflow server pods {} (evaluated as a template)
server.pdb.create Enable/disable a Pod Disruption Budget creation false
server.pdb.minAvailable Minimum number/percentage of pods that should remain scheduled 1
server.pdb.maxUnavailable Maximum number/percentage of pods that may be made unavailable nil
server.autoscaling.enabled Enable autoscaling for Dataflow server false
server.autoscaling.minReplicas Minimum number of Dataflow server replicas nil
server.autoscaling.maxReplicas Maximum number of Dataflow server replicas nil
server.autoscaling.targetCPU Target CPU utilization percentage nil
server.autoscaling.targetMemory Target Memory utilization percentage nil
server.jdwp.enabled Enable Java Debug Wire Protocol (JDWP) false
server.jdwp.port JDWP TCP port 5005
server.extraVolumes Extra Volumes to be set on the Dataflow Server Pod nil
server.extraVolumeMounts Extra VolumeMounts to be set on the Dataflow Container nil

Dataflow Skipper parameters

Parameter Description Default
skipper.enabled Enable Spring Cloud Skipper component true
skipper.image.registry Spring Cloud Skipper image registry docker.io
skipper.image.repository Spring Cloud Skipper image name bitnami/spring-cloud-dataflow
skipper.image.tag Spring Cloud Skipper image tag {TAG_NAME}
skipper.image.pullPolicy Spring Cloud Skipper image pull policy IfNotPresent
skipper.image.pullSecrets Specify docker-registry secret names as an array [] (does not add image pull secrets to deployed pods)
skipper.configuration.accountName The name of the account to configure for the Kubernetes platform default
skipper.configuration.trustK8sCerts Trust K8s certificates when querying the Kubernetes API false
skipper.existingConfigmap Name of existing ConfigMap with Skipper server configuration nil
skipper.extraEnvVars Extra environment variables to be set on Skipper server container {}
skipper.extraEnvVarsCM Name of existing ConfigMap containing extra env vars nil
skipper.extraEnvVarsSecret Name of existing Secret containing extra env vars nil
skipper.replicaCount Number of Skipper server replicas to deploy 1
skipper.strategyType Deployment Strategy Type RollingUpdate
skipper.affinity Affinity for pod assignment {} (evaluated as a template)
skipper.nodeSelector Node labels for pod assignment {} (evaluated as a template)
skipper.tolerations Tolerations for pod assignment [] (evaluated as a template)
skipper.priorityClassName Controller priorityClassName nil
skipper.podSecurityContext Skipper server pods' Security Context { fsGroup: "1001" }
skipper.containerSecurityContext Skipper server containers' Security Context { runAsUser: "1001" }
skipper.resources.limits The resources limits for the Skipper server container {}
skipper.resources.requests The requested resources for the Skipper server container {}
skipper.podAnnotations Annotations for Skipper server pods {}
skipper.livenessProbe Liveness probe configuration for Skipper server Check values.yaml file
skipper.readinessProbe Readiness probe configuration for Skipper server Check values.yaml file
skipper.customLivenessProbe Override default liveness probe nil
skipper.customReadinessProbe Override default readiness probe nil
skipper.service.type Kubernetes service type ClusterIP
skipper.service.port Service HTTP port 8080
skipper.service.nodePort Service HTTP node port nil
skipper.service.clusterIP Skipper server service clusterIP IP None
skipper.service.externalTrafficPolicy Enable client source IP preservation Cluster
skipper.service.loadBalancerIP loadBalancerIP if service type is LoadBalancer nil
skipper.service.loadBalancerSourceRanges Address that are allowed when service is LoadBalancer []
skipper.service.annotations Annotations for Skipper server service {}
skipper.initContainers Add additional init containers to the Skipper pods {} (evaluated as a template)
skipper.sidecars Add additional sidecar containers to the Skipper pods {} (evaluated as a template)
skipper.pdb.create Enable/disable a Pod Disruption Budget creation false
skipper.pdb.minAvailable Minimum number/percentage of pods that should remain scheduled 1
skipper.pdb.maxUnavailable Maximum number/percentage of pods that may be made unavailable nil
skipper.autoscaling.enabled Enable autoscaling for Skipper server false
skipper.autoscaling.minReplicas Minimum number of Skipper server replicas nil
skipper.autoscaling.maxReplicas Maximum number of Skipper server replicas nil
skipper.autoscaling.targetCPU Target CPU utilization percentage nil
skipper.autoscaling.targetMemory Target Memory utilization percentage nil
skipper.jdwp.enabled Enable Java Debug Wire Protocol (JDWP) false
skipper.jdwp.port JDWP TCP port 5005
skipper.extraVolumes Extra Volumes to be set on the Skipper Pod nil
skipper.extraVolumeMounts Extra VolumeMounts to be set on the Skipper Container nil
externalSkipper.host Host of a external Skipper Server localhost
externalSkipper.port External Skipper Server port number 7577

RBAC parameters

Parameter Description Default
serviceAccount.create Enable the creation of a ServiceAccount for Dataflow server and Skipper server pods true
serviceAccount.name Name of the created serviceAccount Generated using the scdf.fullname template
rbac.create Weather to create & use RBAC resources or not true

