使用案例类Spark 2.1.0的显式强制转换.csv [英] Explicit cast reading .csv with case class Spark 2.1.0

问题描述

我有以下案例类:

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

我正在尝试阅读以下csv: https ://github.com/xsankar/fdps-v3/blob/master/data/NW-Order-Details.csv

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

这是我的代码:

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]

错误是:

 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;

如果所有字段均为双精度"值，为什么不能对其进行转换?我不明白什么?

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

Spark版本2.1.0，Scala版本2.11.7

Spark version 2.1.0, Scala version 2.11.7

推荐答案

您只需要将字段显式转换为Double:

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]

顺便提一下，根据Scala(和Java)的约定，您的case类构造函数参数应为小写驼峰式:

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)

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