1. About the documentation

The Spring Cloud Data Flow reference guide is available as html, pdf and epub documents. The latest copy is available at

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

2. Getting help

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

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

Spring Cloud Data Flow Overview

This section provides a brief overview of the Spring Cloud Data Flow reference documentation. Think of it as map for the rest of the document. You can read this reference guide in a linear fashion, or you can skip sections if something doesn’t interest you.

3. Introducing Spring Cloud Data Flow

Spring Cloud Data Flow is a cloud-native orchestration service for composable microservice applications on modern runtimes. With Spring Cloud Data Flow, developers can create and orchestrate data pipelines for common use cases such as data ingest, real-time analytics, and data import/export.

Spring Cloud Data Flow is the cloud native redesign of Spring XD – a project that aimed to simplify development of Big Data applications. The stream and batch modules from Spring XD are refactored as Spring Boot based stream and task/batch microservice applications respectively. These applications are now autonomous deployment units and they can "natively" run in modern runtimes such as Cloud Foundry, Apache YARN, Apache Mesos, and Kubernetes.

Spring Cloud Data Flow offers a collection of patterns and best practices for microservices-based distributed streaming and task/batch data pipelines.

3.1. Features

  • Develop using DSL, REST-APIs, Dashboard, and the drag-and-drop GUI - Flo

  • Create, unit-test, troubleshoot and manage microservice applications in isolation

  • Build data pipelines rapidly using the out-of-the-box stream and task/batch applications

  • Consume microservice applications as maven or docker artifacts

  • Scale data pipelines without interrupting data flows

  • Orchestrate data-centric applications on a variety of modern runtime platforms including Cloud Foundry, Apache YARN, Apache Mesos, and Kubernetes

  • Take advantage of metrics, health checks, and the remote management of each microservice application


4. 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

  • A stream DSL that describes the logical flow of data through multiple applications.

  • 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.

As an example, the 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.

5. 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.

5.1. 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.

6. 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.

6.1. 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,

public class LoggingSink {

    public void log(String 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.

6.2. 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.

7. Streams

7.1. 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.

7.2. 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.

7.3. 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 where the contents of the file are


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.

7.4. 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.

8. 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.

9. 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.

10. Data Flow Server

10.1. 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

10.2. 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.

10.3. 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.

11. Runtime

11.1. 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’.

11.2. 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.

11.3. 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.

11.4. 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.

Getting started

If you’re just getting started with Spring Cloud Data Flow, this is the section for you! Here we answer the basic “what?”, “how?” and “why?” questions. You’ll find a gentle introduction to Spring Cloud Data Flow along with installation instructions. We’ll then build our first Spring Cloud Data Flow application, discussing some core principles as we go.

12. System Requirements

You need Java installed (Java 8 or later), and to build, you need to have Maven installed as well.

You need to have an RDBMS for storing stream, task and app states in the database. The local Data Flow server by default uses embedded H2 database for this.

You also need to have Redis running if you are running any streams that involve analytics applications. Redis may also be required run the unit/integration tests.

For the deployed streams and tasks to communicate, either RabbitMQ or Kafka needs to be installed.

13. Deploying Spring Cloud Data Flow Local Server

  1. Download the Spring Cloud Data Flow Server and Shell apps:

  2. Launch the Data Flow Server

    1. Since the Data Flow Server is a Spring Boot application, you can run it just by using java -jar.

      $ java -jar spring-cloud-dataflow-server-local-1.2.0.M3.jar
  3. Launch the shell:

    $ java -jar spring-cloud-dataflow-shell-1.2.0.M3.jar

    If the Data Flow Server and shell are not running on the same host, point the shell to the Data Flow server URL:

    server-unknown:>dataflow config server
    Successfully targeted

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

    $ dataflow:>app import --uri
    Depending on your environment, you may need to configure the Data Flow Server to point to a custom Maven repository location or configure proxy settings. See Maven Configuration for more information.
  4. 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 --server.port=9000 | log" --deploy
    You will need to wait a little while until the apps are actually deployed successfully before posting data. Look in the log file of the Data Flow server for the location of the log files for the http and log applications. Tail the log file for each application to verify the application has started.

    Now post some data

dataflow:> http post --target http://localhost:9000 --data "hello world"

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

When deploying locally, each app (and each app instance, in case of count>1) gets a dynamically assigned server.port unless you explicitly assign one with --server.port=x. In both cases, this setting is propagated as a configuration property that will override any lower-level setting that you may have used (e.g. in application.yml files).

In case you encounter unexpected errors when executing shell commands, you can retrieve more detailed error information by setting the exception logging level to WARNING in logback.xml:

<logger name="" level="WARNING"/>

13.1. Maven Configuration

If you want to override specific maven configuration properties (remote repositories, proxies, etc.) and/or run the Data Flow Server behind a proxy, you need to specify those properties as command line arguments when starting the Data Flow Server. For example:

$ java -jar spring-cloud-dataflow-server-local-1.2.0.M3.jar --maven.localRepository=mylocal
--maven.proxy.port=9010 --maven.proxy.auth.username=proxyuser1

By default, the protocol is set to http. You can omit the auth properties if the proxy doesn’t need a username and password.

By default, the maven localRepository is set to ${user.home}/.m2/repository/, and will be the only remote repository. Like in the above example, the remote repositories can be specified along with their authentication (if needed). If the remote repositories are behind a proxy, then the proxy properties can be specified as above.

If you want to pass these properties as environment properties, then you need to use SPRING_APPLICATION_JSON to set these properties and pass SPRING_APPLICATION_JSON as environment variable as below:

$ SPRING_APPLICATION_JSON='{ "maven": { "local-repository": null,
"remote-repositories": { "repo1": { "url": "https://repo1", "auth": { "username": "repo1user", "password": "repo1pass" } }, "repo2": { "url": "https://repo2" } },
"proxy": { "host": "proxyhost", "port": 9018, "auth": { "username": "proxyuser", "password": "proxypass" } } } }' java -jar spring-cloud-dataflow-server-local-{project-version}.jar

14. Application Configuration

You can use the following configuration properties of the Data Flow server to customize how applications are deployed. # Directory in which all created processes will run and create log files. # Whether to delete created files and directories on JVM exit.,LANG,LANGUAGE,"LC_.*. # Array of regular expression patterns for environment variables that will be passed to launched applications. # Command to run java. # Max number of seconds to wait for app shutdown. # The Java options to pass to the JVM

When deploying the application you can also set deployer properties prefixed with deployer.<name of application>, So for example to set Java options for the time application in the ticktock stream, use the following stream deployment properties.

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

As a convenience you can set the property deployer.memory to set the Java option -Xmx. So for example,

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

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

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.

15. 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:




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.

