# Spring Cloud Data Flow Reference Guide

Sabby Anandan
Marius Bogoevici
Eric Bottard
Mark Fisher
Ilayaperumal Gopinathan
Gunnar Hillert
Mark Pollack
Patrick Peralta
Glenn Renfro
Thomas Risberg
Dave Syer
David Turanski
Janne Valkealahti
Oleg Zhurakousky
Jay Bryant
Vinicius Carvalho
Chris Schaefer
Damien Vitrac
Christian Tzolov

Version 2.8.0-M1

© 2012-2020 Pivotal Software, Inc.

Copies of this document may be made for your own use and for distribution to others, provided that you do not charge any fee for such copies and further provided that each copy contains this Copyright Notice, whether distributed in print or electronically.

# Preface

## 1. About the documentation

The documentation for this release is available in HTML.

The latest copy of the Spring Cloud Data Flow reference guide can be found here.

Copies of this document may be made for your own use and for distribution to others, provided that you do not charge any fee for such copies and further provided that each copy contains this Copyright Notice, whether distributed in print or electronically.

## 2. Getting help

Having trouble with Spring Cloud Data Flow? We would like to help!

- Ask a question. We monitor stackoverflow.com for questions tagged with `spring-cloud-dataflow`.

- Report bugs with Spring Cloud Data Flow at github.com/spring-cloud/spring-cloud-dataflow/issues.

- Chat with the community and developers on Gitter.

| All of Spring Cloud Data Flow is open source, including the documentation! If you find problems with the docs or if you just want to improve them, please get involved.

# Getting Started

## 3. Getting Started - Local

See the Local Machine section of the microsite for more information on setting up docker compose and manual installation.

Once you have the Data Flow server installed locally, you probably want to get started with orchestrating the deployment of readily available pre-built applications into coherent streaming or batch data pipelines. We have guides to help you get started with both Stream and Batch processing.

## 4. Getting Started - Cloud Foundry

This section covers how to get started with Spring Cloud Data Flow on Cloud Foundry. See the Cloud Foundry section of the microsite for more information on installing Spring Cloud Data Flow on Cloud Foundry.

Once you have the Data Flow server installed on Cloud Foundry, you probably want to get started with orchestrating the deployment of readily available pre-built applications into coherent streaming or batch data pipelines. We have guides to help you get started with both Stream and Batch processing.

## 5. Getting Started - Kubernetes

Spring Cloud Data Flow is a toolkit for building data integration and real-time data-processing pipelines.

Pipelines consist of Spring Boot applications built with 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 project provides support for using Spring Cloud Data Flow with Kubernetes as the runtime for these pipelines, with applications packaged as Docker images.

See the Kubernetes section of the microsite for more information on installing Spring Cloud Data Flow on Kubernetes.

Once you have the Data Flow server installed on Kubernetes, you probably want to get started with orchestrating the deployment of readily available pre-built applications into a coherent streaming or batch data pipelines. We have guides to help you get started with both Stream and Batch processing.

### 5.1. 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-[binder].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`.

#### 5.1.1. Memory and CPU Settings

Applications are deployed with default memory and CPU settings. If you need to, you can adjust these values. 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.<application>.kubernetes.limits.cpu=1000m
    deployer.<application>.kubernetes.limits.memory=1024Mi
    deployer.<application>.kubernetes.requests.cpu=800m
    deployer.<application>.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:

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

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

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

The settings we have used so far affect only 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 set an environment variable to do so. See the next section for details.

#### 5.1.2. 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.<application>.kubernetes.environmentVariables=JAVA_TOOL_OPTIONS=-Xmx1024m

| The `environmentVariables` property accepts a comma-delimited string. If an environment variable contains a value that 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 `<application>` (replace `<application>` with the name of your application).

#### 5.1.3. 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. The liveness and readiness probes are applied only to streams.

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

    deployer.<application>.kubernetes.livenessProbePath=/health
    deployer.<application>.kubernetes.livenessProbeDelay=120
    deployer.<application>.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.<application>.kubernetes.readinessProbePort=7000
    deployer.<application>.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 `<application>` with the name of your application):
    deployer.<application>.kubernetes.livenessProbePath=/health
    deployer.<application>.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.<application>.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 a 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.<application>.kubernetes.probeCredentialsSecret=myprobesecret

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

#### 5.1.4. 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\"} } } }"

#### 5.1.5. 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.<application>.kubernetes.imagePullSecret=mysecret

Replace `<application>` 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:

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

The following example shows how to do so for tasks:

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

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

#### 5.1.6. 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.<application>.kubernetes.podAnnotations=annotationName:annotationValue
    deployer.<application>.kubernetes.serviceAnnotations=annotationName:annotationValue,annotationName2:annotationValue2
    deployer.<application>.kubernetes.jobAnnotations=annotationName:annotationValue

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

#### 5.1.7. 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 applicationor 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 sent on to the container as is.

You can configure an application as follows:

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

Replace `<application>` 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 `entryPointStyle` 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.<application>.kubernetes.environmentVariables` property must not contain `SPRING_APPLICATION_JSON`.

#### 5.1.8. 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.<application>.kubernetes.deploymentServiceAccountName=myserviceaccountname

Replace `<application>` 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.

#### 5.1.9. 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.<application>.kubernetes.imagePullPolicy=Always

Replace `<application>` 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.

#### 5.1.10. 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.<application>.kubernetes.deploymentLabels=myLabelName:myLabelValue

Replace `<application>` 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.<application>.kubernetes.deploymentLabels=myLabelName:myLabelValue,myLabelName2:myLabelValue2

#### 5.1.11. Tolerations

Tolerations work with taints to ensure pods are not scheduled onto particular nodes. Tolerations are set into the pod configuration while taints are set onto nodes. See the Taints and Tolerations section of the Kubernetes reference for more information.

The following example shows how you can individually configure applications:

    deployer.<application>.kubernetes.tolerations=[{key: 'mykey' operator: 'Equal', value: 'myvalue', effect: 'NoSchedule'}]

Replace `<application>` with the name of your application and the key-value pairs according to your desired toleration configuration.

You can configure tolerations at the global server level as well.

The following example shows how to do so for streams:

    data:
      application.yaml: |-
        spring:
          cloud:
            skipper:
              server:
                platform:
                  kubernetes:
                    accounts:
                      default:
                        tolerations:
                          - key: mykey
                            operator: Equal
                            value: myvalue
                            effect: NoSchedule

The following example shows how to do so for tasks:

    data:
      application.yaml: |-
        spring:
          cloud:
            dataflow:
              task:
                platform:
                  kubernetes:
                    accounts:
                      default:
                        tolerations:
                          - key: mykey
                            operator: Equal
                            value: myvalue
                            effect: NoSchedule

Replace the `tolerations` key-value pairs according to your desired toleration configuration.

#### 5.1.12. Secret References

Secrets can be referenced and their entire data contents can be decoded and inserted into the pod environment as individual variables. See the Configure all key-value pairs in a Secret as container environment variables section of the Kubernetes reference for more information.

The following example shows how you can individually configure applications:

    deployer.<application>.kubernetes.secretRefs=testsecret

You can also specify multiple secrets, as follows:

    deployer.<application>.kubernetes.secretRefs=[testsecret,anothersecret]

Replace `<application>` with the name of your application and the `secretRefs` attribute with the appropriate values for your application environment and secret.

You can configure secret references at the global server level as well.

The following example shows how to do so for streams:

    data:
      application.yaml: |-
        spring:
          cloud:
            skipper:
              server:
                platform:
                  kubernetes:
                    accounts:
                      default:
                        secretRefs:
                          - testsecret
                          - anothersecret

The following example shows how to do so for tasks:

    data:
      application.yaml: |-
        spring:
          cloud:
            dataflow:
              task:
                platform:
                  kubernetes:
                    accounts:
                      default:
                        secretRefs:
                          - testsecret
                          - anothersecret

Replace the items of `secretRefs` with one or more secret names.

#### 5.1.13. Secret Key References

Secrets can be referenced and their decoded value can be inserted into the pod environment. See the Using Secrets as Environment Variables section of the Kubernetes reference for more information.

The following example shows how you can individually configure applications:

    deployer.<application>.kubernetes.secretKeyRefs=[{envVarName: 'MY_SECRET', secretName: 'testsecret', dataKey: 'password'}]

Replace `<application>` with the name of your application and the `envVarName`, `secretName`, and `dataKey` attributes with the appropriate values for your application environment and secret.

You can configure secret key references at the global server level as well.

The following example shows how to do so for streams:

    data:
      application.yaml: |-
        spring:
          cloud:
            skipper:
              server:
                platform:
                  kubernetes:
                    accounts:
                      default:
                        secretKeyRefs:
                          - envVarName: MY_SECRET
                            secretName: testsecret
                            dataKey: password

The following example shows how to do so for tasks:

    data:
      application.yaml: |-
        spring:
          cloud:
            dataflow:
              task:
                platform:
                  kubernetes:
                    accounts:
                      default:
                        secretKeyRefs:
                          - envVarName: MY_SECRET
                            secretName: testsecret
                            dataKey: password

Replace the `envVarName`, `secretName`, and `dataKey` attributes with the appropriate values for your secret.

#### 5.1.14. ConfigMap References

A ConfigMap can be referenced and its entire data contents can be decoded and inserted into the pod environment as individual variables. See the Configure all key-value pairs in a ConfigMap as container environment variables section of the Kubernetes reference for more information.

The following example shows how you can individually configure applications:

    deployer.<application>.kubernetes.configMapRefs=testcm

You can also specify multiple ConfigMap instances, as follows:

    deployer.<application>.kubernetes.configMapRefs=[testcm,anothercm]

Replace `<application>` with the name of your application and the `configMapRefs` attribute with the appropriate values for your application environment and ConfigMap.

You can configure ConfigMap references at the global server level as well.

The following example shows how to do so for streams. Edit the appropriate `skipper-config-(binder).yaml`, replacing `(binder)` with the corresponding binder in use:

    data:
      application.yaml: |-
        spring:
          cloud:
            skipper:
              server:
                platform:
                  kubernetes:
                    accounts:
                      default:
                        configMapRefs:
                          - testcm
                          - anothercm

The following example shows how to do so for tasks by editing the `server-config.yaml` file:

    data:
      application.yaml: |-
        spring:
          cloud:
            dataflow:
              task:
                platform:
                  kubernetes:
                    accounts:
                      default:
                        configMapRefs:
                          - testcm
                          - anothercm

Replace the items of `configMapRefs` with one or more secret names.

#### 5.1.15. ConfigMap Key References

A ConfigMap can be referenced and its associated key value inserted into the pod environment. See the Define container environment variables using ConfigMap data section of the Kubernetes reference for more information.

The following example shows how you can individually configure applications:

    deployer.<application>.kubernetes.configMapKeyRefs=[{envVarName: 'MY_CM', configMapName: 'testcm', dataKey: 'platform'}]

Replace `<application>` with the name of your application and the `envVarName`, `configMapName`, and `dataKey` attributes with the appropriate values for your application environment and ConfigMap.

You can configure ConfigMap references at the global server level as well.

The following example shows how to do so for streams. Edit the appropriate `skipper-config-(binder).yaml`, replacing `(binder)` with the corresponding binder in use:

    data:
      application.yaml: |-
        spring:
          cloud:
            skipper:
              server:
                platform:
                  kubernetes:
                    accounts:
                      default:
                        configMapKeyRefs:
                          - envVarName: MY_CM
                            configMapName: testcm
                            dataKey: platform

The following example shows how to do so for tasks by editing the `server-config.yaml` file:

    data:
      application.yaml: |-
        spring:
          cloud:
            dataflow:
              task:
                platform:
                  kubernetes:
                    accounts:
                      default:
                        configMapKeyRefs:
                          - envVarName: MY_CM
                            configMapName: testcm
                            dataKey: platform

Replace the `envVarName`, `configMapName`, and `dataKey` attributes with the appropriate values for your ConfigMap.

#### 5.1.16. Pod Security Context

You can confiure the pod security context to run processes under the specified UID (user ID) or GID (group ID). This is useful when you want to not run processes under the default `root` UID and GID. You can define either the `runAsUser` (UID) or `fsGroup` (GID), and you can configure them to work together. See the Security Context section of the Kubernetes reference for more information.

The following example shows how you can individually configure application pods:

    deployer.<application>.kubernetes.podSecurityContext={runAsUser: 65534, fsGroup: 65534}

Replace `<application>` with the name of your application and the `runAsUser` and/or `fsGroup` attributes with the appropriate values for your container environment.

You can configure the pod security context at the global server level as well.

The following example shows how to do so for streams. Edit the appropriate `skipper-config-(binder).yaml`, replacing `(binder)` with the corresponding binder in use:

    data:
      application.yaml: |-
        spring:
          cloud:
            skipper:
              server:
                platform:
                  kubernetes:
                    accounts:
                      default:
                        podSecurityContext:
                          runAsUser: 65534
                          fsGroup: 65534

The following example shows how to do so for tasks by editing the `server-config.yaml` file:

    data:
      application.yaml: |-
        spring:
          cloud:
            dataflow:
              task:
                platform:
                  kubernetes:
                    accounts:
                      default:
                        podSecurityContext:
                          runAsUser: 65534
                          fsGroup: 65534

Replace the `runAsUser` and/or `fsGroup` attributes with the appropriate values for your container environment.

#### 5.1.17. Service Ports

When you deploy applications, a kubernetes Service object is created with a default port of `8080`. If the `server.port` property is set, it overrides the default port value. You can add additional ports to the Service object on a per-application basis. You can add multiple ports with a comma delimiter.

The following example shows how you can configure additional ports on a Service object for an application:

    deployer.<application>.kubernetes.servicePorts=5000
    deployer.<application>.kubernetes.servicePorts=5000,9000

Replace `<application>` with the name of your application and the value of your ports.

#### 5.1.18. StatefulSet Init Container

When deploying an application by using a StatefulSet, an Init Container is used to set the instance index in the pod. By default, the image used is `busybox`, which you can be customize.

The following example shows how you can individually configure application pods:

    deployer.<application>.kubernetes.statefulSetInitContainerImageName=myimage:mylabel

Replace `<application>` with the name of your application and the `statefulSetInitContainerImageName` attribute with the appropriate value for your environment.

You can configure the StatefulSet Init Container at the global server level as well.

The following example shows how to do so for streams. Edit the appropriate `skipper-config-(binder).yaml`, replacing `(binder)` with the corresponding binder in use:

    data:
      application.yaml: |-
        spring:
          cloud:
            skipper:
              server:
                platform:
                  kubernetes:
                    accounts:
                      default:
                        statefulSetInitContainerImageName: myimage:mylabel

The following example shows how to do so for tasks by editing the `server-config.yaml` file:

    data:
      application.yaml: |-
        spring:
          cloud:
            dataflow:
              task:
                platform:
                  kubernetes:
                    accounts:
                      default:
                        statefulSetInitContainerImageName: myimage:mylabel

Replace the `statefulSetInitContainerImageName` attribute with the appropriate value for your environment.

#### 5.1.19. Init Containers

When you deploy applications, you can set a custom Init Container on a per-application basis. Refer to the Init Containers section of the Kubernetes reference for more information.

The following example shows how you can configure an Init Container for an application:

    deployer.<application>.kubernetes.initContainer={containerName: 'test', imageName: 'busybox:latest', commands: ['sh', '-c', 'echo hello']}

Replace `<application>` with the name of your application and set the values of the `initContainer` attributes appropriate for your Init Container.

# Applications

A selection of pre-built stream and task or batch starter applications for various data integration and processing scenarios to facilitate learning and experimentation. The table in the next section includes the pre-built applications at a glance. For more details, review how to register supported applications.

## 6. Available Applications

Source | Processor | Sink | Task
--- | --- | --- | ---
sftp | tcp-client | mqtt | timestamp
jms | scriptable-transform | log | composed-task-runner
ftp | transform | throughput | timestamp-batch
time | header-enricher | mongodb |
load-generator | python-http | ftp |
syslog | twitter-sentiment | jdbc |
s3 | splitter | cassandra |
loggregator | image-recognition | router |
triggertask (deprecated) | bridge | redis-pubsub |
twitterstream | pmml | file |
mongodb | python-jython | websocket |
gemfire-cq | groovy-transform | s3 |
http | httpclient | rabbit |
rabbit | filter | counter |
tcp | pose-estimation | pgcopy |
trigger | grpc | gpfdist |
mqtt | groovy-filter | sftp |
tcp-client | aggregator | task-launcher-dataflow |
mail | counter | hdfs |
jdbc | tensorflow | tcp |
gemfire | tasklaunchrequest-transform (deprecated) | gemfire |
file | object-detection |  |
sftp-dataflow |  |  |

# Architecture

## 7. Introduction

Spring Cloud Data Flow simplifies the development and deployment of applications that are focused on data-processing use cases.

The Architecture section of the microsite describes Data Flow’s architecture.

# Configuration

## 8. Maven

If you want to override specific Maven configuration properties (remote repositories, proxies, and others) or run the Data Flow Server behind a proxy, you need to specify those properties as command-line arguments when you start the Data Flow Server, as shown in the following example:

    $ java -jar spring-cloud-dataflow-server-2.8.0-M1.jar --spring.config.additional-location=/home/joe/maven.yml

The preceding command assumes a `maven.yaml` similar to the following:

    maven:
      localRepository: mylocal
      remote-repositories:
        repo1:
          url: https://repo1
          auth:
            username: user1
            password: pass1
          snapshot-policy:
            update-policy: daily
            checksum-policy: warn
          release-policy:
            update-policy: never
            checksum-policy: fail
        repo2:
          url: https://repo2
          policy:
            update-policy: always
            checksum-policy: fail
      proxy:
        host: proxy1
        port: "9010"
        auth:
          username: proxyuser1
          password: proxypass1

By default, the protocol is set to `http`. You can omit the auth properties if the proxy does not need a username and password. Also, by default, the maven `localRepository` is set to `${user.home}/.m2/repository/`. As shown in the preceding example, you can specify the remote repositories along with their authentication (if needed). If the remote repositories are behind a proxy, you can specify the proxy properties, as shown in the preceding example.

You can specify the repository policies for each remote repository configuration, as shown in the preceding example. The key `policy` is applicable to both the `snapshot` and the `release` repository policies.

See the Repository Policies topic for the list of supported repository policies.

As these are Spring Boot `@ConfigurationProperties` you need to specify by adding them to the `SPRING_APPLICATION_JSON` environment variable. The following example shows how the JSON is structured:

    $ SPRING_APPLICATION_JSON='
    {
      "maven": {
        "local-repository": null,
        "remote-repositories": {
          "repo1": {
            "url": "https://repo1",
            "auth": {
              "username": "repo1user",
              "password": "repo1pass"
            }
          },
          "repo2": {
            "url": "https://repo2"
          }
        },
        "proxy": {
          "host": "proxyhost",
          "port": 9018,
          "auth": {
            "username": "proxyuser",
            "password": "proxypass"
          }
        }
      }
    }
    '

### 8.1. Wagon

There is a limited support for using `Wagon` transport with Maven. Currently, this exists to support preemptive authentication with `http`-based repositories and needs to be enabled manually.

Wagon-based `http` transport is enabled by setting the `maven.use-wagon` property to `true`. Then you can enable preemptive authentication for each remote repository. Configuration loosely follows the similar patterns found in HttpClient HTTP Wagon. At the time of this writing, documentation in Maven’s own site is slightly misleading and missing most of the possible configuration options.

The `maven.remote-repositories.<repo>.wagon.http` namespace contains all Wagon `http` related settings, and the keys directly under it map to supported `http` methods — namely, `all`, `put`, `get` and `head`, as in Maven’s own configuration. Under these method configurations, you can then set various options, such as `use-preemptive`. A simpl preemptive configuration to send an auth header with all requests to a specified remote repository would look like the following example:

    maven:
      use-wagon: true
      remote-repositories:
        springRepo:
          url: https://repo.example.org
          wagon:
            http:
              all:
                use-preemptive: true
          auth:
            username: user
            password: password

Instead of configuring `all` methods, you can tune settings for `get` and `head` requests only, as follows:

    maven:
      use-wagon: true
      remote-repositories:
        springRepo:
          url: https://repo.example.org
          wagon:
            http:
              get:
                use-preemptive: true
              head:
                use-preemptive: true
                use-default-headers: true
                connection-timeout: 1000
                read-timeout: 1000
                headers:
                  sample1: sample2
                params:
                  http.socket.timeout: 1000
                  http.connection.stalecheck: true
          auth:
            username: user
            password: password

There are settings for `use-default-headers`, `connection-timeout`, `read-timeout`, request `headers`, and HttpClient `params`. For more about parameters, see Wagon ConfigurationUtils.

## 9. Security

By default, the Data Flow server is unsecured and runs on an unencrypted HTTP connection. You can secure your REST endpoints as well as the Data Flow Dashboard by enabling HTTPS and requiring clients to authenticate with OAuth 2.0.

| Appendix Azure contains more information how to setup Azure Active Directory integration.

| By default, the REST endpoints (administration, management, and health) as well as the Dashboard UI do not require authenticated access.

While you can theoretically choose any OAuth provider in conjunction with Spring Cloud Data Flow, we recommend using the CloudFoundry User Account and Authentication (UAA) Server.

Not only is the UAA OpenID certified and is used by Cloud Foundry, but you can also use it in local stand-alone deployment scenarios. Furthermore, the UAA not only provides its own user store, but it also provides comprehensive LDAP integration.

#### 9.1. Enabling HTTPS

By default, the dashboard, management, and health endpoints use HTTP as a transport. You can switch to HTTPS by adding a certificate to your configuration in `application.yml`, as shown in the following example:

    server:
      port: 8443                                         (1)
      ssl:
        key-alias: yourKeyAlias                          (2)
        key-store: path/to/keystore                      (3)
        key-store-password: yourKeyStorePassword         (4)
        key-password: yourKeyPassword                    (5)
        trust-store: path/to/trust-store                 (6)
        trust-store-password: yourTrustStorePassword     (7)

1 | As the default port is `9393`, you may choose to change the port to a more common HTTPs-typical port.
2 | The alias (or name) under which the key is stored in the keystore.
3 | The path to the keystore file. You can also specify classpath resources, by using the classpath prefix - for example: `classpath:path/to/keystore`.
4 | The password of the keystore.
5 | The password of the key.
6 | The path to the truststore file. You can also specify classpath resources, by using the classpath prefix - for example: `classpath:path/to/trust-store`
7 | The password of the trust store.

| If HTTPS is enabled, it completely replaces HTTP as the protocol over which the REST endpoints and the Data Flow Dashboard interact. Plain HTTP requests fail. Therefore, make sure that you configure your Shell accordingly.

##### Using Self-Signed Certificates

For testing purposes or during development, it might be convenient to create self-signed certificates. To get started, execute the following command to create a certificate:

    $ keytool -genkey -alias dataflow -keyalg RSA -keystore dataflow.keystore \
              -validity 3650 -storetype JKS \
              -dname "CN=localhost, OU=Spring, O=Pivotal, L=Kailua-Kona, ST=HI, C=US"  (1)
              -keypass dataflow -storepass dataflow

1 | `CN` is the important parameter here. It should match the domain you are trying to access - for example, `localhost`.

Then add the following lines to your `application.yml` file:

    server:
      port: 8443
      ssl:
        enabled: true
        key-alias: dataflow
        key-store: "/your/path/to/dataflow.keystore"
        key-store-type: jks
        key-store-password: dataflow
        key-password: dataflow

This is all you need to do for the Data Flow Server. Once you start the server, you should be able to access it at `localhost:8443/`. As this is a self-signed certificate, you should hit a warning in your browser, which you need to ignore.

| Never use self-signed certificates in production.

##### Self-Signed Certificates and the Shell

By default, self-signed certificates are an issue for the shell, and additional steps are necessary to make the shell work with self-signed certificates. Two options are available:

- Add the self-signed certificate to the JVM truststore.

- Skip certificate validation.

###### Adding the Self-signed Certificate to the JVM Truststore

In order to use the JVM truststore option, you need to export the previously created certificate from the keystore, as follows:

    $ keytool -export -alias dataflow -keystore dataflow.keystore -file dataflow_cert -storepass dataflow

Next, you need to create a truststore that the shell can use, as follows:

    $ keytool -importcert -keystore dataflow.truststore -alias dataflow -storepass dataflow -file dataflow_cert -noprompt

Now you are ready to launch the Data Flow Shell with the following JVM arguments:

    $ java -Djavax.net.ssl.trustStorePassword=dataflow \
           -Djavax.net.ssl.trustStore=/path/to/dataflow.truststore \
           -Djavax.net.ssl.trustStoreType=jks \
           -jar spring-cloud-dataflow-shell-2.8.0-M1.jar

| If you run into trouble establishing a connection over SSL, you can enable additional logging by using and setting the `javax.net.debug` JVM argument to `ssl`.

Do not forget to target the Data Flow Server with the following command:

    dataflow:> dataflow config server https://localhost:8443/

###### Skipping Certificate Validation

Alternatively, you can also bypass the certification validation by providing the optional `--dataflow.skip-ssl-validation=true` command-line parameter.

If you set this command-line parameter, the shell accepts any (self-signed) SSL certificate.

| If possible, you should avoid using this option. Disabling the trust manager defeats the purpose of SSL and makes your application vulnerable to man-in-the-middle attacks.

#### 9.2. Authentication by using OAuth 2.0

To support authentication and authorization, Spring Cloud Data Flow uses OAuth 2.0. It lets you integrate Spring Cloud Data Flow into Single Sign On (SSO) environments.

| As of Spring Cloud Data Flow 2.0, OAuth2 is the only mechanism for providing authentication and authorization.

The following OAuth2 Grant Types are used:

- Authorization Code: Used for the GUI (browser) integration. Visitors are redirected to your OAuth Service for authentication

- Password: Used by the shell (and the REST integration), so visitors can log in with username and password

- Client Credentials: Retrieves an access token directly from your OAuth provider and passes it to the Data Flow server by using the Authorization HTTP header

| Currently, Spring Cloud Data Flow uses opaque tokens and not transparent tokens (JWT).

You can access the REST endpoints in two ways:

- Basic authentication, which uses the Password Grant Type to authenticate with your OAuth2 service

- Access token, which uses the Client Credentials Grant Type

| When you set up authentication, you really should enable HTTPS as well, especially in production environments.

You can turn on OAuth2 authentication by adding the following to `application.yml` or by setting environment variables. The following example shows the minimal setup needed for CloudFoundry User Account and Authentication (UAA) Server:

    spring:
      security:
        oauth2:                                                           (1)
          client:
            registration:
              uaa:                                                        (2)
                client-id: myclient
                client-secret: mysecret
                redirect-uri: '{baseUrl}/login/oauth2/code/{registrationId}'
                authorization-grant-type: authorization_code
                scope:
                - openid                                                  (3)
            provider:
              uaa:
                jwk-set-uri: http://uaa.local:8080/uaa/token_keys
                token-uri: http://uaa.local:8080/uaa/oauth/token
                user-info-uri: http://uaa.local:8080/uaa/userinfo    (4)
                user-name-attribute: user_name                            (5)
                authorization-uri: http://uaa.local:8080/uaa/oauth/authorize
          resourceserver:
            opaquetoken:
              introspection-uri: http://uaa.local:8080/uaa/introspect (6)
              client-id: dataflow
              client-secret: dataflow

1 | Providing this property activates OAuth2 security.
2 | The provider ID. You can specify more than one provider.
3 | As the UAA is an OpenID provider, you must at least specify the `openid` scope. If your provider also provides additional scopes to control the role assignments, you must specify those scopes here as well.
4 | OpenID endpoint. Used to retrieve user information such as the username. Mandatory.
5 | The JSON property of the response that contains the username.
6 | Used to introspect and validate a directly passed-in token. Mandatory.

You can verify that basic authentication is working properly by using curl, as follows:

    curl -u myusername:mypassword http://localhost:9393/ -H 'Accept: application/json'

As a result, you should see a list of available REST endpoints.

| When you access the Root URL with a web browser and security enabled, you are redirected to the Dashboard UI. To see the list of REST endpoints, specify the `application/json` `Accept` header. Also be sure to add the `Accept` header by using tools such as Postman (Chrome) or RESTClient (Firefox).

Besides Basic Authentication, you can also provide an access token, to access the REST API. To do so, retrieve an OAuth2 Access Token from your OAuth2 provider and pass that access token to the REST Api by using the Authorization HTTP header, as follows:

    $ curl -H "Authorization: Bearer <ACCESS_TOKEN>" http://localhost:9393/ -H 'Accept: application/json'

#### 9.3. Customizing Authorization

The preceding content mostly deals with authentication — that is, how to assess the identity of the user. In this section, we discuss the available authorization options — that is, who can do what.

The authorization rules are defined in `dataflow-server-defaults.yml` (part of the Spring Cloud Data Flow Core module).

Because the determination of security roles is environment-specific, Spring Cloud Data Flow, by default, assigns all roles to authenticated OAuth2 users. The `DefaultDataflowAuthoritiesExtractor` class is used for that purpose.

Alternatively, you can have Spring Cloud Data Flow map OAuth2 scopes to Data Flow roles by setting the boolean property `map-oauth-scopes` for your provider to `true` (the default is `false`). For example, if your provider’s ID is `uaa`, the property would be `spring.cloud.dataflow.security.authorization.provider-role-mappings.uaa.map-oauth-scopes`.

For more details, see the chapter on Role Mappings.

You can also customize the role-mapping behavior by providing your own Spring bean definition that extends Spring Cloud Data Flow’s `AuthorityMapper` interface. In that case, the custom bean definition takes precedence over the default one provided by Spring Cloud Data Flow.

The default scheme uses seven roles to protect the REST endpoints that Spring Cloud Data Flow exposes:

- ROLE_CREATE: For anything that involves creating, such as creating streams or tasks

- ROLE_DEPLOY: For deploying streams or launching tasks

- ROLE_DESTROY: For anything that involves deleting streams, tasks, and so on.

- ROLE_MANAGE: For Boot management endpoints

- ROLE_MODIFY: For anything that involves mutating the state of the system

- ROLE_SCHEDULE: For scheduling related operation (such as scheduling a task)

- ROLE_VIEW: For anything that relates to retrieving state

As mentioned earlier in this section, all authorization-related default settings are specified in `dataflow-server-defaults.yml`, which is part of the Spring Cloud Data Flow Core Module. Nonetheless, you can override those settings, if desired — for example, in `application.yml`. The configuration takes the form of a YAML list (as some rules may have precedence over others). Consequently, you need to copy and paste the whole list and tailor it to your needs (as there is no way to merge lists).

| Always refer to your version of the `application.yml` file, as the following snippet may be outdated.

The default rules are as follows:

    spring:
      cloud:
        dataflow:
          security:
            authorization:
              enabled: true
              loginUrl: "/"
              permit-all-paths: "/authenticate,/security/info,/assets/**,/dashboard/logout-success-oauth.html,/favicon.ico"
              rules:
                # About

                - GET    /about                          => hasRole('ROLE_VIEW')

                # Audit

                - GET /audit-records                     => hasRole('ROLE_VIEW')
                - GET /audit-records/**                  => hasRole('ROLE_VIEW')

                # Boot Endpoints

                - GET /management/**                  => hasRole('ROLE_MANAGE')

                # Apps

                - GET    /apps                           => hasRole('ROLE_VIEW')
                - GET    /apps/**                        => hasRole('ROLE_VIEW')
                - DELETE /apps/**                        => hasRole('ROLE_DESTROY')
                - POST   /apps                           => hasRole('ROLE_CREATE')
                - POST   /apps/**                        => hasRole('ROLE_CREATE')
                - PUT    /apps/**                        => hasRole('ROLE_MODIFY')

                # Completions

                - GET /completions/**                    => hasRole('ROLE_VIEW')

                # Job Executions & Batch Job Execution Steps && Job Step Execution Progress

                - GET    /jobs/executions                => hasRole('ROLE_VIEW')
                - PUT    /jobs/executions/**             => hasRole('ROLE_MODIFY')
                - GET    /jobs/executions/**             => hasRole('ROLE_VIEW')
                - GET    /jobs/thinexecutions            => hasRole('ROLE_VIEW')

                # Batch Job Instances

                - GET    /jobs/instances                 => hasRole('ROLE_VIEW')
                - GET    /jobs/instances/*               => hasRole('ROLE_VIEW')

                # Running Applications

                - GET    /runtime/streams                => hasRole('ROLE_VIEW')
                - GET    /runtime/streams/**             => hasRole('ROLE_VIEW')
                - GET    /runtime/apps                   => hasRole('ROLE_VIEW')
                - GET    /runtime/apps/**                => hasRole('ROLE_VIEW')

                # Stream Definitions

                - GET    /streams/definitions            => hasRole('ROLE_VIEW')
                - GET    /streams/definitions/*          => hasRole('ROLE_VIEW')
                - GET    /streams/definitions/*/related  => hasRole('ROLE_VIEW')
                - POST   /streams/definitions            => hasRole('ROLE_CREATE')
                - DELETE /streams/definitions/*          => hasRole('ROLE_DESTROY')
                - DELETE /streams/definitions            => hasRole('ROLE_DESTROY')

                # Stream Deployments

                - DELETE /streams/deployments/*          => hasRole('ROLE_DEPLOY')
                - DELETE /streams/deployments            => hasRole('ROLE_DEPLOY')
                - POST   /streams/deployments/**         => hasRole('ROLE_MODIFY')
                - GET    /streams/deployments/**         => hasRole('ROLE_VIEW')

                # Stream Validations

                - GET /streams/validation/               => hasRole('ROLE_VIEW')
                - GET /streams/validation/*              => hasRole('ROLE_VIEW')

                # Stream Logs
                - GET /streams/logs/*                    => hasRole('ROLE_VIEW')

                # Task Definitions

                - POST   /tasks/definitions              => hasRole('ROLE_CREATE')
                - DELETE /tasks/definitions/*            => hasRole('ROLE_DESTROY')
                - GET    /tasks/definitions              => hasRole('ROLE_VIEW')
                - GET    /tasks/definitions/*            => hasRole('ROLE_VIEW')

                # Task Executions

                - GET    /tasks/executions               => hasRole('ROLE_VIEW')
                - GET    /tasks/executions/*             => hasRole('ROLE_VIEW')
                - POST   /tasks/executions               => hasRole('ROLE_DEPLOY')
                - POST   /tasks/executions/*             => hasRole('ROLE_DEPLOY')
                - DELETE /tasks/executions/*             => hasRole('ROLE_DESTROY')

                # Task Schedules

                - GET    /tasks/schedules                => hasRole('ROLE_VIEW')
                - GET    /tasks/schedules/*              => hasRole('ROLE_VIEW')
                - GET    /tasks/schedules/instances      => hasRole('ROLE_VIEW')
                - GET    /tasks/schedules/instances/*    => hasRole('ROLE_VIEW')
                - POST   /tasks/schedules                => hasRole('ROLE_SCHEDULE')
                - DELETE /tasks/schedules/*              => hasRole('ROLE_SCHEDULE')

                # Task Platform Account List */

                - GET    /tasks/platforms                => hasRole('ROLE_VIEW')

                # Task Validations

                - GET    /tasks/validation/               => hasRole('ROLE_VIEW')
                - GET    /tasks/validation/*              => hasRole('ROLE_VIEW')

                # Task Logs
                - GET /tasks/logs/*                       => hasRole('ROLE_VIEW')

                # Tools

                - POST   /tools/**                       => hasRole('ROLE_VIEW')

The format of each line is the following:

    HTTP_METHOD URL_PATTERN '=>' SECURITY_ATTRIBUTE

where:

- HTTP_METHOD is one HTTP method (such as PUT or GET), capital case.

- URL_PATTERN is an Ant-style URL pattern.

- SECURITY_ATTRIBUTE is a SpEL expression. See Expression-Based Access Control.

- Each of those is separated by one or whitespace characters (spaces, tabs, and so on).

Be mindful that the above is a YAML list, not a map (thus the use of '-' dashes at the start of each line) that lives under the `spring.cloud.dataflow.security.authorization.rules` key.

##### Authorization — Shell and Dashboard Behavior

When security is enabled, the dashboard and the shell are role-aware, meaning that, depending on the assigned roles, not all functionality may be visible.

For instance, shell commands for which the user does not have the necessary roles are marked as unavailable.

| Currently, the shell’s `help` command lists commands that are unavailable. Please track the following issue: github.com/spring-projects/spring-shell/issues/115

Conversely, for the Dashboard, the UI does not show pages or page elements for which the user is not authorized.

##### Securing the Spring Boot Management Endpoints

When security is enabled, the Spring Boot HTTP Management Endpoints are secured in the same way as the other REST endpoints. The management REST endpoints are available under `/management` and require the `MANAGEMENT` role.

The default configuration in `dataflow-server-defaults.yml` is as follows:

    management:
      endpoints:
        web:
          base-path: /management
      security:
        roles: MANAGE

| Currently, you should not customize the default management path.

#### 9.4. Setting up UAA Authentication

For local deployment scenarios, we recommend using the CloudFoundry User Account and Authentication (UAA) Server, which is OpenID certified. While the UAA is used by Cloud Foundry, it is also a fully featured stand alone OAuth2 server with enterprise features, such as LDAP integration.

##### Requirements

You need to check out, build and run UAA. To do so, make sure that you:

- Use Java 8.

- Have Git installed.

- Have the CloudFoundry UAA Command Line Client installed.

- Use a different host name for UAA when running on the same machine — for example, `uaa/`.

If you run into issues installing uaac, you may have to set the `GEM_HOME` environment variable:

    export GEM_HOME="$HOME/.gem"

You should also ensure that `~/.gem/gems/cf-uaac-4.2.0/bin` has been added to your path.

##### Prepare UAA for JWT

As the UAA is an OpenID provider and uses JSON Web Tokens (JWT), it needs to have a private key for signing those JWTs:

    openssl genrsa -out signingkey.pem 2048
    openssl rsa -in signingkey.pem -pubout -out verificationkey.pem
    export JWT_TOKEN_SIGNING_KEY=$(cat signingkey.pem)
    export JWT_TOKEN_VERIFICATION_KEY=$(cat verificationkey.pem)

Later, once the UAA is started, you can see the keys when you access `uaa:8080/uaa/token_keys`.

| Here, the `uaa` in the URL `uaa:8080/uaa/token_keys` is the hostname.

##### Download and Start UAA

To download and install UAA, run the following commands:

    git clone https://github.com/pivotal/uaa-bundled.git
    cd uaa-bundled
    ./mvnw clean install
    java -jar target/uaa-bundled-1.0.0.BUILD-SNAPSHOT.jar

The configuration of the UAA is driven by a YAML file `uaa.yml`, or you can script the configuration using the UAA Command Line Client:

    uaac target http://uaa:8080/uaa
    uaac token client get admin -s adminsecret
    uaac client add dataflow \
      --name dataflow \
      --secret dataflow \
      --scope cloud_controller.read,cloud_controller.write,openid,password.write,scim.userids,sample.create,sample.view,dataflow.create,dataflow.deploy,dataflow.destroy,dataflow.manage,dataflow.modify,dataflow.schedule,dataflow.view \
      --authorized_grant_types password,authorization_code,client_credentials,refresh_token \
      --authorities uaa.resource,dataflow.create,dataflow.deploy,dataflow.destroy,dataflow.manage,dataflow.modify,dataflow.schedule,dataflow.view,sample.view,sample.create\
      --redirect_uri http://localhost:9393/login \
      --autoapprove openid

    uaac group add "sample.view"
    uaac group add "sample.create"
    uaac group add "dataflow.view"
    uaac group add "dataflow.create"

    uaac user add springrocks -p mysecret --emails springrocks@someplace.com
    uaac user add vieweronly -p mysecret --emails mrviewer@someplace.com

    uaac member add "sample.view" springrocks
    uaac member add "sample.create" springrocks
    uaac member add "dataflow.view" springrocks
    uaac member add "dataflow.create" springrocks
    uaac member add "sample.view" vieweronly

The preceding script sets up the dataflow client as well as two users:

- User springrocks has have both scopes: `sample.view` and `sample.create`.