Metrics parameters

Parameter Description Default
metrics.metrics Enable the export of Prometheus metrics false
metrics.image.registry Prometheus Rsocket Proxy image registry docker.io
metrics.image.repository Prometheus Rsocket Proxy image name bitnami/prometheus-rsocket-proxy
metrics.image.tag Prometheus Rsocket Proxy image tag {TAG_NAME}
metrics.image.pullPolicy Prometheus Rsocket Proxy image pull policy IfNotPresent
metrics.image.pullSecrets Specify docker-registry secret names as an array [] (does not add image pull secrets to deployed pods)
metrics.kafka.service.httpPort Prometheus Rsocket Proxy HTTP port 8080
metrics.kafka.service.rsocketPort Prometheus Rsocket Proxy Rsocket port 8080
metrics.kafka.service.annotations Annotations for Prometheus Rsocket Proxy service Check values.yaml file
metrics.serviceMonitor.enabled if true, creates a Prometheus Operator ServiceMonitor (also requires metrics.enabled to be true) false
metrics.serviceMonitor.namespace Namespace in which Prometheus is running nil
metrics.serviceMonitor.interval Interval at which metrics should be scraped. nil (Prometheus Operator default value)
metrics.serviceMonitor.scrapeTimeout Timeout after which the scrape is ended nil (Prometheus Operator default value)

Init Container parameters

Parameter Description Default
waitForBackends.enabled Wait for the database and other services (such as Kafka or RabbitMQ) used when enabling streaming true
waitForBackends.image.registry Init container wait-for-backend image registry docker.io
waitForBackends.image.repository Init container wait-for-backend image name bitnami/kubectl
waitForBackends.image.tag Init container wait-for-backend image tag {TAG_NAME}
waitForBackends.image.pullPolicy Init container wait-for-backend image pull policy IfNotPresent
waitForBackends.image.pullSecrets Specify docker-registry secret names as an array [] (does not add image pull secrets to deployed pods)
waitForBackends.resources.limits Init container wait-for-backend resource limits {}
waitForBackends.resources.requests Init container wait-for-backend resource requests {}

Database parameters

Parameter Description Default
mariadb.enabled Enable/disable MariaDB chart installation true
mariadb.architecture MariaDB architecture (standalone or replication) standalone
mariadb.auth.database Database name to create dataflow
mariadb.auth.username Username of new user to create dataflow
mariadb.auth.password Password for the new user change-me
mariadb.auth.rootPassword Password for the MariaDB root user random 10 character alphanumeric string
mariadb.initdbScripts Dictionary of initdb scripts Check values.yaml file
externalDatabase.driver The fully qualified name of the JDBC Driver class ""
externalDatabase.scheme The scheme is a vendor-specific or shared protocol string that follows the "jdbc:" of the URL ""
externalDatabase.host Host of the external database localhost
externalDatabase.port External database port number 3306
externalDatabase.password Password for the above username ""
externalDatabase.existingPasswordSecret Existing secret with database password ""
externalDatabase.dataflow.url JDBC URL for dataflow server. Overrides external scheme, host, port, database, and jdbc parameters. ""
externalDatabase.dataflow.username Existing username in the external db to be used by Dataflow server dataflow
externalDatabase.dataflow.database Name of the existing database to be used by Dataflow server dataflow
externalDatabase.skipper.url JDBC URL for skipper. Overrides external scheme, host, port, database, and jdbc parameters. ""
externalDatabase.skipper.username Existing username in the external db to be used by Skipper server skipper
externalDatabase.skipper.database Name of the existing database to be used by Skipper server skipper
externalDatabase.hibernateDialect Hibernate Dialect used by Dataflow/Skipper servers ""

RabbitMQ chart parameters

Parameter Description Default
rabbitmq.enabled Enable/disable RabbitMQ chart installation true
rabbitmq.auth.username RabbitMQ username user
rabbitmq.auth.password RabbitMQ password random 40 character alphanumeric string
externalRabbitmq.enabled Enable/disable external RabbitMQ false
externalRabbitmq.host Host of the external RabbitMQ localhost
externalRabbitmq.port External RabbitMQ port number 5672
externalRabbitmq.username External RabbitMQ username guest
externalRabbitmq.password External RabbitMQ password guest
externalRabbitmq.vhost External RabbitMQ virtual host /
externalRabbitmq.existingPasswordSecret Existing secret with RabbitMQ password ""

Kafka chart parameters

Parameter Description Default
kafka.enabled Enable/disable Kafka chart installation false
kafka.replicaCount Number of Kafka brokers 1
kafka.offsetsTopicReplicationFactor Kafka Secret Key 1
kafka.zookeeper.enabled Enable/disable Zookeeper chart installation nil
kafka.zookeeper.replicaCount Number of Zookeeper replicas 1

Specify each parameter using the --set key=value[,key=value] argument to helm install. For example,

helm install my-release --set server.replicaCount=2 bitnami/spring-cloud-dataflow

The above command install Spring Cloud Data Flow chart with 2 Dataflow server replicas.

