[SPARK-7927] whitespace fixes for SQL core.

sql/core/src/main/scala/org/apache/spark/sql/Column.scala

@@ -349,7 +349,7 @@ class Column(protected[sql] val expr: Expression) extends Logging {
    * @group expr_ops
    * @since 1.4.0
    */
-  def when(condition: Column, value: Any):Column = this.expr match {
+  def when(condition: Column, value: Any): Column = this.expr match {
     case CaseWhen(branches: Seq[Expression]) =>
       CaseWhen(branches ++ Seq(lit(condition).expr, lit(value).expr))
     case _ =>
@@ -378,7 +378,7 @@ class Column(protected[sql] val expr: Expression) extends Logging {
    * @group expr_ops
    * @since 1.4.0
    */
-  def otherwise(value: Any):Column = this.expr match {
+  def otherwise(value: Any): Column = this.expr match {
     case CaseWhen(branches: Seq[Expression]) =>
       if (branches.size % 2 == 0) {
         CaseWhen(branches :+ lit(value).expr)

sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala

@@ -255,7 +255,7 @@ class DataFrame private[sql](
     val newCols = logicalPlan.output.zip(colNames).map { case (oldAttribute, newName) =>
       Column(oldAttribute).as(newName)
     }
-    select(newCols :_*)
+    select(newCols : _*)
   }
 
   /**
@@ -500,7 +500,7 @@ class DataFrame private[sql](
    */
   @scala.annotation.varargs
   def sort(sortCol: String, sortCols: String*): DataFrame = {
-    sort((sortCol +: sortCols).map(apply) :_*)
+    sort((sortCol +: sortCols).map(apply) : _*)
   }
 
   /**
@@ -531,7 +531,7 @@ class DataFrame private[sql](
    * @since 1.3.0
    */
   @scala.annotation.varargs
-  def orderBy(sortCol: String, sortCols: String*): DataFrame = sort(sortCol, sortCols :_*)
+  def orderBy(sortCol: String, sortCols: String*): DataFrame = sort(sortCol, sortCols : _*)
 
   /**
    * Returns a new [[DataFrame]] sorted by the given expressions.
@@ -540,7 +540,7 @@ class DataFrame private[sql](
    * @since 1.3.0
    */
   @scala.annotation.varargs
-  def orderBy(sortExprs: Column*): DataFrame = sort(sortExprs :_*)
+  def orderBy(sortExprs: Column*): DataFrame = sort(sortExprs : _*)
 
   /**
    * Selects column based on the column name and return it as a [[Column]].
@@ -611,7 +611,7 @@ class DataFrame private[sql](
    * @since 1.3.0
    */
   @scala.annotation.varargs
-  def select(col: String, cols: String*): DataFrame = select((col +: cols).map(Column(_)) :_*)
+  def select(col: String, cols: String*): DataFrame = select((col +: cols).map(Column(_)) : _*)
 
   /**
    * Selects a set of SQL expressions. This is a variant of `select` that accepts
@@ -825,7 +825,7 @@ class DataFrame private[sql](
    * @since 1.3.0
    */
   def agg(aggExpr: (String, String), aggExprs: (String, String)*): DataFrame = {
-    groupBy().agg(aggExpr, aggExprs :_*)
+    groupBy().agg(aggExpr, aggExprs : _*)
   }
 
   /**
@@ -863,7 +863,7 @@ class DataFrame private[sql](
    * @since 1.3.0
    */
   @scala.annotation.varargs
-  def agg(expr: Column, exprs: Column*): DataFrame = groupBy().agg(expr, exprs :_*)
+  def agg(expr: Column, exprs: Column*): DataFrame = groupBy().agg(expr, exprs : _*)
 
   /**
    * Returns a new [[DataFrame]] by taking the first `n` rows. The difference between this function
@@ -1039,7 +1039,7 @@ class DataFrame private[sql](
         val name = field.name
         if (resolver(name, colName)) col.as(colName) else Column(name)
       }
-      select(colNames :_*)
+      select(colNames : _*)
     } else {
       select(Column("*"), col.as(colName))
     }
@@ -1262,7 +1262,7 @@ class DataFrame private[sql](
    * @group action
    * @since 1.3.0
    */
-  override def collectAsList(): java.util.List[Row] = java.util.Arrays.asList(rdd.collect() :_*)
+  override def collectAsList(): java.util.List[Row] = java.util.Arrays.asList(rdd.collect() : _*)
 
   /**
    * Returns the number of rows in the [[DataFrame]].

sql/core/src/main/scala/org/apache/spark/sql/DataFrameHolder.scala

@@ -28,5 +28,5 @@ private[sql] case class DataFrameHolder(df: DataFrame) {
   // `rdd.toDF("1")` as invoking this toDF and then apply on the returned DataFrame.
   def toDF(): DataFrame = df
 