16. Database Configuration

Spring Cloud Data Flow provides schemas for H2, HSQLDB, MySQL, Oracle, Postgresql, DB2 and SqlServer that will be automatically created when the server starts.

The JDBC drivers for MySQL (via MariaDB driver), HSQLDB, PostgreSQL along with embedded H2 are available out of the box. If you are using any other database, then the corresponding JDBC driver jar needs to be on the classpath of the server.

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

For instance, If you are using MySQL:

java -jar spring-cloud-dataflow-server-local/target/spring-cloud-dataflow-server-local-1.0.0.BUILD-SNAPSHOT.jar \
    --spring.datasource.url=jdbc:mysql:<db-info> \
    --spring.datasource.username=<user> \
    --spring.datasource.password=<password> \
    --spring.datasource.driver-class-name=org.mariadb.jdbc.Driver &

For PostgreSQL:

java -jar spring-cloud-dataflow-server-local/target/spring-cloud-dataflow-server-local-1.0.0.BUILD-SNAPSHOT.jar \
    --spring.datasource.url=jdbc:postgresql:<db-info> \
    --spring.datasource.username=<user> \
    --spring.datasource.password=<password> \
    --spring.datasource.driver-class-name=org.postgresql.Driver &


java -jar spring-cloud-dataflow-server-local/target/spring-cloud-dataflow-server-local-1.0.0.BUILD-SNAPSHOT.jar \
    --spring.datasource.url=jdbc:hsqldb:<db-info> \
    --spring.datasource.username=SA \
    --spring.datasource.driver-class-name=org.hsqldb.jdbc.JDBCDriver &
There is a schema update to the Spring Cloud Data Flow datastore when upgrading from version 1.0.x to 1.1.x. Migration scripts for specific database types can be found here.
If you wish to use an external H2 database instance instead of the one embedded with Spring Cloud Data Flow set the spring.dataflow.embedded.database.enabled property to false. If spring.dataflow.embedded.database.enabled is set to false or a database other than h2 is specified as the datasource the embedded database will not start.

17. 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 using either:

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

17.1. Enabling HTTPS

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

  port: 8443                                         (1)
    key-alias: yourKeyAlias                          (2)
    key-store: path/to/keystore                      (3)
    key-store-password: yourKeyStorePassword         (4)
    key-password: yourKeyPassword                    (5)
    trust-store: path/to/trust-store                 (6)
    trust-store-password: yourTrustStorePassword     (7)
1 As the default port is 9393, you may choose to change the port to a more common HTTPs-typical port.
2 The alias (or name) under which the key is stored in the keystore.
3 The path to the keystore file. Classpath resources may also be specified, by using the classpath prefix: classpath:path/to/keystore
4 The password of the keystore.
5 The password of the key.
6 The path to the truststore file. Classpath resources may also be specified, by using the classpath prefix: classpath:path/to/trust-store
7 The password of the trust store.
If HTTPS is enabled, it will completely replace HTTP as the protocol over which the REST endpoints and the Data Flow Dashboard interact. Plain HTTP requests will fail - therefore, make sure that you configure your Shell accordingly.

17.1.1. Using Self-Signed Certificates

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

$ keytool -genkey -alias dataflow -keyalg RSA -keystore dataflow.keystore \
          -validity 3650 -storetype JKS \
          -dname "CN=localhost, OU=Spring, O=Pivotal, L=Kailua-Kona, ST=HI, C=US"  (1)
          -keypass dataflow -storepass dataflow
1 CN is the only important parameter here. It should match the domain you are trying to access, e.g. localhost.

Then add the following to your application.yml file:

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

This is all that’s needed for the Data Flow Server. Once you start the server, you should be able to access it via https://localhost:8443/. As this is a self-signed certificate, you will hit a warning in your browser, that you need to ignore.

17.1.2. Self-Signed Certificates and the Shell

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

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

  2. Skip certificate validation

Add the self-signed certificate to the JVM truststore

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

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

Next, we need to create a truststore which the Shell will use:

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

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

$ java \ \ \
       -jar spring-cloud-dataflow-shell-1.2.0.M3.jar

In case you run into trouble establishing a connection via SSL, you can enable additional logging by using and setting the JVM argument to ssl.

Don’t forget to target the Data Flow Server with:

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

Skip Certificate Validation

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

Using this command-line parameter, the shell will accept any (self-signed) SSL certificate.

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

17.2. Basic Authentication

Basic Authentication can be enabled by adding the following to application.yml or via environment variables:

    enabled: true                                                     (1)
    realm: Spring Cloud Data Flow                                     (2)
1 Enables basic authentication. Must be set to true for security to be enabled.
2 (Optional) The realm for Basic authentication. Will default to Spring if not explicitly set.
Current versions of Chrome do not display the realm. Please see the following Chromium issue ticket for more information.

In this use-case, the underlying Spring Boot will auto-create a user called user with an auto-generated password which will be printed out to the console upon startup.

Default Spring Boot user credentials
Figure 4. Default Spring Boot user credentials
Please be aware of inherent issues of Basic Authentication and logging out, since the credentials are cached by the browser and simply browsing back to application pages will log you back in.

If you need to define more than one file-based user account, please take a look at File based authentication.

17.2.1. File based authentication

By default Spring Boot allows you to only specify one single user. Spring Cloud Data Flow also supports the listing of more than one user in a configuration file, as described below. Each user must be assigned a password and one or more roles:

    enabled: true
    realm: Spring Cloud Data Flow
            enabled: true                                                 (1)
            users:                                                        (2)
              bob: bobspassword, ROLE_MANAGE                              (3)
              alice: alicepwd, ROLE_VIEW, ROLE_CREATE
1 Enables file based authentication
2 This is a yaml map of username to password
3 Each map value is made of a corresponding password and role(s), comma separated
As of Spring Cloud Data Flow 1.1, roles are not supported, yet (specified roles are ignored). Due to an issue in Spring Security, though, at least one role must be provided.

17.2.2. LDAP Authentication

Spring Cloud Data Flow also supports authentication against an LDAP server (Lightweight Directory Access Protocol), providing support for the following 2 modes:

  • Direct bind

  • Search and bind

When the LDAP authentication option is activated, the default single user mode is turned off.