Alternatively, a YAML file that specifies the values for the parameters can be provided while installing the chart. For example,

helm install my-release -f values.yaml bitnami/spring-cloud-dataflow

Tip: You can use the default values.yaml

Configuration and installation details

Rolling VS Immutable tags

It is strongly recommended to use immutable tags in a production environment. This ensures your deployment does not change automatically if the same tag is updated with a different image.

Bitnami will release a new chart updating its containers if a new version of the main container, significant changes, or critical vulnerabilities exist.

Production configuration

This chart includes a values-production.yaml file where you can find some parameters oriented to production configuration in comparison to the regular values.yaml. You can use this file instead of the default one.

  • Enable Pod Disruption Budget for Server and Skipper:
- server.pdb.create: false
+ server.pdb.create: true
- skipper.pdb.create: false
+ skipper.pdb.create: true
  • Enable exposing Prometheus Metrics via Prometheus Rsocket Proxy:
- metrics.enabled: false
+ metrics.enabled: true
  • Force users to specify a password and mount secrets as volumes instead of using environment variables on MariaDB:
- mariadb.auth.forcePassword: false
+ mariadb.auth.forcePassword: true
- mariadb.auth.usePasswordFiles: false
+ mariadb.auth.usePasswordFiles: true

Features

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

server.configuration.batchEnabled=true
server.configuration.streamingEnabled=false
skipper.enabled=false
rabbitmq.enabled=false

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

server.configuration.batchEnabled=false
server.configuration.streamingEnabled=true
skipper.enabled=true
rabbitmq.enabled=true

NOTE: Both server.configuration.batchEnabled and server.configuration.streamingEnabled should not be set to false at the same time.

Messaging solutions

There are two supported messaging solutions in this chart:

  • RabbitMQ (default)
  • Kafka

To change the messaging layer to Kafka, use the the following parameters:

rabbitmq.enabled=false
kafka.enabled=true

Only one messaging layer can be used at a given time.

Using an external database

Sometimes you may want to have Spring Cloud components connect to an external database rather than installing one inside your cluster, e.g. to use a managed database service, or use run a single database server for all your applications. To do this, the chart allows you to specify credentials for an external database under the externalDatabase parameter. You should also disable the MariaDB installation with the mariadb.enabled option. For example with the following parameters:

mariadb.enabled=false
externalDatabase.scheme=mariadb
externalDatabase.host=myexternalhost
externalDatabase.port=3306
externalDatabase.password=mypassword
externalDatabase.dataflow.user=mydataflowuser
externalDatabase.dataflow.database=mydataflowdatabase
externalDatabase.dataflow.user=myskipperuser
externalDatabase.dataflow.database=myskipperdatabase

NOTE: When using the indidual propertes (scheme, host, port, database, an optional jdbcParameters) this chart will format the JDBC URL as jdbc:{scheme}://{host}:{port}/{database}{jdbcParameters}. The URL format follows that of the MariaDB database drive but may not work for other database vendors.

To use an alternate database vendor (other than MariaDB) you can use the externalDatabase.dataflow.url and externalDatabase.skipper.url properties to provide the JDBC URLs for the dataflow server and skipper respectively. If these properties are defined, they will take precendence over the individual attributes. As an example of configuring an external MS SQL Server database:

mariadb.enabled=false
externalDatabase.password=mypassword
externalDatabase.dataflow.url=jdbc:sqlserver://mssql-server:1433
externalDatabase.dataflow.user=mydataflowuser
externalDatabase.skipper.url=jdbc:sqlserver://mssql-server:1433
externalDatabase.skipper.user=myskipperuser
externalDatabase.hibernateDialect=org.hibernate.dialect.SQLServer2012Dialect

NOTE: If you disable MariaDB per above you MUST supply values for the externalDatabase connection.

Adding extra flags

In case you want to add extra environment variables to any Spring Cloud component, you can use XXX.extraEnvs parameter(s), where XXX is placeholder you need to replace with the actual component(s). For instance, to add extra flags to Spring Cloud Data Flow, use:

server:
  extraEnvs:
    - name: FOO
      value: BAR

Using custom Dataflow configuration

This helm chart supports using custom configuration for Dataflow server.

You can specify the configuration for Dataflow server setting the server.existingConfigmap parameter to an external ConfigMap with the configuration file.

Using custom Skipper configuration

This helm chart supports using custom configuration for Skipper server.

You can specify the configuration for Skipper server setting the skipper.existingConfigmap parameter to an external ConfigMap with the configuration file.

Sidecars and Init Containers

If you have a need for additional containers to run within the same pod as Dataflow or Skipper components (e.g. an additional metrics or logging exporter), you can do so via the XXX.sidecars parameter(s), where XXX is placeholder you need to replace with the actual component(s). Simply define your container according to the Kubernetes container spec.

server:
  sidecars:
    - name: your-image-name
      image: your-image
      imagePullPolicy: Always
      ports:
        - name: portname
          containerPort: 1234

Similarly, you can add extra init containers using the XXX.initContainers parameter(s).

server:
  initContainers:
    - name: your-image-name
      image: your-image
      imagePullPolicy: Always
      ports:
        - name: portname
          containerPort: 1234

Ingress

This chart provides support for ingress resources. If you have an ingress controller installed on your cluster, such as nginx-ingress or traefik you can utilize the ingress controller to serve your Spring Cloud Data Flow server.

