[
https://issues.apache.org/jira/browse/SYSTEMML-1283?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15886796#comment-15886796
]
Brendan Dwyer commented on SYSTEMML-1283:
-----------------------------------------
[~acs_s] I had my spark driver memory set to 2g. I increased it to 4g and now I
am seeing this error:
{code}
17/02/27 15:33:45 WARN TaskSetManager: Stage 71 contains a task of very large
size (1955 KB). The maximum recommended task size is 100 KB.
[Stage 73:>
(0 + 8) / 8]17/02/27
15:33:51 WARN CodeGenerator: Error calculating stats of compiled class.
java.lang.IllegalArgumentException: Illegal Capacity: -29693
at java.util.ArrayList.<init>(ArrayList.java:156)
at org.codehaus.janino.util.ClassFile.loadAttributes(ClassFile.java:643)
at org.codehaus.janino.util.ClassFile.loadFields(ClassFile.java:623)
at org.codehaus.janino.util.ClassFile.<init>(ClassFile.java:280)
at
org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator$$anonfun$recordCompilationStats$1.apply(CodeGenerator.scala:967)
at
org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator$$anonfun$recordCompilationStats$1.apply(CodeGenerator.scala:964)
at scala.collection.Iterator$class.foreach(Iterator.scala:893)
at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
at scala.collection.IterableLike$class.foreach(IterableLike.scala:72)
at scala.collection.AbstractIterable.foreach(Iterable.scala:54)
at
org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator$.recordCompilationStats(CodeGenerator.scala:964)
at
org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator$.org$apache$spark$sql$catalyst$expressions$codegen$CodeGenerator$$doCompile(CodeGenerator.scala:936)
at
org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator$$anon$1.load(CodeGenerator.scala:998)
at
org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator$$anon$1.load(CodeGenerator.scala:995)
at
org.spark_project.guava.cache.LocalCache$LoadingValueReference.loadFuture(LocalCache.java:3599)
at
org.spark_project.guava.cache.LocalCache$Segment.loadSync(LocalCache.java:2379)
at
org.spark_project.guava.cache.LocalCache$Segment.lockedGetOrLoad(LocalCache.java:2342)
at
org.spark_project.guava.cache.LocalCache$Segment.get(LocalCache.java:2257)
at org.spark_project.guava.cache.LocalCache.get(LocalCache.java:4000)
at
org.spark_project.guava.cache.LocalCache.getOrLoad(LocalCache.java:4004)
at
org.spark_project.guava.cache.LocalCache$LocalLoadingCache.get(LocalCache.java:4874)
at
org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator$.compile(CodeGenerator.scala:890)
at
org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeProjection$.create(GenerateUnsafeProjection.scala:405)
at
org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeProjection$.create(GenerateUnsafeProjection.scala:359)
at
org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeProjection$.create(GenerateUnsafeProjection.scala:32)
at
org.apache.spark.sql.catalyst.expressions.codegen.CodeGenerator.generate(CodeGenerator.scala:874)
at
org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.extractProjection$lzycompute(ExpressionEncoder.scala:266)
at
org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.extractProjection(ExpressionEncoder.scala:266)
at
org.apache.spark.sql.catalyst.encoders.ExpressionEncoder.toRow(ExpressionEncoder.scala:290)
at
org.apache.spark.sql.SparkSession$$anonfun$3.apply(SparkSession.scala:547)
at
org.apache.spark.sql.SparkSession$$anonfun$3.apply(SparkSession.scala:547)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
at
org.apache.spark.util.random.SamplingUtils$.reservoirSampleAndCount(SamplingUtils.scala:42)
at
org.apache.spark.RangePartitioner$$anonfun$9.apply(Partitioner.scala:263)
at
org.apache.spark.RangePartitioner$$anonfun$9.apply(Partitioner.scala:261)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndex$1$$anonfun$apply$26.apply(RDD.scala:843)
at
org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndex$1$$anonfun$apply$26.apply(RDD.scala:843)
at
org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:99)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
{code}
> Out of memory error
> -------------------
>
> Key: SYSTEMML-1283
> URL: https://issues.apache.org/jira/browse/SYSTEMML-1283
> Project: SystemML
> Issue Type: Bug
> Reporter: Brendan Dwyer
> Attachments: SYSTEMML-1283_example.csv
>
>
> Possibly related to [SYSTEMML-1281]
> When a matrix X containing ~13,000 rows is passed into the following DML
> scripts it errors out on my laptop but passes in my 5 node cluster.
