Version 1.2.4.RELEASE

© 2012-2017 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.

Getting started

1. Deploying on Cloud Foundry

Spring Cloud Data Flow can be used to deploy modules in a Cloud Foundry environment. When doing so, the server application can either run itself on Cloud Foundry, or on another installation (e.g. a simple laptop).

The required configuration amounts to the same in either case, and is merely related to providing credentials to the Cloud Foundry instance so that the server can spawn applications itself. Any Spring Boot compatible configuration mechanism can be used (passing program arguments, editing configuration files before building the application, using Spring Cloud Config, using environment variables, etc.), although some may prove more practicable than others when running on Cloud Foundry.

By default, the application registry in Spring Cloud Data Flow’s Cloud Foundry server is empty. It is intentionally designed to allow users to have the flexibility of choosing and registering applications, as they find appropriate for the given use-case requirement. Depending on the message-binder of choice, users can register between RabbitMQ or Apache Kafka based maven artifacts.

1.1. Provision a Redis service instance on Cloud Foundry

Use cf marketplace to discover which plans are available to you, depending on the details of your Cloud Foundry setup. For example when using Pivotal Web Services:

cf create-service rediscloud 30mb redis

A redis instance is required for analytics apps, and would typically be bound to such apps when you create an analytics stream using the per-app-binding feature.

1.2. Provision a Rabbit service instance on Cloud Foundry

Use cf marketplace to discover which plans are available to you, depending on the details of your Cloud Foundry setup. For example when using Pivotal Web Services:

cf create-service cloudamqp lemur rabbit

Rabbit is typically used as a messaging middleware between streaming apps and would be bound to each deployed app thanks to the SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_STREAM_SERVICES setting (see below).

1.3. Provision a MySQL service instance on Cloud Foundry

Use cf marketplace to discover which plans are available to you, depending on the details of your Cloud Foundry setup. For example when using Pivotal Web Services:

cf create-service cleardb spark my_mysql

An RDBMS is used to persist Data Flow state, such as stream definitions and deployment ids. It can also be used for tasks to persist execution history.

1.4. Download the Spring Cloud Data Flow Server and Shell apps

wget http://repo.spring.io/release/org/springframework/cloud/spring-cloud-dataflow-server-cloudfoundry/1.2.4.RELEASE/spring-cloud-dataflow-server-cloudfoundry-1.2.4.RELEASE.jar
wget http://repo.spring.io/release/org/springframework/cloud/spring-cloud-dataflow-shell/1.2.3.RELEASE/spring-cloud-dataflow-shell-1.2.3.RELEASE.jar

1.5. Running the Server

You can either deploy the server application on Cloud Foundry itself or on your local machine. The following two sections explain each way of running the server.

1.5.1. Deploying and Running the Server app on Cloud Foundry

Push the server application on Cloud Foundry, configure it (see below) and start it.

You must use a unique name for your app; an app with the same name in the same organization will cause your deployment to fail
cf push dataflow-server -b java_buildpack -m 2G -k 2G --no-start -p spring-cloud-dataflow-server-cloudfoundry-1.2.4.RELEASE.jar
cf bind-service dataflow-server redis
cf bind-service dataflow-server my_mysql
The recommended minimal memory setting for the server is 2G. Also, to push apps to PCF and obtain application property metadata, the server downloads applications to Maven repository hosted on the local disk. While you can specify up to 2G as a typical maximum value for disk space on a PCF installation, this can be increased to 10G. Read the maximum disk quota section for information on how to configure this PCF property. Also, the Data Flow server itself implements a Last Recently Used algorithm to free disk space when it falls below a low water mark value.
If you are pushing to a space with multiple users, for example on PWS, there may already be a route taken for the applicaiton name you have chosen. You can use the options --random-route to avoid this when pushing the app.

Now we can configure the app. The following configuration is for Pivotal Web Services. You need to fill in {org}, {space}, {email} and {password} before running these commands.

cf set-env dataflow-server SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_URL https://api.run.pivotal.io
cf set-env dataflow-server SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_ORG {org}
cf set-env dataflow-server SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_SPACE {space}
cf set-env dataflow-server SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_DOMAIN cfapps.io
cf set-env dataflow-server SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_STREAM_SERVICES rabbit
cf set-env dataflow-server SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_TASK_SERVICES my_mysql
cf set-env dataflow-server SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_USERNAME {email}
cf set-env dataflow-server SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_PASSWORD {password}
cf set-env dataflow-server SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_SKIP_SSL_VALIDATION false
Only set 'Skip SSL Validation' to true if you’re running on a Cloud Foundry instance using self-signed certs (e.g. in development). Do not use for production.
If you are deploying in an environment that requires you to sign on using the Pivotal Single Sign-On Service, refer to the section Authentication and Cloud Foundry for information on how to configure the server.

Spring Cloud Data Flow server implementations (be it for Cloud Foundry, Mesos, YARN, or Kubernetes) do not have any default remote maven repository configured. This is intentionally designed to provide the flexibility for the users, so they can override and point to a remote repository of their choice. The out-of-the-box applications that are supported by Spring Cloud Data Flow are available in Spring’s repository, so if you want to use them, set it as the remote repository as listed below.

cf set-env dataflow-server SPRING_APPLICATION_JSON '{"maven": { "remote-repositories": { "repo1": { "url": "https://repo.spring.io/libs-release" } } } }'

where repo1 is the alias name for the remote repository.

If you need to configure multiple Maven repositories, a proxy, or authorization for a private repository, see Maven Configuration.

Configuring Defaults for Deployed Apps

You can also set other optional properties that alter the way Spring Cloud Data Flow will deploy stream and task apps:

  • The default memory and disk sizes for a deployed application can be configured. By default they are 1024 MB memory and 1024 MB disk. To change these, as an example to 512 and 2048 respectively, use

    cf set-env dataflow-server SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_STREAM_MEMORY 512
    cf set-env dataflow-server SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_STREAM_DISK 2048
  • The default number of instances to deploy is set to 1, but can be overridden using

    cf set-env dataflow-server SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_STREAM_INSTANCES 1
  • You can set the buildpack that will be 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_DEPLOYER_CLOUDFOUNDRY_STREAM_BUILDPACK java_buildpack_offline
  • The health check mechanism used by Cloud Foundry to assert if apps are running can be customized. Current supported options are port (the default) and none. Change the default like so:

    cf set-env dataflow-server SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_STREAM_HEALTH_CHECK none

These settings can be configured separately for stream and task apps. To alter settings for tasks, simply substitute STREAM with TASK in the property name. As an example,

cf set-env dataflow-server SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_TASK_MEMORY 512
All the properties mentioned above are @ConfigurationProperties of the Cloud Foundry deployer. See CloudFoundryDeploymentProperties.java for more information.

We are now ready to start the app.

cf start dataflow-server

Alternatively, you can run the Admin application locally on your machine which is described in the next section.

1.5.2. Running the Server app locally

To run the server application locally, targeting your Cloud Foundry installation, you you need to configure the application either by passing in command line arguments (see below) or setting a number of environment variables.

To use environment variables set the following:

export SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_URL=https://api.run.pivotal.io
export SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_ORG={org}
export SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_SPACE={space}
export SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_DOMAIN=cfapps.io
export SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_USERNAME={email}
export SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_PASSWORD={password}
export SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_SKIP_SSL_VALIDATION=false

export SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_STREAM_SERVICES=rabbit
# The following is for letting task apps write to their db.
# Note however that when the *server* is running locally, it can't access that db
# task related commands that show executions won't work then
export SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_TASK_SERVICES=my_mysql

You need to fill in {org}, {space}, {email} and {password} before running these commands.

Only set 'Skip SSL Validation' to true if you’re running on a Cloud Foundry instance using self-signed certs (e.g. in development). Do not use for production.

Now we are ready to start the server application:

java -jar spring-cloud-dataflow-server-cloudfoundry-1.2.4.RELEASE.jar [--option1=value1] [--option2=value2] [etc.]
Of course, all other parameterization options that were available when running the server on Cloud Foundry are still available. This is particularly true for configuring defaults for applications. Just substitute cf set-env syntax with export.
The current underlying PCF task capabilities are considered experimental for PCF version versions less than 1.9. See Feature Togglers for how to disable task support in Data Flow.

1.5.3. Sample Manifest Template

As an alternative to setting environment variables via cf set-env command, you can curate all the relevant env-var’s in manifest.yml file and use cf push command to provision the server.

Following is a sample template to provision the server on PCFDev.

---
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_DEPLOYER_CLOUDFOUNDRY_URL: https://api.local.pcfdev.io
    SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_ORG: pcfdev-org
    SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_SPACE: pcfdev-space
    SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_DOMAIN: local.pcfdev.io
    SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_USERNAME: admin
    SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_PASSWORD: admin
    SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_STREAM_SERVICES: rabbit
    SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_TASK_SERVICES: mysql
    SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_SKIP_SSL_VALIDATION: true
    SPRING_APPLICATION_JSON {"maven": { "remote-repositories": { "repo1": { "url": "https://repo.spring.io/libs-release"} } } }
services:
- mysql

Once you’re ready with the relevant properties in this file, you can issue cf push command from the directory where this file is stored.

1.6. Running Spring Cloud Data Flow Shell locally

Run the shell and optionally target the Admin application if not running on the same host (will typically be the case if deployed on Cloud Foundry as explained here)

$ java -jar spring-cloud-dataflow-shell-1.2.3.RELEASE.jar
server-unknown:>dataflow config server http://dataflow-server.cfapps.io
Successfully targeted http://dataflow-server.cfapps.io
dataflow:>

By default, the application registry will be empty. If you would like to register all out-of-the-box stream applications built with the RabbitMQ binder in bulk, you can with the following command. For more details, review how to register applications.

dataflow:>app import --uri http://bit.ly/Avogadro-SR1-stream-applications-rabbit-maven
A Note about application URIs

While Spring Cloud Data Flow for Cloud Foundry leverages the core Data Flow project, and as such theoretically supports registering apps using any scheme, the use of file:// URIs does not really make sense on Cloud Foundry. Indeed, the local filesystem of the Data Flow server is ephemeral and chances are that you don’t want to manually upload your apps there.

When deploying apps using Data Flow for Cloud Foundry, a typical choice is to use maven:// coordinates, or maybe http:// URIs.

You can now use the shell commands to list available applications (source/processors/sink) and create streams. For example:

dataflow:> stream create --name httptest --definition "http | log" --deploy
You will need to wait a little while until the apps are actually deployed successfully before posting data. Tail the log file for each application to verify the application has started.

Now post some data. The URL will be unique to your deployment, the following is just an example

dataflow:> http post --target http://dataflow-AxwwAhK-httptest-http.cfapps.io --data "hello world"

Look to see if hello world ended up in log files for the log application.

To run a simple task application, you can register all the out-of-the-box task applications with the following command.

dataflow:>app import --uri http://bit.ly/Addison-GA-task-applications-maven

Now create a simple timestamp task.

dataflow:>task create mytask --definition "timestamp --format='yyyy'"

Tail the logs, e.g. cf logs mytask and then launch the task in the UI or in the Data Flow Shell

dataflow:>task launch mytask

You will see the year 2017 printed in the logs. The execution status of the task is stored in the database and you can retrieve information about the task execution using the shell commands task execution list and task execution status --id <ID_OF_TASK> or though the Data Flow UI.

2. Application Names and Prefixes

To help avoid clashes with routes across spaces in Cloud Foundry, a naming strategy to provide a random prefix to a deployed application is available and is enabled by default. The default configurations are overridable and the respective properties can be set via cf set-env commands.

For instance, if you’d like to disable the randomization, you can override it through:

cf set-env dataflow-server SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_STREAM_ENABLE_RANDOM_APP_NAME_PREFIX false

2.1. Using Custom Routes

As an alternative to random name, or to get even more control over the hostname used by the deployed apps, one can use custom deployment properties, as such:

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

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

This would result in the http app being bound to the URL myhost.mydomain.com/my-path. Note that this is an example showing all customization options available. One can of course only leverage one or two out of the three.

3. Deploying Docker Applications

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

If you are using Spring Boot and RabbitMQ based Docker images you can provide a common deployment property to facilitate the apps binding 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, something similar to the following could be used, if using 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 apps, your Docker app would have to parse the VCAP_SERVICES variable in order to bind to any available services.

Passing application properties

When using non-boot apps, chances are that you want the application properties passed to your app using traditional environment variables, as opposed to using the special SPRING_APPLICATION_JSON variable. To achieve this, set the following variables for streams and tasks, respectively:

SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_STREAM_USE_SPRING_APPLICATION_JSON=false
SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_TASK_USE_SPRING_APPLICATION_JSON=false

4. 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’d like to provide 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 only bound to jdbc application and the http application wouldn’t get the binding by this method. If you have more than one service to bind, they can be passed as comma separated items (eg: deployer.jdbc.cloudfoundry.services=mysqlService,someService).

5. A Note About 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 UPSs as well, whether for use as the messaging middleware (e.g. if you’d like to use an external Apache Kafka installation) or for ad hoc usage by some of the stream apps (e.g. an Oracle Database).

Let’s review an example of extracting and supplying the connection credentials from an UPS.

  • A sample UPS setup for Apache Kafka.

cf create-user-provided-service kafkacups -p '{”brokers":"HOST:PORT","zkNodes":"HOST:PORT"}'
  • The UPS credentials will be wrapped within VCAP_SERVICES and it can be supplied directly in the stream definition like the following.

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}"

6. Application Rolling Upgrades

Similar to Cloud Foundry’s blue-green deployments, you can perform rolling upgrades on the applications orchestrated by Spring Cloud Data Flow.

Let’s start with the following simple stream definition.

dataflow:>stream create --name foo --definition "time | log" --deploy

List Apps.

→ cf apps
Getting apps in org test-org / space development as [email protected]
OK

name       requested state   instances   memory   disk   urls
foo-log    started           1/1         1G       1G     foo-log.cfapps.io
foo-time   started           1/1         1G       1G     foo-time.cfapps.io

Let’s assume you’ve to make an enhancement to update the "logger" to append extra text in every log statement.

  • Download the Log Sink application starter with "Rabbit binder starter" from start-scs.cfapps.io/

  • Load the downloaded project in an IDE

  • Import the LogSinkConfiguration.class

  • Adapt the handler to add extra text: loggingHandler.setLoggerName("TEST [" + this.properties.getName() + "]");

  • Build the application locally

@SpringBootApplication
@Import(LogSinkConfiguration.class)
public class DemoApplication {

  @Autowired
  private LogSinkProperties properties;

  public static void main(String[] args) {
    SpringApplication.run(DemoApplication.class, args);
  }

  @Bean
  @ServiceActivator(inputChannel = Sink.INPUT)
  public LoggingHandler logSinkHandler() {
    LoggingHandler loggingHandler = new LoggingHandler(this.properties.getLevel().name());
    loggingHandler.setExpression(this.properties.getExpression());
    loggingHandler.setLoggerName("TEST [" + this.properties.getName() + "]");
    return loggingHandler;
  }
}

Let’s deploy the locally built application to Cloud Foundry

→ cf push foo-log-v2 -b java_buildpack -p demo-0.0.1-SNAPSHOT.jar -n foo-log-v2 --no-start

List Apps.

→ cf apps
Getting apps in org test-org / space development as [email protected]
OK

name       requested state   instances   memory   disk   urls
foo-log    started           1/1         1G       1G     foo-log.cfapps.io
foo-time   started           1/1         1G       1G     foo-time.cfapps.io
foo-log-v2 stopped           1/1         1G       1G     foo-log-v2.cfapps.io

The stream applications do not communicate via (Go)Router, so they aren’t generating HTTP traffic. Instead, they communicate via the underlying messaging middleware such as Kafka or RabbitMQ. In order to rolling upgrade to route the payload from old to the new version of the application, you’d have to replicate the SPRING_APPLICATION_JSON environment variable from the old application that includes spring.cloud.stream.bindings.input.destination and spring.cloud.stream.bindings.input.group credentials.

You can find the SPRING_APPLICATION_JSON of the old application via: "cf env foo-log".
cf set-env foo-log-v2 SPRING_APPLICATION_JSON '{"spring.cloud.stream.bindings.input.destination":"foo.time","spring.cloud.stream.bindings.input.group":"foo"}'

Let’s start foo-log-v2 application.

cf start foo-log-v2

As soon as the application bootstraps, you’d now notice the payload being load balanced between two log application instances running on Cloud Foundry. Since they both share the same "destination" and "consumer group", they are now acting as competing consumers.

