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.

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,

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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.

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’.

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.

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.

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

Now create a simple timestamp task.

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

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

When deploying Spring Cloud Data Flow to Cloud Foundry, you can take advantage of the Spring Cloud Single Sign-On Connector, which provides Cloud Foundry specific auto-configuration support for OAuth 2.0, when used in conjunction with the Pivotal Single Sign-On Service.

Simply set security.basic.enabled to true and in Cloud Foundry bind the SSO service to your Data Flow Server app and SSO will be enabled.

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

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

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

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

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

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

Even when configuring the Data Flow server to use 10G of space, there is the possibility of exhausting the available space on the local disk. The server implements a least recently used (LRU) algorithm that will remove maven artifacts from the local maven repository. This is configured using the following configuration property, the default value is 25.

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.

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

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.

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

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:

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

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

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

As a result you should see a list of available REST 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:

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 start.spring.io. 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.

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.

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.

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

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

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

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

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:

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:

The benefits of a companion artifact are manifold:

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

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.

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.

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

For example, all the launched applications can be configured to use a specific Kafka broker by launching the configuration server with the following options:

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

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:

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:

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:

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:

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

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.

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:

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.

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:

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:

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.

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

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.

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:

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.

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.

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

For example adding postgres would look something like this:

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:

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:

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

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

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

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

Listing tasks is as simple as:

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

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

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

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

<type>.<name> = <coordinates>

For example:

task.timestamp=maven://org.springframework.cloud.task.app:timestamp-task:1.0.0.BUILD-SNAPSHOT

processor.transform=maven://org.springframework.cloud.stream.app:transform-processor-rabbit:1.0.3.BUILD-SNAPSHOT

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.

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.

On this screen you can perform the following actions:

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.

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.

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.

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.

(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.

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".

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

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.

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

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.

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.

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.

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

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

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

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 run Redis.

The main build command is

You can also add '-DskipTests' if you like, to avoid running the tests.

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.

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

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.