- User vieweronly has only one scope: `sample.view`.

Once added, you can quickly double-check that the UAA has the users created:

    curl -v -d"username=springrocks&password=mysecret&client_id=dataflow&grant_type=password" -u "dataflow:dataflow" http://uaa:8080/uaa/oauth/token -d 'token_format=opaque'

The preceding command should produce output similar to the following:

    *   Trying 127.0.0.1...
    * TCP_NODELAY set
    * Connected to uaa (127.0.0.1) port 8080 (#0)
    * Server auth using Basic with user 'dataflow'
    > POST /uaa/oauth/token HTTP/1.1
    > Host: uaa:8080
    > Authorization: Basic ZGF0YWZsb3c6ZGF0YWZsb3c=
    > User-Agent: curl/7.54.0
    > Accept: */*
    > Content-Length: 97
    > Content-Type: application/x-www-form-urlencoded
    >
    * upload completely sent off: 97 out of 97 bytes
    < HTTP/1.1 200
    < Cache-Control: no-store
    < Pragma: no-cache
    < X-XSS-Protection: 1; mode=block
    < X-Frame-Options: DENY
    < X-Content-Type-Options: nosniff
    < Content-Type: application/json;charset=UTF-8
    < Transfer-Encoding: chunked
    < Date: Thu, 31 Oct 2019 21:22:59 GMT
    <
    * Connection #0 to host uaa left intact
    {"access_token":"0329c8ecdf594ee78c271e022138be9d","token_type":"bearer","id_token":"eyJhbGciOiJSUzI1NiIsImprdSI6Imh0dHBzOi8vbG9jYWxob3N0OjgwODAvdWFhL3Rva2VuX2tleXMiLCJraWQiOiJsZWdhY3ktdG9rZW4ta2V5IiwidHlwIjoiSldUIn0. .bqYvicyCPB5cIIu_2HEe5_c7nSGXKw7B8-reTvyYjOQ2qXSMq7gzS4LCCQ-CMcb4IirlDaFlQtZJSDE-_UsM33-ThmtFdx--TujvTR1u2nzot4Pq5A_ThmhhcCB21x6-RNNAJl9X9uUcT3gKfKVs3gjE0tm2K1vZfOkiGhjseIbwht2vBx0MnHteJpVW6U0pyCWG_tpBjrNBSj9yLoQZcqrtxYrWvPHaa9ljxfvaIsOnCZBGT7I552O1VRHWMj1lwNmRNZy5koJFPF7SbhiTM8eLkZVNdR3GEiofpzLCfoQXrr52YbiqjkYT94t3wz5C6u1JtBtgc2vq60HmR45bvg","refresh_token":"6ee95d017ada408697f2d19b04f7aa6c-r","expires_in":43199,"scope":"scim.userids openid sample.create cloud_controller.read password.write cloud_controller.write sample.view","jti":"0329c8ecdf594ee78c271e022138be9d"}

By using the `token_format` parameter, you can request the token to be either:

- opaque

- jwt

## 10. Configuration - Local

### 10.1. Feature Toggles

Spring Cloud Data Flow Server offers specific set of features that can be enabled/disabled when launching. These features include all the lifecycle operations and REST endpoints (server and client implementations, including the shell and the UI) for:

- Streams (requires Skipper)

- Tasks

- Task Scheduler

One can enable and disable these features by setting the following boolean properties when launching the Data Flow server:

- `spring.cloud.dataflow.features.streams-enabled`

- `spring.cloud.dataflow.features.tasks-enabled`

- `spring.cloud.dataflow.features.schedules-enabled`

By default, stream (requires Skipper), and tasks are enabled and Task Scheduler is disabled by default.

The REST `/about` endpoint provides information on the features that have been enabled and disabled.

### 10.2. Database

A relational database is used to store stream and task definitions as well as the state of executed tasks. Spring Cloud Data Flow provides schemas for H2, MySQL, Oracle, PostgreSQL, Db2, and SQL Server. The schema is automatically created when the server starts.

By default, Spring Cloud Data Flow offers an embedded instance of the H2 database. The H2 database is good for development purposes but is not recommended for production use.

| H2 database is not supported as an external mode.

The JDBC drivers for MySQL (through the MariaDB driver), PostgreSQL, SQL Server, and embedded H2 are available without additional configuration. If you are using any other database, then you need to put the corresponding JDBC driver jar on the classpath of the server.

The database properties can be passed as environment variables or command-line arguments to the Data Flow Server.

#### 10.2.1. MySQL

The following example shows how to define a MySQL database connection using MariaDB driver.

    java -jar spring-cloud-dataflow-server/target/spring-cloud-dataflow-server-2.8.0-M1.jar \
        --spring.datasource.url=jdbc:mysql://localhost:3306/mydb \
        --spring.datasource.username= \
        --spring.datasource.password= \
        --spring.datasource.driver-class-name=org.mariadb.jdbc.Driver

MySQL versions up to 5.7 can be used with a MariaDB driver. Starting from version 8.0 MySQL’s own driver has to be used.

    java -jar spring-cloud-dataflow-server/target/spring-cloud-dataflow-server-2.8.0-M1.jar \
        --spring.datasource.url=jdbc:mysql://localhost:3306/mydb \
        --spring.datasource.username= \
        --spring.datasource.password= \
        --spring.datasource.driver-class-name=com.mysql.jdbc.Driver

| Due to licensing restrictions we’re unable to bundle MySQL driver. You need to add it to server’s classpath yourself.

#### 10.2.2. MariaDB

The following example shows how to define a MariaDB database connection with command Line arguments

    java -jar spring-cloud-dataflow-server/target/spring-cloud-dataflow-server-2.8.0-M1.jar \
        --spring.datasource.url=jdbc:mariadb://localhost:3306/mydb?useMysqlMetadata=true \
        --spring.datasource.username= \
        --spring.datasource.password= \
        --spring.datasource.driver-class-name=org.mariadb.jdbc.Driver

Starting with MariaDB v2.4.1 connector release, it is required to also add `useMysqlMetadata=true` to the JDBC URL. This is a required workaround until when MySQL and MariaDB entirely switch as two different databases.

MariaDB version 10.3 introduced a support for real database sequences which is yet another breaking change while toolings around these databases fully support MySQL and MariaDB as a separate database types. Workaround is to use older hibernate dialect which doesn’t try to use sequences.

    java -jar spring-cloud-dataflow-server/target/spring-cloud-dataflow-server-2.8.0-M1.jar \
        --spring.datasource.url=jdbc:mariadb://localhost:3306/mydb?useMysqlMetadata=true \
        --spring.datasource.username= \
        --spring.datasource.password= \
        --spring.jpa.properties.hibernate.dialect=org.hibernate.dialect.MariaDB102Dialect \
        --spring.datasource.driver-class-name=org.mariadb.jdbc.Driver

#### 10.2.3. PostgreSQL

The following example shows how to define a PostgreSQL database connection with command line arguments:

    java -jar spring-cloud-dataflow-server/target/spring-cloud-dataflow-server-2.8.0-M1.jar \
        --spring.datasource.url=jdbc:postgresql://localhost:5432/mydb \
        --spring.datasource.username= \
        --spring.datasource.password= \
        --spring.datasource.driver-class-name=org.postgresql.Driver

#### 10.2.4. SQL Server

The following example shows how to define a SQL Server database connection with command line arguments:

    java -jar spring-cloud-dataflow-server/target/spring-cloud-dataflow-server-2.8.0-M1.jar \
        --spring.datasource.url='jdbc:sqlserver://localhost:1433;databaseName=mydb' \
        --spring.datasource.username= \
        --spring.datasource.password= \
        --spring.datasource.driver-class-name=com.microsoft.sqlserver.jdbc.SQLServerDriver

#### 10.2.5. Db2

The following example shows how to define a Db2 database connection with command line arguments:

    java -jar spring-cloud-dataflow-server/target/spring-cloud-dataflow-server-2.8.0-M1.jar \
        --spring.datasource.url=jdbc:db2://localhost:50000/mydb \
        --spring.datasource.username= \
        --spring.datasource.password= \
        --spring.datasource.driver-class-name=com.ibm.db2.jcc.DB2Driver

| Due to licensing restrictions we’re unable to bundle Db2 driver. You need to add it to server’s classpath yourself.

#### 10.2.6. Oracle

The following example shows how to define a Oracle database connection with command line arguments:

    java -jar spring-cloud-dataflow-server/target/spring-cloud-dataflow-server-2.8.0-M1.jar \
        --spring.datasource.url=jdbc:oracle:thin:@localhost:1521/MYDB \
        --spring.datasource.username= \
        --spring.datasource.password= \
        --spring.datasource.driver-class-name=oracle.jdbc.OracleDriver

| Due to licensing restrictions we’re unable to bundle Oracle driver. You need to add it to server’s classpath yourself.

#### 10.2.7. Adding a Custom JDBC Driver

To add a custom driver for the database (for example, Oracle), you should rebuild the Data Flow Server and add the dependency to the Maven `pom.xml` file. You need to modify the maven `pom.xml` of `spring-cloud-dataflow-server` module. There are GA release tags in GitHub repository, so you can switch to desired GA tags to add the drivers on the production-ready codebase.

To add a custom JDBC driver dependency for the Spring Cloud Data Flow server:

1. Select the tag that corresponds to the version of the server you want to rebuild and clone the github repository.

2. Edit the spring-cloud-dataflow-server/pom.xml and, in the `dependencies` section, add the dependency for the database driver required. In the following example , an Oracle driver has been chosen:

    <dependencies>
    ...
      <dependency>
        <groupId>com.oracle.jdbc</groupId>
        <artifactId>ojdbc8</artifactId>
        <version>12.2.0.1</version>
      </dependency>
    ...
    </dependencies>

3. Build the application as described in Building Spring Cloud Data Flow

You can also provide default values when rebuilding the server by adding the necessary properties to the dataflow-server.yml file, as shown in the following example for PostgreSQL:

    spring:
      datasource:
        url: jdbc:postgresql://localhost:5432/mydb
        username: myuser
        password: mypass
        driver-class-name:org.postgresql.Driver

4. Alternatively, you can build a custom Spring Cloud Data Flow server with your build files. There are examples of a custom server builds in our samples repo if there is a need to add a driver jars.

#### 10.2.8. Schema Handling

On default database schema is managed with Flyway which is convenient if it’s possible to give enough permissions to a database user.

Here’s a description what happens when Skipper server is started:

- Flyway checks if `flyway_schema_history` table exists.

- Does a baseline(to version 1) if schema is not empty as Dataflow tables may be in place if a shared DB is used.

- If schema is empty, flyway assumes to start from a scratch.

- Goes through all needed schema migrations.

Here’s a description what happens when Dataflow server is started:

- Flyway checks if `flyway_schema_history_dataflow` table exists.

- Does a baseline(to version 1) if schema is not empty as Skipper tables may be in place if a shared DB is used.

- If schema is empty, flyway assumes to start from a scratch.

- Goes through all needed schema migrations.

- Due to historical reasons, if we detect that schema is from 1.7.x line we convert these to structures needed from 2.0.x onwards and fully continue with flyway.

| We have schema ddl’s in our source code schemas which can be used manually if Flyway is disabled by using configuration `spring.flyway.enabled=false`. This is a good option if company’s databases are restricted and i.e. applications itself cannot create schemas.

### 10.3. Deployer Properties

You can use the following configuration properties of the Local deployer to customize how Streams and Tasks are deployed. When deploying using the Data Flow shell, you can use the syntax `deployer.<appName>.local.<deployerPropertyName>`. See below for an example shell usage. These properties are also used when configuring Local Task Platforms in the Data Flow server and local platforms in Skipper for deploying Streams.

Deployer Property Name | Description | Default Value
--- | --- | ---
workingDirectoriesRoot | Directory in which all created processes will run and create log files. | java.io.tmpdir
envVarsToInherit | Array of regular expression patterns for environment variables that are passed to launched applications. | <"TMP", "LANG", "LANGUAGE", "LC_.*", "PATH", "SPRING_APPLICATION_JSON"> on windows and <"TMP", "LANG", "LANGUAGE", "LC_.*", "PATH"> on Unix
deleteFilesOnExit | Whether to delete created files and directories on JVM exit. | true
javaCmd | Command to run java | java
shutdownTimeout | Max number of seconds to wait for app shutdown. | 30
javaOpts | The Java Options to pass to the JVM, e.g -Dtest=foo | <none>
inheritLogging | allow logging to be redirected to the output stream of the process that triggered child process. | false
debugPort | Port for remote debugging | <none>

As an example, to set Java options for the time application in the `ticktock` stream, use the following stream deployment properties.

    dataflow:> stream create --name ticktock --definition "time --server.port=9000 | log"
    dataflow:> stream deploy --name ticktock --properties "deployer.time.local.javaOpts=-Xmx2048m -Dtest=foo"

As a convenience, you can set the `deployer.memory` property to set the Java option `-Xmx`, as shown in the following example:

    dataflow:> stream deploy --name ticktock --properties "deployer.time.memory=2048m"

At deployment time, if you specify an `-Xmx` option in the `deployer.<app>.local.javaOpts` property in addition to a value of the `deployer.<app>.local.memory` option, the value in the `javaOpts` property has precedence. Also, the `javaOpts` property set when deploying the application has precedence over the Data Flow Server’s `spring.cloud.deployer.local.javaOpts` property.

### 10.4. Logging

Spring Cloud Data Flow `local` server is automatically configured to use `RollingFileAppender` for logging. The logging configuration is located on the classpath contained in a file named `logback-spring.xml`.

By default, the log file is configured to use:

    <property name="LOG_FILE" value="${LOG_FILE:-${LOG_PATH:-${LOG_TEMP:-${java.io.tmpdir:-/tmp}}}/spring-cloud-dataflow-server}"/>

with the logback configuration for the `RollingPolicy`:

    <appender name="FILE"
    			  class="ch.qos.logback.core.rolling.RollingFileAppender">
    		<file>${LOG_FILE}.log</file>
    		<rollingPolicy
    				class="ch.qos.logback.core.rolling.SizeAndTimeBasedRollingPolicy">
    			<!-- daily rolling -->
    			<fileNamePattern>${LOG_FILE}.${LOG_FILE_ROLLING_FILE_NAME_PATTERN:-%d{yyyy-MM-dd}}.%i.gz</fileNamePattern>
    			<maxFileSize>${LOG_FILE_MAX_SIZE:-100MB}</maxFileSize>
    			<maxHistory>${LOG_FILE_MAX_HISTORY:-30}</maxHistory>
    			<totalSizeCap>${LOG_FILE_TOTAL_SIZE_CAP:-500MB}</totalSizeCap>
    		</rollingPolicy>
    		<encoder>
    			<pattern>${FILE_LOG_PATTERN}</pattern>
    		</encoder>
    	</appender>

To check the `java.io.tmpdir` for the current Spring Cloud Data Flow Server `local` server,

    jinfo <pid> | grep "java.io.tmpdir"

If you want to change or override any of the properties `LOG_FILE`, `LOG_PATH`, `LOG_TEMP`, `LOG_FILE_MAX_SIZE`, `LOG_FILE_MAX_HISTORY` and `LOG_FILE_TOTAL_SIZE_CAP`, please set them as system properties.

### 10.5. Streams

Data Flow Server delegates to the Skipper server the management of the Stream’s lifecycle. Set the configuration property `spring.cloud.skipper.client.serverUri` to the location of Skipper, e.g.

    $ java -jar spring-cloud-dataflow-server-2.8.0-M1.jar --spring.cloud.skipper.client.serverUri=https://192.51.100.1:7577/api

The configuration of show streams are deployed and to which platforms, is done by configuration of `platform accounts` on the Skipper server. See the documentation on platforms for more information.

### 10.6. Tasks

The Data Flow server is responsible for deploying Tasks. Tasks that are launched by Data Flow write their state to the same database that is used by the Data Flow server. For Tasks which are Spring Batch Jobs, the job and step execution data is also stored in this database. As with streams launched by Skipper, Tasks can be launched to multiple platforms. If no platform is defined, a platform named `default` is created using the default values of the class LocalDeployerProperties, which is summarized in the table Local Deployer Properties

To configure new platform accounts for the local platform, provide an entry under the `spring.cloud.dataflow.task.platform.local` section in your `application.yaml` file for via another Spring Boot supported mechanism. In the following example, two local platform accounts named `localDev` and `localDevDebug` are created. The keys such as `shutdownTimeout` and `javaOpts` are local deployer properties.

    spring:
      cloud:
        dataflow:
          task:
            platform:
              local:
                accounts:
                  localDev:
                    shutdownTimeout: 60
                    javaOpts: "-Dtest=foo -Xmx1024m"
                  localDevDebug:
                    javaOpts: "-Xdebug -Xmx2048m"

| By defining one platform as `default` allows you to skip using `platformName` where its use would otherwise be required.

When launching a task, pass the value of the platform account name using the task launch option `--platformName` If you do not pass a value for `platformName`, the value `default` will be used.

| When deploying a task to multiple platforms, the configuration of the task needs to connect to the same database as the Data Flow Server.

You can configure the Data Flow server that is running locally to deploy tasks to Cloud Foundry or Kubernetes. See the sections on Cloud Foundry Task Platform Configuration and Kubernetes Task Platform Configuration for more information.

Detailed examples for launching and scheduling tasks across multiple platforms, are available in this section Multiple Platform Support for Tasks on dataflow.spring.io.

##### Start Skipper

    git clone https://github.com/spring-cloud/spring-cloud-skipper.git
    cd spring-cloud/spring-cloud-skipper
    ./mvnw clean package -DskipTests=true
    java -jar spring-cloud-skipper-server/target/spring-cloud-skipper-server-2.2.0.BUILD-SNAPSHOT.jar

##### Start Spring Cloud Data Flow

    git clone https://github.com/spring-cloud/spring-cloud-dataflow.git
    cd spring-cloud-dataflow
    ./mvnw clean package -DskipTests=true
    cd ..

Create a yaml file scdf.yml with the following contents:

    spring:
      cloud:
        dataflow:
          security:
            authorization:
              provider-role-mappings:
                uaa:
                  map-oauth-scopes: true
                  role-mappings:
                    ROLE_CREATE: foo.create
                    ROLE_DEPLOY: foo.create
                    ROLE_DESTROY: foo.create
                    ROLE_MANAGE: foo.create
                    ROLE_MODIFY: foo.create
                    ROLE_SCHEDULE: foo.create
                    ROLE_VIEW: foo.view
      security:
        oauth2:
          client:
            registration:
              uaa:
                redirect-uri: '{baseUrl}/login/oauth2/code/{registrationId}'
                authorization-grant-type: authorization_code
                client-id: dataflow
                client-secret: dataflow
                scope:                                                       (1)
                - openid
                - foo.create
                - foo.view
            provider:
              uaa:
                jwk-set-uri: http://uaa:8080/uaa/token_keys
                token-uri: http://uaa:8080/uaa/oauth/token
                user-info-uri: http://uaa:8080/uaa/userinfo                  (2)
                user-name-attribute: user_name
                authorization-uri: http://uaa:8080/uaa/oauth/authorize
          resourceserver:
            opaquetoken:                                                     (3)
              introspection-uri: http://uaa:8080/uaa/introspect
              client-id: dataflow
              client-secret: dataflow

1 | If you use scopes to identify roles, please make sure to also request the relevant scopes, e.g `dataflow.view`, `dataflow.create` and don’t forget to request the `openid` scope
2 | Used to retrieve profile information, e.g. username for display purposes (mandatory)
3 | Used for token introspection and validation (mandatory)

The `introspection-uri` property is especially important when passing an externally retrieved (opaque) OAuth Access Token to Spring Cloud Data Flow. In that case Spring Cloud Data Flow will take the OAuth Access, and use the UAA’s Introspect Token Endpoint to not only check the validity of the token but also retrieve the associated OAuth scopes from the UAA

Finally startup Spring Cloud Data Flow:

    java -jar spring-cloud-dataflow/spring-cloud-dataflow-server/target/spring-cloud-dataflow-server-2.4.0.BUILD-SNAPSHOT.jar --spring.config.additional-location=scdf.yml

##### Role Mappings

By default all roles are assigned to users that login to Spring Cloud Data Flow. However, you can set the property:

`spring.cloud.dataflow.security.authorization.provider-role-mappings.uaa.map-oauth-scopes: true`

This will instruct the underlying `DefaultAuthoritiesExtractor` to map OAuth scopes to the respective authorities. The following scopes are supported:

- Scope `dataflow.create` maps to the `CREATE` role

- Scope `dataflow.deploy` maps to the `DEPLOY` role

- Scope `dataflow.destroy` maps to the `DESTROY` role

- Scope `dataflow.manage` maps to the `MANAGE` role

- Scope `dataflow.modify` maps to the `MODIFY` role

- Scope `dataflow.schedule` maps to the `SCHEDULE` role

- Scope `dataflow.view` maps to the `VIEW` role

Additionally you can also map arbitrary scopes to each of the Data Flow roles:

    spring:
      cloud:
        dataflow:
          security:
            authorization:
              provider-role-mappings:
                uaa:
                  map-oauth-scopes: true                                    (1)
                  role-mappings:
                    ROLE_CREATE: dataflow.create                            (2)
                    ROLE_DEPLOY: dataflow.deploy
                    ROLE_DESTROY: dataflow.destoy
                    ROLE_MANAGE: dataflow.manage
                    ROLE_MODIFY: dataflow.modify
                    ROLE_SCHEDULE: dataflow.schedule
                    ROLE_VIEW: dataflow.view

1 | Enables explicit mapping support from OAuth scopes to Data Flow roles
2 | When role mapping support is enabled, you must provide a mapping for all 7 Spring Cloud Data Flow roles ROLE_CREATE, ROLE_DEPLOY, ROLE_DESTROY, ROLE_MANAGE, ROLE_MODIFY, ROLE_SCHEDULE, ROLE_VIEW.

| You can assign an OAuth scope to multiple Spring Cloud Data Flow roles, giving you flexible regarding the granularity of your authorization configuration.

#### 10.6.4. LDAP Authentication

LDAP Authentication (Lightweight Directory Access Protocol) is indirectly provided by Spring Cloud Data Flow using the UAA. The UAA itself provides comprehensive LDAP support.

| While you may use your own OAuth2 authentication server, the LDAP support documented here requires using the UAA as authentication server. For any other provider, please consult the documentation for that particular provider.

The UAA supports authentication against an LDAP (Lightweight Directory Access Protocol) server using the following modes:

- Direct bind

- Search and bind

- Search and Compare

| When integrating with an external identity provider such as LDAP, authentication within the UAA becomes chained. UAA first attempts to authenticate with a user’s credentials against the UAA user store before the external provider, LDAP. For more information, see Chained Authentication in the User Account and Authentication LDAP Integration GitHub documentation.

##### LDAP Role Mapping

The OAuth2 authentication server (UAA), provides comprehensive support for mapping LDAP groups to OAuth scopes.

The following options exist:

- `ldap/ldap-groups-null.xml` No groups will be mapped

- `ldap/ldap-groups-as-scopes.xml` Group names will be retrieved from an LDAP attribute. E.g. `CN`

- `ldap/ldap-groups-map-to-scopes.xml` Groups will be mapped to UAA groups using the external_group_mapping table

These values are specified via the configuration property `ldap.groups.file controls`. Under the covers these values reference a Spring XML configuration file.

| During test and development it might be necessary to make frequent changes to LDAP groups and users and see those reflected in the UAA. However, user information is cached for the duration of the login. The following script helps to retrieve the updated information quickly:
    #!/bin/bash
    uaac token delete --all
    uaac target http://localhost:8080/uaa
    uaac token owner get cf <username> -s "" -p  <password>
    uaac token client get admin -s adminsecret
    uaac user get <username>

##### LDAP Security and UAA Example Application

In order to get up and running quickly and to help you understand the security architecture, we provide the LDAP Security and UAA Example on GitHub.

| This is solely a demo/example application and shall not be used in production.

The setup consists of:

- Spring Cloud Data Flow Server

- Skipper Server

- CloudFoundry User Account and Authentication (UAA) Server

- Lightweight Directory Access Protocol (LDAP) Server (provided by Apache Directory Server (ApacheDS))

Ultimately, as part of this example, you will learn how to configure and launch a Composed Task using this security setup.

#### 10.6.5. Spring Security OAuth2 Resource/Authorization Server Sample

For local testing and development, you may also use the Resource and Authorization Server support provided by Spring Security OAuth. It allows you to easily create your own (very basic) OAuth2 Server with the following simple annotations:

- `@EnableResourceServer`

- `@EnableAuthorizationServer`

| In fact the UAA uses Spring Security OAuth2 under the covers, thus the basic endpoints are the same.

A working example application can be found at: https://github.com/ghillert/oauth-test-server/

Clone the project and configure Spring Cloud Data Flow with the respective Client ID and Client Secret:

    security:
      oauth2:
        client:
          client-id: myclient
          client-secret: mysecret
          access-token-uri: http://127.0.0.1:9999/oauth/token
          user-authorization-uri: http://127.0.0.1:9999/oauth/authorize
        resource:
          user-info-uri: http://127.0.0.1:9999/me
          token-info-uri: http://127.0.0.1:9999/oauth/check_token

| This sample application is not intended for production use

#### 10.6.6. Data Flow Shell Authentication

When using the Shell, the credentials can either be provided via username and password or by specifying a credentials-provider command. If your OAuth2 provider supports the Password Grant Type you can start the Data Flow Shell with:

    $ java -jar spring-cloud-dataflow-shell-2.8.0-M1.jar         \
      --dataflow.uri=http://localhost:9393                                \   (1)
      --dataflow.username=my_username                                     \   (2)
      --dataflow.password=my_password                                     \   (3)
      --skip-ssl-validation  true                                         \   (4)

1 | Optional, defaults to localhost:9393.
2 | Mandatory.
3 | If the password is not provided, the user is prompted for it.
4 | Optional, defaults to `false`, ignores certificate errors (when using self-signed certificates). Use cautiously!

| Keep in mind that when authentication for Spring Cloud Data Flow is enabled, the underlying OAuth2 provider must support the Password OAuth2 Grant Type if you want to use the Shell via username/password authentication.

From within the Data Flow Shell you can also provide credentials by using the following command:

    server-unknown:>dataflow config server                                \
      --uri  http://localhost:9393                                        \   (1)
      --username myuser                                                   \   (2)
      --password mysecret                                                 \   (3)
      --skip-ssl-validation  true                                         \   (4)

1 | Optional, defaults to localhost:9393.
2 | Mandatory..
3 | If security is enabled, and the password is not provided, the user is prompted for it.
4 | Optional, ignores certificate errors (when using self-signed certificates). Use cautiously!

The following image shows a typical shell command to connect to and authenticate a Data Flow Server:

Figure 1. Target and Authenticate with the Data Flow Server from within the Shell

Once successfully targeted, you should see the following output:

    dataflow:>dataflow config info
    dataflow config info

    ╔═══════════╤═══════════════════════════════════════╗
    ║Credentials│[username='my_username, password=****']║
    ╠═══════════╪═══════════════════════════════════════╣
    ║Result     │                                       ║
    ║Target     │http://localhost:9393                  ║
    ╚═══════════╧═══════════════════════════════════════╝

Alternatively, you can specify the credentials-provider command in order to pass-in a bearer token directly, instead of providing a username and password. This works from within the shell or by providing the `--dataflow.credentials-provider-command` command-line argument when starting the Shell.

| When using the credentials-provider command, please be aware that your specified command must return a Bearer token (Access Token prefixed with Bearer). For instance, in Unix environments the following simplistic command can be used:
    $ java -jar spring-cloud-dataflow-shell-2.8.0-M1.jar \
      --dataflow.uri=http://localhost:9393 \
      --dataflow.credentials-provider-command="echo Bearer 123456789"

### 10.7. About Configuration

The Spring Cloud Data Flow About Restful API result contains a display name, version, and, if specified, a URL for each of the major dependencies that comprise Spring Cloud Data Flow. The result (if enabled) also contains the sha1 and or sha256 checksum values for the shell dependency. The information that is returned for each of the dependencies is configurable by setting the following properties:

- spring.cloud.dataflow.version-info.spring-cloud-dataflow-core.name: the name to be used for the core.

- spring.cloud.dataflow.version-info.spring-cloud-dataflow-core.version: the version to be used for the core.

- spring.cloud.dataflow.version-info.spring-cloud-dataflow-dashboard.name: the name to be used for the dashboard.

- spring.cloud.dataflow.version-info.spring-cloud-dataflow-dashboard.version: the version to be used for the dashboard.

- spring.cloud.dataflow.version-info.spring-cloud-dataflow-implementation.name: the name to be used for the implementation.

- spring.cloud.dataflow.version-info.spring-cloud-dataflow-implementation.version: the version to be used for the implementation.

- spring.cloud.dataflow.version-info.spring-cloud-dataflow-shell.name: the name to be used for the shell.

- spring.cloud.dataflow.version-info.spring-cloud-dataflow-shell.version: the version to be used for the shell.

- spring.cloud.dataflow.version-info.spring-cloud-dataflow-shell.url: the URL to be used for downloading the shell dependency.

- spring.cloud.dataflow.version-info.spring-cloud-dataflow-shell.checksum-sha1: the sha1 checksum value that is returned with the shell dependency info.

- spring.cloud.dataflow.version-info.spring-cloud-dataflow-shell.checksum-sha256: the sha256 checksum value that is returned with the shell dependency info.

- spring.cloud.dataflow.version-info.spring-cloud-dataflow-shell.checksum-sha1-url: if the `spring.cloud.dataflow.version-info.spring-cloud-dataflow-shell.checksum-sha1` is not specified, SCDF uses the contents of the file specified at this URL for the checksum.

- spring.cloud.dataflow.version-info.spring-cloud-dataflow-shell.checksum-sha256-url: if the `spring.cloud.dataflow.version-info.spring-cloud-dataflow-shell.checksum-sha256` is not specified, SCDF uses the contents of the file specified at this URL for the checksum.

#### 10.7.1. Enabling Shell Checksum values

By default, checksum values are not displayed for the shell dependency. If you need this feature enabled, set the `spring.cloud.dataflow.version-info.dependency-fetch.enabled` property to true.

#### 10.7.2. Reserved Values for URLs

There are reserved values (surrounded by curly braces) that you can insert into the URL that will make sure that the links are up to date:

- repository: if using a build-snapshot, milestone, or release candidate of Data Flow, the repository refers to the repo-spring-io repository. Otherwise, it refers to Maven Central.

- version: Inserts the version of the jar/pom.

For example, `myrepository/org/springframework/cloud/spring-cloud-dataflow-shell/{version}/spring-cloud-dataflow-shell-{version}.jar` produces `myrepository/org/springframework/cloud/spring-cloud-dataflow-shell/1.2.3.RELEASE/spring-cloud-dataflow-shell-1.2.3.RELEASE.jar` if you were using the 1.2.3.RELEASE version of the Spring Cloud Data Flow Shell

## 11. Configuration - Cloud Foundry

This section describes how to configure Spring Cloud Data Flow server’s features, such as security and which relational database to use. It also describes how to configure Spring Cloud Data Flow shell’s features.

### 11.1. Feature Toggles

Data Flow server offers a specific set of features that you can enable or disable when launching. These features include all the lifecycle operations and REST endpoints (server, client implementations including Shell and the UI) for:

- Streams

- Tasks

You can enable or disable these features by setting the following boolean properties when you launch the Data Flow server:

- `spring.cloud.dataflow.features.streams-enabled`

- `spring.cloud.dataflow.features.tasks-enabled`

By default, all features are enabled.

The REST endpoint (`/features`) provides information on the enabled and disabled features.

### 11.2. Deployer Properties

You can use the following configuration properties of the Data Flow server’s Cloud Foundry deployer to customize how applications are deployed. When deploying with the Data Flow shell, you can use the syntax `deployer.<appName>.cloudfoundry.<deployerPropertyName>`. See below for an example shell usage. These properties are also used when configuring the Cloud Foundry Task platforms in the Data Flow server and and Kubernetes platforms in Skipper for deploying Streams.

Deployer Property Name | Description | Default Value
--- | --- | ---
services | The names of services to bind to the deployed application. | <none>
host | The host name to use as part of the route. | hostname derived by Cloud Foundry
domain | The domain to use when mapping routes for the application. | <none>
routes | The list of routes that the application should be bound to. Mutually exclusive with host and domain. | <none>
buildpack | The buildpack to use for deploying the application. Deprecated use buildpacks. | github.com/cloudfoundry/java-buildpack.git#v4.29.1
buildpacks | The list of buildpacks to use for deploying the application. | github.com/cloudfoundry/java-buildpack.git#v4.29.1
memory | The amount of memory to allocate. Default unit is mebibytes, 'M' and 'G" suffixes supported | 1024m
disk | The amount of disk space to allocate. Default unit is mebibytes, 'M' and 'G" suffixes supported. | 1024m
healthCheck | The type of health check to perform on deployed application. Values can be HTTP, NONE, PROCESS, and PORT | PORT
healthCheckHttpEndpoint | The path that the http health check will use, | /health
healthCheckTimeout | The timeout value for health checks in seconds. | 120
instances | The number of instances to run. | 1
enableRandomAppNamePrefix | Flag to enable prefixing the app name with a random prefix. | true
apiTimeout | Timeout for blocking API calls, in seconds. | 360
statusTimeout | Timeout for status API operations in milliseconds | 5000
useSpringApplicationJson | Flag to indicate whether application properties are fed into `SPRING_APPLICATION_JSON` or as separate environment variables. | true
stagingTimeout | Timeout allocated for staging the application. | 15 minutes
startupTimeout | Timeout allocated for starting the application. | 5 minutes
appNamePrefix | String to use as prefix for name of deployed application | The Spring Boot property `spring.application.name` of the application that is using the deployer library.
deleteRoutes | Whether to also delete routes when un-deploying an application. | true
javaOpts | The Java Options to pass to the JVM, e.g -Dtest=foo | <none>
pushTasksEnabled | Whether to push task applications or assume that the application already exists when launched. | true
autoDeleteMavenArtifacts | Whether to automatically delete Maven artifacts from the local repository when deployed. | true
env.<key> | Defines a top level environment variable. This is useful for customizing Java build pack configuration which must be included as top level environment variables in the application manifest, as the Java build pack does not recognize `SPRING_APPLICATION_JSON`. | The deployer determines if the app has Java CfEnv in its classpath. If so, it applies the required configuration.

Here are some examples using the Cloud Foundry deployment properties:

- You can set the buildpack that is used to deploy each application. For example, to use the Java offline buildback, set the following environment variable:

    cf set-env dataflow-server SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_DEPLOYMENT_BUILDPACKS java_buildpack_offline

- Setting `buildpack` is now deprecated in favour of `buildpacks` which allows you to pass on more than one if needed. More about this can be found from How Buildpacks Work.

- You can customize the health check mechanism used by Cloud Foundry to assert whether apps are running by using the `SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_DEPLOYMENT_HEALTH_CHECK` environment variable. The current supported options are `http` (the default), `port`, and `none`.

You can also set environment variables that specify the HTTP-based health check endpoint and timeout: `SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_DEPLOYMENT_HEALTH_CHECK_ENDPOINT` and `SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_DEPLOYMENT_HEALTH_CHECK_TIMEOUT`, respectively. These default to `/health` (the Spring Boot default location) and `120` seconds.

- You can also specify deployment properties by using the DSL. For instance, if you want to set the allocated memory for the `http` application to 512m and also bind a mysql service to the `jdbc` application, you can run the following commands:

    dataflow:> stream create --name mysqlstream --definition "http | jdbc --tableName=names --columns=name"
    dataflow:> stream deploy --name mysqlstream --properties "deployer.http.memory=512, deployer.jdbc.cloudfoundry.services=mysql"

| You can configure these settings separately for stream and task apps. To alter settings for tasks, substitute `TASK` for `STREAM` in the property name, as the following example shows:
    cf set-env dataflow-server SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_DEPLOYMENT_MEMORY 512

### 11.3. Tasks

The Data Flow server is responsible for deploying Tasks. Tasks that are launched by Data Flow write their state to the same database that is used by the Data Flow server. For Tasks which are Spring Batch Jobs, the job and step execution data is also stored in this database. As with Skipper, Tasks can be launched to multiple platforms. When Data Flow is running on Cloud Foundry, a Task platfom must be defined. To configure new platform accounts that target Cloud Foundry, provide an entry under the `spring.cloud.dataflow.task.platform.cloudfoundry` section in your `application.yaml` file for via another Spring Boot supported mechanism. In the following example, two Cloud Foundry platform accounts named `dev` and `qa` are created. The keys such as `memory` and `disk` are Cloud Foundry Deployer Properties.

    spring:
      cloud:
        dataflow:
          task:
            platform:
              cloudfoundry:
                accounts:
                  dev:
                    connection:
                      url: https://api.run.pivotal.io
                      org: myOrg
                      space: mySpace
                      domain: cfapps.io
                      username: user@example.com
                      password: drowssap
                      skipSslValidation: false
                    deployment:
                      memory: 512m
                      disk: 2048m
                      instances: 4
                      services: rabbit,mysql
                      appNamePrefix: dev1
                  qa:
                    connection:
                      url: https://api.run.pivotal.io
                      org: myOrgQA
                      space: mySpaceQA
                      domain: cfapps.io
                      username: user@example.com
                      password: drowssap
                      skipSslValidation: true
                    deployment:
                      memory: 756m
                      disk: 724m
                      instances: 2
                      services: rabbitQA,mysqlQA
                      appNamePrefix: qa1

| By defining one platform as `default` allows you to skip using `platformName` where its use would otherwise be required.

When launching a task, pass the value of the platform account name using the task launch option `--platformName` If you do not pass a value for `platformName`, the value `default` will be used.

| When deploying a task to multiple platforms, the configuration of the task needs to connect to the same database as the Data Flow Server.

You can configure the Data Flow server that is on Cloud Foundry to deploy tasks to Cloud Foundry or Kubernetes. See the section on Kubernetes Task Platform Configuration for more information.

Detailed examples for launching and scheduling tasks across multiple platforms, are available in this section Multiple Platform Support for Tasks on dataflow.spring.io.

### 11.4. Application Names and Prefixes

To help avoid clashes with routes across spaces in Cloud Foundry, a naming strategy that provides a random prefix to a deployed application is available and is enabled by default. You can override the default configurations and set the respective properties by using `cf set-env` commands.

For instance, if you want to disable the randomization, you can override it by using the following command:

    cf set-env dataflow-server SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_DEPLOYMENT_ENABLE_RANDOM_APP_NAME_PREFIX false

### 11.5. Custom Routes

As an alternative to a random name or to get even more control over the hostname used by the deployed apps, you can use custom deployment properties, as the following example shows:

    dataflow:>stream create foo --definition "http | log"

    sdataflow:>stream deploy foo --properties "deployer.http.cloudfoundry.domain=mydomain.com,
                                              deployer.http.cloudfoundry.host=myhost,
                                              deployer.http.cloudfoundry.route-path=my-path"

The preceding example binds the `http` app to the `myhost.mydomain.com/my-path` URL. Note that this example shows all of the available customization options. In practice, you can use only one or two out of the three.

### 11.6. Docker Applications

Starting with version 1.2, it is possible to register and deploy Docker based apps as part of streams and tasks by using Data Flow for Cloud Foundry.