Old App Logs:

2016-08-08T17:11:08.94-0700 [APP/0]      OUT 2016-08-09 00:11:08.942  INFO 19 --- [ foo.time.foo-1] log.sink                                 : 08/09/16 00:11:08
2016-08-08T17:11:10.95-0700 [APP/0]      OUT 2016-08-09 00:11:10.954  INFO 19 --- [ foo.time.foo-1] log.sink                                 : 08/09/16 00:11:10
2016-08-08T17:11:12.94-0700 [APP/0]      OUT 2016-08-09 00:11:12.944  INFO 19 --- [ foo.time.foo-1] log.sink                                 : 08/09/16 00:11:12

New App Logs:

2016-08-08T17:11:07.94-0700 [APP/0]      OUT 2016-08-09 00:11:07.945  INFO 26 --- [ foo.time.foo-1] TEST [log.sink                       : 08/09/16 00:11:07]
2016-08-08T17:11:09.92-0700 [APP/0]      OUT 2016-08-09 00:11:09.925  INFO 26 --- [ foo.time.foo-1] TEST [log.sink                       : 08/09/16 00:11:09]
2016-08-08T17:11:11.94-0700 [APP/0]      OUT 2016-08-09 00:11:11.941  INFO 26 --- [ foo.time.foo-1] TEST [log.sink                       : 08/09/16 00:11:11]

Deleting the old version foo-log from the CF CLI would make all the payload consumed by the foo-log-v2 application. Now, you’ve successfully upgraded an application in the streaming pipeline without bringing it down in entirety to do an adjustment in it.

List Apps.

→ cf apps
Getting apps in org test-org / space development as [email protected]
OK

name       requested state   instances   memory   disk   urls
foo-time   started           1/1         1G       1G     foo-time.cfapps.io
foo-log-v2 started           1/1         1G       1G     foo-log-v2.cfapps.io
A comprehensive canary analysis along with rolling upgrades will be supported via Spinnaker in future releases.

7. Maximum Disk Quota Configuration

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 via Pivotal Cloud Foundry’s (PCF) Ops Manager GUI.

This configuration is relevant for Spring Cloud Data Flow because every stream and 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/reuse. With this happening in the background, there is a possibility the default disk quota (1G) fills up rapidly; especially, when we are experimenting 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.

7.1. PCF’s Operations Manager Configuration

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

7.2. Scale Application

Once the disk quota change is applied successfully and assuming you’ve a running application, you may scale the application with a new disk_limit through CF CLI.

→ 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
→ 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

7.3. Configuring target free disk percentage

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. The server implements a least recently used (LRU) algorithm that will remove maven artifacts from the local maven repository. This is configured using the following configuration property, the default value is 25.

# The low water mark percentage, expressed as in integer between 0 and 100, that triggers cleanup of
# the local maven repository
# (for setting env var use SPRING_CLOUD_DATAFLOW_SERVER_CLOUDFOUNDRY_FREE_DISK_SPACE_PERCENTAGE)
spring.cloud.dataflow.server.cloudfoundry.freeDiskSpacePercentage=25

8. Application Resolution Alternatives

Though we highly recommend using Maven Repository for application resolution and registration in Cloud Foundry, there might be situations where an alternative approach would make sense. Following alternative options could come handy for resolving applications when running on Cloud Foundry.

  • 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/task applications and 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 app with the name http-source.jar via --uri=http://<Route-To-StaticApp>/http-source.jar.

  • The Über-jar’s can be hosted on any external server that’s reachable via 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 via --uri=http://<Raw_GitHub_URI>/http-source.jar.

  • Static Buildpack support in Cloud Foundry is another option. A similar HTTP resolution will work on this model, too.

  • Volume Services is another great option. The required Über-jar’s can be hosted in an external file-system and with the help of volume-services, you can, for example, register the app with the name http-source.jar via --uri=file://<Path-To-FileSystem>/http-source.jar.

Applications

A selection of pre-built stream and task/batch starter apps for various data integration and processing scenarios facilitate learning and experimentation. For more details, review how to register applications

Architecture

9. Introduction

Spring Cloud Data Flow simplifies the development and deployment of applications focused on data processing use-cases. The major concepts of the architecture are Applications, the Data Flow Server, and the target runtime.

Applications come in two flavors

  • Long lived Stream applications where an unbounded amount of data is consumed or produced via messaging middleware.

  • Short lived Task applications that process a finite set of data and then terminate.

Depending on the runtime, applications can be packaged in two ways

  • Spring Boot uber-jar that is hosted in a maven repository, file, http or any other Spring resource implementation.

  • Docker

The runtime is the place where applications execute. The target runtimes for applications are platforms that you may already be using for other application deployments.

The supported runtimes are

  • Cloud Foundry

  • Apache YARN

  • Kubernetes

  • Apache Mesos

  • Local Server for development

There is a deployer Service Provider Interface (SPI) that enables you to extend Data Flow to deploy onto other runtimes, for example to support Docker Swarm. There are community implementations of Hashicorp’s Nomad and RedHat Openshift is available. We look forward to working with the community for further contributions!

The component that is responsible for deploying applications to a runtime is the Data Flow Server. There is a Data Flow Server executable jar provided for each of the target runtimes. The Data Flow server is responsible for:

  • Interpreting and executing a stream DSL that describes the logical flow of data through multiple long lived applications.

  • Launching a long lived task application

  • Interpreting and executing a composed task DSL that describes the logical flow of data through multiple short lived applications.

  • Applyhing a deployment manifest that describes the mapping of applications onto the runtime. For example, to set the initial number of instances, memory requirements, and data partitioning.

  • Providing the runtime status of deployed applications

As an example, the stream DSL to describe the flow of data from an http source to an Apache Cassandra sink would be written as “http | cassandra”. These names in the DSL are registered with the Data Flow Server and map onto application artifacts that can be hosted in Maven or Docker repositories. Many source, processor, and sink applications for common use-cases (e.g. jdbc, hdfs, http, router) are provided by the Spring Cloud Data Flow team. The pipe symbol represents the communication between the two applications via messaging middleware. The two messaging middleware brokers that are supported are

  • Apache Kafka

  • RabbitMQ

In the case of Kafka, when deploying the stream, the Data Flow server is responsible to create the topics that correspond to each pipe symbol and configure each application to produce or consume from the topics so the desired flow of data is achieved.

The interaction of the main components is shown below

The Spring Cloud Data Flow High Level Architecture
Figure 1. The Spring Cloud Data High Level Architecure

In this diagram a DSL description of a stream is POSTed to the Data Flow Server. Based on the mapping of DSL application names to Maven and Docker artifacts, the http-source and cassandra-sink applications are deployed on the target runtime.

10. Microservice Architectural Style

The Data Flow Server deploys applications onto the target runtime that conform to the microservice architectural style. For example, a stream represents a high level application that consists of multiple small microservice applications each running in their own process. Each microservice application can be scaled up or down independent of the other and each has their own versioning lifecycle.

Both Streaming and Task based microservice applications build upon Spring Boot as the foundational library. This gives all microservice applications functionality such as health checks, security, configurable logging, monitoring and management functionality, as well as executable JAR packaging.

It is important to emphasise that these microservice applications are ‘just apps’ that you can run by yourself using ‘java -jar’ and passing in appropriate configuration properties. We provide many common microservice applications for common operations so you don’t have to start from scratch when addressing common use-cases which build upon the rich ecosystem of Spring Projects, e.g Spring Integration, Spring Data, Spring Hadoop and Spring Batch. Creating your own microservice application is similar to creating other Spring Boot applications, you can start using the Spring Initialzr web site or the UI to create the basic scaffolding of either a Stream or Task based microservice.

In addition to passing in the appropriate configuration to the applications, the Data Flow server is responsible for preparing the target platform’s infrastructure so that the application can be deployed. For example, in Cloud Foundry it would be binding specified services to the applications and executing the ‘cf push’ command for each application. For Kubernetes it would be creating the replication controller, service, and load balancer.

The Data Flow Server helps simplify the deployment of multiple applications onto a target runtime, but one could also opt to deploy each of the microservice applications manually and not use Data Flow at all. This approach might be more appropriate to start out with for small scale deployments, gradually adopting the convenience and consistency of Data Flow as you develop more applications. Manual deployment of Stream and Task based microservices is also a useful educational exercise that will help you better understand some of the automatic applications configuration and platform targeting steps that the Data Flow Server provides.

10.1. Comparison to other Platform architectures

Spring Cloud Data Flow’s architectural style is different than other Stream and Batch processing platforms. For example in Apache Spark, Apache Flink, and Google Cloud Dataflow applications run on a dedicated compute engine cluster. The nature of the compute engine gives these platforms a richer environment for performing complex calculations on the data as compared to Spring Cloud Data Flow, but it introduces complexity of another execution environment that is often not needed when creating data centric applications. That doesn’t mean you cannot do real time data computations when using Spring Cloud Data Flow. Refer to the analytics section which describes the integration of Redis to handle common counting based use-cases as well as the RxJava integration for functional API driven analytics use-cases, such as time-sliding-window and moving-average among others.

Similarly, Apache Storm, Hortonworks DataFlow and Spring Cloud Data Flow’s predecessor, Spring XD, use a dedicated application execution cluster, unique to each product, that determines where your code should execute on the cluster and perform health checks to ensure that long lived applications are restarted if they fail. Often, framework specific interfaces are required to be used in order to correctly “plug in” to the cluster’s execution framework.

As we discovered during the evolution of Spring XD, the rise of multiple container frameworks in 2015 made creating our own runtime a duplication of efforts. There is no reason to build your own resource management mechanics, when there are multiple runtime platforms that offer this functionality already. Taking these considerations into account is what made us shift to the current architecture where we delegate the execution to popular runtimes, runtimes that you may already be using for other purposes. This is an advantage in that it reduces the cognitive distance for creating and managing data centric applications as many of the same skills used for deploying other end-user/web applications are applicable.

11. Streaming Applications

While Spring Boot provides the foundation for creating DevOps friendly microservice applications, other libraries in the Spring ecosystem help create Stream based microservice applications. The most important of these is Spring Cloud Stream.

The essence of the Spring Cloud Stream programming model is to provide an easy way to describe multiple inputs and outputs of an application that communicate over messaging middleware. These input and outputs map onto Kafka topics or Rabbit exchanges and queues. Common application configuration for a Source that generates data, a Process that consumes and produces data and a Sink that consumes data is provided as part of the library.

11.1. Imperative Programming Model

Spring Cloud Stream is most closely integrated with Spring Integration’s imperative "event at a time" programming model. This means you write code that handles a single event callback. For example,

@EnableBinding(Sink.class)
public class LoggingSink {

    @StreamListener(Sink.INPUT)
    public void log(String message) {
        System.out.println(message);
    }
}

In this case the String payload of a message coming on the input channel, is handed to the log method. The @EnableBinding annotation is what is used to tie together the input channel to the external middleware.

11.2. Functional Programming Model

However, Spring Cloud Stream can support other programming styles. The use of reactive APIs where incoming and outgoing data is handled as continuous data flows and it defines how each individual message should be handled. You can also use operators that describe functional transformations from inbound to outbound data flows. The upcoming versions will support Apache Kafka’s KStream API in the programming model.

12. Streams

12.1. Topologies

The Stream DSL describes linear sequences of data flowing through the system. For example, in the stream definition http | transformer | cassandra, each pipe symbol connects the application on the left to the one on the right. Named channels can be used for routing and to fan out data to multiple messaging destinations.

Taps can be used to ‘listen in’ to the data that if flowing across any of the pipe symbols. Taps can be used as sources for new streams with an in independent life cycle.

12.2. Concurrency

For an application that will consume events, Spring Cloud stream exposes a concurrency setting that controls the size of a thread pool used for dispatching incoming messages. See the Consumer properties documentation for more information.

12.3. Partitioning

A common pattern in stream processing is to partition the data as it moves from one application to the next. Partitioning is a critical concept in stateful processing, for either performance or consistency reasons, to ensure that all related data is processed together. For example, in a time-windowed average calculation example, it is important that all measurements from any given sensor are processed by the same application instance. Alternatively, you may want to cache some data related to the incoming events so that it can be enriched without making a remote procedure call to retrieve the related data.

Spring Cloud Data Flow supports partitioning by configuring Spring Cloud Stream’s output and input bindings. Spring Cloud Stream provides a common abstraction for implementing partitioned processing use cases in a uniform fashion across different types of middleware. Partitioning can thus be used whether the broker itself is naturally partitioned (e.g., Kafka topics) or not (e.g., RabbitMQ). The following image shows how data could be partitioned into two buckets, such that each instance of the average processor application consumes a unique set of data.

Stream Partitioning Architecture
Figure 2. Spring Cloud Stream Partitioning

To use a simple partitioning strategy in Spring Cloud Data Flow, you only need set the instance count for each application in the stream and a partitionKeyExpression producer property when deploying the stream. The partitionKeyExpression identifies what part of the message will be used as the key to partition data in the underlying middleware. An ingest stream can be defined as http | averageprocessor | cassandra (Note that the Cassandra sink isn’t shown in the diagram above). Suppose the payload being sent to the http source was in JSON format and had a field called sensorId. Deploying the stream with the shell command stream deploy ingest --propertiesFile ingestStream.properties where the contents of the file ingestStream.properties are

deployer.http.count=3
deployer.averageprocessor.count=2
app.http.producer.partitionKeyExpression=payload.sensorId

will deploy the stream such that all the input and output destinations are configured for data to flow through the applications but also ensure that a unique set of data is always delivered to each averageprocessor instance. In this case the default algorithm is to evaluate payload.sensorId % partitionCount where the partitionCount is the application count in the case of RabbitMQ and the partition count of the topic in the case of Kafka.

Please refer to Passing stream partition properties during stream deployment for additional strategies to partition streams during deployment and how they map onto the underlying Spring Cloud Stream Partitioning properties.

Also note, that you can’t currently scale partitioned streams. Read the section Scaling at runtime for more information.

12.4. Message Delivery Guarantees

Streams are composed of applications that use the Spring Cloud Stream library as the basis for communicating with the underlying messaging middleware product. Spring Cloud Stream also provides an opinionated configuration of middleware from several vendors, in particular providing persistent publish-subscribe semantics.

The Binder abstraction in Spring Cloud Stream is what connects the application to the middleware. There are several configuration properties of the binder that are portable across all binder implementations and some that are specific to the middleware.

For consumer applications there is a retry policy for exceptions generated during message handling. The retry policy is configured using the common consumer properties maxAttempts, backOffInitialInterval, backOffMaxInterval, and backOffMultiplier. The default values of these properties will retry the callback method invocation 3 times and wait one second for the first retry. A backoff multiplier of 2 is used for the second and third attempts.

When the number of retry attempts has exceeded the maxAttempts value, the exception and the failed message will become the payload of a message and be sent to the application’s error channel. By default, the default message handler for this error channel logs the message. You can change the default behavior in your application by creating your own message handler that subscribes to the error channel.

Spring Cloud Stream also supports a configuration option for both Kafka and RabbitMQ binder implementations that will send the failed message and stack trace to a dead letter queue. The dead letter queue is a destination and its nature depends on the messaging middleware (e.g in the case of Kafka it is a dedicated topic). To enable this for RabbitMQ set the consumer properties republishtoDlq and autoBindDlq and the producer property autoBindDlq to true when deploying the stream. To always apply these producer and consumer properties when deploying streams, configure them as common application properties when starting the Data Flow server.

Additional messaging delivery guarantees are those provided by the underlying messaging middleware that is chosen for the application for both producing and consuming applications. Refer to the Kafka Consumer and Producer and Rabbit Consumer and Producer documentation for more details. You will find extensive declarative support for all the native QOS options.

13. Analytics

Spring Cloud Data Flow is aware of certain Sink applications that will write counter data to Redis and provides an REST endpoint to read counter data. The types of counters supported are

  • Counter - Counts the number of messages it receives, optionally storing counts in a separate store such as redis.

  • Field Value Counter - Counts occurrences of unique values for a named field in a message payload

  • Aggregate Counter - Stores total counts but also retains the total count values for each minute, hour day and month.

It is important to note that the timestamp that is used in the aggregate counter can come from a field in the message itself so that out of order messages are properly accounted.

14. Task Applications

The Spring Cloud Task programming model provides:

  • Persistence of the Task’s lifecycle events and exit code status.

