Re: spark there is no space on the disk
Yes, we have just modified the configuration, and every thing works fine. Thanks very much for the help. On Thu, Mar 19, 2015 at 5:24 PM, Ted Yu yuzhih...@gmail.com wrote: For YARN, possibly this one ? property nameyarn.nodemanager.local-dirs/name value/hadoop/yarn/local/value /property Cheers On Thu, Mar 19, 2015 at 2:21 PM, Marcelo Vanzin van...@cloudera.com wrote: IIRC you have to set that configuration on the Worker processes (for standalone). The app can't override it (only for a client-mode driver). YARN has a similar configuration, but I don't know the name (shouldn't be hard to find, though). On Thu, Mar 19, 2015 at 11:56 AM, Davies Liu dav...@databricks.com wrote: Is it possible that `spark.local.dir` is overriden by others? The docs say: NOTE: In Spark 1.0 and later this will be overriden by SPARK_LOCAL_DIRS (Standalone, Mesos) or LOCAL_DIRS (YARN) On Sat, Mar 14, 2015 at 5:29 PM, Peng Xia sparkpeng...@gmail.com wrote: Hi Sean, Thank very much for your reply. I tried to config it from below code: sf = SparkConf().setAppName(test).set(spark.executor.memory, 45g).set(spark.cores.max, 62),set(spark.local.dir, C:\\tmp) But still get the error. Do you know how I can config this? Thanks, Best, Peng On Sat, Mar 14, 2015 at 3:41 AM, Sean Owen so...@cloudera.com wrote: It means pretty much what it says. You ran out of space on an executor (not driver), because the dir used for serialization temp files is full (not all volumes). Set spark.local.dirs to something more appropriate and larger. On Sat, Mar 14, 2015 at 2:10 AM, Peng Xia sparkpeng...@gmail.com wrote: Hi I was running a logistic regression algorithm on a 8 nodes spark cluster, each node has 8 cores and 56 GB Ram (each node is running a windows system). And the spark installation driver has 1.9 TB capacity. The dataset I was training on are has around 40 million records with around 6600 features. But I always get this error during the training process: Py4JJavaError: An error occurred while calling o70.trainLogisticRegressionModelWithLBFGS. : org.apache.spark.SparkException: Job aborted due to stage failure: Task 2709 in stage 3.0 failed 4 times, most recent failure: Lost task 2709.3 in stage 3.0 (TID 2766, workernode0.rbaHdInsightCluster5.b6.internal.cloudapp.net): java.io.IOException: There is not enough space on the disk at java.io.FileOutputStream.writeBytes(Native Method) at java.io.FileOutputStream.write(FileOutputStream.java:345) at java.io.BufferedOutputStream.write(BufferedOutputStream.java:122) at org.xerial.snappy.SnappyOutputStream.dumpOutput(SnappyOutputStream.java:300) at org.xerial.snappy.SnappyOutputStream.rawWrite(SnappyOutputStream.java:247) at org.xerial.snappy.SnappyOutputStream.write(SnappyOutputStream.java:107) at java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1876) at java.io.ObjectOutputStream$BlockDataOutputStream.writeByte(ObjectOutputStream.java:1914) at java.io.ObjectOutputStream.writeFatalException(ObjectOutputStream.java:1575) at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:350) at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:42) at org.apache.spark.serializer.SerializationStream.writeAll(Serializer.scala:110) at org.apache.spark.storage.BlockManager.dataSerializeStream(BlockManager.scala:1177) at org.apache.spark.storage.DiskStore.putIterator(DiskStore.scala:78) at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:787) at org.apache.spark.storage.BlockManager.putIterator(BlockManager.scala:638) at org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:145) at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:70) at org.apache.spark.rdd.RDD.iterator(RDD.scala:243) at org.apache.spark.rdd.FilteredRDD.compute(FilteredRDD.scala:34) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:278) at org.apache.spark.rdd.RDD.iterator(RDD.scala:245) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61) at org.apache.spark.scheduler.Task.run(Task.scala:56) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:200) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) at java.lang.Thread.run(Thread.java:745) Driver stacktrace: at
Re: spark there is no space on the disk
