[
https://issues.apache.org/jira/browse/SPARK-19680?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16546891#comment-16546891
]
Gayathiri Duraikannu commented on SPARK-19680:
----------------------------------------------
Ours is a framework and multiple consumers use this to stream the data. We have
thousands of topics to consume the data from. Adding the start offset
explicitly wouldn't work for all of our use cases. Is there any other alternate
approach.
> Offsets out of range with no configured reset policy for partitions
> -------------------------------------------------------------------
>
> Key: SPARK-19680
> URL: https://issues.apache.org/jira/browse/SPARK-19680
> Project: Spark
> Issue Type: Bug
> Components: DStreams
> Affects Versions: 2.1.0
> Reporter: Schakmann Rene
> Priority: Major
>
> I'm using spark streaming with kafka to acutally create a toplist. I want to
> read all the messages in kafka. So I set
> "auto.offset.reset" -> "earliest"
> Nevertheless when I start the job on our spark cluster it is not working I
> get:
> Error:
> {code:title=error.log|borderStyle=solid}
> Job aborted due to stage failure: Task 2 in stage 111.0 failed 4 times,
> most recent failure: Lost task 2.3 in stage 111.0 (TID 1270, 194.232.55.23,
> executor 2): org.apache.kafka.clients.consumer.OffsetOutOfRangeException:
> Offsets out of range with no configured reset policy for partitions:
> {SearchEvents-2=161803385}
> {code}
> This is somehow wrong because I did set the auto.offset.reset property
> Setup:
> Kafka Parameter:
> {code:title=Config.Scala|borderStyle=solid}
> def getDefaultKafkaReceiverParameter(properties: Properties):Map[String,
> Object] = {
> Map(
> "bootstrap.servers" ->
> properties.getProperty("kafka.bootstrap.servers"),
> "group.id" -> properties.getProperty("kafka.consumer.group"),
> "auto.offset.reset" -> "earliest",
> "spark.streaming.kafka.consumer.cache.enabled" -> "false",
> "enable.auto.commit" -> "false",
> "key.deserializer" -> classOf[StringDeserializer],
> "value.deserializer" -> "at.willhaben.sid.DTOByteDeserializer")
> }
> {code}
> Job:
> {code:title=Job.Scala|borderStyle=solid}
> def processSearchKeyWords(stream: InputDStream[ConsumerRecord[String,
> Array[Byte]]], windowDuration: Int, slideDuration: Int, kafkaSink:
> Broadcast[KafkaSink[TopList]]): Unit = {
> getFilteredStream(stream.map(_.value()), windowDuration,
> slideDuration).foreachRDD(rdd => {
> val topList = new TopList
> topList.setCreated(new Date())
> topList.setTopListEntryList(rdd.take(TopListLength).toList)
> CurrentLogger.info("TopList length: " +
> topList.getTopListEntryList.size().toString)
> kafkaSink.value.send(SendToTopicName, topList)
> CurrentLogger.info("Last Run: " + System.currentTimeMillis())
> })
> }
> def getFilteredStream(result: DStream[Array[Byte]], windowDuration: Int,
> slideDuration: Int): DStream[TopListEntry] = {
> val Mapper = MapperObject.readerFor[SearchEventDTO]
> result.repartition(100).map(s => Mapper.readValue[SearchEventDTO](s))
> .filter(s => s != null && s.getSearchRequest != null &&
> s.getSearchRequest.getSearchParameters != null && s.getVertical ==
> Vertical.BAP &&
> s.getSearchRequest.getSearchParameters.containsKey(EspParameterEnum.KEYWORD.getName))
> .map(row => {
> val name =
> row.getSearchRequest.getSearchParameters.get(EspParameterEnum.KEYWORD.getName).getEspSearchParameterDTO.getValue.toLowerCase()
> (name, new TopListEntry(name, 1, row.getResultCount))
> })
> .reduceByKeyAndWindow(
> (a: TopListEntry, b: TopListEntry) => new TopListEntry(a.getKeyword,
> a.getSearchCount + b.getSearchCount, a.getMeanSearchHits +
> b.getMeanSearchHits),
> (a: TopListEntry, b: TopListEntry) => new TopListEntry(a.getKeyword,
> a.getSearchCount - b.getSearchCount, a.getMeanSearchHits -
> b.getMeanSearchHits),
> Minutes(windowDuration),
> Seconds(slideDuration))
> .filter((x: (String, TopListEntry)) => x._2.getSearchCount > 200L)
> .map(row => (row._2.getSearchCount, row._2))
> .transform(rdd => rdd.sortByKey(ascending = false))
> .map(row => new TopListEntry(row._2.getKeyword, row._2.getSearchCount,
> row._2.getMeanSearchHits / row._2.getSearchCount))
> }
> def main(properties: Properties): Unit = {
> val sparkSession = SparkUtil.getDefaultSparkSession(properties, TaskName)
> val kafkaSink =
> sparkSession.sparkContext.broadcast(KafkaSinkUtil.apply[TopList](SparkUtil.getDefaultSparkProperties(properties)))
> val kafkaParams: Map[String, Object] =
> SparkUtil.getDefaultKafkaReceiverParameter(properties)
> val ssc = new StreamingContext(sparkSession.sparkContext, Seconds(30))
> ssc.checkpoint("/home/spark/checkpoints")
> val adEventStream =
> KafkaUtils.createDirectStream[String, Array[Byte]](ssc,
> PreferConsistent, Subscribe[String, Array[Byte]](Array(ReadFromTopicName),
> kafkaParams))
> processSearchKeyWords(adEventStream,
> SparkUtil.getWindowDuration(properties),
> SparkUtil.getSlideDuration(properties), kafkaSink)
> ssc.start()
> ssc.awaitTermination()
> }
> {code}
> As I saw in the code KafkaUtils
> {code:title=Job.Scala|borderStyle=solid}
> logWarning(s"overriding ${ConsumerConfig.AUTO_OFFSET_RESET_CONFIG} to
> none for executor")
> kafkaParams.put(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG, "none")
> {code}
> This means as soon as one worker has a kafka partion that can no be processed
> because the offset is not valid anymore due to retention policy the streaming
> job will stop working
--
This message was sent by Atlassian JIRA
(v7.6.3#76005)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