  • Lifecycle hooks to execute code before or after a task execution.

  • Emit task events to a stream (as a source) during the task lifecycle.

  • Integration with Spring Batch Jobs.

15. Data Flow Server

15.1. Endpoints

The Data Flow Server uses an embedded servlet container and exposes REST endpoints for creating, deploying, undeploying, and destroying streams and tasks, querying runtime state, analytics, and the like. The Data Flow Server is implemented using Spring’s MVC framework and the Spring HATEOAS library to create REST representations that follow the HATEOAS principle.

The Spring Cloud Data Flow Server Architecture
Figure 3. The Spring Cloud Data Flow Server

15.2. Customization

Each Data Flow Server executable jar targets a single runtime by delegating to the implementation of the deployer Service Provider Interface found on the classpath.

We provide a Data Flow Server executable jar that targets a single runtime. The Data Flow server delegates to the implementation of the deployer Service Provider Interface found on the classpath. In the current version, there are no endpoints specific to a target runtime, but may be available in future releases as a convenience to access runtime specific features

While we provide a server executable for each of the target runtimes you can also create your own customized server application using Spring Initialzr. This let’s you add or remove functionality relative to the executable jar we provide. For example, adding additional security implementations, custom endpoints, or removing Task or Analytics REST endpoints. You can also enable or disable some features through the use of feature toggles.

15.3. Security

The Data Flow Server executable jars support basic http, LDAP(S), File-based, and OAuth 2.0 authentication to access its endpoints. Refer to the security section for more information.

Authorization via groups is planned for a future release.

16. Runtime

16.1. Fault Tolerance

The target runtimes supported by Data Flow all have the ability to restart a long lived application should it fail. Spring Cloud Data Flow sets up whatever health probe is required by the runtime environment when deploying the application.

The collective state of all applications that comprise the stream is used to determine the state of the stream. If an application fails, the state of the stream will change from ‘deployed’ to ‘partial’.

16.2. Resource Management

Each target runtime lets you control the amount of memory, disk and CPU that is allocated to each application. These are passed as properties in the deployment manifest using key names that are unique to each runtime. Refer to the each platforms server documentation for more information.

16.3. Scaling at runtime

When deploying a stream, you can set the instance count for each individual application that comprises the stream. Once the stream is deployed, each target runtime lets you control the target number of instances for each individual application. Using the APIs, UIs, or command line tools for each runtime, you can scale up or down the number of instances as required. Future work will provide a portable command in the Data Flow Server to perform this operation.

Currently, this is not supported with the Kafka binder (based on the 0.8 simple consumer at the time of the release), as well as partitioned streams, for which the suggested workaround is redeploying the stream with an updated number of instances. Both cases require a static consumer set up based on information about the total instance count and current instance index, a limitation intended to be addressed in future releases. For example, Kafka 0.9 and higher provides good infrastructure for scaling applications dynamically and will be available as an alternative to the current Kafka 0.8 based binder in the near future. One specific concern regarding scaling partitioned streams is the handling of local state, which is typically reshuffled as the number of instances is changed. This is also intended to be addressed in the future versions, by providing first class support for local state management.

16.4. Application Versioning

Application versioning, that is upgrading or downgrading an application from one version to another, is not directly supported by Spring Cloud Data Flow. You must rely on specific target runtime features to perform these operational tasks.

The roadmap for Spring Cloud Data Flow will deploy applications that are compatible with Spinnaker to manage the complete application lifecycle. This also includes automated canary analysis backed by application metrics. Portable commands in the Data Flow server to trigger pipelines in Spinnaker are also planned.

Server Configuration

In this section you will learn how to configure Spring Cloud Data Flow server’s features such as the relational database to use and security. You will also learn how to configure Spring Cloud Data Flow shell’s features.

17. Feature Toggles

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

  1. Streams

  2. Tasks

  3. Analytics

One can enable, 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.analytics-enabled

By default, all the features are enabled. Note: Since analytics feature is enabled by default, the Data Flow server is expected to have a valid Redis store available as analytic repository as we provide a default implementation of analytics based on Redis. This also means that the Data Flow server’s health depends on the redis store availability as well. If you do not want to enabled HTTP endpoints to read analytics data written to Redis, then disable the analytics feature using the property mentioned above.

The REST endpoint /features provides information on the features enabled/disabled.

18. 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 (i.e: UAA/SSO running on Cloud Foundry), please review the security section from the core reference guide. The security configurations can be configured in dataflow-server.yml or passed as environment variables through cf set-env commands.

18.1. Authentication and Cloud Foundry

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

18.1.1. Pivotal Single Sign-On Service

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

Simply bind the Pivotal Single Sign-On Service to your Data Flow Server app and Single Sign-On (SSO) via OAuth2 will be enabled by default.

Authorization is similarly support as for non-Cloud Foundry security scenarios. Please refer to the security section from the core Data Flow reference guide.

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

This can be customized by providing your own AuthoritiesExtractor.

One possible approach to set the custom AuthoritiesExtractor on the UserInfoTokenServices could be this:

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;
	}
}

And you simply declare it in your configuration class:

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

18.1.2. Cloud Foundry UAA

The availability of this option depends on the used Cloud Foundry environment. In order to provide UAA integration, you have to manually provide the necessary OAuth2 configuration properties, for instance via the SPRING_APPLICATION_JSON property.

{
  "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 property spring.cloud.dataflow.security.cf-use-uaa is set to true. This property will activate a special

AuthoritiesExtractor CloudFoundryDataflowAuthoritiesExtractor.

If CloudFoundry UAA is not used, then make sure to set spring.cloud.dataflow.security.cf-use-uaa to false.

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

If the authenticated user is verified as Space Developer, all roles will be assigned, otherwise no roles whatsoever will be assigned. In that case you may see the following Dashboard screen:

Dashboard without roles
Figure 4. Accessing the Data Flow Dashboard without Roles

19. Configuration Reference

The following pieces of configuration must be provided. These are Spring Boot @ConfigurationProperties so you can set them as environment variables or by any other means that Spring Boot supports. Here is a listing in environment variable format as that is an easy way to get started configuring Boot applications in Cloud Foundry.

# Default values cited after the equal sign.
# Example values, typical for Pivotal Web Services, cited as a comment

# url of the CF API (used when using cf login -a for example), e.g. https://api.run.pivotal.io
# (for setting env var use SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_URL)
spring.cloud.deployer.cloudfoundry.url=

# name of the organization that owns the space above, e.g. youruser-org
# (For Setting Env var use SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_ORG)
spring.cloud.deployer.cloudfoundry.org=

# name of the space into which modules will be deployed, e.g. development
# (for setting env var use SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_SPACE)
spring.cloud.deployer.cloudfoundry.space=

# the root domain to use when mapping routes, e.g. cfapps.io
# (for setting env var use SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_DOMAIN)
spring.cloud.deployer.cloudfoundry.domain=

# username and password of the user to use to create apps
# (for setting env var use SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_USERNAME and SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_PASSWORD)
spring.cloud.deployer.cloudfoundry.username=
spring.cloud.deployer.cloudfoundry.password=

# Whether to allow self-signed certificates during SSL validation
# (for setting env var use SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_SKIP_SSL_VALIDATION)
spring.cloud.deployer.cloudfoundry.skipSslValidation=false

# Comma separated set of service instance names to bind to every stream app deployed.
# Amongst other things, this should include a service that will be used
# for Spring Cloud Stream binding, e.g. rabbit
# (for setting env var use SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_STREAM_SERVICES)
spring.cloud.deployer.cloudfoundry.stream.services=

# Health check type to use for stream apps. Accepts 'none' and 'port'
spring.cloud.deployer.cloudfoundry.stream.health-check=


# Comma separated set of service instance names to bind to every task app deployed.
# Amongst other things, this should include an RDBMS service that will be used
# for Spring Cloud Task execution reporting, e.g. my_mysql
# (for setting env var use SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_TASK_SERVICES)
spring.cloud.deployer.cloudfoundry.task.services=

# Timeout to use, in seconds, when doing blocking API calls to Cloud Foundry.
# (for setting env var use SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_TASK_API_TIMEOUT
# and SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_STREAM_API_TIMEOUT)
spring.cloud.deployer.cloudfoundry.stream.apiTimeout=360
spring.cloud.deployer.cloudfoundry.task.apiTimeout=360

# Timeout to use, in milliseconds, when querying the Cloud Foundry API to compute app status.
# (for setting env var use SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_TASK_STATUS_TIMEOUT
# and SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_STREAM_STATUS_TIMEOUT)
spring.cloud.deployer.cloudfoundry.stream.statusTimeout=5000
spring.cloud.deployer.cloudfoundry.task.statusTimeout=5000

Note that you can set the following properties spring.cloud.deployer.cloudfoundry.services, spring.cloud.deployer.cloudfoundry.buildpack 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. For example

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

will deploy the time source with 2048MB of memory, while the log sink will use the default 1024MB.

19.1. Understanding what’s going on

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 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 is disabled by default.

    spring.cloud.dataflow.server.cloudfoundry.debugReactor = true
  • Deployer and Cloud Foundry client library request/response logs. This allows seeing detailed conversation between the Data Flow server and the Cloud Foundry Cloud Controller.

    logging.level.cloudfoundry-client = DEBUG

19.2. Using Spring Cloud Config Server

Spring Cloud Config Server can be used to centralize configuration properties for Spring Boot applications. Likewise, both Spring Cloud Data Flow and the applications orchestrated using Spring Cloud Data Flow can be integrated with config-server to leverage the same capabilities.

19.2.1. Stream, Task, and Spring Cloud Config Server

Similar to Spring Cloud Data Flow server, it is also possible to configure both the stream and task applications to resolve the centralized properties from config-server. Setting the property spring.cloud.config.uri for the deployed applications is a common way to bind to the Config 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 property spring.cloud.dataflow.applicationProperties.stream for stream apps and spring.cloud.dataflow.applicationProperties.task for task apps can be used to pass the uri of the Config Server to each deployed stream or task application. Refer to the section on Common application properties for more information.

If you’re using applications from the App Starters project, note that these applications already embed the spring-cloud-services-starter-config-client dependency. If you’re building your application from scratch and want to add the client side support for config server, simply add a reference dependency reference to the config server client library. A maven example snippet follows:

...
<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 Spring Cloud Config Server client for Pivotal Cloud Foundry.

You will observe a WARN logging message if the application that uses this library can not connect to the config server when the applicaiton starts and whenever the /health endpoint is accessed. You can disable the client library if you know that you are not using config server functionality by setting the environment variable SPRING_CLOUD_CONFIG_ENABLED=false. Another, more drastic option, is to disable the platform health check with the environment variable SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_STREAM_HEALTH_CHECK=none

19.2.2. Sample Manifest Template

Following manifest.yml template includes the required env-var’s for the Spring Cloud Data Flow server and deployed apps/tasks to successfully run on Cloud Foundry and automatically resolve centralized properties from my-config-server at the 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
    SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_URL: https://api.local.pcfdev.io
    SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_ORG: pcfdev-org
    SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_SPACE: pcfdev-space
    SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_DOMAIN: local.pcfdev.io
    SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_USERNAME: admin
    SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_PASSWORD: admin
    SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_STREAM_SERVICES: rabbit,my-config-server
    SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_TASK_SERVICES: mysql,my-config-server
    SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_SKIP_SSL_VALIDATION: true
    SPRING_APPLICATION_JSON {"maven": { "remote-repositories": { "repo1": { "url": "https://repo.spring.io/libs-release"} } } }
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 both Spring Cloud Data Flow server as well as all the Spring Cloud Stream and Spring Cloud Task applications respectively, we can now resolve centralized properties backed by this service.

19.2.3. 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 config-server for Pivotal Cloud Foundry uses HTTPS for all client-to-service communication, so it is necessary to add a self-signed SSL certificate in environments with no valid certificates.

Using the same manifest.yml template listed in the previous section, for the server, we can provide the self-signed SSL certificate via: TRUST_CERTS: <API_ENDPOINT>.

However, the deployed applications also require TRUST_CERTS as a flat env-var (as opposed to being wrapped inside SPRING_APPLICATION_JSON), so we will have to instruct the server with yet another set of tokens SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_STREAM_USE_SPRING_APPLICATION_JSON: false and SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_TASK_USE_SPRING_APPLICATION_JSON: false for stream and task applications respectively. With this setup, the applications will receive their application properties as regular environment variables

Let’s review the updated manifest.yml with the required changes. Both the Data Flow server and deployed applications would get their config 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-cloudfoundry-VERSION.jar
  env:
    SPRING_APPLICATION_NAME: test-server
    SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_URL: <URL>
    SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_ORG: <ORG>
    SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_SPACE: <SPACE>
    SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_DOMAIN: <DOMAIN>
    SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_USERNAME: <USER>
    SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_PASSWORD: <PASSWORD>
    MAVEN_REMOTE_REPOSITORIES_REPO1_URL: https://repo.spring.io/libs-release
    SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_STREAM_SERVICES: my-config-server #this is so all stream applications bind to my-config-server
    SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_TASK_SERVICES: config-server      #this for so all task applications bind to my-config-server
    SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_STREAM_USE_SPRING_APPLICATION_JSON: false #this is for all the stream applications
    SPRING_CLOUD_DEPLOYER_CLOUDFOUNDRY_TASK_USE_SPRING_APPLICATION_JSON: false #this is for all the task applications
    TRUST_CERTS: <API_ENDPOINT> #this is for the server
    spring.cloud.dataflow.applicationProperties.stream.TRUST_CERTS: <API_ENDPOINT> #this propagates to all streams
    spring.cloud.dataflow.applicationProperties.task.TRUST_CERTS: <API_ENDPOINT>   #this propagates to all tasks
services:
- mysql
- my-config-server #this is for the server

Shell

In this section you will learn about the options for starting the Shell and more advanced functionality relating to how it handles white spaces, quotes, and interpretation of SpEL expressions. The introductory chapters to the Stream DSL and Composed Task DSL is a good place to start for the most common usage of shell commands.

20. Shell Options

The Shell is built upon the Spring Shell project. There are command line options generic to Spring Shell and some specific to Data Flow. The shell takes the following command line options

unix:>java -jar spring-cloud-dataflow-shell-1.2.1.RELEASE.jar --help
Data Flow Options:
  --dataflow.uri=<uri>                              Address of the Data Flow Server [default: http://localhost:9393].
  --dataflow.username=<USER>                        Username of the Data Flow Server [no default].
  --dataflow.password=<PASSWORD>                    Password of the Data Flow Server [no default].
  --dataflow.credentials-provider-command=<COMMAND> Executes an external command which must return an OAuth Access Token [no default].
  --dataflow.skip-ssl-validation=<true|false>       Accept any SSL certificate (even self-signed) [default: no].
  --spring.shell.historySize=<SIZE>                 Default size of the shell log file [default: 3000].
  --spring.shell.commandFile=<FILE>                 Data Flow Shell executes commands read from the file(s) and then exits.
  --help                                            This message.

The spring.shell.commandFile option is of note, as it can be used to point to an existing file which contains all the shell commands to deploy one or many related streams and tasks. This is useful when creating some scripts to help automate the deployment.

There is also a shell command

dataflow:>script --file <YOUR_AWESOME_SCRIPT>

This is useful to help modularize a complex script into multiple indepenent files.

21. Listing available commands

Typing help at the command prompt will give a listing of all available commands. Most of the commands are for Data Flow functionality, but a few are general purpose.

! - 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 shell's properties
version - Displays shell version

Adding the name of the command to help will display 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'

22. Tab Completion

The shell command options can be completed in the shell by hitting the TAB key after the leading --. For example, hitting TAB after stream create -- results in

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

If you type --de and then hit tab, --definition will be expanded.

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 what available sources, processors, or sinks can be used.

23. White space and quote rules

It is only necessary to quote parameter values if they contain spaces or the | character. Here the transform processor is being passed a SpEL expression that will be applied to any data it encounters:

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

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

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

23.1. Quotes and Escaping

There is a Spring Shell based client that talks to the Data Flow Server that is responsible for parsing the DSL. In turn, applications may have applications 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 will encounter when all three components are involved.

It’s not always that complicated

If you don’t use the Data Flow shell, for example you’re using the REST API directly, or if applications properties are not SpEL expressions, then escaping rules are simpler.

23.1.1. 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 (--foo) and corresponding values. There is a special, key-less mapping though, see below

  • a value can not 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)

  • 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

  • Lastly, the key-less mapping is handled in a special way in the sense that if does not need quoting to contain spaces

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

dataflow:>! rm foo

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

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

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

23.1.2. DSL parsing rules

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

  • 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 (without quotes).