If you use Spring Boot and RabbitMQ-based Docker images, you can provide a common deployment property to facilitate binding the apps to the RabbitMQ service. Assuming your RabbitMQ service is named `rabbit`, you can provide the following:

    cf set-env dataflow-server SPRING_APPLICATION_JSON '{"spring.cloud.dataflow.applicationProperties.stream.spring.rabbitmq.addresses": "${vcap.services.rabbit.credentials.protocols.amqp.uris}"}'

For Spring Cloud Task apps, you can use something similar to the following, if you use a database service instance named `mysql`:

    cf set-env SPRING_DATASOURCE_URL '${vcap.services.mysql.credentials.jdbcUrl}'
    cf set-env SPRING_DATASOURCE_USERNAME '${vcap.services.mysql.credentials.username}'
    cf set-env SPRING_DATASOURCE_PASSWORD '${vcap.services.mysql.credentials.password}'
    cf set-env SPRING_DATASOURCE_DRIVER_CLASS_NAME 'org.mariadb.jdbc.Driver'

For non-Java or non-Boot applications, your Docker app must parse the `VCAP_SERVICES` variable in order to bind to any available services.

| Passing application properties When using non-Boot applications, chances are that you want to pass the application properties by using traditional environment variables, as opposed to using the special `SPRING_APPLICATION_JSON` variable. To do so, set the following variables for streams and tasks, respectively:
    SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_DEPLOYMENT_USE_SPRING_APPLICATION_JSON=false

### 11.7. Application-level Service Bindings

When deploying streams in Cloud Foundry, you can take advantage of application-specific service bindings, so not all services are globally configured for all the apps orchestrated by Spring Cloud Data Flow.

For instance, if you want to provide a `mysql` service binding only for the `jdbc` application in the following stream definition, you can pass the service binding as a deployment property:

    dataflow:>stream create --name httptojdbc --definition "http | jdbc"
    dataflow:>stream deploy --name httptojdbc --properties "deployer.jdbc.cloudfoundry.services=mysqlService"

where `mysqlService` is the name of the service specifically bound only to the `jdbc` application and the `http` application does not get the binding by this method.

If you have more than one service to bind, they can be passed as comma-separated items (for example: `deployer.jdbc.cloudfoundry.services=mysqlService,someService`).

### 11.8. Configuring Service binding parameters

The CloudFoundry API supports providing configuration parameters when binding a service instance. Some service brokers require or recommend binding configuration. For example, binding the Google Cloud Platform service using the CF CLI looks something like:

    cf bind-service my-app my-google-bigquery-example -c '{"role":"bigquery.user"}'

Likewise the NFS Volume Service supports binding configuration such as:

    cf bind-service my-app nfs_service_instance -c '{"uid":"1000","gid":"1000","mount":"/var/volume1","readonly":true}'

Starting with version 2.0, Data Flow for Cloud Foundry allows you to provide binding configuration parameters may be provided in the app level or server level `cloudfoundry.services` deployment property. For example, to bind to the nfs service, as above :

    dataflow:> stream deploy --name mystream --properties "deployer.<app>.cloudfoundry.services='nfs_service_instance uid:1000,gid:1000,mount:/var/volume1,readonly:true'"

The format is intended to be compatible with the Data Flow DSL parser. Generally, the `cloudfoundry.services` deployment property accepts a comma delimited value. Since a comma is also used to separate configuration parameters, and to avoid white space issues, any item including configuration parameters must be enclosed in singe quotes. Valid values incude things like:

    rabbitmq,'nfs_service_instance uid:1000,gid:1000,mount:/var/volume1,readonly:true',mysql,'my-google-bigquery-example role:bigquery.user'

| Spaces are permitted within single quotes and `=` may be used instead of `:` to delimit key-value pairs.

### 11.9. User-provided Services

In addition to marketplace services, Cloud Foundry supports User-provided Services (UPS). Throughout this reference manual, regular services have been mentioned, but there is nothing precluding the use of User-provided Services as well, whether for use as the messaging middleware (for example, if you want to use an external Apache Kafka installation) or for use by some of the stream applications (for example, an Oracle Database).

Now we review an example of extracting and supplying the connection credentials from a UPS.

The following example shows a sample UPS setup for Apache Kafka:

    cf create-user-provided-service kafkacups -p '{”brokers":"HOST:PORT","zkNodes":"HOST:PORT"}'

The UPS credentials are wrapped within `VCAP_SERVICES`, and they can be supplied directly in the stream definition, as the following example shows.

    stream create fooz --definition "time | log"
    stream deploy fooz --properties "app.time.spring.cloud.stream.kafka.binder.brokers=${vcap.services.kafkacups.credentials.brokers},app.time.spring.cloud.stream.kafka.binder.zkNodes=${vcap.services.kafkacups.credentials.zkNodes},app.log.spring.cloud.stream.kafka.binder.brokers=${vcap.services.kafkacups.credentials.brokers},app.log.spring.cloud.stream.kafka.binder.zkNodes=${vcap.services.kafkacups.credentials.zkNodes}"

### 11.10. Database Connection Pool

As of Data Flow 2.0, the Spring Cloud Connector library is no longer used to create the DataSource. The library java-cfenv is now used which allows you to set Spring Boot properties to configure the connection pool.

### 11.11. Maximum Disk Quota

By default, every application in Cloud Foundry starts with 1G disk quota and this can be adjusted to a default maximum of 2G. The default maximum can also be overridden up to 10G by using Pivotal Cloud Foundry’s (PCF) Ops Manager GUI.

This configuration is relevant for Spring Cloud Data Flow because every task deployment is composed of applications (typically Spring Boot uber-jar’s), and those applications are resolved from a remote maven repository. After resolution, the application artifacts are downloaded to the local Maven Repository for caching and reuse. With this happening in the background, the default disk quota (1G) can fill up rapidly, especially when we experiment with streams that are made up of unique applications. In order to overcome this disk limitation and depending on your scaling requirements, you may want to change the default maximum from 2G to 10G. Let’s review the steps to change the default maximum disk quota allocation.

#### 11.11.1. PCF’s Operations Manager

From PCF’s Ops Manager, select the “Pivotal Elastic Runtime” tile and navigate to the “Application Developer Controls” tab. Change the “Maximum Disk Quota per App (MB)” setting from 2048 (2G) to 10240 (10G). Save the disk quota update and click “Apply Changes” to complete the configuration override.

### 11.12. Scale Application

Once the disk quota change has been successfully applied and assuming you have a running application, you can scale the application with a new `disk_limit` through the CF CLI, as the following example shows:

    → cf scale dataflow-server -k 10GB

    Scaling app dataflow-server in org ORG / space SPACE as user...
    OK

    ....
    ....
    ....
    ....

         state     since                    cpu      memory           disk           details
    #0   running   2016-10-31 03:07:23 PM   1.8%     497.9M of 1.1G   193.9M of 10G

You can then list the applications and see the new maximum disk space, as the following example shows:

    → cf apps
    Getting apps in org ORG / space SPACE as user...
    OK

    name              requested state   instances   memory   disk   urls
    dataflow-server   started           1/1         1.1G     10G    dataflow-server.apps.io

### 11.13. Managing Disk Use

Even when configuring the Data Flow server to use 10G of space, there is the possibility of exhausting the available space on the local disk. To prevent this, `jar` artifacts downloaded from external sources, i.e., apps registered as `http` or `maven` resources, are automatically deleted whenever the application is deployed, whether or not the deployment request succeeds. This behavior is optimal for production environments in which container runtime stability is more critical than I/O latency incurred during deployment. In development environments deployment happens more frequently. Additionally, the `jar` artifact (or a lighter `metadata` jar) contains metadata describing application configuration properties which is used by various operations related to application configuration, more frequently performed during pre-production activities (see Application Metadata for details). To provide a more responsive interactive developer experience at the expense of more disk usage in pre-production environments, you can set the CloudFoundry deployer property `autoDeleteMavenArtifacts` to `false`.

If you deploy the Data Flow server by using the default `port` health check type, you must explicitly monitor the disk space on the server in order to avoid running out space. If you deploy the server by using the `http` health check type (see the next example), the Data Flow server is restarted if there is low disk space. This is due to Spring Boot’s Disk Space Health Indicator. You can configure the settings of the Disk Space Health Indicator by using the properties that have the `management.health.diskspace` prefix.

For version 1.7, we are investigating the use of Volume Services for the Data Flow server to store `.jar` artifacts before pushing them to Cloud Foundry.

The following example shows how to deploy the `http` health check type to an endpoint called `/management/health`:

    ---
      ...
      health-check-type: http
      health-check-http-endpoint: /management/health

### 11.14. Application Resolution Alternatives

Though we recommend using a Maven Artifactory for application Register a Stream Application, there might be situations where one of the following alternative approaches would make sense.

- We have custom-built and maintain a SCDF APP Tool that can run as a regular Spring Boot application in Cloud Foundry, but it will in turn host and serve the application JARs for SCDF at runtime.

- With the help of Spring Boot, we can serve static content in Cloud Foundry. A simple Spring Boot application can bundle all the required stream and task applications. By having it run on Cloud Foundry, the static application can then serve the über-jar’s. From the shell, you can, for example, register the application with the name `http-source.jar` by using `--uri=http://<Route-To-StaticApp>/http-source.jar`.

- The über-jar’s can be hosted on any external server that’s reachable over HTTP. They can be resolved from raw GitHub URIs as well. From the shell, you can, for example, register the app with the name `http-source.jar` by using `--uri=http://<Raw_GitHub_URI>/http-source.jar`.

- Static Buildpack support in Cloud Foundry is another option. A similar HTTP resolution works on this model, too.

- Volume Services is another great option. The required über-jars can be hosted in an external file system. With the help of volume-services, you can, for example, register the application with the name `http-source.jar` by using `--uri=file://<Path-To-FileSystem>/http-source.jar`.

### 11.15. Security

By default, the Data Flow server is unsecured and runs on an unencrypted HTTP connection. You can secure your REST endpoints (as well as the Data Flow Dashboard) by enabling HTTPS and requiring clients to authenticate. For more details about securing the REST endpoints and configuring to authenticate against an OAUTH backend (UAA and SSO running on Cloud Foundry), see the security section from the core [configuration-local-security]. You can configure the security details in `dataflow-server.yml` or pass them as environment variables through `cf set-env` commands.

#### 11.15.1. Authentication

Spring Cloud Data Flow can either integrate with Pivotal Single Sign-On Service (for example, on PWS) or Cloud Foundry User Account and Authentication (UAA) Server.

##### Pivotal Single Sign-On Service

When deploying Spring Cloud Data Flow to Cloud Foundry, you can bind the application to the Pivotal Single Sign-On Service. By doing so, Spring Cloud Data Flow takes advantage of the Java CFEnv, which provides Cloud Foundry-specific auto-configuration support for OAuth 2.0.

To do so, bind the Pivotal Single Sign-On Service to your Data Flow Server application and provide the following properties:

    SPRING_CLOUD_DATAFLOW_SECURITY_CFUSEUAA: false                                                 (1)
    SECURITY_OAUTH2_CLIENT_CLIENTID: "${security.oauth2.client.clientId}"
    SECURITY_OAUTH2_CLIENT_CLIENTSECRET: "${security.oauth2.client.clientSecret}"
    SECURITY_OAUTH2_CLIENT_ACCESSTOKENURI: "${security.oauth2.client.accessTokenUri}"
    SECURITY_OAUTH2_CLIENT_USERAUTHORIZATIONURI: "${security.oauth2.client.userAuthorizationUri}"
    SECURITY_OAUTH2_RESOURCE_USERINFOURI: "${security.oauth2.resource.userInfoUri}"

1 | It is important that the property `spring.cloud.dataflow.security.cf-use-uaa` is set to `false`

Authorization is similarly supported for non-Cloud Foundry security scenarios. See the security section from the core Data Flow [configuration-local-security].

As the provisioning of roles can vary widely across environments, we by default assign all Spring Cloud Data Flow roles to users.

You can customize this behavior by providing your own `AuthoritiesExtractor`.

The following example shows one possible approach to set the custom `AuthoritiesExtractor` on the `UserInfoTokenServices`:

    public class MyUserInfoTokenServicesPostProcessor
    	implements BeanPostProcessor {

    	@Override
    	public Object postProcessBeforeInitialization(Object bean, String beanName) {
    		if (bean instanceof UserInfoTokenServices) {
    			final UserInfoTokenServices userInfoTokenServices == (UserInfoTokenServices) bean;
    			userInfoTokenServices.setAuthoritiesExtractor(ctx.getBean(AuthoritiesExtractor.class));
    		}
    		return bean;
    	}

    	@Override
    	public Object postProcessAfterInitialization(Object bean, String beanName) {
    		return bean;
    	}
    }

Then you can declare it in your configuration class as follows:

    @Bean
    public BeanPostProcessor myUserInfoTokenServicesPostProcessor() {
    	BeanPostProcessor postProcessor == new MyUserInfoTokenServicesPostProcessor();
    	return postProcessor;
    }

##### Cloud Foundry UAA

The availability of Cloud Foundry User Account and Authentication (UAA) depends on the Cloud Foundry environment. In order to provide UAA integration, you have to provide the necessary OAuth2 configuration properties (for example, by setting the `SPRING_APPLICATION_JSON` property).

The following JSON example shows how to create a security configuration:

    {
      "security.oauth2.client.client-id": "scdf",
      "security.oauth2.client.client-secret": "scdf-secret",
      "security.oauth2.client.access-token-uri": "https://login.cf.myhost.com/oauth/token",
      "security.oauth2.client.user-authorization-uri": "https://login.cf.myhost.com/oauth/authorize",
      "security.oauth2.resource.user-info-uri": "https://login.cf.myhost.com/userinfo"
    }

By default, the `spring.cloud.dataflow.security.cf-use-uaa` property is set to `true`. This property activates a special `AuthoritiesExtractor` called `CloudFoundryDataflowAuthoritiesExtractor`.

If you do not use CloudFoundry UAA, you should set `spring.cloud.dataflow.security.cf-use-uaa` to `false`.

Under the covers, this `AuthoritiesExtractor` calls out to the Cloud Foundry Apps API and ensure that users are in fact Space Developers.

If the authenticated user is verified as a Space Developer, all roles are assigned.

### 11.16. Configuration Reference

You must provide several pieces of configuration. These are Spring Boot `@ConfigurationProperties`, so you can set them as environment variables or by any other means that Spring Boot supports. The following listing is in environment variable format, as that is an easy way to get started configuring Boot applications in Cloud Foundry. Note that in the future, you will be able to deploy tasks to multiple platforms, but for 2.0.0.M1 you can deploy only to a single platform and the name must be `default`.

    # Default values appear after the equal signs.
    # Example values, typical for Pivotal Web Services, are included as comments.

    # URL of the CF API (used when using cf login -a for example) - for example, https://api.run.pivotal.io
    SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_URL=

    # The name of the organization that owns the space above - for example, youruser-org
    SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_ORG=

    # The name of the space into which modules will be deployed - for example, development
    SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_SPACE=

    # The root domain to use when mapping routes - for example, cfapps.io
    SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_DOMAIN=

    # The user name and password of the user to use to create applications
    SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_USERNAME=
    SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_PASSWORD

    # The identity provider to be used when accessing the Cloud Foundry API (optional).
    # The passed string has to be a URL-Encoded JSON Object, containing the field origin with value as origin_key of an identity provider - for example, {"origin":"uaa"}
    SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_LOGIN_HINT=

    # Whether to allow self-signed certificates during SSL validation (you should NOT do so in production)
    SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_SKIP_SSL_VALIDATION

    # A comma-separated set of service instance names to bind to every deployed task application.
    # Among other things, this should include an RDBMS service that is used
    # for Spring Cloud Task execution reporting, such as my_postgres
    SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_DEPLOYMENT_SERVICES
    spring.cloud.deployer.cloudfoundry.task.services=

    # Timeout, in seconds, to use when doing blocking API calls to Cloud Foundry
    SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_DEPLOYMENT_API_TIMEOUT=

    # Timeout, in milliseconds, to use when querying the Cloud Foundry API to compute app status
    SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_DEPLOYMENT_STATUS_TIMEOUT

Note that you can set `spring.cloud.deployer.cloudfoundry.services`, `spring.cloud.deployer.cloudfoundry.buildpacks`, or the Spring Cloud Deployer-standard `spring.cloud.deployer.memory` and `spring.cloud.deployer.disk` as part of an individual deployment request by using the `deployer.<app-name>` shortcut, as the following example shows:

    stream create --name ticktock --definition "time | log"
    stream deploy --name ticktock --properties "deployer.time.memory=2g"

The commands in the preceding example deploy the time source with 2048MB of memory, while the log sink uses the default 1024MB.

When you deploy a stream, you can also pass `JAVA_OPTS` as a deployment property, as the following example shows:

    stream deploy --name ticktock --properties "deployer.time.cloudfoundry.javaOpts=-Duser.timezone=America/New_York"

### 11.17. Debugging

If you want to get better insights into what is happening when your streams and tasks are being deployed, you may want to turn on the following features:

- Reactor “stacktraces”, showing which operators were involved before an error occurred. This feature is helpful, as the deployer relies on project reactor and regular stacktraces may not always allow understanding the flow before an error happened. Note that this comes with a performance penalty, so it is disabled by default.

    spring.cloud.dataflow.server.cloudfoundry.debugReactor == true

- Deployer and Cloud Foundry client library request and response logs. This feature allows seeing a detailed conversation between the Data Flow server and the Cloud Foundry Cloud Controller.

    logging.level.cloudfoundry-client == DEBUG

### 11.18. Spring Cloud Config Server

You can use Spring Cloud Config Server to centralize configuration properties for Spring Boot applications. Likewise, both Spring Cloud Data Flow and the applications orchestrated by Spring Cloud Data Flow can be integrated with a configuration server to use the same capabilities.

#### 11.18.1. Stream, Task, and Spring Cloud Config Server

Similar to Spring Cloud Data Flow server, you can configure both the stream and task applications to resolve the centralized properties from the configuration server. Setting the `spring.cloud.config.uri` property for the deployed applications is a common way to bind to the configuration server. See the Spring Cloud Config Client reference guide for more information. Since this property is likely to be used across all applications deployed by the Data Flow server, the Data Flow server’s `spring.cloud.dataflow.applicationProperties.stream` property for stream applications and `spring.cloud.dataflow.applicationProperties.task` property for task applications can be used to pass the `uri` of the Config Server to each deployed stream or task application. See the section on Common Application Properties for more information.

Note that, if you use applications from the App Starters project, these applications already embed the `spring-cloud-services-starter-config-client` dependency. If you build your application from scratch and want to add the client side support for config server, you can add a dependency reference to the config server client library. The following snippet shows a Maven example:

    ...
    <dependency>
      <groupId>io.pivotal.spring.cloud</groupId>
      <artifactId>spring-cloud-services-starter-config-client</artifactId>
      <version>CONFIG_CLIENT_VERSION</version>
    </dependency>
    ...

where `CONFIG_CLIENT_VERSION` can be the latest release of the Spring Cloud Config Server client for Pivotal Cloud Foundry.

| You may see a `WARN` logging message if the application that uses this library cannot connect to the configuration server when the application starts and whenever the `/health` endpoint is accessed. If you know that you are not using config server functionality, you can disable the client library by setting the `SPRING_CLOUD_CONFIG_ENABLED` environment variable to `false`.

#### 11.18.2. Sample Manifest Template

The following SCDF and Skipper `manifest.yml` templates includes the required environment variables for the Skipper and Spring Cloud Data Flow server and deployed applications and tasks to successfully run on Cloud Foundry and automatically resolve centralized properties from `my-config-server` at runtime:

    ---
    applications:
    - name: data-flow-server
      host: data-flow-server
      memory: 2G
      disk_quota: 2G
      instances: 1
      path: {PATH TO SERVER UBER-JAR}
      env:
        SPRING_APPLICATION_NAME: data-flow-server
        MAVEN_REMOTE_REPOSITORIES_REPO1_URL: https://repo.spring.io/libs-snapshot
        SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_URL: https://api.sys.huron.cf-app.com
        SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_ORG: sabby20
        SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_SPACE: sabby20
        SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_DOMAIN: apps.huron.cf-app.com
        SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_USERNAME: admin
        SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_PASSWORD: ***
        SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_SKIP_SSL_VALIDATION: true
        SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_DEPLOYMENT_SERVICES: mysql
        SPRING_CLOUD_SKIPPER_CLIENT_SERVER_URI: https://<skipper-host-name>/api
    services:
    - mysql
    - my-config-server

    ---
    applications:
    - name: skipper-server
      host: skipper-server
      memory: 1G
      disk_quota: 1G
      instances: 1
      timeout: 180
      buildpack: java_buildpack
      path: <PATH TO THE DOWNLOADED SKIPPER SERVER UBER-JAR>
      env:
        SPRING_APPLICATION_NAME: skipper-server
        SPRING_CLOUD_SKIPPER_SERVER_ENABLE_LOCAL_PLATFORM: false
        SPRING_CLOUD_SKIPPER_SERVER_STRATEGIES_HEALTHCHECK_TIMEOUTINMILLIS: 300000
        SPRING_CLOUD_SKIPPER_SERVER_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_URL: https://api.local.pcfdev.io
        SPRING_CLOUD_SKIPPER_SERVER_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_ORG: pcfdev-org
        SPRING_CLOUD_SKIPPER_SERVER_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_SPACE: pcfdev-space
        SPRING_CLOUD_SKIPPER_SERVER_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_DEPLOYMENT_DOMAIN: cfapps.io
        SPRING_CLOUD_SKIPPER_SERVER_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_USERNAME: admin
        SPRING_CLOUD_SKIPPER_SERVER_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_PASSWORD: admin
        SPRING_CLOUD_SKIPPER_SERVER_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_SKIP_SSL_VALIDATION: false
        SPRING_CLOUD_SKIPPER_SERVER_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_DEPLOYMENT_DELETE_ROUTES: false
        SPRING_CLOUD_SKIPPER_SERVER_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_DEPLOYMENT_SERVICES: rabbit, my-config-server
    services:
    - mysql
      my-config-server

where `my-config-server` is the name of the Spring Cloud Config Service instance running on Cloud Foundry.

By binding the service to Spring Cloud Data Flow server, Spring Cloud Task and via Skipper to all the Spring Cloud Stream applications respectively, we can now resolve centralized properties backed by this service.

#### 11.18.3. Self-signed SSL Certificate and Spring Cloud Config Server

Often, in a development environment, we may not have a valid certificate to enable SSL communication between clients and the backend services. However, the configuration server for Pivotal Cloud Foundry uses HTTPS for all client-to-service communication, so we need to add a self-signed SSL certificate in environments with no valid certificates.

By using the same `manifest.yml` templates listed in the previous section for the server, we can provide the self-signed SSL certificate by setting `TRUST_CERTS: <API_ENDPOINT>`.

However, the deployed applications also require `TRUST_CERTS` as a flat environment variable (as opposed to being wrapped inside `SPRING_APPLICATION_JSON`), so we must instruct the server with yet another set of tokens (`SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_DEPLOYMENT_USE_SPRING_APPLICATION_JSON: false`) for tasks. With this setup, the applications receive their application properties as regular environment variables.

The following listing shows the updated `manifest.yml` with the required changes. Both the Data Flow server and deployed applications get their configuration from the `my-config-server` Cloud Config server (deployed as a Cloud Foundry service).

    ---
    applications:
    - name: test-server
      host: test-server
      memory: 1G
      disk_quota: 1G
      instances: 1
      path: spring-cloud-dataflow-server-VERSION.jar
      env:
        SPRING_APPLICATION_NAME: test-server
        MAVEN_REMOTE_REPOSITORIES_REPO1_URL: https://repo.spring.io/libs-snapshot
        SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_URL: https://api.sys.huron.cf-app.com
        SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_ORG: sabby20
        SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_SPACE: sabby20
        SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_DOMAIN: apps.huron.cf-app.com
        SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_USERNAME: admin
        SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_PASSWORD: ***
        SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_SKIP_SSL_VALIDATION: true
        SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_DEPLOYMENT_SERVICES: mysql, config-server
        SPRING_CLOUD_SKIPPER_CLIENT_SERVER_URI: https://<skipper-host-name>/api
        TRUST_CERTS: <API_ENDPOINT> #this is for the server
        SPRING_CLOUD_DATAFLOW_APPLICATION_PROPERTIES_TASK_TRUST_CERTS: <API_ENDPOINT>   #this propagates to all tasks
    services:
    - mysql
    - my-config-server #this is for the server

Also add the `my-config-server` service to the Skipper’s manifest environment

    ---
    applications:
    - name: skipper-server
      host: skipper-server
      memory: 1G
      disk_quota: 1G
      instances: 1
      timeout: 180
      buildpack: java_buildpack
      path: <PATH TO THE DOWNLOADED SKIPPER SERVER UBER-JAR>
      env:
        SPRING_APPLICATION_NAME: skipper-server
        SPRING_CLOUD_SKIPPER_SERVER_ENABLE_LOCAL_PLATFORM: false
        SPRING_CLOUD_SKIPPER_SERVER_STRATEGIES_HEALTHCHECK_TIMEOUTINMILLIS: 300000
        SPRING_CLOUD_SKIPPER_SERVER_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_URL: <URL>
        SPRING_CLOUD_SKIPPER_SERVER_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_ORG: <ORG>
        SPRING_CLOUD_SKIPPER_SERVER_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_SPACE: <SPACE>
        SPRING_CLOUD_SKIPPER_SERVER_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_DEPLOYMENT_DOMAIN: <DOMAIN>
        SPRING_CLOUD_SKIPPER_SERVER_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_USERNAME: <USER>
        SPRING_CLOUD_SKIPPER_SERVER_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_PASSWORD: <PASSWORD>
        SPRING_CLOUD_SKIPPER_SERVER_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_DEPLOYMENT_SERVICES: rabbit, my-config-server #this is so all stream applications bind to my-config-server
    services:
    - mysql
      my-config-server

### 11.19. Configure Scheduling

This section discusses how to configure Spring Cloud Data Flow to connect to the PCF-Scheduler as its agent to execute tasks.

| Before following these instructions, be sure to have an instance of the PCF-Scheduler service running in your Cloud Foundry space. To create a PCF-Scheduler in your space (assuming it is in your Market Place) execute the following from the CF CLI: `cf create-service scheduler-for-pcf standard <name of service>`. Name of a service is later used to bound running application in PCF.

For scheduling, you must add (or update) the following environment variables in your environment:

- Enable scheduling for Spring Cloud Data Flow by setting `spring.cloud.dataflow.features.schedules-enabled` to `true`.

- Bind the task deployer to your instance of PCF-Scheduler by adding the PCF-Scheduler service name to the `SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_DEPLOYMENT_SERVICES` environment variable.

- Establish the URL to the PCF-Scheduler by setting the `SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_SCHEDULER_SCHEDULER_URL` environment variable.

| After creating the preceding configurations, you must create any task definitions that need to be scheduled.

The following sample manifest shows both environment properties configured (assuming you have a PCF-Scheduler service available with the name `myscheduler`):

    ---
    applications:
    - name: data-flow-server
      host: data-flow-server
      memory: 2G
      disk_quota: 2G
      instances: 1
      path: {PATH TO SERVER UBER-JAR}
      env:
        SPRING_APPLICATION_NAME: data-flow-server
        SPRING_CLOUD_SKIPPER_SERVER_ENABLE_LOCAL_PLATFORM: false
        SPRING_CLOUD_SKIPPER_SERVER_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_URL: <URL>
        SPRING_CLOUD_SKIPPER_SERVER_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_ORG: <ORG>
        SPRING_CLOUD_SKIPPER_SERVER_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_SPACE: <SPACE>
        SPRING_CLOUD_SKIPPER_SERVER_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_DEPLOYMENT_DOMAIN: <DOMAIN>
        SPRING_CLOUD_SKIPPER_SERVER_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_USERNAME: <USER>
        SPRING_CLOUD_SKIPPER_SERVER_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_CONNECTION_PASSWORD: <PASSWORD>
        SPRING_CLOUD_SKIPPER_SERVER_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_DEPLOYMENT_SERVICES: rabbit, myscheduler
        SPRING_CLOUD_DATAFLOW_FEATURES_SCHEDULES_ENABLED: true
        SPRING_CLOUD_SKIPPER_CLIENT_SERVER_URI: https://<skipper-host-name>/api
        SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]_SCHEDULER_SCHEDULER_URL: https://scheduler.local.pcfdev.io
    services:
    - mysql

Where the `SPRING_CLOUD_DATAFLOW_TASK_PLATFORM_CLOUDFOUNDRY_ACCOUNTS[default]SCHEDULER_SCHEDULER_URL` has the following format: `scheduler.<Domain-Name>` (for example, `scheduler.local.pcfdev.io`). Check the actual address from your _PCF environment.

| Detailed examples for launching and scheduling tasks across multiple platforms, are available in this section Multiple Platform Support for Tasks on dataflow.spring.io.

## 12. Configuration - Kubernetes

This section describes how to configure Spring Cloud Data Flow features, such as deployer properties, tasks, and which relational database to use.

### 12.1. Feature Toggles

Data Flow server offers specific set of features that can be enabled or disabled when launching. These features include all the lifecycle operations, REST endpoints (server and client implementations including Shell and the UI) for:

- Streams

- Tasks

- Schedules

You can enable or disable these features by setting the following boolean environment variables when launching the Data Flow server:

- `SPRING_CLOUD_DATAFLOW_FEATURES_STREAMS_ENABLED`

- `SPRING_CLOUD_DATAFLOW_FEATURES_TASKS_ENABLED`

- `SPRING_CLOUD_DATAFLOW_FEATURES_SCHEDULES_ENABLED`

By default, all the features are enabled.

The `/features` REST endpoint provides information on the features that have been enabled and disabled.

### 12.2. Deployer Properties

You can use the following configuration properties the Kubernetes deployer to customize how Streams and Tasks are deployed. When deploying with the Data Flow shell, you can use the syntax `deployer.<appName>.kubernetes.<deployerPropertyName>`. These properties are also used when configuring the Kubernetes task platforms in the Data Flow server and Kubernetes platforms in Skipper for deploying Streams.

Deployer Property Name | Description | Default Value
--- | --- | ---
namespace | Namespace to use | environment variable `KUBERNETES_NAMESPACE`, otherwise `default`
deployment.nodeSelector | The node selectors to apply to the deployment in `key:value` format. Multiple node selectors are comma separated. | <none>
imagePullSecret | Secrets for a access a private registry to pull images. | <none>
imagePullPolicy | The Image Pull Policy to apply when pulling images. Valid options are `Always`, `IfNotPresent`, and `Never`. | IfNotPresent
livenessProbeDelay | Delay in seconds when the Kubernetes liveness check of the app container should start checking its health status. | 10
livenessProbePeriod | Period in seconds for performing the Kubernetes liveness check of the app container. | 60
livenessProbeTimeout | Timeout in seconds for the Kubernetes liveness check of the app container. If the health check takes longer than this value to return it is assumed as 'unavailable'. | 2
livenessProbePath | Path that app container has to respond to for liveness check. | <none>
livenessProbePort | Port that app container has to respond on for liveness check. | <none>
readinessProbeDelay | Delay in seconds when the readiness check of the app container should start checking if the module is fully up and running. | 10
readinessProbePeriod | Period in seconds to perform the readiness check of the app container. | 10
readinessProbeTimeout | Timeout in seconds that the app container has to respond to its health status during the readiness check. | 2
readinessProbePath | Path that app container has to respond to for readiness check. | <none>
readinessProbePort | Port that app container has to respond on for readiness check. | <none>
probeCredentialsSecret | The secret name containing the credentials to use when accessing secured probe endpoints. | <none>
limits.memory | The memory limit, maximum needed value to allocate a pod, Default unit is mebibytes, 'M' and 'G" suffixes supported | <none>
limits.cpu | The CPU limit, maximum needed value to allocate a pod | <none>
requests.memory | The memory request, guaranteed needed value to allocate a pod. | <none>
requests.cpu | The CPU request, guaranteed needed value to allocate a pod. | <none>
statefulSet.volumeClaimTemplate.storageClassName | Name of the storage class for a stateful set | <none>
statefulSet.volumeClaimTemplate.storage | The storage amount. Default unit is mebibytes, 'M' and 'G" suffixes supported | <none>
environmentVariables | List of environment variables to set for any deployed app container | <none>
entryPointStyle | Entry point style used for the Docker image. Used to determine how to pass in properties. Can be `exec`, `shell`, and `boot` | `exec`
createLoadBalancer | Create a "LoadBalancer" for the service created for each app. This facilitates assignment of external IP to app. | false
serviceAnnotations | Service annotations to set for the service created for each application. String of the format `annotation1:value1,annotation2:value2` | <none>
podAnnotations | Pod annotations to set for the pod created for each deployment. String of the format `annotation1:value1,annotation2:value2` | <none>
jobAnnotations | Job annotations to set for the pod or job created for a job. String of the format `annotation1:value1,annotation2:value2` | <none>
minutesToWaitForLoadBalancer | Time to wait for load balancer to be available before attempting delete of service (in minutes). | 5
maxTerminatedErrorRestarts | Maximum allowed restarts for app that fails due to an error or excessive resource use. | 2
maxCrashLoopBackOffRestarts | Maximum allowed restarts for app that is in a CrashLoopBackOff. Values are `Always`, `IfNotPresent`, `Never` | `IfNotPresent`
volumeMounts | volume mounts expressed in YAML format. e.g. ``[{name: 'testhostpath', mountPath: '/test/hostPath'}, {name: 'testpvc', mountPath: '/test/pvc'}, {name: 'testnfs', mountPath: '/test/nfs'}]`` | <none>
volumes | The volumes that a Kubernetes instance supports specifed in YAML format. e.g. ``[{name: testhostpath, hostPath: { path: '/test/override/hostPath' }},{name: 'testpvc', persistentVolumeClaim: { claimName: 'testClaim', readOnly: 'true' }}, {name: 'testnfs', nfs: { server: '10.0.0.1:111', path: '/test/nfs' }}]`` | <none>
hostNetwork | The hostNetwork setting for the deployments, see kubernetes.io/docs/api-reference/v1/definitions/#_v1_podspec | false
createDeployment | Create a "Deployment" with a "Replica Set" instead of a "Replication Controller". | true
createJob | Create a "Job" instead of just a "Pod" when launching tasks. | false
containerCommand | Overrides the default entry point command with the provided command and arguments. | <none>
containerPorts | Adds additional ports to expose on the container. | <none>
createNodePort | The explicit port to use when `NodePort` is the `Service` type. | <none>
deploymentServiceAccountName | Service account name used in app deployments. Note: The service account name used for app deployments is derived from the Data Flow servers deployment. | <none>
deploymentLabels | Additional labels to add to the deployment in `key:value` format. Multiple labels are comma separated. | <none>
bootMajorVersion | The Spring Boot major version to use. Currently only used to configure Spring Boot version specific probe paths automatically. Valid options are `1` or `2`. | 2
tolerations.key | The key to use for the toleration. | <none>
tolerations.effect | The toleration effect. See kubernetes.io/docs/concepts/configuration/taint-and-toleration for valid options. | <none>
tolerations.operator | The toleration operator. See kubernetes.io/docs/concepts/configuration/taint-and-toleration/ for valid options. | <none>
tolerations.tolerationSeconds | The number of seconds defining how long the pod will stay bound to the node after a taint is added. | <none>
tolerations.value | The toleration value to apply, used in conjunction with `operator` to select to appropriate `effect`. | <none>
secretRefs | The name of the secret(s) to load the entire data contents into individual environment variables. Multiple secrets may be comma separated. | <none>
secretKeyRefs.envVarName | The environment variable name to hold the secret data | <none>
secretKeyRefs.secretName | The secret name to access | <none>
secretKeyRefs.dataKey | The key name to obtain secret data from | <none>
configMapRefs | The name of the ConfigMap(s) to load the entire data contents into individual environment variables. Multiple ConfigMaps be comma separated. | <none>
configMapKeyRefs.envVarName | The environment variable name to hold the ConfigMap data | <none>
configMapKeyRefs.configMapName | The ConfigMap name to access | <none>
configMapKeyRefs.dataKey | The key name to obtain ConfigMap data from | <none>
maximumConcurrentTasks | The maximum concurrent tasks allowed for this platform instance. | 20
podSecurityContext.runAsUser | The numeric user ID to run pod container processes under | <none>
podSecurityContext.fsGroup | The numeric group ID to run pod container processes under | <none>
affinity.nodeAffinity | The node affinity expressed in YAML format. e.g. ``{ requiredDuringSchedulingIgnoredDuringExecution: { nodeSelectorTerms: [ { matchExpressions: [ { key: 'kubernetes.io/e2e-az-name', operator: 'In', values: [ 'e2e-az1', 'e2e-az2']}]}]}, preferredDuringSchedulingIgnoredDuringExecution: [ { weight: 1, preference: { matchExpressions: [ { key: 'another-node-label-key', operator: 'In', values: [ 'another-node-label-value' ]}]}}]}`` | <none>
affinity.podAffinity | The pod affinity expressed in YAML format. e.g. ``{ requiredDuringSchedulingIgnoredDuringExecution: { labelSelector: [ { matchExpressions: [ { key: 'app', operator: 'In', values: [ 'store']}]}], topologyKey: 'kubernetes.io/hostnam'}, preferredDuringSchedulingIgnoredDuringExecution: [ { weight: 1, podAffinityTerm: { labelSelector: { matchExpressions: [ { key: 'security', operator: 'In', values: [ 'S2' ]}]}, topologyKey: 'failure-domain.beta.kubernetes.io/zone'}}]}`` | <none>
affinity.podAntiAffinity | The pod anti-affinity expressed in YAML format. e.g. ``{ requiredDuringSchedulingIgnoredDuringExecution: { labelSelector: { matchExpressions: [ { key: 'app', operator: 'In', values: [ 'store']}]}], topologyKey: 'kubernetes.io/hostname'}, preferredDuringSchedulingIgnoredDuringExecution: [ { weight: 1, podAffinityTerm: { labelSelector: { matchExpressions: [ { key: 'security', operator: 'In', values: [ 'S2' ]}]}, topologyKey: 'failure-domain.beta.kubernetes.io/zone'}}]}`` | <none>
statefulSetInitContainerImageName | A custom image name to use for the StatefulSet Init Container | <none>
initContainer | An Init Container experessed in YAML format to be applied to a pod. e.g. ``{containerName: 'test', imageName: 'busybox:latest', commands: ['sh', '-c', 'echo hello']}`` | <none>

### 12.3. Tasks

The Data Flow server is responsible for deploying Tasks. Tasks that are launched by Data Flow write their state to the same database that is used by the Data Flow server. For Tasks which are Spring Batch Jobs, the job and step execution data is also stored in this database. As with Skipper, Tasks can be launched to multiple platforms. When Data Flow is running on Kubernetes, a Task platfom must be defined. To configure new platform accounts that target Kubernetes, provide an entry under the `spring.cloud.dataflow.task.platform.kubernetes` section in your `application.yaml` file for via another Spring Boot supported mechanism. In the following example, two Kubernetes platform accounts named `dev` and `qa` are created. The keys such as `memory` and `disk` are Cloud Foundry Deployer Properties.

    spring:
      cloud:
        dataflow:
          task:
            platform:
              kubernetes:
                accounts:
                  dev:
                    namespace: devNamespace
                    imagePullPolicy: Always
                    entryPointStyle: exec
                    limits:
                      cpu: 4
                  qa:
                    namespace: qaNamespace
                    imagePullPolicy: IfNotPresent
                    entryPointStyle: boot
                    limits:
                      memory: 2048m

| By defining one platform as `default` allows you to skip using `platformName` where its use would otherwise be required.

When launching a task, pass the value of the platform account name using the task launch option `--platformName` If you do not pass a value for `platformName`, the value `default` will be used.

| When deploying a task to multiple platforms, the configuration of the task needs to connect to the same database as the Data Flow Server.

You can configure the Data Flow server that is on Kubernetes to deploy tasks to Cloud Foundry and Kubernetes. See the section on Cloud Foundry Task Platform Configuration for more information.

Detailed examples for launching and scheduling tasks across multiple platforms, are available in this section Multiple Platform Support for Tasks on dataflow.spring.io.

