...

val kafka_streams_scala_version = "0.1.2"
libraryDependencies ++= Seq("com.lightbend" %% "kafka-streams-scala" % kafka_streams_scala_version)

Maven

<dependency>
 <groupId>com.lightbend</groupId>
 <artifactId>kafka-streams-scala_2.12</artifactId>
 <version>0.1.2</version>
</dependency>

...

compile 'com.lightbend:kafka-streams-scala_2.12:0.1.2'

Dependencies

kafka-streams-scala only depends on the Scala standard library and Kafka Streams 1.0.0.

Sample Usage

The library works by wrapping the original Java abstractions of Kafka Streams within a Scala wrapper object and then using implicit conversions between them. All the Scala abstractions are named with an S appended to the name of the corresponding Java abstraction e.g. StreamBuilderS is a wrapper around StreamBuilder, KStreamS is a wrapper around KStream.

Here's an example of the classic Word Count program that uses the Scala builder StreamBuilderS (a wrapper around StreamBuilder) and then builds an instance of KStreamS (a wrapper around KStream) using the wrapped API builder.stream. Then we reify to a table and get a KTableS, which, again is a wrapper around KTable.

The net result is that the following code is structured just like using the Java API, but from Scala and with far fewer type annotations compared to using the Java API directly from Scala. The difference in type annotation usage will be more obvious when we use a more complicated example. The library comes with a test suite of a few examples that demonstrate these capabilities.

import DefaultSerdes._
 
val builder = new StreamsBuilderS
val textLines = builder.stream[String, String](inputTopic)

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

val wordCounts: KTableS[String, Long] =

 textLines.flatMapValues(v => pattern.split(v.toLowerCase))
   .groupBy((k, v) => v)
   .count()
wordCounts.toStream.to(outputTopic)

val streams = new KafkaStreams(builder.build, streamsConfiguration)

streams.start()

In the above code snippet, we don't have to provide any serdes, Serialized, Produced, Consumed or Joined explicitly. They will also not be dependent on any serdes specified in the config - in fact all serdes specified in the config will be ignored by the Scala APIs. All serdes and Serialized, Produced, Consumed or Joined will be handled through implicit serdes as discussed later in the document. The complete independence from configuration based serdes is what makes this library completely type-safe - any missing instances of serdes, Serialized, Produced, Consumed or Joined will be flagged as a compile time error.

// Compute the total per region by summing the individual click counts per region.
val clicksPerRegion: KTableS[String, Long] =
 userClicksStream
   // Join the stream against the table
   .leftJoin(userRegionsTable,
     (clicks: Long, region: String) =>
       (if (region == null) "UNKNOWN" else region, clicks))

   // Change the stream from <user> -> <region, clicks> to <region> -> <clicks>
   .map((_, regionWithClicks) => regionWithClicks)

   // Compute the total per region by summing the individual click counts per region.
   .groupByKey
   .reduce(_ + _)

// DefaultSerdes brings into scope implicit serdes (mostly for primitives)
// that will set up all Serialized, Produced, Consumed and Joined instances.
// So all APIs below that accept Serialized, Produced, Consumed or Joined will
// get these instances automatically
import DefaultSerdes._

val builder = new StreamsBuilderS()

val userClicksStream: KStreamS[String, Long] = builder.stream(userClicksTopic)

val userRegionsTable: KTableS[String, String] = builder.table(userRegionsTopic)

// The following code fragment does not have a single instance of Serialized,
// Produced, Consumed or Joined supplied explicitly.
// All of them are taken care of by the implicit serdes imported by DefaultSerdes
val clicksPerRegion: KTableS[String, Long] =
  userClicksStream
    .leftJoin(userRegionsTable,
      (clicks: Long, region: String) =>
        (if (region == null) "UNKNOWN" else region, clicks))
    .map((_, regionWithClicks) => regionWithClicks)
    .groupByKey
    .reduce(_ + _)

clicksPerRegion.toStream.to(outputTopic)

Compatibility, Deprecation, and Migration Plan

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