Now, let’s imagine we want to test against string messages. If we’d like to compare the payload to the SpEL literal string, "foo", this is how we could do:

filter --expression=payload=='foo'           (1)
filter --expression='payload == ''foo'''     (2)
filter --expression='payload == "foo"'       (3)
1 This works because there are no spaces. Not very legible though
2 This uses single quotes to protect the whole argument, hence actual single quotes need to be doubled
3 But SpEL recognizes String literals with either single or double quotes, so this last method is arguably the best

Please note that the examples above are to be considered outside of the shell, for example if when calling the REST API directly. When entered inside the shell, chances are that the whole stream definition will itself be inside double quotes, which would need escaping. The whole example then becomes:

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

23.1.3. 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 above, String literals are handled in a special way there too. The rules are:

  • 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 vice versa

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 are equivalent:

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

but very different from the following:

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

and other variations.

The first series will simply evaluate to the message payload, while the latter examples will evaluate to the actual literal string payload (again, without quotes).

23.1.4. Putting it all together

As a last, complete example, let’s review how one 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 foo --definition "http | transform --expression='''hello world''' | log" (1)
dataflow:>stream create foo --definition "http | transform --expression='\"hello world\"' | log" (2)
dataflow:>stream create foo --definition "http | transform --expression=\"'hello world'\" | log" (2)
1 This uses single quotes around the string (at the Data Flow parser level), but they need to be doubled because we’re inside a string literal (very first single quote after the equals sign)
2 use single and double quotes respectively to encompass the whole string at the Data Flow parser level. Hence, 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. So double quotes are escaped (at the shell level)

Streams

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

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

24. Introduction

Streams are 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 will typically create a custom Spring Cloud Stream application to implement custom business logic.

24.1. Stream Pipeline DSL

A stream is defined using a unix-inspired Pipeline syntax. The syntax uses vertical bars, also 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 in turn is sent to the input of the less process. Each | symbol will connect the standard ouput of the program 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 via 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 information on how to register Spring Cloud Stream applications in this section. In Data Flow, we classify the Spring Cloud Stream applications as either Sources, Processors, or Sinks.

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

http | file

A stream that involves some processing would be expresed as:

http | filter | transform | file

Stream definitions can be created using the shell’s create stream command. For 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 via the shell’s stream deploy command.

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 shell is calling the Data Flow Servers' REST API. For more information on making HTTP request directly to the server, consult the REST API Guide.

24.2. Application properties

Each application takes properties to customize its behavior. As an example the http source module exposes a port setting which allows the data ingestion port to 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. One may also specify the longhand version as well.

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

This shorthand behavior is discussed more in the section on Whitelisting 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 and also the shell command app info <appType>:<appName> provides additional documentation for all the supported properties.

Supported Stream `<appType>’s are: source, processor, and sink

25. Lifecycle of Streams

25.1. Register a Stream App

Register a Stream App with the App Registry using the Spring Cloud Data Flow Shell app register command. You must provide a unique name, application type, and a URI that can be resolved to the app artifact. For the type, specify "source", "processor", or "sink". Here are a few examples:

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

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

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

When providing a URI with the maven scheme, the format should conform to the following:

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

For example, 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 apps 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, if you would like 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 [eg: 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 apps in bulk, use the app import command and provide the location of the properties file via --uri:

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

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/batch app-starters. You can point to this file and import all the application-URIs in bulk. Otherwise, as explained in previous paragraphs, you can register them individually or have your own custom property file with only the required application-URIs in it. It is recommended, however, to have a "focused" list of desired application-URIs in a custom property file.

List of available Stream Application Starters:

Artifact Type Stable Release SNAPSHOT Release

RabbitMQ + Maven

bit.ly/Bacon-RELEASE-stream-applications-rabbit-maven

bit.ly/Bacon-BUILD-SNAPSHOT-stream-applications-rabbit-maven

RabbitMQ + Docker

bit.ly/Bacon-RELEASE-stream-applications-rabbit-docker

N/A

Kafka 0.9 + Maven

bit.ly/Bacon-RELEASE-stream-applications-kafka-09-maven

bit.ly/Bacon-BUILD-SNAPSHOT-stream-applications-kafka-09-maven

Kafka 0.9 + Docker

bit.ly/Bacon-RELEASE-stream-applications-kafka-09-docker

N/A

Kafka 0.10 + Maven

bit.ly/Bacon-RELEASE-stream-applications-kafka-10-maven

bit.ly/Bacon-BUILD-SNAPSHOT-stream-applications-kafka-10-maven

Kafka 0.10 + Docker

bit.ly/Bacon-RELEASE-stream-applications-kafka-10-docker

N/A

List of available Task Application Starters:

Artifact Type Stable Release SNAPSHOT Release

Maven

bit.ly/Belmont-GA-task-applications-maven

bit.ly/Belmont-BUILD-SNAPSHOT-task-applications-maven

Docker

bit.ly/Belmont-GA-task-applications-docker

N/A

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 RabbitMQ binder in bulk, you can with the following command.

dataflow:>app import --uri http://bit.ly/Bacon-RELEASE-stream-applications-rabbit-maven

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 an app is already registered with the provided name and type, it will not be overridden by default. If you would like to override the pre-existing app coordinates, then 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 doesn’t change (even though the actual resource bytes may be different), then it won’t be re-downloaded. When using maven:// resources on the other hand, using a constant location still may circumvent caching (if using -SNAPSHOT versions).

Moreover, if a stream is already deployed and using some version of a registered app, then (forcibly) re-registering a different app will have no effect until the stream is deployed anew.

In some cases the Resource is resolved on the server side, whereas in others the URI will be passed to a runtime container instance where it is resolved. Consult the specific documentation of each Data Flow Server for more detail.

25.1.1. Whitelisting application properties

Stream and Task applications are Spring Boot applications which are aware of many Common application properties, e.g. server.port but also families of properties such as those with the prefix spring.jmx and logging. When creating your own application it is desirable to whitelist properties so that the shell and the UI can display them first as primary properties when presenting options via TAB completion or in drop-down boxes.

To whitelist application properties create a file named spring-configuration-metadata-whitelist.properties in the META-INF resource directory. There are two property keys that can be used inside this file. The first key is named configuration-properties.classes. The value is a comma separated list of fully qualified @ConfigurationProperty class names. The second key is configuration-properties.names whose value is a comma separated list of property names. This can contain the full name of property, such as server.port or a partial name to whitelist a category of property names, e.g. spring.jmx.

The Spring Cloud Stream application starters are a good place to look for examples of usage. Here is a simple example of the file sink’s spring-configuration-metadata-whitelist.properties file

configuration-properties.classes=org.springframework.cloud.stream.app.file.sink.FileSinkProperties

If we also wanted to add server.port to be white listed, then it would look like this:

configuration-properties.classes=org.springframework.cloud.stream.app.file.sink.FileSinkProperties
configuration-properties.names=server.port

Make sure to add 'spring-boot-configuration-processor' as an optional dependency to generate configuration metadata file for the properties.

<dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-configuration-processor</artifactId>
    <optional>true</optional>
</dependency>

25.1.2. Creating and using a dedicated metadata artifact

You can go a step further in the process of describing the main properties that your stream or task app supports by creating a so-called metadata companion artifact. This simple jar file contains only the Spring boot JSON file about configuration properties metadata, as well as the whitelisting file described in the previous section.

Here is the contents of such an artifact, for the canonical log sink:

$ jar tvf log-sink-rabbit-1.2.1.BUILD-SNAPSHOT-metadata.jar
373848 META-INF/spring-configuration-metadata.json
   174 META-INF/spring-configuration-metadata-whitelist.properties

Note that the spring-configuration-metadata.json file is quite large. This is because it contains the concatenation of all the properties that are available at runtime to the log sink (some of them come from spring-boot-actuator.jar, some of them come from spring-boot-autoconfigure.jar, even some more from spring-cloud-starter-stream-sink-log.jar, etc.) Data Flow always relies on all those properties, even when a companion artifact is not available, but here all have been merged into a single file.

To help with that (as a matter of fact, you don’t want to try to craft this giant JSON file by hand), you can use the following plugin in your build:

<plugin>
 	<groupId>org.springframework.cloud</groupId>
 	<artifactId>spring-cloud-app-starter-metadata-maven-plugin</artifactId>
 	<executions>
 		<execution>
 			<id>aggregate-metadata</id>
 			<phase>compile</phase>
 			<goals>
 				<goal>aggregate-metadata</goal>
 			</goals>
 		</execution>
 	</executions>
 </plugin>
This plugin comes in addition to the spring-boot-configuration-processor that creates the individual JSON files. Be sure to configure the two!

The benefits of a companion artifact are manifold:

  1. being way lighter (usually a few kilobytes, as opposed to megabytes for the actual app), they are quicker to download, allowing quicker feedback when using e.g. app info or the Dashboard UI

  2. as a consequence of the above, they can be used in resource constrained environments (such as PaaS) when metadata is the only piece of information needed

  3. finally, for environments that don’t deal with boot uberjars directly (for example, Docker-based runtimes such as Kubernetes or Mesos), this is the only way to provide metadata about the properties supported by the app.

Remember though, that this is entirely optional when dealing with uberjars. The uberjar itself also includes the metadata in it already.

25.1.3. Using the companion artifact

Once you have a companion artifact at hand, you need to make the system aware of it so that it can be used.

When registering a single app via app register, you can use the optional --metadata-uri option in the shell, like so:

dataflow:>app register --name log --type sink
    --uri maven://org.springframework.cloud.stream.app:log-sink-kafka-10:1.2.1.BUILD-SNAPSHOT
    --metadata-uri=maven://org.springframework.cloud.stream.app:log-sink-kafka-10:jar:metadata:1.2.1.BUILD-SNAPSHOT

When registering several files using the app import command, the file should contain a <type>.<name>.metadata line in addition to each <type>.<name> line. This is optional (i.e. if some apps have it but some others don’t, that’s fine).

Here is an example for a Dockerized app, where the metadata artifact is being hosted in a Maven repository (but retrieving it via http:// or file:// would be equally possible).

...
source.http=docker:springcloudstream/http-source-rabbit:latest
source.http.metadata=maven://org.springframework.cloud.stream.app:http-source-rabbit:jar:metadata:1.2.1.BUILD-SNAPSHOT
...

25.2. Creating custom applications

While there are out of the box source, processor, sink applications available, one can extend these applications or write a custom Spring Cloud Stream application.

The process of creating Spring Cloud Stream applications via Spring Initializr is detailed in the Spring Cloud Stream documentation. It is possible to include multiple binders to an application. If doing so, refer the instructions in Passing Spring Cloud Stream properties for the application on how to configure them.

For supporting property whitelisting, Spring Cloud Stream applications running in Spring Cloud Data Flow may include the Spring Boot configuration-processor as an optional dependency, as in the following example.

<dependencies>
  <!-- other dependencies -->
  <dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-configuration-processor</artifactId>
    <optional>true</optional>
  </dependency>
</dependencies>

Make sure that the spring-boot-maven-plugin is included in the POM. The plugin is necesary for creating the executable jar that will be registered with Spring Cloud Data Flow. Spring Initialzr will include the plugin in the generated POM.

Once a custom application has been created, it can be registered as described in Register a Stream App.

25.3. 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 via the Spring Cloud Data Flow shell. Start the shell as described in the Getting Started section.

New streams are created by with the help of stream definitions. The definitions are built from a simple DSL. For example, let’s walk through what happens if we execute the following shell command:

dataflow:> stream create --definition "time | log" --name ticktock

This defines a stream named ticktock based off the DSL expression time | log. The DSL uses the "pipe" symbol |, to connect a source to a sink.

Then to deploy the stream execute the following shell command (or alternatively add the --deploy flag when creating the stream so that this step is not needed):

dataflow:> stream deploy --name ticktock

The Data Flow Server resolves time and log to maven coordinates and uses those to launch the time and log applications of the stream.

2016-06-01 09:41:21.728  INFO 79016 --- [nio-9393-exec-6] o.s.c.d.spi.local.LocalAppDeployer       : deploying app ticktock.log instance 0
   Logs will be in /var/folders/wn/8jxm_tbd1vj28c8vj37n900m0000gn/T/spring-cloud-dataflow-912434582726479179/ticktock-1464788481708/ticktock.log
2016-06-01 09:41:21.914  INFO 79016 --- [nio-9393-exec-6] o.s.c.d.spi.local.LocalAppDeployer       : deploying app ticktock.time instance 0
   Logs will be in /var/folders/wn/8jxm_tbd1vj28c8vj37n900m0000gn/T/spring-cloud-dataflow-912434582726479179/ticktock-1464788481910/ticktock.time

In this example, the time source simply sends the current time as a message each second, and the log sink outputs it 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 above.

$ 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

25.3.1. 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 via command line arguments or environment variables based on the underlying deployment implementation.

Passing application properties when creating a stream

The following stream

dataflow:> stream create --definition "time | log" --name ticktock

can have application properties defined at the time of stream creation.

The shell command app info <appType>:<appName> displays the white-listed application properties for the application. For more info on the property white listing refer to Whitelisting application properties

Below are the white listed properties for the app time:

dataflow:> app info source:time
╔══════════════════════════════╤══════════════════════════════╤══════════════════════════════╤══════════════════════════════╗
║         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              ║
╚══════════════════════════════╧══════════════════════════════╧══════════════════════════════╧══════════════════════════════╝

Below are the white listed properties for the app log:

dataflow:> app info sink:log
╔══════════════════════════════╤══════════════════════════════╤══════════════════════════════╤══════════════════════════════╗
║         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.        │                              │                              ║
╚══════════════════════════════╧══════════════════════════════╧══════════════════════════════╧══════════════════════════════╝

The application properties for the time and log apps can be specified at the time of stream creation as follows:

dataflow:> stream create --definition "time --fixed-delay=5 | log --level=WARN" --name ticktock

Note that the properties fixed-delay and level defined above for the apps time and log are the 'short-form' property names provided by the shell completion. These 'short-form' property names are applicable only for the white-listed properties and in all other cases, only fully qualified property names should be used.

25.3.2. Deployment properties

When deploying the stream, properties that control the deployment of the apps into the target platform are known as deployment properties. For instance, one can specify how many instances need to be deployed for the specific application defined in the stream using the deployment property called count.

Application properties versus Deployer properties

Starting with version 1.2, the distinction between properties that are meant for the deployed app and properties that govern how this app is deployed (thanks to some implementation of a spring cloud deployer) is more explicit. The former should be passed using the syntax app.<app-name>.<property-name>=<value> while the latter use the deployer.<app-name>.<short-property-name>=<value>

The following table recaps the difference in behavior between the two.

Application Properties Deployer Properties

Example Syntax

app.filter.expression=foo

deployer.filter.count=3

What the application "sees"

expression=foo or <some-prefix>.expression=foo if expression is one of the whitelisted properties

Nothing

What the deployer "sees"

Nothing

spring.cloud.deployer.count=3 The spring.cloud.deployer prefix is automatically and always prepended to the property name

Typical usage

Passing/Overriding application properties, passing Spring Cloud Stream binder or partitionning properties

Setting the number of instances, memory, disk, etc.

Passing instance count as deployment property

If you would like to have multiple instances of an application in the stream, you can include a deployer property with the deploy command:

dataflow:> stream deploy --name ticktock --properties "deployer.time.count=3"

Note that count is the reserved property name used by the underlying deployer. Hence, if the application also has a custom property named count, it is not supported when specified in 'short-form' form during stream deployment as it could conflict with the instance count deployer property. Instead, the count as a custom application property can be specified in its fully qualified form (example: app.foo.bar.count) during stream deployment or it can be specified using 'short-form' or fully qualified form during the stream creation where it will be considered as an app property.

Inline vs file reference properties

When using the Spring Cloud Data Flow Shell, there are two ways to provide deployment properties: either inline or via a file reference. Those two ways are exclusive and documented below:

Inline properties

use the --properties shell option and list properties as a comma separated list of key=value pairs, like so:

stream deploy foo
    --properties "deployer.transform.count=2,app.transform.producer.partitionKeyExpression=payload"
Using a file reference

use the --propertiesFile option and point it to a local .properties, .yaml or .yml file (i.e. that lives in the filesystem of the machine running the shell). Being read as a .properties file, normal rules apply (ISO 8859-1 encoding, =, <space> or : delimiter, etc.) although we recommend using = as a key-value pair delimiter for consistency:

stream deploy foo --propertiesFile myprops.properties

where myprops.properties contains:

deployer.transform.count=2
app.transform.producer.partitionKeyExpression=payload

Both the above properties will be passed as deployment properties for the stream foo above.