In direct bind mode, a pattern is defined for the user’s distinguished name (DN), using a placeholder for the username. The authentication process derives the distinguished name of the user by replacing the placeholder and use it to authenticate a user against the LDAP server, along with the supplied password. You can set up LDAP direct bind as follows:

    enabled: true
    realm: Spring Cloud Data Flow
            enabled: true                                                 (1)
            url: ldap://                             (2)
            userDnPattern: uid={0},ou=people,dc=example,dc=com            (3)
1 Enables LDAP authentication
2 The URL for the LDAP server
3 The distinguished name (DN) pattern for authenticating against the server

The search and bind mode involves connecting to an LDAP server, either anonymously or with a fixed account, and searching for the distinguished name of the authenticating user based on its username, and then using the resulting value and the supplied password for binding to the LDAP server. This option is configured as follows:

    enabled: true
    realm: Spring Cloud Data Flow
            enabled: true                                                 (1)
            url: ldap://localhost:10389                                   (2)
            managerDn: uid=admin,ou=system                                (3)
            managerPassword: secret                                       (4)
            userSearchBase: ou=otherpeople,dc=example,dc=com              (5)
            userSearchFilter: uid={0}                                     (6)
1 Enables LDAP integration
2 The URL of the LDAP server
3 A DN for to authenticate to the LDAP server, if anonymous searches are not supported (optional, required together with next option)
4 A password to authenticate to the LDAP server, if anonymous searches are not supported (optional, required together with previous option)
5 The base for searching the DN of the authenticating user (serves to restrict the scope of the search)
6 The search filter for the DN of the authenticating user
For more information, please also see the chapter LDAP Authentication of the Spring Security reference guide.
LDAP Transport Security

When connecting to an LDAP server, you typically (In the LDAP world) have 2 options in order to establish a connection to an LDAP server securely:

  • LDAP over SSL (LDAPs)

  • Start Transport Layer Security (Start TLS is defined in RFC2830)

As of Spring Cloud Data Flow 1.1.0 only LDAPs is supported out-of-the-box. When using official certificates no special configuration is necessary, in order to connect to an LDAP Server via LDAPs. Just change the url format to ldaps, e.g. ldaps://localhost:636.

In case of using self-signed certificates, the setup for your Spring Cloud Data Flow server becomes slightly more complex. The setup is very similar to Using Self-Signed Certificates (Please read first) and Spring Cloud Data Flow needs to reference a trustStore in order to work with your self-signed certificates.

While useful during development and testing, please never use self-signed certificates in production!

Ultimately you have to provide a set of system properties to reference the trustStore and its credentials when starting the server:

$ java \ \ \
       -jar spring-cloud-starter-dataflow-server-local-1.2.0.M3.jar

As mentioned above, another option to connect to an LDAP server securely is via Start TLS. In the LDAP world, LDAPs is technically even considered deprecated in favor of Start TLS. However, this option is currently not supported out-of-the-box by Spring Cloud Data Flow.

Please follow the following issue tracker ticket to track its implementation. You may also want to look at the Spring LDAP reference documentation chapter on Custom DirContext Authentication Processing for further details.

17.2.3. Customizing authorization

All of the above deals with authentication, i.e. how to assess the identity of the user. Irrespective of the option chosen, you can also customize authorization i.e. who can do what.

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

  • ROLE_VIEW for anything that relates to retrieving state

  • ROLE_CREATE for anything that involves creating, deleting or mutating the state of the system

  • ROLE_MANAGE for boot management endpoints.

All of those defaults are specified in dataflow-server-defaults.yml which is part of the Spring Cloud Data Flow Core Module. Nonetheless, you can override those, if desired, e.g. in application.yml. The configuration takes the form of a YAML list (as some rules may have precedence over others) and so you’ll need to copy/paste the whole list and tailor it to your needs (as there is no way to merge lists). Always refer to your version of application.yml, as the snippet reproduced below may be out-dated. The default rules are as such:

          enabled: true
            # Metrics

            - GET    /metrics/**                     => hasRole('ROLE_VIEW')
            - DELETE /metrics/**                     => hasRole('ROLE_CREATE')

            # Boot Endpoints

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

            # Apps

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

            # Completions

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

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

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

            # Batch Job Instances

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

            # Running Applications

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

            # Stream Definitions

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

            # Stream Deployments

            - DELETE /streams/deployments/*          => hasRole('ROLE_CREATE')
            - DELETE /streams/deployments            => hasRole('ROLE_CREATE')
            - POST   /streams/deployments/*          => hasRole('ROLE_CREATE')

            # Task Definitions

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

            # Task Executions

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

The format of each line is the following:



Be mindful that the above is indeed a YAML list, not a map (thus the use of '-' dashes at the start of each line) that lives under the key.

In case you are solely interested in authentication but not authorization, for instance every user shall have have access to all endpoints, then you can also set

17.2.4. Authorization - Shell and Dashboard Behavior

When authorization is enabled, the Dashboard and the Shell will be role-aware, meaning that depending on the assigned role(s), not all functionality may be visible.

For instance, Shell commands, for which the user does not have the necessary roles for, will be marked as unavailable.

Currently, the Shell’s help command will list commands that are unavailable. Please track the following issue:

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

17.2.5. Authorization with Ldap

When configuring Ldap for authentication, you can also specify the group-role-attribute in conjunction with group-search-base and group-search-filter.

The group role attribure contains the name of the role. If not specified, the ROLE_MANAGE role is populated by default.

For further information, please refer to Configuring an LDAP Server of the Spring Security reference guide.

17.3. OAuth 2.0

OAuth 2.0 allows you to integrate Spring Cloud Data Flow into Single Sign On (SSO) environments. The following 2 OAuth2 Grant Types will be used:

  • Authorization Code - Used for the GUI (Browser) integration. You will be redirected to your OAuth Service for authentication

  • Password - Used by the shell (And the REST integration), so you can login using username and password

The REST endpoints are secured via Basic Authentication but will use the Password Grand Type under the covers to authenticate with your OAuth2 service.

When authentication is set up, it is strongly recommended to enable HTTPS as well, especially in production environments.

You can turn on OAuth2 authentication by adding the following to application.yml or via environment variables:

    enabled: true                                                     (1)
    realm: Spring Cloud Data Flow                                     (2)
  oauth2:                                                             (3)
      client-id: myclient
      client-secret: mysecret
1 Must be set to true for security to be enabled.
2 The realm for Basic authentication
3 OAuth Configuration Section, if you leave off the OAuth2 section, Basic Authentication will be enabled instead.
As of the current version, Spring Cloud Data Flow does not provide finer-grained authorization when OAUTH is used as authentication mechanism. Thus, once you are logged in, you have full access to all functionality.

You can verify that basic authentication is working properly using curl:

$ curl -u myusername:mypassword http://localhost:9393/

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

17.3.1. Authentication using the Spring Cloud Data Flow Shell

If your OAuth2 provider supports the Password Grant Type you can start the Data Flow Shell with:

$ java -jar spring-cloud-dataflow-shell-1.2.0.M3.jar \
  --dataflow.uri=http://localhost:9393 \
  --dataflow.username=my_username --dataflow.password=my_password
Keep in mind that when authentication for Spring Cloud Data Flow is enabled, the underlying OAuth2 provider must support the Password OAuth2 Grant Type, if you want to use the Shell.

