Read json from Kafka and write json to other Kafka topic

Question

I'm trying prepare application for Spark streaming (Spark 2.1, Kafka 0.10)

I need to read data from Kafka topic "input", find correct data and write result to topic "output"

I can read data from Kafka base on KafkaUtils.createDirectStream method.

I converted the RDD to json and prepare filters:

val messages = KafkaUtils.createDirectStream[String, String](
  ssc,
  PreferConsistent,
  Subscribe[String, String](topics, kafkaParams)
)

val elementDstream = messages.map(v => v.value).foreachRDD { rdd =>

  val PeopleDf=spark.read.schema(schema1).json(rdd)
  import spark.implicits._
  PeopleDf.show()
  val PeopleDfFilter = PeopleDf.filter(($"value1".rlike("1"))||($"value2" === 2))
  PeopleDfFilter.show()
}

I can load data from Kafka and write "as is" to Kafka use KafkaProducer:

    messages.foreachRDD( rdd => {
      rdd.foreachPartition( partition => {
        val kafkaTopic = "output"
        val props = new HashMap[String, Object]()
        props.put(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG, "localhost:9092")
        props.put(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG,
          "org.apache.kafka.common.serialization.StringSerializer")
        props.put(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG,
          "org.apache.kafka.common.serialization.StringSerializer")

        val producer = new KafkaProducer[String, String](props)
        partition.foreach{ record: ConsumerRecord[String, String] => {
        System.out.print("########################" + record.value())
        val messageResult = new ProducerRecord[String, String](kafkaTopic, record.value())
        producer.send(messageResult)
        }}
        producer.close()
      })

    })

However, I cannot integrate those two actions > find in json proper value and write findings to Kafka: write PeopleDfFilter in JSON format to "output" Kafka topic.

I have a lot of input messages in Kafka, this is the reason I want to use foreachPartition to create the Kafka producer.

Answer 1

The process is very simple so why not use structured streaming all the way?

import org.apache.spark.sql.functions.from_json

spark
  // Read the data
  .readStream
  .format("kafka")
  .option("kafka.bootstrap.servers", inservers) 
  .option("subscribe", intopic)
  .load()
  // Transform / filter
  .select(from_json($"value".cast("string"), schema).alias("value"))
  .filter(...)  // Add the condition
  .select(to_json($"value").alias("value")
  // Write back
  .writeStream
  .format("kafka")
  .option("kafka.bootstrap.servers", outservers)
  .option("subscribe", outtopic)
  .start()

Answer 2

Try using Structured Streaming for that. Even if you used Spark 2.1, you may implement your own Kafka ForeachWriter as followed:

Kafka sink:

import java.util.Properties
import kafkashaded.org.apache.kafka.clients.producer._
import org.apache.spark.sql.ForeachWriter


 class  KafkaSink(topic:String, servers:String) extends ForeachWriter[(String, String)] {
      val kafkaProperties = new Properties()
      kafkaProperties.put("bootstrap.servers", servers)
      kafkaProperties.put("key.serializer",
        classOf[org.apache.kafka.common.serialization.StringSerializer].toString)
      kafkaProperties.put("value.serializer",
        classOf[org.apache.kafka.common.serialization.StringSerializer].toString)
      val results = new scala.collection.mutable.HashMap[String, String]
      var producer: KafkaProducer[String, String] = _

      def open(partitionId: Long,version: Long): Boolean = {
        producer = new KafkaProducer(kafkaProperties)
        true
      }

      def process(value: (String, String)): Unit = {
          producer.send(new ProducerRecord(topic, value._1 + ":" + value._2))
      }

      def close(errorOrNull: Throwable): Unit = {
        producer.close()
      }
   }

Usage:

val topic = "<topic2>"
val brokers = "<server:ip>"

val writer = new KafkaSink(topic, brokers)

val query =
  streamingSelectDF
    .writeStream
    .foreach(writer)
    .outputMode("update")
    .trigger(ProcessingTime("25 seconds"))
    .start()