Explicit cast reading .csv with case class Spark 2.1.0

Question

I have the following case class:

case class OrderDetails(OrderID : String, ProductID : String, UnitPrice : Double,
                    Qty : Int, Discount : Double)

I am trying read this csv: https://github.com/xsankar/fdps-v3/blob/master/data/NW-Order-Details.csv

This is my code:

val spark = SparkSession.builder.master(sparkMaster).appName(sparkAppName).getOrCreate()
import spark.implicits._
val orderDetails = spark.read.option("header","true").csv( inputFiles + "NW-Order-Details.csv").as[OrderDetails]

And the error is:

 Exception in thread "main" org.apache.spark.sql.AnalysisException: 
 Cannot up cast `UnitPrice` from string to double as it may truncate
 The type path of the target object is:
  - field (class: "scala.Double", name: "UnitPrice")
  - root class: "es.own3dh2so4.OrderDetails"
 You can either add an explicit cast to the input data or choose a higher precision type of the field in the target object;

Why can not it be transformed if all fields are "doubles" values? What do not I understand?

Spark version 2.1.0, Scala version 2.11.7

Answer 1

You just need to explicitly cast your field to a Double:

val orderDetails = spark.read
   .option("header","true")
   .csv( inputFiles + "NW-Order-Details.csv")
   .withColumn("unitPrice", 'UnitPrice.cast(DoubleType))
   .as[OrderDetails]

On a side note, by Scala (and Java) convention, your case class constructor parameters should be lower camel case:

case class OrderDetails(orderID: String, 
                        productID: String, 
                        unitPrice: Double,
                        qty: Int, 
                        discount: Double)