To enable ingress integration, please set server.ingress.enabled to true

Hosts

Most likely you will only want to have one hostname that maps to this Spring Cloud Data Flow installation. If that's your case, the property server.ingress.hostname will set it. However, it is possible to have more than one host. To facilitate this, the server.ingress.extraHosts object is can be specified as an array. You can also use server.ingress.extraTLS to add the TLS configuration for extra hosts.

For each host indicated at server.ingress.extraHosts, please indicate a name, path, and any annotations that you may want the ingress controller to know about.

For annotations, please see this document. Not all annotations are supported by all ingress controllers, but this document does a good job of indicating which annotation is supported by many popular ingress controllers.

Troubleshooting

Find more information about how to deal with common errors related to Bitnami’s Helm charts in this troubleshooting guide.

Upgrading

If you enabled RabbitMQ chart to be used as the messaging solution for Skipper to manage streaming content, then it's necessary to set the rabbitmq.auth.password and rabbitmq.auth.erlangCookie parameters when upgrading for readiness/liveness probes to work properly. Inspect the RabbitMQ secret to obtain the password and the Erlang cookie, then you can upgrade your chart using the command below:

To 2.0.0

On November 13, 2020, Helm v2 support was formally finished, this major version is the result of the required changes applied to the Helm Chart to be able to incorporate the different features added in Helm v3 and to be consistent with the Helm project itself regarding the Helm v2 EOL.

What changes were introduced in this major version?

  • Previous versions of this Helm Chart use apiVersion: v1 (installable by both Helm 2 and 3), this Helm Chart was updated to apiVersion: v2 (installable by Helm 3 only). Here you can find more information about the apiVersion field.
  • Move dependency information from the requirements.yaml to the Chart.yaml
  • After running helm dependency update, a Chart.lock file is generated containing the same structure used in the previous requirements.lock
  • The different fields present in the Chart.yaml file has been ordered alphabetically in a homogeneous way for all the Bitnami Helm Charts

Considerations when upgrading to this version

  • If you want to upgrade to this version from a previous one installed with Helm v3, you shouldn't face any issues
  • If you want to upgrade to this version using Helm v2, this scenario is not supported as this version doesn't support Helm v2 anymore
  • If you installed the previous version with Helm v2 and wants to upgrade to this version with Helm v3, please refer to the official Helm documentation about migrating from Helm v2 to v3

Useful links

v0.x.x

helm upgrade my-release bitnami/spring-cloud-dataflow --set mariadb.rootUser.password=[MARIADB_ROOT_PASSWORD] --set rabbitmq.auth.password=[RABBITMQ_PASSWORD] --set rabbitmq.auth.erlangCookie=[RABBITMQ_ERLANG_COOKIE]

v1.x.x

helm upgrade my-release bitnami/spring-cloud-dataflow --set mariadb.auth.rootPassword=[MARIADB_ROOT_PASSWORD] --set rabbitmq.auth.password=[RABBITMQ_PASSWORD] --set rabbitmq.auth.erlangCookie=[RABBITMQ_ERLANG_COOKIE]

Notable changes

v1.0.0

MariaDB dependency version was bumped to a new major version that introduces several incompatilibites. Therefore, backwards compatibility is not guaranteed unless an external database is used. Check MariaDB Upgrading Notes for more information.

To upgrade to 1.0.0, you will need to reuse the PVC used to hold the MariaDB data on your previous release. To do so, follow the instructions below (the following example assumes that the release name is dataflow):

NOTE: Please, create a backup of your database before running any of those actions.

Obtain the credentials and the name of the PVC used to hold the MariaDB data on your current release:

export MARIADB_ROOT_PASSWORD=$(kubectl get secret --namespace default dataflow-mariadb -o jsonpath="{.data.mariadb-root-password}" | base64 --decode)
export MARIADB_PASSWORD=$(kubectl get secret --namespace default dataflow-mariadb -o jsonpath="{.data.mariadb-password}" | base64 --decode)
export MARIADB_PVC=$(kubectl get pvc -l app=mariadb,component=master,release=dataflow -o jsonpath="{.items[0].metadata.name}")
export RABBITMQ_PASSWORD=$(kubectl get secret --namespace default dataflow-rabbitmq -o jsonpath="{.data.rabbitmq-password}" | base64 --decode)
export RABBITMQ_ERLANG_COOKIE=$(kubectl get secret --namespace default dataflow-rabbitmq -o jsonpath="{.data.rabbitmq-erlang-cookie}" | base64 --decode)

Upgrade your release (maintaining the version) disabling MariaDB and scaling Data Flow replicas to 0:

$ helm upgrade dataflow bitnami/spring-cloud-dataflow --version 0.7.4 \
  --set server.replicaCount=0 \
  --set skipper.replicaCount=0 \
  --set mariadb.enabled=false \
  --set rabbitmq.auth.password=$RABBITMQ_PASSWORD \
  --set rabbitmq.auth.erlangCookie=$RABBITMQ_ERLANG_COOKIE

Finally, upgrade you release to 1.0.0 reusing the existing PVC, and enabling back MariaDB:

$ helm upgrade dataflow bitnami/spring-cloud-dataflow \
  --set mariadb.primary.persistence.existingClaim=$MARIADB_PVC \
  --set mariadb.auth.rootPassword=$MARIADB_ROOT_PASSWORD \
  --set mariadb.auth.password=$MARIADB_PASSWORD \
  --set rabbitmq.auth.password=$RABBITMQ_PASSWORD \
  --set rabbitmq.auth.erlangCookie=$RABBITMQ_ERLANG_COOKIE

You should see the lines below in MariaDB container logs:

$ kubectl logs $(kubectl get pods -l app.kubernetes.io/instance=dataflow,app.kubernetes.io/name=mariadb,app.kubernetes.io/component=primary -o jsonpath="{.items[0].metadata.name}")
...
mariadb 12:13:24.98 INFO  ==> Using persisted data
mariadb 12:13:25.01 INFO  ==> Running mysql_upgrade
...

Expected output

After issuing the helm install command, you should see output similar to the following:

NAME: my-release
LAST DEPLOYED: Sun Nov 22 21:12:29 2020
NAMESPACE: default
STATUS: deployed
REVISION: 1
TEST SUITE: None
NOTES:
** Please be patient while the chart is being deployed **

Spring Cloud Data Flow chart was deployed enabling the following components:

- Spring Cloud Data Flow server
- Spring Cloud Skipper server

Spring Cloud Data Flow can be accessed through the following DNS name from within your cluster:

    my-release-spring-cloud-dataflow-server.default.svc.cluster.local (port 8080)

To access Spring Cloud Data Flow dashboard from outside the cluster execute the following commands:

1. Get the Data Flow dashboard URL by running these commands:

    export SERVICE_PORT=$(kubectl get --namespace default -o jsonpath="{.spec.ports[0].port}" services my-release-spring-cloud-dataflow-server)
    kubectl port-forward --namespace default svc/my-release-spring-cloud-dataflow-server ${SERVICE_PORT}:${SERVICE_PORT} &
    echo "http://127.0.0.1:${SERVICE_PORT}/dashboard"

2. Open a browser and access the Data Flow dashboard using the obtained URL.

If you prefer, the Spring Cloud Data Flow service type may be changed by passing the following set argument to helm install:

--set server.service.type=ServiceType

Where ServiceType would be a valid service name, for example LoadBalancer, NodePort, etc.

It may take a few minutes for the LoadBalancer IP to be available. You can watch the status of the server by running kubectl get svc -w my-release-spring-cloud-dataflow-server

If your using Minikube without load balancer support, you can use the following command to get the URL for the server:

minikube service --url my-release-spring-cloud-dataflow-server

You have just created a new release in the default namespace of your Kubernetes cluster. It takes a couple of minutes for the application and its required services to start. You can check on the status by issuing a kubectl get pod -w command. You need to wait for the READY column to show 1/1 for all pods.

When all pods are ready, you can access the Spring Cloud Data Flow dashboard by accessing http://<SERVICE_ADDRESS>/dashboard where <SERVICE_ADDRESS> is the address returned by either the kubectl or minikube commands above.

Version Compatibility

The following listing shows Spring Cloud Data Flow’s version compatibility with the respective Helm Chart releases:

Deprecated chart mappings from official Helm repository:

SCDF Version Chart Version
SCDF-K8S-Server 1.7.x 1.0.x
SCDF-K8S-Server 2.0.x 2.2.x
SCDF-K8S-Server 2.1.x 2.3.x
SCDF-K8S-Server 2.2.x 2.4.x
SCDF-K8S-Server 2.3.x 2.5.x
SCDF-K8S-Server 2.4.x 2.6.x
SCDF-K8S-Server 2.5.x 2.7.x
SCDF-K8S-Server 2.6.x 2.8.x

Bitnami chart mappings:

SCDF Version Chart Version
SCDF-K8S-Server 2.6.x 1.1.x
SCDF-K8S-Server 2.7.x 2.0.x

Register prebuilt applications

All the prebuilt streaming applications:

  • Are available as Apache Maven artifacts or Docker images.
  • Use RabbitMQ or Apache Kafka.
  • Support monitoring via Prometheus and InfluxDB.
  • Contain metadata for application properties used in the UI and code completion in the shell.

Applications can be registered individually using the app register functionality or as a group using the app import functionality. There are also dataflow.spring.io links that represent the group of prebuilt applications for a specific release which is useful for getting started.

You can register applications using the UI or the shell. Even though we are only using two prebuilt applications, we will register the full set of prebuilt applications.