### 12.4. General Configuration

The Spring Cloud Data Flow server for Kubernetes uses the `spring-cloud-kubernetes` module to process secrets that are mounted under `/etc/secrets`. ConfigMaps must be mounted as `application.yaml` in the `/config` directory that is processed by Spring Boot. To avoid access to the Kubernetes API server the `SPRING_CLOUD_KUBERNETES_CONFIG_ENABLE_API` and `SPRING_CLOUD_KUBERNETES_SECRETS_ENABLE_API` are set to `false`.

#### 12.4.1. Using ConfigMap and Secrets

You can pass configuration properties to the Data Flow Server by using Kubernetes ConfigMap and secrets.

The following example shows one possible configuration, which enables MySQL and sets a memory limit:

    apiVersion: v1
    kind: ConfigMap
    metadata:
      name: scdf-server
      labels:
        app: scdf-server
    data:
      application.yaml: |-
        spring:
          cloud:
            dataflow:
              task:
                platform:
                  kubernetes:
                    accounts:
                      default:
                        limits:
                          memory: 1024Mi
          datasource:
            url: jdbc:mysql://${MYSQL_SERVICE_HOST}:${MYSQL_SERVICE_PORT}/mysql
            username: root
            password: ${mysql-root-password}
            driverClassName: org.mariadb.jdbc.Driver
            testOnBorrow: true
            validationQuery: "SELECT 1"

The preceding example assumes that MySQL is deployed with `mysql` as the service name. Kubernetes publishes the host and port values of these services as environment variables that we can use when configuring the apps we deploy.

We prefer to provide the MySQL connection password in a Secrets file, as the following example shows:

    apiVersion: v1
    kind: Secret
    metadata:
      name: mysql
      labels:
        app: mysql
    data:
      mysql-root-password: eW91cnBhc3N3b3Jk

The password is a base64-encoded value.

### 12.5. Database Configuration

Spring Cloud Data Flow provides schemas for H2, HSQLDB, MySQL, Oracle, PostgreSQL, DB2, and SQL Server. The appropriate schema is automatically created when the server starts, provided the right database driver and appropriate credentials are in the classpath.

The JDBC drivers for MySQL (via MariaDB driver), HSQLDB, PostgreSQL, and embedded H2 are available out of the box. If you use any other database, you need to put the corresponding JDBC driver jar on the classpath of the server.

For instance, if you use MySQL in addition to a password in the secrets file, you could provide the following properties in the ConfigMap:

    data:
      application.yaml: |-
        spring:
          datasource:
            url: jdbc:mysql://${MYSQL_SERVICE_HOST}:${MYSQL_SERVICE_PORT}/mysql
            username: root
            password: ${mysql-root-password}
            driverClassName: org.mariadb.jdbc.Driver
            url: jdbc:mysql://${MYSQL_SERVICE_HOST}:${MYSQL_SERVICE_PORT}/test
            driverClassName: org.mariadb.jdbc.Driver

For PostgreSQL, you could use the following configuration:

    data:
      application.yaml: |-
        spring:
          datasource:
            url: jdbc:postgresql://${PGSQL_SERVICE_HOST}:${PGSQL_SERVICE_PORT}/database
            username: root
            password: ${postgres-password}
            driverClassName: org.postgresql.Driver

For HSQLDB, you could use the following configuration:

    data:
      application.yaml: |-
        spring:
          datasource:
            url: jdbc:hsqldb:hsql://${HSQLDB_SERVICE_HOST}:${HSQLDB_SERVICE_PORT}/database
            username: sa
            driverClassName: org.hsqldb.jdbc.JDBCDriver

The following YAML snippet from a Deployment is an example of mounting a ConfigMap as `application.yaml` under `/config` where Spring Boot will process it plus a Secret mounted under `/etc/secrets` where it will get picked up by the spring-cloud-kubernetes library due to the environment variable `SPRING_CLOUD_KUBERNETES_SECRETS_PATHS` being set to `/etc/secrets`.

    ...
          containers:
          - name: scdf-server
            image: springcloud/spring-cloud-dataflow-server:2.5.0.BUILD-SNAPSHOT
            imagePullPolicy: Always
            volumeMounts:
              - name: config
                mountPath: /config
                readOnly: true
              - name: database
                mountPath: /etc/secrets/database
                readOnly: true
            ports:
    ...
          volumes:
            - name: config
              configMap:
                name: scdf-server
                items:
                - key: application.yaml
                  path: application.yaml
            - name: database
              secret:
                secretName: mysql

You can find migration scripts for specific database types in the spring-cloud-task repo.

### 12.6. Monitoring and Management

We recommend using the `kubectl` command for troubleshooting streams and tasks.

You can list all artifacts and resources used by using the following command:

    kubectl get all,cm,secrets,pvc

You can list all resources used by a specific application or service by using a label to select resources. The following command lists all resources used by the `mysql` service:

    kubectl get all -l app=mysql

You can get the logs for a specific pod by issuing the following command:

    kubectl logs pod <pod-name>

If the pod is continuously getting restarted, you can add `-p` as an option to see the previous log, as follows:

    kubectl logs -p <pod-name>

You can also tail or follow a log by adding an `-f` option, as follows:

    kubectl logs -f <pod-name>

A useful command to help in troubleshooting issues, such as a container that has a fatal error when starting up, is to use the `describe` command, as the following example shows:

    kubectl describe pod ticktock-log-0-qnk72

#### 12.6.1. Inspecting Server Logs

You can access the server logs by using the following command:

    kubectl get pod -l app=scdf=server
    kubectl logs <scdf-server-pod-name>

#### 12.6.2. Streams

Stream applications are deployed with the stream name followed by the name of the application. For processors and sinks, an instance index is also appended.

To see all the pods that are deployed by the Spring Cloud Data Flow server, you can specify the `role=spring-app` label, as follows:

    kubectl get pod -l role=spring-app

To see details for a specific application deployment you can use the following command:

    kubectl describe pod <app-pod-name>

To view the application logs, you can use the following command:

    kubectl logs <app-pod-name>

If you would like to tail a log you can use the following command:

    kubectl logs -f <app-pod-name>

#### 12.6.3. Tasks

Tasks are launched as bare pods without a replication controller. The pods remain after the tasks complete, which gives you an opportunity to review the logs.

To see all pods for a specific task, use the following command:

    kubectl get pod -l task-name=<task-name>

To review the task logs, use the following command:

    kubectl logs <task-pod-name>

You have two options to delete completed pods. You can delete them manually once they are no longer needed or you can use the Data Flow shell `task execution cleanup` command to remove the completed pod for a task execution.

To delete the task pod manually, use the following command:

    kubectl delete pod <task-pod-name>

To use the `task execution cleanup` command, you must first determine the `ID` for the task execution. To do so, use the `task execution list` command, as the following example (with output) shows:

    dataflow:>task execution list
    ╔═════════╤══╤════════════════════════════╤════════════════════════════╤═════════╗
    ║Task Name│ID│         Start Time         │          End Time          │Exit Code║
    ╠═════════╪══╪════════════════════════════╪════════════════════════════╪═════════╣
    ║task1    │1 │Fri May 05 18:12:05 EDT 2017│Fri May 05 18:12:05 EDT 2017│0        ║
    ╚═════════╧══╧════════════════════════════╧════════════════════════════╧═════════╝

Once you have the ID, you can issue the command to cleanup the execution artifacts (the completed pod), as the following example shows:

    dataflow:>task execution cleanup --id 1
    Request to clean up resources for task execution 1 has been submitted

##### Database Credentials for Tasks

By default Spring Cloud Data Flow passes database credentials as properties to the pod at task launch time. If using the `exec` or `shell` entry point styles the DB credentials will be viewable if the user does a `kubectl describe` on the task’s pod. To configure Spring Cloud Data Flow to use Kubernetes Secrets: Set `spring.cloud.dataflow.task.use.kubernetes.secrets.for.db.credentials` property to `true`. If using the yaml files provided by Spring Cloud Data Flow update the `src/kubernetes/server/server-deployment.yaml to add the following environment variable:

    - name: SPRING_CLOUD_DATAFLOW_TASK_USE_KUBERNETES_SECRETS_FOR_DB_CREDENTIALS
      value: 'true'

If upgrading from a previous version of SCDF be sure to verify that `spring.datasource.username` and `spring.datasource.password` environment variables are present in the `secretKeyRefs` in the server-config.yaml. If not, add it as shown in the example below:

    ...
      task:
        platform:
          kubernetes:
            accounts:
              default:
                secretKeyRefs:
                  - envVarName: "spring.datasource.password"
                    secretName: mysql
                    dataKey: mysql-root-password
                  - envVarName: "spring.datasource.username"
                      secretName: mysql
                      dataKey: mysql-root-username
    ...

Also verify that the associated secret(dataKey) is also available in secrets. SCDF provides an example of this for MySql here: `src/kubernetes/mysql/mysql-svc.yaml`.

| Passing of DB credentials via properties by default is to preserve to backwards compatibility. This will be feature will be removed in future release.

### 12.7. Scheduling

This section covers customization of how scheduled tasks are configured. Scheduling of tasks is enabled by default in the Spring Cloud Data Flow Kubernetes Server. Properties are used to influence settings for scheduled tasks and can be configured on a global or per-schedule basis.

| Unless noted, properties set on a per-schedule basis always take precedence over properties set as the server configuration. This arrangement allows for the ability to override global server level properties for a specific schedule.

See `KubernetesSchedulerProperties` for more on the supported options.

#### 12.7.1. Entry Point Style

An Entry Point Style affects how application properties are passed to the task container to be deployed. Currently, three styles are supported:

- `exec`: (default) Passes all application properties as command line arguments.

- `shell`: Passes all application properties as environment variables.

- `boot`: Creates an environment variable called `SPRING_APPLICATION_JSON` that contains a JSON representation of all application properties.

You can configure the entry point style as follows:

    deployer.kubernetes.entryPointStyle=<Entry Point Style>

Replace `<Entry Point Style>` with your desired Entry Point Style.

You can also configure the Entry Point Style at the server level in the container `env` section of a deployment YAML, as the following example shows:

    env:
    - name: SPRING_CLOUD_SCHEDULER_KUBERNETES_ENTRY_POINT_STYLE
      value: entryPointStyle

Replace `entryPointStyle` 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` vs `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 mapped into the `SPRING_APPLICATION_JSON` environment variable rather than command line arguments.

#### 12.7.2. Environment Variables

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

    deployer.kubernetes.environmentVariables=JAVA_TOOL_OPTIONS=-Xmx1024m

| When deploying stream applications or launching task applications where some of the properties may contain sensitive information, use the `shell` or `boot` as the `entryPointStyle`. This is because the `exec` (default) converts all properties to command line arguments and thus may not be secure in some environments.

Additionally you can configure environment variables at the server level in the container `env` section of a deployment YAML, as the following example shows:

| When specifying environment variables in the server configuration and on a per-schedule basis, environment variables will be merged. This allows for the ability to set common environment variables in the server configuration and more specific at the specific schedule level.

    env:
    - name: SPRING_CLOUD_SCHEDULER_KUBERNETES_ENVIRONMENT_VARIABLES
      value: myVar=myVal

Replace `myVar=myVal` with your desired environment variables.

#### 12.7.3. 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 containers:

    deployer.kubernetes.imagePullPolicy=Always

Replace `Always` with your desired image pull policy.

You can configure an image pull policy at the server level in the container `env` section of a deployment YAML, as the following example shows:

    env:
    - name: SPRING_CLOUD_SCHEDULER_KUBERNETES_IMAGE_PULL_POLICY
      value: Always

Replace `Always` with your desired image pull policy.

#### 12.7.4. Private Docker Registry

Docker images that are private and require authentication can be pulled by configuring a Secret. 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, use the `imagePullSecret` property to set the secret to use, as the following example shows:

    deployer.kubernetes.imagePullSecret=mysecret

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

You can also configure the image pull secret at the server level in the container `env` section of a deployment YAML, as the following example shows:

    env:
    - name: SPRING_CLOUD_SCHEDULER_KUBERNETES_IMAGE_PULL_SECRET
      value: mysecret

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

#### 12.7.5. Namespace

By default the namespace used for scheduled tasks is `default`. This value can be set at the server level configuration in the container `env` section of a deployment YAML, as the following example shows:

    env:
    - name: SPRING_CLOUD_SCHEDULER_KUBERNETES_NAMESPACE
      value: mynamespace

#### 12.7.6. Service Account

You can configure a custom service account for scheduled tasks through properties. An existing service account can be used or a new one created. 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 the service account to use on a per-schedule basis as follows:

    deployer.kubernetes.taskServiceAccountName=myserviceaccountname

Replace `myserviceaccountname` with your service account name.

You can also configure the service account name at the server level in the container `env` section of a deployment YAML, as the following example shows:

    env:
    - name: SPRING_CLOUD_SCHEDULER_KUBERNETES_TASK_SERVICE_ACCOUNT_NAME
      value: myserviceaccountname

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

For more information on scheduling tasks see Scheduling Tasks.

### 12.8. Debug Support

Debugging the Spring Cloud Data Flow Kubernetes Server and included components (such as the Spring Cloud Kubernetes Deployer) is supported through the Java Debug Wire Protocol (JDWP). This section outlines an approach to manually enable debugging and another approach that uses configuration files provided with Spring Cloud Data Flow Server Kubernetes to “patch” a running deployment.

| JDWP itself does not use any authentication. This section assumes debugging is being done on a local development environment (such as Minikube), so guidance on securing the debug port is not provided.

#### 12.8.1. Enabling Debugging Manually

To manually enable JDWP, first edit `src/kubernetes/server/server-deployment.yaml` and add an additional `containerPort` entry under `spec.template.spec.containers.ports` with a value of `5005`. Additionally, add the `JAVA_TOOL_OPTIONS` environment variable under `spec.template.spec.containers.env` as the following example shows:

    spec:
      ...
      template:
        ...
        spec:
          containers:
          - name: scdf-server
            ...
            ports:
            ...
    		- containerPort: 5005
            env:
            - name: JAVA_TOOL_OPTIONS
              value: '-agentlib:jdwp=transport=dt_socket,server=y,suspend=n,address=5005'

| The preceding example uses port 5005, but it can be any number that does not conflict with another port. The chosen port number must also be the same for the added `containerPort` value and the `address` parameter of the `JAVA_TOOL_OPTIONS` `-agentlib` flag, as shown in the preceding example.

You can now start the Spring Cloud Data Flow Kubernetes Server. Once the server is up, you can verify the configuration changes on the `scdf-server` deployment, as the following example (with output) shows:

    kubectl describe deployment/scdf-server
    ...
    ...
    Pod Template:
      ...
      Containers:
       scdf-server:
        ...
        Ports:       80/TCP, 5005/TCP
        ...
        Environment:
          JAVA_TOOL_OPTIONS:  -agentlib:jdwp=transport=dt_socket,server=y,suspend=n,address=5005
    	  ...

With the server started and JDWP enabled, you need to configure access to the port. In this example, we use the `port-forward` subcommand of `kubectl`. The following example (with output) shows how to expose a local port to your debug target by using `port-forward`:

    $ kubectl get pod -l app=scdf-server
    NAME                           READY     STATUS    RESTARTS   AGE
    scdf-server-5b7cfd86f7-d8mj4   1/1       Running   0          10m
    $ kubectl port-forward scdf-server-5b7cfd86f7-d8mj4 5005:5005
    Forwarding from 127.0.0.1:5005 -> 5005
    Forwarding from [::1]:5005 -> 5005

You can now attach a debugger by pointing it to `127.0.0.1` as the host and `5005` as the port. The `port-forward` subcommand runs until stopped (by pressing `CTRL+c`, for example).

You can remove debugging support by reverting the changes to `src/kubernetes/server/server-deployment.yaml`. The reverted changes are picked up on the next deployment of the Spring Cloud Data Flow Kubernetes Server. Manually adding debug support to the configuration is useful when debugging should be enabled by default each time the server is deployed.

#### 12.8.2. Enabling Debugging with Patching

Rather than manually changing the `server-deployment.yaml`, Kubernetes objects can be “patched” in place. For convenience, patch files that provide the same configuration as the manual approach are included. To enable debugging by patching, use the following command:

    kubectl patch deployment scdf-server -p "$(cat src/kubernetes/server/server-deployment-debug.yaml)"

Running the preceding command automatically adds the `containerPort` attribute and the `JAVA_TOOL_OPTIONS` environment variable. The following example (with output) shows how toverify changes to the `scdf-server` deployment:

    $ kubectl describe deployment/scdf-server
    ...
    ...
    Pod Template:
      ...
      Containers:
       scdf-server:
        ...
        Ports:       5005/TCP, 80/TCP
        ...
        Environment:
          JAVA_TOOL_OPTIONS:  -agentlib:jdwp=transport=dt_socket,server=y,suspend=n,address=5005
    	  ...

To enable access to the debug port, rather than using the `port-forward` subcommand of `kubectl`, you can patch the `scdf-server` Kubernetes service object. You must first ensure that the `scdf-server` Kubernetes service object has the proper configuration. The following example (with output) shows how to do so:

    kubectl describe service/scdf-server
    Port:                     <unset>  80/TCP
    TargetPort:               80/TCP
    NodePort:                 <unset>  30784/TCP

If the output contains `<unset>`, you must patch the service to add a name for this port. The following example shows how to do so:

    $ kubectl patch service scdf-server -p "$(cat src/kubernetes/server/server-svc.yaml)"

| A port name should only be missing if the target cluster had been created prior to debug functionality being added. Since multiple ports are being added to the `scdf-server` Kubernetes Service Object, each needs to have its own name.

Now you can add the debug port, as the following example shows:

    kubectl patch service scdf-server -p "$(cat src/kubernetes/server/server-svc-debug.yaml)"

The following example (with output) shows how to verify the mapping:

    $ kubectl describe service scdf-server
    Name:                     scdf-server
    ...
    ...
    Port:                     scdf-server-jdwp  5005/TCP
    TargetPort:               5005/TCP
    NodePort:                 scdf-server-jdwp  31339/TCP
    ...
    ...
    Port:                     scdf-server  80/TCP
    TargetPort:               80/TCP
    NodePort:                 scdf-server  30883/TCP
    ...
    ...

The output shows that container port 5005 has been mapped to the NodePort of 31339. The following example (with output) shows how to get the IP address of the Minikube node:

    $ minikube ip
    192.168.99.100

With this information, you can create a debug connection by using a host of 192.168.99.100 and a port of 31339.

The following example shows how to disable JDWP:

    $ kubectl rollout undo deployment/scdf-server
    $ kubectl patch service scdf-server --type json -p='[{"op": "remove", "path": "/spec/ports/0"}]'

The Kubernetes deployment object is rolled back to its state before being patched. The Kubernetes service object is then patched with a `remove` operation to remove port 5005 from the `containerPorts` list.

| `kubectl rollout undo` forces the pod to restart. Patching the Kubernetes Service Object does not re-create the service, and the port mapping to the `scdf-server` deployment remains the same.

See Rolling Back a Deployment for more information on deployment rollbacks, including managing history and Updating API Objects in Place Using kubectl Patch.

# Shell

This section covers the options for starting the shell and more advanced functionality relating to how the shell handles whitespace, quotes, and interpretation of SpEL expressions. The introductory chapters to the Stream DSL and Composed Task DSL are good places to start for the most common usage of shell commands.

## 13. Shell Options

The shell is built upon the Spring Shell project. Some command-line options come from Spring Shell, and some are specific to Data Flow. The shell takes the following command line options:

    unix:>java -jar spring-cloud-dataflow-shell-2.8.0-M1.jar --help
    Data Flow Options:
      --dataflow.uri=                              Address of the Data Flow Server [default: http://localhost:9393].
      --dataflow.username=                        Username of the Data Flow Server [no default].
      --dataflow.password=                    Password of the Data Flow Server [no default].
      --dataflow.credentials-provider-command= Executes an external command which must return an
                                                        OAuth Bearer Token (Access Token prefixed with 'Bearer '),
                                                        e.g. 'Bearer 12345'), [no default].
      --dataflow.skip-ssl-validation=       Accept any SSL certificate (even self-signed) [default: no].
      --dataflow.proxy.uri=                  Address of an optional proxy server to use [no default].
      --dataflow.proxy.username=        Username of the proxy server (if required by proxy server) [no default].
      --dataflow.proxy.password=        Password of the proxy server (if required by proxy server) [no default].
      --spring.shell.historySize=                 Default size of the shell log file [default: 3000].
      --spring.shell.commandFile=                 Data Flow Shell executes commands read from the file(s) and then exits.
      --help                                            This message.

You can use the `spring.shell.commandFile` option to point to an existing file that contains all the shell commands to deploy one or many related streams and tasks. Running multiple files is also supported. They should be passed as a comma-delimited string:

`--spring.shell.commandFile=file1.txt,file2.txt`

This option is useful when creating some scripts to help automate deployment.

Also, the following shell command helps to modularize a complex script into multiple independent files:

`dataflow:>script --file <YOUR_AWESOME_SCRIPT>`

## 14. Listing Available Commands

Typing `help` at the command prompt gives a listing of all available commands. Most of the commands are for Data Flow functionality, but a few are general purpose. The following listing shows the output of the `help` command:

    ! - Allows execution of operating system (OS) commands
    clear - Clears the console
    cls - Clears the console
    date - Displays the local date and time
    exit - Exits the shell
    http get - Make GET request to http endpoint
    http post - POST data to http endpoint
    quit - Exits the shell
    system properties - Shows the shells properties {JB - restore the apostrophe}
    version - Displays shell version

Adding the name of the command to `help` shows additional information on how to invoke the command:

    dataflow:>help stream create
    Keyword:                   stream create
    Description:               Create a new stream definition
     Keyword:                  ** default **
     Keyword:                  name
       Help:                   the name to give to the stream
       Mandatory:              true
       Default if specified:   '__NULL__'
       Default if unspecified: '__NULL__'

     Keyword:                  definition
       Help:                   a stream definition, using the DSL (e.g. "http --port=9000 | hdfs")
       Mandatory:              true
       Default if specified:   '__NULL__'
       Default if unspecified: '__NULL__'

     Keyword:                  deploy
       Help:                   whether to deploy the stream immediately
       Mandatory:              false
       Default if specified:   'true'
       Default if unspecified: 'false'

## 15. Tab Completion

You can complete the shell command options in the shell by pressing the `TAB` key after the leading `--`. For example, pressing `TAB` after `stream create --` results in the following pair of suggestions:

    dataflow:>stream create --
    stream create --definition    stream create --name

If you type `--de` and then press tab, `--definition` expands.

Tab completion is also available inside the stream or composed task DSL expression for application or task properties. You can also use `TAB` to get hints in a stream DSL expression for the available sources, processors, or sinks that you can use.

## 16. Whitespace and Quoting Rules

You need to quote parameter values only if they contain spaces or the `|` character. The following example passes a SpEL expression (which is applied to any data it encounters) to a transform processor:

    transform --expression='new StringBuilder(payload).reverse()'

If the parameter value needs to embed a single quote, use two single quotes, as follows:

    // Query is: Select * from /Customers where name='Smith'
    scan --query='Select * from /Customers where name=''Smith'''

### 16.1. Quotes and Escaping

There is a Spring Shell-based client that talks to the Data Flow Server and is responsible for parsing the DSL. In turn, applications may have application properties that rely on embedded languages, such as the Spring Expression Language.

The Shell, Data Flow DSL parser, and SpEL have rules about how they handle quotes and how syntax escaping works. When combined together, confusion may arise. This section explains the rules that apply and provides examples of the most complicated situations you may encounter when all three components are involved.

| It is not always that complicated If you do not use the Data Flow Shell (for example, if you use the REST API directly) or if application properties are not SpEL expressions, the escaping rules are simpler.

#### 16.1.1. Shell Rules

Arguably, the most complex component when it comes to quotes is the Shell. The rules can be laid out quite simply, though:

- A shell command is made of keys (`--something`) and corresponding values. There is a special, keyless mapping, though, which is described later.

- A value cannot normally contain spaces, as space is the default delimiter for commands.

- Spaces can be added though, by surrounding the value with quotes (either single (`'`) or double (`"`) quotes).

- Values passed inside deployment properties (for example, `deployment <stream-name> --properties " …​"`) should not be quoted again.

- If surrounded with quotes, a value can embed a literal quote of the same kind by prefixing it with a backslash (`\`).

- Other escapes are available, such as `\t`, `\n`, `\r`, `\f` and unicode escapes of the form `\uxxxx`.

- The keyless mapping is handled in a special way such that it does not need quoting to contain spaces.

For example, the shell supports the `!` command to execute native shell commands. The `!` accepts a single keyless argument. This is why the following example works:

    dataflow:>! rm something

The argument here is the whole `rm something` string, which is passed as is to the underlying shell.

As another example, the following commands are strictly equivalent, and the argument value is `something` (without the quotes):

    dataflow:>stream destroy something
    dataflow:>stream destroy --name something
    dataflow:>stream destroy "something"
    dataflow:>stream destroy --name "something"

#### 16.1.2. Property Files Rules

The rules are relaxed when loading the properties from files.

- The special characters used in property files (both Java and YAML) need to be escaped. For example `\` should be replaced by `\\`, `\t` by `\\t` and so forth.

- For Java property files (`--propertiesFile <FILE_PATH>.properties`), the property values should not be surrounded by quotes. It is not needed even if they contain spaces.

      filter.expression=payload > 5

- For YAML property files (`--propertiesFile <FILE_PATH>.yaml`), though, the values need to be surrounded by double quotes.

      app:
          filter:
              filter:
                  expression: "payload > 5"

#### 16.1.3. DSL Parsing Rules

At the parser level (that is, inside the body of a stream or task definition), the rules are as follows:

- Option values are normally parsed until the first space character.

- They can be made of literal strings, though, surrounded by single or double quotes.

- To embed such a quote, use two consecutive quotes of the desired kind.

As such, the values of the `--expression` option to the filter application are semantically equivalent in the following examples:

    filter --expression=payload>5
    filter --expression="payload>5"
    filter --expression='payload>5'
    filter --expression='payload > 5'

Arguably, the last one is more readable. It is made possible thanks to the surrounding quotes. The actual expression is `payload > 5`.

Now, imagine that we want to test against string messages. If we want to compare the payload to the SpEL literal string, `"something"`, we could use the following:

    filter --expression=payload=='something'           (1)
    filter --expression='payload == ''something'''     (2)
    filter --expression='payload == "something"'       (3)

1 | This works because there are no spaces. It is not very legible, though.
2 | This uses single quotes to protect the whole argument. Hence, the actual single quotes need to be doubled.
3 | SpEL recognizes String literals with either single or double quotes, so this last method is arguably the most readable.

Note that the preceding examples are to be considered outside of the shell (for example, when calling the REST API directly). When entered inside the shell, chances are that the whole stream definition is itself inside double quotes, which would need to be escaped. The whole example then becomes the following:

    dataflow:>stream create something --definition "http | filter --expression=payload='something' | log"

    dataflow:>stream create something --definition "http | filter --expression='payload == ''something''' | log"

    dataflow:>stream create something --definition "http | filter --expression='payload == \"something\"' | log"

#### 16.1.4. SpEL Syntax and SpEL Literals

The last piece of the puzzle is about SpEL expressions. Many applications accept options that are to be interpreted as SpEL expressions, and, as seen earlier, String literals are handled in a special way there, too. The rules are as follows:

- Literals can be enclosed in either single or double quotes.

- Quotes need to be doubled to embed a literal quote. Single quotes inside double quotes need no special treatment, and the reverse is also true.

As a last example, assume you want to use the transform processor. This processor accepts an `expression` option which is a SpEL expression. It is to be evaluated against the incoming message, with a default of `payload` (which forwards the message payload untouched).

It is important to understand that the following statements are equivalent:

    transform --expression=payload
    transform --expression='payload'

However, they are different from the following (and variations upon them):

    transform --expression="'payload'"
    transform --expression='''payload'''

The first series evaluates to the message payload, while the latter examples evaluate to the literal string, `payload`.

#### 16.1.5. Putting It All Together

As a last, complete example, consider how you could force the transformation of all messages to the string literal, `hello world`, by creating a stream in the context of the Data Flow shell:

    dataflow:>stream create something --definition "http | transform --expression='''hello world''' | log" (1)

    dataflow:>stream create something --definition "http | transform --expression='\"hello world\"' | log" (2)

    dataflow:>stream create something --definition "http | transform --expression=\"'hello world'\" | log" (2)

1 | In the first line, single quotes surround the string (at the Data Flow parser level), but they need to be doubled because they are inside a string literal (started by the first single quote after the equals sign).
2 | The second and third lines use single and double quotes, respectively, to encompass the whole string at the Data Flow parser level. Consequently, the other kind of quote can be used inside the string. The whole thing is inside the `--definition` argument to the shell, though, which uses double quotes. Consequently, double quotes are escaped (at the shell level).

# Streams

This section goes into more detail about how you can create Streams, which are collections of Spring Cloud Stream applications. It covers topics such as creating and deploying Streams.

If you are just starting out with Spring Cloud Data Flow, you should probably read the Getting Started guide before diving into this section.

## 17. Introduction

A Stream is a collection of long-lived Spring Cloud Stream applications that communicate with each other over messaging middleware. A text-based DSL defines the configuration and data flow between the applications. While many applications are provided for you to implement common use-cases, you typically create a custom Spring Cloud Stream application to implement custom business logic.

The general lifecycle of a Stream is:

1. Register applications.

2. Create a Stream Definition.

3. Deploy the Stream.

4. Undeploy or destroy the Stream.

5. Upgrade or roll back applications in the Stream.

For deploying Streams, the Data Flow Server has to be configured to delegate the deployment to a new server in the Spring Cloud ecosystem named Skipper.

Furthermore, you can configure Skipper to deploy applications to one or more Cloud Foundry orgs and spaces, one or more namespaces on a Kubernetes cluster, or to the local machine. When deploying a stream in Data Flow, you can specify which platform to use at deployment time. Skipper also provides Data Flow with the ability to perform updates to deployed streams. There are many ways the applications in a stream can be updated, but one of the most common examples is to upgrade a processor application with new custom business logic while leaving the existing source and sink applications alone.

### 17.1. Stream Pipeline DSL

A stream is defined by using a Unix-inspired Pipeline syntax. The syntax uses vertical bars, known as “pipes”, to connect multiple commands. The command `ls -l | grep key | less` in Unix takes the output of the `ls -l` process and pipes it to the input of the `grep key` process. The output of `grep` is, in turn, sent to the input of the `less` process. Each `|` symbol connects the standard output of the command on the left to the standard input of the command on the right. Data flows through the pipeline from left to right.

In Data Flow, the Unix command is replaced by a Spring Cloud Stream application and each pipe symbol represents connecting the input and output of applications over messaging middleware, such as RabbitMQ or Apache Kafka.

Each Spring Cloud Stream application is registered under a simple name. The registration process specifies where the application can be obtained (for example, in a Maven Repository or a Docker registry). You can find out more about how to register Spring Cloud Stream applications in this section. In Data Flow, we classify the Spring Cloud Stream applications as Sources, Processors, or Sinks.

As a simple example, consider the collection of data from an HTTP Source and writing to a File Sink. Using the DSL, the stream description is:

`http | file`

A stream that involves some processing would be expressed as:

`http | filter | transform | file`

Stream definitions can be created by using the shell’s `stream create` command, as shown in the following example:

`dataflow:> stream create --name httpIngest --definition "http | file"`

The Stream DSL is passed in to the `--definition` command option.

The deployment of stream definitions is done through the Shell’s `stream deploy` command, as follows:

`dataflow:> stream deploy --name ticktock`

The Getting Started section shows you how to start the server and how to start and use the Spring Cloud Data Flow shell.

Note that the shell calls the Data Flow Server’s REST API. For more information on making HTTP requests directly to the server, see the REST API Guide.

| When naming a stream definition, keep in mind that each application in the stream will be created on the platform with the name in the format of `<stream name>-<app name>`. Thus, the total length of the generated application name can’t exceed 58 characters.

### 17.2. Stream Application DSL

You can use the Stream Application DSL to define custom binding properties for each of the Spring Cloud Stream applications. See the Stream Application DSL section of the microsite for more information.

Consider the following Java interface, which defines an input method and two output methods:

    public interface Barista {

        @Input
        SubscribableChannel orders();

        @Output
        MessageChannel hotDrinks();

        @Output
        MessageChannel coldDrinks();
    }

Further consider the following Java interface, which is typical for creating a Kafka Streams application:

    interface KStreamKTableBinding {

        @Input
        KStream<?, ?> inputStream();

        @Input
        KTable<?, ?> inputTable();
    }

In these cases with multiple input and output bindings, Data Flow cannot make any assumptions about the flow of data from one application to another. Therefore, you need to set the binding properties to “wire up” the application. The Stream Application DSL uses a “double pipe”, instead of the “pipe symbol”, to indicate that Data Flow should not configure the binding properties of the application. Think of `||` as meaning “in parallel”. The following example shows such a “parallel” definition:

    dataflow:> stream create --definition "orderGeneratorApp || baristaApp || hotDrinkDeliveryApp || coldDrinkDeliveryApp" --name myCafeStream

| Breaking Change! Versions of SCDF Local, Cloud Foundry 1.7.0 to 1.7.2 and SCDF Kubernetes 1.7.0 to 1.7.1 used the `comma` character as the separator between applications. This caused breaking changes in the traditional Stream DSL. While not ideal, changing the separator character was felt to be the best solution with the least impact on existing users.

This stream has four applications. `baristaApp` has two output destinations, `hotDrinks` and `coldDrinks`, intended to be consumed by the `hotDrinkDeliveryApp` and `coldDrinkDeliveryApp`, respectively. When deploying this stream, you need to set the binding properties so that the `baristaApp` sends hot drink messages to the `hotDrinkDeliveryApp` destination and cold drink messages to the `coldDrinkDeliveryApp` destination. The following listing does so:

    app.baristaApp.spring.cloud.stream.bindings.hotDrinks.destination=hotDrinksDest
    app.baristaApp.spring.cloud.stream.bindings.coldDrinks.destination=coldDrinksDest
    app.hotDrinkDeliveryApp.spring.cloud.stream.bindings.input.destination=hotDrinksDest
    app.coldDrinkDeliveryApp.spring.cloud.stream.bindings.input.destination=coldDrinksDest

If you want to use consumer groups, you need to set the Spring Cloud Stream application properties, `spring.cloud.stream.bindings.<channelName>.producer.requiredGroups` and `spring.cloud.stream.bindings.<channelName>.group`, on the producer and consumer applications respectively.

Another common use case for the Stream Application DSL is to deploy a HTTP gateway application that sends a synchronous request or reply message to a Kafka or RabbitMQ application. In this case, both the HTTP gateway application and the Kafka or RabbitMQ application can be a Spring Integration application that does not make use of the Spring Cloud Stream library.

It is also possible to deploy only a single application using the Stream application DSL.

### 17.3. Application Properties

Each application takes properties to customize its behavior. As an example, the `http` source module exposes a `port` setting that lets the data ingestion port be changed from the default value:

    dataflow:> stream create --definition "http --port=8090 | log" --name myhttpstream

This `port` property is actually the same as the standard Spring Boot `server.port` property. Data Flow adds the ability to use the shorthand form `port` instead of `server.port`. You can also specify the longhand version:

    dataflow:> stream create --definition "http --server.port=8000 | log" --name myhttpstream

This shorthand behavior is discussed more in the section on Stream Application Properties. If you have registered application property metadata, you can use tab completion in the shell after typing `--` to get a list of candidate property names.

The shell provides tab completion for application properties. The `app info --name <appName> --type <appType>` shell command provides additional documentation for all the supported properties.

| Supported Stream `<appType>` possibilities are: `source`, `processor`, and `sink`.

## 18. Stream Lifecycle

The lifecycle of a stream goes through the following stages:

1. Register a Stream Application

2. Creating a Stream

3. Deploying a Stream

4. Destroying a Stream or Undeploying a Stream

5. Upgrade or roll back applications in the Stream.

Skipper is a server that lets you discover Spring Boot applications and manage their lifecycle on multiple Cloud Platforms.

Applications in Skipper are bundled as packages that contain the application’s resource location, application properties, and deployment properties. You can think of Skipper packages as being analogous to packages found in tools such as `apt-get` or `brew`.

When Data Flow deploys a Stream, it generates and upload a package to Skipper that represents the applications in the Stream. Subsequent commands to upgrade or roll back the applications within the Stream are passed through to Skipper. In addition, the Stream definition is reverse-engineered from the package, and the status of the Stream is also delegated to Skipper.

### 18.1. Register a Stream Application

You can register a versioned stream application by using the `app register` command. You must provide a unique name, an application type, and a URI that can be resolved to the application artifact. For the type, specify `source`, `processor`, or `sink`. The version is resolved from the URI. Here are a few examples:

    dataflow:>app register --name mysource --type source --uri maven://com.example:mysource:0.0.1
    dataflow:>app register --name mysource --type source --uri maven://com.example:mysource:0.0.2
    dataflow:>app register --name mysource --type source --uri maven://com.example:mysource:0.0.3

    dataflow:>app list --id source:mysource
    ╔═══╤══════════════════╤═════════╤════╤════╗
    ║app│      source      │processor│sink│task║
    ╠═══╪══════════════════╪═════════╪════╪════╣
    ║   │> mysource-0.0.1 <│         │    │    ║
    ║   │mysource-0.0.2    │         │    │    ║
    ║   │mysource-0.0.3    │         │    │    ║
    ╚═══╧══════════════════╧═════════╧════╧════╝

    dataflow:>app register --name myprocessor --type processor --uri file:///Users/example/myprocessor-1.2.3.jar

    dataflow:>app register --name mysink --type sink --uri https://example.com/mysink-2.0.1.jar

The application URI should conform to one the following schema formats:

- Maven schema:

      maven://<groupId>:<artifactId>[:<extension>[:<classifier>]]:<version>

- HTTP schema:

      http://<web-path>/<artifactName>-<version>.jar

- File schema:

      file:///<local-path>/<artifactName>-<version>.jar

- Docker schema:

      docker:<docker-image-path>/<imageName>:<version>

| The URI `<version>` part is compulsory for versioned stream applications. Skipper uses the multi-versioned stream applications to allow upgrading or rolling back those applications at runtime by using the deployment properties.

If you would like to register the snapshot versions of the `http` and `log` applications built with the RabbitMQ binder, you could do the following:

    dataflow:>app register --name http --type source --uri maven://org.springframework.cloud.stream.app:http-source-rabbit:1.2.1.BUILD-SNAPSHOT
    dataflow:>app register --name log --type sink --uri maven://org.springframework.cloud.stream.app:log-sink-rabbit:1.2.1.BUILD-SNAPSHOT

If you would like to register multiple applications at one time, you can store them in a properties file, where the keys are formatted as `<type>.<name>` and the values are the URIs.

For example, to register the snapshot versions of the `http` and `log` applications built with the RabbitMQ binder, you could have the following in a properties file (for example, `stream-apps.properties`):

    source.http=maven://org.springframework.cloud.stream.app:http-source-rabbit:1.2.1.BUILD-SNAPSHOT
    sink.log=maven://org.springframework.cloud.stream.app:log-sink-rabbit:1.2.1.BUILD-SNAPSHOT

Then, to import the applications in bulk, use the `app import` command and provide the location of the properties file with the `--uri` switch, as follows:

    dataflow:>app import --uri file:///<YOUR_FILE_LOCATION>/stream-apps.properties

Registering an application by using `--type app` is the same as registering a `source`, `processor` or `sink`. Applications of the type `app` can be used only in the Stream Application DSL (which uses double pipes `||` instead of single pipes `|` in the DSL) and instructs Data Flow not to configure the Spring Cloud Stream binding properties of the application. The application that is registered using `--type app` does not have to be a Spring Cloud Stream application. It can be any Spring Boot application. See the Stream Application DSL introduction for more about using this application type.

You can register multiple versions of the same applications (for example, the same name and type), but you can set only one as the default. The default version is used for deploying Streams.

