Spark Scala 数据集映射在 main 中工作但在函数中不工作

Spark Scala Dataset map works in main but not in function

我有 2 个数据集:

implicit val spark: SparkSession = SparkSession
  .builder()
  .appName("app").master("local[1]")
  .config("spark.executor.memory", "1g")
  .getOrCreate()


import spark.implicits._
val ds1 = /*read csv file*/.as[caseClass1]   
val ds2 = /*read csv file*/.as[caseClass2]  

然后我像这样加入和映射:

  val ds3 = ds1.
  joinWith(ds2, ds1("id") === ds2("id"))
  .map{case(left, right) => (left, Option(right))}

获得预期结果。

问题是我正在尝试用它和其他一些函数来实现 RichDataset,如下所示:

object Extentions {

  implicit class RichDataset[T <: Product](leftDs: Dataset[T]) {

    def leftJoinWith[V <: Product](rightDs: Dataset[V], condition: 
Column)(implicit spark: SparkSession) : Dataset[(T, Option[V])] = {
      import spark.implicits._

      leftDs.joinWith(rightDs, condition, "left")
        .map{case(left, right) => (left, Option(right))}
    }
  }
 }

在 main 中,对 leftJoinWith 的导入 Extentions._ 调用失败:

Error:(15, 13) Unable to find encoder for type stored in a Dataset. Primitive types (Int, String, etc) and Product types (case classes) are supported by importing spark.implicits._ Support for serializing other types will be added in future releases. .map{case(left, right) => (left, Option(right))}

Error:(15, 13) not enough arguments for method map: (implicit evidence: org.apache.spark.sql.Encoder[(T, Option[V])])org.apache.spark.sql.Dataset[(T, Option[V])]. Unspecified value parameter evidence. .map{case(left, right) => (left, Option(right))}

...但是spark.implicits._是在函数内部导入的!

如果return只是join,而不是join + map,它在main和function中都可以工作。

scalaVersion := "2.11.8", sparkVersion := "2.2.0"

提前致谢!

如果您将 TypeTag 添加到泛型类型参数,它会起作用(在 Spark 的源代码中看到这个):

import scala.reflect.runtime.universe.TypeTag
import org.apache.spark.sql.{Column, Dataset, SparkSession}


object Extentions {

  implicit class RichDataset[T <: Product : TypeTag](leftDs: Dataset[T]) {

    def leftJoinWith[V <: Product : TypeTag](rightDs: Dataset[V], condition:
    Column)(implicit spark: SparkSession) : Dataset[(T, Option[V])] = {
      import spark.implicits._

      leftDs.joinWith(rightDs, condition, "left")
        .map{case(left, right) => (left, Option(right))}
    }
  }
}