NotNull 条件不适用于 spark 数据框 scala 中的 withColumn 条件答案

【问题标题】：NotNull condition is not working for withColumn condition in spark data frame scalaNotNull 条件不适用于 spark 数据框 scala 中的 withColumn 条件
【发布时间】：2018-04-23 12:53:44
【问题描述】：

所以我试图在找到它时添加列，但我不想在 xml 架构中不存在列时添加。这就是我正在做的事情，我想我在检查条件时做错了什么。

  val temp = tempNew1
  .withColumn("BookMark", when($"AsReportedItem.fs:BookMark".isNotNull or $"AsReportedItem.fs:BookMark" =!= "", 0))
  .withColumn("DocByteOffset", when($"AsReportedItem.fs:DocByteOffset".isNotNull or $"AsReportedItem.fs:DocByteOffset" =!= "", 0))
  .withColumn("DocByteLength", when($"AsReportedItem.fs:DocByteLength".isNotNull or $"AsReportedItem.fs:DocByteLength" =!= "", 0))
  .withColumn("EditedDescription", when($"AsReportedItem.fs:EditedDescription".isNotNull or $"AsReportedItem.fs:EditedDescription" =!= "", 0))
  .withColumn("EditedDescription", when($"AsReportedItem.fs:EditedDescription._VALUE".isNotNull or $"AsReportedItem.fs:EditedDescription._VALUE" =!= "", 0))
  .withColumn("EditedDescription_languageId", when($"AsReportedItem.fs:EditedDescription._languageId".isNotNull or $"AsReportedItem.fs:EditedDescription._languageId" =!= "", 0))
  .withColumn("ReportedDescription", when($"AsReportedItem.fs:ReportedDescription._VALUE".isNotNull or $"AsReportedItem.fs:ReportedDescription._VALUE" =!= "", 0))
  .withColumn("ReportedDescription_languageId", when($"AsReportedItem.fs:ReportedDescription._languageId".isNotNull or $"AsReportedItem.fs:ReportedDescription._languageId" =!= "", 0))
  .withColumn("FinancialAsReportedLineItemName_languageId", when($"FinancialAsReportedLineItemName._languageId".isNotNull or $"FinancialAsReportedLineItemName._languageId" =!= "", 0))
  .withColumn("FinancialAsReportedLineItemName", when($"FinancialAsReportedLineItemName._VALUE".isNotNull or $"FinancialAsReportedLineItemName._VALUE" =!= "", 0))
  .withColumn("PeriodPermId_objectTypeId", when($"PeriodPermId._objectTypeId".isNotNull or $"PeriodPermId._objectTypeId" =!= "", 0))
  .withColumn("PeriodPermId", when($"PeriodPermId._VALUE".isNotNull or $"PeriodPermId._VALUE" =!= "", 0))
  .drop($"AsReportedItem").drop($"AsReportedItem")

但是当我找到列时，它对我来说很好，但是当 tempNew1 中不存在列时，我得到错误。

如果在架构中找不到标签，我根本不想使用 withColumn。

安慰我在这里失踪。请帮我找出问题。

我得到的错误如下

线程“主”org.apache.spark.sql.AnalysisException 中的异常：无法解析给定输入列的“AsReportedItem.fs:BookMark”： [IsAsReportedCurrencySetManually，

这也是我试过的

    def hasColumn(df: DataFrame, path: String) = Try(df(path)).isSuccess
 val temp = tempNew1.withColumn("BookMark", when(hasColumn(tempNew1,"AsReportedItem.fs:BookMark") == true, $"AsReportedItem.fs:BookMark"))

但无法使其充分发挥作用..

