README.md

spark-kafka-writer

Write your Spark data to Kafka seamlessly

Installation

spark-kafka-writer is available on maven central with the following coordinates depending on whether you're using Kafka 0.8 or 0.10 and your version of Spark:

	Kafka 0.8	Kafka 0.10
Spark 2.4.X	❌	"com.github.benfradet" %% "spark-kafka-writer" % "0.5.0"
Spark 2.2.X	❌	"com.github.benfradet" %% "spark-kafka-writer" % "0.4.0"
Spark 2.1.X	"com.github.benfradet" %% "spark-kafka-0-8-writer" % "0.3.0"	"com.github.benfradet" %% "spark-kafka-0-10-writer" % "0.3.0"
Spark 2.0.X	"com.github.benfradet" %% "spark-kafka-0-8-writer" % "0.2.0"	"com.github.benfradet" %% "spark-kafka-0-10-writer" % "0.2.0"
Spark 1.6.X	"com.github.benfradet" %% "spark-kafka-writer" % "0.1.0"	❌

Usage

Without callbacks

• if you want to save an RDD to Kafka:

import com.github.benfradet.spark.kafka.writer._
import org.apache.kafka.common.serialization.StringSerializer

val topic = "my-topic"
val producerConfig = Map(
  "bootstrap.servers" -> "127.0.0.1:9092",
  "key.serializer" -> classOf[StringSerializer].getName,
  "value.serializer" -> classOf[StringSerializer].getName
)

val rdd: RDD[String] = ...
rdd.writeToKafka(
  producerConfig,
  s => new ProducerRecord[String, String](topic, s)
)

• if you want to save a DStream to Kafka:

import com.github.benfradet.spark.kafka.writer._
import org.apache.kafka.common.serialization.StringSerializer

val dStream: DStream[String] = ...
dStream.writeToKafka(
  producerConfig,
  s => new ProducerRecord[String, String](topic, s)
)

• if you want to save a Dataset to Kafka:

import com.github.benfradet.spark.kafka.writer._
import org.apache.kafka.common.serialization.StringSerializer

case class Foo(a: Int, b: String)
val dataset: Dataset[Foo] = ...
dataset.writeToKafka(
  producerConfig,
  foo => new ProducerRecord[String, String](topic, foo.toString)
)

• if you want to write a DataFrame to Kafka:

import com.github.benfradet.spark.kafka.writer._
import org.apache.kafka.common.serialization.StringSerializer

val dataFrame: DataFrame = ...
dataFrame.writeToKafka(
  producerConfig,
  row => new ProducerRecord[String, String](topic, row.toString)
)

With callbacks

It is also possible to assign a Callback from the Kafka Producer API that will be triggered after each write, this has a default value of None.

The Callback must implement the onCompletion method and the Exception parameter will be null if the write was successful.

Any Callback implementations will need to be serializable to be used in Spark.

For example, if you want to use a Callback when saving an RDD to Kafka:

// replace by kafka08 if you're using Kafka 0.8
import com.github.benfradet.spark.kafka010.writer._
import org.apache.kafka.clients.producer.{Callback, ProducerRecord, RecordMetadata}

@transient lazy val log = org.apache.log4j.Logger.getLogger("spark-kafka-writer")

val rdd: RDD[String] = ...
rdd.writeToKafka(
  producerConfig,
  s => new ProducerRecord[String, String](topic, s),
  Some(new Callback with Serializable {
    override def onCompletion(metadata: RecordMetadata, e: Exception): Unit = {
      if (Option(e).isDefined) {
        log.warn("error sending message", e)
      } else {
        log.info(s"write succeeded! offset: ${metadata.offset()}")
      }
    }
  })
)

Check out the Kafka documentation to know more about callbacks.

Java usage

It's also possible to use the library from Java, for example if we were to write a DStream to Kafka:

// Define a serializable Function1 separately
abstract class SerializableFunc1<T, R> extends AbstractFunction1<T, R> implements Serializable {}

Map<String, Object> producerConfig = new HashMap<String, Object>();
producerConfig.put("bootstrap.servers", "localhost:9092");
producerConfig.put("key.serializer", StringSerializer.class);
producerConfig.put("value.serializer", StringSerializer.class);

KafkaWriter<String> kafkaWriter = new DStreamKafkaWriter<>(javaDStream.dstream(),
    scala.reflect.ClassTag$.MODULE$.apply(String.class));
kafkaWriter.writeToKafka(producerConfig.asScala,
    new SerializableFunc1<String, ProducerRecord<String, String>>() {
        @Override
        public ProducerRecord<String, String> apply(final String s) {
            return new ProducerRecord<>(topic, s);
        }
    },
    //new Some<>((metadata, exception) -> {}), // with callback, define your lambda here.
    Option.empty()                             // or without callback.
);

However, #59 will provide a better Java API.

Scaladoc

You can find the full scaladoc at https://benfradet.github.io/spark-kafka-writer.

Credit

The original code was written by Hari Shreedharan.