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

dataflow config server --uri http://localhost:9393 --username my_username --password my_password

Once successfully targeted, you should see the following output:

dataflow:>dataflow config info
dataflow config info

║Credentials│[username='my_username, password=****']║
║Result     │                                       ║
║Target     │http://localhost:9393                  ║

17.3.2. OAuth2 Authentication Examples

Local OAuth2 Server

With Spring Security OAuth you can easily create your own OAuth2 Server with the following 2 simple annotations:

  • @EnableResourceServer

  • @EnableAuthorizationServer

A working example application can be found at:

Simply clone the project, built and start it. Furthermore configure Spring Cloud Data Flow with the respective Client Id and Client Secret.

Authentication using GitHub

If you rather like to use an existing OAuth2 provider, here is an example for GitHub. First you need to Register a new application under your GitHub account at:

When running a default version of Spring Cloud Data Flow locally, your GitHub configuration should look like the following:

Register an OAuth Application for GitHub
Figure 5. Register an OAuth Application for GitHub
For the Authorization callback URL you will enter Spring Cloud Data Flow’s Login URL, e.g. localhost:9393/login.

Configure Spring Cloud Data Flow with the GitHub relevant Client Id and Secret:

    enabled: true
      client-id: your-github-client-id
      client-secret: your-github-client-secret
GitHub does not support the OAuth2 password grant type. As such you cannot use the Spring Cloud Data Flow Shell in conjunction with GitHub.

17.4. Securing the Spring Boot Management Endpoints

When enabling security, please also make sure that the Spring Boot HTTP Management Endpoints are secured as well. You can enable security for the management endpoints by adding the following to application.yml:

  contextPath: /management
    enabled: true
If you don’t explicitly enable security for the management endpoints, you may end up having unsecured REST endpoints, despite security.basic.enabled being set to true.

18. Monitoring and Management

The Spring Cloud Data Flow server is a Spring Boot application that includes the Actuator library, which adds several production ready features to help you monitor and manage your application.

The Actuator library adds http endpoints under the context path /management that is also a discovery page for available endpoints. For example, there is a health endpoint that shows application health information and an env that lists properties from Spring’s ConfigurableEnvironment. By default only the health and application info endpoints are accessible. The other endpoints are considered to be sensitive and need to be enabled explicitly via configuration. If you are enabling sensitive endpoints then you should also secure the Data Flow server’s endpoints so that information is not inadvertently exposed to unauthenticated users. The local Data Flow server has security disabled by default, so all actuator endpoints are available.

The Data Flow server requires a relational database and if the feature toggled for analytics is enabled, a Redis server is also required. The Data Flow server will autoconfigure the DataSourceHealthIndicator and RedisHealthIndicator if needed. The health of these two services is incorporated to the overall health status of the server through the health endpoint.

18.1. Spring Boot Admin

A nice way to visualize and interact with actuator endpoints is to incorporate the Spring Boot Admin client library into the Spring Cloud Data Flow server. You can create the Spring Boot Admin application by following a few simple steps.

An easy way to include the client library into the Data Flow server is to create a new Data Flow Server project from Type 'flow' in the "Search for dependencies" text box and select the server runtime you want to customize. A simple way to have the Spring Cloud Data Flow server be a client to the Spring Boot Admin Server is by adding a dependency to the Data Flow server’s pom and an additional configuration property as documented in Registering Client Applications.

This will result in a UI with tabs for each of the actuator endpoints.

Spring Boot Admin UI
Figure 6. Spring Boot Admin UI

Additional configuration is required to interact with JMX beans and logging levels. Refer to the Spring Boot admin documentation for more information. As only the info and health endpoints are available to unauthenticated users, you should enable security on the Data Flow Server and also configure Spring Boot Admin server’s security so that it can securely access the actuator endpoints.

18.2. Monitoring Deployed Applications

The applications that are deployed by Spring Cloud Data Flow are based on Spring Boot which contains several features for monitoring your application in production. Each deployed application contains several web endpoints for monitoring and interacting with Stream and Task applications.

In particular, the metrics endpoint contains counters and gauges for HTTP requests, System Metrics (such as JVM stats), DataSource Metrics and Message Channel Metrics (such as message rates). In turn, these metrics can be exported periodically to various application monitoring tools via MetricWriter implementations. You can control how often and which Spring Boot metrics are exported through the use of include and exclude name filters.

The project Spring Cloud Data Flow Metrics provides the foundation for exporting Spring Boot metrics. The main project provides Spring Boots AutoConfiguration to setup the exporting process and common functionality such as defining a metric name prefix appropriate for your environement. For example, you may want to include the region where the application is running in addition to the application’s name and stream/task to which it belongs. The main project also includes a LogMetricWriter so that metrics can be stored into the log file. While very simple in approach, log files are often ingested into application monitoring tools (such as Splunk) where they can be further processed to create dashboards of an application’s performance.

The project Spring Cloud Data Flow Metrics Datadog Metrics provides integration to export Spring Boot metrics to Datadog.

To make use of this functionality, you will need to add additional dependencies into your Stream and Task applications. To customize the "out of the box" Task and Stream applications you can use the Data Flow Initializr to generate a project and then add to the generated Maven pom file the MetricWriter implementation you want to use. The documentation on the Data Flow Metrics project pages provides the additional information you need to get started.


In this section you will learn all about Streams and how to use them with Spring Cloud Data Flow.

19. Introduction

In Spring Cloud Data Flow, a basic stream defines the ingestion of event data from a source to a sink that passes through any number of processors. Streams are composed of Spring Cloud Stream applications and the deployment of stream definitions is done via the Data Flow Server (REST API). The Getting Started section shows you how to start the server and how to start and use the Spring Cloud Data Flow shell.

A high level DSL is used to create stream definitions. The DSL to define a stream that has an http source and a file sink (with no processors) is shown below

http | file

The DSL mimics UNIX pipes and filters syntax. Default values for ports and filenames are used in this example but can be overridden using -- options, such as

http --server.port=8091 | file --directory=/tmp/httpdata/

To create these stream definitions you use the shell or make an HTTP POST request to the Spring Cloud Data Flow Server. For more information on making HTTP request directly to the server, consult the REST API Guide.

20. Stream DSL

In the example above, we connected a source to a sink using the pipe symbol |. You can also pass properties to the source and sink configurations. The property names will depend on the individual app implementations, but as an example, the http source app exposes a server.port setting and it allows you to change the data ingestion port from the default value. To create the stream using port 8000, we would use

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

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

21. 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

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


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://
dataflow:>app register --name log --type sink --uri maven://

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:]:


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>/

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

RabbitMQ + Docker


Kafka 0.9 + Maven

Kafka 0.9 + Docker


Kafka 0.10 + Maven

Kafka 0.10 + Docker


List of available Task Application Starters:

Artifact Type Stable Release SNAPSHOT Release




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

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 stream 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 stream 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.

