Getting Started with Stream Processing with Spring Cloud Data Flow

1. Introduction

Spring Cloud Data Flow is a cloud-native programming and operating model for composable data microservices.

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.

These data pipelines come in two flavors, streaming and batch data pipelines.

In the first case, an unbounded amount of data is consumed or produced via messaging middleware. In the second case, the short-lived task processes a finite set of data and then terminates.

This article will focus on streaming processing.

2. Architectural Overview

The key components of this type of architecture are Applications, the Data Flow Server, the Skipper Server, and the target runtime.

Also in addition to these key components, we usually have a Data Flow Shell and a message broker within the architecture.

Let’s see all these components in more detail.

2.1. Applications

Typically, a streaming data pipeline includes consuming events from external systems, data processing, and polyglot persistence. These phases are commonly referred to as Source, Processor, and Sink in Spring Cloud terminology:

• Source: is the application that consumes events
• Processor: consumes data from the Source, does some processing on it, and emits the processed data to the next application in the pipeline
• Sink: either consumes from a Source or Processor and writes the data to the desired persistence layer

These applications can be packaged in two ways:

3. Install a Message Broker

As we have seen, the applications in the pipeline need a messaging middleware to communicate. For this article, we’ll go with RabbitMQ.

For the full details of the installation, you can follow the instructions on the official site.

4. The Local Data Flow Server and Skipper Server

Download the Spring Cloud Data Flow Server and Spring Cloud Skipper Server by using the following commands:

wget https://repo.maven.apache.org/maven2/org/springframework/cloud/spring-cloud-dataflow-server/2.11.2/spring-cloud-dataflow-server-2.11.2.jar 

wget https://repo.maven.apache.org/maven2/org/springframework/cloud/spring-cloud-skipper-server/2.11.2/spring-cloud-skipper-server-2.11.2.jar

Now you need to start the applications that comprise the server:

Skipper :

In the directory where you downloaded Skipper, run the server by using java -jar, as follows:

java -jar spring-cloud-skipper-server-2.11.2.jar

The application will boot up on port 7577.

Dataflow:

In a different terminal window and in the directory where you downloaded Data Flow, run the server by using java -jar, as follows:

java -jar spring-cloud-dataflow-server-2.11.2.jar

The application will boot up on port 9393.

5. The Data Flow Shell

Download the Spring Cloud Data Shell by using the following command:

wget https://repo.maven.apache.org/maven2/org/springframework/cloud/spring-cloud-dataflow-shell/2.11.2/spring-cloud-dataflow-shell-2.11.2.jar

Now start the Spring Cloud Data Flow Shell with the following command:

java -jar spring-cloud-dataflow-shell-2.11.2.jar

After the shell is running, we can type the help command in the prompt to see a complete list of commands that we can perform.

6. The Source Application

On Initializr, we’ll now create a simple application and add a Stream Rabbit dependency called spring-cloud-starter-stream-rabbit:

<dependency>
    <groupId>org.springframework.cloud</groupId>
    <artifactId>spring-cloud-starter-stream-rabbit</artifactId>
</dependency>

We’ll then add the @EnableBinding(Source.class) annotation to the Spring Boot main class:

@EnableBinding(Source.class)
@SpringBootApplication
public class SpringDataFlowTimeSourceApplication {
    
    public static void main(String[] args) {
        SpringApplication.run(
          SpringDataFlowTimeSourceApplication.class, args);
    }
}

Now we need to define the source of the data that must be processed. This source could be any potentially endless workload (internet-of-things sensor data, 24/7 event processing, online transaction data ingest).

In our sample application, we produce one event (for simplicity a new timestamp) every 10 seconds with a Poller.

The @InboundChannelAdapter annotation sends a message to the source’s output channel, using the return value as the payload of the message:

@Bean
@InboundChannelAdapter(
  value = Source.OUTPUT, 
  poller = @Poller(fixedDelay = "10000", maxMessagesPerPoll = "1")
)
public MessageSource<Long> timeMessageSource() {
    return () -> MessageBuilder.withPayload(new Date().getTime()).build();
}

Our data source is ready.

7. The Processor Application

Next- we’ll create an application and add a Stream Rabbit dependency.

We’ll then add the @EnableBinding(Processor.class) annotation to the Spring Boot main class:

@EnableBinding(Processor.class)
@SpringBootApplication
public class SpringDataFlowTimeProcessorApplication {

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

Next, we need to define a method to process the data that coming from the source application.

To define a transformer, we need to annotate this method with @Transformer annotation:

@Transformer(inputChannel = Processor.INPUT, 
  outputChannel = Processor.OUTPUT)
public Object transform(Long timestamp) {

    DateFormat dateFormat = new SimpleDateFormat("yyyy/MM/dd hh:mm:yy");
    String date = dateFormat.format(timestamp);
    return date;
}

It converts a timestamp from the ‘input’ channel to a formatted date which will be sent to the ‘output’ channel.