In case of using YAML as the format for the deployment properties, use the .yaml or .yml file extention when deploying the stream,

stream deploy foo --propertiesFile myprops.yaml

where myprops.yaml contains:

deployer:
  transform:
    count: 2
app:
  transform:
    producer:
      partitionKeyExpression: payload
Passing application properties when deploying a stream

The application properties can also be specified when deploying a stream. When specified during deployment, these application properties can either be specified as 'short-form' property names (applicable for white-listed properties) or fully qualified property names. The application properties should have the prefix "app.<appName/label>".

For example, the stream

dataflow:> stream create --definition "time | log" --name ticktock

can be deployed with application properties using the 'short-form' property names:

dataflow:>stream deploy ticktock --properties "app.time.fixed-delay=5,app.log.level=ERROR"

When using the app label,

stream create ticktock --definition "a: time | b: log"

the application properties can be defined as:

stream deploy ticktock --properties "app.a.fixed-delay=4,app.b.level=ERROR"
Passing Spring Cloud Stream properties for the application

Spring Cloud Data Flow sets the required Spring Cloud Stream properties for the applications inside the stream. Most importantly, the spring.cloud.stream.bindings.<input/output>.destination is set internally for the apps to bind.

If someone wants to override any of the Spring Cloud Stream properties, they can be set via deployment properties.

For example, for the below stream

dataflow:> stream create --definition "http | transform --expression=payload.getValue('hello').toUpperCase() | log" --name ticktock

if there are multiple binders available in the classpath for each of the applications and the binder is chosen for each deployment then the stream can be deployed with the specific Spring Cloud Stream properties as:

dataflow:>stream deploy ticktock --properties "app.time.spring.cloud.stream.bindings.output.binder=kafka,app.transform.spring.cloud.stream.bindings.input.binder=kafka,app.transform.spring.cloud.stream.bindings.output.binder=rabbit,app.log.spring.cloud.stream.bindings.input.binder=rabbit"
Overriding the destination names is not recommended as Spring Cloud Data Flow takes care of setting this internally.
Passing per-binding producer consumer properties

A Spring Cloud Stream application can have producer and consumer properties set per-binding basis. While Spring Cloud Data Flow supports specifying short-hand notation for per binding producer properties such as partitionKeyExpression, partitionKeyExtractorClass as described in Passing stream partition properties during stream deployment, all the supported Spring Cloud Stream producer/consumer properties can be set as Spring Cloud Stream properties for the app directly as well.

The consumer properties can be set for the inbound channel name with the prefix app.[app/label name].spring.cloud.stream.bindings.<channelName>.consumer. and the producer properties can be set for the outbound channel name with the prefix app.[app/label name].spring.cloud.stream.bindings.<channelName>.producer.. For example, the stream

dataflow:> stream create --definition "time | log" --name ticktock

can be deployed with producer/consumer properties as:

dataflow:>stream deploy ticktock --properties "app.time.spring.cloud.stream.bindings.output.producer.requiredGroups=myGroup,app.time.spring.cloud.stream.bindings.output.producer.headerMode=raw,app.log.spring.cloud.stream.bindings.input.consumer.concurrency=3,app.log.spring.cloud.stream.bindings.input.consumer.maxAttempts=5"

The binder specific producer/consumer properties can also be specified in a similar way.

For instance

dataflow:>stream deploy ticktock --properties "app.time.spring.cloud.stream.rabbit.bindings.output.producer.autoBindDlq=true,app.log.spring.cloud.stream.rabbit.bindings.input.consumer.transacted=true"
Passing stream partition properties during stream deployment

A common pattern in stream processing is to partition the data as it is streamed. This entails deploying multiple instances of a message consuming app and using content-based routing so that messages with a given key (as determined at runtime) are always routed to the same app instance. You can pass the partition properties during stream deployment to declaratively configure a partitioning strategy to route each message to a specific consumer instance.

See below for examples of deploying partitioned streams:

app.[app/label name].producer.partitionKeyExtractorClass

The class name of a PartitionKeyExtractorStrategy (default null)

app.[app/label name].producer.partitionKeyExpression

A SpEL expression, evaluated against the message, to determine the partition key; only applies if partitionKeyExtractorClass is null. If both are null, the app is not partitioned (default null)

app.[app/label name].producer.partitionSelectorClass

The class name of a PartitionSelectorStrategy (default null)

app.[app/label name].producer.partitionSelectorExpression

A SpEL expression, evaluated against the partition key, to determine the partition index to which the message will be routed. The final partition index will be the return value (an integer) modulo [nextModule].count. If both the class and expression are null, the underlying binder’s default PartitionSelectorStrategy will be applied to the key (default null)

In summary, an app is partitioned if its count is > 1 and the previous app has a partitionKeyExtractorClass or partitionKeyExpression (class takes precedence). When a partition key is extracted, the partitioned app instance is determined by invoking the partitionSelectorClass, if present, or the partitionSelectorExpression % partitionCount, where partitionCount is application count in the case of RabbitMQ, and the underlying partition count of the topic in the case of Kafka.

If neither a partitionSelectorClass nor a partitionSelectorExpression is present the result is key.hashCode() % partitionCount.

Passing application content type properties

In a stream definition you can specify that the input or the output of an application need to be converted to a different type. You can use the inputType and outputType properties to specify the content type for the incoming data and outgoing data, respectively.

For example, consider the following stream:

dataflow:>stream create tuple --definition "http | filter --inputType=application/x-spring-tuple
 --expression=payload.hasFieldName('hello') | transform --expression=payload.getValue('hello').toUpperCase()
 | log" --deploy

The http app is expected to send the data in JSON and the filter app receives the JSON data and processes it as a Spring Tuple. In order to do so, we use the inputType property on the filter app to convert the data into the expected Spring Tuple format. The transform application processes the Tuple data and sends the processed data to the downstream log application.

When sending some data to the http application:

dataflow:>http post --data {"hello":"world","foo":"bar"} --contentType application/json --target http://localhost:<http-port>

At the log application you see the content as follows:

INFO 18745 --- [transform.tuple-1] log.sink                                 : WORLD

Depending on how applications are chained, the content type conversion can be specified either as via the --outputType in the upstream app or as an --inputType in the downstream app. For instance, in the above stream, instead of specifying the --inputType on the 'transform' application to convert, the option --outputType=application/x-spring-tuple can also be specified on the 'http' application.

For the complete list of message conversion and message converters, please refer to Spring Cloud Stream documentation.

Overriding application properties during stream deployment

Application properties that are defined during deployment override the same properties defined during the stream creation.

For example, the following stream has application properties defined during stream creation:

dataflow:> stream create --definition "time --fixed-delay=5 | log --level=WARN" --name ticktock

To override these application properties, one can specify the new property values during deployment:

dataflow:>stream deploy ticktock --properties "app.time.fixed-delay=4,app.log.level=ERROR"

25.3.3. Common application properties

In addition to configuration via 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 will pass 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

This will cause the properties spring.cloud.stream.kafka.binder.brokers and spring.cloud.stream.kafka.binder.zkNodes to be passed to all the launched applications.

Properties configured using this mechanism have lower precedence than stream deployment properties. They will be overridden if a property with the same key is specified at stream deployment time (e.g. app.http.spring.cloud.stream.kafka.binder.brokers will override the common property).

25.4. Destroying a Stream

You can delete a stream by issuing the stream destroy command from the shell:

dataflow:> stream destroy --name ticktock

If the stream was deployed, it will be undeployed before the stream definition is deleted.

25.5. Deploying and Undeploying Streams

Often you will want to stop a stream, but retain the name and definition for future use. In that case you can undeploy the stream by name and issue the deploy command at a later time to restart it.

dataflow:> stream undeploy --name ticktock
dataflow:> stream deploy --name ticktock

26. Stream DSL

This section covers additional features of the Stream DSL not covered in the Stream DSL introduction.

26.1. Tap a Stream

Taps can be created at various producer endpoints in a stream. For a stream like this:

stream create --definition "http | step1: transform --expression=payload.toUpperCase() | step2: transform --expression=payload+'!' | log" --name mainstream --deploy

taps can be created at the output of http, step1 and step2.

To create a stream that acts as a 'tap' on another stream requires to specify the source destination name for the tap stream. The syntax for source destination name is:

`:<streamName>.<label/appName>`

To create a tap at the output of http in the stream above, the source destination name is mainstream.http To create a tap at the output of the first transform app in the stream above, the source destination name is mainstream.step1

The tap stream DSL looks like this:

stream create --definition ":mainstream.http > counter" --name tap_at_http --deploy

stream create --definition ":mainstream.step1 > jdbc" --name tap_at_step1_transformer --deploy

Note the colon (:) prefix before the destination names. The colon allows the parser to recognize this as a destination name instead of an app name.

26.2. Using Labels in a Stream

When a stream is comprised of multiple apps with the same name, they must be qualified with labels:

stream create --definition "http | firstLabel: transform --expression=payload.toUpperCase() | secondLabel: transform --expression=payload+'!' | log" --name myStreamWithLabels --deploy

26.3. Named Destinations

Instead of referencing a source or sink applications, you can use a named destination. A named destination corresponds to a specific destination name in the middleware broker (Rabbit, Kafka, etc.,). When using the | symbol, applications are connected to each other using messaging middleware destination names created by the Data Flow server. In keeping with the unix analogy, one can redirect standard input and output using the less-than < greater-than > charaters. To specify the name of the destination, prefix it with a colon :. For example the following stream has the destination name in the source position:

dataflow:>stream create --definition ":myDestination > log" --name ingest_from_broker --deploy

This stream receives messages from the destination myDestination located at the broker and connects it to the log app. You can also create additional streams that will consume data from the same named destination.

The following stream has the destination name in the sink position:

dataflow:>stream create --definition "http > :myDestination" --name ingest_to_broker --deploy

It is also possible to connect two different destinations (source and sink positions) at the broker in a stream.

dataflow:>stream create --definition ":destination1 > :destination2" --name bridge_destinations --deploy

In the above stream, both the destinations (destination1 and destination2) are located in the broker. The messages flow from the source destination to the sink destination via a bridge app that connects them.

26.4. Fan-in and Fan-out

Using named destinations, you can support Fan-in and Fan-out use cases. Fan-in use cases are when multiple sources all send data to the same named destination. For example

s3 > :data
ftp > :data
http > :data

Would direct the data payloads from the Amazon S3, FTP, and HTTP sources to the same named destination called data. Then an additional stream created with the DSL expression

:data > file

would have all the data from those three sources sent to the file sink.

The Fan-out use case is when you determine the destination of a stream based on some information that is only known at runtime. In this case, the Router Application can be used to specify how to direct the incoming message to one of N named destinations.

27. 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, it is important to make sure the applications are configured appropriately with their binder configurations. For example, let’s consider the following stream:

http | transform --expression=payload.toUpperCase() | log

and in this stream, each application connects to messaging middleware in the following way:

Http source sends events to RabbitMQ (rabbit1)
Transform processor receives events from RabbitMQ (rabbit1) and sends the processed events into Kafka (kafka1)
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 will have the following binder(s) in their classpath 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 via deployment properties when the stream is deployed.

For example,

dataflow:>stream create --definition "http | transform --expression=payload.toUpperCase() | log" --name mystream
dataflow:>stream deploy mystream --properties "app.http.spring.cloud.stream.bindings.output.binder=rabbit1,app.transform.spring.cloud.stream.bindings.input.binder=rabbit1,
app.transform.spring.cloud.stream.bindings.output.binder=kafka1,app.log.spring.cloud.stream.bindings.input.binder=kafka1"

One can override any of the binder configuration properties by specifying them via deployment properties.

28. Examples

28.1. Simple Stream Processing

As an example of a simple processing step, we can transform the payload of the HTTP posted data to upper case using the stream definitions

http | transform --expression=payload.toUpperCase() | log

To create this stream enter the following command in the shell

dataflow:> stream create --definition "http | transform --expression=payload.toUpperCase() | log" --name mystream --deploy

Posting some data (using a shell command)

dataflow:> http post --target http://localhost:1234 --data "hello"

Will result in an uppercased 'HELLO' in the log

2016-06-01 09:54:37.749  INFO 80083 --- [  kafka-binder-] log.sink    : HELLO

28.2. Stateful Stream Processing

To demonstrate the data partitioning functionality, let’s deploy the following 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

You’ll see the following in the server logs.

2016-06-05 18:33:24.982  INFO 58039 --- [nio-9393-exec-9] o.s.c.d.spi.local.LocalAppDeployer       : deploying app words.log instance 0
   Logs will be in /var/folders/c3/ctx7_rns6x30tq7rb76wzqwr0000gp/T/spring-cloud-dataflow-694182453710731989/words-1465176804970/words.log
2016-06-05 18:33:24.988  INFO 58039 --- [nio-9393-exec-9] o.s.c.d.spi.local.LocalAppDeployer       : deploying app words.log instance 1
   Logs will be in /var/folders/c3/ctx7_rns6x30tq7rb76wzqwr0000gp/T/spring-cloud-dataflow-694182453710731989/words-1465176804970/words.log

Review the words.log instance 0 logs:

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

Review the words.log instance 1 logs:

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 shows that payload splits that contain the same word are routed to the same application instance.

28.3. Other Source and Sink Application Types

Let’s try something a bit more complicated and swap out the time source for something else. Another supported source type is http, which accepts data for ingestion over HTTP POSTs. 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 using an http source, but still using the same log sink, we would change the original command above to

dataflow:> stream create --definition "http | log" --name myhttpstream --deploy

which will produce the following output from the server

2016-06-01 09:47:58.920  INFO 79016 --- [io-9393-exec-10] o.s.c.d.spi.local.LocalAppDeployer       : deploying app myhttpstream.log instance 0
   Logs will be in /var/folders/wn/8jxm_tbd1vj28c8vj37n900m0000gn/T/spring-cloud-dataflow-912434582726479179/myhttpstream-1464788878747/myhttpstream.log
2016-06-01 09:48:06.396  INFO 79016 --- [io-9393-exec-10] o.s.c.d.spi.local.LocalAppDeployer       : deploying app myhttpstream.http instance 0
   Logs will be in /var/folders/wn/8jxm_tbd1vj28c8vj37n900m0000gn/T/spring-cloud-dataflow-912434582726479179/myhttpstream-1464788886383/myhttpstream.http

Note that we don’t see any other output this time until we actually post some data (using a shell command). In order to see the randomly assigned port on which the http source is listening, execute:

dataflow:> runtime apps

You should see that the corresponding http source has a url property containing the host and port information on which it is listening. You are now ready to post to that url, e.g.:

dataflow:> http post --target http://localhost:1234 --data "hello"
dataflow:> http post --target http://localhost:1234 --data "goodbye"

and the stream will then funnel the data from the http source to the output log implemented by the log sink

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

Of course, 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 apps which are available. You can also define your own apps.

Tasks

This section goes into more detail about how you can work with Spring Cloud Task. It covers topics such as creating and running task applications.

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

29. Introduction

A task executes a process on demand. In this case a task is a Spring Boot application that is annotated with @EnableTask. Hence a user launches a task that performs a certain process, and once complete the task ends. An example of a task would be a boot application that exports data from a JDBC repository to an HDFS instance. Tasks record the start time and the end time as well as the boot exit code in a relational database. The task implementation is based on the Spring Cloud Task project.

30. The Lifecycle of a Task

Before we dive deeper into the details of creating Tasks, we need to 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

30.1. Creating a Task Application

While Spring Cloud Task does provide a number of out of the box applications (via the spring-cloud-task-app-starters), most task applications will be custom developed. In order to create a custom task application:

  1. Create a new project via Spring Initializer via either the website or your IDE making sure to select the following starters:

    1. Cloud Task - This dependency is the spring-cloud-starter-task.

    2. JDBC - This is the dependency for the spring-jdbc starter.

  2. Within your new project, create a new class that will serve as your main class:

@EnableTask
@SpringBootApplication
public class MyTask {

    public static void main(String[] args) {
		SpringApplication.run(MyTask.class, args);
	}
}
  1. With this, you’ll need one or more CommandLineRunner or ApplicationRunner 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).