The easiest way to install Data Flow on Kubernetes is using the Helm chart that uses RabbitMQ as the default messaging middleware. The command to import the Kafka version of the applications is

dataflow:>app import --uri https://dataflow.spring.io/kafka-docker-latest

Change kafka to rabbitmq in the above URL if you set kafka.enabled=true in the helm chart or followed the manual kubectl based installation instructions for installing Data Flow on Kubernetes and chose to use Kafka as the messaging middleware.

Only applications registered with a --uri property pointing to a Docker resource are supported by the Data Flow Server for Kubernetes. However, we do support Maven resources for the --metadata-uri property, which is used to list the properties supported by each application. For example, the following application registration is valid:

app register --type source --name time --uri docker://springcloudstream/time-source-rabbit:{docker-time-source-rabbit-version} --metadata-uri maven://org.springframework.cloud.stream.app:time-source-rabbit:jar:metadata:{docker-time-source-rabbit-version}

Any application registered with a Maven, HTTP, or File resource or the executable jar (by using a --uri property prefixed with maven://, http:// or file://) is not supported.

Application and Server Properties

This section covers how you can customize the deployment of your applications. You can use a number of properties to influence settings for the applications that are deployed. Properties can be applied on a per-application basis or in the appropriate server configuration for all deployed applications.

Properties set on a per-application basis always take precedence over properties set as the server configuration. This arrangement lets you override global server level properties on a per-application basis.

Properties to be applied for all deployed Tasks are defined in the src/kubernetes/server/server-config.yaml file and for Streams in src/kubernetes/skipper/skipper-config-(binder).yaml. Replace (binder) with the messaging middleware you are using — for example, rabbit or kafka.

Memory and CPU Settings

Applications are deployed with default memory and CPU settings. If needed, these values can be adjusted. The following example shows how to set Limits to 1000m for CPU and 1024Mi for memory and Requests to 800m for CPU and 640Mi for memory:

deployer.<app>.kubernetes.limits.cpu=1000m
deployer.<app>.kubernetes.limits.memory=1024Mi
deployer.<app>.kubernetes.requests.cpu=800m
deployer.<app>.kubernetes.requests.memory=640Mi

Those values results in the following container settings being used:

Limits:
cpu: 1
memory: 1Gi
Requests:
cpu: 800m
memory: 640Mi

You can also control the default values to which to set the cpu and memory globally.

The following example shows how to set the CPU and memory for streams and tasks:

data:
  application.yaml: |-
    spring:
      cloud:
        skipper:
          server:
            platform:
              kubernetes:
                accounts:
                  default:
                    limits:
                      memory: 640mi
                      cpu: 500m
data:
  application.yaml: |-
    spring:
      cloud:
        dataflow:
          task:
            platform:
              kubernetes:
                accounts:
                  default:
                    limits:
                      memory: 640mi
                      cpu: 500m

The settings we have used so far only affect the settings for the container. They do not affect the memory setting for the JVM process in the container. If you would like to set JVM memory settings, you can provide an environment variable to do so. See the next section for details.

Environment Variables

To influence the environment settings for a given application, you can use the spring.cloud.deployer.kubernetes.environmentVariables deployer property. For example, a common requirement in production settings is to influence the JVM memory arguments. You can do so by using the JAVA_TOOL_OPTIONS environment variable, as the following example shows:

deployer.<app>.kubernetes.environmentVariables=JAVA_TOOL_OPTIONS=-Xmx1024m

The environmentVariables property accepts a comma-delimited string. If an environment variable contains a value which is also a comma-delimited string, it must be enclosed in single quotation marks — for example,

spring.cloud.deployer.kubernetes.environmentVariables=spring.cloud.stream.kafka.binder.brokers='somehost:9092, anotherhost:9093'

This overrides the JVM memory setting for the desired <app> (replace <app> with the name of your application).

Liveness and Readiness Probes

The liveness and readiness probes use paths called /health and /info, respectively. They use a delay of 10 for both and a period of 60 and 10 respectively. You can change these defaults when you deploy the stream by using deployer properties. Liveness and readiness probes are only applied to streams.

The following example changes the liveness probe (replace <app> with the name of your application) by setting deployer properties:

deployer.<app>.kubernetes.livenessProbePath=/health
deployer.<app>.kubernetes.livenessProbeDelay=120
deployer.<app>.kubernetes.livenessProbePeriod=20

You can declare the same as part of the server global configuration for streams, as the following example shows:

data:
  application.yaml: |-
    spring:
      cloud:
        skipper:
          server:
            platform:
              kubernetes:
                accounts:
                  default:
                    livenessProbePath: /health
                    livenessProbeDelay: 120
                    livenessProbePeriod: 20

Similarly, you can swap liveness for readiness to override the default readiness settings.