> {code}
> # # encode dml function for one hot encoding
> encode_onehot = function(matrix[double] X) return(matrix[double] Y) {
> N = nrow(X)
> Y = table(seq(1, N, 1), X)
> }
> # a dummy read, which allows sysML to attach variables
> X = read("")
>
> col_idx = $onehot_index
>
> nc = ncol(X)
> if (col_idx < 1 | col_idx > nc) {
> stop("one hot index out of range")
> }
> Y = matrix(0, rows=1, cols=1)
> oneHot = encode_onehot(X[,col_idx:col_idx])
> if (col_idx == 1) {
> if (col_idx < nc) {
> X_tmp = X[, col_idx+1:nc]
> Y = append(oneHot, X_tmp)
> } else {
> Y = oneHot
> }
> } else if (1 < col_idx & col_idx < nc) {
> Y = append(append(X[,1:col_idx-1], oneHot), X[, col_idx+1:nc])
> } else { # col_idx == nc
> Y = append(X[,1:col_idx-1], oneHot)
> }
> # a dummy write, which allows sysML to attach varibles
> write(Y, "")
> {code}
> Error:
> {code}
> 17/02/17 16:57:35 ERROR Executor: Exception in task 0.0 in stage 63.0 (TID
> 1739)
> java.lang.OutOfMemoryError: GC overhead limit exceeded
> at java.lang.Double.valueOf(Double.java:519)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificSafeProjection.apply_853$(Unknown
> Source)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificSafeProjection.apply(Unknown
> Source)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
> at org.apache.spark.util.Utils$$anon$4.next(Utils.scala:1778)
> at org.apache.spark.util.Utils$$anon$4.next(Utils.scala:1772)
> at
> scala.collection.convert.Wrappers$IteratorWrapper.next(Wrappers.scala:31)
> at
> org.apache.sysml.runtime.instructions.spark.utils.FrameRDDConverterUtils$DataFrameToBinaryBlockFunction.call(FrameRDDConverterUtils.java:748)
> at
> org.apache.sysml.runtime.instructions.spark.utils.FrameRDDConverterUtils$DataFrameToBinaryBlockFunction.call(FrameRDDConverterUtils.java:715)
> at
> org.apache.spark.api.java.JavaRDDLike$$anonfun$fn$7$1.apply(JavaRDDLike.scala:186)
> at
> org.apache.spark.api.java.JavaRDDLike$$anonfun$fn$7$1.apply(JavaRDDLike.scala:186)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:796)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:796)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
> at org.apache.spark.scheduler.Task.run(Task.scala:99)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread.java:745)
> 17/02/17 16:57:35 ERROR TaskSetManager: Task 0 in stage 63.0 failed 1 times;
> aborting job
> 17/02/17 16:57:36 ERROR SparkUncaughtExceptionHandler: Uncaught exception in
> thread Thread[Executor task launch worker-20,5,main]
> java.lang.OutOfMemoryError: GC overhead limit exceeded
> at java.lang.Double.valueOf(Double.java:519)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificSafeProjection.apply_853$(Unknown
> Source)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificSafeProjection.apply(Unknown
> Source)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
> at org.apache.spark.util.Utils$$anon$4.next(Utils.scala:1778)
> at org.apache.spark.util.Utils$$anon$4.next(Utils.scala:1772)
> at
> scala.collection.convert.Wrappers$IteratorWrapper.next(Wrappers.scala:31)
> at
> org.apache.sysml.runtime.instructions.spark.utils.FrameRDDConverterUtils$DataFrameToBinaryBlockFunction.call(FrameRDDConverterUtils.java:748)
> at
> org.apache.sysml.runtime.instructions.spark.utils.FrameRDDConverterUtils$DataFrameToBinaryBlockFunction.call(FrameRDDConverterUtils.java:715)
> at
> org.apache.spark.api.java.JavaRDDLike$$anonfun$fn$7$1.apply(JavaRDDLike.scala:186)
> at
> org.apache.spark.api.java.JavaRDDLike$$anonfun$fn$7$1.apply(JavaRDDLike.scala:186)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:796)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:796)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
> at org.apache.spark.scheduler.Task.run(Task.scala:99)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread.java:745)
> 17/02/17 16:57:36 ERROR RBackendHandler: executeScript on 117277 failed
> java.lang.reflect.InvocationTargetException
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:498)
> at
> org.apache.spark.api.r.RBackendHandler.handleMethodCall(RBackendHandler.scala:167)
> at
> org.apache.spark.api.r.RBackendHandler.channelRead0(RBackendHandler.scala:108)
> at
> org.apache.spark.api.r.RBackendHandler.channelRead0(RBackendHandler.scala:40)
> at
> io.netty.channel.SimpleChannelInboundHandler.channelRead(SimpleChannelInboundHandler.java:105)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:367)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:353)
> at
> io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:346)
> at
> io.netty.handler.timeout.IdleStateHandler.channelRead(IdleStateHandler.java:266)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:367)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:353)
> at
> io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:346)
