How to Fix "java.lang.NullPointerException at org.apache.spark.sql.execution.datasources.orc.OrcColumnVector.getLong(OrcColumnVector.java:141)"

Question

I'm trying to combine all columns in dataframe to one column named value.

Mycode :

    val df = sparkSession.sql(sql)

    val dfwithValue = df.withColumn("value",df.col("topic"))

    dfwithValue.selectExpr("CAST(value AS STRING)").show() // no error

    import org.apache.spark.sql.functions._

    val cols = df.columns.map({ col =>
      df.col(col)
    }).toSeq

     val newdf = df.withColumn("value", struct(cols : _*))

     newdf.selectExpr("CAST(value AS STRING)").show() // error

when I use the second way, I encounter error

Caused by: java.lang.NullPointerException
    at org.apache.spark.sql.execution.datasources.orc.OrcColumnVector.getLong(OrcColumnVector.java:141)
    at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
    at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
    at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$13$$anon$1.hasNext(WholeStageCodegenExec.scala:636)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409)
    at org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:125)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99)
    at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:55)
    at org.apache.spark.scheduler.Task.run(Task.scala:121)
    at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:403)
    at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:409)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)

Can someone help?

Answer 1

I do not get errors with either approach but I find the solution overly complicated for the task. Also this code returns a dataframe of rows of Row[WarappedArray[String]] rather than Row[String]

try:

df.map(_.mkString("")).toDF("value").show()