Typesafe连接与Spark数据集的安全性低于我的预期

在试图制造这个 solution 到 Perform a typed join in Scala with Spark Datasets 我无意中碰到了一些我不明白的事情。

在下面的测试中,签名 innerJoin 是 def innerJoin[U, K](ds2: Dataset[U])(f: T => K, g: U => K)(implicit e1: Encoder[(K, T)], e2: Encoder[(K, U)], e3: Encoder[(T, U)]) f: Foo => String 和 g: Bar => Int . 我希望在编译时出现错误,但它编译得很好。为什么?

java.lang.ClassNotFoundException: scala.Any 当Spark试图创建产品编码器时 ((K, Foo),(K, Bar)) Any 显示为 Int 和 String .

import org.apache.spark.sql.{Dataset, Encoder, SparkSession}
import org.scalatest.Matchers
import org.scalatest.testng.TestNGSuite
import org.testng.annotations.Test

case class Foo(a: String)

case class Bar(b: Int)

class JoinTest extends TestNGSuite with Matchers {
  import JoinTest._

  @Test
  def testJoin(): Unit = {
    val spark = SparkSession.builder()
      .master("local")
      .appName("test").getOrCreate()

    import spark.implicits._

    val ds1 = spark.createDataset(Seq(Foo("a")))
    val ds2 = spark.createDataset(Seq(Bar(123)))

    val jd = ds1.innerJoin(ds2)(_.a, _.b)

    jd.count shouldBe 0
  }
 }

object JoinTest {
  implicit class Joins[T](ds1: Dataset[T]) {
    def innerJoin[U, K](ds2: Dataset[U])(f: T => K, g: U => K)
     (implicit e1: Encoder[(K, T)], e2: Encoder[(K, U)], e3: Encoder[(T, U)]): Dataset[(T, U)] = 
     {
       val ds1_ = ds1.map(x => (f(x), x))
       val ds2_ = ds2.map(x => (g(x), x))
       ds1_.joinWith(ds2_, ds1_("_1") === ds2_("_1")).map(x => (x._1._2, x._2._2))
    }
   }
}