21.1. 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 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 file

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

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


21.2. 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-1.2.0.BUILD-SNAPSHOT-metadata.jar
373848 META-INF/spring-configuration-metadata.json
   174 META-INF/

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:

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.

21.2.1. 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://

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).


22. 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.

  <!-- other 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 [spring-cloud-dataflow-register-appss].

23. 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

23.1. 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.

23.1.1. 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.               │                              │                              ║
║           │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             ║
║                      │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.

23.2. 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.

23.2.1. 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



What the application "sees"

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


What the deployer "sees"

Nothing The 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.

23.2.2. 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: 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.

23.2.3. 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

where contains:


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:

    count: 2
      partitionKeyExpression: payload

23.2.4. 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"

23.2.5. 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<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 ",,,"
Overriding the destination names is not recommended as Spring Cloud Data Flow takes care of setting this internally.

23.2.6. 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]<channelName>.consumer. and the producer properties can be set for the outbound channel name with the prefix app.[app/label name]<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 ",,,"

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

For instance

dataflow:>stream deploy ticktock --properties ","

23.2.7. 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.

23.2.8. 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.

23.2.9. 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"

24. 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. 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. 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.

27. 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. 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

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.

29. 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:


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.

30. 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

31. Explicit Broker Destinations in a Stream

One can connect to a specific destination name located in the broker (Rabbit, Kafka etc.,) either at the source or at the sink position.

The following stream has the destination name at the source position:

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.

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

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

This stream sends the messages from the http app to the destination myDestination located at the broker.

From the above streams, notice that the http and log apps are interacting with each other via the broker (through the destination myDestination) rather than having a pipe directly between http and log within a single stream.

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

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.

32. Directed Graphs in a Stream

If directed graphs are needed instead of the simple linear streams described above, two features are relevant.

First, named destinations may be used as a way to combine the output from multiple streams or for multiple consumers to share the output from a single stream. This can be done using the DSL syntax http > :mydestination or :mydestination > log.

Second, you may need to determine the output channel of a stream based on some information that is only known at runtime. In that case, a router may be used in the sink position of a stream definition. For more information, refer to the Router Sink starter’s README.

32.1. 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 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 configuration server with the following options:

This will cause the properties and 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. will override the common property).

33. 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 ",,,"

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


This section goes into more detail about how you can work with Spring Cloud Tasks. 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.

34. Introducing Spring Cloud Task

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.

35. 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. Register a Task App

  2. Create a Task Definition

  3. Launch a Task

  4. Task Execution

  5. Destroy a Task Definition

35.1. Creating a custom 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:

public class MyTask {

    public static void main(String[] args) {, 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.

35.2. 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

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


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:

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

app import --uri file:///tmp/

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



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

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.

35.3. Creating a Task

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.

35.4. 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"

35.5. 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.

35.6. Destroying a Task

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.

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

36. Task Repository

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

36.1. Configuring the Task Execution Repository

To add a driver for the database that will store the Task Execution information, a dependency for the driver will need to be added to a maven pom file and the Spring Cloud Data Flow will need to be rebuilt. Since Spring Cloud Data Flow is comprised of an SPI for each environment it supports, please review the SPI’s documentation on which POM should be updated to add the dependency and how to build. This document will cover how to setup the dependency for local SPI.

36.1.1. Local

  1. Open the spring-cloud-dataflow-server-local/pom.xml in your IDE.

  2. In the dependencies section add the dependency for the database driver required. In the sample below postgresql has been chosen.

  1. Save the changed pom.xml

  2. Build the application as described here: Building Spring Cloud Data Flow

36.1.2. Task Application Repository

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.

36.2. Datasource

To configure the datasource Add the following properties to the dataflow-server.yml or via environment variables:

  1. spring.datasource.url

  2. spring.datasource.username

  3. spring.datasource.password

  4. spring.datasource.driver-class-name

For example adding postgres would look something like this:

  • Environment variables:

export spring_datasource_url=jdbc:postgresql://localhost:5432/mydb
export spring_datasource_username=myuser
export spring_datasource_password=mypass
export spring_datasource_driver-class-name="org.postgresql.Driver"
  • dataflow-server.yml

    url: jdbc:postgresql://localhost:5432/mydb
    username: myuser
    password: mypass

37. 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 ""
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



Task events


Job Execution events


Step Execution events


Item Read events


Item Process events


Item Write events


Skip events


38. Launching Tasks from a Stream

You can launch a task from a stream by using one of the available task-launcher sinks. Currently the only available task-launcher sink is the task-launcher-local, which will launch a task on your local machine.

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 properties and command line arguments 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:

app register --name task-launcher-local --type sink --uri maven://

38.1. 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. An example of this would be to launch the timestamp task once every 5 seconds, the stream to implement this would look like:

stream create foo --definition "triggertask --triggertask.uri=maven:// --trigger.fixed-delay=5 | task-launcher-local" --deploy

38.2. Translator

Another option to start a task using the task-launcher would be to create a stream using a your own translator (as a processor) to translate a message payload to a TaskLaunchRequest. For example:

http --server.port=9000 | my-task-processor | task-launcher-local


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:


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.

Note: The default Dashboard server port is 9393

The Spring Cloud Data Flow Dashboard
Figure 7. 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 8. 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:



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 9. 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 10. 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 11. 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 12. 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 13. 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.

Note: You will also use this tab to create Batch Jobs.

List of Task Apps
Figure 14. 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.

Note: 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 15. 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 16. 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.

44.2.2. 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 17. 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 18. 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 19. 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 20. 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 have created the springtweets stream and the corresponding counter in the Counter chapter, 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.

‘How-to’ guides

This section provides answers to some common ‘how do I do that…​’ type of questions that often arise when using Spring Cloud Data Flow.

If you are having a specific problem that we don’t cover here, you might want to check out to see if someone has already provided an answer; this is also a great place to ask new questions (please use the spring-cloud-dataflow tag).

We’re also more than happy to extend this section; If you want to add a ‘how-to’ you can send us a pull request.

47. Configure Maven Properties

You can set the maven properties such as local maven repository location, remote maven repositories and their authentication credentials including the proxy server properties via commandline properties when starting the Dataflow server or using the SPRING_APPLICATION_JSON environment property for the Dataflow server.

The remote maven repositories need to be configured explicitly if the apps are resolved using maven repository except for local Data Flow server. The other Data Flow server implementations (that use maven resources for app artifacts resolution) have no default value for remote repositories. The local server has as the default remote repository.

