Chouaieb Nemri created TOREE-523:
------------------------------------
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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