> at
> io.netty.handler.codec.MessageToMessageDecoder.channelRead(MessageToMessageDecoder.java:102)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:367)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:353)
> at
> io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:346)
> at
> io.netty.handler.codec.ByteToMessageDecoder.fireChannelRead(ByteToMessageDecoder.java:293)
> at
> io.netty.handler.codec.ByteToMessageDecoder.channelRead(ByteToMessageDecoder.java:267)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:367)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:353)
> at
> io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:346)
> at
> io.netty.channel.DefaultChannelPipeline$HeadContext.channelRead(DefaultChannelPipeline.java:1294)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:367)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:353)
> at
> io.netty.channel.DefaultChannelPipeline.fireChannelRead(DefaultChannelPipeline.java:911)
> at
> io.netty.channel.nio.AbstractNioByteChannel$NioByteUnsafe.read(AbstractNioByteChannel.java:131)
> at
> io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:652)
> at
> io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:575)
> at
> io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:489)
> at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:451)
> at
> io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:140)
> at
> io.netty.util.concurrent.DefaultThreadFactory$DefaultRunnableDecorator.run(DefaultThreadFactory.java:144)
> at java.lang.Thread.run(Thread.java:745)
> Caused by: org.apache.sysml.runtime.DMLRuntimeException:
> org.apache.sysml.runtime.DMLRuntimeException: ERROR: Runtime error in program
> block generated from statement block between lines 16 and 17 -- Error
> evaluating instruction: SPARK°rangeReIndex°X- MATRIX- DOUBLE°1- SCALAR- INT-
> true°_Var178- SCALAR- INT- false°9- SCALAR- INT- true°9- SCALAR- INT-
> true°_mVar179- MATRIX- DOUBLE°MULTI_BLOCK
> at
> org.apache.sysml.runtime.controlprogram.Program.execute(Program.java:130)
> at
> org.apache.sysml.api.MLContext.executeUsingSimplifiedCompilationChain(MLContext.java:1655)
> at
> org.apache.sysml.api.MLContext.compileAndExecuteScript(MLContext.java:1520)
> at org.apache.sysml.api.MLContext.executeScript(MLContext.java:1469)
> at org.apache.sysml.api.MLContext.executeScript(MLContext.java:1455)
> at org.apache.sysml.api.MLContext.executeScript(MLContext.java:1413)
> at org.apache.sysml.api.MLContext.executeScript(MLContext.java:1419)
> ... 36 more
> Caused by: org.apache.sysml.runtime.DMLRuntimeException: ERROR: Runtime error
> in program block generated from statement block between lines 16 and 17 --
> Error evaluating instruction: SPARK°rangeReIndex°X- MATRIX- DOUBLE°1- SCALAR-
> INT- true°_Var178- SCALAR- INT- false°9- SCALAR- INT- true°9- SCALAR- INT-
> true°_mVar179- MATRIX- DOUBLE°MULTI_BLOCK
> at
> org.apache.sysml.runtime.controlprogram.ProgramBlock.executeSingleInstruction(ProgramBlock.java:320)
> at
> org.apache.sysml.runtime.controlprogram.ProgramBlock.executeInstructions(ProgramBlock.java:221)
> at
> org.apache.sysml.runtime.controlprogram.ProgramBlock.execute(ProgramBlock.java:168)
> at
> org.apache.sysml.runtime.controlprogram.Program.execute(Program.java:123)
> ... 42 more
> Caused by: org.apache.spark.SparkException: Job aborted due to stage failure:
> Task 0 in stage 63.0 failed 1 times, most recent failure: Lost task 0.0 in
> stage 63.0 (TID 1739, localhost, executor driver):
> java.lang.OutOfMemoryError: GC overhead limit exceeded
> at java.lang.Double.valueOf(Double.java:519)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificSafeProjection.apply_853$(Unknown
> Source)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificSafeProjection.apply(Unknown
> Source)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
> at org.apache.spark.util.Utils$$anon$4.next(Utils.scala:1778)
> at org.apache.spark.util.Utils$$anon$4.next(Utils.scala:1772)
> at
> scala.collection.convert.Wrappers$IteratorWrapper.next(Wrappers.scala:31)
> at
> org.apache.sysml.runtime.instructions.spark.utils.FrameRDDConverterUtils$DataFrameToBinaryBlockFunction.call(FrameRDDConverterUtils.java:748)
> at
> org.apache.sysml.runtime.instructions.spark.utils.FrameRDDConverterUtils$DataFrameToBinaryBlockFunction.call(FrameRDDConverterUtils.java:715)
> at
> org.apache.spark.api.java.JavaRDDLike$$anonfun$fn$7$1.apply(JavaRDDLike.scala:186)
> at
> org.apache.spark.api.java.JavaRDDLike$$anonfun$fn$7$1.apply(JavaRDDLike.scala:186)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:796)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:796)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
> at org.apache.spark.scheduler.Task.run(Task.scala:99)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread.java:745)
