spark 错误id意义
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可以推广到其他,由系统的错误id类报错找到问题的所在。
1 command exit code 137
1 为系统资源不够 ,从节点spark内存超出实际系统内存 系统杀死excutor进程造成的错误
2可以用ulimit -a 看哪个系统限制可以调整 ulimit -v unlimited
2 Too many open files
设置最大文件数限制
ulimit -n 124000
/etc/security/limits.conf中soft和hard是限制修改
3 exit code 255
status of 255 error
错误类型:
java.io.IOException: Task process exit with nonzero status of 255.
at org.apache.hadoop.mapred.TaskRunner.run(TaskRunner.java:424)
错误原因:
Set mapred.jobtracker.retirejob.interval and mapred.userlog.retain.hours to higher value. By default, their values are 24 hours. These might be the reason for failure, though I'm not sure
参考这里:http://grepalex.com/2012/11/12/hadoop-logging/
http://218.245.3.161/2013/03/31/5965
413/11/07 08:06:17 INFO cluster.ClusterTaskSetManager: Loss was due to org.apache.spark.SparkException
org.apache.spark.SparkException: Error communicating with MapOutputTracker
at org.apache.spark.MapOutputTracker.askTracker(MapOutputTracker.scala:84)
at org.apache.spark.MapOutputTracker.getServerStatuses(MapOutputTracker.scala:170)
at org.apache.spark.BlockStoreShuffleFetcher.fetch(BlockStoreShuffleFetcher.scala:39)
at org.apache.spark.CoGroupedRDD$$anonfun$compute$2.apply(CoGroupedRDD.scala:125)
at org.apache.spark.CoGroupedRDD$$anonfun$compute$2.apply(CoGroupedRDD.scala:116)
at scala.collection.LinearSeqOptimized$class.foreach(LinearSeqOptimized.scala:59)
at scala.collection.immutable.List.foreach(List.scala:76)
源码在
private[spark] class MapOutputTracker extends Logging {
private val timeout = Duration.create(System.getProperty("spark.akka.askTimeout", "10").toLong, "seconds")
// Set to the MapOutputTrackerActor living on the driver
var trackerActor: ActorRef = _
private var mapStatuses = new TimeStampedHashMap[Int, Array[MapStatus]]
// Incremented every time a fetch fails so that client nodes know to clear
// their cache of map output locations if this happens.
private var epoch: Long = 0
private val epochLock = new java.lang.Object
// Cache a serialized version of the output statuses for each shuffle to send them out faster
var cacheEpoch = epoch
private val cachedSerializedStatuses = new TimeStampedHashMap[Int, Array[Byte]]
val metadataCleaner = new MetadataCleaner("MapOutputTracker", this.cleanup)
// Send a message to the trackerActor and get its result within a default timeout, or
// throw a SparkException if this fails.
def askTracker(message: Any): Any = {
try {
val future = trackerActor.ask(message)(timeout)
return Await.result(future, timeout)
} catch {
case e: Exception =>
throw new SparkException("Error communicating with MapOutputTracker", e)
}
}
尝试 调大 -Dspark.akka.askTimeout
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