Code of method "initialize(I)V" of class "org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection" grows beyond 64 KB

18/01/19 06:33:15 INFO CodeGenerator: Code generated in 246.168393 ms$
18/01/19 06:33:21 ERROR CodeGenerator: failed to compile: org.codehaus.janino.JaninoRuntimeException: Code of method "initialize(I)V" of class "org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection" grows beyond 64 KB$
/* 001 */ public java.lang.Object generate(Object[] references) {$
/* 002 */ return new SpecificUnsafeProjection(references);$
/* 003 */ }$
/* 004 */$
/* 005 */ class SpecificUnsafeProjection extends org.apache.spark.sql.catalyst.expressions.UnsafeProjection {$
/* 006 */$
/* 007 */ private Object[] references;$
/* 008 */ private org.apache.spark.util.random.XORShiftRandom rng;$
/* 009 */ private org.apache.spark.util.random.XORShiftRandom rng1;$
/* 010 */ private org.apache.spark.util.random.XORShiftRandom rng2;$
/* 011 */ private org.apache.spark.util.random.XORShiftRandom rng3;$
/* 012 */ private org.apache.spark.util.random.XORShiftRandom rng4;$

import org.apache.spark.sql.types._
import org.apache.spark.sql.{Column, DataFrame, SparkSession}
class RandomData(val numberOfColumns: Int, val numberOfRows: Int) extends Serializable {
  private val minEpoch = Timestamp.valueOf("1800-01-01 00:00:00").getTime
  private val maxEpoch = Timestamp.valueOf("2200-01-01 00:00:00").getTime
  val idColumn = "id"
  import org.apache.spark.sql.functions._
def generateData(path: String): Unit = {
  val spark: SparkSession = SparkSession.builder().getOrCreate()
  materializeTable(spark).write.parquet(path + "/source")
}

private def materializeTable(spark: SparkSession): DataFrame = {
  var sourceDF = spark.sqlContext.range(0, numberOfRows).withColumnRenamed("id", 
 idColumn)
  val columns = sourceDF(idColumn) +: (0 until numberOfColumns)
  .flatMap(x => Seq(getTimeColumn(x), getNumberColumn(x), getCategoryColumn(x)))
sourceDF.select(columns: _*)
}

private def getTimeColumn(seed: Int): Column = {
  val uniqueSeed = seed + numberOfColumns * 3
  rand(seed = uniqueSeed)
   .multiply(maxEpoch - minEpoch)
   .divide(1000).cast("long")
   .plus(minEpoch / 1000).cast(TimestampType).alias(s"time$seed")
}
private def getNumberColumn(seed: Int, namePrefix: String = "number"): Column = {
  val uniqueSeed = seed + numberOfColumns * 4
  randn(seed = uniqueSeed).alias(s"$namePrefix$seed")
}
private def getCategoryColumn(seed: Int): Column = {
  val uniqueSeed = seed + numberOfColumns * 4
  rand(seed = uniqueSeed).multiply(100).cast("int").alias(s"category$seed")
}

}

object GenerateData{
def main(args: Array[String]): Unit = {
  new RandomData(args(0).toInt, args(1).toInt).generateData(args(2))
}
}

spark-submit --master yarn \
 --driver-memory 12g \
 --executor-memory 12g \
 --deploy-mode cluster \
 --class GenerateData \
 --master yarn \
 100 5000 "hdfs:///tmp/parquet"