> Driver stacktrace:
> at
> org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1435)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1423)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1422)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> at
> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1422)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
> at scala.Option.foreach(Option.scala:257)
> at
> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:802)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1650)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1605)
> at
> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1594)
> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
> at
> org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:628)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1918)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1931)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1944)
> at org.apache.spark.SparkContext.runJob(SparkContext.scala:1958)
> at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:935)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
> at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
> at org.apache.spark.rdd.RDD.collect(RDD.scala:934)
> at
> org.apache.spark.api.java.JavaRDDLike$class.collect(JavaRDDLike.scala:361)
> at
> org.apache.spark.api.java.AbstractJavaRDDLike.collect(JavaRDDLike.scala:45)
> at
> org.apache.sysml.runtime.controlprogram.context.SparkExecutionContext.toMatrixBlock(SparkExecutionContext.java:783)
> at
> org.apache.sysml.runtime.instructions.spark.MatrixIndexingSPInstruction.processInstruction(MatrixIndexingSPInstruction.java:151)
> at
> org.apache.sysml.runtime.controlprogram.ProgramBlock.executeSingleInstruction(ProgramBlock.java:290)
> ... 45 more
> Caused by: java.lang.OutOfMemoryError: GC overhead limit exceeded
> at java.lang.Double.valueOf(Double.java:519)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificSafeProjection.apply_853$(Unknown
> Source)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificSafeProjection.apply(Unknown
> Source)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
> at org.apache.spark.util.Utils$$anon$4.next(Utils.scala:1778)
> at org.apache.spark.util.Utils$$anon$4.next(Utils.scala:1772)
> at
> scala.collection.convert.Wrappers$IteratorWrapper.next(Wrappers.scala:31)
> at
> org.apache.sysml.runtime.instructions.spark.utils.FrameRDDConverterUtils$DataFrameToBinaryBlockFunction.call(FrameRDDConverterUtils.java:748)
> at
> org.apache.sysml.runtime.instructions.spark.utils.FrameRDDConverterUtils$DataFrameToBinaryBlockFunction.call(FrameRDDConverterUtils.java:715)
> at
> org.apache.spark.api.java.JavaRDDLike$$anonfun$fn$7$1.apply(JavaRDDLike.scala:186)
> at
> org.apache.spark.api.java.JavaRDDLike$$anonfun$fn$7$1.apply(JavaRDDLike.scala:186)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:796)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:796)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
> at org.apache.spark.scheduler.Task.run(Task.scala:99)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> ... 1 more
> Show Traceback
>
> Rerun with Debug
> Error: HydraR[sysml.execute]: DML returned error: Error in
> handleErrors(returnStatus, conn):
> org.apache.sysml.runtime.DMLRuntimeException:
> org.apache.sysml.runtime.DMLRuntimeException: ERROR: Runtime error in program
> block generated from statement block between lines 16 and 17 -- Error
> evaluating instruction: SPARK°rangeReIndex°X- MATRIX- DOUBLE°1- SCALAR- INT-
> true°_Var178- SCALAR- INT- false°9- SCALAR- INT- true°9- SCALAR- INT-
> true°_mVar179- MATRIX- DOUBLE°MULTI_BLOCK
> at
> org.apache.sysml.runtime.controlprogram.Program.execute(Program.java:130)
> at
> org.apache.sysml.api.MLContext.executeUsingSimplifiedCompilationChain(MLContext.java:1655)
> at
> org.apache.sysml.api.MLContext.compileAndExecuteScript(MLContext.java:1520)
> at org.apache.sysml.api.MLContext.executeScript(MLContext.java:1469)
> at org.apache.sysml.api.MLContext.executeScript(MLContext.java:1455)
> at org.apache.sysml.api.MLContext.executeScript(MLContext.java:1413)
> at org.apache.sysml.api.MLContext.executeScript(MLContext.java:1419)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:498)
> at
> org.apache.spark.api.r.RBackendHandler.handleMethodCall(RBackendHandler.scala:167)
> at
> org.apache.spark.api.r.RBackendHandler.channelRead0(RBackendHandler.scala:108)
> at
> org.apache.spark.api.r.RBackendHandler.channelRead0(RBackendHandler.scala:40)
> at
> io.netty.channel.SimpleChannelInboundHandler.channelRead(SimpleChannelInboundHandler.java:105)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:367)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:353)