The first time an application is registered, it is marked as default. The default application version can be altered with the `app default` command:

    dataflow:>app default --id source:mysource --version 0.0.2
    dataflow:>app list --id source:mysource
    ╔═══╤══════════════════╤═════════╤════╤════╗
    ║app│      source      │processor│sink│task║
    ╠═══╪══════════════════╪═════════╪════╪════╣
    ║   │mysource-0.0.1    │         │    │    ║
    ║   │> mysource-0.0.2 <│         │    │    ║
    ║   │mysource-0.0.3    │         │    │    ║
    ╚═══╧══════════════════╧═════════╧════╧════╝

The `app list --id <type:name>` command lists all versions for a given stream application.

The `app unregister` command has an optional `--version` parameter to specify the application version to unregister:

    dataflow:>app unregister --name mysource --type source --version 0.0.1
    dataflow:>app list --id source:mysource
    ╔═══╤══════════════════╤═════════╤════╤════╗
    ║app│      source      │processor│sink│task║
    ╠═══╪══════════════════╪═════════╪════╪════╣
    ║   │> mysource-0.0.2 <│         │    │    ║
    ║   │mysource-0.0.3    │         │    │    ║
    ╚═══╧══════════════════╧═════════╧════╧════╝

If `--version` is not specified, the default version is unregistered.

| All applications in a stream should have a default version set for the stream to be deployed. Otherwise, they are treated as unregistered application during the deployment. Use the `app default` command to set the defaults.

    app default --id source:mysource --version 0.0.3
    dataflow:>app list --id source:mysource
    ╔═══╤══════════════════╤═════════╤════╤════╗
    ║app│      source      │processor│sink│task║
    ╠═══╪══════════════════╪═════════╪════╪════╣
    ║   │mysource-0.0.2    │         │    │    ║
    ║   │> mysource-0.0.3 <│         │    │    ║
    ╚═══╧══════════════════╧═════════╧════╧════╝

The `stream deploy` necessitates default application versions being set. The `stream update` and `stream rollback` commands, though, can use all (default and non-default) registered application versions.

The following command creates a stream that uses the default mysource version (0.0.3):

    dataflow:>stream create foo --definition "mysource | log"

Then we can update the version to 0.0.2:

    dataflow:>stream update foo --properties version.mysource=0.0.2

| Only pre-registered applications can be used to `deploy`, `update`, or `rollback` a Stream.

An attempt to update the `mysource` to version `0.0.1` (not registered) fails.

#### 18.1.1. Register Supported Applications and Tasks

For convenience, we have the static files with application-URIs (for both Maven and Docker) available for all the out-of-the-box stream and task or batch app-starters. You can point to this file and import all the application-URIs in bulk. Otherwise, as explained previously, you can register them individually or have your own custom property file with only the required application-URIs in it. We recommend, however, having a “focused” list of desired application-URIs in a custom property file.

##### Spring Cloud Stream App Starters

The following table includes the `dataflow.spring.io` links to the available Stream Application Starters based on Spring Cloud Stream 2.1.x and Spring Boot 2.1.x:

Artifact Type | Stable Release | SNAPSHOT Release
--- | --- | ---
RabbitMQ + Maven | dataflow.spring.io/rabbitmq-maven-latest | dataflow.spring.io/Einstein-BUILD-SNAPSHOT-stream-applications-rabbit-maven
RabbitMQ + Docker | dataflow.spring.io/rabbitmq-docker-latest | dataflow.spring.io/Einstein-BUILD-SNAPSHOT-stream-applications-rabbit-docker
Apache Kafka + Maven | dataflow.spring.io/kafka-maven-latest | dataflow.spring.io/Einstein-BUILD-SNAPSHOT-stream-applications-kafka-maven
Apache Kafka + Docker | dataflow.spring.io/kafka-docker-latest | dataflow.spring.io/Einstein-BUILD-SNAPSHOT-stream-applications-kafka-docker

| By default, App Starter actuator endpoints are secured. You can disable security by deploying streams with the `app.*.spring.autoconfigure.exclude=org.springframework.boot.autoconfigure.security.servlet.SecurityAutoConfiguration` property. On Kubernetes, see the Liveness and readiness probes section for how to configure security for actuator endpoints.

| Starting with the Spring Cloud Stream 2.1 GA release, we now have robust interoperability with the Spring Cloud Function programming model. Building on that, with the Einstein release-train, it is now possible to pick a few Stream App Starters and compose them into a single application by using the functional-style programming model. Check out the "Composed Function Support in Spring Cloud Data Flow" blog to learn more about the developer and orchestration-experience with an example.

##### Spring Cloud Task App Starters

The following table includes the available Task Application Starters based on Spring Cloud Task 2.1.x and Spring Boot 2.1.x:

Artifact Type | Stable Release | SNAPSHOT Release
--- | --- | ---
Maven | dataflow.spring.io/task-maven-latest | dataflow.spring.io/Elston-BUILD-SNAPSHOT-task-applications-maven
Docker | dataflow.spring.io/task-docker-latest | dataflow.spring.io/Elston-BUILD-SNAPSHOT-task-applications-docker

You can find more information about the available task starters in the Task App Starters Project Page and related reference documentation. For more information about the available stream starters, look at the Stream App Starters Project Page and related reference documentation.

As an example, if you would like to register all out-of-the-box stream applications built with the Kafka binder in bulk, you can use the following command:

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

Alternatively, you can register all the stream applications with the Rabbit binder, as follows:

    $ dataflow:>app import --uri https://dataflow.spring.io/rabbitmq-maven-latest

You can also pass the `--local` option (which is `true` by default) to indicate whether the properties file location should be resolved within the shell process itself. If the location should be resolved from the Data Flow Server process, specify `--local false`.

| When you use either `app register` or `app import`, if an application is already registered with the provided name and type and version, it is, by default, not overridden. If you would like to override the pre-existing application `uri` or `metadata-uri` coordinates, include the `--force` option. Note, however, that, once downloaded, applications may be cached locally on the Data Flow server, based on the resource location. If the resource location does not change (even though the actual resource bytes may be different), it is not re-downloaded. When using `maven://` resources, on the other hand, using a constant location may still circumvent caching (if using `-SNAPSHOT` versions). Moreover, if a stream is already deployed and uses some version of a registered app, then (forcibly) re-registering a different application has no effect until the stream is deployed again.

| In some cases, the resource is resolved on the server side. In others, the URI is passed to a runtime container instance, where it is resolved. See the specific documentation of each Data Flow Server for more detail.

#### 18.1.2. Creating Custom Applications

While Data Flow includes source, processor, sink applications, you can extend these applications or write a custom Spring Cloud Stream application.

The process of creating Spring Cloud Stream applications with Spring Initializr is detailed in the Spring Cloud Stream documentation. You can include multiple binders to an application. If you do so, see the instructions in [passing_producer_consumer_properties] for how to configure them.

To support allowing properties, Spring Cloud Stream applications running in Spring Cloud Data Flow can include the Spring Boot `configuration-processor` as an optional dependency, as shown in the following example:

    <dependencies>
      <!-- other dependencies -->
      <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-configuration-processor</artifactId>
        <optional>true</optional>
      </dependency>
    </dependencies>

NOTE:Make sure that the `spring-boot-maven-plugin` is included in the POM. The plugin is necessary for creating the executable jar that is registered with Spring Cloud Data Flow. Spring Initialzr includes the plugin in the generated POM.

Once you have created a custom application, you can register it, as described in Register a Stream Application.

### 18.2. Creating a Stream

The Spring Cloud Data Flow Server exposes a full RESTful API for managing the lifecycle of stream definitions, but the easiest way to use is it is through the Spring Cloud Data Flow shell. The Getting Started section describes how to start the shell.

New streams are created with the help of stream definitions. The definitions are built from a simple DSL. For example, consider what happens if we run the following shell command:

    dataflow:> stream create --definition "time | log" --name ticktock

This defines a stream named `ticktock` that is based off of the DSL expression `time | log`. The DSL uses the “pipe” symbol (`|`), to connect a source to a sink.

The `stream info` command shows useful information about the stream, as shown (with its output) in the following example:

    dataflow:>stream info ticktock
    ╔═══════════╤═════════════════╤══════════╗
    ║Stream Name│Stream Definition│  Status  ║
    ╠═══════════╪═════════════════╪══════════╣
    ║ticktock   │time | log       │undeployed║
    ╚═══════════╧═════════════════╧══════════╝

#### 18.2.1. Stream Application Properties

Application properties are the properties associated with each application in the stream. When the application is deployed, the application properties are applied to the application through command-line arguments or environment variables, depending on the underlying deployment implementation.

The following stream can have application properties defined at the time of stream creation:

    dataflow:> stream create --definition "time | log" --name ticktock

The `app info --name <appName> --type <appType>` shell command displays the exposed application properties for the application. For more about exposed properties, see Application Metadata.

The following listing shows the exposed properties for the `time` application:

    dataflow:> app info --name time --type source
    ╔══════════════════════════════╤══════════════════════════════╤══════════════════════════════╤══════════════════════════════╗
    ║         Option Name          │         Description          │           Default            │             Type             ║
    ╠══════════════════════════════╪══════════════════════════════╪══════════════════════════════╪══════════════════════════════╣
    ║trigger.time-unit             │The TimeUnit to apply to delay│<none>                        │java.util.concurrent.TimeUnit ║
    ║                              │values.                       │                              │                              ║
    ║trigger.fixed-delay           │Fixed delay for periodic      │1                             │java.lang.Integer             ║
    ║                              │triggers.                     │                              │                              ║
    ║trigger.cron                  │Cron expression value for the │<none>                        │java.lang.String              ║
    ║                              │Cron Trigger.                 │                              │                              ║
    ║trigger.initial-delay         │Initial delay for periodic    │0                             │java.lang.Integer             ║
    ║                              │triggers.                     │                              │                              ║
    ║trigger.max-messages          │Maximum messages per poll, -1 │1                             │java.lang.Long                ║
    ║                              │means infinity.               │                              │                              ║
    ║trigger.date-format           │Format for the date value.    │<none>                        │java.lang.String              ║
    ╚══════════════════════════════╧══════════════════════════════╧══════════════════════════════╧══════════════════════════════╝

The following listing shows the exposed properties for the `log` application:

    dataflow:> app info --name log --type sink
    ╔══════════════════════════════╤══════════════════════════════╤══════════════════════════════╤══════════════════════════════╗
    ║         Option Name          │         Description          │           Default            │             Type             ║
    ╠══════════════════════════════╪══════════════════════════════╪══════════════════════════════╪══════════════════════════════╣
    ║log.name                      │The name of the logger to use.│<none>                        │java.lang.String              ║
    ║log.level                     │The level at which to log     │<none>                        │org.springframework.integratio║
    ║                              │messages.                     │                              │n.handler.LoggingHandler$Level║
    ║log.expression                │A SpEL expression (against the│payload                       │java.lang.String              ║
    ║                              │incoming message) to evaluate │                              │                              ║
    ║                              │as the logged message.        │                              │                              ║
    ╚══════════════════════════════╧══════════════════════════════╧══════════════════════════════╧══════════════════════════════╝

You can specify the application properties for the `time` and `log` apps at the time of `stream` creation, as follows:

    dataflow:> stream create --definition "time --fixed-delay=5 | log --level=WARN" --name ticktock

Note that, in the preceding example, the `fixed-delay` and `level` properties defined for the `time` and `log` applications are the “short-form” property names provided by the shell completion. These “short-form” property names are applicable only for the exposed properties. In all other cases, you should use only fully qualified property names.

#### 18.2.2. Common Application Properties

In addition to configuration through DSL, Spring Cloud Data Flow provides a mechanism for setting common properties to all the streaming applications that are launched by it. This can be done by adding properties prefixed with `spring.cloud.dataflow.applicationProperties.stream` when starting the server. When doing so, the server passes all the properties, without the prefix, to the instances it launches.

For example, all the launched applications can be configured to use a specific Kafka broker by launching the Data Flow server with the following options:

    --spring.cloud.dataflow.applicationProperties.stream.spring.cloud.stream.kafka.binder.brokers=192.168.1.100:9092
    --spring.cloud.dataflow.applicationProperties.stream.spring.cloud.stream.kafka.binder.zkNodes=192.168.1.100:2181

Doing so causes the `spring.cloud.stream.kafka.binder.brokers` and `spring.cloud.stream.kafka.binder.zkNodes` properties to be passed to all the launched applications.

| Properties configured with this mechanism have lower precedence than stream deployment properties. They are overridden if a property with the same key is specified at stream deployment time (for example, `app.http.spring.cloud.stream.kafka.binder.brokers` overrides the common property).

### 18.3. Deploying a Stream

This section describes how to deploy a Stream when the Spring Cloud Data Flow server is responsible for deploying the stream. It covers the deployment and upgrade of Streams by using the Skipper service. The description of how to set deployment properties applies to both approaches of Stream deployment.

Consider the `ticktock` stream definition:

    dataflow:> stream create --definition "time | log" --name ticktock

To deploy the stream, use the following shell command:

    dataflow:> stream deploy --name ticktock

The Data Flow Server delegates to Skipper the resolution and deployment of the `time` and `log` applications.

The `stream info` command shows useful information about the stream, including the deployment properties:

    dataflow:>stream info --name ticktock
    ╔═══════════╤═════════════════╤═════════╗
    ║Stream Name│Stream Definition│  Status ║
    ╠═══════════╪═════════════════╪═════════╣
    ║ticktock   │time | log       │deploying║
    ╚═══════════╧═════════════════╧═════════╝

    Stream Deployment properties: {
      "log" : {
        "resource" : "maven://org.springframework.cloud.stream.app:log-sink-rabbit",
        "spring.cloud.deployer.group" : "ticktock",
        "version" : "2.0.1.RELEASE"
      },
      "time" : {
        "resource" : "maven://org.springframework.cloud.stream.app:time-source-rabbit",
        "spring.cloud.deployer.group" : "ticktock",
        "version" : "2.0.1.RELEASE"
      }
    }

There is an important optional command argument (called `--platformName`) to the `stream deploy` command. Skipper can be configured to deploy to multiple platforms. Skipper is pre-configured with a platform named `default`, which deploys applications to the local machine where Skipper is running. The default value of the `--platformName` command line argument is `default`. If you commonly deploy to one platform, when installing Skipper, you can override the configuration of the `default` platform. Otherwise, specify the `platformName` to be one of the values returned by the `stream platform-list` command.

In the preceding example, the time source sends the current time as a message each second, and the log sink outputs it by using the logging framework. You can tail the `stdout` log (which has an `<instance>` suffix). The log files are located within the directory displayed in the Data Flow Server’s log output, as shown in the following listing:

    $ tail -f /var/folders/wn/8jxm_tbd1vj28c8vj37n900m0000gn/T/spring-cloud-dataflow-912434582726479179/ticktock-1464788481708/ticktock.log/stdout_0.log
    2016-06-01 09:45:11.250  INFO 79194 --- [  kafka-binder-] log.sink    : 06/01/16 09:45:11
    2016-06-01 09:45:12.250  INFO 79194 --- [  kafka-binder-] log.sink    : 06/01/16 09:45:12
    2016-06-01 09:45:13.251  INFO 79194 --- [  kafka-binder-] log.sink    : 06/01/16 09:45:13

You can also create and deploy the stream in one step by passing the `--deploy` flag when creating the stream, as follows:

    dataflow:> stream create --definition "time | log" --name ticktock --deploy

However, it is not common in real-world use cases to create and deploy the stream in one step. The reason is that when you use the `stream deploy` command, you can pass in properties that define how to map the applications onto the platform (for example, what is the memory size of the container to use, the number of each application to run, and whether to enable data partitioning features). Properties can also override application properties that were set when creating the stream. The next sections cover this feature in detail.

#### 18.3.1. Deployment Properties

When deploying a stream, you can specify properties that can control how applications are deployed and configured. See the Deployment Properties section of the microsite for more information.

### 18.4. Destroying a Stream

You can delete a stream by issuing the `stream destroy` command from the shell, as follows:

    dataflow:> stream destroy --name ticktock

If the stream was deployed, it is undeployed before the stream definition is deleted.

### 18.5. Undeploying a Stream

Often, you want to stop a stream but retain the name and definition for future use. In that case, you can `undeploy` the stream by name:

    dataflow:> stream undeploy --name ticktock
    dataflow:> stream deploy --name ticktock

You can issue the `deploy` command at a later time to restart it:

    dataflow:> stream deploy --name ticktock

### 18.6. Validating a Stream

Sometimes, an application contained within a stream definition contains an invalid URI in its registration. This can caused by an invalid URI being entered at application registration time or by the application being removed from the repository from which it was to be drawn. To verify that all the applications contained in a stream are resolve-able, a user can use the `validate` command:

    dataflow:>stream validate ticktock
    ╔═══════════╤═════════════════╗
    ║Stream Name│Stream Definition║
    ╠═══════════╪═════════════════╣
    ║ticktock   │time | log       ║
    ╚═══════════╧═════════════════╝

    ticktock is a valid stream.
    ╔═══════════╤═════════════════╗
    ║ App Name  │Validation Status║
    ╠═══════════╪═════════════════╣
    ║source:time│valid            ║
    ║sink:log   │valid            ║
    ╚═══════════╧═════════════════╝

In the preceding example, the user validated their ticktock stream. Both the `source:time` and `sink:log` are valid. Now we can see what happens if we have a stream definition with a registered application with an invalid URI:

    dataflow:>stream validate bad-ticktock
    ╔════════════╤═════════════════╗
    ║Stream Name │Stream Definition║
    ╠════════════╪═════════════════╣
    ║bad-ticktock│bad-time | log   ║
    ╚════════════╧═════════════════╝

    bad-ticktock is an invalid stream.
    ╔═══════════════╤═════════════════╗
    ║   App Name    │Validation Status║
    ╠═══════════════╪═════════════════╣
    ║source:bad-time│invalid          ║
    ║sink:log       │valid            ║
    ╚═══════════════╧═════════════════╝

In this case, Spring Cloud Data Flow states that the stream is invalid because `source:bad-time` has an invalid URI.

### 18.7. Updating a Stream

To update the stream, use the `stream update` command, which takes either `--properties` or `--propertiesFile` as a command argument. Skipper has an important new top-level prefix: `version`. The following commands deploy `http | log` stream (and the version of `log` which registered at the time of deployment was `1.1.0.RELEASE`):

    dataflow:> stream create --name httptest --definition "http --server.port=9000 | log"
    dataflow:> stream deploy --name httptest
    dataflow:>stream info httptest
    ╔══════════════════════════════╤══════════════════════════════╤════════════════════════════╗
    ║             Name             │             DSL              │          Status            ║
    ╠══════════════════════════════╪══════════════════════════════╪════════════════════════════╣
    ║httptest                      │http --server.port=9000 | log │deploying                   ║
    ╚══════════════════════════════╧══════════════════════════════╧════════════════════════════╝

    Stream Deployment properties: {
      "log" : {
        "spring.cloud.deployer.indexed" : "true",
        "spring.cloud.deployer.group" : "httptest",
        "maven://org.springframework.cloud.stream.app:log-sink-rabbit" : "1.1.0.RELEASE"
      },
      "http" : {
        "spring.cloud.deployer.group" : "httptest",
        "maven://org.springframework.cloud.stream.app:http-source-rabbit" : "1.1.0.RELEASE"
      }
    }

Then the following command updates the stream to use the `1.2.0.RELEASE` version of the log application. Before updating the stream with the specific version of the application, we need to make sure that the application is registered with that version:

    dataflow:>app register --name log --type sink --uri maven://org.springframework.cloud.stream.app:log-sink-rabbit:1.2.0.RELEASE
    Successfully registered application 'sink:log'

Then we can update the application:

    dataflow:>stream update --name httptest --properties version.log=1.2.0.RELEASE

| You can use only pre-registered application versions to `deploy`, `update`, or `rollback` a stream.

To verify the deployment properties and the updated version, we can use `stream info`, as shown (with its output) in the following example:

    dataflow:>stream info httptest
    ╔══════════════════════════════╤══════════════════════════════╤════════════════════════════╗
    ║             Name             │             DSL              │          Status            ║
    ╠══════════════════════════════╪══════════════════════════════╪════════════════════════════╣
    ║httptest                      │http --server.port=9000 | log │deploying                   ║
    ╚══════════════════════════════╧══════════════════════════════╧════════════════════════════╝

    Stream Deployment properties: {
      "log" : {
        "spring.cloud.deployer.indexed" : "true",
        "spring.cloud.deployer.count" : "1",
        "spring.cloud.deployer.group" : "httptest",
        "maven://org.springframework.cloud.stream.app:log-sink-rabbit" : "1.2.0.RELEASE"
      },
      "http" : {
        "spring.cloud.deployer.group" : "httptest",
        "maven://org.springframework.cloud.stream.app:http-source-rabbit" : "1.1.0.RELEASE"
      }
    }

### 18.8. Forcing an Update of a Stream

When upgrading a stream, you can use the `--force` option to deploy new instances of currently deployed applications even if no application or deployment properties have changed. This behavior is needed for when configuration information is obtained by the application itself at startup time — for example, from Spring Cloud Config Server. You can specify the applications for which to force an upgrade by using the `--app-names` option. If you do not specify any application names, all the applications are forced to upgrade. You can specify the `--force` and `--app-names` options together with the `--properties` or `--propertiesFile` options.

### 18.9. Stream Versions

Skipper keeps a history of the streams that were deployed. After updating a Stream, there is a second version of the stream. You can query for the history of the versions by using the `stream history --name <name-of-stream>` command:

    dataflow:>stream history --name httptest
    ╔═══════╤════════════════════════════╤════════╤════════════╤═══════════════╤════════════════╗
    ║Version│        Last updated        │ Status │Package Name│Package Version│  Description   ║
    ╠═══════╪════════════════════════════╪════════╪════════════╪═══════════════╪════════════════╣
    ║2      │Mon Nov 27 22:41:16 EST 2017│DEPLOYED│httptest    │1.0.0          │Upgrade complete║
    ║1      │Mon Nov 27 22:40:41 EST 2017│DELETED │httptest    │1.0.0          │Delete complete ║
    ╚═══════╧════════════════════════════╧════════╧════════════╧═══════════════╧════════════════╝

### 18.10. Stream Manifests

Skipper keeps a “manifest” of the all of the applications, their application properties, and their deployment properties after all values have been substituted. This represents the final state of what was deployed to the platform. You can view the manifest for any of the versions of a Stream by using the following command:

    stream manifest --name <name-of-stream> --releaseVersion <optional-version>

If the `--releaseVersion` is not specified, the manifest for the last version is returned.

The following example shows the use of the manifest:

    dataflow:>stream manifest --name httptest

Using the command results in the following output:

    # Source: log.yml
    apiVersion: skipper.spring.io/v1
    kind: SpringCloudDeployerApplication
    metadata:
      name: log
    spec:
      resource: maven://org.springframework.cloud.stream.app:log-sink-rabbit
      version: 1.2.0.RELEASE
      applicationProperties:
        spring.cloud.dataflow.stream.app.label: log
        spring.cloud.stream.bindings.input.group: httptest
        spring.cloud.dataflow.stream.name: httptest
        spring.cloud.dataflow.stream.app.type: sink
        spring.cloud.stream.bindings.input.destination: httptest.http
      deploymentProperties:
        spring.cloud.deployer.indexed: true
        spring.cloud.deployer.group: httptest
        spring.cloud.deployer.count: 1

    ---
    # Source: http.yml
    apiVersion: skipper.spring.io/v1
    kind: SpringCloudDeployerApplication
    metadata:
      name: http
    spec:
      resource: maven://org.springframework.cloud.stream.app:http-source-rabbit
      version: 1.2.0.RELEASE
      applicationProperties:
        spring.cloud.dataflow.stream.app.label: http
        spring.cloud.stream.bindings.output.producer.requiredGroups: httptest
        server.port: 9000
        spring.cloud.stream.bindings.output.destination: httptest.http
        spring.cloud.dataflow.stream.name: httptest
        spring.cloud.dataflow.stream.app.type: source
      deploymentProperties:
        spring.cloud.deployer.group: httptest

The majority of the deployment and application properties were set by Data Flow to enable the applications to talk to each other and to send application metrics with identifying labels.

### 18.11. Rollback a Stream

You can roll back to a previous version of the stream by using the `stream rollback` command:

    dataflow:>stream rollback --name httptest

The optional `--releaseVersion` command argument adds the version of the stream. If not specified, the rollback operation goes to the previous stream version.

### 18.12. Application Count

The application count is a dynamic property of the system used to specify the number of instances of applications. See the Application Count section of the microsite for more information.

### 18.13. Skipper’s Upgrade Strategy

Skipper has a simple “red/black” upgrade strategy. It deploys the new version of the applications, using as many instances as the currently running version, and checks the `/health` endpoint of the application. If the health of the new application is good, the previous application is undeployed. If the health of the new application is bad, all new applications are undeployed, and the upgrade is considered to be not successful.

The upgrade strategy is not a rolling upgrade, so, if five instances of the application are running, then, in a sunny-day scenario, five of the new applications are also running before the older version is undeployed.

## 19. Stream DSL

This section covers additional features of the Stream DSL not covered in the Stream DSL introduction.

### 19.1. Tap a Stream

Taps can be created at various producer endpoints in a stream. See the Tapping a Stream section of the microsite for more information.

### 19.2. Using Labels in a Stream

When a stream is made up of multiple applications with the same name, they must be qualified with labels. See the Labeling Applications section of the microsite for more information.

### 19.3. Named Destinations

Instead of referencing a source or sink application, you can use a named destination. See the Named Destinations section of the microsite for more information.

### 19.4. Fan-in and Fan-out

By using named destinations, you can support fan-in and fan-out use cases. See the Fan-in and Fan-out section of the microsite for more information.

## 20. Stream Java DSL

Instead of using the shell to create and deploy streams, you can use the Java-based DSL provided by the `spring-cloud-dataflow-rest-client` module. See the Java DSL section of the microsite for more information.

## 21. Stream Applications with Multiple Binder Configurations

In some cases, a stream can have its applications bound to multiple spring cloud stream binders when they are required to connect to different messaging middleware configurations. In those cases, you should make sure the applications are configured appropriately with their binder configurations. For example, a multi-binder transformer that supports both Kafka and Rabbit binders is the processor in the following stream:

    http | multibindertransform --expression=payload.toUpperCase() | log

| In the preceding example, you would write your own `multibindertransform` application.

In this stream, each application connects to messaging middleware in the following way:

1. The HTTP source sends events to RabbitMQ (`rabbit1`).

2. The Multi-Binder Transform processor receives events from RabbitMQ (`rabbit1`) and sends the processed events into Kafka (`kafka1`).

3. The log sink receives events from Kafka (`kafka1`).

Here, `rabbit1` and `kafka1` are the binder names given in the Spring Cloud Stream application properties. Based on this setup, the applications have the following binders in their classpaths with the appropriate configuration:

- HTTP: Rabbit binder

- Transform: Both Kafka and Rabbit binders

- Log: Kafka binder

The `spring-cloud-stream` `binder` configuration properties can be set within the applications themselves. If not, they can be passed through `deployment` properties when the stream is deployed:

    dataflow:>stream create --definition "http | multibindertransform --expression=payload.toUpperCase() | log" --name mystream

    dataflow:>stream deploy mystream --properties "app.http.spring.cloud.stream.bindings.output.binder=rabbit1,app.multibindertransform.spring.cloud.stream.bindings.input.binder=rabbit1,
    app.multibindertransform.spring.cloud.stream.bindings.output.binder=kafka1,app.log.spring.cloud.stream.bindings.input.binder=kafka1"

You can override any of the binder configuration properties by specifying them through deployment properties.

## 22. Function Composition

Function composition lets you attach a functional logic dynamically to an existing event streaming application. See the Function Composition section of the microsite for more details.

## 23. Functional Applications

With Spring Cloud Stream 3.x adding functional support, you can build `Source`, `Sink` and `Processor` applications merely by implementing the Java Util’s `Supplier`, `Consumer`, and `Function` interfaces respectively. See the Functional Application Recipe of the SCDF site for more about this feature.

## 24. Examples

This chapter includes the following examples:

- Simple Stream Processing

- Stateful Stream Processing

- Other Source and Sink Application Types

You can find links to more samples in the “Samples” chapter.

### 24.1. Simple Stream Processing

As an example of a simple processing step, we can transform the payload of the HTTP-posted data to upper case by using the following stream definition:

    http | transform --expression=payload.toUpperCase() | log

To create this stream, enter the following command in the shell:

    dataflow:> stream create --definition "http --server.port=9000 | transform --expression=payload.toUpperCase() | log" --name mystream --deploy

The following example uses a shell command to post some data:

    dataflow:> http post --target http://localhost:9000 --data "hello"

The preceding example results in an upper-case `HELLO` in the log, as follows:

    2016-06-01 09:54:37.749  INFO 80083 --- [  kafka-binder-] log.sink    : HELLO

### 24.2. Stateful Stream Processing

To demonstrate the data partitioning functionality, the following listing deploys a stream with Kafka as the binder:

    dataflow:>stream create --name words --definition "http --server.port=9900 | splitter --expression=payload.split(' ') | log"
    Created new stream 'words'

    dataflow:>stream deploy words --properties "app.splitter.producer.partitionKeyExpression=payload,deployer.log.count=2"
    Deployed stream 'words'

    dataflow:>http post --target http://localhost:9900 --data "How much wood would a woodchuck chuck if a woodchuck could chuck wood"
    > POST (text/plain;Charset=UTF-8) http://localhost:9900 How much wood would a woodchuck chuck if a woodchuck could chuck wood
    > 202 ACCEPTED

    dataflow:>runtime apps
    ╔════════════════════╤═══════════╤═══════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════╗
    ║App Id / Instance Id│Unit Status│                                                               No. of Instances / Attributes                                                               ║
    ╠════════════════════╪═══════════╪═══════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════╣
    ║words.log-v1        │ deployed  │                                                                             2                                                                             ║
    ╟┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┼┈┈┈┈┈┈┈┈┈┈┈┼┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈╢
    ║                    │           │       guid = 24166                                                                                                                                        ║
    ║                    │           │        pid = 33097                                                                                                                                        ║
    ║                    │           │       port = 24166                                                                                                                                        ║
    ║words.log-v1-0      │ deployed  │     stderr = /var/folders/js/7b_pn0t575l790x7j61slyxc0000gn/T/spring-cloud-deployer-6467595568759190742/words-1542803461063/words.log-v1/stderr_0.log     ║
    ║                    │           │     stdout = /var/folders/js/7b_pn0t575l790x7j61slyxc0000gn/T/spring-cloud-deployer-6467595568759190742/words-1542803461063/words.log-v1/stdout_0.log     ║
    ║                    │           │        url = https://192.168.0.102:24166                                                                                                                   ║
    ║                    │           │working.dir = /var/folders/js/7b_pn0t575l790x7j61slyxc0000gn/T/spring-cloud-deployer-6467595568759190742/words-1542803461063/words.log-v1                  ║
    ╟┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┼┈┈┈┈┈┈┈┈┈┈┈┼┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈╢
    ║                    │           │       guid = 41269                                                                                                                                        ║
    ║                    │           │        pid = 33098                                                                                                                                        ║
    ║                    │           │       port = 41269                                                                                                                                        ║
    ║words.log-v1-1      │ deployed  │     stderr = /var/folders/js/7b_pn0t575l790x7j61slyxc0000gn/T/spring-cloud-deployer-6467595568759190742/words-1542803461063/words.log-v1/stderr_1.log     ║
    ║                    │           │     stdout = /var/folders/js/7b_pn0t575l790x7j61slyxc0000gn/T/spring-cloud-deployer-6467595568759190742/words-1542803461063/words.log-v1/stdout_1.log     ║
    ║                    │           │        url = https://192.168.0.102:41269                                                                                                                   ║
    ║                    │           │working.dir = /var/folders/js/7b_pn0t575l790x7j61slyxc0000gn/T/spring-cloud-deployer-6467595568759190742/words-1542803461063/words.log-v1                  ║
    ╟────────────────────┼───────────┼───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╢
    ║words.http-v1       │ deployed  │                                                                             1                                                                             ║
    ╟┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┼┈┈┈┈┈┈┈┈┈┈┈┼┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈╢
    ║                    │           │       guid = 9900                                                                                                                                         ║
    ║                    │           │        pid = 33094                                                                                                                                        ║
    ║                    │           │       port = 9900                                                                                                                                         ║
    ║words.http-v1-0     │ deployed  │     stderr = /var/folders/js/7b_pn0t575l790x7j61slyxc0000gn/T/spring-cloud-deployer-6467595568759190742/words-1542803461054/words.http-v1/stderr_0.log    ║
    ║                    │           │     stdout = /var/folders/js/7b_pn0t575l790x7j61slyxc0000gn/T/spring-cloud-deployer-6467595568759190742/words-1542803461054/words.http-v1/stdout_0.log    ║
    ║                    │           │        url = https://192.168.0.102:9900                                                                                                                    ║
    ║                    │           │working.dir = /var/folders/js/7b_pn0t575l790x7j61slyxc0000gn/T/spring-cloud-deployer-6467595568759190742/words-1542803461054/words.http-v1                 ║
    ╟────────────────────┼───────────┼───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╢
    ║words.splitter-v1   │ deployed  │                                                                             1                                                                             ║
    ╟┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┼┈┈┈┈┈┈┈┈┈┈┈┼┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈╢
    ║                    │           │       guid = 33963                                                                                                                                        ║
    ║                    │           │        pid = 33093                                                                                                                                        ║
    ║                    │           │       port = 33963                                                                                                                                        ║
    ║words.splitter-v1-0 │ deployed  │     stderr = /var/folders/js/7b_pn0t575l790x7j61slyxc0000gn/T/spring-cloud-deployer-6467595568759190742/words-1542803437542/words.splitter-v1/stderr_0.log║
    ║                    │           │     stdout = /var/folders/js/7b_pn0t575l790x7j61slyxc0000gn/T/spring-cloud-deployer-6467595568759190742/words-1542803437542/words.splitter-v1/stdout_0.log║
    ║                    │           │        url = https://192.168.0.102:33963                                                                                                                   ║
    ║                    │           │working.dir = /var/folders/js/7b_pn0t575l790x7j61slyxc0000gn/T/spring-cloud-deployer-6467595568759190742/words-1542803437542/words.splitter-v1             ║
    ╚════════════════════╧═══════════╧═══════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════╝

When you review the `words.log-v1-0` logs, you should see the following:

    2016-06-05 18:35:47.047  INFO 58638 --- [  kafka-binder-] log.sink                                 : How
    2016-06-05 18:35:47.066  INFO 58638 --- [  kafka-binder-] log.sink                                 : chuck
    2016-06-05 18:35:47.066  INFO 58638 --- [  kafka-binder-] log.sink                                 : chuck

When you review the `words.log-v1-1` logs, you should see the following:

    2016-06-05 18:35:47.047  INFO 58639 --- [  kafka-binder-] log.sink                                 : much
    2016-06-05 18:35:47.066  INFO 58639 --- [  kafka-binder-] log.sink                                 : wood
    2016-06-05 18:35:47.066  INFO 58639 --- [  kafka-binder-] log.sink                                 : would
    2016-06-05 18:35:47.066  INFO 58639 --- [  kafka-binder-] log.sink                                 : a
    2016-06-05 18:35:47.066  INFO 58639 --- [  kafka-binder-] log.sink                                 : woodchuck
    2016-06-05 18:35:47.067  INFO 58639 --- [  kafka-binder-] log.sink                                 : if
    2016-06-05 18:35:47.067  INFO 58639 --- [  kafka-binder-] log.sink                                 : a
    2016-06-05 18:35:47.067  INFO 58639 --- [  kafka-binder-] log.sink                                 : woodchuck
    2016-06-05 18:35:47.067  INFO 58639 --- [  kafka-binder-] log.sink                                 : could
    2016-06-05 18:35:47.067  INFO 58639 --- [  kafka-binder-] log.sink                                 : wood

This example has shown that payload splits that contain the same word are routed to the same application instance.

### 24.3. Other Source and Sink Application Types

This example shows something a bit more complicated: swapping out the `time` source for something else. Another supported source type is `http`, which accepts data for ingestion over HTTP POST requests. Note that the `http` source accepts data on a different port from the Data Flow Server (default 8080). By default, the port is randomly assigned.

To create a stream that uses an `http` source but still uses the same `log` sink, we would change the original command in the Simple Stream Processing example to the following:

    dataflow:> stream create --definition "http | log" --name myhttpstream --deploy

Note that, this time, we do not see any other output until we actually post some data (by using a shell command). To see the randomly assigned port on which the `http` source is listening, run the following command:

    dataflow:>runtime apps

    ╔══════════════════════╤═══════════╤═════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════╗
    ║ App Id / Instance Id │Unit Status│                                                                    No. of Instances / Attributes                                                                    ║
    ╠══════════════════════╪═══════════╪═════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════╣
    ║myhttpstream.log-v1   │ deploying │                                                                                  1                                                                                  ║
    ╟┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┼┈┈┈┈┈┈┈┈┈┈┈┼┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈╢
    ║                      │           │       guid = 39628                                                                                                                                                  ║
    ║                      │           │        pid = 34403                                                                                                                                                  ║
    ║                      │           │       port = 39628                                                                                                                                                  ║
    ║myhttpstream.log-v1-0 │ deploying │     stderr = /var/folders/js/7b_pn0t575l790x7j61slyxc0000gn/T/spring-cloud-deployer-6467595568759190742/myhttpstream-1542803867070/myhttpstream.log-v1/stderr_0.log ║
    ║                      │           │     stdout = /var/folders/js/7b_pn0t575l790x7j61slyxc0000gn/T/spring-cloud-deployer-6467595568759190742/myhttpstream-1542803867070/myhttpstream.log-v1/stdout_0.log ║
    ║                      │           │        url = https://192.168.0.102:39628                                                                                                                             ║
    ║                      │           │working.dir = /var/folders/js/7b_pn0t575l790x7j61slyxc0000gn/T/spring-cloud-deployer-6467595568759190742/myhttpstream-1542803867070/myhttpstream.log-v1              ║
    ╟──────────────────────┼───────────┼─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╢
    ║myhttpstream.http-v1  │ deploying │                                                                                  1                                                                                  ║
    ╟┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┼┈┈┈┈┈┈┈┈┈┈┈┼┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈┈╢
    ║                      │           │       guid = 52143                                                                                                                                                  ║
    ║                      │           │        pid = 34401                                                                                                                                                  ║
    ║                      │           │       port = 52143                                                                                                                                                  ║
    ║myhttpstream.http-v1-0│ deploying │     stderr = /var/folders/js/7b_pn0t575l790x7j61slyxc0000gn/T/spring-cloud-deployer-6467595568759190742/myhttpstream-1542803866800/myhttpstream.http-v1/stderr_0.log║
    ║                      │           │     stdout = /var/folders/js/7b_pn0t575l790x7j61slyxc0000gn/T/spring-cloud-deployer-6467595568759190742/myhttpstream-1542803866800/myhttpstream.http-v1/stdout_0.log║
    ║                      │           │        url = https://192.168.0.102:52143                                                                                                                             ║
    ║                      │           │working.dir = /var/folders/js/7b_pn0t575l790x7j61slyxc0000gn/T/spring-cloud-deployer-6467595568759190742/myhttpstream-1542803866800/myhttpstream.http-v1             ║
    ╚══════════════════════╧═══════════╧═════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════════╝

You should see that the corresponding `http` source has a `url` property that contains the host and port information on which it is listening. You are now ready to post to that url, as shown in the following example:

    dataflow:> http post --target http://localhost:1234 --data "hello"
    dataflow:> http post --target http://localhost:1234 --data "goodbye"

The stream then funnels the data from the `http` source to the output log implemented by the `log` sink, yielding output similar to the following:

    2016-06-01 09:50:22.121  INFO 79654 --- [  kafka-binder-] log.sink    : hello
    2016-06-01 09:50:26.810  INFO 79654 --- [  kafka-binder-] log.sink    : goodbye

We could also change the sink implementation. You could pipe the output to a file (`file`), to hadoop (`hdfs`), or to any of the other sink applications that are available. You can also define your own applications.

