我改编了这个问题here 的答案。您还可以在 Json 文件中添加 joinTypes 以在 runtume 中读取。您可以查看此答案以了解 json 对象处理JsonParsing
更新1：我更新答案以遵循Spark文档方式JoinType
import org.apache.spark._
import org.apache.spark.sql._
import org.apache.spark.sql.expressions._
import org.apache.spark.sql.functions._
object SparkSandbox extends App {
case class Row(id: Int, value: String)
private[this] implicit val spark = SparkSession.builder().master("local[*]").getOrCreate()
import spark.implicits._
spark.sparkContext.setLogLevel("ERROR")
val r1 = Seq(Row(1, "A1"), Row(2, "A2"), Row(3, "A3"), Row(4, "A4")).toDS()
val r2 = Seq(Row(3, "A3"), Row(4, "A4"), Row(4, "A4_1"), Row(5, "A5"), Row(6, "A6")).toDS()
val validUserJoinType = "inner"
val inValiedUserJoinType = "nothing"
val joinTypes = Seq("inner", "outer", "full", "full_outer", "left", "left_outer", "right", "right_outer", "left_semi", "left_anti")
inValiedUserJoinType match {
case x => if (joinTypes.contains(x)) {
println("do some logic")
joinTypes foreach { joinType =>
println(s"${joinType.toUpperCase()} JOIN")
r1.join(right = r2, usingColumns = Seq("id"), joinType = joinType).orderBy("id").show()
}
}
case _ =>
val supported = Seq(
"inner",
"outer", "full", "fullouter", "full_outer",
"leftouter", "left", "left_outer",
"rightouter", "right", "right_outer",
"leftsemi", "left_semi",
"leftanti", "left_anti",
"cross")
throw new IllegalArgumentException(s"Unsupported join type '$inValiedUserJoinType'. " +
"Supported join types include: " + supported.mkString("'", "', '", "'") + ".")
}
}