java.nio.channels.ClosedChannelException

spark-user | أنس الليثي | 10 months ago
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  1. 0

    Re: Spark Streaming Job get killed after running for about 1 hour

    spark-user | 10 months ago | أنس الليثي
    java.nio.channels.ClosedChannelException
  2. 0

    GitHub comment 30#75908086

    GitHub | 2 years ago | luck02
    java.nio.channels.ClosedChannelException
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  4. 0

    Kafka producer fails fetching metadata from broker

    Stack Overflow | 1 year ago | kareblak
    java.nio.channels.ClosedChannelException

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    Root Cause Analysis

    1. java.nio.channels.ClosedChannelException

      No message provided

      at kafka.network.BlockingChannel.send()
    2. Apache Kafka
      DefaultEventHandler$$anonfun$partitionAndCollate$1.apply
      1. kafka.network.BlockingChannel.send(BlockingChannel.scala:110)
      2. kafka.producer.SyncProducer.liftedTree1$1(SyncProducer.scala:75)
      3. kafka.producer.SyncProducer.kafka$producer$SyncProducer$$doSend(SyncProducer.scala:74)
      4. kafka.producer.SyncProducer.send(SyncProducer.scala:119)
      5. kafka.client.ClientUtils$.fetchTopicMetadata(ClientUtils.scala:59)
      6. kafka.producer.BrokerPartitionInfo.updateInfo(BrokerPartitionInfo.scala:82)
      7. kafka.producer.BrokerPartitionInfo.getBrokerPartitionInfo(BrokerPartitionInfo.scala:49)
      8. kafka.producer.async.DefaultEventHandler.kafka$producer$async$DefaultEventHandler$$getPartitionListForTopic(DefaultEventHandler.scala:188)
      9. kafka.producer.async.DefaultEventHandler$$anonfun$partitionAndCollate$1.apply(DefaultEventHandler.scala:152)
      10. kafka.producer.async.DefaultEventHandler$$anonfun$partitionAndCollate$1.apply(DefaultEventHandler.scala:151)
      10 frames
    3. Scala
      ArrayBuffer.foreach
      1. scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
      2. scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
      2 frames
    4. Apache Kafka
      Producer.send
      1. kafka.producer.async.DefaultEventHandler.partitionAndCollate(DefaultEventHandler.scala:151)
      2. kafka.producer.async.DefaultEventHandler.dispatchSerializedData(DefaultEventHandler.scala:96)
      3. kafka.producer.async.DefaultEventHandler.handle(DefaultEventHandler.scala:73)
      4. kafka.producer.Producer.send(Producer.scala:77)
      5. kafka.javaapi.producer.Producer.send(Producer.scala:33)
      5 frames
    5. org.css.java
      GnipSparkStreamer$1$1.call
      1. org.css.java.gnipStreaming.GnipSparkStreamer$1$1.call(GnipSparkStreamer.java:59)
      2. org.css.java.gnipStreaming.GnipSparkStreamer$1$1.call(GnipSparkStreamer.java:51)
      2 frames
    6. Spark
      Task.run
      1. org.apache.spark.api.java.JavaRDDLike$$anonfun$foreachPartition$1.apply(JavaRDDLike.scala:225)
      2. org.apache.spark.api.java.JavaRDDLike$$anonfun$foreachPartition$1.apply(JavaRDDLike.scala:225)
      3. org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$33.apply(RDD.scala:920)
      4. org.apache.spark.rdd.RDD$$anonfun$foreachPartition$1$$anonfun$apply$33.apply(RDD.scala:920)
      5. org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
      6. org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
      7. org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
      8. org.apache.spark.scheduler.Task.run(Task.scala:89)
      8 frames