Helm Installation

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.

Installing the Spring Cloud Data Flow Server and Required Services

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

Common parameters

Dataflow Server parameters

Dataflow Skipper parameters

RBAC parameters

Metrics parameters

Init Container parameters

Database parameters

RabbitMQ chart parameters

Kafka chart parameters

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 precedence 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.

Setting Pod's affinity

This chart allows you to set your custom affinity using the XXX.affinity parameter(s). Find more information about Pod's affinity in the kubernetes documentation.

As an alternative, you can use of the preset configurations for pod affinity, pod anti-affinity, and node affinity available at the bitnami/common chart. To do so, set the XXX.podAffinityPreset, XXX.podAntiAffinityPreset, or XXX.nodeAffinityPreset parameters.

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

• https://docs.bitnami.com/tutorials/resolve-helm2-helm3-post-migration-issues/
• https://helm.sh/docs/topics/v2_v3_migration/
• https://helm.sh/blog/migrate-from-helm-v2-to-helm-v3/

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.

--set server.service.type=ServiceType

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:

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.

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}

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 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

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

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:

   Copy

   echo -n "user:pass" | base64
   dXNlcjpwYXNz

2. With the encoded credentials, create a file (for example, myprobesecret.yml) with the following contents:

   Copy

   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:

   Copy

   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:

   Copy

   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.

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.

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.

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.