README.md

kafka-topic-loader

Reads the contents of provided Kafka topics, either the topics in their entirety or up until a consumer groups last committed Offset depending on which LoadTopicStrategy you provide.

As of version 1.3.0, data can be loaded either from complete topics using load or loadAndRun.

Since version 1.4.0 the library is cross compiled for scala versions 2.12 and 2.13.

Since version 1.6.0 the library is cross compiled for scala versions 2.12, 2.13 and 3.

Since version 2.0.0 the library is no longer cross compiles for version 2.12, and the package has been renamed from com.sky to uk.sky.

Add the following to your build.sbt:

libraryDependencies += "uk.sky" %% "kafka-topic-loader" % "<version>"

import uk.sky.kafka.topicloader.{LoadAll, TopicLoader}
import org.apache.kafka.common.serialization.Deserializer}

implicit val as: ActorSystem = ActorSystem()
implicit val stringDeserializer: Deserializer[String] = new StringDeserializer

val stream = TopicLoader.load[String, String](NonEmptyList.one("topic-to-load"), LoadAll)
      .mapAsync(1)(_ => ??? /* store records in akka.Actor for example */)
      .runWith(Sink.ignore)

loadAndRun will load the topics, complete the Future[Done] from the materialised value and then carry on running, emitting any new records that appear on the topics. An example use-case for this is a REST API that holds the contents of a Kafka topic in memory. This kind of application doesn't need to commit offsets and can use the Future[Done] to determine readiness.

object Main extends App {

  implicit val system = ActorSystem()
  implicit val mat    = ActorMaterializer()

  import system.dispatcher

  implicit val keyDeserializer: Deserializer[String]        = new StringDeserializer
  implicit val valueDeserializer: Deserializer[Array[Byte]] = new ByteArrayDeserializer

  val state = new SimplifiedState

  val (initialLoadingFuture, controlF): (Future[Done], Future[Consumer.Control]) =
    TopicLoader
      .loadAndRun[String, Array[Byte]](NonEmptyList.one("topic-to-load"))
      .to(Sink.foreach(record => state.store.put(record.key, record.value)))
      .run()

  initialLoadingFuture.foreach(_ => state.isAppReady.set(true))
}

class SimplifiedState {

  /**
    * API requests may query this state
    */
  val store = new ConcurrentHashMap[String, Array[Byte]]()

  /**
    * A readiness endpoint could be created that queries this
    */
  val isAppReady = new AtomicBoolean()
}

Configuration

Topic loader

The config in reference.conf can be overridden by providing your own application.conf.

By default, Akka's ConsumerConfig will inherit the consumer client.id from the application kafka-topic-loader is running from. To separate the client id of your application and the kafka-topic-loader, provide it in your application.conf:

topic-loader {
  client-id = "custom-client-id"
}

Akka-kafka

You should configure the akka.kafka.consumer.kafka-clients.group.id to match that of your application, e.g.:

akka.kafka {
  consumer.kafka-clients {
    bootstrap.servers = ${?KAFKA_BROKERS}
    group.id = assembler-consumer-group
  }
  producer.kafka-clients {
    bootstrap.servers = ${?KAFKA_BROKERS}
  }
}

Source per partition

This is deprecated in favour of a new API for partitioned loading which is coming soon.

Data can also be loaded from specific partitions using fromPartitions. By loading from specific partitions the topic loader can be used by multiple application instances with separate streams per set of partitions (see Alpakka kafka and below).

implicit val system = ActorSystem()

val consumerSettings: ConsumerSettings[String, Long]              = ???
val doBusinessLogic: ConsumerRecord[String, Long] => Future[Unit] = ???

val stream: Source[ConsumerMessage.CommittableMessage[String, Long], Consumer.Control] =
  Consumer
    .committablePartitionedSource(consumerSettings, Subscriptions.topics("topic-to-load"))
    .flatMapConcat {
      case (topicPartition, source) =>
        TopicLoader
          .fromPartitions(LoadAll, NonEmptyList.one(topicPartition), doBusinessLogic, new LongDeserializer())
          .flatMapConcat(_ => source)
    }