Is it possible that `spark.local.dir` is overriden by others? The docs say: NOTE: In Spark 1.0 and later this will be overriden by SPARK_LOCAL_DIRS (Standalone, Mesos) or LOCAL_DIRS (YARN) On Sat, Mar 14, 2015 at 5:29 PM, Peng Xia sparkpeng...@gmail.com wrote: Hi Sean, Thank very much for your reply. I tried to config it from below code: sf = SparkConf().setAppName(test).set(spark.executor.memory, 45g).set(spark.cores.max, 62),set(spark.local.dir, C:\\tmp) But still get the error. Do you know how I can config this? Thanks, Best, Peng On Sat, Mar 14, 2015 at 3:41 AM, Sean Owen so...@cloudera.com wrote: It means pretty much what it says. You ran out of space on an executor (not driver), because the dir used for serialization temp files is full (not all volumes). Set spark.local.dirs to something more appropriate and larger. On Sat, Mar 14, 2015 at 2:10 AM, Peng Xia sparkpeng...@gmail.com wrote: Hi I was running a logistic regression algorithm on a 8 nodes spark cluster, each node has 8 cores and 56 GB Ram (each node is running a windows system). And the spark installation driver has 1.9 TB capacity. The dataset I was training on are has around 40 million records with around 6600 features. But I always get this error during the training process: Py4JJavaError: An error occurred while calling o70.trainLogisticRegressionModelWithLBFGS. : org.apache.spark.SparkException: Job aborted due to stage failure: Task 2709 in stage 3.0 failed 4 times, most recent failure: Lost task 2709.3 in stage 3.0 (TID 2766, workernode0.rbaHdInsightCluster5.b6.internal.cloudapp.net): java.io.IOException: There is not enough space on the disk at java.io.FileOutputStream.writeBytes(Native Method) at java.io.FileOutputStream.write(FileOutputStream.java:345) at java.io.BufferedOutputStream.write(BufferedOutputStream.java:122) at org.xerial.snappy.SnappyOutputStream.dumpOutput(SnappyOutputStream.java:300) at org.xerial.snappy.SnappyOutputStream.rawWrite(SnappyOutputStream.java:247) at org.xerial.snappy.SnappyOutputStream.write(SnappyOutputStream.java:107) at java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1876) at java.io.ObjectOutputStream$BlockDataOutputStream.writeByte(ObjectOutputStream.java:1914) at java.io.ObjectOutputStream.writeFatalException(ObjectOutputStream.java:1575) at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:350) at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:42) at org.apache.spark.serializer.SerializationStream.writeAll(Serializer.scala:110) at org.apache.spark.storage.BlockManager.dataSerializeStream(BlockManager.scala:1177) at org.apache.spark.storage.DiskStore.putIterator(DiskStore.scala:78) at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:787) at org.apache.spark.storage.BlockManager.putIterator(BlockManager.scala:638) at org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:145) at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:70) at org.apache.spark.rdd.RDD.iterator(RDD.scala:243) at org.apache.spark.rdd.FilteredRDD.compute(FilteredRDD.scala:34) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:278) at org.apache.spark.rdd.RDD.iterator(RDD.scala:245) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61) at org.apache.spark.scheduler.Task.run(Task.scala:56) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:200) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) at java.lang.Thread.run(Thread.java:745) Driver stacktrace: at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1214) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1203) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1202) at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1202) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:696) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:696) at
Re: spark there is no space on the disk
IIRC you have to set that configuration on the Worker processes (for standalone). The app can't override it (only for a client-mode driver). YARN has a similar configuration, but I don't know the name (shouldn't be hard to find, though). On Thu, Mar 19, 2015 at 11:56 AM, Davies Liu dav...@databricks.com wrote: Is it possible that `spark.local.dir` is overriden by others? The docs say: NOTE: In Spark 1.0 and later this will be overriden by SPARK_LOCAL_DIRS (Standalone, Mesos) or LOCAL_DIRS (YARN) On Sat, Mar 14, 2015 at 5:29 PM, Peng Xia sparkpeng...@gmail.com wrote: Hi Sean, Thank very much for your reply. I tried to config it from below code: sf = SparkConf().setAppName(test).set(spark.executor.memory, 45g).set(spark.cores.max, 62),set(spark.local.dir, C:\\tmp) But still get the error. Do you know how I can config this? Thanks, Best, Peng On Sat, Mar 14, 2015 at 3:41 AM, Sean Owen so...