-  def toDF(colNames: String*): DataFrame = df.toDF(colNames :_*)
+  def toDF(colNames: String*): DataFrame = df.toDF(colNames : _*)
 }

sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala

@@ -247,7 +247,7 @@ class GroupedData protected[sql](
    */
   @scala.annotation.varargs
   def mean(colNames: String*): DataFrame = {
-    aggregateNumericColumns(colNames:_*)(Average)
+    aggregateNumericColumns(colNames : _*)(Average)
   }
 
   /**
@@ -259,7 +259,7 @@ class GroupedData protected[sql](
    */
   @scala.annotation.varargs
   def max(colNames: String*): DataFrame = {
-    aggregateNumericColumns(colNames:_*)(Max)
+    aggregateNumericColumns(colNames : _*)(Max)
   }
 
   /**
@@ -271,7 +271,7 @@ class GroupedData protected[sql](
    */
   @scala.annotation.varargs
   def avg(colNames: String*): DataFrame = {
-    aggregateNumericColumns(colNames:_*)(Average)
+    aggregateNumericColumns(colNames : _*)(Average)
   }
 
   /**
@@ -283,7 +283,7 @@ class GroupedData protected[sql](
    */
   @scala.annotation.varargs
   def min(colNames: String*): DataFrame = {
-    aggregateNumericColumns(colNames:_*)(Min)
+    aggregateNumericColumns(colNames : _*)(Min)
   }
 
   /**
@@ -295,6 +295,6 @@ class GroupedData protected[sql](
    */
   @scala.annotation.varargs
   def sum(colNames: String*): DataFrame = {
-    aggregateNumericColumns(colNames:_*)(Sum)
+    aggregateNumericColumns(colNames : _*)(Sum)
   }
 }

sql/core/src/main/scala/org/apache/spark/sql/SQLContext.scala

@@ -298,7 +298,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
      */
     implicit class StringToColumn(val sc: StringContext) {
       def $(args: Any*): ColumnName = {
-        new ColumnName(sc.s(args :_*))
+        new ColumnName(sc.s(args : _*))
       }
     }
 

sql/core/src/main/scala/org/apache/spark/sql/SparkSQLParser.scala

@@ -54,15 +54,15 @@ private[sql] class SparkSQLParser(fallback: String => LogicalPlan) extends Abstr
     }
   }
 
-  protected val AS      = Keyword("AS")
-  protected val CACHE   = Keyword("CACHE")
-  protected val CLEAR   = Keyword("CLEAR")
-  protected val IN      = Keyword("IN")
-  protected val LAZY    = Keyword("LAZY")
-  protected val SET     = Keyword("SET")
-  protected val SHOW    = Keyword("SHOW")
-  protected val TABLE   = Keyword("TABLE")
-  protected val TABLES  = Keyword("TABLES")
+  protected val AS = Keyword("AS")
+  protected val CACHE = Keyword("CACHE")
+  protected val CLEAR = Keyword("CLEAR")
+  protected val IN = Keyword("IN")
+  protected val LAZY = Keyword("LAZY")
+  protected val SET = Keyword("SET")
+  protected val SHOW = Keyword("SHOW")
+  protected val TABLE = Keyword("TABLE")
+  protected val TABLES = Keyword("TABLES")
   protected val UNCACHE = Keyword("UNCACHE")
 
   override protected lazy val start: Parser[LogicalPlan] = cache | uncache | set | show | others

sql/core/src/main/scala/org/apache/spark/sql/columnar/InMemoryColumnarTableScan.scala

@@ -236,7 +236,7 @@ private[sql] case class InMemoryColumnarTableScan(
     case GreaterThanOrEqual(a: AttributeReference, l: Literal) => l <= statsFor(a).upperBound
     case GreaterThanOrEqual(l: Literal, a: AttributeReference) => statsFor(a).lowerBound <= l
 
-    case IsNull(a: Attribute)    => statsFor(a).nullCount > 0
+    case IsNull(a: Attribute) => statsFor(a).nullCount > 0
     case IsNotNull(a: Attribute) => statsFor(a).count - statsFor(a).nullCount > 0
   }
 

sql/core/src/main/scala/org/apache/spark/sql/execution/Exchange.scala

@@ -296,7 +296,7 @@ private[sql] case class EnsureRequirements(sqlContext: SQLContext) extends Rule[
           .sliding(2)
           .map {
             case Seq(a) => true
-            case Seq(a,b) => a compatibleWith b
+            case Seq(a, b) => a.compatibleWith(b)
           }.exists(!_)
 
