Thanks Jeff, is there a workaround in order to make it work now? Il giorno gio 5 lug 2018 alle ore 12:42 Jeff Zhang <zjf...@gmail.com> ha scritto:
> > This is due to hadoop version used in embedded spark is 2.3 which is too > lower. I created https://issues.apache.org/jira/browse/ZEPPELIN-3586 for > this issue. Suppose it will be fixed in o.8.1 > > > > Andrea Santurbano <sant...@gmail.com>于2018年7月5日周四 下午3:35写道： > >> I agree that is not for production, but if want to do a simple blog post >> (and that's what I'm doing) I think it's a well suited solution. >> Is it possible to fix this? >> Thanks >> Andrea >> >> Il giorno gio 5 lug 2018 alle ore 02:29 Jeff Zhang <zjf...@gmail.com> ha >> scritto: >> >>> >>> This might be due to the embedded spark version. I would recommend you >>> to specify SPARK_HOME instead of using the embedded spark, the embedded >>> spark is not for production. >>> >>> >>> Andrea Santurbano <sant...@gmail.com>于2018年7月5日周四 上午12:07写道： >>> >>>> I have the same issue... >>>> Il giorno mar 3 lug 2018 alle 23:18 Adamantios Corais < >>>> adamantios.cor...@gmail.com> ha scritto: >>>> >>>>> Hi Jeff, I am using the embedded Spark. >>>>> >>>>> FYI, this is how I start the dockerized (yet old) version of Zeppelin >>>>> that works as expected. >>>>> >>>>> #!/bin/bash >>>>>> docker run --rm \ >>>>>> --name zepelin \ >>>>>> -p 127.0.0.1:9090:8080 \ >>>>>> -p 127.0.0.1:5050:4040 \ >>>>>> -v $(pwd):/zeppelin/notebook \ >>>>>> apache/zeppelin:0.7.3 >>>>> >>>>> >>>>> And this is how I start the binarized (yet stable) version of Zeppelin >>>>> that >>>>> is supposed to work (but it doesn't). >>>>> >>>>> #!/bin/bash >>>>>> wget >>>>>> http://www-eu.apache.org/dist/zeppelin/zeppelin-0.8.0/zeppelin-0.8.0-bin-all.tgz >>>>>> tar zxvf zeppelin-0.8.0-bin-all.tgz >>>>>> cd ./zeppelin-0.8.0-bin-all/ >>>>>> bash ./bin/zeppelin.sh >>>>> >>>>> >>>>> Thanks. >>>>> >>>>> >>>>> >>>>> >>>>> *// **Adamantios Corais* >>>>> >>>>> On Tue, Jul 3, 2018 at 2:24 AM, Jeff Zhang <zjf...@gmail.com> wrote: >>>>> >>>>>> >>>>>> Do you use the embeded spark or specify SPARK_HOME ? If you set >>>>>> SPARK_HOME, which spark version and hadoop version do you use ? >>>>>> >>>>>> >>>>>> >>>>>> Adamantios Corais <adamantios.cor...@gmail.com>于2018年7月3日周二 >>>>>> 上午12:32写道： >>>>>> >>>>>>> Hi, >>>>>>> >>>>>>> I have downloaded the latest binary package of Zeppelin (ver. >>>>>>> 0.8.0), extracted, and started as follows: `./bin/zeppelin.sh` >>>>>>> >>>>>>> Next, I tried a very simple example: >>>>>>> >>>>>>> `spark.read.parquet("./bin/userdata1.parquet").show()` >>>>>>> >>>>>>> Which unfortunately returns the following error. Note that the same >>>>>>> example works fine with the official docker version of Zeppelin (ver. >>>>>>> 0.7.3). Any ideas? >>>>>>> >>>>>>> org.apache.spark.SparkException: Job aborted due to stage failure: >>>>>>>> Task 0 in stage 7.0 failed 1 times, most recent failure: Lost task 0.0 >>>>>>>> in >>>>>>>> stage 7.0 (TID 7, localhost, executor driver): >>>>>>>> java.lang.NoSuchMethodError: >>>>>>>> org.apache.hadoop.fs.FileSystem$Statistics.getThreadStatistics()Lorg/apache/hadoop/fs/FileSystem$Statistics$StatisticsData; >>>>>>>> at >>>>>>>> org.apache.spark.deploy.SparkHadoopUtil$$anonfun$1$$anonfun$apply$mcJ$sp$1.apply(SparkHadoopUtil.scala:149) >>>>>>>> at >>>>>>>> org.apache.spark.deploy.SparkHadoopUtil$$anonfun$1$$anonfun$apply$mcJ$sp$1.apply(SparkHadoopUtil.scala:149) >>>>>>>> at >>>>>>>> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234) >>>>>>>> at >>>>>>>> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234) >>>>>>>> at scala.collection.Iterator$class.foreach(Iterator.scala:893) >>>>>>>> at scala.collection.AbstractIterator.foreach(Iterator.scala:1336) >>>>>>>> at >>>>>>>> scala.collection.IterableLike$class.foreach(IterableLike.scala:72) >>>>>>>> at scala.collection.AbstractIterable.foreach(Iterable.scala:54) >>>>>>>> at >>>>>>>> scala.collection.TraversableLike$class.map(TraversableLike.scala:234) >>>>>>>> at scala.collection.AbstractTraversable.map(Traversable.scala:104) >>>>>>>> at >>>>>>>> org.apache.spark.deploy.SparkHadoopUtil$$anonfun$1.apply$mcJ$sp(SparkHadoopUtil.scala:149) >>>>>>>> at >>>>>>>> org.apache.spark.deploy.SparkHadoopUtil.getFSBytesReadOnThreadCallback(SparkHadoopUtil.scala:150) >>>>>>>> at >>>>>>>> org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.