SparkSQL 算子接口调用，数据编码报错

问题背景

具体信息分析过程

编写spark-sql 算子ut，自定构造数据，调用列式filter验证对null处理。

1、方法调用栈

org.apache.spark.sql.execution.ColumnarFilterExecSuite *** ABORTED ***
  java.lang.UnsupportedOperationException: No Encoder found for Any
- field (class: "java.lang.Object", name: "_4")
- root class: "scala.Tuple4"
  at org.apache.spark.sql.catalyst.ScalaReflection$.$anonfun$serializerFor$1(ScalaReflection.scala:591)
  at scala.reflect.internal.tpe.TypeConstraints$UndoLog.undo(TypeConstraints.scala:73)
  at org.apache.spark.sql.catalyst.ScalaReflection.cleanUpReflectionObjects(ScalaReflection.scala:904)
  at org.apache.spark.sql.catalyst.ScalaReflection.cleanUpReflectionObjects$(ScalaReflection.scala:903)
  at org.apache.spark.sql.catalyst.ScalaReflection$.cleanUpReflectionObjects(ScalaReflection.scala:49)
  at org.apache.spark.sql.catalyst.ScalaReflection$.serializerFor(ScalaReflection.scala:432)
  at org.apache.spark.sql.catalyst.ScalaReflection$.$anonfun$serializerFor$6(ScalaReflection.scala:577)
  at scala.collection.immutable.List.map(List.scala:297)
  at org.apache.spark.sql.catalyst.ScalaReflection$.$anonfun$serializerFor$1(ScalaReflection.scala:562)
  at scala.reflect.internal.tpe.TypeConstraints$UndoLog.undo(TypeConstraints.scala:73)

报错的数据（未指定数据类型）

inputDfWithNull = Seq(
  (null, "", 4, 2.0),
  (null, null, 1, 1.0),
  (" add", "World", 8, null),
  (" yeah  ", "yeah", 10, 8.0),
  (" yeah  ", "yeah", 10, 8.0)
).toDF("a", "b", "c", "d")

不报错的数据（指定数据类型）

inputDfWithNull = Seq[(String, String, java.lang.Integer, java.lang.Double)](
  (null, "", 4, 2.0),
  (null, null, 1, 1.0),
  (" add", "World", 8, null),
  (" yeah  ", "yeah", 10, 8.0),
  (" yeah  ", "yeah", 10, 8.0)
).toDF("a", "b", "c", "d")

1、调用原生spark是否报错？ 一样报错
2、报错是类型编码问题，可能是数据转换原因，指定编码？ 指定编码比较麻烦
3、使用其他创建df的方式，在创建时指定每列数据类型（参考原生spark 算子ut代码）
创建方式1

sparkContext.parallelize(Seq(
      Row(1, 2.0),
      Row(1, 2.0),
      Row(2, 1.0),
      Row(2, 1.0),
      Row(3, 3.0),
      Row(null, null),
      Row(null, 5.0),
      Row(6, null)
    )), new StructType().add("a", IntegerType).add("b", DoubleType))

创建方式2

spark.createDataframe(
    sparkContext.parallelize(Seq(
      Row(1, 2.0),
      Row(1, 2.0),
      Row(2, 1.0),
      Row(2, 1.0),
      Row(3, 3.0),
      Row(null, null),
      Row(null, 5.0),
      Row(6, null)
    )), new StructType().add("a", IntegerType).add("b", DoubleType))