# Stream Developer Guide

See the Stream Developer Guides on the microsite for more about how to create, test, and run Spring Cloud Stream applications on your local machine.

# Stream Monitoring

See the Stream Monitoring Guide on the microsite for more about how to monitor the applications that were deployed as part of a Stream.

# Tasks

This section goes into more detail about how you can orchestrate Spring Cloud Task applications on Spring Cloud Data Flow.

If you are just starting out with Spring Cloud Data Flow, you should probably read the Getting Started guide for “Local” , “Cloud Foundry”, or “Kubernetes” before diving into this section.

## 25. Introduction

A task application is short-lived, meaning that it stops running on purpose and can be run on demand or scheduled for later. One use case might be to scrape a web page and write to the database.

The Spring Cloud Task framework is based on Spring Boot and adds the ability for Boot applications to record the lifecycle events of a short-lived application, such as when it starts, when it ends, and the exit status. The `TaskExecution` documentation shows which information is stored in the database. The entry point for code execution in a Spring Cloud Task application is most often an implementation of Boot’s `CommandLineRunner` interface, as shown in this example.

The Spring Batch project is probably what comes to mind for Spring developers writing short-lived applications. Spring Batch provides a much richer set of functionality than Spring Cloud Task and is recommended when processing large volumes of data. One use case might be to read many CSV files, transform each row of data, and write each transformed row to a database. Spring Batch provides its own database schema with a much more rich set of information about the execution of a Spring Batch job. Spring Cloud Task is integrated with Spring Batch so that, if a Spring Cloud Task application defines a Spring Batch `Job`, a link between the Spring Cloud Task and Spring Cloud Batch execution tables is created.

When running Data Flow on your local machine, Tasks are launched in a separate JVM. When running on Cloud Foundry, tasks are launched by using Cloud Foundry’s Task functionality. When running on Kubernetes, tasks are launched by using either a `Pod` or a `Job` resource.

## 26. The Lifecycle of a Task

Before you dive deeper into the details of creating Tasks, you should understand the typical lifecycle for tasks in the context of Spring Cloud Data Flow:

1. Creating a Task Application

2. Registering a Task Application

3. Creating a Task Definition

4. Launching a Task

5. Reviewing Task Executions

6. Destroying a Task Definition

7. Continuous Deployment

### 26.1. Creating a Task Application

While Spring Cloud Task does provide a number of out-of-the-box applications (at spring-cloud-task-app-starters), most task applications require custom development. To create a custom task application:

1. Use the Spring Initializer to create a new project, making sure to select the following starters:

   1. `Cloud Task`: This dependency is the `spring-cloud-starter-task`.

   2. `JDBC`: This dependency is the `spring-jdbc` starter.

   3. Select your database dependency: Enter the database dependency that Data Flow is currently using. For example: `H2`.

2. Within your new project, create a new class to serve as your main class, as follows:

       @EnableTask
       @SpringBootApplication
       public class MyTask {

           public static void main(String[] args) {
       		SpringApplication.run(MyTask.class, args);
       	}
       }

3. With this class, you need one or more `CommandLineRunner` or `ApplicationRunner` implementations within your application. You can either implement your own or use the ones provided by Spring Boot (there is one for running batch jobs, for example).

4. Packaging your application with Spring Boot into an über jar is done through the standard Spring Boot conventions. The packaged application can be registered and deployed as noted below.

#### 26.1.1. Task Database Configuration

| When launching a task application, be sure that the database driver that is being used by Spring Cloud Data Flow is also a dependency on the task application. For example, if your Spring Cloud Data Flow is set to use Postgresql, be sure that the task application also has Postgresql as a dependency.

| When you run tasks externally (that is, from the command line) and you want Spring Cloud Data Flow to show the TaskExecutions in its UI, be sure that common datasource settings are shared among them both. By default, Spring Cloud Task uses a local H2 instance, and the execution is recorded to the database used by Spring Cloud Data Flow.

### 26.2. Registering a Task Application

You can register a Task application with the App Registry by using the Spring Cloud Data Flow Shell `app register` command. You must provide a unique name and a URI that can be resolved to the application artifact. For the type, specify `task`. The following listing shows three examples:

    dataflow:>app register --name task1 --type task --uri maven://com.example:mytask:1.0.2

    dataflow:>app register --name task2 --type task --uri file:///Users/example/mytask-1.0.2.jar

    dataflow:>app register --name task3 --type task --uri https://example.com/mytask-1.0.2.jar

When providing a URI with the `maven` scheme, the format should conform to the following:

    maven://<groupId>:<artifactId>[:<extension>[:<classifier>]]:<version>

If you would like to register multiple applications at one time, you can store them in a properties file where the keys are formatted as `<type>.<name>` and the values are the URIs. For example, the following listing would be a valid properties file:

    task.cat=file:///tmp/cat-1.2.1.BUILD-SNAPSHOT.jar
    task.hat=file:///tmp/hat-1.2.1.BUILD-SNAPSHOT.jar

Then you can use the `app import` command and provide the location of the properties file by using the `--uri` option, as follows:

    app import --uri file:///tmp/task-apps.properties

For example, if you would like to register all the task applications that ship with Data Flow in a single operation, you can do so with the following command:

    dataflow:>app import --uri https://dataflow.spring.io/task-maven-latest

You can also pass the `--local` option (which is `TRUE` by default) to indicate whether the properties file location should be resolved within the shell process itself. If the location should be resolved from the Data Flow Server process, specify `--local false`.

When using either `app register` or `app import`, if a task application is already registered with the provided name and version, it is not overridden by default. If you would like to override the pre-existing task application with a different `uri` or `uri-metadata` location, include the `--force` option.

| In some cases, the resource is resolved on the server side. In other cases, the URI is passed to a runtime container instance, where it is resolved. Consult the specific documentation of each Data Flow Server for more detail.

### 26.3. Creating a Task Definition

You can create a task definition from a task application by providing a definition name as well as properties that apply to the task execution. You can create a task definition through the RESTful API or the shell. To create a task definition by using the shell, use the `task create` command to create the task definition, as shown in the following example:

    dataflow:>task create mytask --definition "timestamp --format=\"yyyy\""
    Created new task 'mytask'

You can obtain a listing of the current task definitions through the RESTful API or the shell. To get the task definition list by using the shell, use the `task list` command.

#### 26.3.1. Maximum Task Definition Name Length

The maximum character length of a task definition name is dependent on the platform.

| Consult the platform documents for specifics on resource naming. The Local platform stores the task definition name in a database column with a maximum size of 255.

Table 1. Maximum Task Definition Name Character Length by Platform
Kubernetes Bare Pods | Kubernetes Jobs | Cloud Foundry | Local
--- | --- | --- | ---
63 | 52 | 63 | 255

#### 26.3.2. Automating the Creation of Task Definitions

As of version 2.3.0, you can configure the Data Flow server to automatically create task definitions by setting `spring.cloud.dataflow.task.autocreate-task-definitions` to `true`. This is not the default behavior but is provided as a convenience. When this property is enabled, a task launch request can specify the registered task application name as the task name. If the task application is registered, the server creates a basic task definition that specifies only the application name, as required. This eliminates a manual step similar to:

    dataflow:>task create mytask --definition "mytask"

You can still specify command-line arguments and deployment properties for each task launch request.

### 26.4. Launching a Task

An ad hoc task can be launched through the RESTful API or the shell. To launch an ad hoc task through the shell, use the `task launch` command, as shown in the following example:

    dataflow:>task launch mytask
    Launched task 'mytask'

When a task is launched, you can set any properties that need to be passed as command-line arguments to the task application when you launch the task, as follows:

    dataflow:>task launch mytask --arguments "--server.port=8080 --custom=value"

| The arguments need to be passed as space-delimited values.

You can pass in additional properties meant for a `TaskLauncher` itself by using the `--properties` option. The format of this option is a comma-separated string of properties prefixed with `app.<task definition name>.<property>`. Properties are passed to `TaskLauncher` as application properties. It is up to an implementation to choose how those are passed into an actual task application. If the property is prefixed with `deployer` instead of `app`, it is passed to `TaskLauncher` as a deployment property, and its meaning may be `TaskLauncher` implementation specific.

    dataflow:>task launch mytask --properties "deployer.timestamp.custom1=value1,app.timestamp.custom2=value2"

#### 26.4.1. Application properties

Each application takes properties to customize its behavior. For example, the `timestamp` task `format` setting establishes an output format that is different from the default value.

    dataflow:> task create --definition "timestamp --format=\"yyyy\"" --name printTimeStamp

This `timestamp` property is actually the same as the `timestamp.format` property specified by the timestamp application. Data Flow adds the ability to use the shorthand form `format` instead of `timestamp.format`. You can also specify the longhand version as well, as shown in the following example:

    dataflow:> task create --definition "timestamp --timestamp.format=\"yyyy\"" --name printTimeStamp

This shorthand behavior is discussed more in the section on Stream Application Properties. If you have registered application property metadata, you can use tab completion in the shell after typing `--` to get a list of candidate property names.

The shell provides tab completion for application properties. The `app info --name <appName> --type <appType>` shell command provides additional documentation for all the supported properties. The supported task `<appType>` is `task`.

| When restarting Spring Batch Jobs on Kubernetes, you must use the entry point of `shell` or `boot`.

##### Application Properties With Sensitive Information on Kubernetes

When launching task applications where some of the properties may contain sensitive information, use the `shell` or `boot` as the `entryPointStyle`. This is because the `exec` (default) converts all properties to command-line arguments and, as a result, may not be secure in some environments.

#### 26.4.2. Common application properties

In addition to configuration through DSL, Spring Cloud Data Flow provides a mechanism for setting properties that are common to all the task applications that are launched by it. You can do so by adding properties prefixed with `spring.cloud.dataflow.applicationProperties.task` when starting the server. The server then passes all the properties, without the prefix, to the instances it launches.

For example, you can configure all the launched applications to use the `prop1` and `prop2` properties by launching the Data Flow server with the following options:

    --spring.cloud.dataflow.applicationProperties.task.prop1=value1
    --spring.cloud.dataflow.applicationProperties.task.prop2=value2

This causes the `prop1=value1` and `prop2=value2` properties to be passed to all the launched applications.

| Properties configured by using this mechanism have lower precedence than task deployment properties. They are overridden if a property with the same key is specified at task launch time (for example, `app.trigger.prop2` overrides the common property).

### 26.5. Limit the number concurrent task launches

Spring Cloud Data Flow lets a user limit the maximum number of concurrently running tasks for each configured platform to prevent the saturation of IaaS or hardware resources. By default, the limit is set to `20` for all supported platforms. If the number of concurrently running tasks on a platform instance is greater than or equal to the limit, the next task launch request fails, and an error message is returned through the RESTful API, the Shell, or the UI. You can configure this limit for a platform instance by setting the corresponding deployer property, `spring.cloud.dataflow.task.platform.<platform-type>.accounts[<account-name>].maximumConcurrentTasks`, where `<account-name>` is the name of a configured platform account (`default` if no accounts are explicitly configured). The `<platform-type>` refers to one of the currently supported deployers: `local` or `kubernetes`. For `cloudfoundry`, the property is `spring.cloud.dataflow.task.platform.<platform-type>.accounts[<account-name>].deployment.maximumConcurrentTasks`. (The difference is that `deployment` has been added to the path).

The `TaskLauncher` implementation for each supported platform determines the number of currently running tasks by querying the underlying platform’s runtime state, if possible. The method for identifying a `task` varies by platform. For example, launching a task on the local host uses the `LocalTaskLauncher`. `LocalTaskLauncher` runs a process for each launch request and keeps track of these processes in memory. In this case, we do not query the underlying OS, as it is impractical to identify tasks this way. For Cloud Foundry, tasks are a core concept supported by its deployment model. The state of all tasks ) is available directly through the API. This means that every running task container in the account’s organization and space is included in the running execution count, whether or not it was launched by using Spring Cloud Data Flow or by invoking the `CloudFoundryTaskLauncher` directly. For Kubernetes, launching a task through the `KubernetesTaskLauncher`, if successful, results in a running pod, which we expect to eventually complete or fail. In this environment, there is generally no easy way to identify pods that correspond to a task. For this reason, we count only pods that were launched by the `KubernetesTaskLauncher`. Since the task launcher provides `task-name` label in the pod’s metadata, we filter all running pods by the presence of this label.

### 26.6. Reviewing Task Executions

Once the task is launched, the state of the task is stored in a relational database. The state includes:

- Task Name

- Start Time

- End Time

- Exit Code

- Exit Message

- Last Updated Time

- Parameters

You can check the status of your task executions through the RESTful API or the shell. To display the latest task executions through the shell, use the `task execution list` command.

To get a list of task executions for just one task definition, add `--name` and the task definition name — for example, `task execution list --name foo`. To retrieve full details for a task execution, use the `task execution status` command with the ID of the task execution, for example `task execution status --id 549`.

### 26.7. Destroying a Task Definition

Destroying a task definition removes the definition from the definition repository. This can be done through the RESTful API or the shell. To destroy a task through the shell, use the `task destroy` command, as shown in the following example:

    dataflow:>task destroy mytask
    Destroyed task 'mytask'

The `task destroy` command also has an option to `cleanup` the task executions of the task being destroyed, as shown in the following example:

    dataflow:>task destroy mytask --cleanup
    Destroyed task 'mytask'

By default, the `cleanup` option is set to `false` (that is, by default, the task executions are not cleaned up when the task is destroyed).

To destroy all tasks through the shell, use the `task all destroy` command as shown in the following example:

    dataflow:>task all destroy
    Really destroy all tasks? [y, n]: y
    All tasks destroyed

If need be, you can use the force switch:

    dataflow:>task all destroy --force
    All tasks destroyed

The task execution information for previously launched tasks for the definition remains in the task repository.

| This does not stop any currently running tasks for this definition. Instead, it removes the task definition from the database.

| `task destroy <task-name>` deletes only the definition and not the task deployed on Cloud Foundry. The only way to do delete the task is through the CLI in two steps: + . Obtain a list of the apps by using the `cf apps` command. . Identify the task application to be deleted and run the `cf delete <task-name>` command.

### 26.8. Validating a Task

Sometimes, an application contained within a task definition has an invalid URI in its registration. This can be caused by an invalid URI being entered at application-registration time or the by the application being removed from the repository from which it was to be drawn. To verify that all the applications contained in a task are resolve-able, use the `validate` command, as follows:

    dataflow:>task validate time-stamp
    ╔══════════╤═══════════════╗
    ║Task Name │Task Definition║
    ╠══════════╪═══════════════╣
    ║time-stamp│timestamp      ║
    ╚══════════╧═══════════════╝

    time-stamp is a valid task.
    ╔═══════════════╤═════════════════╗
    ║   App Name    │Validation Status║
    ╠═══════════════╪═════════════════╣
    ║task:timestamp │valid            ║
    ╚═══════════════╧═════════════════╝

In the preceding example, the user validated their time-stamp task. The `task:timestamp` application is valid. Now we can see what happens if we have a stream definition with a registered application that has an invalid URI:

    dataflow:>task validate bad-timestamp
    ╔═════════════╤═══════════════╗
    ║  Task Name  │Task Definition║
    ╠═════════════╪═══════════════╣
    ║bad-timestamp│badtimestamp   ║
    ╚═════════════╧═══════════════╝

    bad-timestamp is an invalid task.
    ╔══════════════════╤═════════════════╗
    ║     App Name     │Validation Status║
    ╠══════════════════╪═════════════════╣
    ║task:badtimestamp │invalid          ║
    ╚══════════════════╧═════════════════╝

In this case, Spring Cloud Data Flow states that the task is invalid because `task:badtimestamp` has an invalid URI.

### 26.9. Stopping a Task Execution

In some cases, a task that is running on a platform may not stop because of a problem on the platform or the application business logic itself. For such cases, Spring Cloud Data Flow offers the ability to send a request to the platform to end the task. To do this, submit a `task execution stop` for a given set of task executions, as follows:

    task execution stop --ids 5

    Request to stop the task execution with id(s): 5 has been submitted

With the preceding command, the trigger to stop the execution of `id=5` is submitted to the underlying deployer implementation. As a result, the operation stops that task. When we view the result for the task execution, we see that the task execution completed with a 0 exit code:

    dataflow:>task execution list
    ╔══════════╤══╤════════════════════════════╤════════════════════════════╤═════════╗
    ║Task Name │ID│         Start Time         │          End Time          │Exit Code║
    ╠══════════╪══╪════════════════════════════╪════════════════════════════╪═════════╣
    ║batch-demo│5 │Mon Jul 15 13:58:41 EDT 2019│Mon Jul 15 13:58:55 EDT 2019│0        ║
    ║timestamp │1 │Mon Jul 15 09:26:41 EDT 2019│Mon Jul 15 09:26:41 EDT 2019│0        ║
    ╚══════════╧══╧════════════════════════════╧════════════════════════════╧═════════╝

If you submit a stop for a task execution that has child task executions associated with it, such as a composed task, a stop request is sent for each of the child task executions.

| When stopping a task execution that has a running Spring Batch job, the job is left with a batch status of `STARTED`. Each of the supported platforms sends a SIG-INT to the task application when a stop is requested. That allows Spring Cloud Task to capture the state of the app. However, Spring Batch does not handle a SIG-INT and, as a result, the job stops but remains in the STARTED status.

#### 26.9.1. Stopping a Task Execution that was Started Outside of Spring Cloud Data Flow

You may wish to stop a task that has been launched outside of Spring Cloud Data Flow. An example of this is the worker applications launched by a remote batch partitioned application. In such cases, the remote batch partitioned application stores the `external-execution-id` for each of the worker applications. However, no platform information is stored. So when Spring Cloud Data Flow has to stop a remote batch partitioned application and its worker applications, you need to specify the platform name, as follows:

    dataflow:>task execution stop --ids 1 --platform myplatform
    Request to stop the task execution with id(s): 1 for platform myplatform has been submitted

## 27. Subscribing to Task and Batch Events

You can also tap into various task and batch events when the task is launched. If the task is enabled to generate task or batch events (with the additional dependencies of `spring-cloud-task-stream` and, in the case of Kafka as the binder, `spring-cloud-stream-binder-kafka`), those events are published during the task lifecycle. By default, the destination names for those published events on the broker (Rabbit, Kafka, and others) are the event names themselves (for instance: `task-events`, `job-execution-events`, and so on).

    dataflow:>task create myTask --definition "myBatchJob"
    dataflow:>stream create task-event-subscriber1 --definition ":task-events > log" --deploy
    dataflow:>task launch myTask

You can control the destination name for those events by specifying explicit names when launching the task, as follows:

    dataflow:>stream create task-event-subscriber2 --definition ":myTaskEvents > log" --deploy
    dataflow:>task launch myTask --properties "app.myBatchJob.spring.cloud.stream.bindings.task-events.destination=myTaskEvents"

The following table lists the default task and batch event and destination names on the broker:

Table 2. Task and Batch Event Destinations
Event | Destination
Task events | `task-events`
Job Execution events | `job-execution-events`
Step Execution events | `step-execution-events`
Item Read events | `item-read-events`
Item Process events | `item-process-events`
Item Write events | `item-write-events`
Skip events | `skip-events`

## 28. Composed Tasks

Spring Cloud Data Flow lets you create a directed graph, where each node of the graph is a task application. This is done by using the DSL for composed tasks. You can create a composed task through the RESTful API, the Spring Cloud Data Flow Shell, or the Spring Cloud Data Flow UI.

### 28.1. Configuring the Composed Task Runner

Composed tasks are run through a task application called the Composed Task Runner.

#### 28.1.1. Registering the Composed Task Runner

By default, Spring Cloud Data Flow retrieves the composed task runner application from Maven Central for Cloud Foundry and local deployments and DockerHub for Kubernetes. It retrieves the composed task runner upon the first use of composed tasks.

If Maven Central or DockerHub cannot be reached for a given Spring Cloud Data Flow deployment, you can specify a new URI from which to retrieve the composed task runner by setting the `spring.cloud.dataflow.task.composedtaskrunner.uri` property.

#### 28.1.2. Configuring the Composed Task Runner

The composed task runner application has a `dataflow-server-uri` property that is used for validation and for launching child tasks. This defaults to `localhost:9393`. If you run a distributed Spring Cloud Data Flow server, as you would if you deploy the server on Cloud Foundry or Kubernetes, you need to provide the URI that can be used to access the server. You can either provide this by setting the `dataflow-server-uri` property for the composed task runner application when launching a composed task or by setting the `spring.cloud.dataflow.server.uri` property for the Spring Cloud Data Flow server when it is started. For the latter case, the `dataflow-server-uri` composed task runner application property is automatically set when a composed task is launched.

##### Configuration Options

The `ComposedTaskRunner` task has the following options:

- `composed-task-arguments` The command line arguments to be used for each of the tasks. (String, default: <none>).

- `increment-instance-enabled` Allows a single `ComposedTaskRunner` instance to be run again without changing the parameters. The default is `false`, which means a `ComposedTaskRunner` instance can be started only once with a given set of parameters. If `true` it can be re-started. (Boolean, default: `false`). ComposedTaskRunner is built by using Spring Batch. As a result, upon a successful execution, the batch job is considered to be complete. To launch the same `ComposedTaskRunner` definition multiple times, you must set the `increment-instance-enabled` property to `true` or change the parameters for the definition for each launch. When using this option, it must be applied for all task launches for the desired application, including the first launch.

- `interval-time-between-checks` The amount of time, in milliseconds, that the `ComposedTaskRunner` waits between checks of the database to see if a task has completed. (Integer, default: `10000`). `ComposedTaskRunner` uses the datastore to determine the status of each child tasks. This interval indicates to `ComposedTaskRunner` how often it should check the status its child tasks.

- `max-wait-time` The maximum amount of time, in milliseconds, that an individual step can run before the execution of the Composed task is failed (Integer, default: 0). Determines the maximum time each child task is allowed to run before the CTR ends with a failure. The default of `0` indicates no timeout.

- `split-thread-allow-core-thread-timeout` Specifies whether to allow split core threads to timeout. (Boolean, default: `false`) Sets the policy governing whether core threads may timeout and terminate if no tasks arrive within the keep-alive time, being replaced if needed when new tasks arrive.

- `split-thread-core-pool-size` Split’s core pool size. (Integer, default: `1`) Each child task contained in a split requires a thread in order to execute. So, for example, a definition such as `<AAA || BBB || CCC> && <DDD || EEE>` would require a `split-thread-core-pool-size` of `3`. This is because the largest split contains three child tasks. A count of `2` would mean that `AAA` and `BBB` would run in parallel, but CCC would wait until either `AAA` or `BBB` finish in order to run. Then `DDD` and `EEE` would run in parallel.

- `split-thread-keep-alive-seconds` Split’s thread keep alive seconds. (Integer, default: `60`) If the pool currently has more than `corePoolSize` threads, excess threads are stopped if they have been idle for more than the `keepAliveTime`.

- `split-thread-max-pool-size` Split’s maximum pool size. (Integer, default: `Integer.MAX_VALUE`). Establish the maximum number of threads allowed for the thread pool.

- split-thread-queue-capacity Capacity for Split’s `BlockingQueue`. (Integer, default: `Integer.MAX_VALUE`)

  - If fewer than `corePoolSize` threads are running, the `Executor` always prefers adding a new thread rather than queuing.

  - If `corePoolSize` or more threads are running, the `Executor` always prefers queuing a request rather than adding a new thread.

  - If a request cannot be queued, a new thread is created unless this would exceed `maximumPoolSize`. In that case, the task is rejected.

- `split-thread-wait-for-tasks-to-complete-on-shutdown` Whether to wait for scheduled tasks to complete on shutdown, not interrupting running tasks and running all tasks in the queue. (Boolean, default: `false`)

- `dataflow-server-uri` The URI for the Data Flow server that receives task launch requests. (String, default: `localhost:9393`)

- `dataflow-server-username` The optional username for the Data Flow server that receives task launch requests. Used to access the the Data Flow server by using Basic Authentication. Not used if `dataflow-server-access-token` is set.

- `dataflow-server-password` The optional password for the Data Flow server that receives task launch requests. Used to access the the Data Flow server by using Basic Authentication. Not used if `dataflow-server-access-token` is set.

- `dataflow-server-access-token` This property sets an optional OAuth2 Access Token. Typically, the value is automatically set by using the token from the currently logged-in user, if available. However, for special use-cases, this value can also be set explicitly.

A special boolean property, `dataflow-server-use-user-access-token`, exists for when you want to use the access token of the currently logged-in user and propagate it to the Composed Task Runner. This property is used by Spring Cloud Data Flow and, if set to `true`, auto-populates the `dataflow-server-access-token` property. When using `dataflow-server-use-user-access-token`, it must be passed for each task execution. In some cases, it may be preferred that the user’s `dataflow-server-access-token` must be passed for each composed task launch by default. In this case, set the Spring Cloud Data Flow `spring.cloud.dataflow.task.useUserAccessToken` property to `true`.

To set a property for Composed Task Runner you will need to prefix the property with `app.composed-task-runner.`. For example to set the `dataflow-server-uri` property the property will look like `app.composed-task-runner.dataflow-server-uri`.

### 28.2. The Lifecycle of a Composed Task

The lifecycle of a composed task has three parts:

- Creating a Composed Task

- Stopping a Composed Task

- Restarting a Composed Task

#### 28.2.1. Creating a Composed Task

The DSL for the composed tasks is used when creating a task definition through the task create command, as shown in the following example:

    dataflow:> app register --name timestamp --type task --uri maven://org.springframework.cloud.task.app:timestamp-task:
    dataflow:> app register --name mytaskapp --type task --uri file:///home/tasks/mytask.jar
    dataflow:> task create my-composed-task --definition "mytaskapp && timestamp"
    dataflow:> task launch my-composed-task

In the preceding example, we assume that the applications to be used by our composed task have not yet been registered. Consequently, in the first two steps, we register two task applications. We then create our composed task definition by using the `task create` command. The composed task DSL in the preceding example, when launched, runs `mytaskapp` and then runs the timestamp application.

But before we launch the `my-composed-task` definition, we can view what Spring Cloud Data Flow generated for us. This can be done by using the task list command, as shown (including its output) in the following example:

    dataflow:>task list
    ╔══════════════════════════╤══════════════════════╤═══════════╗
    ║        Task Name         │   Task Definition    │Task Status║
    ╠══════════════════════════╪══════════════════════╪═══════════╣
    ║my-composed-task          │mytaskapp && timestamp│unknown    ║
    ║my-composed-task-mytaskapp│mytaskapp             │unknown    ║
    ║my-composed-task-timestamp│timestamp             │unknown    ║
    ╚══════════════════════════╧══════════════════════╧═══════════╝

In the example, Spring Cloud Data Flow created three task definitions, one for each of the applications that makes up our composed task (`my-composed-task-mytaskapp` and `my-composed-task-timestamp`) as well as the composed task (`my-composed-task`) definition. We also see that each of the generated names for the child tasks is made up of the name of the composed task and the name of the application, separated by a hyphen `-` (as in my-composed-task `-` mytaskapp).

##### Task Application Parameters

The task applications that make up the composed task definition can also contain parameters, as shown in the following example:

    dataflow:> task create my-composed-task --definition "mytaskapp --displayMessage=hello && timestamp --format=YYYY"

#### 28.2.2. Launching a Composed Task

Launching a composed task is done in the same way as launching a stand-alone task, as follows:

    task launch my-composed-task

Once the task is launched, and assuming all the tasks complete successfully, you can see three task executions when you run a `task execution list`, as shown in the following example:

    dataflow:>task execution list
    ╔══════════════════════════╤═══╤════════════════════════════╤════════════════════════════╤═════════╗
    ║        Task Name         │ID │         Start Time         │          End Time          │Exit Code║
    ╠══════════════════════════╪═══╪════════════════════════════╪════════════════════════════╪═════════╣
    ║my-composed-task-timestamp│713│Wed Apr 12 16:43:07 EDT 2017│Wed Apr 12 16:43:07 EDT 2017│0        ║
    ║my-composed-task-mytaskapp│712│Wed Apr 12 16:42:57 EDT 2017│Wed Apr 12 16:42:57 EDT 2017│0        ║
    ║my-composed-task          │711│Wed Apr 12 16:42:55 EDT 2017│Wed Apr 12 16:43:15 EDT 2017│0        ║
    ╚══════════════════════════╧═══╧════════════════════════════╧════════════════════════════╧═════════╝

In the preceding example, we see that `my-compose-task` launched and that the other tasks were also launched in sequential order. Each of them ran successfully with an `Exit Code` as `0`.

##### Passing Properties to the Child Tasks

To set the properties for child tasks in a composed task graph at task launch time, use the following format: `app.<composed task definition name>.<child task app name>.<property>`. The following listing shows a composed task definition as an example:

    dataflow:> task create my-composed-task --definition "mytaskapp  && mytimestamp"

To have `mytaskapp` display 'HELLO' and set the `mytimestamp` timestamp format to `YYYY` for the composed task definition, use the following task launch format:

    task launch my-composed-task --properties "app.my-composed-task.mytaskapp.displayMessage=HELLO,app.my-composed-task.mytimestamp.timestamp.format=YYYY"

Similar to application properties, you can also set the `deployer` properties for child tasks by using the following format: `deployer.<composed task definition name>.<child task app name>.<deployer-property>`:

    task launch my-composed-task --properties "deployer.my-composed-task.mytaskapp.memory=2048m,app.my-composed-task.mytimestamp.timestamp.format=HH:mm:ss"
    Launched task 'a1'

##### Passing Arguments to the Composed Task Runner

You can pass command-line arguments for the composed task runner by using the `--arguments` option:

    dataflow:>task create my-composed-task --definition "<aaa: timestamp || bbb: timestamp>"
    Created new task 'my-composed-task'

    dataflow:>task launch my-composed-task --arguments "--increment-instance-enabled=true --max-wait-time=50000 --split-thread-core-pool-size=4" --properties "app.my-composed-task.bbb.timestamp.format=dd/MM/yyyy HH:mm:ss"
    Launched task 'my-composed-task'

##### Launching a Composed Task by using a Custom Composed Task Runner

In some cases, you need to launch a composed task by using a custom version of a composed task runner other than the default application that is shipped out-of-the-box. To do this, you need to register the custom version of the composed task runner and then specify the `composedTaskRunnerName` property to point to the custom application at task launch, as follows:

    dataflow:>app register --name best-ctr --type task --uri maven://the.best.ctr.composed-task-runner:1.0.0.RELEASE

    dataflow:>task create mycomposedtask --definition "te:timestamp &&  tr:timestamp"
    Created new task 'mycomposedtask'

    dataflow:>task launch --name mycomposedtask --composedTaskRunnerName best-ctr

| The application specified by the `composedTaskRunnerName` needs to be a task registered in the Application Registry.

##### Exit Statuses

The following list shows how the exit status is set for each step (task) contained in the composed task following each step execution:

- If the `TaskExecution` has an `ExitMessage`, that is used as the `ExitStatus`.

- If no `ExitMessage` is present and the `ExitCode` is set to zero, the `ExitStatus` for the step is `COMPLETED`.

- If no `ExitMessage` is present and the `ExitCode` is set to any non-zero number, the `ExitStatus` for the step is `FAILED`.

#### 28.2.3. Destroying a Composed Task

The command used to destroy a stand-alone task is the same as the command used to destroy a composed task. The only difference is that destroying a composed task also destroys the child tasks associated with it. The following example shows the task list before and after using the `destroy` command:

    dataflow:>task list
    ╔══════════════════════════╤══════════════════════╤═══════════╗
    ║        Task Name         │   Task Definition    │Task Status║
    ╠══════════════════════════╪══════════════════════╪═══════════╣
    ║my-composed-task          │mytaskapp && timestamp│COMPLETED  ║
    ║my-composed-task-mytaskapp│mytaskapp             │COMPLETED  ║
    ║my-composed-task-timestamp│timestamp             │COMPLETED  ║
    ╚══════════════════════════╧══════════════════════╧═══════════╝
    ...
    dataflow:>task destroy my-composed-task
    dataflow:>task list
    ╔═════════╤═══════════════╤═══════════╗
    ║Task Name│Task Definition│Task Status║
    ╚═════════╧═══════════════╧═══════════╝

#### 28.2.4. Stopping a Composed Task

In cases where a composed task execution needs to be stopped, you can do so through the:

- RESTful API

- Spring Cloud Data Flow Dashboard

To stop a composed task through the dashboard, select the Jobs tab and click the *Stop() button next to the job execution that you want to stop.

The composed task run is stopped when the currently running child task completes. The step associated with the child task that was running at the time that the composed task was stopped is marked as `STOPPED` as well as the composed task job execution.

#### 28.2.5. Restarting a Composed Task

In cases where a composed task fails during execution and the status of the composed task is `FAILED`, the task can be restarted. You can do so through the:

- RESTful API

- The shell

- Spring Cloud Data Flow Dashboard

To restart a composed task through the shell, launch the task with the same parameters. To restart a composed task through the dashboard, select the Jobs tab and click the Restart button next to the job execution that you want to restart.

| Restarting a composed task job that has been stopped (through the Spring Cloud Data Flow Dashboard or RESTful API) relaunches the `STOPPED` child task and then launches the remaining (unlaunched) child tasks in the specified order.

## 29. Composed Tasks DSL

Composed tasks can be run in three ways:

- Conditional Execution

- Transitional Execution

- Split Execution

### 29.1. Conditional Execution

Conditional execution is expressed by using a double ampersand symbol (`&&`). This lets each task in the sequence be launched only if the previous task successfully completed, as shown in the following example:

    task create my-composed-task --definition "task1 && task2"

When the composed task called `my-composed-task` is launched, it launches the task called `task1` and, if `task1` completes successfully, the task called `task2` is launched. If `task1` fails, `task2` does not launch.

You can also use the Spring Cloud Data Flow Dashboard to create your conditional execution, by using the designer to drag and drop applications that are required and connecting them together to create your directed graph, as shown in the following image:

Figure 2. Conditional Execution

The preceding diagram is a screen capture of the directed graph as it being created by using the Spring Cloud Data Flow Dashboard. You can see that four components in the diagram comprise a conditional execution:

- Start icon: All directed graphs start from this symbol. There is only one.

- Task icon: Represents each task in the directed graph.

- End icon: Represents the end of a directed graph.

- Solid line arrow: Represents the flow conditional execution flow between:

  - Two applications.

  - The start control node and an application.

  - An application and the end control node.

- End icon: All directed graphs end at this symbol.

| You can view a diagram of your directed graph by clicking the Detail button next to the composed task definition on the Definitions tab.

### 29.2. Transitional Execution

The DSL supports fine-grained control over the transitions taken during the execution of the directed graph. Transitions are specified by providing a condition for equality that is based on the exit status of the previous task. A task transition is represented by the following symbol `->`.

#### 29.2.1. Basic Transition

A basic transition would look like the following:

    task create my-transition-composed-task --definition "foo 'FAILED' -> bar 'COMPLETED' -> baz"

In the preceding example, `foo` would launch, and, if it had an exit status of `FAILED`, the `bar` task would launch. If the exit status of `foo` was `COMPLETED`, `baz` would launch. All other statuses returned by `cat` have no effect, and the task would end normally.

Using the Spring Cloud Data Flow Dashboard to create the same “basic transition” would resemble the following image:

Figure 3. Basic Transition

The preceding diagram is a screen capture of the directed graph as it being created in the Spring Cloud Data Flow Dashboard. Notice that there are two different types of connectors:

- Dashed line: Represents transitions from the application to one of the possible destination applications.

- Solid line: Connects applications in a conditional execution or a connection between the application and a control node (start or end).

To create a transitional connector:

1. When creating a transition, link the application to each possible destination by using the connector.

2. Once complete, go to each connection and select it by clicking it.

3. A bolt icon appears.

4. Click that icon.

5. Enter the exit status required for that connector.

6. The solid line for that connector turns to a dashed line.

#### 29.2.2. Transition With a Wildcard

Wildcards are supported for transitions by the DSL, as shown in the following example:

    task create my-transition-composed-task --definition "foo 'FAILED' -> bar '*' -> baz"

In the preceding example, `foo` would launch, and, if it had an exit status of `FAILED`, `bar` task would launch. For any exit status of `cat` other than `FAILED`, `baz` would launch.

Using the Spring Cloud Data Flow Dashboard to create the same “transition with wildcard” would resemble the following image:

Figure 4. Basic Transition With Wildcard

#### 29.2.3. Transition With a Following Conditional Execution

A transition can be followed by a conditional execution, so long as the wildcard is not used, as shown in the following example:

    task create my-transition-conditional-execution-task --definition "foo 'FAILED' -> bar 'UNKNOWN' -> baz && qux && quux"

In the preceding example, `foo` would launch, and, if it had an exit status of `FAILED`, the `bar` task would launch. If `foo` had an exit status of `UNKNOWN`, `baz` would launch. For any exit status of `foo` other than `FAILED` or `UNKNOWN`, `qux` would launch and, upon successful completion, `quux` would launch.

Using the Spring Cloud Data Flow Dashboard to create the same “transition with conditional execution” would resemble the following image:

Figure 5. Transition With Conditional Execution

| In this diagram, the dashed line (transition) connects the `foo` application to the target applications, but a solid line connects the conditional executions between `foo`, `qux`, and `quux`.

### 29.3. Split Execution

Splits let multiple tasks within a composed task be run in parallel. It is denoted by using angle brackets (`<>`) to group tasks and flows that are to be run in parallel. These tasks and flows are separated by the double pipe `||` symbol, as shown in the following example:

    task create my-split-task --definition "<foo || bar || baz>"

The preceding example launches tasks `foo`, `bar` and `baz` in parallel.

Using the Spring Cloud Data Flow Dashboard to create the same “split execution” would resemble the following image:

Figure 6. Split

With the task DSL, you can also run multiple split groups in succession, as shown in the following example:

    task create my-split-task --definition "<foo || bar || baz> && <qux || quux>"

In the preceding example, the `foo`, `bar`, and `baz` tasks are launched in parallel. Once they all complete, then the `qux` and `quux` tasks are launched in parallel. Once they complete, the composed task ends. However, if `foo`, `bar`, or `baz` fails, the split containing `qux` and `quux` does not launch.

Using the Spring Cloud Data Flow Dashboard to create the same “split with multiple groups” would resemble the following image:

Figure 7. Split as a part of a conditional execution

Notice that there is a `SYNC` control node that is inserted by the designer when connecting two consecutive splits.

| Tasks that are used in a split should not set the their `ExitMessage`. Setting the `ExitMessage` is only to be used with transitions.

#### 29.3.1. Split Containing Conditional Execution

A split can also have a conditional execution within the angle brackets, as shown in the following example:

    task create my-split-task --definition "<foo && bar || baz>"

In the preceding example, we see that `foo` and `baz` are launched in parallel. However, `bar` does not launch until `foo` completes successfully.

Using the Spring Cloud Data Flow Dashboard to create the same " `split containing conditional execution` " resembles the following image:

Figure 8. Split with conditional execution

#### 29.3.2. Establishing the Proper Thread Count for Splits

Each child task contained in a split requires a thread in order to run. To set this properly, you want to look at your graph and find the split that has the largest number of child tasks. The number of child tasks in that split is the number of threads you need. To set the thread count, use the `split-thread-core-pool-size property` (defaults to `1`). So, for example, a definition such as `<AAA || BBB || CCC> && <DDD || EEE>` requires a `split-thread-core-pool-size` of `3`. This is because the largest split contains three child tasks. A count of two would mean that `AAA` and `BBB` would run in parallel but CCC would wait for either `AAA` or `BBB` to finish in order to run. Then `DDD` and `EEE` would run in parallel.

## 30. Launching Tasks from a Stream

You can launch a task from a stream by using the `task-launcher-dataflow` sink. The sink connects to a Data Flow server and uses its REST API to launch any defined task. The sink accepts a JSON payload representing a `task launch request`, which provides the name of the task to launch and may include command line arguments and deployment properties.