<init>(FileScanRDD.scala:78) >>>>>>>> at >>>>>>>> org.apache.spark.sql.execution.datasources.FileScanRDD.compute(FileScanRDD.scala:71) >>>>>>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323) >>>>>>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287) >>>>>>>> at >>>>>>>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >>>>>>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323) >>>>>>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287) >>>>>>>> at >>>>>>>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >>>>>>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323) >>>>>>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287) >>>>>>>> at >>>>>>>> org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87) >>>>>>>> at org.apache.spark.scheduler.Task.run(Task.scala:108) >>>>>>>> at >>>>>>>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335) >>>>>>>> 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) >>>>>>>> Driver stacktrace: >>>>>>>> at org.apache.spark.scheduler.DAGScheduler.org >>>>>>>> $apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1499) >>>>>>>> at >>>>>>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1487) >>>>>>>> at >>>>>>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1486) >>>>>>>> at >>>>>>>> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) >>>>>>>> at >>>>>>>> scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48) >>>>>>>> at >>>>>>>> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1486) >>>>>>>> at >>>>>>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814) >>>>>>>> at >>>>>>>> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814) >>>>>>>> at scala.Option.foreach(Option.scala:257) >>>>>>>> at >>>>>>>> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:814) >>>>>>>> at >>>>>>>> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1714) >>>>>>>> at >>>>>>>> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1669) >>>>>>>> at >>>>>>>> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1658) >>>>>>>> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48) >>>>>>>> at >>>>>>>> org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:630) >>>>>>>> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2022) >>>>>>>> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2043) >>>>>>>> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2062) >>>>>>>> at >>>>>>>> org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:336) >>>>>>>> at >>>>>>>> org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38) >>>>>>>> at org.apache.spark.sql.Dataset.org >>>>>>>> $apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:2853) >>>>>>>> at >>>>>>>> org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2153) >>>>>>>> at >>>>>>>> org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2153) >>>>>>>> at >>>>>>>> org.apache.spark.sql.Dataset$$anonfun$55.apply(Dataset.scala:2837) >>>>>>>> at >>>>>>>> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:65) >>>>>>>> at org.apache.spark.sql.Dataset.withAction(Dataset.scala:2836) >>>>>>>> at org.apache.spark.sql.Dataset.head(Dataset.scala:2153) >>>>>>>> at org.apache.spark.sql.Dataset.take(Dataset.scala:2366) >>>>>>>> at org.apache.spark.sql.Dataset.showString(Dataset.scala:245) >>>>>>>> at org.apache.spark.sql.Dataset.show(Dataset.scala:644) >>>>>>>> at org.apache.spark.sql.Dataset.show(Dataset.scala:603) >>>>>>>> at org.apache.spark.sql.Dataset.show(Dataset.scala:612) >>>>>>>> ... 52 elided >>>>>>>> Caused by: java.lang.NoSuchMethodError: >>>>>>>> org.apache.hadoop.fs.FileSystem$Statistics.getThreadStatistics()Lorg/apache/hadoop/fs/FileSystem$Statistics$StatisticsData; >>>>>>>> at >>>>>>>> org.apache.spark.deploy.SparkHadoopUtil$$anonfun$1$$anonfun$apply$mcJ$sp$1.apply(SparkHadoopUtil.scala:149) >>>>>>>> at >>>>>>>> org.apache.spark.deploy.SparkHadoopUtil$$anonfun$1$$anonfun$apply$mcJ$sp$1.apply(SparkHadoopUtil.scala:149) >>>>>>>> at >>>>>>>> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234) >>>>>>>> at >>>>>>>> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234) >>>>>>>> at scala.collection.Iterator$class.foreach(Iterator.scala:893) >>>>>>>> at scala.collection.AbstractIterator.foreach(Iterator.scala:1336) >>>>>>>> at >>>>>>>> scala.collection.IterableLike$class.foreach(IterableLike.scala:72) >>>>>>>> at scala.collection.AbstractIterable.foreach(Iterable.scala:54) >>>>>>>> at >>>>>>>> scala.collection.TraversableLike$class.map(TraversableLike.scala:234) >>>>>>>> at scala.collection.AbstractTraversable.map(Traversable.scala:104) >>>>>>>> at >>>>>>>> org.apache.spark.deploy.SparkHadoopUtil$$anonfun$1.apply$mcJ$sp(SparkHadoopUtil.scala:149) >>>>>>>> at >>>>>>>> org.apache.spark.deploy.SparkHadoopUtil.getFSBytesReadOnThreadCallback(SparkHadoopUtil.scala:150) >>>>>>>> at >>>>>>>> org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.<init>(FileScanRDD.scala:78) >>>>>>>> at >>>>>>>> org.apache.spark.sql.execution.datasources.FileScanRDD.compute(FileScanRDD.scala:71) >>>>>>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323) >>>>>>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287) >>>>>>>> at >>>>>>>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >>>>>>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323) >>>>>>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287) >>>>>>>> at >>>>>>>> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) >>>>>>>> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323) >>>>>>>> at org.apache.spark.rdd.RDD.iterator(RDD.scala:287) >>>>>>>> at >>>>>>>> org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87) >>>>>>>> at org.apache.spark.scheduler.Task.run(Task.scala:108) >>>>>>>> at >>>>>>>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335) >>>>>>>> ... 3 more >>>>>>> >>>>>>> >>>>>>> >>>>>
