[jira] [Created] (TOREE-523) Problem reading Kudu tables using Spark (Jupyer Notebook with Apache Toree - Scala Kernel )

["Chouaieb Nemri (Jira)" <ji...@apache.org>]

Chouaieb Nemri created TOREE-523:
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             Summary: Problem reading Kudu tables using Spark (Jupyer Notebook with Apache Toree - Scala Kernel )
                 Key: TOREE-523
                 URL: https://issues.apache.org/jira/browse/TOREE-523
             Project: TOREE
          Issue Type: Bug
          Components: Kernel
    Affects Versions: 0.4.0, 0.3.0
         Environment: Jupyter Notebook - Spark version : 2.2.0 Scala version : 2.11 Apache Toree version : 0.3
            Reporter: Chouaieb Nemri


I am trying to read a Kudu table using Apache Spark within a Jupyter Notebook running with an Apache Toree - Scala Kernel.

Spark version : 2.2.0 Scala version : 2.11 Apache Toree version : 0.3

This is the code I am using to read the Kudu table
{code:java}
val kuduMasterAddresses = KUDU_MASTER_ADDRESSES_HERE
val kuduMasters: String = Seq(kuduMasterAddresses).mkString(",")

val kuduContext = new KuduContext(kuduMasters, spark.sparkContext)

val table = TABLE_NAME_HERE

def readKudu(table: String) = {
    val tableKuduOptions: Map[String, String] = Map(
    "kudu.table"  -> table,
    "kudu.master" -> kuduMasters
    )
    spark.sqlContext.read.options(tableKuduOptions).kudu
}

val kuduTableDF = readKudu(table)
{code}
 

Using _kuduContext.tableExists(table)_ returns _true_. Using _kuduTableDF.columns_ gives an array of String with the right column names.

The problem occurs when I try to apply an action like count, show etc ... the current exception is thrown:
{quote}Name: org.apache.spark.SparkException Message: Job aborted due to stage failure: Exception while getting task result: java.io.IOException: java.lang.ClassNotFoundException: org.apache.kudu.spark.kudu.KuduContext$TimestampAccumulator StackTrace: at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1567) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1555) at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1554) at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48) at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1554) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:803) at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:803) at scala.Option.foreach(Option.scala:257) at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:803) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1782) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1737) at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1726) at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:619) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2031) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2052) at org.apache.spark.SparkContext.runJob(SparkContext.scala:2071) at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:336) at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38) at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collectFromPlan(Dataset.scala:2865) at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2154) at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2154) at org.apache.spark.sql.Dataset$$anonfun$55.apply(Dataset.scala:2846) at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:65) at org.apache.spark.sql.Dataset.withAction(Dataset.scala:2845) at org.apache.spark.sql.Dataset.head(Dataset.scala:2154) at org.apache.spark.sql.Dataset.take(Dataset.scala:2367) at org.apache.spark.sql.Dataset.showString(Dataset.scala:241) at org.apache.spark.sql.Dataset.show(Dataset.scala:641) at org.apache.spark.sql.Dataset.show(Dataset.scala:600) at org.apache.spark.sql.Dataset.show(Dataset.scala:609)
{quote}
I have already used the _AddDeps_ magic in the Apache Toree notebook as follows:
{code:java}
%AddDeps org.apache.kudu kudu-spark2_2.11 1.6.0 --transitive --trace
%AddDeps org.apache.kudu kudu-client 1.6.0 --transitive --trace
{code}
I have no problems doing the following import :
{code:java}
import org.apache.kudu.spark.kudu._{code}



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