Couldn’t you readStream from Kafka, do your transformations, map your rows from the transformed input into what you want need to send to Kafka, then writeStream to Kafka?
From: Liana Napalkova <liana.napalk...@eurecat.org> Date: Monday, December 18, 2017 at 10:07 AM To: Silvio Fiorito <silvio.fior...@granturing.com>, "user@spark.apache.org" <user@spark.apache.org> Subject: Re: How to properly execute `foreachPartition` in Spark 2.2 I need to firstly read from Kafka queue into a DataFrame. Then I should perform some transformations with the data. Finally, for each row in the DataFrame I should conditionally apply KafkaProducer in order to send some data to Kafka. So, I am both consuming and producing the data from/to Kafka. ________________________________ From: Silvio Fiorito <silvio.fior...@granturing.com> Sent: 18 December 2017 16:00:39 To: Liana Napalkova; user@spark.apache.org Subject: Re: How to properly execute `foreachPartition` in Spark 2.2 Why don’t you just use the Kafka sink for Spark 2.2? https://spark.apache.org/docs/2.2.0/structured-streaming-kafka-integration.html#creating-a-kafka-sink-for-streaming-queries From: Liana Napalkova <liana.napalk...@eurecat.org> Date: Monday, December 18, 2017 at 9:45 AM To: "user@spark.apache.org" <user@spark.apache.org> Subject: How to properly execute `foreachPartition` in Spark 2.2 Hi, I wonder how to properly execute `foreachPartition` in Spark 2.2. Below I explain the problem is details. I appreciate any help. In Spark 1.6 I was doing something similar to this: DstreamFromKafka.foreachRDD(session => { session.foreachPartition { partitionOfRecords => println("Setting the producer.") val producer = Utils.createProducer(mySet.value("metadataBrokerList"), mySet.value("batchSize"), mySet.value("lingerMS")) partitionOfRecords.foreach(s => { //... However, I cannot find the proper way to do the similar thing in Spark 2.2. I tried to write my own class by extending `ForeachWriter`, but I get Task Serialization error when passing `KafkaProducer`. class MyTestClass( // val inputparams: String) extends Serializable { val spark = SparkSession .builder() .appName("TEST") //.config("spark.sql.warehouse.dir", kafkaData) .enableHiveSupport() .getOrCreate() import spark.implicits._ val df: Dataset[String] = spark.readStream .format("kafka") .option("kafka.bootstrap.servers", "localhost:9092") .option("subscribe", "test") .option("startingOffsets", "latest") .option("failOnDataLoss", "true") .load() .selectExpr("CAST(key AS STRING)", "CAST(value AS STRING)").as[(String, String)] // Kafka sends bytes .map(_._2) val producer = // create KafkaProducer val writer = new MyForeachWriter(producer: KafkaProducer[String,String]) val query = df .writeStream .foreach(writer) .start query.awaitTermination() spark.stop() class MyForeachWriter extends ForeachWriter[String] with Serializable { var producer: KafkaProducer[String,String] = _ def this(producer: KafkaProducer[String,String]) { this() this.producer = producer } override def process(row: String): Unit = { // ... } override def close(errorOrNull: Throwable): Unit = {} override def open(partitionId: Long, version: Long): Boolean = { true } } Liana Napalkova, PhD Big Data Analytics Unit ________________________________ [http://cdn.eurecat.org/imgs/logomailEurecat.jpg] T +34 93 238 14 00 (ext. 1248) M +34 633 426 677 liana.napalk...@eurecat.org ________________________________ Carrer Camí Antic de València 54-56, Edifici A - 08005 - Barcelona www.eurecat.org Ascamm, BDigital, Barcelona Media i Cetemmsa ara som Eurecat ________________________________ DISCLAIMER: Aquest missatge pot contenir informació confidencial. 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