这是可行的，但我怎样才能为所有列编写它。

val temp = if (hasColumn(tempNew1, "AsReportedItem")) {
      tempNew1
        .withColumn("BookMark", $"AsReportedItem.fs:BookMark")
        .withColumn("DocByteOffset", $"AsReportedItem.fs:DocByteOffset")
        .withColumn("DocByteLength", $"AsReportedItem.fs:DocByteLength")
        .withColumn("EditedDescription", $"AsReportedItem.fs:EditedDescription")
        .withColumn("EditedDescription", $"AsReportedItem.fs:EditedDescription._VALUE")
        .withColumn("EditedDescription_languageId", $"AsReportedItem.fs:EditedDescription._languageId")
        .withColumn("ReportedDescription", $"AsReportedItem.fs:ReportedDescription._VALUE")
        .withColumn("ReportedDescription_languageId", $"AsReportedItem.fs:ReportedDescription._languageId")
        .withColumn("FinancialAsReportedLineItemName_languageId", $"FinancialAsReportedLineItemName._languageId")
        .withColumn("FinancialAsReportedLineItemName", $"FinancialAsReportedLineItemName._VALUE")
        .withColumn("PeriodPermId_objectTypeId", $"PeriodPermId._objectTypeId")
        .withColumn("PeriodPermId", $"PeriodPermId._VALUE")
        .drop($"AsReportedItem")
    } else {
      tempNew1
        .withColumn("BookMark", lit(null))
        .withColumn("DocByteOffset", lit(null))
        .withColumn("DocByteLength", lit(null))
        .withColumn("EditedDescription", lit(null))
        .withColumn("EditedDescription", lit(null))
        .withColumn("EditedDescription_languageId", lit(null))
        .withColumn("ReportedDescription", lit(null))
        .withColumn("ReportedDescription_languageId", lit(null))
        .withColumn("FinancialAsReportedLineItemName_languageId", $"FinancialAsReportedLineItemName._languageId")
        .withColumn("FinancialAsReportedLineItemName", $"FinancialAsReportedLineItemName._VALUE")
        .withColumn("PeriodPermId_objectTypeId", $"PeriodPermId._objectTypeId")
        .withColumn("PeriodPermId", $"PeriodPermId._VALUE")
        .drop($"AsReportedItem")

    }

添加主数据框的架构

root
 |-- DataPartition: string (nullable = true)
 |-- TimeStamp: string (nullable = true)
 |-- PeriodId: long (nullable = true)
 |-- SourceId: long (nullable = true)
 |-- FinancialStatementLineItem_lineItemId: long (nullable = true)
 |-- FinancialStatementLineItem_lineItemInstanceKey: long (nullable = true)
 |-- StatementCurrencyId: long (nullable = true)
 |-- StatementTypeCode: string (nullable = true)
 |-- uniqueFundamentalSet: long (nullable = true)
 |-- AuditID: string (nullable = true)
 |-- EstimateMethodCode: string (nullable = true)
 |-- EstimateMethodId: long (nullable = true)
 |-- FinancialAsReportedLineItemName: struct (nullable = true)
 |    |-- _VALUE: string (nullable = true)
 |    |-- _languageId: long (nullable = true)
 |-- FinancialStatementLineItemSequence: long (nullable = true)
 |-- FinancialStatementLineItemValue: double (nullable = true)
 |-- FiscalYear: long (nullable = true)
 |-- IsAnnual: boolean (nullable = true)
 |-- IsAsReportedCurrencySetManually: boolean (nullable = true)
 |-- IsCombinedItem: boolean (nullable = true)
 |-- IsDerived: boolean (nullable = true)
 |-- IsExcludedFromStandardization: boolean (nullable = true)
 |-- IsFinal: boolean (nullable = true)
 |-- IsTotal: boolean (nullable = true)
 |-- PeriodEndDate: string (nullable = true)
 |-- PeriodPermId: struct (nullable = true)
 |    |-- _VALUE: long (nullable = true)
 |    |-- _objectTypeId: long (nullable = true)
 |-- ReportedCurrencyId: long (nullable = true)
 |-- StatementSectionCode: string (nullable = true)
 |-- StatementSectionId: long (nullable = true)
 |-- StatementSectionIsCredit: boolean (nullable = true)
 |-- SystemDerivedTypeCode: string (nullable = true)
 |-- SystemDerivedTypeCodeId: long (nullable = true)
 |-- Unit: double (nullable = true)
 |-- UnitEnumerationId: long (nullable = true)
 |-- FFAction|!|: string (nullable = true)
 |-- PartitionYear: long (nullable = true)
 |-- PartitionStatement: string (nullable = true)

在模式中出现列后添加模式

|-- uniqueFundamentalSet: long (nullable = true)
 |-- AsReportedItem: struct (nullable = true)
 |    |-- fs:BookMark: string (nullable = true)
 |    |-- fs:DocByteLength: long (nullable = true)
 |    |-- fs:DocByteOffset: long (nullable = true)
 |    |-- fs:EditedDescription: struct (nullable = true)
 |    |    |-- _VALUE: string (nullable = true)
 |    |    |-- _languageId: long (nullable = true)
 |    |-- fs:ItemDisplayedNegativeFlag: boolean (nullable = true)
 |    |-- fs:ItemDisplayedValue: double (nullable = true)
 |    |-- fs:ItemScalingFactor: long (nullable = true)
 |    |-- fs:ReportedDescription: struct (nullable = true)
 |    |    |-- _VALUE: string (nullable = true)
 |    |    |-- _languageId: long (nullable = true)
 |    |-- fs:ReportedValue: double (nullable = true)
 |-- EstimateMethodCode: string (nullable = true)
 |-- EstimateMethodId: long (nullable = true)
 |-- FinancialAsReportedLineItemName: struct (nullable = true)
 |    |-- _VALUE: string (nullable = true)
 |    |-- _languageId: long (nullable = true)
 |-- FinancialLineItemSource: long (nullable = true)