@cloudera.com wrote: It means pretty much what it says. You ran out of space on an executor (not driver), because the dir used for serialization temp files is full (not all volumes). Set spark.local.dirs to something more appropriate and larger. On Sat, Mar 14, 2015 at 2:10 AM, Peng Xia sparkpeng...@gmail.com wrote: Hi I was running a logistic regression algorithm on a 8 nodes spark cluster, each node has 8 cores and 56 GB Ram (each node is running a windows system). And the spark installation driver has 1.9 TB capacity. The dataset I was training on are has around 40 million records with around 6600 features. But I always get this error during the training process: Py4JJavaError: An error occurred while calling o70.trainLogisticRegressionModelWithLBFGS. : org.apache.spark.SparkException: Job aborted due to stage failure: Task 2709 in stage 3.0 failed 4 times, most recent failure: Lost task 2709.3 in stage 3.0 (TID 2766, workernode0.rbaHdInsightCluster5.b6.internal.cloudapp.net): java.io.IOException: There is not enough space on the disk at java.io.FileOutputStream.writeBytes(Native Method) at java.io.FileOutputStream.write(FileOutputStream.java:345) at java.io.BufferedOutputStream.write(BufferedOutputStream.java:122) at org.xerial.snappy.SnappyOutputStream.dumpOutput(SnappyOutputStream.java:300) at org.xerial.snappy.SnappyOutputStream.rawWrite(SnappyOutputStream.java:247) at org.xerial.snappy.SnappyOutputStream.write(SnappyOutputStream.java:107) at java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1876) at java.io.ObjectOutputStream$BlockDataOutputStream.writeByte(ObjectOutputStream.java:1914) at java.io.ObjectOutputStream.writeFatalException(ObjectOutputStream.java:1575) at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:350) at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:42) at org.apache.spark.serializer.SerializationStream.writeAll(Serializer.scala:110) at org.apache.spark.storage.BlockManager.dataSerializeStream(BlockManager.scala:1177) at org.apache.spark.storage.DiskStore.putIterator(DiskStore.scala:78) at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:787) at org.apache.spark.storage.BlockManager.putIterator(BlockManager.scala:638) at org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:145) at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:70) at org.apache.spark.rdd.RDD.iterator(RDD.scala:243) at org.apache.spark.rdd.FilteredRDD.compute(FilteredRDD.scala:34) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:278) at org.apache.spark.rdd.RDD.iterator(RDD.scala:245) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61) at org.apache.spark.scheduler.Task.run(Task.scala:56) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:200) 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) Driver stacktrace: at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1214) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1203) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1202) at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) at
Re: spark there is no space on the disk
For YARN, possibly this one ? property nameyarn.nodemanager.local-dirs/name value/hadoop/yarn/local/value /property Cheers On Thu, Mar 19, 2015 at 2:21 PM, Marcelo Vanzin van...@cloudera.com wrote: IIRC you have to set that configuration on the Worker processes (for standalone). The app can't override it (only for a client-mode driver). YARN has a similar configuration, but I don't know the name (shouldn't be hard to find, though). On Thu, Mar 19, 2015 at 11:56 AM, Davies Liu dav...@databricks.com wrote: Is it possible that `spark.local.dir` is overriden by others? The docs say: NOTE: In Spark 1.0 and later this will be overriden by SPARK_LOCAL_DIRS (Standalone, Mesos) or LOCAL_DIRS (YARN) On Sat, Mar 14, 2015 at 5:29 PM, Peng Xia sparkpeng...@gmail.com wrote: Hi Sean, Thank very much for your reply. I tried to config it from below code: sf = SparkConf().setAppName(test).set(spark.executor.memory, 45g).set(spark.cores.max, 62),set(spark.local.dir, C:\\tmp) But still get the error. Do you know how I can config this? Thanks, Best, Peng On Sat, Mar 14, 2015 at 3:41 AM, Sean Owen so...@cloudera.com wrote: It means pretty much what it says. You ran out of space on an executor (not driver), because the dir used for serialization temp files is full (not all volumes). Set spark.local.dirs to something more appropriate and larger. On Sat, Mar 14, 2015 at 2:10 AM, Peng Xia sparkpeng...