This issue has been fixed few days ago in master branch. Here is the PR https://github.com/apache/incubator-zeppelin/pull/673
And related issues filed in JIRA before https://issues.apache.org/jira/browse/ZEPPELIN-194 https://issues.apache.org/jira/browse/ZEPPELIN-381 With the latest master branch, we recommend you to load dependencies via interpreter setting menu instead of %dep interpreter. If you want to know how to set dependencies with latest master branch, please check doc <https://zeppelin.incubator.apache.org/docs/0.6.0-incubating-SNAPSHOT/manual/dependencymanagement.html> and let me know if it works. Cheers, Mina On Tue, Feb 2, 2016 at 12:50 PM, Lin, Yunfeng <yunfeng....@citi.com> wrote: > I’ve created an issue in jira > > > > https://issues.apache.org/jira/browse/ZEPPELIN-648 > > > > *From:* Benjamin Kim [mailto:bbuil...@gmail.com] > *Sent:* Tuesday, February 02, 2016 3:34 PM > *To:* us...@zeppelin.incubator.apache.org > *Cc:* dev@zeppelin.incubator.apache.org > *Subject:* Re: csv dependencies loaded in %spark but not %sql in spark > 1.6/zeppelin 0.5.6 > > > > Same here. I want to know the answer too. > > > > > > On Feb 2, 2016, at 12:32 PM, Jonathan Kelly <jonathaka...@gmail.com> > wrote: > > > > Hey, I just ran into that same exact issue yesterday and wasn't sure if I > was doing something wrong or what. Glad to know it's not just me! > Unfortunately I have not yet had the time to look any deeper into it. Would > you mind filing a JIRA if there isn't already one? > > > > On Tue, Feb 2, 2016 at 12:29 PM Lin, Yunfeng <yunfeng....@citi.com> wrote: > > Hi guys, > > > > I load spark-csv dependencies in %spark, but not in %sql using apache > zeppelin 0.5.6 with spark 1.6.0. Everything is working fine in zeppelin > 0.5.5 with spark 1.5 through > > > > Do you have similar problems? > > > > I am loading spark csv dependencies ( > https://github.com/databricks/spark-csv > <https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_databricks_spark-2Dcsv&d=BQMFaQ&c=j-EkbjBYwkAB4f8ZbVn1Fw&r=b2BXWa66OlJ_NWqk5P310M6mGfus8eDC5O4J0-nePFY&m=dSXRZCZNlnU1tx9rtyX9UWfdjT0EPbafKr2NyIrXP-o&s=zUPPWKYhZiNUuIUWmlXXGF_94ImGHQ4qHpFCU0xSEzg&e=> > ) > > > > Using: > > %dep > > z.load(“PATH/commons-csv-1.1.jar”) > > z.load(“PATH/spark-csv_2.10-1.3.0.jar”) > > z.load(“PATH/univocity-parsers-1.5.1.jar:) > > z.load(“PATH/scala-library-2.10.5.jar”) > > > > I am able to load a csv from hdfs using data frame API in spark. It is > running perfect fine. > > %spark > > val df = sqlContext.read > > .format("com.databricks.spark.csv") > > .option("header", "false") // Use finrst line of all files as header > > .option("inferSchema", "true") // Automatically infer data types > > .load("hdfs://sd-6f48-7fe6:8020/tmp/people.txt") // this is a file > in HDFS > > df.registerTempTable("people") > > df.show() > > > > This also work: > > %spark > > val df2=sqlContext.sql(“select * from people”) > > df2.show() > > > > But this doesn’t work…. > > %sql > > select * from people > > > > java.lang.ClassNotFoundException: > com.databricks.spark.csv.CsvRelation$$anonfun$1$$anonfun$2 at > java.net.URLClassLoader$1.run(URLClassLoader.java:366) at > java.net.URLClassLoader$1.run(URLClassLoader.java:355) at > java.security.AccessController.doPrivileged(Native Method) at > java.net.URLClassLoader.findClass(URLClassLoader.java:354) at > java.lang.ClassLoader.loadClass(ClassLoader.java:425) at > sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:308) at > java.lang.ClassLoader.loadClass(ClassLoader.java:358) at > java.lang.Class.forName0(Native Method) at > java.lang.Class.forName(Class.java:270) at > org.apache.spark.util.InnerClosureFinder$$anon$4.visitMethodInsn(ClosureCleaner.scala:435) > at org.apache.xbean.asm5.ClassReader.a(Unknown Source) at > org.apache.xbean.asm5.ClassReader.b(Unknown Source) at > org.apache.xbean.asm5.ClassReader.accept(Unknown Source) at > org.apache.xbean.asm5.ClassReader.accept(Unknown Source) at > org.apache.spark.util.ClosureCleaner$.getInnerClosureClasses(ClosureCleaner.scala:84) > at > org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:187) > at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:122) at > org.apache.spark.SparkContext.clean(SparkContext.scala:2055) at > org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1.apply(RDD.scala:707) at > org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1.apply(RDD.scala:706) at > org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150) > at > org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111) > at org.apache.spark.rdd.RDD.withScope(RDD.scala:316) at > org.apache.spark.rdd.RDD.mapPartitions(RDD.scala:706) at > com.databricks.spark.csv.CsvRelation.tokenRdd(CsvRelation.scala:90) at > com.databricks.spark.csv.CsvRelation.buildScan(CsvRelation.scala:104) at > com.databricks.spark.csv.CsvRelation.buildScan(CsvRelation.scala:152) at > org.apache.spark.sql.execution.datasources.DataSourceStrategy$$anonfun$4.apply(DataSourceStrategy.scala:64) > at > org.apache.spark.sql.execution.datasources.DataSourceStrategy$$anonfun$4.apply(DataSourceStrategy.scala:64) > at > org.apache.spark.sql.execution.datasources.DataSourceStrategy$$anonfun$pruneFilterProject$1.apply(DataSourceStrategy.scala:274) > at > org.apache.spark.sql.execution.datasources.DataSourceStrategy$$anonfun$pruneFilterProject$1.apply(DataSourceStrategy.scala:273) > at > org.apache.spark.sql.execution.datasources.DataSourceStrategy$.pruneFilterProjectRaw(DataSourceStrategy.scala:352) > at > org.apache.spark.sql.execution.datasources.DataSourceStrategy$.pruneFilterProject(DataSourceStrategy.scala:269) > at > org.apache.spark.sql.execution.datasources.DataSourceStrategy$.apply(DataSourceStrategy.scala:60) > at > org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:58) > at > org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:58) > at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371) at > org.apache.spark.sql.catalyst.planning.QueryPlanner.plan(QueryPlanner.scala:59) > at > org.apache.spark.sql.catalyst.planning.QueryPlanner.planLater(QueryPlanner.scala:54) > at > org.apache.spark.sql.execution.SparkStrategies$BasicOperators$.apply(SparkStrategies.scala:349) > at > org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:58) > at > org.apache.spark.sql.catalyst.planning.QueryPlanner$$anonfun$1.apply(QueryPlanner.scala:58) > > at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371) at > org.apache.spark.sql.catalyst.planning.QueryPlanner.plan(QueryPlanner.scala:59) > at > org.apache.spark.sql.execution.QueryExecution.sparkPlan$lzycompute(QueryExecution.scala:47) > at > org.apache.spark.sql.execution.QueryExecution.sparkPlan(QueryExecution.scala:45) > at > org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:52) > at > org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:52) > at org.apache.spark.sql.DataFrame.withCallback(DataFrame.scala:2134) at > org.apache.spark.sql.DataFrame.head(DataFrame.scala:1413) at > org.apache.spark.sql.DataFrame.take(DataFrame.scala:1495) at > sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at > sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57) > at > sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) > at java.lang.reflect.Method.invoke(Method.java:606) at > org.apache.zeppelin.spark.ZeppelinContext.showDF(ZeppelinContext.java:297) > at > org.apache.zeppelin.spark.SparkSqlInterpreter.interpret(SparkSqlInterpreter.java:144) > at > org.apache.zeppelin.interpreter.ClassloaderInterpreter.interpret(ClassloaderInterpreter.java:57) > at > org.apache.zeppelin.interpreter.LazyOpenInterpreter.interpret(LazyOpenInterpreter.java:93) > at > org.apache.zeppelin.interpreter.remote.RemoteInterpreterServer$InterpretJob.jobRun(RemoteInterpreterServer.java:300) > at org.apache.zeppelin.scheduler.Job.run(Job.java:169) at > org.apache.zeppelin.scheduler.FIFOScheduler$1.run(FIFOScheduler.java:134) > at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471) > at java.util.concurrent.FutureTask.run(FutureTask.java:262) at > java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:178) > at > java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:292) > at > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) > at > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) > at java.lang.Thread.run(Thread.java:745) > > > > > > > > I notice that in the source code of spark interpreter (class: > org.apache.zeppelin.spark.ZeppelinContext. There are something different > between spark 1.5 and spark 1.6. Spark 1.5 is using SQLContext while Spark > 1.6 is HiveContext. Unfortunately, no matter true or false are set in > zeppelin.spark.useHiveContext, %sql just can’t find csv dependencies … > > > > > > try { > // Use reflection because of classname returned by queryExecution > changes from > // Spark <1.5.2 org.apache.spark.sql.SQLContext$QueryExecution > // Spark 1.6.0> org.apache.spark.sql.hive.HiveContext$QueryExecution > Object qe = df.getClass().getMethod("queryExecution").invoke(df); > Object a = qe.getClass().getMethod("analyzed").invoke(qe); > scala.collection.Seq seq = (scala.collection.Seq) > a.getClass().getMethod("output").invoke(a); > > columns = (List<Attribute>) scala.collection.JavaConverters. > *seqAsJavaListConverter*(seq) > .asJava(); > } catch (NoSuchMethodException | SecurityException | > IllegalAccessException > | IllegalArgumentException | InvocationTargetException e) { > throw new InterpreterException(e); > } > > > > Yunfeng > > >
