[jira] [Commented] (SPARK-14804) Graph vertexRDD/EdgeRDD checkpoint results ClassCastException:

2017-01-04 Thread Joseph K. Bradley (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-14804?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15799094#comment-15799094
 ] 

Joseph K. Bradley commented on SPARK-14804:
---

I think this is a separate issue from local checkpointing.  The problem appears 
to be that Graph checkpointing incorrectly uses internal checkpointing APIs.

> Graph vertexRDD/EdgeRDD checkpoint results ClassCastException: 
> ---
>
> Key: SPARK-14804
> URL: https://issues.apache.org/jira/browse/SPARK-14804
> Project: Spark
>  Issue Type: Bug
>  Components: GraphX
>Affects Versions: 1.6.1
>Reporter: SuYan
>Priority: Minor
>
> {code}
> graph3.vertices.checkpoint()
> graph3.vertices.count()
> graph3.vertices.map(_._2).count()
> {code}
> 16/04/21 21:04:43 WARN scheduler.TaskSetManager: Lost task 0.0 in stage 4.0 
> (TID 13, localhost): java.lang.ClassCastException: 
> org.apache.spark.graphx.impl.ShippableVertexPartition cannot be cast to 
> scala.Tuple2
>   at 
> com.xiaomi.infra.codelab.spark.Graph2$$anonfun$main$1.apply(Graph2.scala:80)
>   at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>   at org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1597)
>   at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1161)
>   at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1161)
>   at 
> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1863)
>   at 
> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1863)
>   at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
>   at org.apache.spark.scheduler.Task.run(Task.scala:91)
>   at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:219)
>   at 
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
>   at 
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
>   at java.lang.Thread.run(Thread.java:745)
> look at the code:
> {code}
>   private[spark] def computeOrReadCheckpoint(split: Partition, context: 
> TaskContext): Iterator[T] =
>   {
> if (isCheckpointedAndMaterialized) {
>   firstParent[T].iterator(split, context)
> } else {
>   compute(split, context)
> }
>   }
>  private[spark] def isCheckpointedAndMaterialized: Boolean = isCheckpointed
>  override def isCheckpointed: Boolean = {
>firstParent[(PartitionID, EdgePartition[ED, VD])].isCheckpointed
>  }
> {code}
> for VertexRDD or EdgeRDD, first parent is its partitionRDD  
> RDD[ShippableVertexPartition[VD]]/RDD[(PartitionID, EdgePartition[ED, VD])]
> 1. we call vertexRDD.checkpoint, it partitionRDD will checkpoint, so 
> VertexRDD.isCheckpointedAndMaterialized=true.
> 2. then we call vertexRDD.iterator, because checkoint=true it called 
> firstParent.iterator(which is not CheckpointRDD, actually is partitionRDD). 
>  
> so returned iterator is iterator[ShippableVertexPartition] not expect 
> iterator[(VertexId, VD)]]



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[jira] [Commented] (SPARK-14804) Graph vertexRDD/EdgeRDD checkpoint results ClassCastException:

2016-10-11 Thread Apache Spark (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-14804?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15567714#comment-15567714
 ] 

Apache Spark commented on SPARK-14804:
--

User 'apivovarov' has created a pull request for this issue:
https://github.com/apache/spark/pull/15447

