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scala - 基于scala中拆分列值的Spark数据帧重复行

问题描述

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("*")

此数据框 fullCertificateSourceDf 包含以下数据：

原始数据

为了简洁，我隐藏了一些列。

我希望数据看起来像这样：

目标数据

我们分为两列：ProcessBreakdownSummaryList 和 ProcessBreakdownUp1SummaryList。它们都是逗号分隔的列表。

请注意 ProcessBreakdownSummaryList (CS10-100-22-10 - Mine Intake Air Fan Heater System, CS10-100-81 -10 - Mine Services Switchgear) 和 ProcessBreakdownUp1SummaryList (CS10-100-22 - Service Shaft Ventilation, CS10- 100-81 - 维修轴电气）是相同的，我们应该只拆分一次。

但是，如果它们与 ProcessBreakdownSummaryList(CS10-100-22-10 - Mine Intake Air Fan Heater System, CS10-100-81 -10 - Mine Services Switchgear) 和 ProcessBreakdownUp1SummaryList (CS10-100-22 - Service Shaft Ventilation, CS10-100-34 - 维修轴电气）它应该再次分开第三排。

预先感谢您对此的帮助。

标签： scalaapache-sparkapache-spark-sqldatabricks

解决方案

您可以通过多种方式解决它，我认为复杂处理最简单的方法是使用 scala。您可以读取包括“ProcessBreakdownSummaryList”和“ProcessBreakdownUp1SummaryList”在内的所有列，比较它们的值是否相同/不同，并为单个输入行发出多行。然后在输出上进行平面映射以获取包含您需要的所有行的数据框。

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"...)

这是将一行拆分为多个的示例

    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)

结果看起来像

+--------+------+-----+
|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    |
+--------+------+-----+

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