@gmail.com wrote: Hi I was running a logistic regression algorithm on a 8 nodes spark cluster, each node has 8 cores and 56 GB Ram (each node is running a windows system). And the spark installation driver has 1.9 TB capacity. The dataset I was training on are has around 40 million records with around 6600 features. But I always get this error during the training process: Py4JJavaError: An error occurred while calling o70.trainLogisticRegressionModelWithLBFGS. : org.apache.spark.SparkException: Job aborted due to stage failure: Task 2709 in stage 3.0 failed 4 times, most recent failure: Lost task 2709.3 in stage 3.0 (TID 2766, workernode0.rbaHdInsightCluster5.b6.internal.cloudapp.net): java.io.IOException: There is not enough space on the disk at java.io.FileOutputStream.writeBytes(Native Method) at java.io.FileOutputStream.write(FileOutputStream.java:345) at java.io.BufferedOutputStream.write(BufferedOutputStream.java:122) at org.xerial.snappy.SnappyOutputStream.dumpOutput(SnappyOutputStream.java:300) at org.xerial.snappy.SnappyOutputStream.rawWrite(SnappyOutputStream.java:247) at org.xerial.snappy.SnappyOutputStream.write(SnappyOutputStream.java:107) at java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1876) at java.io.ObjectOutputStream$BlockDataOutputStream.writeByte(ObjectOutputStream.java:1914) at java.io.ObjectOutputStream.writeFatalException(ObjectOutputStream.java:1575) at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:350) at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:42) at org.apache.spark.serializer.SerializationStream.writeAll(Serializer.scala:110) at org.apache.spark.storage.BlockManager.dataSerializeStream(BlockManager.scala:1177) at org.apache.spark.storage.DiskStore.putIterator(DiskStore.scala:78) at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:787) at org.apache.spark.storage.BlockManager.putIterator(BlockManager.scala:638) at org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:145) at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:70) at org.apache.spark.rdd.RDD.iterator(RDD.scala:243) at org.apache.spark.rdd.FilteredRDD.compute(FilteredRDD.scala:34) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:278) at org.apache.spark.rdd.RDD.iterator(RDD.scala:245) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61) at org.apache.spark.scheduler.Task.run(Task.scala:56) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:200) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) at java.lang.Thread.run(Thread.java:745) Driver stacktrace: at org.apache.spark.scheduler.DAGScheduler.org $apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1214) at
Re: spark there is no space on the disk
Hi Sean, Thank very much for your reply. I tried to config it from below code: sf = SparkConf().setAppName(test).set(spark.executor.memory, 45g).set(spark.cores.max, 62),set(spark.local.dir, C:\\tmp) But still get the error. Do you know how I can config this? Thanks, Best, Peng On Sat, Mar 14, 2015 at 3:41 AM, Sean Owen so...@cloudera.com wrote: It means pretty much what it says. You ran out of space on an executor (not driver), because the dir used for serialization temp files is full (not all volumes). Set spark.local.dirs to something more appropriate and larger. On Sat, Mar 14, 2015 at 2:10 AM, Peng Xia sparkpeng...@gmail.com wrote: Hi I was running a logistic regression algorithm on a 8 nodes spark cluster, each node has 8 cores and 56 GB Ram (each node is running a windows system). And the spark installation driver has 1.9 TB capacity. The dataset I was training on are has around 40 million records with around 6600 features. But I always get this error during the training process: Py4JJavaError: An error occurred while calling o70.trainLogisticRegressionModelWithLBFGS. : org.apache.spark.SparkException: Job aborted due to stage failure: Task 2709 in stage 3.0 failed 4 times, most recent failure: Lost task 2709.3 in stage 3.0 (TID 2766, workernode0.rbaHdInsightCluster5.b6.internal.cloudapp.net): java.io.IOException: There is not enough space on the disk at java.io.FileOutputStream.writeBytes(Native Method) at java.io.FileOutputStream.write(FileOutputStream.java:345) at java.io.BufferedOutputStream.write(BufferedOutputStream.java:122) at org.xerial.snappy.SnappyOutputStream.dumpOutput(SnappyOutputStream.java:300) at org.xerial.snappy.SnappyOutputStream.rawWrite(SnappyOutputStream.java:247) at org.xerial.snappy.SnappyOutputStream.write(SnappyOutputStream.java:107) at