       // Adds Exchange or Sort operators as required

sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala

@@ -243,8 +243,9 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
                 case (predicate, None) => predicate
                 // Filter needs to be applied above when it contains partitioning
                 // columns
-                case (predicate, _) if(!predicate.references.map(_.name).toSet
-                  .intersect (partitionColNames).isEmpty) => predicate
+                case (predicate, _)
+                  if !predicate.references.map(_.name).toSet.intersect(partitionColNames).isEmpty =>
+                  predicate
               }
             }
           } else {
@@ -270,7 +271,7 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
           projectList,
           filters,
           identity[Seq[Expression]], // All filters still need to be evaluated.
-          InMemoryColumnarTableScan(_,  filters, mem)) :: Nil
+          InMemoryColumnarTableScan(_, filters, mem)) :: Nil
       case _ => Nil
     }
   }

sql/core/src/main/scala/org/apache/spark/sql/execution/joins/BroadcastLeftSemiJoinHash.scala

@@ -39,7 +39,7 @@ case class BroadcastLeftSemiJoinHash(
   override def output: Seq[Attribute] = left.output
 
   protected override def doExecute(): RDD[Row] = {
-    val buildIter= buildPlan.execute().map(_.copy()).collect().toIterator
+    val buildIter = buildPlan.execute().map(_.copy()).collect().toIterator
     val hashSet = new java.util.HashSet[Row]()
     var currentRow: Row = null
 

sql/core/src/main/scala/org/apache/spark/sql/execution/stat/FrequentItems.scala

@@ -89,7 +89,7 @@ private[sql] object FrequentItems extends Logging {
       (name, originalSchema.fields(index).dataType)
     }
 
-    val freqItems = df.select(cols.map(Column(_)):_*).rdd.aggregate(countMaps)(
+    val freqItems = df.select(cols.map(Column(_)) : _*).rdd.aggregate(countMaps)(
       seqOp = (counts, row) => {
         var i = 0
         while (i < numCols) {
@@ -110,7 +110,7 @@ private[sql] object FrequentItems extends Logging {
       }
     )
     val justItems = freqItems.map(m => m.baseMap.keys.toSeq)
-    val resultRow = Row(justItems:_*)
+    val resultRow = Row(justItems : _*)
     // append frequent Items to the column name for easy debugging
     val outputCols = colInfo.map { v =>
       StructField(v._1 + "_freqItems", ArrayType(v._2, false))

sql/core/src/main/scala/org/apache/spark/sql/functions.scala

@@ -187,7 +187,7 @@ object functions {
    */
   @scala.annotation.varargs
   def countDistinct(columnName: String, columnNames: String*): Column =
-    countDistinct(Column(columnName), columnNames.map(Column.apply) :_*)
+    countDistinct(Column(columnName), columnNames.map(Column.apply) : _*)
 
   /**
    * Aggregate function: returns the approximate number of distinct items in a group.

sql/core/src/main/scala/org/apache/spark/sql/jdbc/JDBCRDD.scala

@@ -52,6 +52,7 @@ private[sql] object JDBCRDD extends Logging {
       scale: Int,
       signed: Boolean): DataType = {
     val answer = sqlType match {
+      // scalastyle:off
       case java.sql.Types.ARRAY         => null
       case java.sql.Types.BIGINT        => if (signed) { LongType } else { DecimalType.Unlimited }
       case java.sql.Types.BINARY        => BinaryType
@@ -92,7 +93,8 @@ private[sql] object JDBCRDD extends Logging {
       case java.sql.Types.TINYINT       => IntegerType
       case java.sql.Types.VARBINARY     => BinaryType
       case java.sql.Types.VARCHAR       => StringType
-      case _ => null
+      case _                            => null
+      // scalastyle:on
     }
 
     if (answer == null) throw new SQLException("Unsupported type " + sqlType)
@@ -323,19 +325,19 @@ private[sql] class JDBCRDD(
    */
   def getConversions(schema: StructType): Array[JDBCConversion] = {
     schema.fields.map(sf => sf.dataType match {
-      case BooleanType           => BooleanConversion
-      case DateType              => DateConversion
+      case BooleanType => BooleanConversion
+      case DateType => DateConversion
       case DecimalType.Unlimited => DecimalConversion(None)
-      case DecimalType.Fixed(d)  => DecimalConversion(Some(d))
-      case DoubleType            => DoubleConversion
-      case FloatType             => FloatConversion
-      case IntegerType           => IntegerConversion
-      case LongType              =>
+      case DecimalType.Fixed(d) => DecimalConversion(Some(d))
+      case DoubleType => DoubleConversion
+      case FloatType => FloatConversion
+      case IntegerType => IntegerConversion
+      case LongType =>
         if (sf.metadata.contains("binarylong")) BinaryLongConversion else LongConversion
-      case StringType            => StringConversion
-      case TimestampType         => TimestampConversion
-      case BinaryType            => BinaryConversion
-      case _                     => throw new IllegalArgumentException(s"Unsupported field $sf")
+      case StringType => StringConversion
+      case TimestampType => TimestampConversion
+      case BinaryType => BinaryConversion
+      case _ => throw new IllegalArgumentException(s"Unsupported field $sf")
     }).toArray
   }
 