By default, port 8080 is used as the probe port. You can change the defaults for both liveness and readiness probe ports by using deployer properties, as the following example shows:

deployer.<app>.kubernetes.readinessProbePort=7000
deployer.<app>.kubernetes.livenessProbePort=7000

You can declare the same as part of the global configuration for streams, as the following example shows:

data:
  application.yaml: |-
    spring:
      cloud:
        skipper:
          server:
            platform:
              kubernetes:
                accounts:
                  default:
                    readinessProbePort: 7000
                    livenessProbePort: 7000

By default, the liveness and readiness probe paths use Spring Boot 2.x+ actuator endpoints. To use Spring Boot 1.x actuator endpoint paths, you must adjust the liveness and readiness values, as the following example shows (replace <app> with the name of your application):

deployer.<app>.kubernetes.livenessProbePath=/health
deployer.<app>.kubernetes.readinessProbePath=/info

To automatically set both liveness and readiness endpoints on a per-application basis to the default Spring Boot 1.x paths, you can set the following property:

deployer.<app>.kubernetes.bootMajorVersion=1

You can access secured probe endpoints by using credentials stored in a Kubernetes secret. You can use an existing secret, provided the credentials are contained under the credentials key name of the secret’s data block. You can configure probe authentication on a per-application basis. When enabled, it is applied to both the liveness and readiness probe endpoints by using the same credentials and authentication type. Currently, only Basic authentication is supported.

To create a new secret:

  1. Generate the base64 string with the credentials used to access the secured probe endpoints.

    Basic authentication encodes a username and password as a base64 string in the format of username:password.

    The following example (which includes output and in which you should replace user and pass with your values) shows how to generate a base64 string:

    echo -n "user:pass" | base64
    dXNlcjpwYXNz
  2. With the encoded credentials, create a file (for example, myprobesecret.yml) with the following contents:

    apiVersion: v1
    kind: Secret
    metadata:
    name:
    myprobesecret type:
    Opaque data:
    credentials: GENERATED_BASE64_STRING
  3. Replace GENERATED_BASE64_STRING with the base64-encoded value generated earlier.
  4. Create the secret by using kubectl, as the following example shows:

    kubectl create -f ./myprobesecret.yml
    secret "myprobesecret" created
  5. Set the following deployer properties to use authentication when accessing probe endpoints, as the following example shows:

    deployer.<app>.kubernetes.probeCredentialsSecret=myprobesecret

    Replace <app> with the name of the application to which to apply authentication.

Using SPRING_APPLICATION_JSON

You can use a SPRING_APPLICATION_JSON environment variable to set Data Flow server properties (including the configuration of maven repository settings) that are common across all of the Data Flow server implementations. These settings go at the server level in the container env section of a deployment YAML. The following example shows how to do so:

env:
  - name: SPRING_APPLICATION_JSON
    value: |-
    {
      "maven": {
        "local-repository": null,
        "remote-repositories": {
          "repo1": {
            "url": "https://repo.spring.io/libs-snapshot"
          }
        }
      }
    }

Private Docker Registry

You can pull Docker images from a private registry on a per-application basis. First, you must create a secret in the cluster. Follow the Pull an Image from a Private Registry guide to create the secret.

Once you have created the secret, you can use the imagePullSecret property to set the secret to use, as the following example shows:

deployer.<app>.kubernetes.imagePullSecret=mysecret

Replace <app> with the name of your application and mysecret with the name of the secret you created earlier.

You can also configure the image pull secret at the global server level.

The following example shows how to do so for streams and tasks:

data:
  application.yaml: |-
    spring:
      cloud:
        skipper:
          server:
            platform:
              kubernetes:
                accounts:
                  default:
                    imagePullSecret: mysecret
data:
  application.yaml: |-
    spring:
      cloud:
        dataflow:
          task:
            platform:
              kubernetes:
                accounts:
                  default:
                    imagePullSecret: mysecret

Replace mysecret with the name of the secret you created earlier.

Volume Mounted Secretes

Data Flow uses the application metadata stored in a container image label. To access the metadata labels in a private registry, you have to extend the Data Flow deployment configuration and mount the registry secrets as a Secrets PropertySource:

    spec:
      containers:
      - name: scdf-server
        ...
        volumeMounts:
          - name: mysecret
            mountPath: /etc/secrets/mysecret
            readOnly: true
        ...
      volumes:
        - name: mysecret
          secret:
            secretName: mysecret

Annotations

You can add annotations to Kubernetes objects on a per-application basis. The supported object types are pod Deployment, Service, and Job. Annotations are defined in a key:value format, allowing for multiple annotations separated by a comma. For more information and use cases on annotations, see Annotations.

The following example shows how you can configure applications to use annotations:

deployer.<app>.kubernetes.podAnnotations=annotationName:annotationValue
deployer.<app>.kubernetes.serviceAnnotations=annotationName:annotationValue,annotationName2:annotationValue2
deployer.<app>.kubernetes.jobAnnotations=annotationName:annotationValue

Replace <app> with the name of your application and the value of your annotations.

Entry Point Style

An entry point style affects how application properties are passed to the container to be deployed. Currently, three styles are supported:

  • exec (default): Passes all application properties and command line arguments in the deployment request as container arguments. Application properties are transformed into the format of --key=value.
  • shell: Passes all application properties and command line arguments as environment variables. Each of the application and command line argument properties is transformed into an uppercase string and . characters are replaced with _.
  • boot: Creates an environment variable called SPRING_APPLICATION_JSON that contains a JSON representation of all application properties. Command line arguments from the deployment request are set as container args.

