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https://issues.apache.org/jira/browse/SPARK-35915?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17371160#comment-17371160
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Hyukjin Kwon commented on SPARK-35915:
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cc [~kabhwan] FYI
> Kafka doesn't recover from data loss
> ------------------------------------
>
> Key: SPARK-35915
> URL: https://issues.apache.org/jira/browse/SPARK-35915
> Project: Spark
> Issue Type: Bug
> Components: Structured Streaming
> Affects Versions: 3.1.1
> Reporter: Yuval Yellin
> Priority: Major
>
> I configured a strcutured streaming source for kafka with
> failOnDataLoss=false,
> Getting this error when checkopint offsets are not found :
>
> {code:java}
> Caused by: org.apache.spark.SparkException: Job aborted due to stage failure:
> Task 7 in stage 5.0 failed 1 times, most recent failure: Lost task 7.0 in
> stage 5.0 (TID 113) ( executor driver): java.lang.IllegalStateException: This
> consumer has already been closed.
> at
> org.apache.kafka.clients.consumer.KafkaConsumer.acquireAndEnsureOpen(KafkaConsumer.java:2439)
> at
> org.apache.kafka.clients.consumer.KafkaConsumer.seekToBeginning(KafkaConsumer.java:1656)
> at
> org.apache.spark.sql.kafka010.consumer.InternalKafkaConsumer.getAvailableOffsetRange(KafkaDataConsumer.scala:108)
> at
> org.apache.spark.sql.kafka010.consumer.KafkaDataConsumer.getEarliestAvailableOffsetBetween(KafkaDataConsumer.scala:385)
> at
> org.apache.spark.sql.kafka010.consumer.KafkaDataConsumer.$anonfun$get$1(KafkaDataConsumer.scala:332)
> at
> org.apache.spark.util.UninterruptibleThread.runUninterruptibly(UninterruptibleThread.scala:77)
> at
> org.apache.spark.sql.kafka010.consumer.KafkaDataConsumer.runUninterruptiblyIfPossible(KafkaDataConsumer.scala:604)
> at
> org.apache.spark.sql.kafka010.consumer.KafkaDataConsumer.get(KafkaDataConsumer.scala:287)
> at
> org.apache.spark.sql.kafka010.KafkaBatchPartitionReader.next(KafkaBatchPartitionReader.scala:63)
> at
> org.apache.spark.sql.execution.datasources.v2.PartitionIterator.hasNext(DataSourceRDD.scala:79)
> at
> org.apache.spark.sql.execution.datasources.v2.MetricsIterator.hasNext(DataSourceRDD.scala:112)
> at
> org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37)
> {code}
>
> The issue seems to me to be related to the OffsetOutOfRange exception in
> (line (323 in KafkaDataConsumer):
>
> {code:java}
> case e: OffsetOutOfRangeException =>
> // When there is some error thrown, it's better to use a new consumer to
> drop all cached
> // states in the old consumer. We don't need to worry about the
> performance because this
> // is not a common path.
> releaseConsumer()
> fetchedData.reset()
> reportDataLoss(topicPartition, groupId, failOnDataLoss,
> s"Cannot fetch offset $toFetchOffset", e)
> toFetchOffset = getEarliestAvailableOffsetBetween(consumer,
> toFetchOffset, untilOffset)
> }
> {code}
> seems like releaseConsumer will destoy the consumer , which later is used ...
>
>
>
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