> Graph vertexRDD/EdgeRDD checkpoint results ClassCastException: 
> ---
>
> Key: SPARK-14804
> URL: https://issues.apache.org/jira/browse/SPARK-14804
> Project: Spark
>  Issue Type: Bug
>  Components: GraphX
>Affects Versions: 1.6.1
>Reporter: SuYan
>Priority: Minor
>
> {code}
> graph3.vertices.checkpoint()
> graph3.vertices.count()
> graph3.vertices.map(_._2).count()
> {code}
> 16/04/21 21:04:43 WARN scheduler.TaskSetManager: Lost task 0.0 in stage 4.0 
> (TID 13, localhost): java.lang.ClassCastException: 
> org.apache.spark.graphx.impl.ShippableVertexPartition cannot be cast to 
> scala.Tuple2
>   at 
> com.xiaomi.infra.codelab.spark.Graph2$$anonfun$main$1.apply(Graph2.scala:80)
>   at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>   at org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1597)
>   at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1161)
>   at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1161)
>   at 
> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1863)
>   at 
> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1863)
>   at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
>   at org.apache.spark.scheduler.Task.run(Task.scala:91)
>   at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:219)
>   at 
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
>   at 
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
>   at java.lang.Thread.run(Thread.java:745)
> look at the code:
> {code}
>   private[spark] def computeOrReadCheckpoint(split: Partition, context: 
> TaskContext): Iterator[T] =
>   {
> if (isCheckpointedAndMaterialized) {
>   firstParent[T].iterator(split, context)
> } else {
>   compute(split, context)
> }
>   }
>  private[spark] def isCheckpointedAndMaterialized: Boolean = isCheckpointed
>  override def isCheckpointed: Boolean = {
>firstParent[(PartitionID, EdgePartition[ED, VD])].isCheckpointed
>  }
> {code}
> for VertexRDD or EdgeRDD, first parent is its partitionRDD  
> RDD[ShippableVertexPartition[VD]]/RDD[(PartitionID, EdgePartition[ED, VD])]
> 1. we call vertexRDD.checkpoint, it partitionRDD will checkpoint, so 
> VertexRDD.isCheckpointedAndMaterialized=true.
> 2. then we call vertexRDD.iterator, because checkoint=true it called 
> firstParent.iterator(which is not CheckpointRDD, actually is partitionRDD). 
>  
> so returned iterator is iterator[ShippableVertexPartition] not expect 
> iterator[(VertexId, VD)]]



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[jira] [Commented] (SPARK-14804) Graph vertexRDD/EdgeRDD checkpoint results ClassCastException:

2016-10-10 Thread Eyal Farago (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-14804?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15561452#comment-15561452
 ] 

Eyal Farago commented on SPARK-14804:
-

I think this relates to SPARK-12431, is it possible to 'merge' both efforts?

> Graph vertexRDD/EdgeRDD checkpoint results ClassCastException: 
> ---
>
> Key: SPARK-14804
> URL: https://issues.apache.org/jira/browse/SPARK-14804
> Project: Spark
>  Issue Type: Bug
>  Components: GraphX
>Affects Versions: 1.6.1
>Reporter: SuYan
>Priority: Minor
>
> {code}
> graph3.vertices.checkpoint()
> graph3.vertices.count()
> graph3.vertices.map(_._2).count()
> {code}
> 16/04/21 21:04:43 WARN scheduler.TaskSetManager: Lost task 0.0 in stage 4.0 
> (TID 13, localhost): java.lang.ClassCastException: 
> org.apache.spark.graphx.impl.ShippableVertexPartition cannot be cast to 
> scala.Tuple2
>   at 
> com.xiaomi.infra.codelab.spark.Graph2$$anonfun$main$1.apply(Graph2.scala:80)
>   at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>   at org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1597)
>   at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1161)
>   at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1161)
>   at 
> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1863)
>   at 
> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1863)
>   at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
>   at org.apache.spark.scheduler.Task.run(Task.scala:91)
>   at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:219)
>   at 
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
>   at 
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
>   at java.lang.Thread.run(Thread.java:745)
> look at the code:
> {code}
>   private[spark] def computeOrReadCheckpoint(split: Partition, context: 
> TaskContext): Iterator[T] =
>   {
> if (isCheckpointedAndMaterialized) {
>   firstParent[T].iterator(split, context)
> } else {
>   compute(split, context)
> }
>   }
>  private[spark] def isCheckpointedAndMaterialized: Boolean = isCheckpointed
>  override def isCheckpointed: Boolean = {
>firstParent[(PartitionID, EdgePartition[ED, VD])].isCheckpointed
>  }
> {code}
> for VertexRDD or EdgeRDD, first parent is its partitionRDD  
> RDD[ShippableVertexPartition[VD]]/RDD[(PartitionID, EdgePartition[ED, VD])]
> 1. we call vertexRDD.checkpoint, it partitionRDD will checkpoint, so 
> VertexRDD.isCheckpointedAndMaterialized=true.
> 2. then we call vertexRDD.iterator, because checkoint=true it called 
> firstParent.iterator(which is not CheckpointRDD, actually is partitionRDD). 
>  
> so returned iterator is iterator[ShippableVertexPartition] not expect 
> iterator[(VertexId, VD)]]



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[jira] [Commented] (SPARK-14804) Graph vertexRDD/EdgeRDD checkpoint results ClassCastException:

2016-10-07 Thread Apache Spark (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-14804?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15556429#comment-15556429
 ] 

Apache Spark commented on SPARK-14804:
--

User 'tdas' has created a pull request for this issue:
https://github.com/apache/spark/pull/15396

> Graph vertexRDD/EdgeRDD checkpoint results ClassCastException: 
> ---
>
> Key: SPARK-14804
> URL: https://issues.apache.org/jira/browse/SPARK-14804
> Project: Spark
>  Issue Type: Bug
>  Components: GraphX
>Affects Versions: 1.6.1
>Reporter: SuYan
>Priority: Minor
>
> {code}
> graph3.vertices.checkpoint()
> graph3.vertices.count()
> graph3.vertices.map(_._2).count()
> {code}
> 16/04/21 21:04:43 WARN scheduler.TaskSetManager: Lost task 0.0 in stage 4.0 
> (TID 13, localhost): java.lang.ClassCastException: 
> org.apache.spark.graphx.impl.ShippableVertexPartition cannot be cast to 
> scala.Tuple2
>   at 
> com.xiaomi.infra.codelab.spark.Graph2$$anonfun$main$1.apply(Graph2.scala:80)
>   at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>   at org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1597)
>   at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1161)
>   at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1161)
>   at 
> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1863)
>   at 
> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1863)
>   at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
>   at org.apache.spark.scheduler.Task.run(Task.scala:91)
>   at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:219)
>   at 
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
>   at 
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
>   at java.lang.Thread.run(Thread.java:745)
> look at the code:
> {code}
>   private[spark] def computeOrReadCheckpoint(split: Partition, context: 
> TaskContext): Iterator[T] =
>   {
> if (isCheckpointedAndMaterialized) {
>   firstParent[T].iterator(split, context)
> } else {
>   compute(split, context)
> }
>   }
>  private[spark] def isCheckpointedAndMaterialized: Boolean = isCheckpointed
>  override def isCheckpointed: Boolean = {
>firstParent[(PartitionID, EdgePartition[ED, VD])].isCheckpointed
>  }
> {code}
> for VertexRDD or EdgeRDD, first parent is its partitionRDD  
> RDD[ShippableVertexPartition[VD]]/RDD[(PartitionID, EdgePartition[ED, VD])]
> 1. we call vertexRDD.checkpoint, it partitionRDD will checkpoint, so 
> VertexRDD.isCheckpointedAndMaterialized=true.
> 2. then we call vertexRDD.iterator, because checkoint=true it called 
> firstParent.iterator(which is not CheckpointRDD, actually is partitionRDD). 
>  
> so returned iterator is iterator[ShippableVertexPartition] not expect 
> iterator[(VertexId, VD)]]



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[jira] [Commented] (SPARK-14804) Graph vertexRDD/EdgeRDD checkpoint results ClassCastException:

2016-06-01 Thread Anderson de Andrade (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-14804?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15311681#comment-15311681
 ] 

Anderson de Andrade commented on SPARK-14804:
-

The proposed PR is hacky. It disables independent checkpointing on VertexRDD 
and EdgeRDD. I have solved this issue for VertexRDD in 
https://issues.apache.org/jira/browse/SPARK-15717

