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< h1 > Kafka Streams API< / h1 >
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< h3 class = "streams_intro" > The easiest way to write mission-critical real-time applications and microservices< / h3 >
< p class = "streams__description" > 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.< / p >
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< h3 > TOUR OF THE STREAMS API< / h3 >
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< span class = "number" > 1< / span > < span class = "video__text" > Intro to Streams< / span >
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< span class = "number" > 2< / span > < span class = "video__text" > Creating a Streams Application< / span >
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< span class = "number" > 3< / span > < span class = "video__text" > Transforming Data Pt. 1< / span >
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< span class = "number" > 4< / span > < span class = "video__text" > Transforming Data Pt. 11< / span >
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< h3 > Why you'll love using Kafka Streams!< / h3 >
< ul class = "use-feature-list" >
< li > Elastic, highly scalable, fault-tolerant< / li >
< li > Deploy to containers, VMs, bare metal, cloud< / li >
< li > Equally viable for small, medium, & large use cases< / li >
< li > Fully integrated with Kafka security< / li >
< li > Write standard Java applications< / li >
< li > Exactly-once processing semantics< / li >
< li > No seperate processing cluster required< / li >
< li > Develop on Mac, Linux, Windows< / li >
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< a href = "/{{version}}/documentation/streams/tutorial" class = "first__app__btn" > Write your first app< / a >
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< h3 class = "stream__text" > Streams API use cases< / h3 >
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< div class = "customer__item streams_logo_grid streams__ny__grid" >
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< span class = "grid__item__logo" style = "background-image: url('/images/powered-by/NYT.jpg');" > < / span >
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< p class = "grid__item__customer__description" >
< a href = "https://www.confluent.io/blog/publishing-apache-kafka-new-york-times/" > The New York Times uses Apache Kafka < / a > and the Kafka Streams API to store and distribute, in real-time, published content to the various applications and systems that make it available to the readers.
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< div class = "customer__item streams_logo_grid streams__line__grid" >
< a href = "https://linecorp.com/" target = "_blank" class = "grid__logo__link" >
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< p class = "grid__item__customer__description" > LINE uses Apache Kafka as a central datahub for our services to communicate to one another. Hundreds of billions of messages are produced daily and are used to execute various business logic, threat detection, search indexing and data analysis. LINE leverages Kafka Streams to reliably transform and filter topics enabling sub topics consumers can efficiently consume, meanwhile retaining easy maintainability thanks to its sophisticated yet minimal code base.< / p >
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< div class = "customer__item streams_logo_grid streams__zalando__grid" >
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< / a >
< p class = "grid__item__customer__description" > As the leading online fashion retailer in Europe, Zalando uses Kafka as an ESB (Enterprise Service Bus), which helps us in transitioning from a monolithic to a micro services architecture. Using Kafka for processing
< a href = "" ' https: = "" kafka-summit . org = "" sessions = "" using-kstreams-ktables-calculate-real-time-domain-rankings = "" ' = " " target = "blank'" > event streams< / a > enables our technical team to do near-real time business intelligence.
