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

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

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

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 the features are enabled. Note: Since analytics feature is enabled by default, the Data Flow server is expected to have a valid Redis store available as analytic repository. This also means that the Data Flow server’s health depends on the redis store availability as well. Hence it is recommended to disable the analytics feature (using the property mentioned above) if redis store is not available.

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 &

	Note
	There is a schema update to the Spring Cloud Dataflow datastore when upgrading from version 1.0.x to 1.1.x. Migration scripts for specific database types can be found here.

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

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

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-1.1.0.M2.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 Basic Authentication

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

security:
  basic:
    enabled: true                                                     
    realm: Spring Cloud Data Flow                                     

	Enables basic authentication. Must be set to true for security to be enabled.
	(Optional) The realm for Basic authentication. Will default to Spring if not explicitly set.

	Note
	Current versions of Chrome do not display the realm. Please see the following Chromium issue ticket for more information.

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

Figure 16.1. Default Spring Boot user credentials

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

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

16.2.1 File based authentication

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

security:
  basic:
    enabled: true
    realm: Spring Cloud Data Flow
dataflow:
  security:
    authentication:
      file:
        enabled: true                                                 
        users:                                                        
          bob: bobspassword, ROLE_ADMIN                               
          alice: alicepwd, ROLE_VIEW, ROLE_CREATE

	Enables file based authentication
	This is a yaml map of username to password
	Each map value is made of a corresponding password and role(s), comma separated

	Important
	As of Spring Cloud Data Flow 1.1, roles are not supported, yet (specified roles are ignored). Due to an issue in Spring Security, though, at least one role must be provided.

16.2.2 LDAP Authentication

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

• Direct bind
• Search and bind

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

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

security:
  basic:
    enabled: true
    realm: Spring Cloud Data Flow
dataflow:
  security:
    authentication:
      ldap:
        enabled: true                                                 
        url: ldap://ldap.example.com:3309                             
        userDnPattern: uid={0},ou=people,dc=example,dc=com            

	Enables LDAP authentication
	The URL for the LDAP server
	The distinguished name (DN) pattern for authenticating against the server

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

security:
  basic:
    enabled: true
    realm: Spring Cloud Data Flow
dataflow:
  security:
    authentication:
      ldap:
        enabled: true                                                 
        url: ldap://localhost:10389                                   
        managerDn: uid=admin,ou=system                                
        managerPassword: secret                                       
        userSearchBase: ou=otherpeople,dc=example,dc=com              
        userSearchFilter: uid={0}                                     

	Enables LDAP integration
	The URL of the LDAP server
	A DN for to authenticate to the LDAP server, if anonymous searches are not supported (optional, required together with next option)
	A password to authenticate to the LDAP server, if anonymous searches are not supported (optional, required together with previous option)
	The base for searching the DN of the authenticating user (serves to restrict the scope of the search)
	The search filter for the DN of the authenticating user

	Tip
	For more information, please also see the chapter LDAP Authentication of the Spring Security reference guide.

16.3 OAuth 2.0

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

• Authorization Code - Used for the GUI (Browser) integration. You will be redirected to your OAuth Service for authentication
• Password - Used by the shell (And the REST integration), so you can login using username and password

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

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

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

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, if you leave off the OAuth2 section, Basic Authentication will be enabled instead.

	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.

16.3.1 Authentication using the Spring Cloud Data Flow Shell

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

$ java -jar spring-cloud-dataflow-shell-1.1.0.M2.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                  ║
╚═══════════╧═══════════════════════════════════════╝

16.3.2 OAuth2 Authentication Examples

Local OAuth2 Server

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

• @EnableResourceServer
• @EnableAuthorizationServer

A working example application can be found at:

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.

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 16.2. 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.

16.4 Securing the Spring Boot Management Endpoints

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

management:
  contextPath: /management
  security:
    enabled: true

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

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

List of available Stream Application Starters:

List of available Task Applicaiton Starters:

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/1-0-4-GA-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 Section 29.1, “Common application properties”, e.g. server.port but also families of properties such as those with the prefix spring.jmx and logging. When creating your own application it is desirable to whitelist properties so that the shell and the UI can display them first as primary properties when presenting options via TAB completion or in drop-down boxes.

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

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

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

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

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

	Important
	Make sure to add 'spring-boot-configuration-processor' as an optional dependency to generate configuration metadata file for the properties. <dependency> <groupId>org.springframework.boot</groupId> <artifactId>spring-boot-configuration-processor</artifactId> <optional>true</optional> </dependency>

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

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.

