Kafka streams in Starlight for Kafka

bin/pulsar-admin topics create-partitioned-topic \
  persistent://my-tenant-1/kafka/streams-plaintext-input \
  --partitions 4

bin/pulsar-admin topics create-partitioned-topic \
  persistent://my-tenant-1/kafka/streams-plaintext-output \
  --partitions 4

import org.apache.kafka.common.config.TopicConfig;
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.kstream.KStream;
import org.apache.kafka.streams.kstream.KTable;
import org.apache.kafka.streams.kstream.Printed;
import org.apache.kafka.streams.kstream.Produced;

import java.util.Arrays;
import java.util.Properties;
import java.util.regex.Pattern;

public class WordCountExample {

    static final String username = "my-tenant-1";
    static final String password= "token:XXXX-JWT-TOKEN";
    static final String broker = "kafka-aws-useast1.streaming.datastax.com:9093";

    static final String inputTopic = "streams-plaintext-input";
    static final String outputTopic = "streams-wordcount-output";

    /**
     * The Streams application as a whole can be launched like any normal Java application that has a `main()` method.
     */
    public static void main(final String[] args) {
        final String bootstrapServers = args.length > 0 ? args[0] : broker;

        // Configure the Streams application.
        final Properties streamsConfiguration = getStreamsConfiguration(bootstrapServers);

        // Define the processing topology of the Streams application.
        final StreamsBuilder builder = new StreamsBuilder();
        createWordCountStream(builder);
        final KafkaStreams streams = new KafkaStreams(builder.build(), streamsConfiguration);

        // Always (and unconditionally) clean local state prior to starting the processing topology.
        // We opt for this unconditional call here because this will make it easier for you to play around with the example
        // when resetting the application for doing a re-run (via the Application Reset Tool,
        // https://docs.confluent.io/platform/current/streams/developer-guide/app-reset-tool.html).
        //
        // The drawback of cleaning up local state prior is that your app must rebuilt its local state from scratch, which
        // will take time and will require reading all the state-relevant data from the Kafka cluster over the network.
        // Thus in a production scenario you typically do not want to clean up always as we do here but rather only when it
        // is truly needed, i.e., only under certain conditions (e.g., the presence of a command line flag for your app).
        // See `ApplicationResetExample.java` for a production-like example.
        streams.cleanUp();

        // Now run the processing topology via `start()` to begin processing its input data.
        streams.start();

        // Add shutdown hook to respond to SIGTERM and gracefully close the Streams application.
        Runtime.getRuntime().addShutdownHook(new Thread(streams::close));
    }

    /**
     * Configure the Streams application.
     *
     * Various Kafka Streams related settings are defined here such as the location of the target Kafka cluster to use.
     * Additionally, you could also define Kafka Producer and Kafka Consumer settings when needed.
     *
     * @param bootstrapServers Kafka cluster address
     * @return Properties getStreamsConfiguration
     */
    static Properties getStreamsConfiguration(final String bootstrapServers) {
        final Properties streamsConfiguration = new Properties();
        // Give the Streams application a unique name.  The name must be unique in the Kafka cluster
        // against which the application is run.
        streamsConfiguration.put(StreamsConfig.APPLICATION_ID_CONFIG, "wordcount-lambda-example");
        streamsConfiguration.put(StreamsConfig.CLIENT_ID_CONFIG, "wordcount-lambda-example-client");
        // Where to find Kafka broker(s).
        streamsConfiguration.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, bootstrapServers);
        // Specify default (de)serializers for record keys and for record values.
        streamsConfiguration.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass().getName());
        streamsConfiguration.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass().getName());
        // Records should be flushed every 10 seconds. This is less than the default
        // in order to keep this example interactive.
        streamsConfiguration.put(StreamsConfig.COMMIT_INTERVAL_MS_CONFIG, 10 * 1000);
        // For illustrative purposes we disable record caches.
        streamsConfiguration.put(StreamsConfig.CACHE_MAX_BYTES_BUFFERING_CONFIG, 0);
        streamsConfiguration.put(StreamsConfig.topicPrefix(TopicConfig.MIN_IN_SYNC_REPLICAS_CONFIG), 1);

        // Authentication
        streamsConfiguration.put("sasl.jaas.config", "org.apache.kafka.common.security.plain.PlainLoginModule required "+
        "username=\""+username+"\" " +
        "password=\""+password+"\";");
        streamsConfiguration.put("security.protocol","SASL_SSL");
        streamsConfiguration.put("sasl.mechanism","PLAIN");

        // in Starlight for Kafka replication is handled by BookKeeper
        // and you usually set it at namespace level
        // in KStreams you can set it to 1, but the Broker
        // will apply the namespace policies
        streamsConfiguration.put("replication.factor","1");


        return streamsConfiguration;
    }

    /**
     * Define the processing topology for Word Count.
     *
     * @param builder StreamsBuilder to use
     */
    static void createWordCountStream(final StreamsBuilder builder) {
        // Construct a `KStream` from the input topic "streams-plaintext-input", where message values
        // represent lines of text (for the sake of this example, we ignore whatever may be stored
        // in the message keys).  The default key and value serdes will be used.
        final KStream<String, String> textLines = builder.stream(inputTopic);

        final Pattern pattern = Pattern.compile("\\W+", Pattern.UNICODE_CHARACTER_CLASS);

        final KTable<String, Long> wordCounts = textLines
                // Split each text line, by whitespace, into words.  The text lines are the record
                // values, i.e. we can ignore whatever data is in the record keys and thus invoke
                // `flatMapValues()` instead of the more generic `flatMap()`.
                .flatMapValues(value -> Arrays.asList(pattern.split(value.toLowerCase())))
                // Group the split data by word so that we can subsequently count the occurrences per word.
                // This step re-keys (re-partitions) the input data, with the new record key being the words.
                // Note: No need to specify explicit serdes because the resulting key and value types
                // (String and String) match the application's default serdes.
                .groupBy((keyIgnored, word) -> word)
                // Count the occurrences of each word (record key).
                .count();

        // Write the `KTable<String, Long>` to the output topic.
        wordCounts.toStream().to(outputTopic, Produced.with(Serdes.String(), Serdes.Long()));

        wordCounts.toStream().print(Printed.toSysOut());
    }

}

[wordcount-lambda-example-client-StreamThread-1] INFO org.apache.kafka.clients.consumer.KafkaConsumer - [Consumer clientId=wordcount-lambda-example-client-StreamThread-1-consumer, groupId=wordcount-lambda-example] Requesting the log end offset for streams-plaintext-input-0 in order to compute lag
[wordcount-lambda-example-client-StreamThread-1] INFO org.apache.kafka.clients.consumer.KafkaConsumer - [Consumer clientId=wordcount-lambda-example-client-StreamThread-1-consumer, groupId=wordcount-lambda-example] Requesting the log end offset for wordcount-lambda-example-KSTREAM-AGGREGATE-STATE-STORE-0000000003-repartition-0 in order to compute lag
[KTABLE-TOSTREAM-0000000010]: hello, 1
[KTABLE-TOSTREAM-0000000010]: hello, 2
[KTABLE-TOSTREAM-0000000010]: again, 1
[KTABLE-TOSTREAM-0000000010]: hello, 3
[KTABLE-TOSTREAM-0000000010]: hello, 4
[KTABLE-TOSTREAM-0000000010]: again, 2