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Scala 当函数在自动检测模式的spark数据帧中不工作时

scala apache-spark

Scala 当函数在自动检测模式的spark数据帧中不工作时,scala,apache-spark,spark-dataframe,Scala,Apache Spark,Spark Dataframe,我有一个文本文件，我在spark数据框中以CSV文件的形式读取它。 现在加入后当我写一个函数时，为了选择我下面得到的列异常 这里是我的代码加载csv文件 val df = sqlContext.read.format("csv").option("header", "true").option("delimiter", "|").option("inferSchema","true").load("s3://trfsdisu/SPARK/FinancialLineItem/MAIN") val

我有一个文本文件，我在spark数据框中以CSV文件的形式读取它。 现在加入后当我写一个函数时，为了选择我下面得到的列异常

这里是我的代码加载csv文件

val df = sqlContext.read.format("csv").option("header", "true").option("delimiter", "|").option("inferSchema","true").load("s3://trfsdisu/SPARK/FinancialLineItem/MAIN")

val df1With_ = df.toDF(df.columns.map(_.replace(".", "_")): _*)
val column_to_keep = df1With_.columns.filter(v => (!v.contains("^") && !v.contains("!") && !v.contains("_c"))).toSeq
val df1result = df1With_.select(column_to_keep.head, column_to_keep.tail: _*)


val df2 = sqlContext.read.format("csv").option("header", "true").option("delimiter", "|").option("inferSchema","true").load("s3://trfsdisu/SPARK/FinancialLineItem/INCR")
val df2With_ = df2.toDF(df2.columns.map(_.replace(".", "_")): _*)
val df2column_to_keep = df2With_.columns.filter(v => (!v.contains("^") && !v.contains("!") && !v.contains("_c"))).toSeq
val df2result = df2With_.select(df2column_to_keep.head, df2column_to_keep.tail: _*)



import org.apache.spark.sql.expressions._
val windowSpec = Window.partitionBy("LineItem_organizationId", "LineItem_lineItemId").orderBy($"TimeStamp".cast(LongType).desc) 
val latestForEachKey = df2result.withColumn("rank", rank().over(windowSpec)).filter($"rank" === 1).drop("rank", "TimeStamp")
这是我的模式

latestForEachKey.printSchema()

root
 |-- DataPartiotion: string (nullable = true)
 |-- LineItem_organizationId: long (nullable = true)
 |-- LineItem_lineItemId: integer (nullable = true)
 |-- StatementTypeCode_1: string (nullable = true)
 |-- LineItemName_1: string (nullable = true)
 |-- LocalLanguageLabel_1: string (nullable = true)
 |-- FinancialConceptLocal_1: string (nullable = true)
 |-- FinancialConceptGlobal_1: string (nullable = true)
 |-- IsDimensional_1: boolean (nullable = true)
 |-- InstrumentId_1: string (nullable = true)
 |-- LineItemSequence_1: string (nullable = true)
 |-- PhysicalMeasureId_1: string (nullable = true)
 |-- FinancialConceptCodeGlobalSecondary_1: string (nullable = true)
 |-- IsRangeAllowed_1: string (nullable = true)
 |-- IsSegmentedByOrigin_1: string (nullable = true)
 |-- SegmentGroupDescription_1: string (nullable = true)
 |-- SegmentChildDescription_1: string (nullable = true)
 |-- SegmentChildLocalLanguageLabel_1: string (nullable = true)
 |-- LocalLanguageLabel_languageId_1: string (nullable = true)
 |-- LineItemName_languageId_1: integer (nullable = true)
 |-- SegmentChildDescription_languageId_1: string (nullable = true)
 |-- SegmentChildLocalLanguageLabel_languageId_1: string (nullable = true)
 |-- SegmentGroupDescription_languageId_1: string (nullable = true)
 |-- SegmentMultipleFundbDescription_1: string (nullable = true)
 |-- SegmentMultipleFundbDescription_languageId_1: string (nullable = true)
 |-- IsCredit_1: string (nullable = true)
 |-- FinancialConceptLocalId_1: string (nullable = true)
 |-- FinancialConceptGlobalId_1: string (nullable = true)
 |-- FinancialConceptCodeGlobalSecondaryId_1: string (nullable = true)
 |-- FFAction_1: string (nullable = true)

df1result.printSchema()

