Spark Struct structfield names getting changed in UDF - apache-spark

Question

I am trying to pass a struct in spark to udf. It is changing the field names and renaming to the column position. How do I fix it?
object TestCSV {
def main(args: Array[String]) {
val conf = new SparkConf().setAppName("localTest").setMaster("local")
val sc = new SparkContext(conf)
val sqlContext = new SQLContext(sc)
val inputData = sqlContext.read.format("com.databricks.spark.csv")
.option("delimiter","|")
.option("header", "true")
.load("test.csv")
inputData.printSchema()
inputData.show()
val groupedData = inputData.withColumn("name",struct(inputData("firstname"),inputData("lastname")))
val udfApply = groupedData.withColumn("newName",processName(groupedData("name")))
udfApply.show()
}
def processName = udf((input:Row) =>{
println(input)
println(input.schema)
Map("firstName" -> input.getAs[String]("firstname"), "lastName" -> input.getAs[String]("lastname"))
})
}
Output:
root
|-- id: string (nullable = true)
|-- firstname: string (nullable = true)
|-- lastname: string (nullable = true)
+---+---------+--------+
| id|firstname|lastname|
+---+---------+--------+
| 1| jack| reacher|
| 2| john| Doe|
+---+---------+--------+
Error:
[jack,reacher]
StructType(StructField(i[1],StringType,true), > StructField(i[2],StringType,true))
17/03/08 09:45:35 ERROR Executor: Exception in task 0.0 in stage 2.0 (TID 2)
java.lang.IllegalArgumentException: Field "firstname" does not exist.

Answer 1

What you are encountering is really strange. After playing around a bit I finally figured out that it may be related to a problem with the optimizer engine. It seems that the problem is not the UDF but the struct function.
I get it to work (Spark 1.6.3) when I cache the groupedData, without caching I get your reported exception:
import org.apache.spark.sql.Row
import org.apache.spark.sql.hive.HiveContext
import org.apache.spark.{SparkConf, SparkContext}
object Demo {
def main(args: Array[String]): Unit = {
val sc = new SparkContext(new SparkConf().setAppName("Demo").setMaster("local[1]"))
val sqlContext = new HiveContext(sc)
import sqlContext.implicits._
import org.apache.spark.sql.functions._
def processName = udf((input: Row) => {
Map("firstName" -> input.getAs[String]("firstname"), "lastName" -> input.getAs[String]("lastname"))
})
val inputData =
sc.parallelize(
Seq(("1", "Kevin", "Costner"))
).toDF("id", "firstname", "lastname")
val groupedData = inputData.withColumn("name", struct(inputData("firstname"), inputData("lastname")))
.cache() // does not work without cache
val udfApply = groupedData.withColumn("newName", processName(groupedData("name")))
udfApply.show()
}
}
Alternatively you can use the RDD API to make your struct, but this is not really nice:
case class Name(firstname:String,lastname:String) // define outside main
val groupedData = inputData.rdd
.map{r =>
(r.getAs[String]("id"),
Name(
r.getAs[String]("firstname"),
r.getAs[String]("lastname")
)
)
}
.toDF("id","name")