java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1876) at java.io.ObjectOutputStream$BlockDataOutputStream.writeByte(ObjectOutputStream.java:1914) at java.io.ObjectOutputStream.writeFatalException(ObjectOutputStream.java:1575) at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:350) at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:42) at org.apache.spark.serializer.SerializationStream.writeAll(Serializer.scala:110) at org.apache.spark.storage.BlockManager.dataSerializeStream(BlockManager.scala:1177) at org.apache.spark.storage.DiskStore.putIterator(DiskStore.scala:78) at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:787) at org.apache.spark.storage.BlockManager.putIterator(BlockManager.scala:638) at org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:145) at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:70) at org.apache.spark.rdd.RDD.iterator(RDD.scala:243) at org.apache.spark.rdd.FilteredRDD.compute(FilteredRDD.scala:34) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:278) at org.apache.spark.rdd.RDD.iterator(RDD.scala:245) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61) at org.apache.spark.scheduler.Task.run(Task.scala:56) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:200) 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) Driver stacktrace: at org.apache.spark.scheduler.DAGScheduler.org $apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1214) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1203) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1202) at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1202) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:696) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:696) at scala.Option.foreach(Option.scala:236) at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:696) at org.apache.spark.scheduler.DAGSchedulerEventProcessActor$$anonfun$receive$2.applyOrElse(DAGScheduler.scala:1420) at
Re: spark there is no space on the disk
And I have 2 TB free space on C driver. On Sat, Mar 14, 2015 at 8:29 PM, Peng Xia sparkpeng...@gmail.com wrote: Hi Sean, Thank very much for your reply. I tried to config it from below code: sf = SparkConf().setAppName(test).set(spark.executor.memory, 45g).set(spark.cores.max, 62),set(spark.local.dir, C:\\tmp) But still get the error. Do you know how I can config this? Thanks, Best, Peng On Sat, Mar 14, 2015 at 3:41 AM, Sean Owen so...@cloudera.com wrote: It means pretty much what it says. You ran out of space on an executor (not driver), because the dir used for serialization temp files is full (not all volumes). Set spark.local.dirs to something more appropriate and larger. On Sat, Mar 14, 2015 at 2:10 AM, Peng Xia sparkpeng...@gmail.com wrote: Hi I was running a logistic regression algorithm on a 8 nodes spark cluster, each node has 8 cores and 56 GB Ram (each node is running a windows system). And the spark installation driver has 1.9 TB capacity. The dataset I was training on are has around 40 million records with around 6600 features. But I always get this error during the training process: Py4JJavaError: An error occurred while calling o70.trainLogisticRegressionModelWithLBFGS. : org.apache.spark.SparkException: Job aborted due to stage failure: Task 2709 in stage 3.0 failed 4 times, most recent failure: Lost task 2709.3 in stage 3.0 (TID 2766, workernode0.rbaHdInsightCluster5.b6.internal.cloudapp.net): java.io.IOException: There is not enough space on the disk at java.io.FileOutputStream.writeBytes(Native Method) at java.io.FileOutputStream.write(FileOutputStream.java:345) at java.io.BufferedOutputStream.write(BufferedOutputStream.java:122) at org.xerial.snappy.SnappyOutputStream.dumpOutput(SnappyOutputStream.java:300) at org.xerial.snappy.SnappyOutputStream.rawWrite(SnappyOutputStream.java:247) at org.xerial.snappy.SnappyOutputStream.write(SnappyOutputStream.java:107) at java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1876) at java.io.ObjectOutputStream$BlockDataOutputStream.writeByte(ObjectOutputStream.java:1914) at java.io.ObjectOutputStream.writeFatalException(ObjectOutputStream.java:1575) at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:350) at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:42) at org.apache.spark.serializer.SerializationStream.writeAll(Serializer.scala:110) at org.apache.spark.storage.BlockManager.dataSerializeStream(BlockManager.scala:1177) at