  2. Packaging your application up via Spring Boot into an über jar is done via the standard Boot conventions.

  3. The packaged application can be registered and deployed as noted below.

30.1.1. 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 executing tasks externally (i.e. command line) and you wish for Spring Cloud Data Flow to show the TaskExecutions in its UI, be sure that common datasource settings are shared among the both. By default Spring Cloud Task will use a local H2 instance and the execution will not be recorded to the database used by Spring Cloud Data Flow.

30.2. Registering a Task Application

Register a Task App with the App Registry 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 app artifact. For the type, specify "task". Here are a few 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 http://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 apps 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, this would be a valid properties file:

task.foo=file:///tmp/foo.jar
task.bar=file:///tmp/bar.jar

Then use the app import command and provide the location of the properties file via --uri:

app import --uri file:///tmp/task-apps.properties

For convenience, we have the static files with application-URIs (for both maven and docker) available for all the out-of-the-box Task app-starters. You can point to this file and import all the application-URIs in bulk. Otherwise, as explained in previous paragraphs, you can register them individually or have your own custom property file with only the required application-URIs in it. It is recommended, however, to have a "focused" list of desired application-URIs in a custom property file.

List of available static property files:

Artifact Type Stable Release SNAPSHOT Release

Maven

http://bit.ly/Belmont-GA-task-applications-maven

http://bit.ly/Belmont-BUILD-SNAPSHOT-task-applications-maven

Docker

http://bit.ly/Belmont-GA-task-applications-docker

http://bit.ly/Belmont-BUILD-SNAPSHOT-task-applications-docker

For example, if you would like to register all out-of-the-box task applications in bulk, you can with the following command.

dataflow:>app import --uri http://bit.ly/Belmont-GA-task-applications-maven

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 app is already registered with the provided name, it will not be overridden by default. If you would like to override the pre-existing task app, then include the --force option.

In some cases the Resource is resolved on the server side, whereas in others the URI will be passed to a runtime container instance where it is resolved. Consult the specific documentation of each Data Flow Server for more detail.

30.3. Creating a Task Definition

Create a Task Definition from a Task App by providing a definition name as well as properties that apply to the task execution. Creating a task definition can be done via the restful API or the shell. To create a task definition using the shell, use the task create command to create the task definition. For example:

dataflow:>task create mytask --definition "timestamp --format=\"yyyy\""
 Created new task 'mytask'

A listing of the current task definitions can be obtained via the restful API or the shell. To get the task definition list using the shell, use the task list command.

30.4. Launching a Task

An adhoc task can be launched via the restful API or via the shell. To launch an ad-hoc task via the shell use the task launch command. For example:

dataflow:>task launch mytask
 Launched task 'mytask'

When a task is launched, any properties that need to be passed as the command line arguments to the task application can be set when launching the task as follows:

dataflow:>task launch mytask --arguments "--server.port=8080,--foo=bar"

Additional properties meant for a TaskLauncher itself can be passed in using a --properties option. Format of this option is a comma delimited string of properties prefixed with app.<task definition name>.<property>. Properties are passed to TaskLauncher as application properties and 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.foo1=bar1,app.timestamp.foo2=bar2"

30.4.1. Common application properties

In addition to configuration via DSL, Spring Cloud Data Flow provides a mechanism for setting common properties to all the task applications that are launched by it. This can be done by adding properties prefixed with spring.cloud.dataflow.applicationProperties.task when starting the server. When doing so, the server will pass all the properties, without the prefix, to the instances it launches.

For example, all the launched applications can be configured to use the properties foo and fizz by launching the Data Flow server with the following options:

--spring.cloud.dataflow.applicationProperties.task.foo=bar
--spring.cloud.dataflow.applicationProperties.task.fizz=bar2

This will cause the properties foo=bar and fizz=bar2 to be passed to all the launched applications.

Properties configured using this mechanism have lower precedence than task deployment properties. They will be overridden if a property with the same key is specified at task launch time (e.g. app.trigger.fizz will override the common property).

30.5. Reviewing Task Executions

Once the task is launched the state of the task is stored in a relational DB. The state includes:

  • Task Name

  • Start Time

  • End Time

  • Exit Code

  • Exit Message

  • Last Updated Time

  • Parameters

A user can check the status of their task executions via the restful API or by the shell. To display the latest task executions via 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 display command with the id of the task execution, for example task display --id 549.

30.6. Destroying a Task Definition

Destroying a Task Definition will remove the definition from the definition repository. This can be done via the restful API or via the shell. To destroy a task via the shell use the task destroy command. For example:

dataflow:>task destroy mytask
 Destroyed task 'mytask'

The task execution information for previously launched tasks for the definition will remain in the task repository.

This will not stop any currently executing tasks for this definition, instead it just removes the task definition from the database.

31. Subscribing to Task/Batch Events

You can also tap into various task/batch events when the task is launched. If the task is enabled to generate task and/or batch events (with the additional dependencies spring-cloud-task-stream and spring-cloud-stream-binder-kafka, in the case of Kafka as the binder), those events are published during the task lifecycle. By default, the destination names for those published events on the broker (rabbit, kafka etc.,) are the event names themselves (for instance: task-events, job-execution-events etc.,).

dataflow:>task create myTask --definition “myBatchJob"
dataflow:>task launch myTask
dataflow:>stream create task-event-subscriber1 --definition ":task-events > log" --deploy

You can control the destination name for those events by specifying explicit names when launching the task such as:

dataflow:>task launch myTask --properties "spring.cloud.stream.bindings.task-events.destination=myTaskEvents"
dataflow:>stream create task-event-subscriber2 --definition ":myTaskEvents > log" --deploy

The default Task/Batch event and destination names on the broker are enumerated below:

Table 1. Task/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

32. Launching Tasks from a Stream

You can launch a task from a stream by using one of the available task-launcher sinks. Currently the platforms supported via the task-launcher sinks are local, Cloud Foundry, and Yarn.

task-launcher-local is meant for development purposes only.

A task-launcher sink expects a message containing a TaskLaunchRequest object in its payload. From the TaskLaunchRequest object the task-launcher will obtain the URI of the artifact to be launched as well as the environment properties, command line arguments, deployment properties and application name to be used by the task.

The task-launcher-local can be added to the available sinks by executing the app register command as follows (for the Rabbit Binder):

app register --name task-launcher-local --type sink --uri maven://org.springframework.cloud.stream.app:task-launcher-local-sink-rabbit:jar:1.2.0.RELEASE

In the case of a maven based task that is to be launched, the task-launcher application is responsible for downloading the artifact. You must configure the task-launcher with the appropriate configuration of Maven Properties such as --maven.remote-repositories.repo1.url=http://repo.spring.io/libs-milestone" to resolve artifacts, in this case against a milestone repo. Note that this repo can be different than the one used to register the task-launcher application itself.

32.1. TriggerTask

One way to launch a task using the task-launcher is to use the triggertask source. The triggertask source will emit a message with a TaskLaunchRequest object containing the required launch information. The triggertask can be added to the available sources by executing the app register command as follows (for the Rabbit Binder):

app register --type source --name triggertask --uri maven://org.springframework.cloud.stream.app:triggertask-source-rabbit:1.2.0.RELEASE

An example of this would be to launch the timestamp task once every 60 seconds, the stream to implement this would look like:

stream create foo --definition "triggertask --triggertask.uri=maven://org.springframework.cloud.task.app:timestamp-task:jar:1.2.0.RELEASE --trigger.fixed-delay=60 --triggertask.environment-properties=spring.datasource.url=jdbc:h2:tcp://localhost:19092/mem:dataflow,spring.datasource.username=sa | task-launcher-local --maven.remote-repositories.repo1.url=http://repo.spring.io/libs-release" --deploy

If you execute runtime apps you can find the log file for the task launcher sink. Tailing that file you can find the log file for the launched tasks. The setting of triggertask.environment-properties is so that all the task executions can be collected in the same H2 database used in the local version of the Data Flow Server. You can then see the list of task executions using the shell command task execution list

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        ║
╚════════════════════╧══╧════════════════════════════╧════════════════════════════╧═════════╝

32.2. TaskLaunchRequest-transform

Another option to start a task using the task-launcher would be to create a stream using the Tasklaunchrequest-transform processor to translate a message payload to a TaskLaunchRequest.

The tasklaunchrequest-transform can be added to the available processors by executing the app register command as follows (for the Rabbit Binder):

app register --type processor --name tasklaunchrequest-transform --uri maven://org.springframework.cloud.stream.app:tasklaunchrequest-transform-processor-rabbit:1.2.0.RELEASE

For example:

stream create task-stream --definition "http --port=9000 | tasklaunchrequest-transform --uri=maven://org.springframework.cloud.task.app:timestamp-task:jar:1.2.0.RELEASE | task-launcher-local --maven.remote-repositories.repo1.url=http://repo.spring.io/libs-release"

33. Composed Tasks

Spring Cloud Data Flow allows a user to create a directed graph where each node of the graph is a task application. This is done by using the DSL for composed tasks. A composed task can be created via the RESTful API, the Spring Cloud Data Flow Shell, or the Spring Cloud Data Flow UI.

33.1. Configuring the Composed Task Runner

Composed tasks are executed via a task application called the Composed Task Runner.

33.1.1. Registering the Composed Task Runner

Out of the box the Composed Task Runner application is not registered with Spring Cloud Data Flow. So, to launch composed tasks we must first register the Composed Task Runner as an application with Spring Cloud Data Flow as follows:

app register --name composed-task-runner --type task --uri maven://org.springframework.cloud.task.app:composedtaskrunner-task:<DESIRED_VERSION>

You can also configure Spring Cloud Data Flow to use a different task definition name for the composed task runner. This can be done by setting the spring.cloud.dataflow.task.composedTaskRunnerName property to the name of your choice. You can then register the composed task runner application with the name you set using that property.

33.1.2. 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, like you would do if you deploy the server on Cloud Foundry, YARN or Kubernetes, then you need to provide the URI that can be used to access the server. You can either provide this dataflow.server.uri property for the Composed Task Runner application when launching a composed task, or you can provide a 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 will be automatically set when a composed task is launched.

33.2. The Lifecycle of a Composed Task

33.2.1. Creating a Composed Task

The DSL for the composed tasks is used when creating a task definition via the task create command. For example:

dataflow:> app register --name timestamp --type task --uri maven://org.springframework.cloud.task.app:timestamp-task:<DESIRED_VERSION>
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 example above we assume that the applications to be used by our composed task have not been registered yet. So 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 example above will, when launched, execute mytaskapp and then execute 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 executing the task list command.

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    ║
╚══════════════════════════╧══════════════════════╧═══════════╝

Spring Cloud Data Flow created three task definitions, one for each of the applications that comprises 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 comprised of the name of the composed task and the name of the application separated by a dash -. i.e. my-composed-task - mytaskapp.

Task Application Parameters

The task applications that comprise the composed task definition can also contain parameters. For example:

dataflow:> task create my-composed-task --definition "mytaskapp --displayMessage=hello && timestamp --format=YYYY"

33.2.2. Launching a Composed Task

Launching a composed task is done the same way as launching a stand-alone task. i.e.

task launch my-composed-task

Once the task is launched and assuming all the tasks complete successfully you will see three task executions when executing a task execution list. For 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 example above we see that my-compose-task launched and it also launched the other tasks in sequential order and all of them executed successfully with "Exit Code" as 0.

Exit Statuses

The following list shows how the Exit Status will be set for each step (task) contained in the composed task following each step execution.

  • If the TaskExecution has an ExitMessage that will be used as the ExitStatus

  • If no ExitMessage is present and the ExitCode is set to zero then the ExitStatus for the step will be COMPLETED.

  • If no ExitMessage is present and the ExitCode is set to any non zero number then the ExitStatus for the step will be FAILED.

33.2.3. Destroying a Composed Task

The same command used to destroy a stand-alone task is the same as destroying a composed task. The only difference is that destroying a composed task will also destroy the child tasks associated with it. For example

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║
╚═════════╧═══════════════╧═══════════╝

33.2.4. Stopping a Composed Task

In cases where a composed task execution needs to be stopped. This can be done via the:

  • RESTful API

  • Spring Cloud Data Flow Dashboard by selecting the Job’s tab and then clicking the stop button by the job execution that needs to be stopped.

The composed task run will be 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 will be marked as STOPPED as well as the composed task job execution.

33.2.5. Restarting a Composed Task

In cases where a composed task fails during execution and the status of the composed task is FAILED then the task can be restarted. This can be done via the:

  • RESTful API

  • Shell by launching the task using the same parameters

  • Spring Cloud Data Flow Dashboard by selecting the Job’s tab and then clicking the restart button by the job execution that needs to be restarted.

Restarting a Composed Task job that has been stopped (via the Spring Cloud Data Flow Dashboard or RESTful API), will relaunch the STOPPED child task, and then launch the remaining (unlaunched) child tasks in the specified order.

34. Composed Tasks DSL

34.1. Conditional Execution

Conditional execution is expressed using a double ampersand symbol &&. This allows each task in the sequence to be launched only if the previous task successfully completed. For example:

task create my-composed-task --definition "foo && bar"

When the composed task my-composed-task is launched, it will launch the task foo and if it completes successfully, then the task bar will be launched. If the foo task fails, then the task bar will 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. For example:

Composed Task Conditional Execution
Figure 5. Conditional Execution

The diagram above is a screen capture of the directed graph as it being created using the Spring Cloud Data Flow Dashboard. We see that are 4 components in the diagram that comprise a conditional execution:

  • Start icon - All directed graphs start from this symbol. There will only be one.

  • Task icon - Represents each task in the directed graph.

  • End icon - Represents the termination 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

You can view a diagram of your directed graph by clicking the detail button next to the composed task definition on the definitions tab.

34.2. 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 based on the exit status of the previous task. A task transition is represented by the following symbol ->.

34.2.1. Basic Transition

A basic transition would look like the following:

task create my-transition-composed-task --definition "foo 'FAILED' -> bar 'COMPLETED' -> baz"

In the example above foo would launch and if it had an exit status of FAILED, then the bar task would launch. If the exit status of foo was COMPLETED then baz would launch. All other statuses returned by foo will have no effect and task would terminate normally.

Using the Spring Cloud Data Flow Dashboard to create the same "basic transition" would look like:

Composed Task Basic Transition
Figure 6. Basic Transition

The diagram above is a screen capture of the directed graph as it being created using the Spring Cloud Data Flow Dashboard. Notice that there are 2 different types of connectors:

  • Dashed line - Is the line used to represent transitions from the application to one of the possible destination applications.

  • Solid line - Used to connect applications in a conditional execution or a connection between the application and a control node (end, start).

When creating a transition, link the application to each of possible destination using the connector. Once complete go to each connection and select it by clicking it. A bolt icon should appear, click that icon and enter the exit status required for that connector. The solid line for that connector will turn to a dashed line.

34.2.2. Transition With a Wildcard

Wildcards are supported for transitions by the DSL for example:

task create my-transition-composed-task --definition "foo 'FAILED' -> bar '*' -> baz"

In the example above foo would launch and if it had an exit status of FAILED, then the bar task would launch. Any exit status of foo other than FAILED then baz would launch.

Using the Spring Cloud Data Flow Dashboard to create the same "transition with wildcard" would look like:

Composed Task Basic Transition with Wildcard
Figure 7. Basic Transition With Wildcard

34.2.3. Transition With a Following Conditional Execution

A transition can be followed by a conditional execution so long as the wildcard is not used. For example:

task create my-transition-conditional-execution-task --definition "foo 'FAILED' -> bar 'UNKNOWN' -> baz && qux && quux"

In the example above foo would launch and if it had an exit status of FAILED, then the bar task would launch. If foo had an exit status of UNKNOWN then baz would launch. Any exit status of foo other than FAILED or UNKNOWN then 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 look like:

Composed Task Transition with Conditional Execution
Figure 8. Transition With Conditional Execution
In this diagram we see the dashed line (transition) connecting the foo application to the target applications, but a solid line connecting the conditional executions between foo, qux, and quux.

34.3. Split Execution

Splits allow for multiple tasks within a composed task to 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 || . For example:

task create my-split-task --definition "<foo || bar || baz>"

The example above will launch tasks foo, bar and baz in parallel.

Using the Spring Cloud Data Flow Dashboard to create the same "split execution" would look like:

Composed Task Split
Figure 9. Split

With the task DSL a user may also execute multiple split groups in succession. For example:

task create my-split-task --definition "<foo || bar || baz> && <qux || quux>"

In the example above tasks foo, bar and baz will be launched in parallel, once they all complete then tasks qux, quux will be launched in parallel. Once they complete the composed task will end. However if foo, bar, or baz fails then, the split containing qux and quux will not launch.

