Spark/Zeppelin: Issue with showing data - scala

Question

I'm having an issue trying to show data that's in my DF.(or more generally put, i want to visualize the data. Preferably with the SQL interperter)
I've gotten a flat text file (log excerpt) that i'm formatting through a class and then creating a DF based on the result.
The data itself looks like this (single line excerpt) :
2017-02-09 15:56:15,411 INFO [pool-2-thread-1] n.t.f.c.LoggingConsumer [LoggingConsumer.java:27] [Floor=b827eb074534, tile=00051] Data received: '[false, false, false, false, false, false, false, true]', batch size= 0
Here's my code:
import org.apache.commons.io.IOUtils
import java.net.URL
import java.nio.charset.Charset
import org.apache.spark.sql.SparkSession
import sqlContext.implicits._
val realdata = sc.textFile("/root/application.txt")
case class testClass(date: String, time: String, level: String, unknown1: String, unknownConsumer: String, unknownConsumer2: String, vloer: String, tegel: String, msg: String, sensor1: String, sensor2: String, sensor3: String, sensor4: String, sensor5: String, sensor6: String, sensor7: String, sensor8: String, batchsize: String, troepje1: String, troepje2: String)
val mapData = realdata
.filter(line => line.contains("data") && line.contains("INFO"))
.map(s => s.split(" ").toList)
.map(
s => testClass(s(0),
s(1).split(",")(0),
s(1).split(",")(1),
s(3),
s(4),
s(5),
s(6),
s(7),
s(8),
s(15),
s(16),
s(17),
s(18),
s(19),
s(20),
s(21),
s(22),
"",
"",
""
)
).toDF
When i print the resulting schema it seems to do what i want:
root
|-- date: string (nullable = true)
|-- time: string (nullable = true)
|-- level: string (nullable = true)
|-- unknown1: string (nullable = true)
|-- unknownConsumer: string (nullable = true)
|-- unknownConsumer2: string (nullable = true)
|-- vloer: string (nullable = true)
|-- tegel: string (nullable = true)
|-- msg: string (nullable = true)
|-- sensor1: string (nullable = true)
|-- sensor2: string (nullable = true)
|-- sensor3: string (nullable = true)
|-- sensor4: string (nullable = true)
|-- sensor5: string (nullable = true)
|-- sensor6: string (nullable = true)
|-- sensor7: string (nullable = true)
|-- sensor8: string (nullable = true)
|-- batchsize: string (nullable = true)
|-- troepje1: string (nullable = true)
|-- troepje2: string (nullable = true)
Now granted, it's not very pretty (and i do apologise for the dutch) but that seems to work.
But when i do a mapData.show or anything like that i get:
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 3.0 failed 1 times, most recent failure: Lost task 0.0 in stage 3.0 (TID 3, localhost, executor driver): java.io.IOException: No such file or directory
at java.io.UnixFileSystem.createFileExclusively(Native Method)
at java.io.File.createTempFile(File.java:2024)
at org.apache.spark.util.Utils$.downloadFile(Utils.scala:503)
at org.apache.spark.util.Utils$.doFetchFile(Utils.scala:639)
at org.apache.spark.util.Utils$.fetchFile(Utils.scala:463)
at org.apache.spark.executor.Executor$$anonfun$org$apache$spark$executor$Executor$$updateDependencies$5.apply(Executor.scala:508)
at org.apache.spark.executor.Executor$$anonfun$org$apache$spark$executor$Executor$$updateDependencies$5.apply(Executor.scala:500)
at scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:733)
at scala.collection.mutable.HashMap$$anonfun$foreach$1.apply(HashMap.scala:99)
at scala.collection.mutable.HashMap$$anonfun$foreach$1.apply(HashMap.scala:99)
at scala.collection.mutable.HashTable$class.foreachEntry(HashTable.scala:230)
at scala.collection.mutable.HashMap.foreachEntry(HashMap.scala:40)
at scala.collection.mutable.HashMap.foreach(HashMap.scala:99)
at scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:732)
at org.apache.spark.executor.Executor.org$apache$spark$executor$Executor$$updateDependencies(Executor.scala:500)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:257)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1435)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1423)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1422)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1422)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:802)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1650)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1605)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1594)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:628)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1918)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1931)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1944)
at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:333)
at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
at org.apache.spark.sql.Dataset$$anonfun$org$apache$spark$sql$Dataset$$execute$1$1.apply(Dataset.scala:2371)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:57)
at org.apache.spark.sql.Dataset.withNewExecutionId(Dataset.scala:2765)
at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$execute$1(Dataset.scala:2370)
at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collect(Dataset.scala:2377)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2113)
at org.apache.spark.sql.Dataset$$anonfun$head$1.apply(Dataset.scala:2112)
at org.apache.spark.sql.Dataset.withTypedCallback(Dataset.scala:2795)
at org.apache.spark.sql.Dataset.head(Dataset.scala:2112)
at org.apache.spark.sql.Dataset.take(Dataset.scala:2327)
at org.apache.spark.sql.Dataset.showString(Dataset.scala:248)
at org.apache.spark.sql.Dataset.show(Dataset.scala:636)
at org.apache.spark.sql.Dataset.show(Dataset.scala:595)
... 56 elided
Caused by: java.io.IOException: No such file or directory
at java.io.UnixFileSystem.createFileExclusively(Native Method)
at java.io.File.createTempFile(File.java:2024)
at org.apache.spark.util.Utils$.downloadFile(Utils.scala:503)
at org.apache.spark.util.Utils$.doFetchFile(Utils.scala:639)
at org.apache.spark.util.Utils$.fetchFile(Utils.scala:463)
at org.apache.spark.executor.Executor$$anonfun$org$apache$spark$executor$Executor$$updateDependencies$5.apply(Executor.scala:508)
at org.apache.spark.executor.Executor$$anonfun$org$apache$spark$executor$Executor$$updateDependencies$5.apply(Executor.scala:500)
at scala.collection.TraversableLike$WithFilter$$anonfun$foreach$1.apply(TraversableLike.scala:733)
at scala.collection.mutable.HashMap$$anonfun$foreach$1.apply(HashMap.scala:99)
at scala.collection.mutable.HashMap$$anonfun$foreach$1.apply(HashMap.scala:99)
at scala.collection.mutable.HashTable$class.foreachEntry(HashTable.scala:230)
at scala.collection.mutable.HashMap.foreachEntry(HashMap.scala:40)
at scala.collection.mutable.HashMap.foreach(HashMap.scala:99)
at scala.collection.TraversableLike$WithFilter.foreach(TraversableLike.scala:732)
at org.apache.spark.executor.Executor.org$apache$spark$executor$Executor$$updateDependencies(Executor.scala:500)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:257)
... 3 more
Is there anybody that can tell me what i'm doing wrong or if there's a better way to visualize the data ?
Thank you so much for any help or guidance.