In all cases, environment variables defined at the server-level configuration and on a per-application basis are set onto the container as is.

You can configure applications as follows:

deployer.<app>.kubernetes.entryPointStyle=<Entry Point Style>

Replace <app> with the name of your application and <Entry Point Style> with your desired entry point style.

You can also configure the entry point style at the global server level.

The following example shows how to do so for streams:

data:
  application.yaml: |-
    spring:
      cloud:
        skipper:
          server:
            platform:
              kubernetes:
                accounts:
                  default:
                    entryPointStyle: entryPointStyle

The following example shows how to do so for tasks:

data:
  application.yaml: |-
    spring:
      cloud:
        dataflow:
          task:
            platform:
              kubernetes:
                accounts:
                  default:
                    entryPointStyle: entryPointStyle

Replace entryPointStye with the desired entry point style.

You should choose an Entry Point Style of either exec or shell, to correspond to how the ENTRYPOINT syntax is defined in the container’s Dockerfile. For more information and uses cases on exec versus shell, see the ENTRYPOINT section of the Docker documentation.

Using the boot entry point style corresponds to using the exec style ENTRYPOINT. Command line arguments from the deployment request are passed to the container, with the addition of application properties being mapped into the SPRING_APPLICATION_JSON environment variable rather than command line arguments.

When you use the boot Entry Point Style, the deployer.<app>.kubernetes.environmentVariables property must not contain SPRING_APPLICATION_JSON.

Deployment Service Account

You can configure a custom service account for application deployments through properties. You can use an existing service account or create a new one. One way to create a service account is by using kubectl, as the following example shows:

kubectl create serviceaccount myserviceaccountname
serviceaccount "myserviceaccountname" created

Then you can configure individual applications as follows:

deployer.<app>.kubernetes.deploymentServiceAccountName=myserviceaccountname

Replace <app> with the name of your application and myserviceaccountname with your service account name.

You can also configure the service account name at the global server level.

The following example shows how to do so for streams:

data:
  application.yaml: |-
    spring:
      cloud:
        skipper:
          server:
            platform:
              kubernetes:
                accounts:
                  default:
                    deploymentServiceAccountName: myserviceaccountname

The following example shows how to do so for tasks:

data:
  application.yaml: |-
    spring:
      cloud:
        dataflow:
          task:
            platform:
              kubernetes:
                accounts:
                  default:
                    deploymentServiceAccountName: myserviceaccountname

Replace myserviceaccountname with the service account name to be applied to all deployments.

Image Pull Policy

An image pull policy defines when a Docker image should be pulled to the local registry. Currently, three policies are supported:

  • IfNotPresent (default): Do not pull an image if it already exists.
  • Always: Always pull the image regardless of whether it already exists.
  • Never: Never pull an image. Use only an image that already exists.

The following example shows how you can individually configure applications:

deployer.<app>.kubernetes.imagePullPolicy=Always

Replace <app> with the name of your application and Always with your desired image pull policy.

You can configure an image pull policy at the global server level.

The following example shows how to do so for streams:

data:
  application.yaml: |-
    spring:
      cloud:
        skipper:
          server:
            platform:
              kubernetes:
                accounts:
                  default:
                    imagePullPolicy: Always

The following example shows how to do so for tasks:

data:
  application.yaml: |-
    spring:
      cloud:
        dataflow:
          task:
            platform:
              kubernetes:
                accounts:
                  default:
                    imagePullPolicy: Always

Replace Always with your desired image pull policy.

Deployment Labels

You can set custom labels on objects related to Deployment. See Labels for more information on labels. Labels are specified in key:value format.

The following example shows how you can individually configure applications:

deployer.<app>.kubernetes.deploymentLabels=myLabelName:myLabelValue

Replace <app> with the name of your application, myLabelName with your label name, and myLabelValue with the value of your label.

Additionally, you can apply multiple labels, as the following example shows:

deployer.<app>.kubernetes.deploymentLabels=myLabelName:myLabelValue,myLabelName2:myLabelValue2

NodePort

Applications are deployed using a Service type of ClusterIP which is the default Kubernetes Service type if not defined otherwise. ClusterIP services are only reachable from within the cluster itself.

To expose the deployed application to be available externally, one option is to use NodePort. See the NodePort documentation for more information.

The following example shows how you can individually configure applications using Kubernetes assigned ports:

deployer.<app>.kubernetes.createNodePort=true

Replace <app> with the name of your application.

Additionally, you can define the port to use for the NodePort Service as shown below:

deployer.<app>.kubernetes.createNodePort=31101

Replace <app> with the name of your application and the value of 31101 with your desired port.

When defining the port manually, the port must not already be in use and within the defined NodePort range. Per NodePort the default port range is 30000-32767.

Monitoring

To learn more about the monitoring experience in Data Flow using Prometheus running on Kubernetes, please refer to the Stream Monitoring or Task Monitoring guides.