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 >>>>> >>>>> >>>>> >>>
