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	Note
	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 is a cloud native programming and operating model for composable data microservices 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 streaming and batch modules from Spring XD are refactored into Spring Boot data microservice applications that 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 batch data pipelines.

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

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

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

There is a deployer Service Provider Interface (SPI) that enables you to extend Data Flow to deploy onto other runtimes, for example to support Hashicorp’s Nomad or Docker Swarm. Contributions are welcome!

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

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

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

Figure 4.1. 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 application are deployed on the target runtime.

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.

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.

@EnableBinding(Sink.class)
public class LoggingSink {

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

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

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.

The Spring Cloud Task programming model provides:

You need Java installed (Java 7 or better, we recommend Java 8), 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. The local server registers sources, sink, processors and tasks the are published from the Spring Cloud Stream App Starters and Spring Cloud Task App Starters repository. By default the server registers these applications that use Kafka, but setting the property binding to rabbit will register a list of applications that use RabbitMQ as the message broker.

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:

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

By default, all these features are enabled for local dataflow server.

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

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 RDBMS, then the corresponding JDBC driver jar needs to be on the classpath of the server.

The RDBMS 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 &

For HSQLDB:

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 &

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.

16.1 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.

server:
  port: 8443                                         
  ssl:
    key-alias: yourKeyAlias                          
    key-store: path/to/keystore                      
    key-store-password: yourKeyStorePassword         
    key-password: yourKeyPassword                    
    trust-store: path/to/trust-store                 
    trust-store-password: yourTrustStorePassword     

	As the default port is 9393, you may choose to change the port to a more common HTTPs-typical port.
	The alias (or name) under which the key is stored in the keystore.
	The path to the keystore file. Classpath resources may also be specified, by using the classpath prefix: classpath:path/to/keystore
	The password of the keystore.
	The password of the key.
	The path to the truststore file. Classpath resources may also be specified, by using the classpath prefix: classpath:path/to/trust-store
	The password of the trust store.

	Note
	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.

16.1.1 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"  
          -keypass dataflow -storepass dataflow

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:

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

This is all 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.

This issue also is relevant for the Data Flow Shell. Therefore additional steps are necessary to make the Shell work with self-signed certificates. First, 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 -Djavax.net.ssl.trustStorePassword=dataflow \
       -Djavax.net.ssl.trustStore=/path/to/dataflow.truststore \
       -Djavax.net.ssl.trustStoreType=jks \
       -jar spring-cloud-dataflow-shell-1.0.0.RELEASE.jar

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

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

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

16.2 Enabling Authentication

By default, the REST endpoints (administration, management and health), as well as the Dashboard UI do not require authenticated access. However, authentication can be provided via OAuth 2.0, thus allowing you to also 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.

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

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

security:
  basic:
    enabled: true                                                     
    realm: Spring Cloud Data Flow                                     
  oauth2:                                                             
    client:
      client-id: myclient
      client-secret: mysecret
      access-token-uri: http://127.0.0.1:9999/oauth/token
      user-authorization-uri: http://127.0.0.1:9999/oauth/authorize
    resource:
      user-info-uri: http://127.0.0.1:9999/me

	Must be set to true for security to be enabled.
	The realm for Basic authentication
	OAuth Configuration Section

	Note
	As of version 1.0 Spring Cloud Data Flow does not provide finer-grained authorization. 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.

16.3 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-1.0.0.RELEASE.jar \
  --dataflow.uri=http://localhost:9393 \
  --dataflow.username=my_username --dataflow.password=my_password

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

16.4 Authentication Examples

16.4.1 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:

https://github.com/ghillert/oauth-test-server/

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

16.4.2 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:

https://github.com/settings/developers

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

Figure 16.1. Register an OAuth Application for GitHub

	Note
	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:

security:
  basic:
    enabled: true
  oauth2:
    client:
      client-id: your-github-client-id
      client-secret: your-github-client-secret
      access-token-uri: https://github.com/login/oauth/access_token
      user-authorization-uri: https://github.com/login/oauth/authorize
    resource:
      user-info-uri: https://api.github.com/user

	Important
	GitHub does not support the OAuth2 password grant type. As such you cannot use the Spring Cloud Data Flow Shell in conjunction with GitHub.

