Kafka streams in Starlight for Kafka

{} can run Kafka® Streams applications with minimal configuration changes. This document will show you how to make those changes and provide an example Java application to try out Kstreams with Starlight for Kafka.

Configuring username and password authentication (SASL with PLAIN mechanism)

To use the username and password authentication in Kstreams, set the following properties through Kafka JAAS.

sasl.jaas.config=org.apache.kafka.common.security.plain.PlainLoginModule required username="tenantname"
# security.protocol=SASL_SSL if SSL connection is being used

Configuring replication factor

In Starlight for Kafka, the replication factor is handled by BookKeeper and set at the namespace level.
Kafka Streams sets its own internal replication.factor number, but Pulsar will apply its namespace replication factor from BookKeeper.
Set replication.factor=1 in KStreams, or any positive integer.

Application code

This Java application will accept a stream of words from a KStreams input, process them, and print the number of occurences of each word to the KStreams output.

  1. Create two topics in the kafka namespace of your Pulsar tenant called streams-plaintext-input and streams-plaintext-output:

    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
  2. Add your username (Pulsar tenant), auth token, and broker URL, and run the program.

    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();
            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.
            // Now run the processing topology via `start()` to begin processing its input data.
            // 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+"\" " +
            // 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
            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).
            // Write the `KTable<String, Long>` to the output topic.
            wordCounts.toStream().to(outputTopic, Produced.with(Serdes.String(), Serdes.Long()));
  3. Running the program will generate an output like this:

    [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

If you are on an M1 Mac and encounter a jnilib error, add this dependency to the example application’s pom.xml file:


Starlight for Kafka video

Follow along with this video from our Five Minutes About Pulsar series to migrate from Kafka to Pulsar.

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