To pass the properties as commandline options:

$ java -jar <dataflow-server>.jar --maven.localRepository=mylocal
--maven.proxy.port=9018 --maven.proxy.auth.username=proxyuser

or, using the SPRING_APPLICATION_JSON environment property:

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

Formatted JSON:

  "maven": {
    "local-repository": "local",
    "remote-repositories": {
      "repo1": {
        "url": "https://repo1",
        "auth": {
          "username": "repo1user",
          "password": "repo1pass"
      "repo2": {
        "url": "https://repo2"
    "proxy": {
      "host": "proxyhost",
      "port": 9018,
      "auth": {
        "username": "proxyuser",
        "password": "proxypass"
Depending on Spring Cloud Data Flow server implementation, you may have to pass the environment properties using the platform specific environment-setting capabilities. For instance, in Cloud Foundry, you’d be passing them as cf set-env SPRING_APPLICATION_JSON.

48. Logging

Spring Cloud Data Flow is built upon several Spring projects, but ultimately the dataflow-server is a Spring Boot app, so the logging techniques that apply to any Spring Boot application are applicable here as well.

While troubleshooting, following are the two primary areas where enabling the DEBUG logs could be useful.

48.1. Deployment Logs

Spring Cloud Data Flow builds upon Spring Cloud Deployer SPI and the platform specific dataflow-server uses the respective SPI implementations. Specifically, if we were to troubleshoot deployment specific issues; such as the network errors, it’d be useful to enable the DEBUG logs at the underlying deployer and the libraries used by it.

  1. For instance, if you’d like to enable DEBUG logs for the local-deployer, you’d be starting the server with following.

    $ java -jar <dataflow-server>.jar

    (where, is the global package for everything local-deployer related)

  2. For instance, if you’d like to enable DEBUG logs for the cloudfoundry-deployer, you’d be setting the following environment variable and upon restaging the dataflow-server, we will see more logs around request, response and the elaborate stack traces (upon failures). The cloudfoundry-deployer uses cf-java-client, so we will have to enable DEBUG logs for this library.

    $ cf set-env dataflow-server JAVA_OPTS '-Dlogging.level.cloudfoundry-client=DEBUG'
    $ cf restage dataflow-server

    (where, cloudfoundry-client is the global package for everything cf-java-client related)

  3. If there’s a need to review Reactor logs, which is used by the cf-java-client, then the following would be helpful.

    $ cf set-env dataflow-server JAVA_OPTS '-Dlogging.level.cloudfoundry-client=DEBUG -Dlogging.level.reactor.ipc.netty=DEBUG'
    $ cf restage dataflow-server

    (where, reactor.ipc.netty is the global package for everything reactor-netty related)

Similar to the local-deployer and cloudfoundry-deployer options as discussed above, there are equivalent settings available for Apache YARN, Apache Mesos and Kubernetes variants, too. Check out the respective SPI implementations to find out more details about the packages to configure for logging.

48.2. Application Logs

The streaming applications in Spring Cloud Data Flow are Spring Boot applications and they can be independently setup with logging configurations.

For instance, if you’d have to troubleshoot the header and payload specifics that are being passed around source, processor and sink channels, you’d be deploying the stream with the following options.

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

(where, org.springframework.integration is the global package for everything Spring Integration related, which is responsible for messaging channels)

These properties can also be specified via deployment properties when deploying the stream.

dataflow:>stream deploy foo --properties "app.*"


In this section you will learn all about the Spring Cloud Data Flow REST API.

49. Overview

Spring Cloud Data Flow provides a REST API allowing you to access all aspects of the server. In fact the Spring Cloud Data Flow Shell is a first-class consumer of that API.

If you plan on using the REST API using Java, please also consider using the provided Java client (DataflowTemplate) that uses the REST API internally.

49.1. HTTP verbs

Spring Cloud Data Flow tries to adhere as closely as possible to standard HTTP and REST conventions in its use of HTTP verbs.

Verb Usage


Used to retrieve a resource


Used to create a new resource


Used to update an existing resource, including partial updates. Also used for resources that imply the concept of restarts such as Tasks.


Used to delete an existing resource

49.2. HTTP status codes

RESTful note tries to adhere as closely as possible to standard HTTP and REST conventions in its use of HTTP status codes.

Status code Usage

200 OK

The request completed successfully

201 Created

A new resource has been created successfully. The resource’s URI is available from the response’s Location header

204 No Content

An update to an existing resource has been applied successfully

400 Bad Request

The request was malformed. The response body will include an error providing further information

404 Not Found

The requested resource did not exist

409 Conflict

The requested resource already exists, e.g. the task already exists or the stream was already being deployed

422 Unprocessable Entity

Returned in cases the Job Execution cannot be stopped or restarted

49.3. Headers

Every response has the following header(s):

Name Description


The Content-Type of the payload, e.g. application/hal+json

49.4. Errors

Path Type Description



The HTTP error that occurred, e.g. Bad Request



A description of the cause of the error



The path to which the request was made



The HTTP status code, e.g. 400



The time, in milliseconds, at which the error occurred

49.5. Hypermedia

Spring Cloud Data Flow uses hypermedia and resources include links to other resources in their responses. Responses are in Hypertext Application from resource to resource Language (HAL) format. Links can be found beneath the _links key. Users of the API should not create URIs themselves, instead they should use the above-described links to navigate.

50. Resources

50.1. Index

The index provides the entry point into Spring Cloud Data Flow’s REST API.

50.1.1. Accessing the index

A GET request is used to access the index

Request structure
GET / HTTP/1.1
Host: localhost:8080
Example request
$ curl 'http://localhost:8080/' -i
Response structure
Path Type Description



Links to other resources



Incremented each time a change is implemented in this REST API

Example response
HTTP/1.1 200 OK
Content-Type: application/hal+json;charset=UTF-8
Content-Length: 3880