org.apache.spark.storage.DiskStore.putIterator(DiskStore.scala:78) at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:787) at org.apache.spark.storage.BlockManager.putIterator(BlockManager.scala:638) at org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:145) at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:70) at org.apache.spark.rdd.RDD.iterator(RDD.scala:243) at org.apache.spark.rdd.FilteredRDD.compute(FilteredRDD.scala:34) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:278) at org.apache.spark.rdd.RDD.iterator(RDD.scala:245) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61) at org.apache.spark.scheduler.Task.run(Task.scala:56) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:200) 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) Driver stacktrace: at org.apache.spark.scheduler.DAGScheduler.org $apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1214) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1203) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1202) at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1202) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:696) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:696) at scala.Option.foreach(Option.scala:236) at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:696) at
Re: spark there is no space on the disk
It means pretty much what it says. You ran out of space on an executor (not driver), because the dir used for serialization temp files is full (not all volumes). Set spark.local.dirs to something more appropriate and larger. On Sat, Mar 14, 2015 at 2:10 AM, Peng Xia sparkpeng...@gmail.com wrote: Hi I was running a logistic regression algorithm on a 8 nodes spark cluster, each node has 8 cores and 56 GB Ram (each node is running a windows system). And the spark installation driver has 1.9 TB capacity. The dataset I was training on are has around 40 million records with around 6600 features. But I always get this error during the training process: Py4JJavaError: An error occurred while calling o70.trainLogisticRegressionModelWithLBFGS. : org.apache.spark.SparkException: Job aborted due to stage failure: Task 2709 in stage 3.0 failed 4 times, most recent failure: Lost task 2709.3 in stage 3.0 (TID 2766, workernode0.rbaHdInsightCluster5.b6.internal.cloudapp.net): java.io.IOException: There is not enough space on the disk at java.io.FileOutputStream.writeBytes(Native Method) at java.io.FileOutputStream.write(FileOutputStream.java:345) at java.io.BufferedOutputStream.write(BufferedOutputStream.java:122) at org.xerial.snappy.SnappyOutputStream.dumpOutput(SnappyOutputStream.java:300) at org.xerial.snappy.SnappyOutputStream.rawWrite(SnappyOutputStream.java:247) at org.xerial.snappy.SnappyOutputStream.write(SnappyOutputStream.java:107) at java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1876) at java.io.ObjectOutputStream$BlockDataOutputStream.writeByte(ObjectOutputStream.java:1914) at java.io.ObjectOutputStream.writeFatalException(ObjectOutputStream.java:1575) at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:350) at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:42) at org.apache.spark.serializer.SerializationStream.writeAll(Serializer.scala:110) at org.apache.spark.storage.BlockManager.dataSerializeStream(BlockManager.scala:1177) at org.apache.spark.storage.DiskStore.putIterator(DiskStore.scala:78) at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:787) at org.apache.spark.storage.BlockManager.putIterator(BlockManager.scala:638) at org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:145) at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:70) at org.apache.spark.rdd.RDD.iterator(RDD.scala:243) at org.apache.spark.rdd.FilteredRDD.compute(FilteredRDD.scala:34) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:278) at org.apache.spark.rdd.RDD.iterator(RDD.scala:245) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61) at org.apache.spark.scheduler.Task.run(Task.scala:56) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:200) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) at java.lang.Thread.run(Thread.java:745) Driver stacktrace: at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1214) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1203) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1202) at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1202) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:696) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:696) at scala.Option.foreach(Option.scala:236) at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:696) at org.apache.spark.scheduler.DAGSchedulerEventProcessActor$$anonfun$receive$2.applyOrElse(DAGScheduler.scala:1420) at akka.actor.Actor$class.aroundReceive(Actor.scala:465) at org.apache.spark.scheduler.DAGSchedulerEventProcessActor.aroundReceive(DAGScheduler.scala:1375) at akka.actor.ActorCell.receiveMessage(ActorCell.scala:516) at akka.actor.ActorCell.invoke(ActorCell.scala:487) at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:238) at akka.dispatch.Mailbox.run(Mailbox.scala:220) at
