Spark Kafka Streaming CommitAsync Error [duplicate]

问题

I am new to Scala and RDD concept. Reading message from kafka using Kafka stream api in Spark and trying to commit after business work. but I am getting error.

Note: Using repartition for Parallel work

How to read offset from stream APi and commit it to Kafka ?

scalaVersion := "2.11.8" val sparkVersion = "2.2.0" val connectorVersion = "2.0.7" val kafka_stream_version = "1.6.3"

Code

    val ssc = new StreamingContext(spark.sparkContext, Seconds(2))
    ssc.checkpoint("C:/Gnana/cp")

    val kafkaStream = {

      val kafkaParams = Map[String, Object](
        "bootstrap.servers" -> "localhost:9092",
        "key.deserializer" -> classOf[StringDeserializer],
        "value.deserializer" -> classOf[StringDeserializer],
        "group.id" -> "ignite3",

        "auto.offset.reset" -> "latest",
        "enable.auto.commit" -> (false: java.lang.Boolean)
      )

      val topics = Array("test")
      val numPartitionsOfInputTopic = 2
      val streams = (1 to numPartitionsOfInputTopic) map {
        _ => KafkaUtils.createDirectStream[String, String]( ssc, PreferConsistent, Subscribe[String, String](topics, kafkaParams) ).map(_.value())
      }

      val unifiedStream = ssc.union(streams)
      val sparkProcessingParallelism = 1
      unifiedStream.repartition(sparkProcessingParallelism)
    }
//Finding offsetRanges
kafkaStream
  .transform {
    rdd =>
      offsetRanges = rdd.asInstanceOf[HasOffsetRanges].offsetRanges
      rdd
  }
//do business operation and persist offset to kafka
kafkaStream.foreachRDD(rdd=> {
  println("offsetRanges:"+offsetRanges)
  rdd.foreach(conRec=> {
    println(conRec)
    kafkaStream.asInstanceOf[CanCommitOffsets].commitAsync(offsetRanges)
  })
})

    println(" Spark parallel reader is ready !!!")

   ssc.start()
    ssc.awaitTermination()
  }

Error

java.io.NotSerializableException: Object of org.apache.spark.streaming.dstream.TransformedDStream is being serialized possibly as a part of closure of an RDD operation. This is because the DStream object is being referred to from within the closure. Please rewrite the RDD operation inside this DStream to avoid this. This has been enforced to avoid bloating of Spark tasks with unnecessary objects. at org.apache.spark.streaming.dstream.DStream$$anonfun$writeObject$1.apply$mcV$sp(DStream.scala:525) at org.apache.spark.streaming.dstream.DStream$$anonfun$writeObject$1.apply(DStream.scala:512) at org.apache.spark.streaming.dstream.DStream$$anonfun$writeObject$1.apply(DStream.scala:512) at org.apache.spark.util.Utils$.tryOrIOException(Utils.scala:1303) at org.apache.spark.streaming.dstream.DStream.writeObject(DStream.scala:512) at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) at java.lang.reflect.Method.invoke(Method.java:498) at java.io.ObjectStreamClass.invokeWriteObject(ObjectStreamClass.java:1028) at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1496) at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432) at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178) at java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548) at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509) at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432) at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178) at java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1548) at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1509) at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1432) at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1178) at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:348) at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:43)

回答1:

Don't repartition before calculating offset ranges. If you do so then will encounter this issue. To test you simply remove repartition and then try running this application.

来源：https://stackoverflow.com/questions/49344461/spark-kafka-streaming-commitasync-error

标签

scala

apache-spark

spark-streaming

rdd

scala-streams