The `app-starters-task-launch-request-common` component, in conjunction with Spring Cloud Stream functional composition, can transform the output of any source or processor to a task launch request.

Adding a dependency to `app-starters-task-launch-request-common` auto-configures a `java.util.function.Function` implementation, registered through Spring Cloud Function as a `taskLaunchRequest`.

For example, you can start with the time source, add the following dependency, build it, and register it as a custom source. We call it `time-tlr` in this example:

    <dependency>
        <groupId>org.springframework.cloud.stream.app</groupId>
        <artifactId>app-starters-task-launch-request-common</artifactId>
    </dependency>

| Spring Cloud Stream Initializr provides a great starting point for creating stream applications.

Next, register the `task-launcher-dataflow` sink and create a task (we use the provided timestamp task):

    stream create --name task-every-minute --definition "time-tlr --trigger.fixed-delay=60 --spring.cloud.stream.function.definition=taskLaunchRequest --task.launch.request.task-name=timestamp-task | task-launcher-dataflow" --deploy

The preceding stream produces a task launch request every minute. The request provides the name of the task to launch: `{"name":"timestamp-task"}`.

The following stream definition illustrates the use of command line arguments. It produces messages such as `{"args":["foo=bar","time=12/03/18 17:44:12"],"deploymentProps":{},"name":"timestamp-task"}` to provide command-line arguments to the task:

    stream create --name task-every-second --definition "time-tlr --spring.cloud.stream.function.definition=taskLaunchRequest --task.launch.request.task-name=timestamp-task --task.launch.request.args=foo=bar --task.launch.request.arg-expressions=time=payload | task-launcher-dataflow" --deploy

Note the use of SpEL expressions to map each message payload to the `time` command-line argument, along with a static argument (`foo=bar`).

You can then see the list of task executions by using the shell command `task execution list`, as shown (with its output) in the following example:

    dataflow:>task execution list
    ╔════════════════════╤══╤════════════════════════════╤════════════════════════════╤═════════╗
    ║     Task Name      │ID│         Start Time         │          End Time          │Exit Code║
    ╠════════════════════╪══╪════════════════════════════╪════════════════════════════╪═════════╣
    ║timestamp-task_26176│4 │Tue May 02 12:13:49 EDT 2017│Tue May 02 12:13:49 EDT 2017│0        ║
    ║timestamp-task_32996│3 │Tue May 02 12:12:49 EDT 2017│Tue May 02 12:12:49 EDT 2017│0        ║
    ║timestamp-task_58971│2 │Tue May 02 12:11:50 EDT 2017│Tue May 02 12:11:50 EDT 2017│0        ║
    ║timestamp-task_13467│1 │Tue May 02 12:10:50 EDT 2017│Tue May 02 12:10:50 EDT 2017│0        ║
    ╚════════════════════╧══╧════════════════════════════╧════════════════════════════╧═════════╝

In this example, we have shown how to use the `time` source to launch a task at a fixed rate. This pattern may be applied to any source to launch a task in response to any event.

### 30.1. Launching a Composed Task From a Stream

A composed task can be launched with the `task-launcher-dataflow` sink, as discussed here. Since we use the `ComposedTaskRunner` directly, we need to set up the task definitions for the composed task runner itself, along with the composed tasks, prior to the creation of the composed task launching stream. Suppose we wanted to create the following composed task definition: `AAA && BBB`. The first step would be to create the task definitions, as shown in the following example:

    task create composed-task-runner --definition "composed-task-runner"
    task create AAA --definition "timestamp"
    task create BBB --definition "timestamp"

| Releases of `ComposedTaskRunner` can be found here.

Now that the task definitions we need for composed task definition are ready, we need to create a stream that launches `ComposedTaskRunner`. So, in this case, we create a stream with:

- The `time` source customized to emit task launch requests, as shown earlier.

- The `task-launcher-dataflow` sink that launches the `ComposedTaskRunner`

The stream should resemble the following:

    stream create ctr-stream --definition "time --fixed-delay=30 --task.launch.request.task-name=composed-task-launcher --task.launch.request.args=--graph=AAA&&BBB,--increment-instance-enabled=true | task-launcher-dataflow"

For now, we focus on the configuration that is required to launch the `ComposedTaskRunner`:

- `graph`: This is the graph that is to be executed by the `ComposedTaskRunner`. In this case it is `AAA&&BBB`.

- `increment-instance-enabled`: This lets each execution of `ComposedTaskRunner` be unique. `ComposedTaskRunner` is built by using Spring Batch. Thus, we want a new Job Instance for each launch of the `ComposedTaskRunner`. To do this, we set `increment-instance-enabled` to be `true`.

## 31. Sharing Spring Cloud Data Flow’s Datastore with Tasks

As discussed in the Tasks documentation, Spring Cloud Data Flow lets you view Spring Cloud Task application executions. So, in this section, we discuss what is required for a task application and Spring Cloud Data Flow to share the task execution information.

### 31.1. A Common DataStore Dependency

Spring Cloud Data Flow supports many databases out-of-the-box, so all you typically need to do is declare the `spring_datasource_*` environment variables to establish what data store Spring Cloud Data Flow needs. Regardless of which database you decide to use for Spring Cloud Data Flow, make sure that your task also includes that database dependency in its `pom.xml` or `gradle.build` file. If the database dependency that is used by Spring Cloud Data Flow is not present in the Task Application, the task fails and the task execution is not recorded.

### 31.2. A Common Data Store

Spring Cloud Data Flow and your task application must access the same datastore instance. This is so that the task executions recorded by the task application can be read by Spring Cloud Data Flow to list them in the Shell and Dashboard views. Also, the task application must have read and write privileges to the task data tables that are used by Spring Cloud Data Flow.

Given this understanding of the datasource dependency between Task applications and Spring Cloud Data Flow, you can now review how to apply them in various Task orchestration scenarios.

#### 31.2.1. Simple Task Launch

When launching a task from Spring Cloud Data Flow, Data Flow adds its datasource properties (`spring.datasource.url`, `spring.datasource.driverClassName`, `spring.datasource.username`, `spring.datasource.password`) to the application properties of the task being launched. Thus, a task application records its task execution information to the Spring Cloud Data Flow repository.

#### 31.2.2. Composed Task Runner

Spring Cloud Data Flow lets you create a directed graph where each node of the graph is a task application. This is done through the composed task runner. In this case, the rules that applied to a simple task launch or task launcher sink apply to the composed task runner as well. All child applications must also have access to the datastore that is being used by the composed task runner. Also, all child applications must have the same database dependency as the composed task runner enumerated in their `pom.xml` or `gradle.build` file.

#### 31.2.3. Launching a Task Externally from Spring Cloud Data Flow

You can launch Spring Cloud Task applications by using another method (scheduler, for example) but still track the task execution in Spring Cloud Data Flow. You can do so, provided the task applications observe the rules specified here and here.

| If you want to use Spring Cloud Data Flow to view your Spring Batch jobs, make sure that your batch application uses the `@EnableTask` annotation and follow the rules enumerated here and here. More information is available here.

## 32. Scheduling Tasks

Spring Cloud Data Flow lets you schedule the execution of tasks with a `cron` expression. You can create a schedule through the RESTful API or the Spring Cloud Data Flow UI.

### 32.1. The Scheduler

Spring Cloud Data Flow schedules the execution of its tasks through a scheduling agent that is available on the cloud platform. When using the Cloud Foundry platform, Spring Cloud Data Flow uses the PCF Scheduler. When using Kubernetes, a CronJob will be used.

| Scheduled tasks do not implement the continuous deployment feature. Any changes to application version or properties for a task definition in Spring Cloud Data Flow will not affect scheduled tasks.

Figure 9. Architectural Overview

### 32.2. Enabling Scheduling

By default, Spring Cloud Data Flow leaves the scheduling feature disabled. To enable the scheduling feature, set the following feature properties to `true`:

- `spring.cloud.dataflow.features.schedules-enabled`

- `spring.cloud.dataflow.features.tasks-enabled`

### 32.3. The Lifecycle of a Schedule

The lifecycle of a schedule has three parts:

- Scheduling a Task Execution

- Deleting a Schedule

- Listing Schedules

#### 32.3.1. Scheduling a Task Execution

You can schedule a task execution via the:

- Spring Cloud Data Flow Shell

- Spring Cloud Data Flow Dashboard

- Spring Cloud Data Flow RESTful API

#### 32.3.2. Scheduling a Task

To schedule a task using the shell, use the `task schedule create` command to create the schedule, as shown in the following example:

    dataflow:>task schedule create --definitionName mytask --name mytaskschedule --expression '*/1 * * * *'
    Created schedule 'mytaskschedule'

In the earlier example, we created a schedule called `mytaskschedule` for the task definition called `mytask`. This schedule launches `mytask` once a minute.

| If using Cloud Foundry, the `cron` expression above would be: `*/1 * ? * *`. This is because Cloud Foundry uses the Quartz `cron` expression format.

##### Maximum Length for a Schedule Name

The maximum character length of a schedule name is dependent on the platform.

Table 3. Maximum Schedule Name Character Length by Platform
Kubernetes | Cloud Foundry | Local
--- | --- | ---
52 | 63 | N/A

#### 32.3.3. Deleting a Schedule

You can delete a schedule by using the:

- Spring Cloud Data Flow Shell

- Spring Cloud Data Flow Dashboard

- Spring Cloud Data Flow RESTful API

To delete a task schedule by using the shell, use the `task schedule destroy` command, as shown in the following example:

    dataflow:>task schedule destroy --name mytaskschedule
    Deleted task schedule 'mytaskschedule'

#### 32.3.4. Listing Schedules

You can view the available schedules by using the:

- Spring Cloud Data Flow Shell

- Spring Cloud Data Flow Dashboard

- Spring Cloud Data Flow RESTful API

To view your schedules from the shell, use the `task schedule list` command, as shown in the following example:

    dataflow:>task schedule list
    ╔══════════════════════════╤════════════════════╤════════════════════════════════════════════════════╗
    ║      Schedule Name       │Task Definition Name│                     Properties                     ║
    ╠══════════════════════════╪════════════════════╪════════════════════════════════════════════════════╣
    ║mytaskschedule            │mytask              │spring.cloud.scheduler.cron.expression = */1 * * * *║
    ╚══════════════════════════╧════════════════════╧════════════════════════════════════════════════════╝

| Instructions to create, delete, and list schedules by using the Spring Cloud Data Flow UI can be found here.

## 33. Continuous Deployment

As task applications evolve, you want to get your updates to production. This section walks through the capabilities that Spring Cloud Data Flow provides around being able to update task applications.

When a task application is registered (see Registering a Task Application), a version is associated with it. A task application can have multiple versions associated with it, with one selected as the default. The following image illustrates an application with multiple versions associated with it (see the timestamp entry).

Versions of an application are managed by registering multiple applications with the same name and coordinates, except the version. For example, if you were to register an application with the following values, you would get one application registered with two versions (2.1.0.RELEASE and 2.1.1.RELEASE):

- Application 1

  - Name: `timestamp`

  - Type: `task`

  - URI: `maven://org.springframework.cloud.task.app:timestamp-task:2.1.0.RELEASE`

- Application 2

  - Name: `timestamp`

  - Type: `task`

  - URI: `maven://org.springframework.cloud.task.app:timestamp-task:2.1.1.RELEASE`

Besides having multiple versions, Spring Cloud Data Flow needs to know which version to run on the next launch. This is indicated by setting a version to be the default version. Whatever version of a task application is configured as the default version is the one to be run on the next launch request. You can see which version is the default in the UI, as this image shows:

### 33.1. Task Launch Lifecycle

In previous versions of Spring Cloud Data Flow, when the request to launch a task was received, Spring Cloud Data Flow would deploy the application (if needed) and run it. If the application was being run on a platform that did not need to have the application deployed every time (CloudFoundry, for example), the previously deployed application was used. This flow has changed in 2.3. The following image shows what happens when a task launch request comes in now:

There are three main flows to consider in the preceding diagram. Launching the first time or launching with no changes is one. The other two are launching when there are changes but the appliction is not currently and launching when there are changes and the application is running. We look at the flow with no changes first.

#### 33.1.1. Launching a Task With No Changes

1. A launch request comes into Data Flow. Data Flow determines that an upgrade is not required, since nothing has changed (no properties, deployment properties, or versions have changed since the last execution).

5. On platforms that cache a deployed artifact (CloudFoundry, at this writing), Data Flow checks whether the application was previously deployed.

6. If the application needs to be deployed, Data Flow deploys the task application.

7. Data Flow launches the application.

This flow is the default behavior and, if nothing has changed, occurs every time a request comes in. Note that this is the same flow that Data Flow has always use for launching tasks.

#### 33.1.2. Launching a Task With Changes That Is Not Currently Running

The second flow to consider when launching a task is when a task is not running but there is a change in any of the task application version, application properties, or deployment properties. In this case, the following flow is executed:

1. A launch request comes into Data Flow. Data Flow determines that an upgrade is required, since there was a change in the task application version, the application properties, or the deployment properties.

2. Data Flow checks to see whether another instance of the task definition is currently running.

4. If there is no other instance of the task definition currently running, the old deployment is deleted.

5. On platforms that cache a deployed artifact (CloudFoundry, at this writing), Data Flow checks whether the application was previously deployed (this check evaluates to `false` in this flow, since the old deployment was deleted).

6. Data Flow does the deployment of the task application with the updated values (new application version, new merged properties, and new merged deployment properties).

7. Data Flow launches the application.

This flow is what fundamentally enables continuous deployment for Spring Cloud Data Flow.

#### 33.1.3. Launch a Task With Changes While Another Instance Is Running

The last main flow is when a launch request comes to Spring Cloud Data Flow to do an upgrade but the task definition is currently running. In this case, the launch is blocked due to the requirement to delete the current application. On some platforms (CloudFoundry, at this writing), deleting the application causes all currently running applications to be shut down. This feature prevents that from happening. The following process describes what happens when a task changes while another instance is running:

1. A launch request comes into Data Flow. Data Flow determines that an upgrade is required, since there was a change in the task application version, the application properties, or the deployment properties.

2. Data Flow checks to see whether another instance of the task definition is currently running.

3. Data Flow prevents the launch from happening, because other instances of the task definition are running.

| Any launch that requires an upgrade of a task definition that is running at the time of the request is blocked from running due to the need to delete any currently running tasks.

# Task Developer Guide

See the Batch Developer section of the microsite for more about how to create, test, and run Spring Cloud Task applications on your local machine.

# Task Monitoring

See the Task Monitoring Guide of the microsite for more about how to monitor the applications that were deployed as part of a task.

# Dashboard

This section describes how to use the dashboard of Spring Cloud Data Flow.

## 34. Introduction

Spring Cloud Data Flow provides a browser-based GUI called the Dashboard to manage the following information:

- Apps: The Apps tab lists all available applications and provides the controls to register and unregister them.

- Runtime: The Runtime tab provides the list of all running applications.

- Streams: The Streams tab lets you list, design, create, deploy, and destroy Stream Definitions.

- Tasks: The Tasks tab lets you list, create, launch, schedule, and destroy Task Definitions.

- Jobs: The Jobs tab lets you perform batch job related functions.

Upon starting Spring Cloud Data Flow, the dashboard is available at:

`<host>:<port>/dashboard`

For example, if Spring Cloud Data Flow is running locally, the dashboard is available at `localhost:9393/dashboard`.

If you have enabled HTTPS, the dashboard is available at `localhost:9393/dashboard`. If you have enabled security, a login form is available at `localhost:9393/dashboard/#/login`.

| The default Dashboard server port is `9393`.

The following image shows the opening page of the Spring Cloud Data Flow dashboard:

Figure 10. The Spring Cloud Data Flow Dashboard

## 35. Apps

The Applications tab of the dashboard lists all the available applications and provides the controls to register and unregister them (if applicable). You can import a number of applications at once by using the Bulk Import Applications action.

The following image shows a typical list of available applications within the dashboard:

Figure 11. List of Available Applications

### 35.1. Bulk Import of Applications

Applications can be imported in numerous ways which are available on the "Applications" page. For bulk import, the application definitions are expected to be expressed in a properties style, as follows:

    <type>.<name> = <coordinates>

The following examples show typical application definitions:

    task.timestamp=maven://org.springframework.cloud.task.app:timestamp-task:1.2.0.RELEASE
    processor.transform=maven://org.springframework.cloud.stream.app:transform-processor-rabbit:1.2.0.RELEASE

In the "Import application coordinates from an HTTP URI location" section, you can specify a URI that points to a properties file stored elsewhere, it should contain properties formatted as shown in the previous example. Alternatively, by using the Apps as Properties textbox in the "Import application coordinates from a properties file" section , you can directly list each property string. Finally, if the properties are stored in a local file, the Import a File option opens a local file browser to select the file. After setting your definitions through one of these routes, click Import Application(s).

The following image shows an example page of one way to bulk import applications:

Figure 12. Bulk Import Applications

## 36. Runtime

The Runtime tab of the Dashboard application shows the list of all running applications. For each runtime applicaiton, the state of the deployment and the number of deployed instances is shown. A list of the used deployment properties is available by clicking on the application ID.

The following image shows an example of the Runtime tab in use:

Figure 13. List of Running Applications

## 37. Streams

The Streams tab has two child tabs: Definitions and Create Stream. The following topics describe how to work with each one:

- Working with Stream Definitions

- Creating a Stream

- Deploying a Stream

- Accessing Stream Logs

### 37.1. Working with Stream Definitions

The Streams section of the Dashboard includes the Definitions tab that provides a listing of stream definitions. There you have the option to deploy or undeploy those stream definitions. Additionally, you can remove the definition by clicking on Destroy. Each row includes an arrow on the left, which you can click to see a visual representation of the definition. Hovering over the boxes in the visual representation shows more details about the applications, including any options passed to them.

In the following screenshot, the `timer` stream has been expanded to show the visual representation:

Figure 14. List of Stream Definitions

If you click the details button, the view changes to show a visual representation of that stream and any related streams. In the preceding example, if you click details for the `timer` stream, the view changes to the following view, which clearly shows the relationship between the three streams (two of them are tapping into the `timer` stream):

Figure 15. Stream Details Page

### 37.2. Creating a Stream

The Streams section of the Dashboard includes the Create Stream tab, which makes the Spring Flo designer available. The designer is a canvas application that offers an interactive graphical interface for creating data pipelines.

In this tab, you can:

- Create, manage, and visualize stream pipelines by using DSL, a graphical canvas, or both

- Write pipelines by using DSL with content-assist and auto-complete

- Use auto-adjustment and grid-layout capabilities in the GUI for simpler and interactive organization of pipelines

You should watch this screencast that highlights some of the "Flo for Spring Cloud Data Flow" capabilities. The Spring Flo wiki includes more detailed content on core Flo capabilities.

The following image shows the Flo designer in use:

Figure 16. Flo for Spring Cloud Data Flow

### 37.3. Deploying a Stream

The stream deploy page includes tabs that provide different ways to set up the deployment properties and deploy the stream. The following screenshots show the stream deploy page for `foobar` (`time | log`).

You can define deployments properties by using:

- Form builder tab: a builder that helps you to define deployment properties (deployer, application properties, and so on)

- Free text tab: a free text area (for key-value pairs)

You can switch between both views.

| The form builder offers stronger validation of the inputs.

Figure 17. The following image shows the form builder
Figure 18. The following image shows the same properties in the free text

### 37.4. Accessing Stream Logs

Once the stream applications are deployed, their logs can be accessed from the Stream `summary` page, as the following image shows:

### 37.5. Creating Fan-In and Fan-Out Streams

In the Fan-in and Fan-out chapter, you can learn how to support fan-in and fan-out use cases by using named destinations. The UI provides dedicated support for named destinations as well:

Figure 19. Flo for Spring Cloud Data Flow

In this example, we have data from an HTTP Source and a JDBC Source that is being sent to the sharedData channel, which represents a fan-in use case. On the other end we have a Cassandra Sink and a File Sink subscribed to the sharedData channel, which represents a fan-out use case.

### 37.6. Creating a Tap Stream

Creating taps by using the Dashboard is straightforward. Suppose you have a stream consisting of an HTTP Source and a File Sink and you would like to tap into the stream to also send data to a JDBC Sink. To create the tap stream, connect the output connector of the HTTP Source to the JDBC Sink. The connection is displayed as a dotted line, indicating that you created a tap stream.

Figure 20. Creating a Tap Stream

The primary stream (HTTP Source to File Sink) will be automatically named, in case you did not provide a name for the stream, yet. When creating tap streams, the primary stream must always be explicitly named. In the preceding image, the primary stream was named HTTP_INGEST.

By using the Dashboard, you can also switch the primary stream so that it becomes the secondary tap stream.

Figure 21. Change Primary Stream to Secondary Tap Stream

Hover over the existing primary stream, the line between HTTP Source and File Sink. Several control icons appear, and, by clicking on the icon labeled Switch to/from tap, you change the primary stream into a tap stream. Do the same for the tap stream and switch it to a primary stream.

Figure 22. End Result of Switching the Primary Stream

| When interacting directly with named destinations, there can be "n" combinations (Inputs/Outputs). This allows you to create complex topologies involving a wide variety of data sources and destinations.

### 37.7. Import and Export Streams

The Import/Export tab of the Dashboard includes a page that provides the option to import and export streams.

The following image shows the streams export page:

Figure 23. Stream Utils Export page

When importing the streams, you have to import from a valid JSON file. You can either manually draft the file or export the file from the streams export page.

Figure 24. Stream Utils Import page

After importing the file, you get confirmation of whether the operation completed successfully.

Figure 25. Stream Utils Import Result page

## 38. Tasks

The Tasks tab of the Dashboard currently has three tabs:

- Apps

- Definitions

- Executions

- Scheduling

### 38.1. Apps

Each application encapsulates a unit of work into a reusable component. Within the Data Flow runtime environment, applications let you create definitions for streams as well as tasks. Consequently, the Apps tab within the Tasks tab lets you create task definitions.

| You can also use this tab to create Batch Jobs.

The following image shows a typical list of task applications:

Figure 26. List of Task Apps

On this screen, you can perform the following actions:

- View details, such as the task application options.

- Create a task definition from the respective application.

#### 38.1.1. View Task Application Details

On this page, you can view the details of a selected task application, including the list of available options (properties) for that application.

### 38.2. Definitions

This page lists the Data Flow task definitions and provides actions to launch or destroy those tasks.

The following image shows the Definitions page:

Figure 27. List of Task Definitions

#### 38.2.1. Create a Task Definition

The following image shows a task definition composed of the timestamp application as well as the list of task applications that can be used to create a task definiton:

On this page, you can also specify various properties that are used during the deployment of the application. Once you are satisfied with the task definition, you can click the CREATE TASK button. A dialog box then asks for a task definition name and description. At a minimum, you must provide a name for the new definition.

#### 38.2.2. Creating Composed Task Definitions

The dashboard includes the Create Composed Task tab, which provides an interactive graphical interface for creating composed tasks.

In this tab, you can:

- Create and visualize composed tasks by using DSL, a graphical canvas, or both.

- Use auto-adjustment and grid-layout capabilities in the GUI for simpler and interactive organization of the composed task.

On the Create Composed Task screen, you can define one or more task parameters by entering both the parameter key and the parameter value.

| Task parameters are not typed.

The following image shows the composed task designer:

Figure 28. Composed Task Designer

#### 38.2.3. Launching Tasks

Once the task definition has been created, you can launch the tasks through the dashboard. To do so, click the Tasks tab and select the task you want to launch by pressing `Launch`. The following image shows the Task Launch page:

Figure 29. Task Launch Page

#### 38.2.4. Import/Export Tasks

The Import/Export page provides the option to import and export tasks. This is done by clicking the Import/Export option on the left side of page. From here, click the Export task(s): Create a JSON file with the selected tasks option. The `Export Tasks(s)` page appears.

The following image shows the tasks export page:

Figure 30. Tasks Utils Export page

Similarly, you can import task definitions. To do so, click the Import/Export option on the left side of page. From here, click the Import task(s): Import tasks from a JSON file option to show the Import Tasks page. On the Import Tasks page, you have to import from a valid JSON file. You can either manually draft the file or export the file from the Tasks Export page.

Figure 31. Tasks Utils Import page

After importing the file, you get confirmation on whether the operation completed successfully.

Figure 32. Tasks Utils Import Result page

### 38.3. Executions

The Task Executions tab shows the current running and completed task executions. From this page, you can drill down into the Task Execution details page. Furthermore, you can relaunch a Task Execution or stop a running execution.

Finally, you can clean up one or more task executions. This operation removes any associated task or batch job from the underlying persistence store. This operation can only be triggered for parent task executions and cascades down to the child task executions (if there are any).

The following image shows the Executions tab:

Figure 33. List of Task Executions

### 38.4. Execution Detail

For each task execution on the Task Executions tab, you can retrieve detailed information about a specific execution by clicking the Execution ID of the task execution.

On this screen, you can view not only the information from the task executions page but also:

- Task Arguments

- External Execution ID

- Batch Job Indicator (indicates if the task execution contained Spring Batch jobs.)

- Job Execution IDs links (Clicking the Job Execution Id will take you to the Job Execution Details for that Job Execution ID.)

- Task Execution Duration

- Task Execution Exit Message

- Logging output from the Task Execution

Additionally, you can trigger the following operations:

- Relaunch a task

- Stop a running task

- Task execution cleanup (for parent task executions only)

#### 38.4.1. Stop Executing Tasks

To submit a stop task execution request to the platform, click the drop down button next to the task execution that needs to be stopped. Now click the Stop task option. The dashboard presents a dialog box asking if you are sure that you want to stop the task execution. If so, click `Stop Task Execution(s)`.

| Child Spring Cloud Task applications launched via Spring Batch applications that use remote partitioning are not stopped.

## 39. Jobs

The Job Executions tab of the Dashboard lets you inspect batch jobs. The main section of the screen provides a list of job executions. Batch jobs are tasks that each execute one or more batch jobs. Each job execution has a reference to the task execution ID (in the Task ID column).

The list of job executions also shows the state of the underlying Job Definition. Thus, if the underlying definition has been deleted, “No definition found” appears in the Status column.

You can take the following actions for each job:

- Restart (for failed jobs).

- Stop (for running jobs).

- View execution details.

| Clicking the stop button actually sends a stop request to the running job, which may not immediately stop.

The following image shows the Jobs tab:

Figure 34. List of Job Executions

### 39.1. Job Execution Details

After you have launched a batch job, the Job Execution Details page shows information about the job.

The following image shows the Job Execution Details page:

Figure 35. Job Execution Details

The Job Execution Details page contains a list of the executed steps. You can further drill into the details of each step’s execution by clicking the magnifying glass icon.

### 39.2. Step Execution Details

The Step Execution Details page provides information about an individual step within a job.

The following image shows the Step Execution Details page:

Figure 36. Step Execution Details

The Step Execution Details screen provides a complete list of all Step Execution Context key-value pairs.

| For exceptions, the Exit Description field contains additional error information. However, this field can have a maximum of 2500 characters. Therefore, in the case of long exception stack traces, trimming of error messages may occur. When that happens, check the server log files for further details.

### 39.3. Step Execution History

Under Step Execution History, you can also view various metrics associated with the selected step, such as duration, read counts, write counts, and others.

## 40. Scheduling

You can create schedules from the SCDF Dashboard for the Task Definitions. See the Scheduling Batch Jobs section of the microsite for more information.

## 41. Auditing

The Auditing page of the Dashboard gives you access to recorded audit events. Audit events are recorded for:

- Streams

  - Create

  - Delete

  - Deploy

  - Undeploy

- Tasks

  - Create

  - Delete

  - Launch

- Scheduling of Tasks

  - Create Schedule

  - Delete Schedule

The following image shows the Audit Records page:

Figure 37. List Overview of Audit Records

By clicking the show details icon (the “i” in a circle on the right), you can obtain further details regarding the auditing details:

Figure 38. List Details of an Audit Record

Generally, auditing provides the following information:

- When was the record created?

- The name of the user who triggered the audit event (if security is enabled)

- Audit operation (Schedule, Stream, or Task)

- The performed action (Create, Delete, Deploy, Rollback, Undeploy, or Update)

- Correlation ID, such as the Stream or Task name

- Audit Data

The written value of the audit data property depends on the performed audit operation and the action type. For example, when a schedule is being created, the name of the task definition, task definition properties, deployment properties, and command line arguments are written to the persistence store.

Sensitive information is sanitized prior to saving the Audit Record, in a best-effort manner. Any of the following keys are being detected and their sensitive values are masked:

- password

- secret

- key

- token

- .*credentials.*

- vcap_services

# Samples

This section shows the available samples.

## 42. Links

Several samples have been created to help you get started on implementing higher-level use cases than the basic Streams and Tasks shown in the reference guide. The samples are part of a separate repository and have their own reference documentation.

The following samples are available:

General

- Java DSL

- HTTP to Cassandra

- HTTP to MySQL

- HTTP to Gemfire

- Gemfire CQ to Log Demo

- Gemfire to Log Demo

- Custom Processor

Task and Batch

- Batch Job on Cloud Foundry

- Batch File Ingest

Data Science

- Species Prediction

Functions

- Using Spring Cloud Function

# REST API Guide

This section describes the Spring Cloud Data Flow REST API.

## 43. Overview

Spring Cloud Data Flow provides a REST API that lets you access all aspects of the server. In fact, the Spring Cloud Data Flow shell is a first-class consumer of that API.

| If you plan to use the REST API with Java, you should consider using the provided Java client (`DataflowTemplate`) that uses the REST API internally.

### 43.1. HTTP verbs

Spring Cloud Data Flow tries to adhere as closely as possible to standard HTTP and REST conventions in its use of HTTP verbs, as described in the following table:

Verb | Usage
--- | ---
`GET` | Used to retrieve a resource.
`POST` | Used to create a new resource.
`PUT` | Used to update an existing resource, including partial updates. Also used for resources that imply the concept of `restarts`, such as tasks.
`DELETE` | Used to delete an existing resource.

### 43.2. HTTP Status Codes

Spring Cloud Data Flow tries to adhere as closely as possible to standard HTTP and REST conventions in its use of HTTP status codes, as shown in the following table:

Status code | Usage
--- | ---
`200 OK` | The request completed successfully.
`201 Created` | A new resource has been created successfully. The resource’s URI is available from the response’s `Location` header.
`204 No Content` | An update to an existing resource has been applied successfully.
`400 Bad Request` | The request was malformed. The response body includes an error description that provides further information.
`404 Not Found` | The requested resource did not exist.
`409 Conflict` | The requested resource already exists. For example, the task already exists or the stream was already being deployed
`422 Unprocessable Entity` | Returned in cases where the job execution cannot be stopped or restarted.

### 43.3. Headers

Every response has the following headers:

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### 43.4. Errors

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### 43.5. Hypermedia

Spring Cloud Data Flow uses hypermedia, and resources include links to other resources in their responses. Responses are in the Hypertext Application from resource-to-resource Language (HAL) format. Links can be found beneath the `_links` key. Users of the API should not create URIs themselves. Instead, they should use the above-described links to navigate.

## 44. Resources

The API includes the following resources:

- Index

- Server Meta Information

- Audit Records

- Registered Applications

- Stream Definitions

- Stream Deployments

- Stream Validation

- Task Definitions

- Task Executions

- Task Scheduler

- Task Validation

- Job Executions

- Job Instances

- Job Step Executions

- Runtime Information about Applications

- Stream Logs

- Task Logs

### 44.1. Index

The index provides the entry point into Spring Cloud Data Flow’s REST API. The following topics provide more details:

- Accessing the index

- Request Structure

- Example Request

- Response Structure

- Example Response

- Links

#### 44.1.1. Accessing the index

Use a `GET` request to access the index.

##### Request Structure

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##### Example Request

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##### Response Structure

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##### Example Response

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##### Links

The main element of the index are the links, as they let you traverse the API and execute the desired functionality:

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### 44.2. Server Meta Information

The server meta information endpoint provides more information about the server itself. The following topics provide more details:

- Retrieving information about the server

- Request Structure

- Example Request

- Response Structure

#### 44.2.1. Retrieving information about the server

A `GET` request returns meta information for Spring Cloud Data Flow, including:

- Runtime environment information

- Information regarding which features are enabled

- Dependency information of Spring Cloud Data Flow Server

- Security information

##### Request Structure

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##### Example Request

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##### Response Structure

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### 44.3. Registered Applications

The registered applications endpoint provides information about the applications that are registered with the Spring Cloud Data Flow server. The following topics provide more details:

- Listing Applications

- Getting Information on a Particular Application

- Registering a New Application

- Registering a New Application with version

- Registering Applications in Bulk

- Set the Default Application Version

- Unregistering an Application

- Unregistering all Applications

#### 44.3.1. Listing Applications

A `GET` request lists all of the applications known to Spring Cloud Data Flow. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

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##### Request Parameters

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##### Example Request

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##### Response Structure

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#### 44.3.2. Getting Information on a Particular Application

A `GET` request on `/apps/<type>/<name>` gets info on a particular application. The following topics provide more details:

- Request Structure

- Path Parameters

- Example Request

- Response Structure

##### Request Structure

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##### Request Parameters

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##### Path Parameters

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##### Example Request

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##### Response Structure

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#### 44.3.3. Registering a New Application

A `POST` request on `/apps/<type>/<name>` allows registration of a new application. The following topics provide more details:

- Request Structure

- Request Parameters

- Path Parameters

- Example Request

- Response Structure

##### Request Structure

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##### Request Parameters

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##### Path Parameters

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##### Example Request

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##### Response Structure

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#### 44.3.4. Registering a New Application with version

A `POST` request on `/apps/<type>/<name>/<version>` allows registration of a new application. The following topics provide more details:

- Request Structure

- Request Parameters

- Path Parameters

- Example Request

- Response Structure

##### Request Structure

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##### Request Parameters

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##### Path Parameters

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##### Example Request

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##### Response Structure

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#### 44.3.5. Registering Applications in Bulk

A `POST` request on `/apps` allows registering multiple applications at once. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

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##### Request Parameters

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##### Example Request

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##### Response Structure

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#### 44.3.6. Set the Default Application Version

For an application with the same `name` and `type`, you can register multiple versions. In this case, you can choose one of the versions as the default application.

The following topics provide more details:

- Request Structure

- Path Parameters

- Example Request

- Response Structure

##### Request Structure

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##### Path Parameters

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##### Example Request

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##### Response Structure

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#### 44.3.7. Unregistering an Application

A `DELETE` request on `/apps/<type>/<name>` unregisters a previously registered application. The following topics provide more details:

- Request Structure

- Path Parameters

- Example Request

- Response Structure

##### Request Structure

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##### Path Parameters

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##### Example Request

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##### Response Structure

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#### 44.3.8. Unregistering all Applications

A `DELETE` request on `/apps` unregisters all the applications. The following topics provide more details:

- Request Structure

- Example Request

- Response Structure

##### Request Structure

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##### Example Request

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##### Response Structure

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### 44.4. Audit Records

The audit records endpoint provides information about the audit records. The following topics provide more details:

- List All Audit Records

- Retrieve Audit Record Detail

- List all the Audit Action Types

- List all the Audit Operation Types

#### 44.4.1. List All Audit Records

The audit records endpoint lets you retrieve audit trail information.

The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

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##### Request Parameters

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##### Example Request

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##### Response Structure

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#### 44.4.2. Retrieve Audit Record Detail

The audit record endpoint lets you get a single audit record. The following topics provide more details:

- Request Structure

- Path Parameters

- Example Request

- Response Structure

##### Request Structure

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##### Path Parameters

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##### Example Request

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##### Response Structure

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#### 44.4.3. List all the Audit Action Types

The audit record endpoint lets you get the action types. The following topics provide more details:

- Request Structure

- Example Request

- Response Structure

##### Request Structure

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##### Example Request

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##### Response Structure

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#### 44.4.4. List all the Audit Operation Types

The audit record endpoint lets you get the operation types. The following topics provide more details:

- Request Structure

- Example Request

- Response Structure

##### Request Structure

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##### Example Request

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##### Response Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/audit-records-documentation/get-audit-operation-types/http-response.adoc[]

### 44.5. Stream Definitions

The registered applications endpoint provides information about the stream definitions that are registered with the Spring Cloud Data Flow server. The following topics provide more details:

- Creating a New Stream Definition

- List All Stream Definitions

- List Related Stream Definitions

- Retrieve Stream Definition Detail

- Delete a Single Stream Definition

- Delete All Stream Definitions

- Deploying Stream Definition

- Undeploy Stream Definition

- Undeploy All Stream Definitions

#### 44.5.1. Creating a New Stream Definition

Creating a stream definition is achieved by creating a POST request to the stream definitions endpoint. A curl request for a `ticktock` stream might resemble the following:

    curl -X POST -d "name=ticktock&definition=time | log" localhost:9393/streams/definitions?deploy=false

A stream definition can also contain additional parameters. For instance, in the example shown under “Request Structure”, we also provide the date-time format.

The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-definitions-documentation/create-definition/http-request.adoc[]

##### Request Parameters

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-definitions-documentation/create-definition/request-parameters.adoc[]

##### Example Request

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-definitions-documentation/create-definition/curl-request.adoc[]

##### Response Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-definitions-documentation/create-definition/http-response.adoc[]

#### 44.5.2. List All Stream Definitions

The streams endpoint lets you list all the stream definitions. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-definitions-documentation/list-all-stream-definitions/http-request.adoc[]

##### Request Parameters

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-definitions-documentation/list-all-stream-definitions/request-parameters.adoc[]

##### Example Request

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-definitions-documentation/list-all-stream-definitions/curl-request.adoc[]

##### Response Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-definitions-documentation/list-all-stream-definitions/http-response.adoc[]

#### 44.5.3. List Related Stream Definitions

The streams endpoint lets you list related stream definitions. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-definitions-documentation/list-related-stream-definitions/http-request.adoc[]

##### Request Parameters

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-definitions-documentation/list-related-stream-definitions/request-parameters.adoc[]

##### Example Request

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-definitions-documentation/list-related-stream-definitions/curl-request.adoc[]

##### Response Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-definitions-documentation/list-related-stream-definitions/http-response.adoc[]

#### 44.5.4. Retrieve Stream Definition Detail

The stream definition endpoint lets you get a single stream definition. The following topics provide more details:

- Request Structure

- Path Parameters

- Example Request

- Response Structure

##### Request Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-definitions-documentation/get-stream-definition/http-request.adoc[]

##### Path Parameters

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-definitions-documentation/get-stream-definition/path-parameters.adoc[]

##### Example Request

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-definitions-documentation/get-stream-definition/curl-request.adoc[]

##### Response Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-definitions-documentation/get-stream-definition/http-response.adoc[]

#### 44.5.5. Delete a Single Stream Definition

The streams endpoint lets you delete a single stream definition. (See also: Delete All Stream Definitions.) The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-definitions-documentation/stream-definition-delete1/http-request.adoc[]

##### Request Parameters

There are no request parameters for this endpoint.

##### Example Request

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-definitions-documentation/stream-definition-delete1/curl-request.adoc[]

##### Response Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-definitions-documentation/stream-definition-delete1/http-response.adoc[]

#### 44.5.6. Delete All Stream Definitions

The streams endpoint lets you delete all single stream definitions. (See also: Delete a Single Stream Definition.) The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-definitions-documentation/stream-definition-delete-all/http-request.adoc[]

##### Request Parameters

There are no request parameters for this endpoint.