In Spring Cloud Data Flow, a basic stream defines the ingestion of event driven 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 these servers 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 a 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. More details can be found in the sections below.

In the examples 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 which 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 provides some additional documentation.

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

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

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

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

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

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

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

dataflow:>app register --name http --type source --uri maven://org.springframework.cloud.stream.app:http-source-rabbit:1.0.0.BUILD-SNAPSHOT
dataflow:>app register --name log --type sink --uri maven://org.springframework.cloud.stream.app:log-sink-rabbit:1.0.0.BUILD-SNAPSHOT

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

For example, if you would like to register the snapshot versions of the http and log applications built with the RabbitMQ binder, you could have the following in a properties file [eg: stream-apps.properties]:

source.http=maven://org.springframework.cloud.stream.app:http-source-rabbit:1.0.0.BUILD-SNAPSHOT
sink.log=maven://org.springframework.cloud.stream.app:log-sink-rabbit:1.0.0.BUILD-SNAPSHOT

Then to import the apps in bulk, use the app import command and provide the location of the properties file via --uri:

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

For convenience, we have the static files with application-URIs (for both maven and docker) available for all the out-of-the-box Stream 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 stream applications built with the RabbitMQ binder in bulk, you can with the following command.

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

You can also pass the --local option (which is TRUE by default) to indicate whether the properties file location should be resolved within the shell process itself. If the location should be resolved from the Data Flow Server process, specify --local false.

When using either app register or app import, if 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.

	Note
	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.

19.1 Whitelisting application properties

Stream 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 source’s spring-configuration-metadata-whitelist.properties file

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

If for some reason we also wanted to add file.prefix to this file, it would look like

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

Important

As of Spring Cloud Data Flow 1.0.0.RELEASE the whitelisting of application properties is only explicitly supported for Spring Boot 1.3.x based application. Milestone releases of the upcoming Spring Boot 1.4.0 release are not explicitly supported, yet. The spring-boot-maven-plugin used in 1.4.x has a different approach in handling the nested archives inside the jar. As a result you will notice that the application properties are not listed using app info command at all. As a temporary workaround, you can override the managed version of your app’s spring-boot-maven-plugin explicitly and revert to a version of the latest 1.3.x release: For example, if your app’s pom.xml specifies to use Spring Boot 1.4.0.M3: <parent> <artifactId>spring-boot-starter-parent</artifactId> <groupId>org.springframework.boot</groupId> <version>1.4.0.M3</version> <relativePath></relativePath> </parent> Then you can override the managed version of the spring-boot-maven-plugin with: <plugin> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-maven-plugin</artifactId> <version>1.3.5.RELEASE</version> </plugin> Overriding the managed version 1.4.0.M3. Also, if you have your own dataflow server built using @EnableDataflowServer and using Spring Boot 1.4.x in that, you would need to explicitly override the spring-boot-maven-plugin with any of 1.3.x releases.

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 posting 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

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

dataflow:> app info source:time
╔══════════════════════════════╤══════════════════════════════╤══════════════════════════════╤══════════════════════════════╗
║         Option Name          │         Description          │           Default            │             Type             ║
╠══════════════════════════════╪══════════════════════════════╪══════════════════════════════╪══════════════════════════════╣
║trigger.time-unit             │The TimeUnit to apply to delay│<none>                        │java.util.concurrent.TimeUnit ║
║                              │values.                       │                              │                              ║
║trigger.fixed-delay           │Fixed delay for periodic      │1                             │java.lang.Integer             ║
║                              │triggers.                     │                              │                              ║
║trigger.cron                  │Cron expression value for the │<none>                        │java.lang.String              ║
║                              │Cron Trigger.                 │                              │                              ║
║trigger.initial-delay         │Initial delay for periodic    │0                             │java.lang.Integer             ║
║                              │triggers.                     │                              │                              ║
║trigger.max-messages          │Maximum messages per poll, -1 │1                             │java.lang.Long                ║
║                              │means infinity.               │                              │                              ║
║trigger.date-format           │Format for the date value.    │<none>                        │java.lang.String              ║
╚══════════════════════════════╧══════════════════════════════╧══════════════════════════════╧══════════════════════════════╝

