Almost finished casting XML for DataFrames

run_project.sh

@@ -0,0 +1,4 @@
+#!/usr/bin/env bash
+
+cd target
+spark-submit --class ClusterSOData.Main --master local KMeans-0.0.1.jar

src/main/scala/Main.scala

@@ -26,6 +26,13 @@ object Main {
 
     // get the users XML file
     val users = df("users")
-    users.show()
+    // Show 20 entries from the user dataset
+    //users.show()
+    // Show types for the user dataset
+    users.printSchema()
+
+    // create new dataframe with only the reputation of the users
+    users.select("Reputation").distinct.show()
   }
 }
+

src/main/scala/XMLParser.scala

@@ -23,28 +23,33 @@ object DataParser {
     // Define XML file locations and a string of attribute tags to retrieve
     // from each xml element.
     val xmlInfos = Array(
+      /*
       ("badges", "../stackoverflow_dataset/badges.txt", "Id UserId Name Date"),
       ("comments", "../stackoverflow_dataset/comments.txt", "Id PostId Score Text CreationDate UserId"),
       ("posts", "../stackoverflow_dataset/posts.txt", "Id PostTypeId ParentID AcceptedAnswerId CreationDate Score ViewCount Body OwnerUserId LastEditorUserId LastEditorDisplayName LastEditDate LastActivityDate CommunityOwnedDate ClosedDate Title Tags AnswerCount CommentCount FavoriteCount"),
       ("postHistory", "../stackoverflow_dataset/postHistory.txt","Id PostHistoryTypeId PostId RevisionGUID CreationDate UserId UserDisplayName Comment Text CloseReasonId"),
       ("postLinks", "../stackoverflow_dataset/postLinks.txt", "Id CreationDate PostId RelatedPostId PostLinkTypeId"),
-      ("users", "../stackoverflow_dataset/users.txt", "Reputation CreationDate DisplayName EmailHash LastAccessDate WebsiteUrl Location Age AboutMe Views UpVotes DownVotes"),
+      */
+      ("users", "../stackoverflow_dataset/users.txt", "Reputation CreationDate DisplayName EmailHash LastAccessDate WebsiteUrl Location Age AboutMe Views UpVotes DownVotes", Array[DataType](IntegerType, StringType, StringType, StringType, StringType, StringType, StringType, IntegerType, StringType, IntegerType, IntegerType, IntegerType))
+      /*
       ("votes", "../stackoverflow_dataset/votes.txt", "Id PostId VoteTypeId UserId CreationDate")
+      */
     )
     
     // Store each file's DataFrame in an array of DataFrames.
-    val parsedData = xmlInfos.map(x => (x._1, ParseXMLInfo((x._2, x._3)))).toMap
+    val parsedData = xmlInfos.map(x => (x._1, ParseXMLInfo((x._2, x._3, x._4)))).toMap
 
     return parsedData
   }
 
-  private def ParseXMLInfo(xmlInfo: (String, String)) : DataFrame = {
+  private def ParseXMLInfo(xmlInfo: (String, String, Array[DataType])) : DataFrame = {
     // Get the XML attributes used for generating the table columns
     var schemaString = xmlInfo._2
+    var schemaType = xmlInfo._3
     // Generate schema using XML attribute string
-    var schema = GenerateSchemaFromString(schemaString)
+    var schema = GenerateSchemaFromString(schemaString, schemaType)
     // Generate RDD of data from the XML file
-    var rdd = ParseInput(xmlInfo._1, schemaString)
+    var rdd = ParseInput(xmlInfo._1, schemaString, schemaType)
     // Convert RDD to DataFrame for easier processing
     var data = Main.sqlContext.createDataFrame(rdd, schema)
 
@@ -55,9 +60,9 @@ object DataParser {
   /*
    * Generate a schema based on the string of XML attributes
    */
-  private def GenerateSchemaFromString(schemaString: String) : StructType = {
-    val fields = schemaString.split(" ")
-      .map(fieldName => StructField(fieldName, StringType, nullable = true))
+  private def GenerateSchemaFromString(schemaString: String, schemaType: Array[DataType]) : StructType = {
+    var schemaPairs = schemaString.split(" ") zip schemaType
+    val fields = schemaPairs.map{case (fieldName: String, dataType: DataType) => StructField(fieldName, dataType, nullable = true)}
     val schema = StructType(fields)
     return schema
   }
@@ -68,12 +73,12 @@ object DataParser {
    * inputFilepath: Filepath to XML file
    * schemaString: Space seperated attribute values
    */
-  private def ParseInput(inputFilepath: String, schemaString: String) : RDD[Row] = {
+  private def ParseInput(inputFilepath: String, schemaString: String, schemaType: Array[DataType]) : RDD[Row] = {
     // Create spark text file object
     val inputFile = Main.sc.textFile(inputFilepath)
 
     // Map the input file data to an RDD
-    val Data = inputFile.map(line => ParsingFunc(line, schemaString))
+    val Data = inputFile.map(line => ParsingFunc(line, schemaString, schemaType))
     return Data
   }
 
@@ -83,14 +88,26 @@ object DataParser {
    * line: XML file line
    * schemaString: Space seperated attribute values
    */
-  private def ParsingFunc(line: String, schemaString: String) : Row = {
+  private def ParsingFunc(line: String, schemaString: String, schemaType: Array[DataType]) : Row = {
     // Parse line of XML using Scala's built in XML library
     val xmlLine = scala.xml.XML.loadString(line)
+    var schemaPairs = schemaString.split(" ") zip schemaType
     // Create array of values with element for each attribute in schemaString
-    var lineData = schemaString.split(" ").map(fieldName => getXMLAttribute(xmlLine, fieldName))
+    var lineData = schemaPairs.map { case (fieldName: String, dType: DataType) => castToDType(getXMLAttribute(xmlLine, fieldName), dType) }
 
     return Row.fromSeq(lineData)
   }
+  private def castToDType(attribute: String, dType: DataType) : Any = {
+    dType match {
+      case StringType => return attribute
+      case IntegerType =>   try {
+                                  return attribute.toInt
+                                } catch {
+                                  case e: Exception => return -1
+                                }
+      case DateType => return attribute
+    }
+  }
 
   /*
    * Handle NullPointerError raised when an attribute doesn't exist