> Graph vertexRDD/EdgeRDD checkpoint results ClassCastException: 
> ---
>
> Key: SPARK-14804
> URL: https://issues.apache.org/jira/browse/SPARK-14804
> Project: Spark
>  Issue Type: Bug
>  Components: GraphX
>Affects Versions: 1.6.1
>Reporter: SuYan
>Priority: Minor
>
> {code}
> graph3.vertices.checkpoint()
> graph3.vertices.count()
> graph3.vertices.map(_._2).count()
> {code}
> 16/04/21 21:04:43 WARN scheduler.TaskSetManager: Lost task 0.0 in stage 4.0 
> (TID 13, localhost): java.lang.ClassCastException: 
> org.apache.spark.graphx.impl.ShippableVertexPartition cannot be cast to 
> scala.Tuple2
>   at 
> com.xiaomi.infra.codelab.spark.Graph2$$anonfun$main$1.apply(Graph2.scala:80)
>   at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>   at org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1597)
>   at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1161)
>   at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1161)
>   at 
> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1863)
>   at 
> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1863)
>   at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
>   at org.apache.spark.scheduler.Task.run(Task.scala:91)
>   at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:219)
>   at 
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
>   at 
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
>   at java.lang.Thread.run(Thread.java:745)
> look at the code:
> {code}
>   private[spark] def computeOrReadCheckpoint(split: Partition, context: 
> TaskContext): Iterator[T] =
>   {
> if (isCheckpointedAndMaterialized) {
>   firstParent[T].iterator(split, context)
> } else {
>   compute(split, context)
> }
>   }
>  private[spark] def isCheckpointedAndMaterialized: Boolean = isCheckpointed
>  override def isCheckpointed: Boolean = {
>firstParent[(PartitionID, EdgePartition[ED, VD])].isCheckpointed
>  }
> {code}
> for VertexRDD or EdgeRDD, first parent is its partitionRDD  
> RDD[ShippableVertexPartition[VD]]/RDD[(PartitionID, EdgePartition[ED, VD])]
> 1. we call vertexRDD.checkpoint, it partitionRDD will checkpoint, so 
> VertexRDD.isCheckpointedAndMaterialized=true.
> 2. then we call vertexRDD.iterator, because checkoint=true it called 
> firstParent.iterator(which is not CheckpointRDD, actually is partitionRDD). 
>  
> so returned iterator is iterator[ShippableVertexPartition] not expect 
> iterator[(VertexId, VD)]]



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[jira] [Commented] (SPARK-14804) Graph vertexRDD/EdgeRDD checkpoint results ClassCastException:

2016-04-21 Thread Apache Spark (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-14804?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15253230#comment-15253230
 ] 

Apache Spark commented on SPARK-14804:
--

User 'suyanNone' has created a pull request for this issue:
https://github.com/apache/spark/pull/12576

> Graph vertexRDD/EdgeRDD checkpoint results ClassCastException: 
> ---
>
> Key: SPARK-14804
> URL: https://issues.apache.org/jira/browse/SPARK-14804
> Project: Spark
>  Issue Type: Bug
>  Components: GraphX
>Affects Versions: 1.6.1
>Reporter: SuYan
>Priority: Minor
>
> {code}
> graph3.vertices.checkpoint()
> graph3.vertices.count()
> graph3.vertices.map(_._2).count()
> {code}
> 16/04/21 21:04:43 WARN scheduler.TaskSetManager: Lost task 0.0 in stage 4.0 
> (TID 13, localhost): java.lang.ClassCastException: 
> org.apache.spark.graphx.impl.ShippableVertexPartition cannot be cast to 
> scala.Tuple2
>   at 
> com.xiaomi.infra.codelab.spark.Graph2$$anonfun$main$1.apply(Graph2.scala:80)
>   at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>   at org.apache.spark.util.Utils$.getIteratorSize(Utils.scala:1597)
>   at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1161)
>   at org.apache.spark.rdd.RDD$$anonfun$count$1.apply(RDD.scala:1161)
>   at 
> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1863)
>   at 
> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1863)
>   at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
>   at org.apache.spark.scheduler.Task.run(Task.scala:91)
>   at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:219)
>   at 
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
>   at 
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
>   at java.lang.Thread.run(Thread.java:745)
> look at the code:
> {code}
>   private[spark] def computeOrReadCheckpoint(split: Partition, context: 
> TaskContext): Iterator[T] =
>   {
> if (isCheckpointedAndMaterialized) {
>   firstParent[T].iterator(split, context)
> } else {
>   compute(split, context)
> }
>   }
>  private[spark] def isCheckpointedAndMaterialized: Boolean = isCheckpointed
>  override def isCheckpointed: Boolean = {
>firstParent[(PartitionID, EdgePartition[ED, VD])].isCheckpointed
>  }
> {code}
> for VertexRDD or EdgeRDD, first parent is its partitionRDD  
> RDD[ShippableVertexPartition[VD]]/RDD[(PartitionID, EdgePartition[ED, VD])]
> 1. we call vertexRDD.checkpoint, it partitionRDD will checkpoint, so 
> VertexRDD.isCheckpointedAndMaterialized=true.
> 2. then we call vertexRDD.iterator, because checkoint=true it called 
> firstParent.iterator(which is not CheckpointRDD, actually is partitionRDD). 
>  
> so returned iterator is iterator[ShippableVertexPartition] not expect 
> iterator[(VertexId, VD)]]



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