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https://issues.apache.org/jira/browse/SPARK-19680?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16433100#comment-16433100
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Nicholas Verbeck commented on SPARK-19680:
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KAFKA-3370 is a good solution to the bad preforming jobs problem from a central
point.
However irregardless of that, Spark shouldn't just dictate functionality to
users like that. It should instead leave it up to the user to assume
responsibility if they wish to enable that setting. Making notes and comments
within sparks docs of the potential issues until either Kafka and/or Spark can
come up with a solution that isn't the removal of functionality.
As a side solution, until Kafka can be fixed, would be for Spark to eval the
setting itself. If set go about looking up the current offsets at start and
handling moving them to the latest/earliest as requested. Then switching to
NONE for the continued run. This would prevent the issues that you appear to be
wanting to prevent. While letting the users maintain, somewhat the key part of
the functionality they are looking for.
> 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
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