dataflow:> app info sink:log
╔══════════════════════════════╤══════════════════════════════╤══════════════════════════════╤══════════════════════════════╗
║         Option Name          │         Description          │           Default            │             Type             ║
╠══════════════════════════════╪══════════════════════════════╪══════════════════════════════╪══════════════════════════════╣
║log.name                      │The name of the logger to use.│<none>                        │java.lang.String              ║
║log.level                     │The level at which to log     │<none>                        │org.springframework.integratio║
║                              │messages.                     │                              │n.handler.LoggingHandler$Level║
║log.expression                │A SpEL expression (against the│payload                       │java.lang.String              ║
║                              │incoming message) to evaluate │                              │                              ║
║                              │as the logged message.        │                              │                              ║
╚══════════════════════════════╧══════════════════════════════╧══════════════════════════════╧══════════════════════════════╝

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

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

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

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

stream deploy ticktock --properties "app.a.fixed-delay=4,app.b.level=ERROR"

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

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

20.2 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.

20.2.1 Passing instance count as deployment property

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

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

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

	Important
	See Chapter 27, Using Labels in a Stream.

20.2.2 Inline vs file reference properties

When using the Spring Cloud Dataflow 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 "app.transform.count=2,app.transform.producer.partitionKeyExpression=payload"

Using a file reference

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

stream deploy foo --propertiesFile myprops.properties

where myprops.properties contains:

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

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

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.

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

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.

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

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,app.log.count=2"
Deployed stream 'words'

dataflow:>http post --target http://localhost:9900 --data "How much wood would a woodchuck chuck if a woodchuck could chuck wood"
> POST (text/plain;Charset=UTF-8) http://localhost:9900 How much wood would a woodchuck chuck if a woodchuck could chuck wood
> 202 ACCEPTED

You’ll see the following in the server logs.

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

Review the words.log instance 0 logs:

2016-06-05 18:35:47.047  INFO 58638 --- [  kafka-binder-] log.sink                                 : How
2016-06-05 18:35:47.066  INFO 58638 --- [  kafka-binder-] log.sink                                 : chuck
2016-06-05 18:35:47.066  INFO 58638 --- [  kafka-binder-] log.sink                                 : chuck

Review the words.log instance 1 logs:

2016-06-05 18:35:47.047  INFO 58639 --- [  kafka-binder-] log.sink                                 : much
2016-06-05 18:35:47.066  INFO 58639 --- [  kafka-binder-] log.sink                                 : wood
2016-06-05 18:35:47.066  INFO 58639 --- [  kafka-binder-] log.sink                                 : would
2016-06-05 18:35:47.066  INFO 58639 --- [  kafka-binder-] log.sink                                 : a
2016-06-05 18:35:47.066  INFO 58639 --- [  kafka-binder-] log.sink                                 : woodchuck
2016-06-05 18:35:47.067  INFO 58639 --- [  kafka-binder-] log.sink                                 : if
2016-06-05 18:35:47.067  INFO 58639 --- [  kafka-binder-] log.sink                                 : a
2016-06-05 18:35:47.067  INFO 58639 --- [  kafka-binder-] log.sink                                 : woodchuck
2016-06-05 18:35:47.067  INFO 58639 --- [  kafka-binder-] log.sink                                 : could
2016-06-05 18:35:47.067  INFO 58639 --- [  kafka-binder-] log.sink                                 : wood

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

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:

`:<stream-name>.<label/app-name>`

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.

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

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.

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.

