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title: Introduction description: weight: 1 tags: [‘kafka’, ‘docs’] aliases: keywords: type: docs

Kafka Streams

The easiest way to write mission-critical real-time applications and microservices

Kafka Streams is a client library for building applications and microservices, where the input and output data are stored in Kafka clusters. It combines the simplicity of writing and deploying standard Java and Scala applications on the client side with the benefits of Kafka's server-side cluster technology.

Tour of the Streams API

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Why you'll love using Kafka Streams!

• Elastic, highly scalable, fault-tolerant
• Deploy to containers, VMs, bare metal, cloud
• Equally viable for small, medium, & large use cases
• Fully integrated with Kafka security
• Write standard Java and Scala applications
• Exactly-once processing semantics
• No separate processing cluster required
• Develop on Mac, Linux, Windows

Write your first app

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Kafka Streams use cases

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Hello Kafka Streams

The code example below implements a WordCount application that is elastic, highly scalable, fault-tolerant, stateful, and ready to run in production at large scale

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import org.apache.kafka.common.serialization.Serdes;
import org.apache.kafka.common.utils.Bytes;
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.Materialized;
import org.apache.kafka.streams.kstream.Produced;
import org.apache.kafka.streams.state.KeyValueStore;

import java.util.Arrays;
import java.util.Properties;

public class WordCountApplication {

   public static void main(final String[] args) throws Exception {
       Properties props = new Properties();
       props.put(StreamsConfig.APPLICATION_ID_CONFIG, "wordcount-application");
       props.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "kafka-broker1:9092");
       props.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
       props.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass());

       StreamsBuilder builder = new StreamsBuilder();
       KStream<String, String> textLines = builder.stream("TextLinesTopic");
       KTable<String, Long> wordCounts = textLines
           .flatMapValues(textLine -> Arrays.asList(textLine.toLowerCase().split("\W+")))
           .groupBy((key, word) -> word)
           .count(Materialized.<String, Long, KeyValueStore<Bytes, byte[]>>as("counts-store"));
       wordCounts.toStream().to("WordsWithCountsTopic", Produced.with(Serdes.String(), Serdes.Long()));

       KafkaStreams streams = new KafkaStreams(builder.build(), props);
       streams.start();
   }

}

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import java.util.Properties
import java.util.concurrent.TimeUnit

import org.apache.kafka.streams.kstream.Materialized
import org.apache.kafka.streams.scala.ImplicitConversions._
import org.apache.kafka.streams.scala._
import org.apache.kafka.streams.scala.kstream._
import org.apache.kafka.streams.{KafkaStreams, StreamsConfig}

object WordCountApplication extends App {
  import Serdes._

  val props: Properties = {
    val p = new Properties()
    p.put(StreamsConfig.APPLICATION_ID_CONFIG, "wordcount-application")
    p.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "kafka-broker1:9092")
    p
  }

  val builder: StreamsBuilder = new StreamsBuilder
  val textLines: KStream[String, String] = builder.stream[String, String]("TextLinesTopic")
  val wordCounts: KTable[String, Long] = textLines
    .flatMapValues(textLine => textLine.toLowerCase.split("\W+"))
    .groupBy((_, word) => word)
    .count()(Materialized.as("counts-store"))
  wordCounts.toStream.to("WordsWithCountsTopic")

  val streams: KafkaStreams = new KafkaStreams(builder.build(), props)
  streams.start()

  sys.ShutdownHookThread {
     streams.close(10, TimeUnit.SECONDS)
  }
}

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