Using the Spring Cloud Data Flow Dashboard to create the same "split with multiple groups" would look like:

Composed Task Split
Figure 10. Split as a part of a conditional execution

Notice that there is a SYNC control node that is by the designer when connecting two consecutive splits.

34.3.1. Split Containing Conditional Execution

A split can also have a conditional execution within the angle brackets. For example:

task create my-split-task --definition "<foo && bar || baz>"

In the example above we see that foo and baz will be launched in parallel, however bar will not launch until foo completes successfully.

Using the Spring Cloud Data Flow Dashboard to create the same "split containing conditional execution" would look like:

Composed Task Split With Conditional Execution
Figure 11. Split with conditional execution

Tasks on Cloud Foundry

Spring Cloud Data Flow’s task functionality exposes new task capabilities within the Pivotal Cloud Foundry runtime. It is important to note that the current underlying PCF task capabilities are considered experimental for PCF version versions less than 1.9. See Feature Toggles for how to disable task support in Data Flow.

35. Version Compatibility

The task functionality depends on the latest versions of PCF for runtime support. This release requires PCF version 1.7.12 or higher to run tasks. Tasks are an experimental feature in PCF 1.7 and 1.8 and a GA feature in PCF 1.9.

36. Tooling

It is important to note that there is no Apps Manager support for tasks as of this release. When running applications as tasks through Spring Cloud Data Flow, the only way is to view them within the context of CF CLI.

37. Task Database Schema

The database schema for Task applications was changed slighlty from the 1.0.x to 1.1.x version of Spring Cloud Task. Since Spring Cloud Data Flow automatically creates the database schema if it is not present upon server startup, you may need to update the schema if you ran a 1.0.x version of the Data Flow server and now are upgrading to the 1.1.x version. You can find the migration scripts here in the Spring Cloud Task Github repository. The documentation for Accessing Services with Diego SSH and this blog entry for connecting a GUI tools to the MySQL Service in PCF should help you to update the schema.

38. Running Task Applications

Running a task application within Spring Cloud Data Flow goes through a slightly different lifecycle than running a stream application. Both types of applications need to be registered with the appropriate artifact coordinates. Both need a definition created via the SCDF DSL. However, that’s where the similarities end.

With stream based applications, you "deploy" them with the intent that they run until they are undeployed. A stream definition is only deployed once (it can be scaled, but only deployed as one instance of the stream as a whole). However, tasks are launched. A single task definition can be launched many times. With each launch, they will start, execute, and shut down with PCF cleaning up the resources once the shutdown has occurred. The following sections outline the process of creating, launching, destroying, and viewing tasks.

38.1. Create a Task

Similar to streams, creating a task application is done via the SCDF DSL or through the dashboard. To create a task definition in SCDF, you’ve to either develop a task application or use one of the out-of-the-box task app-starters. The maven coordinates of the task application should be registered in SCDF. For more details on how to register task applications, review register task applications section from the core docs.

Let’s see an example that uses the out-of-the-box timestamp task application.

dataflow:>task create --name foo --definition "timestamp"
Created new task 'foo'
Tasks in SCDF do not require explicit deployment. They are required to be launched and with that there are different ways to launch them - refer to this section for more details.

38.2. Launch a Task

Unlike streams, tasks in SCDF requires an explicit launch trigger or it can be manually kicked-off.

dataflow:>task launch foo
Launched task 'foo'

38.3. View Task Logs

As previously mentioned, the CL CLI is the way to interact with tasks on PCF, including viewing the logs. In order to view the logs as a task is executing use the following command where foo is the name of the task you are executing:

cf v3-logs foo
Tailing logs for app foo...

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

2016-08-19T09:44:49.11-0700 [APP/TASK/bar1/0]OUT 2016-08-19 16:44:49.111  INFO 7 --- [           main] o.s.c.t.a.t.TimestampTaskApplication     : Started TimestampTaskApplication in 2.734 seconds (JVM running for 3.288)
2016-08-19T09:44:49.13-0700 [APP/TASK/bar1/0]OUT Exit status 0
2016-08-19T09:44:49.19-0700 [APP/TASK/bar1/0]OUT Destroying container
2016-08-19T09:44:50.41-0700 [APP/TASK/bar1/0]OUT Successfully destroyed container
Logs are only viewable through the CF CLI as the app is running. Historic logs are not available.

38.4. List Tasks

Listing tasks is as simple as:

dataflow:>task list
╔══════════════════════╤═════════════════════════╤═══════════╗
║      Task Name       │     Task Definition     │Task Status║
╠══════════════════════╪═════════════════════════╪═══════════╣
║foo                   │timestamp                │complete   ║
╚══════════════════════╧═════════════════════════╧═══════════╝

38.5. List Task Executions

If you’d like to view the execution details of the launched task, you could do the following.

dataflow:>task execution list
╔════════════════════════╤══╤═════════════════════════╤═════════════════════════╤════════╗
║       Task Name        │ID│       Start Time        │        End Time         │  Exit  ║
║                        │  │                         │                         │  Code  ║
╠════════════════════════╪══╪═════════════════════════╪═════════════════════════╪════════╣
║foo:cloud:              │1 │ Fri Aug 19 09:44:49 PDT │Fri Aug 19 09:44:49 PDT  │0       ║
╚════════════════════════╧══╧═════════════════════════╧═════════════════════════╧════════╝

38.6. Destroy a Task

Destroying the task application from SCDF removes the task definition from task repository.

dataflow:>task destroy foo
Destroyed task 'foo'
dataflow:>task list
╔═════════╤═══════════════╤═══════════╗
║Task Name│Task Definition│Task Status║
╚═════════╧═══════════════╧═══════════╝

38.7. Deleting Task From Cloud Foundry

Currently Spring Cloud Data Flow does not delete tasks deployed on a Cloud Foundry instance once they have been pushed. The only way to do this now is via CLI on a Cloud Foundry instance version 1.9 or above. This is done in 2 steps:

  1. Obtain a list of the apps via the cf apps command.

  2. Identify the task app to be deleted and execute the cf delete <task-name> command.

The task destroy <task-name> only deletes the definition and not the task deployed on Cloud Foundry.

Dashboard

This section describe how to use the Dashboard of Spring Cloud Data Flow.

39. Introduction

Spring Cloud Data Flow provides a browser-based GUI and it currently includes 6 tabs:

  • Apps Lists all available applications and provides the control to register/unregister them

  • Runtime Provides the Data Flow cluster view with the list of all running applications

  • Streams List, create, deploy, and destroy Stream Definitions

  • Tasks List, create, launch and destroy Task Definitions

  • Jobs Perform Batch Job related functions

  • Analytics Create data visualizations for the various analytics applications

Upon starting Spring Cloud Data Flow, the Dashboard is available at:

http://<host>:<port>/dashboard

If you have enabled https, then it will be located at https://localhost:9393/dashboard. If you have enabled security, a login form is available at http://localhost:9393/dashboard/#/login.

The default Dashboard server port is 9393
The Spring Cloud Data Flow Dashboard
Figure 12. The Spring Cloud Data Flow Dashboard

40. Apps

The Apps section of the Dashboard lists all the available applications and provides the control to register/unregister them (if applicable). It is possible to import a number of applications at once using the Bulk Import Applications action.

List of available applications
Figure 13. List of Available Applications

40.1. Bulk Import of Applications

The bulk import applications page provides numerous options for defining and importing a set of applications in one go. For bulk import the application definitions are expected to be expressed in a properties style:

<type>.<name> = <coordinates>

For example:

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

At the top of the bulk import page an Uri can be specified that points to a properties file stored elsewhere, it should contain properties formatted as above. Alternatively, using the textbox labeled Apps as Properties it is possible to directly list each property string. Finally, if the properties are stored in a local file the Select Properties File option will open a local file browser to select the file. After setting your definitions via one of these routes, click Import.

At the bottom of the page there are quick links to the property files for common groups of stream apps and task apps. If those meet your needs, simply select your appropriate variant (rabbit, kafka, docker, etc) and click the Import action on those lines to immediately import all those applications.

Bulk Import Applications
Figure 14. Bulk Import Applications

41. Runtime

The Runtime section of the Dashboard application shows the Spring Cloud Data Flow cluster view with the list of all running applications. For each runtime app 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 app id.

List of running applications
Figure 15. List of Running Applications

42. Streams

The Streams section of the Dashboard provides 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 can be clicked to see a visual representation of the definition. Hovering over the boxes in the visual representation will show more details about the apps including any options passed to them. In this screenshot the timer stream has been expanded to show the visual representation:

List of Stream Definitions
Figure 16. List of Stream Definitions

If the details button is clicked the view will change to show a visual representation of that stream and also any related streams. In the above example, if clicking details for the timer stream, the view will change to the one shown below which clearly shows the relationship between the three streams (two of them are tapping into the timer stream).

Stream Details Page
Figure 17. Stream Details Page

43. Create Stream

The Create Stream section of the Dashboard includes the Spring Flo designer tab that provides the canvas application, offering a interactive graphical interface for creating data pipelines.

In this tab, you can:

  • Create, manage, and visualize stream pipelines using DSL, a graphical canvas, or both

  • Write pipelines via DSL with content-assist and auto-complete

  • Use auto-adjustment and grid-layout capabilities in the GUI for simpler and interactive organization of pipelines

Watch this screencast that highlights some of the "Flo for Spring Cloud Data Flow" capabilities. Spring Flo wiki includes more detailed content on core Flo capabilities.

Flo for Spring Cloud Data Flo
Figure 18. Flo for Spring Cloud Data Flow

44. Tasks

The Tasks section of the Dashboard currently has three tabs:

  • Apps

  • Definitions

  • Executions

44.1. Apps

Apps encapsulate a unit of work into a reusable component. Within the Data Flow runtime environment Apps allow users to create definitions for Streams as well as Tasks. Consequently, the Apps tab within the Tasks section allows users to create Task definitions.

You will also use this tab to create Batch Jobs.
List of Task Apps
Figure 19. List of Task Apps

On this screen you can perform the following actions:

  • View details such as the task app options.

  • Create a Task Definition from the respective App.

44.1.1. Create a Task Definition from a selected Task App

On this screen you can create a new Task Definition. As a minimum you must provide a name for the new definition. You will also have the option to specify various properties that are used during the deployment of the app.

Each parameter is only included if the Include checkbox is selected.

44.1.2. View Task App Details

On this page you can view the details of a selected task app, including the list of available options (properties) for that app.

44.2. Definitions

This page lists the Data Flow Task definitions and provides actions to launch or destroy those tasks. It also provides a shortcut operation to define one or more tasks using simple textual input, indicated by the bulk define tasks button.

List of Task Definitions
Figure 20. List of Task Definitions

44.2.1. Creating Task Definitions using the bulk define interface

After pressing bulk define tasks, the following screen will be shown.

Bulk Define Tasks
Figure 21. Bulk Define Tasks

It includes a textbox where one or more definitions can be entered and then various actions performed on those definitions. The required input text format for task definitions is very basic, each line should be of the form:

<task-definition-name> = <task-application> <options>

For example:

demo-timestamp = timestamp --format=hhmmss

After entering any data a validator will run asynchronously to verify both the syntax and that the application name entered is a valid application and it supports the options specified. If validation fails the editor will show the errors with more information via tooltips.

To make it easier to enter definitions into the text area, content assist is supported. Pressing Ctrl+Space will invoke content assist to suggest simple task names (based on the line on which it is invoked), task applications and task application options. Press ESCape to close the content assist window without taking a selection.

If the validator should not verify the applications or the options (for example if specifying non-whitelisted options to the applications) then turn off that part of validation by toggling the checkbox off on the Verify Apps button - the validator will then only perform syntax checking. When correctly validated, the create button will be clickable and on pressing it the UI will proceed to create each task definition. If there are any errors during creation then after creation finishes the editor will show any lines of input, as it cannot be used in task definitions. These can then be fixed up and creation repeated. There is an import file button to open a file browser on the local file system if the definitions are in a file and it is easier to import than copy/paste.

Bulk loading of composed task definitions is not currently supported.

44.2.2. Creating Composed Task Definitions

The dashboard includes the Create Composed Task tab that provides the canvas application, offering a interactive graphical interface for creating composed tasks.

In this tab, you can:

  • Create and visualize composed tasks 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

Composed Task Designer
Figure 22. Composed Task Designer

44.2.3. Launching Tasks

Once the task definition is created, they can be launched through the Dashboard as well. Navigate to the Definitions tab. Select the Task you want to launch by pressing Launch.

On the following screen, you can define one or more Task parameters by entering:

  • Parameter Key

  • Parameter Value

Task parameters are not typed.

44.3. Executions

List of Task Executions
Figure 23. List of Task Executions

45. Jobs

The Jobs section of the Dashboard allows you to inspect Batch Jobs. The main section of the screen provides a list of Job Executions. Batch Jobs are Tasks that were executing one or more Batch Job. As such each Job Execution has a back reference to the Task Execution Id (Task Id).

In case of a failed job, you can also restart the task. When dealing with long-running Batch Jobs, you can also request to stop it.

List of Job Executions
Figure 24. List of Job Executions

45.1. List job executions

This page lists the Batch Job Executions and provides the option to restart or stop a specific job execution, provided the operation is available. Furthermore, you have the option to view the Job execution details.

The list of Job Executions also shows the state of the underlying Job Definition. Thus, if the underlying definition has been deleted, deleted will be shown.

45.1.1. Job execution details

Job Execution Details
Figure 25. Job Execution Details

The Job Execution Details screen also contains a list of the executed steps. You can further drill into the Step Execution Details by clicking onto the magnifying glass.

45.1.2. Step execution details

On the top of the page, you will see progress indicator the respective step, with the option to refresh the indicator. Furthermore, a link is provided to view the step execution history.

The Step Execution details screen provides a complete list of all Step Execution Context key/value pairs.

In case of exceptions, the Exit Description field will contain additional error information. Please be aware, though, that this field can only have a maximum of 2500 characters. Therefore, in case of long exception stacktraces, trimming of error messages may occur. In that case, please refer to the server log files for further details.

45.1.3. Step Execution Progress

On this screen, you can see a progress bar indicator in regards to the execution of the current step. Under the Step Execution History, you can also view various metrics associated with the selected step such as duration, read counts, write counts etc.

Step Execution History
Figure 26. Step Execution History

46. Analytics

The Analytics section of the Dashboard provided data visualization capabilities for the various analytics applications available in Spring Cloud Data Flow:

  • Counters

  • Field-Value Counters

  • Aggregate Counters

For example, if you create a stream with a Counter application, you can now easily create the corresponding graph from within the Dashboard tab:

  1. Under Metric Type, select Counters from the select box

  2. Under Stream, select tweetcount

  3. Under Visualization, select the desired chart option, Bar Chart

Using the icons to the right, you can add additional charts to the Dashboard, re-arange the order of created dashboards or remove data visualizations.

REST API Guide

You can find the documentation about the Data Flow REST API in the core documentation.

Appendices

Having trouble with Spring Cloud Data Flow, We’d like to help!

Appendix A: Data Flow Template

As described in the previous chapter, Spring Cloud Data Flow’s functionality is completely exposed via 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 entrypoint is the DataFlowTemplate class in package org.springframework.cloud.dataflow.rest.client.

This class implements the interface DataFlowOperations and delegates to 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 will be discovered via the REST relations, which are provided by HATEOAS.[1]

If a resource cannot be resolved, the respective sub-template will result in being NULL. A common cause is that Spring Cloud Data Flow offers for specific sets of features to be enabled/disabled when launching. For more information see Feature Toggles.

A.1. Using the Data Flow Template

When using the Data Flow Template the only needed Data Flow dependency is the Spring Cloud Data Flow Rest Client:

<dependency>
  <groupId>org.springframework.cloud</groupId>
  <artifactId>spring-cloud-dataflow-rest-client</artifactId>
  <version>1.2.4.RELEASE</version>
</dependency>

With that dependency you will get the DataFlowTemplate class as well as all needed dependencies to make calls to a Spring Cloud Data Flow server.

When instantiating the DataFlowTemplate, you will also pass in a RestTemplate. Please be aware 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 will suffice:

  @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;
  }

Now you can instantiate the DataFlowTemplate with:

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 like to get a list of currently available applications you can execute:

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()));
}

Appendix B: Spring XD to SCDF

In this section you will learn all about the migration path from Spring XD to Spring Cloud Data Flow along with the tips and tricks.