##### Example Request

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-definitions-documentation/stream-definition-delete-all/curl-request.adoc[]

##### Response Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-definitions-documentation/stream-definition-delete-all/http-response.adoc[]

### 44.6. Stream Validation

The stream validation endpoint lets you validate the apps in a stream definition. The following topics provide more details:

- Request Structure

- Path Parameters

- Example Request

- Response Structure

#### 44.6.1. Request Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-validation-documentation/validate-stream/http-request.adoc[]

#### 44.6.2. Path Parameters

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-validation-documentation/validate-stream/path-parameters.adoc[]

#### 44.6.3. Example Request

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-validation-documentation/validate-stream/curl-request.adoc[]

#### 44.6.4. Response Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-validation-documentation/validate-stream/http-response.adoc[]

### 44.7. Stream Deployments

The deployment definitions endpoint provides information about the deployments that are registered with the Spring Cloud Data Flow server. The following topics provide more details:

- Deploying Stream Definition

- Undeploy Stream Definition

- Undeploy All Stream Definitions

- Update Deployed Stream

- Rollback Stream Definition

- Get Manifest

- Get Deployment History

- Get Deployment Platforms

- Scale Stream Definition

#### 44.7.1. Deploying Stream Definition

The stream definition endpoint lets you deploy a single stream definition. Optionally, you can pass application parameters as properties in the request body. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-deployments-documentation/deploy/http-request.adoc[]

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-deployments-documentation/deploy/path-parameters.adoc[]

##### Request Parameters

There are no request parameters for this endpoint.

##### Example Request

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-deployments-documentation/deploy/curl-request.adoc[]

##### Response Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-deployments-documentation/deploy/http-response.adoc[]

#### 44.7.2. Undeploy Stream Definition

The stream definition endpoint lets you undeploy a single stream definition. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-deployments-documentation/un-deploy/http-request.adoc[]

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-deployments-documentation/un-deploy/path-parameters.adoc[]

##### Request Parameters

There are no request parameters for this endpoint.

##### Example Request

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-deployments-documentation/un-deploy/curl-request.adoc[]

##### Response Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-deployments-documentation/un-deploy/http-response.adoc[]

#### 44.7.3. Undeploy All Stream Definitions

The stream definition endpoint lets you undeploy all single stream definitions. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-deployments-documentation/un-deploy-all/http-request.adoc[]

##### Request Parameters

There are no request parameters for this endpoint.

##### Example Request

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-deployments-documentation/un-deploy-all/curl-request.adoc[]

##### Response Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-deployments-documentation/un-deploy-all/http-response.adoc[]

#### 44.7.4. Update Deployed Stream

Thanks to Skipper, you can update deployed streams, and provide additional deployment properties.

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-deployments-documentation/stream-update/http-request.adoc[]

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-deployments-documentation/stream-update/path-parameters.adoc[]

##### Request Parameters

There are no request parameters for this endpoint.

##### Example Request

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-deployments-documentation/stream-update/curl-request.adoc[]

##### Response Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-deployments-documentation/stream-update/http-response.adoc[]

#### 44.7.5. Rollback Stream Definition

Rollback the stream to the previous or a specific version of the stream.

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-deployments-documentation/rollback/http-request.adoc[]

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-deployments-documentation/rollback/path-parameters.adoc[]

##### Request Parameters

There are no request parameters for this endpoint.

##### Example Request

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-deployments-documentation/rollback/curl-request.adoc[]

##### Response Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-deployments-documentation/rollback/http-response.adoc[]

#### 44.7.6. Get Manifest

Return a manifest of a released version. For packages with dependencies, the manifest includes the contents of those dependencies.

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-deployments-documentation/manifest/http-request.adoc[]

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-deployments-documentation/manifest/path-parameters.adoc[]

##### Request Parameters

There are no request parameters for this endpoint.

##### Example Request

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-deployments-documentation/manifest/curl-request.adoc[]

##### Response Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-deployments-documentation/manifest/http-response.adoc[]

#### 44.7.7. Get Deployment History

Get the stream’s deployment history.

- Request Structure

- Example Request

- Response Structure

##### Request Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-deployments-documentation/history/http-request.adoc[]

##### Example Request

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-deployments-documentation/history/curl-request.adoc[]

##### Response Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-deployments-documentation/history/http-response.adoc[]

#### 44.7.8. Get Deployment Platforms

Retrieve a list of supported deployment platforms.

- Request Structure

- Example Request

- Response Structure

##### Request Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-deployments-documentation/platform-list/http-request.adoc[]

##### Example Request

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-deployments-documentation/platform-list/curl-request.adoc[]

##### Response Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-deployments-documentation/platform-list/http-response.adoc[]

#### 44.7.9. Scale Stream Definition

The stream definition endpoint lets you scale a single app in a stream definition. Optionally, you can pass application parameters as properties in the request body. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-deployments-documentation/scale/http-request.adoc[]

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-deployments-documentation/scale/path-parameters.adoc[]

##### Request Parameters

There are no request parameters for this endpoint.

##### Example Request

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-deployments-documentation/scale/curl-request.adoc[]

##### Response Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/stream-deployments-documentation/scale/http-response.adoc[]

### 44.8. Task Definitions

The task definitions endpoint provides information about the task definitions that are registered with the Spring Cloud Data Flow server. The following topics provide more details:

- Creating a New Task Definition

- List All Task Definitions

- Retrieve Task Definition Detail

- Delete Task Definition

#### 44.8.1. Creating a New Task Definition

The task definition endpoint lets you create a new task definition. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/task-definitions-documentation/create-definition/http-request.adoc[]

##### Request Parameters

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/task-definitions-documentation/create-definition/request-parameters.adoc[]

##### Example Request

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/task-definitions-documentation/create-definition/curl-request.adoc[]

##### Response Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/task-definitions-documentation/create-definition/http-response.adoc[]

#### 44.8.2. List All Task Definitions

The task definition endpoint lets you get all task definitions. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/task-definitions-documentation/list-all-task-definitions/http-request.adoc[]

##### Request Parameters

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/task-definitions-documentation/list-all-task-definitions/request-parameters.adoc[]

##### Example Request

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/task-definitions-documentation/list-all-task-definitions/curl-request.adoc[]

##### Response Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/task-definitions-documentation/list-all-task-definitions/http-response.adoc[]

#### 44.8.3. Retrieve Task Definition Detail

The task definition endpoint lets you get a single task definition. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/task-definitions-documentation/display-detail/http-request.adoc[]

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/task-definitions-documentation/display-detail/path-parameters.adoc[]

##### Request Parameters

There are no request parameters for this endpoint.

##### Example Request

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/task-definitions-documentation/display-detail/curl-request.adoc[]

##### Response Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/task-definitions-documentation/display-detail/http-response.adoc[]

#### 44.8.4. Delete Task Definition

The task definition endpoint lets you delete a single task definition. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/task-definitions-documentation/task-definition-delete/http-request.adoc[]

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/task-definitions-documentation/task-definition-delete/path-parameters.adoc[]

##### Request Parameters

There are no request parameters for this endpoint.

##### Example Request

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##### Response Structure

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### 44.9. Task Scheduler

The task scheduler endpoint provides information about the task schedules that are registered with the Scheduler Implementation. The following topics provide more details:

- Creating a New Task Schedule

- List All Schedules

- List Filtered Schedules

- Delete Task Schedule

#### 44.9.1. Creating a New Task Schedule

The task schedule endpoint lets you create a new task schedule. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/task-scheduler-documentation/create-schedule/http-request.adoc[]

##### Request Parameters

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##### Example Request

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##### Response Structure

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#### 44.9.2. List All Schedules

The task schedules endpoint lets you get all task schedules. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

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##### Request Parameters

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##### Example Request

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##### Response Structure

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#### 44.9.3. List Filtered Schedules

The task schedules endpoint lets you get all task schedules that have the specified task definition name. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

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##### Request Parameters

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##### Example Request

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##### Response Structure

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#### 44.9.4. Delete Task Schedule

The task schedule endpoint lets you delete a single task schedule. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/task-scheduler-documentation/delete-schedule/http-request.adoc[]

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/task-scheduler-documentation/delete-schedule/path-parameters.adoc[]

##### Request Parameters

There are no request parameters for this endpoint.

##### Example Request

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##### Response Structure

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### 44.10. Task Validation

The task validation endpoint lets you validate the apps in a task definition. The following topics provide more details:

- Request Structure

- Path Parameters

- Example Request

- Response Structure

#### 44.10.1. Request Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/task-validation-documentation/validate-task/http-request.adoc[]

#### 44.10.2. Path Parameters

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#### 44.10.3. Example Request

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#### 44.10.4. Response Structure

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### 44.11. Task Executions

The task executions endpoint provides information about the task executions that are registered with the Spring Cloud Data Flow server. The following topics provide more details:

- Launching a Task

- Stopping a Task

- List All Task Executions

- List All Task Executions With a Specified Task Name

- Task Execution Detail

- Delete Task Execution

- Task Execution Current Count

#### 44.11.1. Launching a Task

Launching a task is done by requesting the creation of a new task execution. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/task-executions-documentation/launch-task/http-request.adoc[]

##### Request Parameters

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##### Example Request

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/task-executions-documentation/launch-task/curl-request.adoc[]

##### Response Structure

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#### 44.11.2. Stopping a Task

Stopping a task is done by posting the id of an existing task execution. The following topics provide more details:

- Request Structure

- Path Parameters

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/task-executions-documentation/stop-task/http-request.adoc[]

##### Path Parameters

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##### Request Parameters

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##### Example Request

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##### Response Structure

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#### 44.11.3. List All Task Executions

The task executions endpoint lets you list all task executions. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

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##### Request Parameters

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##### Example Request

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##### Response Structure

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#### 44.11.4. List All Task Executions With a Specified Task Name

The task executions endpoint lets you list task executions with a specified task name. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/task-executions-documentation/list-task-executions-by-name/http-request.adoc[]

##### Request Parameters

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##### Example Request

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##### Response Structure

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#### 44.11.5. Task Execution Detail

The task executions endpoint lets you get the details about a task execution. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/task-executions-documentation/launch-task-display-detail/http-request.adoc[]

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/task-executions-documentation/launch-task-display-detail/path-parameters.adoc[]

##### Request Parameters

There are no request parameters for this endpoint.

##### Example Request

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##### Response Structure

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#### 44.11.6. Delete Task Execution

The task execution endpoint lets you:

- Clean up resources used to deploy the task

- Remove relevant task data as well as possibly associated Spring Batch job data from the persistence store

| The cleanup implementation (first option) is platform specific. Both operations can be triggered at once or separately.

The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

Please refer to the following section in regards to Deleting Task Execution Data.

##### Request Structure

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| You must provide task execution IDs that actually exist. Otherwise, a `404` (Not Found) HTTP status is returned. In the case of submitting multiple task execution IDs, the invalidity of a single task execution ID causes the entire request to fail, without performing any operation.

##### Request Parameters

This endpoint supports one optional request parameter named action. It is an enumeration and supports the following values:

- CLEANUP

- REMOVE_DATA

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/task-executions-documentation/task-execution-remove-and-task-data-remove/request-parameters.adoc[]

##### Example Request

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##### Response Structure

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#### 44.11.7. Deleting Task Execution Data

Not only can you clean up resources that were used to deploy tasks but you can also delete the data associated with task executions from the underlying persistence store. Also, if a task execution is associated with one or more batch job executions, these are removed as well.

The following example illustrates how a request can be made using multiple task execution IDs and multiple actions:

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/task-executions-documentation/task-execution-remove-and-task-data-remove/curl-request.adoc[]

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| When deleting data from the persistence store by using the `REMOVE_DATA` action parameter, you must provide task execution IDs that represent parent task executions. When you provide child task executions (executed as part of a composed task), a `400` (Bad Request) HTTP status is returned.

#### 44.11.8. Task Execution Current Count

The task executions current endpoint lets you retrieve the current number of running executions. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/task-executions-documentation/launch-task-current-count/http-request.adoc[]

##### Request Parameters

There are no request parameters for this endpoint.

##### Example Request

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##### Response Structure

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### 44.12. Job Executions

The job executions endpoint provides information about the job executions that are registered with the Spring Cloud Data Flow server. The following topics provide more details:

- List All Job Executions

- List All Job Executions Without Step Executions Included

- List All Job Executions With a Specified Job Name

- List All Job Executions With a Specified Job Name Without Step Executions Included

- List All Job Executions For A Specified Date Range Without Step Executions Included

- List All Job Executions For A Specified Job Instance Id Without Step Executions Included

- List All Job Executions For A Specified Task Execution Id Without Step Executions Included

- Job Execution Detail

- Stop Job Execution

- Restart Job Execution

#### 44.12.1. List All Job Executions

The job executions endpoint lets you list all job executions. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/job-executions-documentation/list-job-executions/http-request.adoc[]

##### Request Parameters

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##### Example Request

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##### Response Structure

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#### 44.12.2. List All Job Executions Without Step Executions Included

The job executions endpoint lets you list all job executions without step executions included. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/job-executions-documentation/list-thin-job-executions/http-request.adoc[]

##### Request Parameters

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##### Example Request

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##### Response Structure

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#### 44.12.3. List All Job Executions With a Specified Job Name

The job executions endpoint lets you list all job executions. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/job-executions-documentation/list-job-executions-by-name/http-request.adoc[]

##### Request Parameters

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##### Example Request

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##### Response Structure

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#### 44.12.4. List All Job Executions With a Specified Job Name Without Step Executions Included

The job executions endpoint lets you list all job executions. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

Unresolved directive in api-guide.adoc - include::/home/runner/work/spring-cloud-dataflow/spring-cloud-dataflow/spring-cloud-dataflow-docs/../spring-cloud-dataflow-classic-docs/target/generated-snippets/job-executions-documentation/list-thin-job-executions-by-name/http-request.adoc[]

##### Request Parameters

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##### Example Request

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##### Response Structure

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#### 44.12.5. List All Job Executions For A Specified Date Range Without Step Executions Included

The job executions endpoint lets you list all job executions. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

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##### Request Parameters

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##### Example Request

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##### Response Structure

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#### 44.12.6. List All Job Executions For A Specified Job Instance Id Without Step Executions Included

The job executions endpoint lets you list all job executions. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

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##### Request Parameters

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##### Example Request

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##### Response Structure

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#### 44.12.7. List All Job Executions For A Specified Task Execution Id Without Step Executions Included

The job executions endpoint lets you list all job executions. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

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##### Request Parameters

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##### Example Request

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##### Response Structure

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#### 44.12.8. Job Execution Detail

The job executions endpoint lets you get the details about a job execution. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

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##### Request Parameters

There are no request parameter for this endpoint.

##### Example Request

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##### Response Structure

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#### 44.12.9. Stop Job Execution

The job executions endpoint lets you stop a job execution. The following topics provide more details:

- Request structure

- Request parameters

- Example request

- Response structure

##### Request structure

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##### Request parameters

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##### Example request

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##### Response structure

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#### 44.12.10. Restart Job Execution

The job executions endpoint lets you restart a job execution. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

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##### Request Parameters

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##### Example Request

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##### Response Structure

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### 44.13. Job Instances

The job instances endpoint provides information about the job instances that are registered with the Spring Cloud Data Flow server. The following topics provide more details:

- List All Job Instances

- Job Instance Detail

#### 44.13.1. List All Job Instances

The job instances endpoint lets you list all job instances. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

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##### Request Parameters

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##### Example Request

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##### Response Structure

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#### 44.13.2. Job Instance Detail

The job instances endpoint lets you list all job instances. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

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##### Request Parameters

There are no request parameters for this endpoint.

##### Example Request

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##### Response Structure

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### 44.14. Job Step Executions

The job step executions endpoint provides information about the job step executions that are registered with the Spring Cloud Data Flow server. The following topics provide more details:

- List All Step Executions For a Job Execution

- Job Step Execution Detail

- Job Step Execution Progress

#### 44.14.1. List All Step Executions For a Job Execution

The job step executions endpoint lets you list all job step executions. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

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##### Request Parameters

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##### Example Request

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##### Response Structure

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#### 44.14.2. Job Step Execution Detail

The job step executions endpoint lets you get details about a job step execution. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

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##### Request Parameters

There are no request parameters for this endpoint.

##### Example Request

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##### Response Structure

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#### 44.14.3. Job Step Execution Progress

The job step executions endpoint lets you get details about the progress of a job step execution. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

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##### Request Parameters

There are no request parameters for this endpoint.

##### Example Request

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##### Response Structure

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### 44.15. Runtime Information about Applications

You can get information about running apps known to the system, either globally or individually. The following topics provide more details:

- Listing All Applications at Runtime

- Querying All Instances of a Single App

- Querying a Single Instance of a Single App

#### 44.15.1. Listing All Applications at Runtime

To retrieve information about all instances of all apps, query the `/runtime/apps` endpoint by using `GET`. The following topics provide more details:

- Request Structure

- Example Request

- Response Structure

##### Request Structure

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##### Example Request

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##### Response Structure

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#### 44.15.2. Querying All Instances of a Single App

To retrieve information about all instances of a particular app, query the `/runtime/apps/<appId>/instances` endpoint by using `GET`. The following topics provide more details:

- Request Structure

- Example Request

- Response Structure

##### Request Structure

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##### Example Request

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##### Response Structure

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#### 44.15.3. Querying a Single Instance of a Single App

To retrieve information about a particular instance of a particular application, query the `/runtime/apps/<appId>/instances/<instanceId>` endpoint by using `GET`. The following topics provide more details:

- Request Structure

- Example Request

- Response Structure

##### Request Structure

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##### Example Request

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##### Response Structure

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### 44.16. Stream Logs

You can get the application logs of the stream for the entire stream or a specific application inside the stream. The following topics provide more details:

- Get the applications' logs by the stream name

- Get the logs of a specific application from the stream

#### 44.16.1. Get the applications' logs by the stream name

Use the HTTP `GET` method with the `/streams/logs/<streamName>` REST endpoint to retrieve all the applications' logs for the given stream name. The following topics provide more details:

- Request Structure

- Example Request

- Response Structure

##### Request Structure

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##### Example Request

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##### Response Structure

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#### 44.16.2. Get the logs of a specific application from the stream

To retrieve the logs of a specific application from the stream, query the `/streams/logs/<streamName>/<appName>` endpoint using the `GET` HTTP method. The following topics provide more details:

- Request Structure

- Example Request

- Response Structure

##### Request Structure

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##### Example Request

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##### Response Structure

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### 44.17. Task Logs

You can get the task execution log for a specific task execution.

The following topic provides more details:

- Get the task execution log

#### 44.17.1. Get the task execution log

To retrieve the logs of the task execution, query the `/tasks/logs/<ExternalTaskExecutionId>` endpoint by using the HTTP `GET` method.. The following topics provide more details:

- Request Structure

- Request Parameters

- Example Request

- Response Structure

##### Request Structure

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##### Request Parameters

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##### Example Request

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##### Response Structure

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# Appendices

Having trouble with Spring Cloud Data Flow, We’d like to help!

- Ask a question. We monitor stackoverflow.com for questions tagged with `spring-cloud-dataflow`.

- Report bugs with Spring Cloud Data Flow at github.com/spring-cloud/spring-cloud-dataflow/issues.

## Appendix A: Data Flow Template

As described in API Guide chapter, Spring Cloud Data Flow’s functionality is completely exposed through REST endpoints. While you can use those endpoints directly, Spring Cloud Data Flow also provides a Java-based API, which makes using those REST endpoints even easier.

The central entry point is the `DataFlowTemplate` class in the `org.springframework.cloud.dataflow.rest.client` package.

This class implements the `DataFlowOperations` interface and delegates to the following sub-templates that provide the specific functionality for each feature-set:

Interface | Description
--- | ---
`StreamOperations` | REST client for stream operations
`CounterOperations` | REST client for counter operations
`FieldValueCounterOperations` | REST client for field value counter operations
`AggregateCounterOperations` | REST client for aggregate counter operations
`TaskOperations` | REST client for task operations
`JobOperations` | REST client for job operations
`AppRegistryOperations` | REST client for app registry operations
`CompletionOperations` | REST client for completion operations
`RuntimeOperations` | REST Client for runtime operations

When the `DataFlowTemplate` is being initialized, the sub-templates can be discovered through the REST relations, which are provided by HATEOAS (Hypermedia as the Engine of Application State).

| If a resource cannot be resolved, the respective sub-template results in NULL. A common cause is that Spring Cloud Data Flow allows for specific sets of features to be enabled or disabled when launching. For more information, see one of the local, Cloud Foundry, or Kubernetes configuration chapters, depending on where you deploy your application.

### A.1. Using the Data Flow Template

When you use the Data Flow Template, the only needed Data Flow dependency is the Spring Cloud Data Flow Rest Client, as shown in the following Maven snippet:

    <dependency>
      <groupId>org.springframework.cloud</groupId>
      <artifactId>spring-cloud-dataflow-rest-client</artifactId>
      <version>2.8.0-M1</version>
    </dependency>

With that dependency, you get the `DataFlowTemplate` class as well as all the dependencies needed to make calls to a Spring Cloud Data Flow server.

When instantiating the `DataFlowTemplate`, you also pass in a `RestTemplate`. Note that the needed `RestTemplate` requires some additional configuration to be valid in the context of the `DataFlowTemplate`. When declaring a `RestTemplate` as a bean, the following configuration suffices:

      @Bean
      public static RestTemplate restTemplate() {
        RestTemplate restTemplate = new RestTemplate();
        restTemplate.setErrorHandler(new VndErrorResponseErrorHandler(restTemplate.getMessageConverters()));
        for(HttpMessageConverter<?> converter : restTemplate.getMessageConverters()) {
          if (converter instanceof MappingJackson2HttpMessageConverter) {
            final MappingJackson2HttpMessageConverter jacksonConverter =
                (MappingJackson2HttpMessageConverter) converter;
            jacksonConverter.getObjectMapper()
                .registerModule(new Jackson2HalModule())
                .addMixIn(JobExecution.class, JobExecutionJacksonMixIn.class)
                .addMixIn(JobParameters.class, JobParametersJacksonMixIn.class)
                .addMixIn(JobParameter.class, JobParameterJacksonMixIn.class)
                .addMixIn(JobInstance.class, JobInstanceJacksonMixIn.class)
                .addMixIn(ExitStatus.class, ExitStatusJacksonMixIn.class)
                .addMixIn(StepExecution.class, StepExecutionJacksonMixIn.class)
                .addMixIn(ExecutionContext.class, ExecutionContextJacksonMixIn.class)
                .addMixIn(StepExecutionHistory.class, StepExecutionHistoryJacksonMixIn.class);
          }
        }
        return restTemplate;
      }

| You can also get a pre-configured `RestTemplate` by using `DataFlowTemplate.getDefaultDataflowRestTemplate();`

Now you can instantiate the `DataFlowTemplate` with the following code:

    DataFlowTemplate dataFlowTemplate = new DataFlowTemplate(
        new URI("http://localhost:9393/"), restTemplate);         (1)

1 | The `URI` points to the ROOT of your Spring Cloud Data Flow Server.

Depending on your requirements, you can now make calls to the server. For instance, if you want to get a list of the currently available applications, you can run the following code:

    PagedResources<AppRegistrationResource> apps = dataFlowTemplate.appRegistryOperations().list();

    System.out.println(String.format("Retrieved %s application(s)",
        apps.getContent().size()));

    for (AppRegistrationResource app : apps.getContent()) {
      System.out.println(String.format("App Name: %s, App Type: %s, App URI: %s",
        app.getName(),
        app.getType(),
        app.getUri()));
    }

### A.2. Data Flow Template and Security

When using the `DataFlowTemplate`, you can also provide all the security-related options as if you were using the Data Flow Shell. In fact, the Data Flow Shell uses the `DataFlowTemplate` for all its operations.

To let you get started, we provide a `HttpClientConfigurer` that uses the builder pattern to set the various security-related options:

    	HttpClientConfigurer
    		.create(targetUri)                                             (1)
    		.basicAuthCredentials(username, password)                      (2)
    		.skipTlsCertificateVerification()                              (3)
    		.withProxyCredentials(proxyUri, proxyUsername, proxyPassword)  (4)
    		.addInterceptor(interceptor)                                   (5)
    		.buildClientHttpRequestFactory()                               (6)

1 | Creates a HttpClientConfigurer with the provided target URI.
2 | Sets the credentials for basic authentication (Using OAuth2 Password Grant)
3 | Skip SSL certificate verification (Use for DEVELOPMENT ONLY!)
4 | Configure any Proxy settings
5 | Add a custom interceptor e.g. to set the OAuth2 Authorization header. This allows you to pass an OAuth2 Access Token instead of username/password credentials.
6 | Builds the `ClientHttpRequestFactory` that can be set on the `RestTemplate`.

Once the `HttpClientConfigurer` is configured, you can use its `buildClientHttpRequestFactory` to build the `ClientHttpRequestFactory` and then set the corresponding property on the `RestTemplate`. You can then instantiate the actual `DataFlowTemplate` using that `RestTemplate`.

To configure Basic Authentication, the following setup is required:

    	RestTemplate restTemplate = DataFlowTemplate.getDefaultDataflowRestTemplate();
    	HttpClientConfigurer httpClientConfigurer = HttpClientConfigurer.create("http://localhost:9393");

    	httpClientConfigurer.basicAuthCredentials("my_username", "my_password");
    	restTemplate.setRequestFactory(httpClientConfigurer.buildClientHttpRequestFactory());

    	DataFlowTemplate dataFlowTemplate = new DataFlowTemplate("http://localhost:9393", restTemplate);

You can find a sample application as part of the spring-cloud-dataflow-samples repository on GitHub.

## Appendix B: “How-to” guides

This section provides answers to some common ‘how do I do that…​’ questions that often arise when people use Spring Cloud Data Flow.

If you have a specific problem that we do not cover here, you might want to check out stackoverflow.com to see if someone has already provided an answer. That is also a great place to ask new questions (use the `spring-cloud-dataflow` tag).

We are also more than happy to extend this section. If you want to add a “how-to”, you can send us a pull request.

### B.1. Configure Maven Properties

You can set the Maven properties, such as the local Maven repository location, remote Maven repositories, authentication credentials, and proxy server properties through command-line properties when you start the Data Flow server. Alternatively, you can set the properties by setting the `SPRING_APPLICATION_JSON` environment property for the Data Flow server.

The remote Maven repositories need to be configured explicitly if the applications are resolved by using the Maven repository, except for a `local` Data Flow server. The other Data Flow server implementations (which use Maven resources for application artifacts resolution) have no default value for remote repositories. The `local` server has `repo.spring.io/libs-snapshot` as the default remote repository.

To pass the properties as command-line options, run the server with a command similar to the following:

    $ java -jar <dataflow-server>.jar --maven.localRepository=mylocal
    --maven.remote-repositories.repo1.url=https://repo1
    --maven.remote-repositories.repo1.auth.username=repo1user
    --maven.remote-repositories.repo1.auth.password=repo1pass
    --maven.remote-repositories.repo2.url=https://repo2 --maven.proxy.host=proxyhost
    --maven.proxy.port=9018 --maven.proxy.auth.username=proxyuser
    --maven.proxy.auth.password=proxypass

You can also use the `SPRING_APPLICATION_JSON` environment property:

    export SPRING_APPLICATION_JSON='{ "maven": { "local-repository": "local","remote-repositories": { "repo1": { "url": "https://repo1", "auth": { "username": "repo1user", "password": "repo1pass" } },
    "repo2": { "url": "https://repo2" } }, "proxy": { "host": "proxyhost", "port": 9018, "auth": { "username": "proxyuser", "password": "proxypass" } } } }'

Here is the same content in nicely formatted JSON:

    SPRING_APPLICATION_JSON='{
      "maven": {
        "local-repository": "local",
        "remote-repositories": {
          "repo1": {
            "url": "https://repo1",
            "auth": {
              "username": "repo1user",
              "password": "repo1pass"
            }
          },
          "repo2": {
            "url": "https://repo2"
          }
        },
        "proxy": {
          "host": "proxyhost",
          "port": 9018,
          "auth": {
            "username": "proxyuser",
            "password": "proxypass"
          }
        }
      }
    }'

| Depending on the Spring Cloud Data Flow server implementation, you may have to pass the environment properties by using the platform specific environment-setting capabilities. For instance, in Cloud Foundry, you would pass them as `cf set-env SPRING_APPLICATION_JSON`.

### B.2. Troubleshooting

This section covers how to troubleshoot Spring Cloud Data Flow on your platform of choice. See the Troubleshooting sections of the microsite for Stream and Batch processing.

### B.3. Frequently Asked Questions

In this section, we review the frequently asked questions for Spring Cloud Data Flow. See the Frequently Asked Questions section of the microsite for more information.

## Appendix C: Building

This appendix describes how to build Spring Cloud Data Flow.

To build the source, you need to install JDK 1.8.

The build uses the Maven wrapper so that you do not have to install a specific version of Maven.

The main build command is as follows:

    $ ./mvnw clean install

To speed up the build, you can add `-DskipTests` to avoid running the tests.

| You can also install Maven (>=3.3.3) yourself and run the `mvn` command in place of `./mvnw` in the examples below. If you do that, you also might need to add `-P spring` if your local Maven settings do not contain repository declarations for Spring pre-release artifacts.

| You might need to increase the amount of memory available to Maven by setting a `MAVEN_OPTS` environment variable with a value similar to `-Xmx512m -XX:MaxPermSize=128m`. We try to cover this in the `.mvn` configuration, so, if you find you have to do it to make a build succeed, please raise a ticket to get the settings added to source control.

### C.1. Documentation

There is a `full` profile that generates documentation. You can build only the documentation by using the following command:

    $ ./mvnw clean package -DskipTests -P full -pl {project-artifactId} -am

### C.2. Working with the Code

If you do not have a favorite IDE, we recommend that you use Spring Tools Suite or Eclipse when working with the code. We use the m2eclipse Eclipse plugin for Maven support. Other IDEs and tools generally also work without issue.

#### C.2.1. Importing into Eclipse with m2eclipse

We recommend the m2eclipe eclipse plugin when working with Eclipse. If you do not already have m2eclipse installed, it is available from the Eclipse marketplace.

Unfortunately, m2e does not yet support Maven 3.3. Consequently, once the projects are imported into Eclipse, you also need to tell m2eclipse to use the `.settings.xml` file for the projects. If you do not do this, you may see many different errors related to the POMs in the projects. To do so:

1. Open your Eclipse preferences.

2. Expand the Maven preferences.

3. Select User Settings.

4. In the User Settings field, click Browse and navigate to the Spring Cloud project you imported.

5. Select the `.settings.xml` file in that project.

6. Click Apply.

7. Click OK.

| Alternatively, you can copy the repository settings from Spring Cloud’s `.settings.xml` file into your own `~/.m2/settings.xml`.

#### C.2.2. Importing into Eclipse without m2eclipse

If you prefer not to use m2eclipse, you can generate Eclipse project metadata by using the following command:

    $ ./mvnw eclipse:eclipse

You can import the generated Eclipse projects by selecting Import existing projects from the File menu.

## Appendix D: Contributing

Spring Cloud is released under the non-restrictive Apache 2.0 license and follows a very standard Github development process, using Github tracker for issues and merging pull requests into the master branch. If you want to contribute even something trivial, please do not hesitate, but do please follow the guidelines in this appendix.

### D.1. Sign the Contributor License Agreement

Before we accept a non-trivial (anything more than correcting a typographical error) patch or pull request, we need you to sign the contributor’s agreement. Signing the contributor’s agreement does not grant anyone commit rights to the main repository, but it does mean that we can accept your contributions, and you get an author credit if we do. Active contributors might be asked to join the core team and be given the ability to merge pull requests.

### D.2. Code Conventions and Housekeeping

None of the following guidelines is essential for a pull request, but they all help your fellow developers understand and work with your code. They can also be added after the original pull request but before a merge.

- Use the Spring Framework code format conventions. If you use Eclipse, you can import formatter settings by using the `eclipse-code-formatter.xml` file from the Spring Cloud Build project. If you use IntelliJ, you can use the Eclipse Code Formatter Plugin to import the same file.

- Make sure all new `.java` files have a simple Javadoc class comment with at least an `@author` tag identifying you, and preferably at least a paragraph describing the class’s purpose.

- Add the ASF license header comment to all new `.java` files (to do so, copy it from existing files in the project).

- Add yourself as an `@author` to the .java files that you modify substantially (more than cosmetic changes).

- Add some Javadocs and, if you change the namespace, some XSD doc elements.

- A few unit tests would help a lot as well. Someone has to do it, and your fellow developers appreciate the effort.

- If no one else uses your branch, rebase it against the current master (or other target branch in the main project).

- When writing a commit message, follow these conventions. If you fix an existing issue, add `Fixes gh-XXXX` (where XXXX is the issue number) at the end of the commit message.

  == Identity Providers

This appendix contains information how specific providers can be set up to work with Data Flow security.

At this writing, Azure is the only identity provider.

### D.3. Azure

Azure AD (Active Directory) is a fully fledged identity provider that provide a wide range of features around authentication and authorization. As with any other provider, it has its own nuances, meaning care must be taken to set it up.

In this section, we go through how OAuth2 setup is done for AD and Spring Cloud Data Flow.

| You need full organization access rights to set up everything correctly.

#### D.3.1. Creating a new AD Environment

To get started, create a new Active Directory environment. Choose a type as Azure Active Directory (not the b2c type) and then pick your organization name and initial domain. The following image shows the settings:

#### D.3.2. Creating a New App Registration

App registration is where OAuth clients are created to get used by OAuth applications. At minimum, you need to create two clients, one for the Data Flow and Skipper servers and one for the Data Flow shell, as these two have slightly different configurations. Server applications can be considered to be trusted applications while shell is not trusted (because users can see its full configuration).

NOTE: We recommend using the same OAuth client for both the Data Flow and the Skipper servers. While you can use different clients, it currently would not provide any value, as the configurations needs to be the same.

The following image shows the settings for creating a a new app registration:

| A client secret, when needed, is created under `Certificates & secrets` in AD.

#### D.3.3. Expose Dataflow APIs

To prepare OAuth scopes, create one for each Data Flow security role. In this example, those would be

- `api://dataflow-server/dataflow.create`

- `api://dataflow-server/dataflow.deploy`

- `api://dataflow-server/dataflow.destroy`

- `api://dataflow-server/dataflow.manage`

- `api://dataflow-server/dataflow.schedule`

- `api://dataflow-server/dataflow.modify`

- `api://dataflow-server/dataflow.view`

The following image shows the APIs to expose:

Previously created scopes needs to be added as API Permissions, as the following image shows:

#### D.3.4. Creating a Privileged Client

For the OAuth client, which is about to use password grants, the same API permissions need to be created for the OAuth client as were used for the server (described in the previous section).

| All these permissions need to be granted with admin privileges.

The following image shows the privileged settings:

| Privileged client needs a client secret, which needs to be exposed to a client configuration when used in a shell. If you do not want to expose that secret, use the Creating a Public Client public client.

#### D.3.5. Creating a Public Client

A public client is basically a client without a client secret and with its type set to public.

The following image shows the configuration of a public client:

#### D.3.6. Configuration Examples

This section contains configuration examples for the Data Flow and Skipper servers and the shell.

To starting a Data Flow server:

    $ java -jar spring-cloud-dataflow-server.jar \
      --spring.config.additional-location=dataflow-azure.yml

dataflow-azure.yml

    spring:
      cloud:
        dataflow:
          security:
            authorization:
              provider-role-mappings:
                dataflow-server:
                  map-oauth-scopes: true
                  role-mappings:
                    ROLE_VIEW: dataflow.view
                    ROLE_CREATE: dataflow.create
                    ROLE_MANAGE: dataflow.manage
                    ROLE_DEPLOY: dataflow.deploy
                    ROLE_DESTROY: dataflow.destroy
                    ROLE_MODIFY: dataflow.modify
                    ROLE_SCHEDULE: dataflow.schedule
      security:
        oauth2:
          client:
            registration:
              dataflow-server:
                provider: azure
                redirect-uri: '{baseUrl}/login/oauth2/code/{registrationId}'
                client-id: <client id>
                client-secret: <client secret>
                scope:
                - openid
                - profile
                - email
                - offline_access
                - api://dataflow-server/dataflow.view
                - api://dataflow-server/dataflow.deploy
                - api://dataflow-server/dataflow.destroy
                - api://dataflow-server/dataflow.manage
                - api://dataflow-server/dataflow.modify
                - api://dataflow-server/dataflow.schedule
                - api://dataflow-server/dataflow.create
            provider:
              azure:
                issuer-uri: https://login.microsoftonline.com/799dcfde-b9e3-4dfc-ac25-659b326e0bcd/v2.0
                user-name-attribute: name
          resourceserver:
            jwt:
              jwk-set-uri: https://login.microsoftonline.com/799dcfde-b9e3-4dfc-ac25-659b326e0bcd/discovery/v2.0/keys

To start a Skipper server:

    $ java -jar spring-cloud-skipper-server.jar \
      --spring.config.additional-location=skipper-azure.yml

skipper-azure.yml

    spring:
      cloud:
        skipper:
          security:
            authorization:
              provider-role-mappings:
                skipper-server:
                  map-oauth-scopes: true
                  role-mappings:
                    ROLE_VIEW: dataflow.view
                    ROLE_CREATE: dataflow.create
                    ROLE_MANAGE: dataflow.manage
                    ROLE_DEPLOY: dataflow.deploy
                    ROLE_DESTROY: dataflow.destroy
                    ROLE_MODIFY: dataflow.modify
                    ROLE_SCHEDULE: dataflow.schedule
      security:
        oauth2:
          client:
            registration:
              skipper-server:
                provider: azure
                redirect-uri: '{baseUrl}/login/oauth2/code/{registrationId}'
                client-id: <client id>
                client-secret: <client secret>
                scope:
                - openid
                - profile
                - email
                - offline_access
                - api://dataflow-server/dataflow.view
                - api://dataflow-server/dataflow.deploy
                - api://dataflow-server/dataflow.destroy
                - api://dataflow-server/dataflow.manage
                - api://dataflow-server/dataflow.modify
                - api://dataflow-server/dataflow.schedule
                - api://dataflow-server/dataflow.create
            provider:
              azure:
                issuer-uri: https://login.microsoftonline.com/799dcfde-b9e3-4dfc-ac25-659b326e0bcd/v2.0
                user-name-attribute: name
          resourceserver:
            jwt:
              jwk-set-uri: https://login.microsoftonline.com/799dcfde-b9e3-4dfc-ac25-659b326e0bcd/discovery/v2.0/keys

To start a shell and (optionally) pass credentials as options:

    $ java -jar spring-cloud-dataflow-shell.jar \
      --spring.config.additional-location=dataflow-azure-shell.yml \
      --dataflow.username=<USERNAME> \
      --dataflow.password=<PASSWORD>

dataflow-azure-shell.yml

      security:
        oauth2:
          client:
            registration:
              dataflow-shell:
                provider: azure
                client-id: <client id>
                client-secret: <client secret>
                authorization-grant-type: password
                scope:
                - offline_access
                - api://dataflow-server/dataflow.create
                - api://dataflow-server/dataflow.deploy
                - api://dataflow-server/dataflow.destroy
                - api://dataflow-server/dataflow.manage
                - api://dataflow-server/dataflow.modify
                - api://dataflow-server/dataflow.schedule
                - api://dataflow-server/dataflow.view
            provider:
              azure:
                issuer-uri: https://login.microsoftonline.com/799dcfde-b9e3-4dfc-ac25-659b326e0bcd/v2.0

Starting a public shell and (optionally) pass credentials as options:

    $ java -jar spring-cloud-dataflow-shell.jar \
      --spring.config.additional-location=dataflow-azure-shell-public.yml \
      --dataflow.username=<USERNAME> \
      --dataflow.password=<PASSWORD>

dataflow-azure-shell-public.yml

    spring:
      security:
        oauth2:
          client:
            registration:
              dataflow-shell:
                provider: azure
                client-id: <client id>
                authorization-grant-type: password
                client-authentication-method: post
                scope:
                - offline_access
                - api://dataflow-server/dataflow.create
                - api://dataflow-server/dataflow.deploy
                - api://dataflow-server/dataflow.destroy
                - api://dataflow-server/dataflow.manage
                - api://dataflow-server/dataflow.modify
                - api://dataflow-server/dataflow.schedule
                - api://dataflow-server/dataflow.view
            provider:
              azure:
                issuer-uri: https://login.microsoftonline.com/799dcfde-b9e3-4dfc-ac25-659b326e0bcd/v2.0