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https://issues.apache.org/jira/browse/SPARK-38379?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Dongjoon Hyun updated SPARK-38379:
----------------------------------
Fix Version/s: (was: 3.2.2)
> Kubernetes: NoSuchElementException: spark.app.id when using PersistentVolumes
> ------------------------------------------------------------------------------
>
> Key: SPARK-38379
> URL: https://issues.apache.org/jira/browse/SPARK-38379
> Project: Spark
> Issue Type: Bug
> Components: Kubernetes
> Affects Versions: 3.2.1
> Reporter: Thomas Graves
> Assignee: Thomas Graves
> Priority: Major
> Fix For: 3.3.0
>
>
> I'm using Spark 3.2.1 on a kubernetes cluster and starting a spark-shell in
> client mode. I'm using persistent local volumes to mount nvme under /data in
> the executors and on startup the driver always throws the warning below.
> using these options:
> --conf
> spark.kubernetes.executor.volumes.persistentVolumeClaim.spark-local-dir-1.options.claimName=OnDemand
> \
> --conf
> spark.kubernetes.executor.volumes.persistentVolumeClaim.spark-local-dir-1.options.storageClass=fast-disks
> \
> --conf
> spark.kubernetes.executor.volumes.persistentVolumeClaim.spark-local-dir-1.options.sizeLimit=500Gi
> \
> --conf
> spark.kubernetes.executor.volumes.persistentVolumeClaim.spark-local-dir-1.mount.path=/data
> \
> --conf
> spark.kubernetes.executor.volumes.persistentVolumeClaim.spark-local-dir-1.mount.readOnly=false
>
>
> {code:java}
> 22/03/01 20:21:22 WARN ExecutorPodsSnapshotsStoreImpl: Exception when
> notifying snapshot subscriber.
> java.util.NoSuchElementException: spark.app.id
> at org.apache.spark.SparkConf.$anonfun$get$1(SparkConf.scala:245)
> at scala.Option.getOrElse(Option.scala:189)
> at org.apache.spark.SparkConf.get(SparkConf.scala:245)
> at org.apache.spark.SparkConf.getAppId(SparkConf.scala:450)
> at
> org.apache.spark.deploy.k8s.features.MountVolumesFeatureStep.$anonfun$constructVolumes$4(MountVolumesFeatureStep.scala:88)
> at
> scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:286)
> at scala.collection.Iterator.foreach(Iterator.scala:943)
> at scala.collection.Iterator.foreach$(Iterator.scala:943)
> at scala.collection.AbstractIterator.foreach(Iterator.scala:1431)
> at scala.collection.IterableLike.foreach(IterableLike.scala:74)
> at scala.collection.IterableLike.foreach$(IterableLike.scala:73)
> at scala.collection.AbstractIterable.foreach(Iterable.scala:56)
> at scala.collection.TraversableLike.map(TraversableLike.scala:286)
> at scala.collection.TraversableLike.map$(TraversableLike.scala:279)
> at scala.collection.AbstractTraversable.map(Traversable.scala:108)
> at
> org.apache.spark.deploy.k8s.features.MountVolumesFeatureStep.constructVolumes(MountVolumesFeatureStep.scala:57)
> at
> org.apache.spark.deploy.k8s.features.MountVolumesFeatureStep.configurePod(MountVolumesFeatureStep.scala:34)
> at
> org.apache.spark.scheduler.cluster.k8s.KubernetesExecutorBuilder.$anonfun$buildFromFeatures$4(KubernetesExecutorBuilder.scala:64)
> at
> scala.collection.LinearSeqOptimized.foldLeft(LinearSeqOptimized.scala:126)
> at
> scala.collection.LinearSeqOptimized.foldLeft$(LinearSeqOptimized.scala:122)
> at scala.collection.immutable.List.foldLeft(List.scala:91)
> at
> org.apache.spark.scheduler.cluster.k8s.KubernetesExecutorBuilder.buildFromFeatures(KubernetesExecutorBuilder.scala:63)
> at
> org.apache.spark.scheduler.cluster.k8s.ExecutorPodsAllocator.$anonfun$requestNewExecutors$1(ExecutorPodsAllocator.scala:391)
> at scala.collection.immutable.Range.foreach$mVc$sp(Range.scala:158)
> at
> org.apache.spark.scheduler.cluster.k8s.ExecutorPodsAllocator.requestNewExecutors(ExecutorPodsAllocator.scala:382)
> at
> org.apache.spark.scheduler.cluster.k8s.ExecutorPodsAllocator.$anonfun$onNewSnapshots$36(ExecutorPodsAllocator.scala:346)
> at
> org.apache.spark.scheduler.cluster.k8s.ExecutorPodsAllocator.$anonfun$onNewSnapshots$36$adapted(ExecutorPodsAllocator.scala:339)
> at
> scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
> at
> scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
> at
> org.apache.spark.scheduler.cluster.k8s.ExecutorPodsAllocator.onNewSnapshots(ExecutorPodsAllocator.scala:339)
> at
> org.apache.spark.scheduler.cluster.k8s.ExecutorPodsAllocator.$anonfun$start$3(ExecutorPodsAllocator.scala:117)
> at
> org.apache.spark.scheduler.cluster.k8s.ExecutorPodsAllocator.$anonfun$start$3$adapted(ExecutorPodsAllocator.scala:117)
> at
> org.apache.spark.scheduler.cluster.k8s.ExecutorPodsSnapshotsStoreImpl$SnapshotsSubscriber.org$apache$spark$scheduler$cluster$k8s$ExecutorPodsSnapshotsStoreImpl$SnapshotsSubscriber$$processSnapshotsInternal(ExecutorPodsSnapshotsStoreImpl.scala:138)
> at
> org.apache.spark.scheduler.cluster.k8s.ExecutorPodsSnapshotsStoreImpl$SnapshotsSubscriber.processSnapshots(ExecutorPodsSnapshotsStoreImpl.scala:126)
> at
> org.apache.spark.scheduler.cluster.k8s.ExecutorPodsSnapshotsStoreImpl.$anonfun$addSubscriber$1(ExecutorPodsSnapshotsStoreImpl.scala:81)
> at
> java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
> at java.util.concurrent.FutureTask.runAndReset(FutureTask.java:308)
> at
> java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$301(ScheduledThreadPoolExecutor.java:180)
> at
> java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:294)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
> at java.lang.Thread.run(Thread.java:748)
> {code}
>
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