问题
I have a data frame which comes as like below
+---------------------------------------------------------------------+
|value |
+---------------------------------------------------------------------+
|[WrappedArray(LineItem_organizationId, LineItem_lineItemId)] |
|[WrappedArray(OrganizationId, LineItemId, SegmentSequence_segmentId)]|
+---------------------------------------------------------------------+
From the above two rows I want to create a string which is in this format
"LineItem_organizationId", "LineItem_lineItemId"
"OrganizationId", "LineItemId", "SegmentSequence_segmentId"
I want to create this as dynamic so in first column third value is present my string will have one more , separated columns value .
How can I do this in Scala .
this is what I am doing in order to create data frame
val xmlFiles = "C://Users//u6034690//Desktop//SPARK//trfsmallfffile//XML"
val discriptorFileLOcation = "C://Users//u6034690//Desktop//SPARK//trfsmallfffile//FinancialLineItem//REFXML"
import sqlContext.implicits._
val dfDiscriptor = sqlContext.read.format("com.databricks.spark.xml").option("rowTag", "FlatFileDescriptor").load(discriptorFileLOcation)
dfDiscriptor.printSchema()
val firstColumn = dfDiscriptor.select($"FFFileType.FFRecord.FFField").as("FFField")
val FirstColumnOfHeaderFile = firstColumn.select(explode($"FFField")).as("ColumnsDetails").select(explode($"col")).first.get(0).toString().split(",")(5)
println(FirstColumnOfHeaderFile)
//dfDiscriptor.printSchema()
val primaryKeyColumnsFinancialLineItem = dfDiscriptor.select(explode($"FFFileType.FFRecord.FFPrimKey.FFPrimKeyCol"))
primaryKeyColumnsFinancialLineItem.show(false)
Adding the full schema
root
|-- FFColumnDelimiter: string (nullable = true)
|-- FFContentItem: struct (nullable = true)
| |-- _VALUE: string (nullable = true)
| |-- _ffMajVers: long (nullable = true)
| |-- _ffMinVers: double (nullable = true)
|-- FFFileEncoding: string (nullable = true)
|-- FFFileType: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- FFPhysicalFile: array (nullable = true)
| | | |-- element: struct (containsNull = true)
| | | | |-- FFFileName: string (nullable = true)
| | | | |-- FFRowCount: long (nullable = true)
| | |-- FFRecord: struct (nullable = true)
| | | |-- FFField: array (nullable = true)
| | | | |-- element: struct (containsNull = true)
| | | | | |-- FFColumnNumber: long (nullable = true)
| | | | | |-- FFDataType: string (nullable = true)
| | | | | |-- FFFacets: struct (nullable = true)
| | | | | | |-- FFMaxLength: long (nullable = true)
| | | | | | |-- FFTotalDigits: long (nullable = true)
| | | | | |-- FFFieldIsOptional: boolean (nullable = true)
| | | | | |-- FFFieldName: string (nullable = true)
| | | | | |-- FFForKey: struct (nullable = true)
| | | | | | |-- FFForKeyCol: string (nullable = true)
| | | | | | |-- FFForKeyRecord: string (nullable = true)
| | | |-- FFPrimKey: struct (nullable = true)
| | | | |-- FFPrimKeyCol: array (nullable = true)
| | | | | |-- element: string (containsNull = true)
| | | |-- FFRecordType: string (nullable = true)
|-- FFHeaderRow: boolean (nullable = true)
|-- FFId: string (nullable = true)
|-- FFRowDelimiter: string (nullable = true)
|-- FFTimeStamp: string (nullable = true)
|-- _env: string (nullable = true)
|-- _ffMajVers: long (nullable = true)
|-- _ffMinVers: double (nullable = true)
|-- _ffPubstyle: string (nullable = true)
|-- _schemaLocation: string (nullable = true)
|-- _sr: string (nullable = true)
|-- _xmlns: string (nullable = true)
|-- _xsi: string (nullable = true)
回答1:
Looking at your given dataframe
+---------------------------------------------------------------------+
|value |
+---------------------------------------------------------------------+
|[WrappedArray(LineItem_organizationId, LineItem_lineItemId)] |
|[WrappedArray(OrganizationId, LineItemId, SegmentSequence_segmentId)]|
+---------------------------------------------------------------------+
it must have the following schema
|-- value: array (nullable = true)
| |-- element: array (containsNull = true)
| | |-- element: string (containsNull = true)
If the above assumption are true then you should write a udf
function as
import org.apache.spark.sql.functions._
def arrayToString = udf((arr: collection.mutable.WrappedArray[collection.mutable.WrappedArray[String]]) => arr.flatten.mkString(", "))
And use it in the dataframe
as
df.withColumn("value", arrayToString($"value"))
And you should have
+-----------------------------------------------------+
|value |
+-----------------------------------------------------+
|LineItem_organizationId, LineItem_lineItemId |
|OrganizationId, LineItemId, SegmentSequence_segmentId|
+-----------------------------------------------------+
|-- value: string (nullable = true)
来源:https://stackoverflow.com/questions/48987566/get-wrappedarray-row-valule-and-convert-it-into-string-in-scala