@@ -376,8 +378,8 @@ private[sql] class JDBCRDD(
         while (i < conversions.length) {
           val pos = i + 1
           conversions(i) match {
-            case BooleanConversion    => mutableRow.setBoolean(i, rs.getBoolean(pos))
-            case DateConversion       =>
+            case BooleanConversion => mutableRow.setBoolean(i, rs.getBoolean(pos))
+            case DateConversion =>
               // DateUtils.fromJavaDate does not handle null value, so we need to check it.
               val dateVal = rs.getDate(pos)
               if (dateVal != null) {
@@ -407,14 +409,14 @@ private[sql] class JDBCRDD(
               } else {
                 mutableRow.update(i, Decimal(decimalVal))
               }
-            case DoubleConversion     => mutableRow.setDouble(i, rs.getDouble(pos))
-            case FloatConversion      => mutableRow.setFloat(i, rs.getFloat(pos))
-            case IntegerConversion    => mutableRow.setInt(i, rs.getInt(pos))
-            case LongConversion       => mutableRow.setLong(i, rs.getLong(pos))
+            case DoubleConversion => mutableRow.setDouble(i, rs.getDouble(pos))
+            case FloatConversion => mutableRow.setFloat(i, rs.getFloat(pos))
+            case IntegerConversion => mutableRow.setInt(i, rs.getInt(pos))
+            case LongConversion => mutableRow.setLong(i, rs.getLong(pos))
             // TODO(davies): use getBytes for better performance, if the encoding is UTF-8
-            case StringConversion     => mutableRow.setString(i, rs.getString(pos))
-            case TimestampConversion  => mutableRow.update(i, rs.getTimestamp(pos))
-            case BinaryConversion     => mutableRow.update(i, rs.getBytes(pos))
+            case StringConversion => mutableRow.setString(i, rs.getString(pos))
+            case TimestampConversion => mutableRow.update(i, rs.getTimestamp(pos))
+            case BinaryConversion => mutableRow.update(i, rs.getBytes(pos))
             case BinaryLongConversion => {
               val bytes = rs.getBytes(pos)
               var ans = 0L

sql/core/src/main/scala/org/apache/spark/sql/json/InferSchema.scala

@@ -124,7 +124,7 @@ private[sql] object InferSchema {
           case ArrayType(NullType, containsNull) => ArrayType(StringType, containsNull)
           case ArrayType(struct: StructType, containsNull) =>
             ArrayType(nullTypeToStringType(struct), containsNull)
-          case struct: StructType =>nullTypeToStringType(struct)
+          case struct: StructType => nullTypeToStringType(struct)
           case other: DataType => other
         }
 

sql/core/src/main/scala/org/apache/spark/sql/json/JacksonGenerator.scala

@@ -33,7 +33,7 @@ private[sql] object JacksonGenerator {
     */
   def apply(rowSchema: StructType, gen: JsonGenerator)(row: Row): Unit = {
     def valWriter: (DataType, Any) => Unit = {
-      case (_, null) | (NullType, _)  => gen.writeNull()
+      case (_, null) | (NullType, _) => gen.writeNull()
       case (StringType, v: String) => gen.writeString(v)
       case (TimestampType, v: java.sql.Timestamp) => gen.writeString(v.toString)
       case (IntegerType, v: Int) => gen.writeNumber(v)
@@ -48,16 +48,16 @@ private[sql] object JacksonGenerator {
       case (DateType, v) => gen.writeString(v.toString)
       case (udt: UserDefinedType[_], v) => valWriter(udt.sqlType, udt.serialize(v))
 
-      case (ArrayType(ty, _), v: Seq[_] ) =>
+      case (ArrayType(ty, _), v: Seq[_]) =>
         gen.writeStartArray()
-        v.foreach(valWriter(ty,_))
+        v.foreach(valWriter(ty, _))
         gen.writeEndArray()
 
-      case (MapType(kv,vv, _), v: Map[_,_]) =>
+      case (MapType(kv, vv, _), v: Map[_, _]) =>
         gen.writeStartObject()
         v.foreach { p =>
           gen.writeFieldName(p._1.toString)
-          valWriter(vv,p._2)
+          valWriter(vv, p._2)
         }
         gen.writeEndObject()