  "_links" : {
    "dashboard" : {
      "href" : "http://localhost:8080/dashboard"
    "streams/definitions" : {
      "href" : "http://localhost:8080/streams/definitions"
    "streams/definitions/definition" : {
      "href" : "http://localhost:8080/streams/definitions/{name}",
      "templated" : true
    "streams/deployments" : {
      "href" : "http://localhost:8080/streams/deployments"
    "streams/deployments/deployment" : {
      "href" : "http://localhost:8080/streams/deployments/{name}",
      "templated" : true
    "runtime/apps" : {
      "href" : "http://localhost:8080/runtime/apps"
    "runtime/apps/app" : {
      "href" : "http://localhost:8080/runtime/apps/{appId}",
      "templated" : true
    "runtime/apps/instances" : {
      "href" : "http://localhost:8080/runtime/apps/interface%20org.springframework.web.util.UriComponents%24UriTemplateVariables/instances"
    "tasks/definitions" : {
      "href" : "http://localhost:8080/tasks/definitions"
    "tasks/definitions/definition" : {
      "href" : "http://localhost:8080/tasks/definitions/{name}",
      "templated" : true
    "tasks/composed-definitions/compose" : {
      "href" : "http://localhost:8080/tasks/composed-definitions"
    "tasks/executions" : {
      "href" : "http://localhost:8080/tasks/executions"
    "tasks/executions/name" : {
      "href" : "http://localhost:8080/tasks/executions{?name}",
      "templated" : true
    "tasks/executions/execution" : {
      "href" : "http://localhost:8080/tasks/executions/{id}",
      "templated" : true
    "jobs/executions" : {
      "href" : "http://localhost:8080/jobs/executions"
    "jobs/executions/name" : {
      "href" : "http://localhost:8080/jobs/executions{?name}",
      "templated" : true
    "jobs/executions/execution" : {
      "href" : "http://localhost:8080/jobs/executions/{id}",
      "templated" : true
    "jobs/executions/execution/steps" : {
      "href" : "http://localhost:8080/jobs/executions/{jobExecutionId}/steps",
      "templated" : true
    "jobs/executions/execution/steps/step" : {
      "href" : "http://localhost:8080/jobs/executions/{jobExecutionId}/steps/{stepId}",
      "templated" : true
    "jobs/executions/execution/steps/step/progress" : {
      "href" : "http://localhost:8080/jobs/executions/{jobExecutionId}/steps/{stepId}/progress",
      "templated" : true
    "jobs/instances/name" : {
      "href" : "http://localhost:8080/jobs/instances{?name}",
      "templated" : true
    "jobs/instances/instance" : {
      "href" : "http://localhost:8080/jobs/instances/{id}",
      "templated" : true
    "counters" : {
      "href" : "http://localhost:8080/metrics/counters"
    "counters/counter" : {
      "href" : "http://localhost:8080/metrics/counters/{name}",
      "templated" : true
    "field-value-counters" : {
      "href" : "http://localhost:8080/metrics/field-value-counters"
    "field-value-counters/counter" : {
      "href" : "http://localhost:8080/metrics/field-value-counters/{name}",
      "templated" : true
    "aggregate-counters" : {
      "href" : "http://localhost:8080/metrics/aggregate-counters"
    "aggregate-counters/counter" : {
      "href" : "http://localhost:8080/metrics/aggregate-counters/{name}",
      "templated" : true
    "apps" : {
      "href" : "http://localhost:8080/apps"
    "about" : {
      "href" : "http://localhost:8080/about"
    "completions/stream" : {
      "href" : "http://localhost:8080/completions/stream{?start,detailLevel}",
      "templated" : true
    "completions/task" : {
      "href" : "http://localhost:8080/completions/task{?start,detailLevel}",
      "templated" : true
  "api.revision" : 12
Example "stream create" request for a ticktock stream
curl -X POST -d "name=ticktock&definition=time | log" localhost:9393/streams/definitions?deploy=false
Example "stream deploy" request for a ticktock stream
curl -X POST http://localhost:9393/streams/deployments/ticktock
HTTP/1.1 200 OK
Content-Type: application/hal+json;charset=UTF-8
Content-Length: 3880

  "_links" : {
    "dashboard" : {
      "href" : "http://localhost:8080/dashboard"
    "streams/definitions" : {
      "href" : "http://localhost:8080/streams/definitions"
    "streams/definitions/definition" : {
      "href" : "http://localhost:8080/streams/definitions/{name}",
      "templated" : true
    "streams/deployments" : {
      "href" : "http://localhost:8080/streams/deployments"
    "streams/deployments/deployment" : {
      "href" : "http://localhost:8080/streams/deployments/{name}",
      "templated" : true
    "runtime/apps" : {
      "href" : "http://localhost:8080/runtime/apps"
    "runtime/apps/app" : {
      "href" : "http://localhost:8080/runtime/apps/{appId}",
      "templated" : true
    "runtime/apps/instances" : {
      "href" : "http://localhost:8080/runtime/apps/interface%20org.springframework.web.util.UriComponents%24UriTemplateVariables/instances"
    "tasks/definitions" : {
      "href" : "http://localhost:8080/tasks/definitions"
    "tasks/definitions/definition" : {
      "href" : "http://localhost:8080/tasks/definitions/{name}",
      "templated" : true
    "tasks/composed-definitions/compose" : {
      "href" : "http://localhost:8080/tasks/composed-definitions"
    "tasks/executions" : {
      "href" : "http://localhost:8080/tasks/executions"
    "tasks/executions/name" : {
      "href" : "http://localhost:8080/tasks/executions{?name}",
      "templated" : true
    "tasks/executions/execution" : {
      "href" : "http://localhost:8080/tasks/executions/{id}",
      "templated" : true
    "jobs/executions" : {
      "href" : "http://localhost:8080/jobs/executions"
    "jobs/executions/name" : {
      "href" : "http://localhost:8080/jobs/executions{?name}",
      "templated" : true
    "jobs/executions/execution" : {
      "href" : "http://localhost:8080/jobs/executions/{id}",
      "templated" : true
    "jobs/executions/execution/steps" : {
      "href" : "http://localhost:8080/jobs/executions/{jobExecutionId}/steps",
      "templated" : true
    "jobs/executions/execution/steps/step" : {
      "href" : "http://localhost:8080/jobs/executions/{jobExecutionId}/steps/{stepId}",
      "templated" : true
    "jobs/executions/execution/steps/step/progress" : {
      "href" : "http://localhost:8080/jobs/executions/{jobExecutionId}/steps/{stepId}/progress",
      "templated" : true
    "jobs/instances/name" : {
      "href" : "http://localhost:8080/jobs/instances{?name}",
      "templated" : true
    "jobs/instances/instance" : {
      "href" : "http://localhost:8080/jobs/instances/{id}",
      "templated" : true
    "counters" : {
      "href" : "http://localhost:8080/metrics/counters"
    "counters/counter" : {
      "href" : "http://localhost:8080/metrics/counters/{name}",
      "templated" : true
    "field-value-counters" : {
      "href" : "http://localhost:8080/metrics/field-value-counters"
    "field-value-counters/counter" : {
      "href" : "http://localhost:8080/metrics/field-value-counters/{name}",
      "templated" : true
    "aggregate-counters" : {
      "href" : "http://localhost:8080/metrics/aggregate-counters"
    "aggregate-counters/counter" : {
      "href" : "http://localhost:8080/metrics/aggregate-counters/{name}",
      "templated" : true
    "apps" : {
      "href" : "http://localhost:8080/apps"
    "about" : {
      "href" : "http://localhost:8080/about"
    "completions/stream" : {
      "href" : "http://localhost:8080/completions/stream{?start,detailLevel}",
      "templated" : true
    "completions/task" : {
      "href" : "http://localhost:8080/completions/task{?start,detailLevel}",
      "templated" : true
  "api.revision" : 12