root
 |-- LineItem_organizationId: long (nullable = true)
 |-- LineItem_lineItemId: integer (nullable = true)
 |-- StatementTypeCode: string (nullable = true)
 |-- LineItemName: string (nullable = true)
 |-- LocalLanguageLabel: string (nullable = true)
 |-- FinancialConceptLocal: string (nullable = true)
 |-- FinancialConceptGlobal: string (nullable = true)
 |-- IsDimensional: boolean (nullable = true)
 |-- InstrumentId: string (nullable = true)
 |-- LineItemSequence: string (nullable = true)
 |-- PhysicalMeasureId: string (nullable = true)
 |-- FinancialConceptCodeGlobalSecondary: string (nullable = true)
 |-- IsRangeAllowed: boolean (nullable = true)
 |-- IsSegmentedByOrigin: boolean (nullable = true)
 |-- SegmentGroupDescription: string (nullable = true)
 |-- SegmentChildDescription: string (nullable = true)
 |-- SegmentChildLocalLanguageLabel: string (nullable = true)
 |-- LocalLanguageLabel_languageId: integer (nullable = true)
 |-- LineItemName_languageId: integer (nullable = true)
 |-- SegmentChildDescription_languageId: integer (nullable = true)
 |-- SegmentChildLocalLanguageLabel_languageId: integer (nullable = true)
 |-- SegmentGroupDescription_languageId: integer (nullable = true)
 |-- SegmentMultipleFundbDescription: string (nullable = true)
 |-- SegmentMultipleFundbDescription_languageId: integer (nullable = true)
 |-- IsCredit: boolean (nullable = true)
 |-- FinancialConceptLocalId: integer (nullable = true)
 |-- FinancialConceptGlobalId: integer (nullable = true)
 |-- FinancialConceptCodeGlobalSecondaryId: string (nullable = true)
 |-- FFAction: string (nullable = true)
这就是我出错的地方

val dfMainOutput = df1result.join(latestForEachKey, Seq("LineItem_organizationId", "LineItem_lineItemId"), "outer")
  .select($"LineItem_organizationId", $"LineItem_lineItemId",
    when($"StatementTypeCode_1".isNotNull, $"StatementTypeCode_1").otherwise($"StatementTypeCode").as("StatementTypeCode"),
    when($"LocalLanguageLabel_1".isNotNull, $"LocalLanguageLabel_1").otherwise($"LocalLanguageLabel").as("LocalLanguageLabel"),
    when($"FinancialConceptLocal_1".isNotNull, $"FinancialConceptLocal_1").otherwise($"FinancialConceptLocal").as("FinancialConceptLocal"),
    when($"FinancialConceptGlobal_1".isNotNull, $"FinancialConceptGlobal_1").otherwise($"FinancialConceptGlobal").as("FinancialConceptGlobal"),
    when($"IsDimensional_1".isNotNull, $"IsDimensional_1").otherwise($"IsDimensional").as("IsDimensional"),
    when($"InstrumentId_1".isNotNull, $"InstrumentId_1").otherwise($"InstrumentId").as("InstrumentId"),
    when($"LineItemLineItemName_1".isNotNull, $"LineItemLineItemName_1").otherwise($"LineItemLineItemName").as("LineItemLineItemName"),
    when($"PhysicalMeasureId_1".isNotNull, $"PhysicalMeasureId_1").otherwise($"PhysicalMeasureId").as("PhysicalMeasureId"),
    when($"FinancialConceptCodeGlobalSecondary_1".isNotNull, $"FinancialConceptCodeGlobalSecondary_1").otherwise($"FinancialConceptCodeGlobalSecondary").as("FinancialConceptCodeGlobalSecondary"),
    when($"IsRangeAllowed_1".isNotNull, $"IsRangeAllowed_1").otherwise($"IsRangeAllowed").as("IsRangeAllowed"),
    when($"IsSegmentedByOrigin_1".isNotNull, $"IsSegmentedByOrigin_1").otherwise($"IsSegmentedByOrigin").as("IsSegmentedByOrigin"),
    when($"SegmentGroupDescription_1".isNotNull, $"SegmentGroupDescription_1").otherwise($"SegmentGroupDescription").as("SegmentGroupDescription"),
    when($"SegmentChildDescription_1".isNotNull, $"SegmentChildDescription_1").otherwise($"SegmentChildDescription").as("SegmentChildDescription"),
    when($"SegmentChildLocalLanguageLabel_1".isNotNull, $"SegmentChildLocalLanguageLabel_1").otherwise($"SegmentChildLocalLanguageLabel").as("SegmentChildLocalLanguageLabel"),
    when($"LocalLanguageLabel_languageId_1".isNotNull, $"LocalLanguageLabel_languageId_1").otherwise($"LocalLanguageLabel_languageId").as("LocalLanguageLabel_languageId"),
    when($"LineItemName_languageId_1".isNotNull, $"LineItemName_languageId_1").otherwise($"LineItemName_languageId").as("LineItemName_languageId"),
    when($"SegmentChildDescription_languageId_1".isNotNull, $"SegmentChildDescription_languageId_1").otherwise($"SegmentChildDescription_languageId").as("SegmentChildDescription_languageId"),
    when($"SegmentChildLocalLanguageLabel_languageId_1".isNotNull, $"SegmentChildLocalLanguageLabel_languageId_1").otherwise($"SegmentChildLocalLanguageLabel_languageId").as("SegmentChildLocalLanguageLabel_languageId"),
    when($"SegmentGroupDescription_languageId_1".isNotNull, $"SegmentGroupDescription_languageId_1").otherwise($"SegmentGroupDescription_languageId").as("SegmentGroupDescription_languageId"),
    when($"SegmentMultipleFundbDescription_1".isNotNull, $"SegmentMultipleFundbDescription_1").otherwise($"SegmentMultipleFundbDescription").as("SegmentMultipleFundbDescription"),
    when($"SegmentMultipleFundbDescription_languageId_1".isNotNull, $"SegmentMultipleFundbDescription_languageId_1").otherwise($"SegmentMultipleFundbDescription_languageId").as("SegmentMultipleFundbDescription_languageId"),
    when($"IsCredit_1".isNotNull, $"IsCredit_1").otherwise($"IsCredit").as("IsCredit"),
    when($"FinancialConceptLocalId_1".isNotNull, $"FinancialConceptLocalId_1").otherwise($"FinancialConceptLocalId").as("FinancialConceptLocalId"),
    when($"FinancialConceptGlobalId_1".isNotNull, $"FinancialConceptGlobalId_1").otherwise($"FinancialConceptGlobalId").as("FinancialConceptGlobalId"),
    when($"FinancialConceptCodeGlobalSecondaryId_1".isNotNull, $"FinancialConceptCodeGlobalSecondaryId_1").otherwise($"FinancialConceptCodeGlobalSecondaryId").as("FinancialConceptCodeGlobalSecondaryId"),
    when($"FFAction_1".isNotNull, $"FFAction_1").otherwise($"FFAction").as("FFAction"))
    .filter(!$"FFAction".contains("D"))


