Working with Stream Processing in Kafka

Kafka streams provide the ability to perform powerful data processing operations against Kafka data in real-time. In this lab, we will have the opportunity to work with Kafka streams by building a Java application capable of transforming data about individual purchases into a running total of each item that was bought. In addition to general stream processing, this lab will provide hands-on experience with topics such as stateless and stateful transformations, data aggregation, and type conversion.

Learning Objectives

Successfully complete this lab by achieving the following learning objectives:

Clone the Starter Project from GitHub and Perform a Test Run

Clone the starter project from GitHub:

cd ~/
git clone https://github.com/linuxacademy/content-ccdak-kafka-streams-lab.git

Perform a test to ensure that the code can compile and run:
```
cd content-ccdak-kafka-streams-lab/
./gradlew run
```
The output should contain the message printed by the Main class: Hello, world!

Implement and Run the Kafka Streams Application

Access the build.gradle file that must be edited:
```
vi build.gradle
```

Add the following dependencies to the dependencies {...} section of the file:

implementation 'org.apache.kafka:kafka-streams:2.2.1'
implementation 'org.apache.kafka:kafka-clients:2.2.1'
...

Implement our streams logic in Main.java:

vi src/main/java/com/linuxacademy/ccdak/streams/Main.java

Note: Remember to check the code comments for some helpful info, or the solution video for a more thorough explanation.

Here is an example of Main.java:

package com.linuxacademy.ccdak.streams;

import java.util.Properties;
import java.util.concurrent.CountDownLatch;
import org.apache.kafka.common.serialization.Serdes;
import org.apache.kafka.streams.KafkaStreams;
import org.apache.kafka.streams.StreamsBuilder;
import org.apache.kafka.streams.StreamsConfig;
import org.apache.kafka.streams.Topology;
import org.apache.kafka.streams.kstream.Grouped;
import org.apache.kafka.streams.kstream.KStream;
import org.apache.kafka.streams.kstream.KTable;
import org.apache.kafka.streams.kstream.Produced;

public class Main {

    public static void main(String[] args) {
        // Set up the configuration.
        final Properties props = new Properties();
        props.put(StreamsConfig.APPLICATION_ID_CONFIG, "inventory-data");
        props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092");
        props.put(StreamsConfig.CACHE_MAX_BYTES_BUFFERING_CONFIG, 0);
        // Since the input topic uses Strings for both key and value, set the default Serdes to String.
        props.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass().getName());
        props.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass().getName());

        // Get the source stream.
        final StreamsBuilder builder = new StreamsBuilder();
        final KStream<String, String> source = builder.stream("inventory_purchases");

        // Convert the value from String to Integer. If the value is not a properly-formatted number, print a message and set it to 0.
        final KStream<String, Integer> integerValuesSource = source.mapValues(value -> {
            try {
                return Integer.valueOf(value);
            } catch (NumberFormatException e) {
                System.out.println("Unable to convert to Integer: "" + value + """);
                return 0;
            }
        });

        // Group by the key and reduce to provide a total quantity for each key.
        final KTable<String, Integer> productCounts = integerValuesSource
            .groupByKey(Grouped.with(Serdes.String(), Serdes.Integer()))
            .reduce((total, newQuantity) -> total + newQuantity);

        // Output to the output topic.
        productCounts
            .toStream()
            .to("total_purchases", Produced.with(Serdes.String(), Serdes.Integer()));

        final Topology topology = builder.build();
        final KafkaStreams streams = new KafkaStreams(topology, props);
        // Print the topology to the console.
        System.out.println(topology.describe());
        final CountDownLatch latch = new CountDownLatch(1);

        // Attach a shutdown handler to catch control-c and terminate the application gracefully.
        Runtime.getRuntime().addShutdownHook(new Thread("streams-wordcount-shutdown-hook") {
            @Override
            public void run() {
                streams.close();
                latch.countDown();
            }
        });

        try {
            streams.start();
            latch.await();
        } catch (final Throwable e) {
            System.out.println(e.getMessage());
            System.exit(1);
        }
        System.exit(0);
    }

}

Run the code:
```
./gradlew run
```

We can check the output of the application by consuming from the output topic:

kafka-console-consumer --bootstrap-server localhost:9092 --topic total_purchases --from-beginning --property print.key=true --property value.deserializer=org.apache.kafka.common.serialization.IntegerDeserializer

If we want to visually compare the output with the input, in a new terminal window, we can consume from the input topic with:
```
kafka-console-consumer --bootstrap-server localhost:9092 --topic inventory_purchases --property print.key=true
```

What are Hands-on Labs

Hands-on Labs are real environments created by industry experts to help you learn. These environments help you gain knowledge and experience, practice without compromising your system, test without risk, destroy without fear, and let you learn from your mistakes. Hands-on Labs: practice your skills before delivering in the real world.

Working with Stream Processing in Kafka

About this Hands-on Lab

Learning Objectives

Additional Resources

What are Hands-on Labs

Newsletter