Scala中Spark DataFrame基于多列的左外连接实现求助

要实现基于ID、date、location三列的左外连接，需要做两处关键修改：

1. 确保每个参与连接的DataFrame都包含连接所需的三列，修改getRelevantSegmentInfo函数的查询逻辑
2. 更新join操作的连接条件，加入date和location列

以下是修改后的完整代码：

import org.apache.spark.sql.DataFrame
import org.apache.spark.sql.SparkSession

val spark = SparkSession.builder.master("local").appName("testing").enableHiveSupport().getOrCreate()
import spark.implicits._

def getRelevantSegmentInfo(tableName: String, segmentName: String, pocs: Seq[String])(implicit
    spark: SparkSession
): DataFrame = {
  spark
    .table(tableName)
    .select(segmentName, "ID", "date", "location") // 修改：新增date和location列的查询
}

val firstDF: DataFrame = getRelevantSegmentInfo(
  result_as_sequence.head.tableName, 
  result_as_sequence.head.segmentName,
  result_as_sequence.head.pocs
)(spark)

val finalDF = result_as_sequence.tail.foldLeft(firstDF) {
  case (leftDF, segmentStruct) =>
    leftDF.join(
      getRelevantSegmentInfo(
        segmentStruct.tableName,
        segmentStruct.segmentName,
        segmentStruct.pocs
      )(spark),
      Seq("ID", "date", "location"), // 修改：连接条件加入date和location
      "left_outer"
    )
}

另外修正了原代码中val conf =的错误定义，改为val spark =避免变量类型不匹配问题。

内容的提问来源于stack exchange，提问作者vicmartin