If there is no other way, then I will follow this recommendation.
________________________________ From: Silvio Fiorito <silvio.fior...@granturing.com> Sent: 18 December 2017 16:20:03 To: Liana Napalkova; user@spark.apache.org Subject: Re: How to properly execute `foreachPartition` in Spark 2.2 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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