Apache kafka 读卡夫卡8.1，写卡夫卡9.0

我有一个要求，即我应该阅读来自kafka v.8.1（在集群a中）的消息并写入kafka v.9.0（在集群B中）

我正在使用spark streaming来阅读卡夫卡，并使用spark原生卡夫卡类将消息推送到卡夫卡B中

它给了我以下的错误

16/10/20 15:29:34 INFO VerifiableProperties: Property zookeeper.connect is overridden to
16/10/20 15:29:34 ERROR Executor: Exception in task 0.0 in stage 1.0 (TID 1)
java.lang.IllegalArgumentException
        at java.nio.Buffer.limit(Buffer.java:275)
        at kafka.api.FetchResponsePartitionData$.readFrom(FetchResponse.scala:37)
        at kafka.api.TopicData$$anonfun$1.apply(FetchResponse.scala:99)
        at kafka.api.TopicData$$anonfun$1.apply(FetchResponse.scala:97)
        at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
        at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
        at scala.collection.immutable.Range.foreach(Range.scala:141)


16/10/20 15:29:34 ERROR Executor: Exception in task 1.0 in stage 1.0 (TID 2)
java.lang.IllegalArgumentException
        at java.nio.Buffer.limit(Buffer.java:275)
        at kafka.api.FetchResponsePartitionData$.readFrom(FetchResponse.scala:37)
        at kafka.api.TopicData$$anonfun$1.apply(FetchResponse.scala:99)
        at kafka.api.TopicData$$anonfun$1.apply(FetchResponse.scala:97)
        at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
        at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
        at scala.collection.immutable.Range.foreach(Range.scala:141)

请在下面查找代码段：

val topicsAndOffsets: scala.collection.immutable.Map[TopicAndPartition, Long] = sc.newAPIHadoopRDD(dhrconf, classOf[TableInputFormat],
classOf[ImmutableBytesWritable], classOf[Result]) .map(x => x._2)     .map(result => ( TopicAndPartition( 
HBaseHelper.getValue(result, HBASE_DEFAULT_CF, HBASE_COL_COUNTER_TOP), 
HBaseHelper.getValueAsInt(result, HBASE_DEFAULT_CF, HBASE_COL_COUNTER_KPART)), 
HBaseHelper.getValueAsLong(result, HBASE_DEFAULT_CF, HBASE_COL_COUNTER_NEXT))) 
.collectAsMap.toMap

 val kafkaParams = Map[String, String]( "metadata.broker.list" -> brokerlist_src, "auto.offset.reset" -> "smallest")

//reading kafka as stream 
    var messages: Option[InputDStream[(String, String)]] = None if (topicsAndOffsets.isEmpty) 
{ // Start the stream 
messages = Some(KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder]( streaming, kafkaParams, Set(topic_src))) 
}
 else 
{ 
messages =    Some(KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder, (String, String)]( streaming, kafkaParams, topicsAndOffsets, (mmd: MessageAndMetadata[String, String]) => (mmd.key, mmd.message))) 
}

    messages.get.foreachRDD { rdd =>

if (!rdd.isEmpty) { val offsetRanges =   rdd.asInstanceOf[HasOffsetRanges].offsetRanges 
val nParts = rdd.partitions.size

rdd.foreachPartition { sparkPartition =>

if (!sparkPartition.isEmpty) { //kafka partition details 
val currid =  TaskContext.get.partitionId 
val osr: OffsetRange = offsetRanges(currid) 
val untilOffset = osr.untilOffset //exclusive 
val id = osr.partition 
val topic_src = osr.topic

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需要帮助来解决此问题…感谢您的帮助：）

向我们展示您的代码。