29.1 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 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:

--spring.cloud.dataflow.applicationProperties.stream.spring.cloud.stream.kafka.binder.brokers=192.168.1.100:9092
--spring.cloud.dataflow.applicationProperties.stream.spring.cloud.stream.kafka.binder.zkNodes=192.168.1.100:2181

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

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

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.

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:

31.1 Registering a Task Application

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

dataflow:>app register --name task1 --type task --uri maven://com.example:mytask:1.0.2

dataflow:>app register --name task2 --type task --uri file:///Users/example/mytask-1.0.2.jar

dataflow:>app register --name task3 --type task --uri http://example.com/mytask-1.0.2.jar

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

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

If you would like to register multiple apps at one time, you can store them in a properties file where the keys are formatted as <type>.<name> and the values are the URIs. For example, this would be a valid properties file:

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

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

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

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

List of available static property files:

• Maven based Task Applications: bit.ly/task-applications-maven
• Docker based Task Applications: bit.ly/task-applications-docker

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

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

You can also pass the --local option (which is TRUE by default) to indicate whether the properties file location should be resolved within the shell process itself. If the location should be resolved from the Data Flow Server process, specify --local false.

When using either app register or app import, if a task app is already registered with the provided name, it will not be overridden by default. If you would like to override the pre-existing task app, then include the --force option.

	Note
	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.

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

dataflow:>task launch mytask
 Launched task 'mytask'

dataflow:>task destroy mytask
 Destroyed task 'mytask'

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.

<dependencies>
...
    <dependency>
        <groupId>org.postgresql</groupId>
        <artifactId>postgresql</artifactId>
    </dependency>
...
</dependencies>

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"

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

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

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

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

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

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

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.

	Note
	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://org.springframework.cloud.stream.app:task-launcher-local-sink-kafka:jar:1.0.0.BUILD-SNAPSHOT

stream create foo --definition "triggertask --triggertask.uri=maven://org.springframework.cloud.task.app:timestamp-task:jar:1.0.0.BUILD-SNAPSHOT --trigger.fixed-delay=5 | task-launcher-local" --deploy

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

Spring Cloud Data Flow provides a browser-based GUI which currently has 6 sections:

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

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

For example: http://localhost:9393/dashboard

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

Note: The default Dashboard server port is 9393

Figure 35.1. The Spring Cloud Data Flow Dashboard

The Apps section of the Dashboard lists all the available applications and provides the control to register/unregister them (if applicable). By clicking on the magnifying glass, you will get a listing of available definition properties.

Figure 36.1. List of Available Applications

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.

Figure 37.1. List of Running Applications

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.

Figure 38.1. List of Stream Definitions

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:

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.

Figure 39.1. Flo for Spring Cloud Data Flow

The Tasks section of the Dashboard currently has three tabs:

40.1 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.

Figure 40.1. 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.

40.1.1 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.

40.1.2 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.

40.2 Definitions

This page lists the Data Flow Task definitions and provides actions to launch or destroy those tasks.

Figure 40.2. List of Task Definitions

40.2.1 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.

40.3 Executions

Figure 40.3. List of Task Executions

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.

Figure 41.1. List of Job Executions

41.1 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.

41.1.1 Job execution details

Figure 41.2. 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.

41.1.2 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.

	Important
	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.

41.1.3 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.

Figure 41.3. Step Execution History

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

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:

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.

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 as except local Data Flow server, other Data Flow server implementations (that use maven resources for app artifacts resolution) have no default value for remote repositories. The local server has repo.spring.io/libs-snapshot as the default remote repository.

To pass the properties as commandline options:

$ java -jar <dataflow-server>.jar --maven.localRepository=mylocal
--maven.remote-repositories.repo1.url=https://repo1
--maven.remote-repositories.repo1.auth.username=repo1user
--maven.remote-repositories.repo1.auth.password=repo1pass
--maven.remote-repositories.repo2.url=https://repo2 --maven.proxy.host=proxyhost
--maven.proxy.port=9018 --maven.proxy.auth.username=proxyuser
--maven.proxy.auth.password=proxypass

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:

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"
      }
    }
  }
}'

	Note
	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.