spark there is no space on the disk
Hi I was running a logistic regression algorithm on a 8 nodes spark cluster, each node has 8 cores and 56 GB Ram (each node is running a windows system). And the spark installation driver has 1.9 TB capacity. The dataset I was training on are has around 40 million records with around 6600 features. But I always get this error during the training process: Py4JJavaError: An error occurred while calling o70.trainLogisticRegressionModelWithLBFGS.: org.apache.spark.SparkException: Job aborted due to stage failure: Task 2709 in stage 3.0 failed 4 times, most recent failure: Lost task 2709.3 in stage 3.0 (TID 2766, workernode0.rbaHdInsightCluster5.b6.internal.cloudapp.net): java.io.IOException: There is not enough space on the disk at java.io.FileOutputStream.writeBytes(Native Method) at java.io.FileOutputStream.write(FileOutputStream.java:345) at java.io.BufferedOutputStream.write(BufferedOutputStream.java:122) at org.xerial.snappy.SnappyOutputStream.dumpOutput(SnappyOutputStream.java:300) at org.xerial.snappy.SnappyOutputStream.rawWrite(SnappyOutputStream.java:247) at org.xerial.snappy.SnappyOutputStream.write(SnappyOutputStream.java:107) at java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1876) at java.io.ObjectOutputStream$BlockDataOutputStream.writeByte(ObjectOutputStream.java:1914) at java.io.ObjectOutputStream.writeFatalException(ObjectOutputStream.java:1575) at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:350) at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:42) at org.apache.spark.serializer.SerializationStream.writeAll(Serializer.scala:110) at org.apache.spark.storage.BlockManager.dataSerializeStream(BlockManager.scala:1177) at org.apache.spark.storage.DiskStore.putIterator(DiskStore.scala:78) at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:787) at org.apache.spark.storage.BlockManager.putIterator(BlockManager.scala:638) at org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:145) at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:70) at org.apache.spark.rdd.RDD.iterator(RDD.scala:243) at org.apache.spark.rdd.FilteredRDD.compute(FilteredRDD.scala:34) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:278) at org.apache.spark.rdd.RDD.iterator(RDD.scala:245) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:61) at org.apache.spark.scheduler.Task.run(Task.scala:56) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:200) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) at java.lang.Thread.run(Thread.java:745) Driver stacktrace: at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1214) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1203) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1202) at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1202) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:696) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:696) at scala.Option.foreach(Option.scala:236) at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:696) at org.apache.spark.scheduler.DAGSchedulerEventProcessActor$$anonfun$receive$2.applyOrElse(DAGScheduler.scala:1420) at akka.actor.Actor$class.aroundReceive(Actor.scala:465) at org.apache.spark.scheduler.DAGSchedulerEventProcessActor.aroundReceive(DAGScheduler.scala:1375) at akka.actor.ActorCell.receiveMessage(ActorCell.scala:516) at akka.actor.ActorCell.invoke(ActorCell.scala:487) at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:238) at akka.dispatch.Mailbox.run(Mailbox.scala:220) at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:393) at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260) at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339) at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979) at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107)