> at
> io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:346)
> at
> io.netty.handler.timeout.IdleStateHandler.channelRead(IdleStateHandler.java:266)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:367)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:353)
> at
> io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:346)
> at
> io.netty.handler.codec.MessageToMessageDecoder.channelRead(MessageToMessageDecoder.java:102)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:367)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:353)
> at
> io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:346)
> at
> io.netty.handler.codec.ByteToMessageDecoder.fireChannelRead(ByteToMessageDecoder.java:293)
> at
> io.netty.handler.codec.ByteToMessageDecoder.channelRead(ByteToMessageDecoder.java:267)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:367)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:353)
> at
> io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:346)
> at
> io.netty.channel.DefaultChannelPipeline$HeadContext.channelRead(DefaultChannelPipeline.java:1294)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:367)
> at
> io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:353)
> at
> io.netty.channel.DefaultChannelPipeline.fireChannelRead(DefaultChannelPipeline.java:911)
> at
> io.netty.channel.nio.AbstractNioByteChannel$NioByteUnsafe.read(AbstractNioByteChannel.java:131)
> at
> io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:652)
> at
> io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:575)
> at
> io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:489)
> at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:451)
> at
> io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:140)
> at
> io.netty.util.concurrent.DefaultThreadFactory$DefaultRunnableDecorator.run(DefaultThreadFactory.java:144)
> at java.lang.Thread.run(Thread.java:745)
> Caused by: org.apache.sysml.runtime.DMLRuntimeException: ERROR: Runtime error
> in program block generated from statement block between lines 16 and 17 --
> Error evaluating instruction: SPARK°rangeReIndex°X- MATRIX- DOUBLE°1- SCALAR-
> INT- true°_Var178- SCALAR- INT- false°9- SCALAR- INT- true°9- SCALAR- INT-
> true°_mVar179- MATRIX- DOUBLE°MULTI_BLOCK
> at
> org.apache.sysml.runtime.controlprogram.ProgramBlock.executeSingleInstruction(ProgramBlock.java:320)
> at
> org.apache.sysml.runtime.controlprogram.ProgramBlock.executeInstructions(ProgramBlock.java:221)
> at
> org.apache.sysml.runtime.controlprogram.ProgramBlock.execute(ProgramBlock.java:168)
> at
> org.apache.sysml.runtime.controlprogram.Program.execute(Program.java:123)
> ... 42 more
> Caused by: org.apache.spark.SparkException: Job aborted due to stage failure:
> Task 0 in stage 63.0 failed 1 times, most recent failure: Lost task 0.0 in
> stage 63.0 (TID 1739, localhost, executor driver):
> java.lang.OutOfMemoryError: GC overhead limit exceeded
> at java.lang.Double.valueOf(Double.java:519)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificSafeProjection.apply_853$(Unknown
> Source)
> at
> org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificSafeProjection.apply(Unknown
> Source)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
> at org.apache.spark.util.Utils$$anon$4.next(Utils.scala:1778)
> at org.apache.spark.util.Utils$$anon$4.next(Utils.scala:1772)
> at
> scala.collection.convert.Wrappers$IteratorWrapper.next(Wrappers.scala:31)
> at
> org.apache.sysml.runtime.instructions.spark.utils.FrameRDDConverterUtils$DataFrameToBinaryBlockFunction.call(FrameRDDConverterUtils.java:748)
> at
> org.apache.sysml.runtime.instructions.spark.utils.FrameRDDConverterUtils$DataFrameToBinaryBlockFunction.call(FrameRDDConverterUtils.java:715)
> at
> org.apache.spark.api.java.JavaRDDLike$$anonfun$fn$7$1.apply(JavaRDDLike.scala:186)
> at
> org.apache.spark.api.java.JavaRDDLike$$anonfun$fn$7$1.apply(JavaRDDLike.scala:186)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:796)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:796)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at
> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
> at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
> at org.apache.spark.scheduler.Task.run(Task.scala:99)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread.java:745)
> Driver stacktrace:
> at
> org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1435)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1423)
> at
> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1422)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> at
> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1422)
> at org.apache.spark.scheduler.DAGSchedul
> {code}
--
This message was sent by Atlassian JIRA
(v6.3.15#6346)