B.1. Terminology Changes

Old New

XD-Admin

Server (implementations: local, cloud foundry, apache yarn, kubernetes, and apache mesos)

XD-Container

N/A

Modules

Applications

Admin UI

Dashboard

Message Bus

Binders

Batch / Job

Task

B.2. Modules to Applications

If you have custom Spring XD modules, you’d have to refactor them to use Spring Cloud Stream and Spring Cloud Task annotations, with updated dependencies and built as normal Spring Boot "applications".

B.2.1. Custom Applications

  • Spring XD’s stream and batch modules are refactored into Spring Cloud Stream and Spring Cloud Task application-starters, respectively. These applications can be used as the reference while refactoring Spring XD modules

  • There are also some samples for Spring Cloud Stream and Spring Cloud Task applications for reference

  • If you’d like to create a brand new custom application, use the getting started guide for Spring Cloud Stream and Spring Cloud Task applications and as well as review the development guide

  • Alternatively, if you’d like to patch any of the out-of-the-box stream applications, you can follow the procedure here

B.2.2. Application Registration

  • Custom Stream/Task application requires being installed to a maven repository for Local, YARN, and CF implementations or as docker images, when deploying to Kubernetes and Mesos. Other than maven and docker resolution, you can also resolve application artifacts from http, file, or as hdfs coordinates

  • Unlike Spring XD, you do not have to upload the application bits while registering custom applications anymore; instead, you’re expected to register the application coordinates that are hosted in the maven repository or by other means as discussed in the previous bullet

  • By default, none of the out-of-the-box applications are preloaded already. It is intentionally designed to provide the flexibility to register app(s), as you find appropriate for the given use-case requirement

  • Depending on the binder choice, you can manually add the appropriate binder dependency to build applications specific to that binder-type. Alternatively, you can follow the Spring Initialzr procedure to create an application with binder embedded in it

B.2.3. Application Properties

  • counter-sink:

    • The peripheral redis is not required in Spring Cloud Data Flow. If you intend to use the counter-sink, then redis becomes required, and you’re expected to have your own running redis cluster

  • field-value-counter-sink:

    • The peripheral redis is not required in Spring Cloud Data Flow. If you intend to use the field-value-counter-sink, then redis becomes required, and you’re expected to have your own running redis cluster

  • aggregate-counter-sink:

    • The peripheral redis is not required in Spring Cloud Data Flow. If you intend to use the aggregate-counter-sink, then redis becomes required, and you’re expected to have your own running redis cluster

B.3. Message Bus to Binders

Terminology wise, in Spring Cloud Data Flow, the message bus implementation is commonly referred to as binders.

B.3.1. Message Bus

Similar to Spring XD, there’s an abstraction available to extend the binder interface. By default, we take the opinionated view of Apache Kafka and RabbitMQ as the production-ready binders and are available as GA releases.

B.3.2. Binders

Selecting a binder is as simple as providing the right binder dependency in the classpath. If you’re to choose Kafka as the binder, you’d register stream applications that are pre-built with Kafka binder in it. If you were to create a custom application with Kafka binder, you’d add the following dependency in the classpath.

<dependency>
    <groupId>org.springframework.cloud</groupId>
    <artifactId>spring-cloud-stream-binder-kafka</artifactId>
    <version>1.0.2.RELEASE</version>
</dependency>
  • Spring Cloud Stream supports Apache Kafka, RabbitMQ and experimental Google PubSub and Solace JMS. All binder implementations are maintained and managed in their individual repositories

  • Every Stream/Task application can be built with a binder implementation of your choice. All the out-of-the-box applications are pre-built for both Kafka and Rabbit and they’re readily available for use as maven artifacts [Spring Cloud Stream / Spring Cloud Task or docker images [Spring Cloud Stream / Spring Cloud Task Changing the binder requires selecting the right binder dependency. Alternatively, you can download the pre-built application from this version of Spring Initializr with the desired “binder-starter” dependency

B.3.3. Named Channels

Fundamentally, all the messaging channels are backed by pub/sub semantics. Unlike Spring XD, the messaging channels are backed only by topics or topic-exchange and there’s no representation of queues in the new architecture.

  • ${xd.module.index} is not supported anymore; instead, you can directly interact with named destinations

  • stream.index changes to :<stream-name>.<label/app-name>

    • for instance: ticktock.0 changes to :ticktock.time

  • “topic/queue” prefixes are not required to interact with named-channels

    • for instance: topic:foo changes to :foo

    • for instance: stream create stream1 --definition ":foo > log"

B.3.4. Directed Graphs

If you’re building non-linear streams, you could take advantage of named destinations to build directed graphs.

for instance, in Spring XD:

stream create f --definition "queue:foo > transform --expression=payload+'-foo' | log" --deploy
stream create b --definition "queue:bar > transform --expression=payload+'-bar' | log" --deploy
stream create r --definition "http | router --expression=payload.contains('a')?'queue:foo':'queue:bar'" --deploy

for instance, in Spring Cloud Data Flow:

stream create f --definition ":foo > transform --expression=payload+'-foo' | log" --deploy
stream create b --definition ":bar > transform --expression=payload+'-bar' | log" --deploy
stream create r --definition "http | router --expression=payload.contains('a')?'foo':'bar'" --deploy

B.4. Batch to Tasks

A Task by definition, is any application that does not run forever, including Spring Batch jobs, and they end/stop at some point. Task applications can be majorly used for on-demand use-cases such as database migration, machine learning, scheduled operations etc. Using Spring Cloud Task, users can build Spring Batch jobs as microservice applications.

  • Spring Batch jobs from Spring XD are being refactored to Spring Boot applications a.k.a Spring Cloud Task applications

  • Unlike Spring XD, these “Tasks” don’t require explicit deployment; instead, a task is ready to be launched directly once the definition is declared

B.5. Shell/DSL Commands

Old Command New Command

module upload

app register / app import

module list

app list

module info

app info

admin config server

dataflow config server

job create

task create

job launch

task launch

job list

task list

job status

task status

job display

task display

job destroy

task destroy

job execution list

task execution list

runtime modules

runtime apps

B.6. REST-API

Old API New API

/modules

/apps

/runtime/modules

/runtime/apps

/runtime/modules/{moduleId}

/runtime/apps/{appId}

/jobs/definitions

/task/definitions

/jobs/deployments

/task/deployments

B.7. UI / Flo

The Admin-UI is now renamed as Dashboard. The URI for accessing the Dashboard is changed from localhost:9393/admin-ui to localhost:9393/dashboard

  • (New) Apps: Lists all the registered applications that are available for use. This view includes informational details such as the URI and the properties supported by each application. You can also register/unregister applications from this view

  • Runtime: Container changes to Runtime. The notion of xd-container is gone, replaced by out-of-the-box applications running as autonomous Spring Boot applications. The Runtime tab displays the applications running in the runtime platforms (implementations: cloud foundry, apache yarn, apache mesos, or kubernetes). You can click on each application to review relevant details about the application such as where it is running with, and what resources etc.

  • Spring Flo is now an OSS product. Flo for Spring Cloud Data Flow’s “Create Stream”, the designer-tab comes pre-built in the Dashboard

  • (New) Tasks:

    • The sub-tab “Modules” is renamed to “Apps”

    • The sub-tab “Definitions” lists all the Task definitions, including Spring Batch jobs that are orchestrated as Tasks

    • The sub-tab “Executions” lists all the Task execution details similar to Spring XD’s Job executions

B.8. Architecture Components

Spring Cloud Data Flow comes with a significantly simplified architecture. In fact, when compared with Spring XD, there are less peripherals that are necessary to operationalize Spring Cloud Data Flow.

B.8.1. ZooKeeper

ZooKeeper is not used in the new architecture.

B.8.2. RDBMS

Spring Cloud Data Flow uses an RDBMS instead of Redis for stream/task definitions, application registration, and for job repositories.The default configuration uses an embedded H2 instance, but Oracle, DB2, SqlServer, MySQL/MariaDB, PostgreSQL, H2, and HSQLDB databases are supported. To use Oracle, DB2 and SqlServer you will need to create your own Data Flow Server using Spring Initializr and add the appropriate JDBC driver dependency.

B.8.3. Redis

Running a Redis cluster is only required for analytics functionality. Specifically, when the counter-sink, field-value-counter-sink, or aggregate-counter-sink applications are used, it is expected to also have a running instance of Redis cluster.

B.8.4. Cluster Topology

Spring XD’s xd-admin and xd-container server components are replaced by stream and task applications themselves running as autonomous Spring Boot applications. The applications run natively on various platforms including Cloud Foundry, Apache YARN, Apache Mesos, or Kubernetes. You can develop, test, deploy, scale +/-, and interact with (Spring Boot) applications individually, and they can evolve in isolation.

B.9. Central Configuration

To support centralized and consistent management of an application’s configuration properties, Spring Cloud Config client libraries have been included into the Spring Cloud Data Flow server as well as the Spring Cloud Stream applications provided by the Spring Cloud Stream App Starters. You can also pass common application properties to all streams when the Data Flow Server starts.

B.10. Distribution

Spring Cloud Data Flow is a Spring Boot application. Depending on the platform of your choice, you can download the respective release uber-jar and deploy/push it to the runtime platform (cloud foundry, apache yarn, kubernetes, or apache mesos). For example, if you’re running Spring Cloud Data Flow on Cloud Foundry, you’d download the Cloud Foundry server implementation and do a cf push as explained in the reference guide.

B.11. Hadoop Distribution Compatibility

The hdfs-sink application builds upon Spring Hadoop 2.4.0 release, so this application is compatible with following Hadoop distributions.

  • Cloudera - cdh5

  • Pivotal Hadoop - phd30

  • Hortonworks Hadoop - hdp24

  • Hortonworks Hadoop - hdp23

  • Vanilla Hadoop - hadoop26

  • Vanilla Hadoop - 2.7.x (default)

B.12. YARN Deployment

Spring Cloud Data Flow can be deployed and used with Apche YARN in two different ways.

B.13. Use Case Comparison

Let’s review some use-cases to compare and contrast the differences between Spring XD and Spring Cloud Data Flow.

B.13.1. Use Case #1

(It is assumed both XD and SCDF distributions are already downloaded)

Description: Simple ticktock example using local/singlenode.

Spring XD Spring Cloud Data Flow

Start xd-singlenode server from CLI

→ xd-singlenode

Start a binder of your choice

Start local-server implementation of SCDF from the CLI

→ java -jar spring-cloud-dataflow-server-local-1.0.0.BUILD-SNAPSHOT.jar

Start xd-shell server from the CLI

→ xd-shell

Start dataflow-shell server from the CLI

→ java -jar spring-cloud-dataflow-shell-1.0.0.BUILD-SNAPSHOT.jar

Create ticktock stream

xd:>stream create ticktock --definition “time | log” --deploy

Create ticktock stream

dataflow:>stream create ticktock --definition “time | log” --deploy

Review ticktock results in the xd-singlenode server console

Review ticktock results by tailing the ticktock.log/stdout_log application logs

B.13.2. Use Case #2

(It is assumed both XD and SCDF distributions are already downloaded)

Description: Stream with custom module/application.

Spring XD Spring Cloud Data Flow

Start xd-singlenode server from CLI

→ xd-singlenode

Start a binder of your choice

Start local-server implementation of SCDF from the CLI

→ java -jar spring-cloud-dataflow-server-local-1.0.0.BUILD-SNAPSHOT.jar

Start xd-shell server from the CLI

→ xd-shell

Start dataflow-shell server from the CLI

→ java -jar spring-cloud-dataflow-shell-1.0.0.BUILD-SNAPSHOT.jar

Register custom “processor” module to transform payload to a desired format

xd:>module upload --name toupper --type processor --file <CUSTOM_JAR_FILE_LOCATION>

Register custom “processor” application to transform payload to a desired format

dataflow:>app register --name toupper --type processor --uri <MAVEN_URI_COORDINATES>

Create a stream with custom module

xd:>stream create testupper --definition “http | toupper | log” --deploy

Create a stream with custom application

dataflow:>stream create testupper --definition “http | toupper | log” --deploy

Review results in the xd-singlenode server console

Review results by tailing the testupper.log/stdout_log application logs

B.13.3. Use Case #3

(It is assumed both XD and SCDF distributions are already downloaded)

Description: Simple batch-job.

Spring XD Spring Cloud Data Flow

Start xd-singlenode server from CLI

→ xd-singlenode

Start local-server implementation of SCDF from the CLI

→ java -jar spring-cloud-dataflow-server-local-1.0.0.BUILD-SNAPSHOT.jar

Start xd-shell server from the CLI

→ xd-shell

Start dataflow-shell server from the CLI

→ java -jar spring-cloud-dataflow-shell-1.0.0.BUILD-SNAPSHOT.jar

Register custom “batch-job” module

xd:>module upload --name simple-batch --type job --file <CUSTOM_JAR_FILE_LOCATION>

Register custom “batch-job” as task application

dataflow:>app register --name simple-batch --type task --uri <MAVEN_URI_COORDINATES>

Create a job with custom batch-job module

xd:>job create batchtest --definition “simple-batch”

Create a task with custom batch-job application

dataflow:>task create batchtest --definition “simple-batch”

Deploy job

xd:>job deploy batchtest

NA

Launch job

xd:>job launch batchtest

Launch task

dataflow:>task launch batchtest

Review results in the xd-singlenode server console as well as Jobs tab in UI (executions sub-tab should include all step details)

Review results by tailing the batchtest/stdout_log application logs as well as Task tab in UI (executions sub-tab should include all step details)

Appendix C: Building

To build the source you will need to install JDK 1.8.

The build uses the Maven wrapper so you don’t have to install a specific version of Maven. To enable the tests for Redis you should run the server before bulding. See below for more information on how to run Redis.

The main build command is

$ ./mvnw clean install

You can also add '-DskipTests' if you like, 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.
Be aware that you might need to increase the amount of memory available to Maven by setting a MAVEN_OPTS environment variable with a value like -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.

The projects that require middleware generally include a docker-compose.yml, so consider using Docker Compose to run the middeware servers in Docker containers. See the README in the scripts demo repository for specific instructions about the common cases of mongo, rabbit and redis.

C.1. Documentation

There is a "full" profile that will generate documentation. You can build just the documentation by executing

$ ./mvnw clean package -DskipTests -P full -pl spring-cloud-dataflow-server-cloudfoundry-docs -am

C.2. Working with the code

If you don’t have an IDE preference we would recommend that you use Spring Tools Suite or Eclipse when working with the code. We use the m2eclipe eclipse plugin for maven support. Other IDEs and tools should also work without issue.

C.2.1. Importing into eclipse with m2eclipse

We recommend the m2eclipe eclipse plugin when working with eclipse. If you don’t already have m2eclipse installed it is available from the "eclipse marketplace".

Unfortunately m2e does not yet support Maven 3.3, so once the projects are imported into Eclipse you will 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. Open your Eclipse preferences, expand the Maven preferences, and select User Settings. In the User Settings field click Browse and navigate to the Spring Cloud project you imported selecting the .settings.xml file in that project. Click Apply and then OK to save the preference changes.

Alternatively you can copy the repository settings from .settings.xml 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 using the following command:

$ ./mvnw eclipse:eclipse

The generated eclipse projects can be imported 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 master. If you want to contribute even something trivial please do not hesitate, but follow the guidelines below.

D.1. Sign the Contributor License Agreement

Before we accept a non-trivial patch or pull request we will 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 will get an author credit if we do. Active contributors might be asked to join the core team, and given the ability to merge pull requests.

D.2. Code Conventions and Housekeeping

None of these is essential for a pull request, but they will all help. 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 using the eclipse-code-formatter.xml file from the Spring Cloud Build project. If using IntelliJ, you can use the Eclipse Code Formatter Plugin to import the same file.

  • Make sure all new .java files to have a simple Javadoc class comment with at least an @author tag identifying you, and preferably at least a paragraph on what the class is for.

  • Add the ASF license header comment to all new .java files (copy 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.

  • If no-one else is using your branch, please rebase it against the current master (or other target branch in the main project).

  • When writing a commit message please follow these conventions, if you are fixing an existing issue please add Fixes gh-XXXX at the end of the commit message (where XXXX is the issue number).


1. HATEOAS stands for Hypermedia as the Engine of Application State