20.2.3 Passing application properties when deploying a stream

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

For example, the stream

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

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

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

When using the app label,

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

the application properties can be defined as:

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

20.2.4 Passing Spring Cloud Stream properties for the application

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

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

For example, for the below stream

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

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

dataflow:>stream deploy ticktock --properties "app.time.spring.cloud.stream.bindings.output.binder=kafka,app.transform.spring.cloud.stream.bindings.input.binder=kafka,app.transform.spring.cloud.stream.bindings.output.binder=rabbit,app.log.spring.cloud.stream.bindings.input.binder=rabbit"

	Note
	Overriding the destination names is not recommended as Spring Cloud Data Flow takes care of setting this internally.

20.2.5 Passing per-binding producer consumer properties

A Spring Cloud Stream application can have producer and consumer properties set per-binding basis. While Spring Cloud Data Flow supports specifying short-hand notation for per binding producer properties such as partitionKeyExpression, partitionKeyExtractorClass as described in Section 20.2.6, “Passing stream partition properties during stream deployment”, all the supported Spring Cloud Stream producer/consumer properties can be set as Spring Cloud Stream properties for the app directly as well.

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

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

can be deployed with producer/consumer properties as:

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

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

For instance

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

20.2.6 Passing stream partition properties during stream deployment

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

See below for examples of deploying partitioned streams:

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

The class name of a PartitionKeyExtractorStrategy (default null)

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

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

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

The class name of a PartitionSelectorStrategy (default null)

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

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

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

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

20.2.7 Passing application content type properties

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

For example, consider the following stream:

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

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

When sending some data to the http application:

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

At the log application you see the content as follows:

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

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

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

20.2.8 Overriding application properties during stream deployment

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

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

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

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

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

20.3 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.

20.3.1 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.

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

stream deploy foo --propertiesFile myprops.properties

where myprops.properties contains:

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

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

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

stream deploy foo --propertiesFile myprops.yaml

where myprops.yaml contains:

app:
  transform:
    count: 2
    producer:
      partitionKeyExpression: payload

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).

 In some cases, a stream can have its applications bound to multiple spring cloud stream binders when they are required to connect to different messaging
middleware configurations. In those cases, it is important to make sure the applications are configured appropriately with their binder
configurations. For example, let's consider the following stream:

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

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

Http source sends events to RabbitMQ (rabbit1)
Transform processor receives events from RabbitMQ (rabbit1) and sends the processed events into Kafka (kafka1)
Log sink receives events from Kafka (kafka1)

Here, rabbit1 and kafka1 are the binder names given in the spring cloud stream application properties. Based on this setup, the applications will have the following binder(s) in their classpath with the appropriate configuration:

Http - Rabbit binder
Transform - Both Kafka and Rabbit binders
Log - Kafka binder

The spring-cloud-stream binder configuration properties can be set within the applications themselves. If not, they can be passed via deployment properties when the stream is deployed.

For example,

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

dataflow:>stream deploy mystream --properties "app.http.spring.cloud.stream.bindings.output.binder=rabbit1,app.transform.spring.cloud.stream.bindings.input.binder=rabbit1,
app.transform.spring.cloud.stream.bindings.output.binder=kafka1,app.log.spring.cloud.stream.bindings.input.binder=kafka1"

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

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:

32.1 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 launch mytask --arguments "--server.port=8080,--foo=bar"

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.

33.1 Configuring the Task Execution Repository

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

33.1.1 Local

1. Open the spring-cloud-dataflow-server-local/pom.xml in your IDE.
2. In the dependencies section add the dependency for the database driver required. In the sample below postgresql has been chosen.

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

3. Save the changed pom.xml
4. Build the application as described here: Building Spring Cloud Data Flow

33.1.2 Task Application Repository

When launching a task application be sure that the database driver that is being used by Spring Cloud Data Flow is also a dependency on the task application. For example if your Spring Cloud Dataflow is set to use Postgresql be sure that the task application also has Postgresql as a dependency.

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

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 36.1. 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). It is possible to import a number of applications at once using the Bulk Import Applications action.

Figure 37.1. List of Available Applications

37.1 Bulk Import of Applications

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

<type>.<name> = <coordinates>

For example:

task.timestamp=maven://org.springframework.cloud.task.app:timestamp-task:1.0.0.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 a Uri can be specified that points to a properties file stored elsewhere, it should contain properties formatted as above. Alternatively, using the textbox labelled 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.