The main element of the index are the links as they allow you to traverse the API and execute the desired functionality:

Relation Description


Access meta information, including enabled features, security info, version information


Access the dashboard UI


Handle registered applications


Exposes the DSL completion features for Stream


Exposes the DSL completion features for Task


Provides the JobExecution resource


Provides details for a specific JobExecution


Provides the steps for a JobExecution


Returns the details for a specific step


Provides progress information for a specific step


Retrieve Job Executions by Job name


Provides the job instance resource for a specific job instance


Provides the Job instance resource for a specific job name


Provides the runtime application resource


Exposes the runtime status for a specific app


Provides the status for app instances


Provides the task definition resource


Provides details for a specific task definition


Requests the creation of a composed task definition


Returns Task executions and allows lanching of tasks


Returns all task executions for a given Task name


Provides details for a specific task execution


Exposes the Streams resource


Handle a specific Stream definition


Provides Stream deployment operations


Request (un-)deployment of an existing stream definition


Exposes the resource for dealing with Counters


Handle a specific counter


Provides the resource for dealing with aggregate counters


Handle a specific aggregate counter


Provides the resource for dealing with field-value-counters


Handle a specific field-value-counter

50.2. Server Meta Information

A GET request will return meta information for Spring Cloud Data Flow. This includes:

  • Runtime Environment Information

  • Information regarding which features are enabled

  • Dependency information of Spring Cloud Data Flow Server

  • Security information

50.2.1. Request structure

GET /about HTTP/1.1
Accept: application/json
Host: localhost:8080

50.2.2. Request parameters

Unresolved directive in api-guide.adoc - include::/opt/bamboo-home/xml-data/build-dir/SCD-BMASTER-JOB1/spring-cloud-dataflow-docs/target/generated-snippets/about-documentation/get-meta-information/request-parameters.adoc[]

50.2.3. Example request

$ curl 'http://localhost:8080/about' -i -H 'Accept: application/json'

50.2.4. Response structure

HTTP/1.1 200 OK
Content-Type: application/json;charset=UTF-8
Content-Length: 1884

  "featureInfo" : {
    "analyticsEnabled" : true,
    "streamsEnabled" : true,
    "tasksEnabled" : true
  "versionInfo" : {
    "implementation" : {
      "name" : "spring-cloud-starter-dataflow-server-local",
      "version" : "1.2.0.M3"
    "core" : {
      "name" : "Spring Cloud Data Flow Core",
      "version" : "1.2.0.M3"
    "dashboard" : {
      "name" : "Spring Cloud Dataflow UI",
      "version" : "1.2.0.M2"
    "commitId" : null,
    "shortCommitId" : null,
    "commitTime" : null,
    "branch" : null
  "securityInfo" : {
    "authenticationEnabled" : false,
    "authorizationEnabled" : true,
    "formLogin" : false,
    "authenticated" : false,
    "username" : null,
    "roles" : [ ]
  "runtimeEnvironment" : {
    "appDeployer" : {
      "deployerImplementationVersion" : "1.2.0.M3",
      "deployerName" : "LocalAppDeployer",
      "deployerSpiVersion" : "1.2.0.M3",
      "javaVersion" : "1.8.0_121",
      "platformApiVersion" : "Linux 4.4.0-66-generic",
      "platformClientVersion" : "4.4.0-66-generic",
      "platformHostVersion" : "4.4.0-66-generic",
      "platformSpecificInfo" : { },
      "platformType" : "Local",
      "springBootVersion" : "1.5.2.RELEASE",
      "springVersion" : "4.3.6.RELEASE"
    "taskLauncher" : {
      "deployerImplementationVersion" : "1.2.0.M3",
      "deployerName" : "LocalTaskLauncher",
      "deployerSpiVersion" : "1.2.0.M3",
      "javaVersion" : "1.8.0_121",
      "platformApiVersion" : "Linux 4.4.0-66-generic",
      "platformClientVersion" : "4.4.0-66-generic",
      "platformHostVersion" : "4.4.0-66-generic",
      "platformSpecificInfo" : { },
      "platformType" : "Local",
      "springBootVersion" : "1.5.2.RELEASE",
      "springVersion" : "4.3.6.RELEASE"
  "_links" : {
    "self" : {
      "href" : "http://localhost:8080/about"

50.3. Listing Applications

A GET request will list all applications known to Spring Cloud Data Flow.

50.3.1. Request structure

GET /apps?type=source HTTP/1.1
Accept: application/json
Host: localhost:8080

50.3.2. Request parameters

Parameter Description


Restrict the returned apps to the type of the app.

50.3.3. Example request

$ curl 'http://localhost:8080/apps?type=source' -i -H 'Accept: application/json'

50.3.4. Response structure

HTTP/1.1 200 OK
Content-Type: application/json;charset=UTF-8
Content-Length: 185

  "_links" : {
    "self" : {
      "href" : "http://localhost:8080/apps"
  "page" : {
    "size" : 0,
    "totalElements" : 0,
    "totalPages" : 1,
    "number" : 0

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.

51. Overview

The central entrypoint is the DataFlowTemplate class in package

This class implements the interface DataFlowOperations and delegates to sub-templates that provide the specific functionality for each feature-set:

Interface Description


REST client for stream operations


REST client for counter operations


REST client for field value counter operations


REST client for aggregate counter operations


REST client for task operations


REST client for job operations


REST client for app registry operations


REST client for completion operations


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.

52. 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:


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:

  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;
            .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)",

for (AppRegistrationResource app : apps.getContent()) {
  System.out.println(String.format("App Name: %s, App Type: %s, App URI: %s",


Appendix A: Migrating from Spring XD to Spring Cloud Data Flow

A.1. Terminology Changes

Old New


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





Admin UI


Message Bus


Batch / Job


A.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".

A.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

A.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

A.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

A.3. Message Bus to Binders

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

A.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.

A.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.

  • 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

A.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"

A.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

A.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 link: 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

A.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













A.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

A.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.

A.8.1. ZooKeeper

ZooKeeper is not used in the new architecture.

A.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.

A.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.

A.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.

A.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.

A.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.

A.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)

A.12. YARN Deployment

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

A.13. Use Case Comparison

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

A.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

A.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

A.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


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 B: 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.

B.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-docs -am

B.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.

B.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.

B.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 C: 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.

C.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.

C.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