 dfMainOutput.write
  .format("csv")
  .option("quote", "\uFEFF")
  .option("codec", "gzip")
  .save("s3://trfsdisu/SPARK/FinancialLineItem/output")
以下是我的例外

 org.apache.spark.sql.AnalysisException: cannot resolve 'CASE WHEN (`IsRangeAllowed_1` IS NOT NULL) THEN `IsRangeAllowed_1` ELSE `IsRangeAllowed` END' due to data type mismatch: THEN and ELSE expressions should all be same type or coercible to a common type;
我在加载CSV时没有提到任何类型

我将这两个文件都加载为csv文件，但其中一个IsRangeAllowed_1是字符串，而另一个IsRangeAllowed_1是BooleanType

有了这个，我想再问一个问题

如何删除数据帧输出中的默认分隔符，并将自定义分隔符与分区和gzip压缩一起放置

dfMainOutput.rdd.saveAsTextFile("s3://trfsdisu/SPARK/FinancialLineItem/output")
对于第一个问题，即eWHEN-THEN和ELSE表达式都应该是相同类型或公共类型

但是这里
IsRangeAllowed
是
Boolean
，
IsRangeAllowed\u 1
是
String
。因此，将其中一列转换为字符串或布尔值。因此，代码更改可能是

import org.apache.spark.sql.types.DataTypes

when($"IsRangeAllowed_1".isNotNull, $"IsRangeAllowed_1")
    .otherwise($"IsRangeAllowed".cast(DataTypes.StringType))
  .as("IsRangeAllowed")
如何删除数据帧输出中的默认分隔符，并将自定义分隔符与分区和gzip压缩一起放置

数据帧
可以使用
分隔符
和
编解码器
直接保存，而无需调用底层的
rdd
，例如
dfMainOutput.rdd
。i、 e:

dfMainOutput.write
  .format("csv")
  .option("delimiter", "!")
  .option("codec", "gzip")
  .save("s3://trfsdisu/SPARK/FinancialLineItem/output")
编辑：根据concat_ws示例的注释

df.withColumn("colmn", concat_ws("|!|", $"IsRangeAllowed_1", "IsRangeAllowed", ...)
  .selectExpr("colmn")
  .show()

//to add all columns in df
df.withColumn("colmn", concat_ws("|!|", df.cols:_*))
  .selectExpr("colmn")
  .show()
当函数正常工作时，谢谢…我现在正在搜索分隔符。如果我进行分区，那么我正在进行分区的列将显示在输出中。是的，分区列将不会显示在输出文件中。但是存在于路径中。是否有任何方法可以将其添加到输出文件中..并且在spark create目录中不是文件..我是否必须手动使用别名复制分区列，以便fileLet us中可以存在一列。