Figure 37.2. Bulk Import 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 38.1. 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. Each row includes an arrow on the left which can be clicked to see a visual representation of the definition. Hovering over the boxes in the visual representation will show more details about the apps including any options passed to them. In this screenshot the timer stream has been expanded to show the visual representation:

Figure 39.1. List of Stream Definitions

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

Figure 39.2. Stream Details Page

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 40.1. Flo for Spring Cloud Data Flow

The Tasks section of the Dashboard currently has three tabs:

41.1 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 41.1. 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.

41.1.1 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.

41.1.2 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.

41.2 Definitions

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

Figure 41.2. List of Task Definitions

41.2.1 Creating Task Definitions using the bulk define interface

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

Figure 41.3. Bulk Define Tasks

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

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

For example:

demo-timestamp = timestamp --format=hhmmss

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

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

41.2.2 Launching Tasks

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

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

• Parameter Key
• Parameter Value

Task parameters are not typed.

41.3 Executions

Figure 41.4. 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 42.1. List of Job Executions

42.1 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.

42.1.1 Job execution details

Figure 42.2. 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.

42.1.2 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.

42.1.3 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 42.3. 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.

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

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

45.1 Deployment Logs

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

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

   $ java -jar <dataflow-server>.jar --logging.level.org.springframework.cloud.deployer.spi.local=DEBUG

   (where, org.springframework.cloud.deployer.spi.local is the global package for everything local-deployer related)

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

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

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

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

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

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

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

dataflow:>stream create foo --definition "http --logging.level.org.springframework.integration=DEBUG | transform --logging.level.org.springframework.integration=DEBUG | log --logging.level.org.springframework.integration=DEBUG" --deploy

dataflow:>stream deploy foo --properties "app.*.logging.level.org.springframework.integration=DEBUG"

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

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

The central entrypoint is the DataFlowTemplate class in package org.springframework.cloud.dataflow.rest.client.

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

When the DataFlowTemplate is being initialized, the sub-templates will be discovered via the REST relations, which are provided by HATEOAS.^[1]

	Important
	If a resource cannot be resolved, the respective sub-template will result in being NULL. A common cause is that Spring Cloud Data Flow offers for specific sets of features to be enabled/disabled when launching. For more information see Chapter 13, Controlling features with Data Flow server.

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

<dependency>
  <groupId>org.springframework.cloud</groupId>
  <artifactId>spring-cloud-dataflow-rest-client</artifactId>
  <version>1.1.0.M2</version>
</dependency>

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

When instantiating the DataFlowTemplate, you will also pass in a RestTemplate. Please be aware that the needed RestTemplate requires some additional configuration to be valid in the context of the DataFlowTemplate. When declaring a RestTemplate as a bean, the following configuration will suffice:

  @Bean
  public static RestTemplate restTemplate() {
    RestTemplate restTemplate = new RestTemplate();
    restTemplate.setErrorHandler(new VndErrorResponseErrorHandler(restTemplate.getMessageConverters()));
    for(HttpMessageConverter<?> converter : restTemplate.getMessageConverters()) {
      if (converter instanceof MappingJackson2HttpMessageConverter) {
        final MappingJackson2HttpMessageConverter jacksonConverter =
            (MappingJackson2HttpMessageConverter) converter;
        jacksonConverter.getObjectMapper()
            .registerModule(new Jackson2HalModule())
            .addMixIn(JobExecution.class, JobExecutionJacksonMixIn.class)
            .addMixIn(JobParameters.class, JobParametersJacksonMixIn.class)
            .addMixIn(JobParameter.class, JobParameterJacksonMixIn.class)
            .addMixIn(JobInstance.class, JobInstanceJacksonMixIn.class)
            .addMixIn(ExitStatus.class, ExitStatusJacksonMixIn.class)
            .addMixIn(StepExecution.class, StepExecutionJacksonMixIn.class)
            .addMixIn(ExecutionContext.class, ExecutionContextJacksonMixIn.class)
            .addMixIn(StepExecutionHistory.class, StepExecutionHistoryJacksonMixIn.class);
      }
    }
    return restTemplate;
  }

Now you can instantiate the DataFlowTemplate with:

DataFlowTemplate dataFlowTemplate = new DataFlowTemplate(
    new URI("http://localhost:9393/"), restTemplate);         

Depending on your requirements, you can now make calls to the server. For instance, if you like to get a list of currently available applications you can execute:

PagedResources<AppRegistrationResource> apps = dataFlowTemplate.appRegistryOperations().list();

System.out.println(String.format("Retrieved %s application(s)",
    apps.getContent().size()));

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