README.md

Monitor Spring Cloud Applications with Kpow

Integrated Spring Cloud Stream Wordcount Kafka Streams example application with the Kpow Streams Agent.

Run this project with the original instructions below, we have integrated the Kpow Agent. You will see log-lines like:

Kpow: sent [112] streams metrics for application.id hello-word-count-sample

Once started, run Kpow with the target cluster and navigate to 'Streams' to view the live topology and metrics.

Quickstart

• Follow the original project setup steps (instructions below)
• Put data on the wordcount topic (instructions below)
• Start Kpow (see: Kpow Local for local evaluation + trial licenses)
  • If using the single-node Kafka Cluster from this project, set REPLICATION_FACTOR=1 when running Kpow
• Navigate to localhost:3000 > Streams
• View WordCount Topology + Metrics
• Navigate to Consumers to reset WordCount offsets

How We Integrated WordCount Streams with the Kpow Agent

Get the Kpow Streams Dependency

Include the Kpow Streams Agent library in your application:

<dependency>
  <groupId>io.operatr</groupId>
  <artifactId>kpow-streams-agent</artifactId>
  <version>0.2.8</version>
</dependency>

Integrate the Agent

Start the Kpow Streams Agent (view full source)

public static void main(String[] args) {
        ApplicationContext context = SpringApplication.run(KafkaStreamsWordCountApplication.class, args);

        // The StreamsBuilderFactoryBean name is '&stream-builder-' + your function name from config, .e.g
        //
        //   spring.cloud.stream:
        //      function:
        //        definition: process <-- '&stream-builder-' + this name here
        //
        // We use the SBFB to obtain the streams and topology of your built Spring Kafka Streams application
        
        StreamsBuilderFactoryBean streamsBuilderFactoryBean = context.getBean("&stream-builder-process", StreamsBuilderFactoryBean.class);
        KafkaStreams streams = streamsBuilderFactoryBean.getKafkaStreams();
        Topology topology = streamsBuilderFactoryBean.getTopology();

        // Create connection properties for the StreamsRegistry producer to send metrics to internal Kpow topics
        // You should be able to use streamsBuilderFactoryBean.getStreamsConfiguration() but in this particular case
        // Those properties contain 'bootstrap.servers = [[localhost:9092]]' which errors on startup
        
        Properties properties = new Properties();
        properties.setProperty("bootstrap.servers", "127.0.0.1:9092");

        // Create a Kpow StreamsRegistry
        
        StreamsRegistry registry = new StreamsRegistry(properties);

        // Register your KafkaStreams and Topology instances with the StreamsRegistry
        
        registry.register(streams, topology);
    }

---

Original Project Readme Follows

What is this app?

This is an example of a Spring Cloud Stream processor using Kafka Streams support.

The example is based on the word count application from the reference documentation.

It uses a single input and a single output. In essence, the application receives text messages from an input topic and computes word occurrence counts in a configurable time window and report that in an output topic. The sample uses a default timewindow of 30 seconds.

Running the app:

Go to the root of the repository.

docker-compose up -d

./mvnw clean package

java -jar target/kafka-streams-word-count-0.0.1-SNAPSHOT.jar

Assuming you are running the dockerized Kafka cluster as above.

Issue the following commands:

docker exec -it kafka-wordcount /opt/kafka/bin/kafka-console-producer.sh --broker-list 127.0.0.1:9092 --topic words

Or if you prefer kafkacat:

kafkacat -b localhost:9092 -t words -P

On another terminal:

docker exec -it kafka-wordcount /opt/kafka/bin/kafka-console-consumer.sh --bootstrap-server 127.0.0.1:9092 --topic counts

Or if you prefer kafkacat:

kafkacat -b localhost:9092 -t counts

Enter some text in the console producer and watch the output in the console consumer.

Once you are done, stop the Kafka cluster: docker-compose down