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< div class = "customer__item streams_logo_grid streams__rabobank__grid" >
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< p class = "grid__item__customer__description" > Rabobank is one of the 3 largest banks in the Netherlands. Its digital nervous system, the Business Event Bus, is powered by Apache Kafka. It is used by an increasing amount of financial processes and services, one of which is Rabo Alerts. This service alerts customers in real-time upon financial events and is built using Kafka Streams.< / p >
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< h3 style = "margin-top: 5.3rem;" > Hello Kafka Streams< / h3 >
< p > 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< / p >
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< a class = "selected b-java-8" data-section = "java-8" > Java 8+< / a >
< a class = "b-java-7" data-section = "java-7" > Java 7< / a >
< a class = "b-scala" data-section = "scala" > Scala< / a >
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< div class = "code-example__snippet b-java-8 selected" >
< pre class = "brush: java;" >
import org.apache.kafka.common.serialization.Serdes;
import org.apache.kafka.streams.KafkaStreams;
import org.apache.kafka.streams.StreamsConfig;
import org.apache.kafka.streams.kstream.KStream;
import org.apache.kafka.streams.kstream.KStreamBuilder;
import org.apache.kafka.streams.kstream.KTable;
import java.util.Arrays;
import java.util.Properties;
public class WordCountApplication {
public static void main(final String[] args) throws Exception {
Properties config = new Properties();
config.put(StreamsConfig.APPLICATION_ID_CONFIG, "wordcount-application");
config.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "kafka-broker1:9092");
config.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
config.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass());
KStreamBuilder builder = new KStreamBuilder();
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("Counts");
wordCounts.to(Serdes.String(), Serdes.Long(), "WordsWithCountsTopic");
KafkaStreams streams = new KafkaStreams(builder, config);
streams.start();
}
}
< / pre >
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< div class = "code-example__snippet b-java-7" >
< pre class = "brush: java;" >
import org.apache.kafka.common.serialization.Serdes;
import org.apache.kafka.streams.KafkaStreams;
import org.apache.kafka.streams.StreamsConfig;
import org.apache.kafka.streams.kstream.KStream;
import org.apache.kafka.streams.kstream.KStreamBuilder;
import org.apache.kafka.streams.kstream.KTable;
import org.apache.kafka.streams.kstream.KeyValueMapper;
import org.apache.kafka.streams.kstream.ValueMapper;
import java.util.Arrays;
import java.util.Properties;
public class WordCountApplication {
public static void main(final String[] args) throws Exception {
Properties config = new Properties();
config.put(StreamsConfig.APPLICATION_ID_CONFIG, "wordcount-application");
config.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "kafka-broker1:9092");
config.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass());
config.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass());
KStreamBuilder builder = new KStreamBuilder();
KStream< String, String> textLines = builder.stream("TextLinesTopic");
KTable< String, Long> wordCounts = textLines
.flatMapValues(new ValueMapper< String, Iterable< String> > () {
@Override
public Iterable< String> apply(String textLine) {
return Arrays.asList(textLine.toLowerCase().split("\\W+"));
}
})
.groupBy(new KeyValueMapper< String, String, String> () {
@Override
public String apply(String key, String word) {
return word;
}
})
.count("Counts");
wordCounts.to(Serdes.String(), Serdes.Long(), "WordsWithCountsTopic");
KafkaStreams streams = new KafkaStreams(builder, config);
streams.start();
}
}
< / pre >
< / div >
< div class = "code-example__snippet b-scala" >
< pre class = "brush: scala;" >
import java.lang.Long
import java.util.Properties
import java.util.concurrent.TimeUnit
import org.apache.kafka.common.serialization._
import org.apache.kafka.streams._
import org.apache.kafka.streams.kstream.{KStream, KStreamBuilder, KTable}
import scala.collection.JavaConverters.asJavaIterableConverter
object WordCountApplication {
def main(args: Array[String]) {
val config: Properties = {
val p = new Properties()
p.put(StreamsConfig.APPLICATION_ID_CONFIG, "wordcount-application")
p.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "kafka-broker1:9092")
p.put(StreamsConfig.DEFAULT_KEY_SERDE_CLASS_CONFIG, Serdes.String().getClass)
p.put(StreamsConfig.DEFAULT_VALUE_SERDE_CLASS_CONFIG, Serdes.String().getClass)
p
}
val builder: KStreamBuilder = new KStreamBuilder()
val textLines: KStream[String, String] = builder.stream("TextLinesTopic")
val wordCounts: KTable[String, Long] = textLines
.flatMapValues(textLine => textLine.toLowerCase.split("\\W+").toIterable.asJava)
.groupBy((_, word) => word)
.count("Counts")
wordCounts.to(Serdes.String(), Serdes.Long(), "WordsWithCountsTopic")
val streams: KafkaStreams = new KafkaStreams(builder, config)
streams.start()
Runtime.getRuntime.addShutdownHook(new Thread(() => {
streams.close(10, TimeUnit.SECONDS)
}))
}
}
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