8. The Sink Application

The last application to create is the Sink application.

Again, go to the Spring Initializr and choose a Group, an Artifact name. After downloading the project let’s add a Stream Rabbit dependency.

Then add the @EnableBinding(Sink.class) annotation to the Spring Boot main class:

@EnableBinding(Sink.class)
@SpringBootApplication
public class SpringDataFlowLoggingSinkApplication {

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

Now we need a method to intercept the messages coming from the processor application.

To do this, we need to add the @StreamListener(Sink.INPUT) annotation to our method:

@StreamListener(Sink.INPUT)
public void loggerSink(String date) {
    logger.info("Received: " + date);
}

The method simply prints the timestamp transformed in a formatted date to a log file.

9. Register a Stream App

The Spring Cloud Data Flow Shell allows us to Register a Stream App with the App Registry using the app register command.

We 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“.

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

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

To register the Source, Processor, and Sink applications previously created, go to the Spring Cloud Data Flow Shell and issue the following commands from the prompt:

app register --name time-source --type source --uri file://local machine path to/time-source-0.0.1-SNAPSHOT.jar

app register --name time-processor --type processor --uri file://local machine path to/time-processor-0.0.1-SNAPSHOT.jar

app register --name log-sink --type sink --uri file://local machine path to/log-sink-0.0.1-SNAPSHOT.jar

10. Create and Deploy the Stream

To create a new stream definition go to the Spring Cloud Data Flow Shell and execute the following shell command:

stream create --name time-to-log --definition 'time-source | time-processor | log-sink'

This defines a stream named time-to-log based on the DSL expression ‘time-source | time-processor | log-sink’.

Then to deploy the stream execute the following shell command:

stream deploy --name time-to-log

The Data Flow Server resolves time-source, time-processor, and log-sink to maven coordinates and uses those to launch the time-source, time-processor, and log-sink applications of the stream.

If the stream is correctly deployed you’ll see in the Data Flow Server logs that the modules have been started and tied together:

2024-04-15 15:15:27.153  INFO 23568 — [pool-3-thread-1] o.s.c.s.s.d.DefaultReleaseManager        : Getting status for org.springframework.cloud.skipper.domain.Release@4de8f9c3 using deploymentIds time-to-log2.time-processor-v1,time-to-log2.time-source1-v1,time-to-log2.log-sink-v1
2024-04-15 15:15:32.156  INFO 23568 — [pool-3-thread-1] o.s.c.s.s.d.DefaultReleaseManager        : Getting status for org.springframework.cloud.skipper.domain.Release@4d72121e using deploymentIds time-to-log2.time-processor-v1,time-to-log2.time-source1-v1,time-to-log2.log-sink-v1
2024-04-15 15:15:37.157  INFO 23568 — [pool-3-thread-1] o.s.c.s.s.d.DefaultReleaseManager        : Getting status for org.springframework.cloud.skipper.domain.Release@9a35173 using deploymentIds time-to-log2.time-processor-v1,time-to-log2.time-source1-v1,time-to-log2.log-sink-v1
2024-04-15 15:15:42.160  INFO 23568 — [pool-3-thread-1] o.s.c.s.s.d.DefaultReleaseManager        : Getting status for org.springframework.cloud.skipper.domain.Release@1ec5d911 using deploymentIds time-to-log2.time-processor-v1,time-to-log2.time-source1-v1,time-to-log2.log-sink-v1

11. Reviewing the Result

In this example, the source sends the current timestamp as a message each second, the processor formats it and the log sink outputs the formatted timestamp using the logging framework.

The log files are located within the directory displayed in the Data Flow Server’s log output, as shown above. To see the result, we can tail the log:

tail -f PATH_TO_LOG/spring-cloud-dataflow-1276836171391672089/time-to-log-1472034549734/time-to-log.logging-sink/stdout_0.log
2016-08-24 12:40:42.029  INFO 9488 --- [r.time-to-log-1] s.c.SpringDataFlowLoggingSinkApplication : Received: 2016/08/24 11:40:01
2016-08-24 12:40:52.035  INFO 9488 --- [r.time-to-log-1] s.c.SpringDataFlowLoggingSinkApplication : Received: 2016/08/24 11:40:11
2016-08-24 12:41:02.030  INFO 9488 --- [r.time-to-log-1] s.c.SpringDataFlowLoggingSinkApplication : Received: 2016/08/24 11:40:21

12. Conclusion

This article shows how to build a data pipeline for stream processing using Spring Cloud Data Flow.

Also, we saw the role of Source, Processor, and Sink applications inside the stream and how to plug and tie this module inside a Data Flow Server using Data Flow Shell.

The example code can be found in the GitHub project.