Spark数据帧复制行基于scala中拆分列值

我在scala中有以下代码：

val  fullCertificateSourceDf = certificateSourceDf         
              .withColumn("Stage", when(col("Data.WorkBreakdownUp1Summary").isNotNull && col("Data.WorkBreakdownUp1Summary")=!="",                                                     rtrim(regexp_extract($"Data.WorkBreakdownUp1Summary","^.*?(?= - *[a-zA-Z])",0))).otherwise(""))
              .withColumn("SubSystem", when(col("Data.ProcessBreakdownSummaryList").isNotNull && col("Data.ProcessBreakdownSummaryList")=!="",                                         regexp_extract($"Data.ProcessBreakdownSummaryList","^.*?(?= - *[a-zA-Z])",0)).otherwise(""))
              .withColumn("System", when(col("Data.ProcessBreakdownUp1SummaryList").isNotNull && col("Data.ProcessBreakdownUp1SummaryList")=!="",                                         regexp_extract($"Data.ProcessBreakdownUp1SummaryList","^.*?(?= - *[a-zA-Z])",0)).otherwise(""))
              .withColumn("Facility", when(col("Data.ProcessBreakdownUp2Summary").isNotNull && col("Data.ProcessBreakdownUp2Summary")=!="",                                         regexp_extract($"Data.ProcessBreakdownUp2Summary","^.*?(?= - *[a-zA-Z])",0)).otherwise(""))
              .withColumn("Area", when(col("Data.ProcessBreakdownUp3Summary").isNotNull && col("Data.ProcessBreakdownUp3Summary")=!="",                                         regexp_extract($"Data.ProcessBreakdownUp3Summary","^.*?(?= - *[a-zA-Z])",0)).otherwise(""))
              .select("Data.ID",
                      "Data.CertificateID",
                      "Data.CertificateTag",
                      "Data.CertificateDescription",
                      "Data.WorkBreakdownUp1Summary",
                      "Data.ProcessBreakdownSummaryList",
                      "Data.ProcessBreakdownUp1SummaryList",
                      "Data.ProcessBreakdownUp2Summary",
                      "Data.ProcessBreakdownUp3Summary",
                      "Data.ActualStartDate",
                      "Data.ActualEndDate",
                      "Data.ApprovedDate",
                      "Data.CurrentState",
                      "DataType",
                      "PullDate",
                      "PullTime",
                      "Stage",
                      "System",
                      "SubSystem",
                      "Facility",
                      "Area"
                     )
                     .filter((col("Stage").isNotNull) && (length(col("Stage"))>0))
                     .filter(((col("SubSystem").isNotNull) && (length(col("SubSystem"))>0)) || ((col("System").isNotNull) && (length(col("System"))>0)) || ((col("Facility").isNotNull) && (length(col("Facility"))>0)) || ((col("Area").isNotNull) && (length(col("Area"))>0))
                      )
                     .select("*")

此dataframe fullCertificateSourceDf包含以下数据：

为了简洁起见，我隐藏了一些专栏

我希望数据如下所示：

我们分为两列：ProcessBreakdownSummaryList和ProcessBreakdownP1SummaryList。它们都是逗号分隔的列表

请注意，如果过程故障汇总表（CS10-100-22-10-矿井进气风扇加热器系统，CS10-100-81-10-矿井服务开关设备）和过程故障汇总表（CS10-100-22-服务井通风，CS10-100-81-服务井电气）中的值相同，我们只应拆分一次

但是，如果它们不同于过程故障汇总表（CS10-100-22-10-矿井进气风扇加热器系统，CS10-100-81-10-矿井服务开关设备）和过程故障汇总表（CS10-100-22-服务井通风，CS10-100-34-服务井电气）它应该再次拆分为第三行

---

提前感谢您在这方面的帮助。

您可以通过多种方式解决它，我认为对于复杂的处理，最简单的方法是使用scala。您可以读取所有列，包括“ProcessBreakdownSummaryList”和“ProcessBreakdownUp1SummaryList”，比较它们的值是否相同/不同，并为单个输入行发出多行。然后对输出进行flatmap，以获得包含所有所需行的数据帧

val fullCertificateSourceDf = // your code

fullCertificateSourceDf.map{ row =>
val id = row.getAs[String]("Data.ID")
... read all columns

val processBreakdownSummaryList = row.getAs[String]("Data.ProcessBreakdownSummaryList")
val processBreakdownUp1SummaryList = row.getAs[String]("Data.ProcessBreakdownUp1SummaryList")

//split processBreakdownSummaryList on ","
//split processBreakdownUp1SummaryList on ","
//compare then for equality 
//lets say you end up with 4 rows.

//return Seq of those 4 rows in a list processBreakdownSummary
//return a List of tuple of strings like List((id, certificateId, certificateTag, ..distinct values of processBreakdownUp1SummaryList...), (...) ...)
//all columns id, certificateId, certificateTag etc are repeated for each distinct value of processBreakdownUp1SummaryList and processBreakdownSummaryList

}.flatMap(identity(_)).toDF("column1","column2"...)

下面是一个将一行拆分为多行的示例

    val employees = spark.createDataFrame(Seq(("E1",100.0,"a,b"), ("E2",200.0,"e,f"),("E3",300.0,"c,d"))).toDF("employee","salary","clubs")

    employees.map{ r =>
      val clubs = r.getAs[String]("clubs").split(",")
      for{
        c : String <- clubs
      }yield(r.getAs[String]("employee"),r.getAs[Double]("salary"), c)
    }.flatMap(identity(_)).toDF("employee","salary","clubs").show(false)

---

谢谢你，萨利姆。如果您能举个例子，我们将不胜感激。请查看提供的例子。这太棒了@Salim。您将如何处理此问题（使用null）`val employees=spark.createDataFrame（Seq（（（“E1”，100.0，“a，b”），（“E2”，200.0，“e，f”），（“E3”，300.0，“c，d”），（“E4”，300.0，null））。toDF（“员工”，“工资”，“俱乐部”）`请避免数字列为null，scala不会长时间识别null。而是对数值使用选项或默认值。对于字符串，可以使用null或默认值。我可以给你举个例子，如果你问另一个问题或者投票给这个问题。

+--------+------+-----+
|employee|salary|clubs|
+--------+------+-----+
|E1      |100.0 |a    |
|E1      |100.0 |b    |
|E2      |200.0 |e    |
|E2      |200.0 |f    |
|E3      |300.0 |c    |
|E3      |300.0 |d    |
+--------+------+-----+