【问题讨论】：

您可以检查tempNew1数据集的columns属性是否存在AsReportedItem.fs:BookMark列，并根据结果有条件地调用withColumn。请参阅 (stackoverflow.com/questions/35904136/…) 了解更多详情
@AlexSavitsky 但我有 10 个这样的专栏，我必须一一做吗？
是的。但是，您可以将列放在 Seq 中，根据数据集列对其进行过滤，然后使用 withColumn 折叠数据集，使其具有某种功能风格
@AlexSavitsky 我刚刚尝试使用 hasColumn 但缺少一些东西..如果你能抽出一些时间，请看一下语法..
正如@AlexSavitsky 指出你要使用 foldleft，使用这个想法，但提供的代码将不起作用。您将不得不使用他的想法来处理您拥有的结构列，并且您将解决它；）

标签： scala spark-dataframe apache-spark-xml

【解决方案1】：

我将向您展示在 AsReportedItem 结构列上应用逻辑的一般方法（为了清楚起见，我在代码中进行了注释）

//required column names even though the elements are not present in AsReportedItem struct column
val requiredAsReportedItemColumns = Array("BookMark", "DocByteOffset", "DocByteLength", "EditedDescription", "EditedDescription", "EditedDescription_languageId", "ReportedDescription", "ReportedDescription_languageId")
//selecting the elements of AsReportedItem struct columns for checking condition using when
//checking for structfields inside the selected struct field
def getFields(parent: String, schema: StructType): Seq[String] = schema.fields.flatMap {
  case StructField(name, t: StructType, _, _) => getFields(parent + name + ".", t)
  case StructField(name, _, _, _) => Seq(s"$parent$name")
}

//checking for struct column if present the get the fields of nested structs as well
val AsReportedItemColumns = if(tempNew1.columns.contains("AsReportedItem")) getFields("", tempNew1.select("AsReportedItem.*").schema).toArray.map(x => x.substring(3, x.length)) else Array.empty[String]

//finding difference between required number of columns and the elements present in AsReportedItem struct column
val notInAsReportedItemColumns = requiredAsReportedItemColumns.diff(AsReportedItemColumns.map(x => x.toString.replace(".", "")))

//checking condition for the elements present in AsReportedItem struct column
val temp_for_AsReportedItem = AsReportedItemColumns.foldLeft(tempNew1){(tempdf, name) => tempdf.withColumn(name.replace(".", ""), col(s"AsReportedItem.fs:$name"))}
//populating nulls for the columns that are not present in AsReportedItem struct column
val final_AsReportedItem = notInAsReportedItemColumns.foldLeft(temp_for_AsReportedItem){(tempdf, name)=> tempdf.withColumn(name, lit(null))}.drop("AsReportedItem")

对其余两个结构列 FinancialAsReportedLineItemName 和 PeriodPermId 应用相同的逻辑，但在转换后的数据帧上，即 在 final_AsReportedItem 和 不在 tempNew1 >

归功于 https://stackoverflow.com/a/47104148/5880706

【讨论】：

收到错误Exception in thread "main" org.apache.spark.sql.AnalysisException: cannot resolve 'AsReportedItem.*' give input columns 'PartitionYear, EstimateMethodId, EstimateMethodCode
我在问题中添加了这一点
好的，所以这不会引发任何错误..但是我需要在获得列后爆炸..
我正在寻找一个对不起延迟的例子
你应该看看stackoverflow.com/questions/47103823/…

【解决方案2】：

将其作为答案，因为它对于 cmets 来说太大了。

假设您有一组要添加的列：

val cols = Seq("BookMark")

您需要在原来的DataFrame 上反复调用withColumn，将结果分配给新的DataFrame。有一个函数式操作可以做到这一点，称为fold：

val result = cols.foldLeft(tempNew1)((df, name) =>
  df.withColumn(name, if (df.column.contains(s"AsReportedItem.fs:$name"))
    col(s"AsReportedItem.fs:$name") else lit("null")))

fold 接受第一个参数（在您的情况下为tempNew1）并为cols 中的每个元素调用提供的函数，每次将结果分配给新的DataFrame

【讨论】：

我没有收到错误，但对于所有记录，我只收到空值