[SPARK-3850] Trim trailing spaces for SQL.

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/CatalystTypeConverters.scala

@@ -233,7 +233,7 @@ object CatalystTypeConverters {
     case other => other
   }
 
-  /** 
+  /**
    * Converts Catalyst types used internally in rows to standard Scala types
    * This method is slow, and for batch conversion you should be using converter
    * produced by createToScalaConverter.

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/analysis/HiveTypeCoercion.scala

@@ -296,8 +296,8 @@ trait HiveTypeCoercion {
   object InConversion extends Rule[LogicalPlan] {
     def apply(plan: LogicalPlan): LogicalPlan = plan transformAllExpressions {
       // Skip nodes who's children have not been resolved yet.
-      case e if !e.childrenResolved => e 
-      
+      case e if !e.childrenResolved => e
+
       case i @ In(a, b) if b.exists(_.dataType != a.dataType) =>
         i.makeCopy(Array(a, b.map(Cast(_, a.dataType))))
     }

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/SortOrder.scala

@@ -29,7 +29,7 @@ case object Descending extends SortDirection
  * An expression that can be used to sort a tuple.  This class extends expression primarily so that
  * transformations over expression will descend into its child.
  */
-case class SortOrder(child: Expression, direction: SortDirection) extends Expression 
+case class SortOrder(child: Expression, direction: SortDirection) extends Expression
     with trees.UnaryNode[Expression] {
 
   override def dataType: DataType = child.dataType

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/aggregates.scala

@@ -394,13 +394,13 @@ case class Sum(child: Expression) extends PartialAggregate with trees.UnaryNode[
  * Combining    PartitionLevel   InputData
  *                           <-- null
  * Zero     <-- Zero         <-- null
- *                              
+ *
  *          <-- null         <-- no data
- * null     <-- null         <-- no data 
+ * null     <-- null         <-- no data
  */
 case class CombineSum(child: Expression) extends AggregateExpression {
   def this() = this(null)
-  
+
   override def children: Seq[Expression] = child :: Nil
   override def nullable: Boolean = true
   override def dataType: DataType = child.dataType
@@ -616,7 +616,7 @@ case class SumFunction(expr: Expression, base: AggregateExpression) extends Aggr
 
   private val sum = MutableLiteral(null, calcType)
 
-  private val addFunction = 
+  private val addFunction =
     Coalesce(Seq(Add(Coalesce(Seq(sum, zero)), Cast(expr, calcType)), sum, zero))
 
   override def update(input: Row): Unit = {
@@ -634,7 +634,7 @@ case class SumFunction(expr: Expression, base: AggregateExpression) extends Aggr
 
 case class CombineSumFunction(expr: Expression, base: AggregateExpression)
   extends AggregateFunction {
-  
+
   def this() = this(null, null) // Required for serialization.
 
   private val calcType =
@@ -649,12 +649,12 @@ case class CombineSumFunction(expr: Expression, base: AggregateExpression)
 
   private val sum = MutableLiteral(null, calcType)
 
-  private val addFunction = 
+  private val addFunction =
     Coalesce(Seq(Add(Coalesce(Seq(sum, zero)), Cast(expr, calcType)), sum, zero))
-  
+
   override def update(input: Row): Unit = {
     val result = expr.eval(input)
-    // partial sum result can be null only when no input rows present 
+    // partial sum result can be null only when no input rows present
     if(result != null) {
       sum.update(addFunction, input)
     }

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/arithmetic.scala

@@ -180,7 +180,7 @@ case class Divide(left: Expression, right: Expression) extends BinaryArithmetic
     case it: IntegralType => it.integral.asInstanceOf[Integral[Any]].quot
     case other => sys.error(s"Type $other does not support numeric operations")
   }
-  
+
   override def eval(input: Row): Any = {
     val evalE2 = right.eval(input)
     if (evalE2 == null || evalE2 == 0) {

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/complexTypes.scala

@@ -26,7 +26,7 @@ import org.apache.spark.sql.types._
 case class CreateArray(children: Seq[Expression]) extends Expression {
 
   override def foldable: Boolean = children.forall(_.foldable)
-  
+
   lazy val childTypes = children.map(_.dataType).distinct
 
   override lazy val resolved =

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/mathfuncs/binary.scala

@@ -26,7 +26,7 @@ import org.apache.spark.sql.types._
  * @param f The math function.
  * @param name The short name of the function
  */
-abstract class BinaryMathExpression(f: (Double, Double) => Double, name: String) 
+abstract class BinaryMathExpression(f: (Double, Double) => Double, name: String)
   extends BinaryExpression with Serializable with ExpectsInputTypes { self: Product =>
 
   override def symbol: String = null

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/random.scala

@@ -24,7 +24,7 @@ import org.apache.spark.util.random.XORShiftRandom
 
 /**
  * A Random distribution generating expression.
- * TODO: This can be made generic to generate any type of random distribution, or any type of  
+ * TODO: This can be made generic to generate any type of random distribution, or any type of
  * StructType.
  *
  * Since this expression is stateful, it cannot be a case object.

sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/stringOperations.scala

@@ -133,7 +133,7 @@ trait CaseConversionExpression extends ExpectsInputTypes {
  * A function that converts the characters of a string to uppercase.
  */
 case class Upper(child: Expression) extends UnaryExpression with CaseConversionExpression {
-  
+
   override def convert(v: UTF8String): UTF8String = v.toUpperCase()
 
   override def toString: String = s"Upper($child)"
@@ -143,7 +143,7 @@ case class Upper(child: Expression) extends UnaryExpression with CaseConversionE
  * A function that converts the characters of a string to lowercase.
  */
 case class Lower(child: Expression) extends UnaryExpression with CaseConversionExpression {
-  
+
   override def convert(v: UTF8String): UTF8String = v.toLowerCase()
 
   override def toString: String = s"Lower($child)"
@@ -223,7 +223,7 @@ case class Substring(str: Expression, pos: Expression, len: Expression)
   @inline
   def slicePos(startPos: Int, sliceLen: Int, length: () => Int): (Int, Int) = {
     // Hive and SQL use one-based indexing for SUBSTR arguments but also accept zero and
-    // negative indices for start positions. If a start index i is greater than 0, it 
+    // negative indices for start positions. If a start index i is greater than 0, it
     // refers to element i-1 in the sequence. If a start index i is less than 0, it refers
     // to the -ith element before the end of the sequence. If a start index i is 0, it
     // refers to the first element.

sql/catalyst/src/main/scala/org/apache/spark/sql/types/StructType.scala

@@ -265,7 +265,7 @@ object StructType {
       case _ =>
         throw new SparkException(s"Failed to merge incompatible data types $left and $right")
     }
-  
+
   private[sql] def fieldsMap(fields: Array[StructField]): Map[String, StructField] = {
     import scala.collection.breakOut
     fields.map(s => (s.name, s))(breakOut)

sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/expressions/ExpressionEvaluationSuite.scala

@@ -1209,15 +1209,15 @@ class ExpressionEvaluationSuite extends ExpressionEvaluationBaseSuite {
   }
 
   /**
-   * Used for testing math functions for DataFrames. 
+   * Used for testing math functions for DataFrames.
    * @param c The DataFrame function
    * @param f The functions in scala.math
    * @param domain The set of values to run the function with
    * @param expectNull Whether the given values should return null or not
    * @tparam T Generic type for primitives
    */
   def unaryMathFunctionEvaluation[@specialized(Int, Double, Float, Long) T](
-      c: Expression => Expression, 
+      c: Expression => Expression,
       f: T => T,
       domain: Iterable[T] = (-20 to 20).map(_ * 0.1),
       expectNull: Boolean = false): Unit = {

sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/CombiningLimitsSuite.scala

@@ -71,15 +71,15 @@ class CombiningLimitsSuite extends PlanTest {
 
     comparePlans(optimized, correctAnswer)
   }
-  
+
   test("limits: combines two limits after ColumnPruning") {
     val originalQuery =
       testRelation
         .select('a)
         .limit(2)
         .select('a)
         .limit(5)
-        
+
     val optimized = Optimize.execute(originalQuery.analyze)
     val correctAnswer =
       testRelation

sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ConstantFoldingSuite.scala

@@ -248,7 +248,7 @@ class ConstantFoldingSuite extends PlanTest {
 
     comparePlans(optimized, correctAnswer)
   }
-  
+
   test("Constant folding test: Fold In(v, list) into true or false") {
     var originalQuery =
       testRelation

sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/FilterPushdownSuite.scala

@@ -93,7 +93,7 @@ class FilterPushdownSuite extends PlanTest {
 
     comparePlans(optimized, correctAnswer)
   }
-  
+
   test("column pruning for Project(ne, Limit)") {
     val originalQuery =
       testRelation
@@ -109,7 +109,7 @@ class FilterPushdownSuite extends PlanTest {
 
     comparePlans(optimized, correctAnswer)
   }
-  
+
   // After this line is unimplemented.
   test("simple push down") {
     val originalQuery =

sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/OptimizeInSuite.scala

@@ -57,7 +57,7 @@ class OptimizeInSuite extends PlanTest {
 
     comparePlans(optimized, correctAnswer)
   }
-  
+
   test("OptimizedIn test: In clause not optimized in case filter has attributes") {
     val originalQuery =
       testRelation

sql/catalyst/src/test/scala/org/apache/spark/sql/types/DataTypeSuite.scala

@@ -71,7 +71,7 @@ class DataTypeSuite extends SparkFunSuite {
 
   test("fieldsMap returns map of name to StructField") {
     val struct = StructType(
-      StructField("a", LongType) :: 
+      StructField("a", LongType) ::
       StructField("b", FloatType) :: Nil)
 
     val mapped = StructType.fieldsMap(struct.fields)
@@ -90,7 +90,7 @@ class DataTypeSuite extends SparkFunSuite {
 
     val right = StructType(List())
     val merged = left.merge(right)
-    
+
     assert(merged === left)
   }
 
@@ -133,7 +133,7 @@ class DataTypeSuite extends SparkFunSuite {
 
     val right = StructType(
       StructField("b", LongType) :: Nil)
-    
+
     intercept[SparkException] {
       left.merge(right)
     }

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

@@ -249,7 +249,7 @@ class GroupedData protected[sql](
   def mean(colNames: String*): DataFrame = {
     aggregateNumericColumns(colNames : _*)(Average)
   }
- 
+
   /**
    * Compute the max value for each numeric columns for each group.
    * The resulting [[DataFrame]] will also contain the grouping columns.

sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala

@@ -106,7 +106,7 @@ private[r] object SQLUtils {
 
     dfCols.map { col =>
       colToRBytes(col)
-    } 
+    }
   }
 
   def convertRowsToColumns(localDF: Array[Row], numCols: Int): Array[Array[Any]] = {
@@ -121,7 +121,7 @@ private[r] object SQLUtils {
     val numRows = col.length
     val bos = new ByteArrayOutputStream()
     val dos = new DataOutputStream(bos)
-    
+
     SerDe.writeInt(dos, numRows)
 
     col.map { item =>

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

@@ -138,15 +138,15 @@ case class GeneratedAggregate(
           case UnscaledValue(e) => e
           case _ => expr
         }
-        // partial sum result can be null only when no input rows present 
+        // partial sum result can be null only when no input rows present
         val updateFunction = If(
           IsNotNull(actualExpr),
           Coalesce(
             Add(
-              Coalesce(currentSum :: zero :: Nil), 
+              Coalesce(currentSum :: zero :: Nil),
               Cast(expr, calcType)) :: currentSum :: zero :: Nil),
           currentSum)
-          
+
         val result =
           expr.dataType match {
             case DecimalType.Fixed(_, _) =>
@@ -155,7 +155,7 @@ case class GeneratedAggregate(
           }
 
         AggregateEvaluation(currentSum :: Nil, initialValue :: Nil, updateFunction :: Nil, result)
-        
+
       case m @ Max(expr) =>
         val currentMax = AttributeReference("currentMax", expr.dataType, nullable = true)()
         val initialValue = Literal.create(null, expr.dataType)

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

@@ -65,7 +65,7 @@ case class Filter(condition: Expression, child: SparkPlan) extends UnaryNode {
  * :: DeveloperApi ::
  * Sample the dataset.
  * @param lowerBound Lower-bound of the sampling probability (usually 0.0)
- * @param upperBound Upper-bound of the sampling probability. The expected fraction sampled 
+ * @param upperBound Upper-bound of the sampling probability. The expected fraction sampled
  *                   will be ub - lb.
  * @param withReplacement Whether to sample with replacement.
  * @param seed the random seed

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

@@ -62,7 +62,7 @@ private[sql] object FrequentItems extends Logging {
   }
 
   /**
-   * Finding frequent items for columns, possibly with false positives. Using the 
+   * Finding frequent items for columns, possibly with false positives. Using the
    * frequent element count algorithm described in
    * [[http://dx.doi.org/10.1145/762471.762473, proposed by Karp, Schenker, and Papadimitriou]].
    * The `support` should be greater than 1e-4.
@@ -75,7 +75,7 @@ private[sql] object FrequentItems extends Logging {
    * @return A Local DataFrame with the Array of frequent items for each column.
    */
   private[sql] def singlePassFreqItems(
-      df: DataFrame, 
+      df: DataFrame,
       cols: Seq[String],
       support: Double): DataFrame = {
     require(support >= 1e-4, s"support ($support) must be greater than 1e-4.")
@@ -88,7 +88,7 @@ private[sql] object FrequentItems extends Logging {
       val index = originalSchema.fieldIndex(name)
       (name, originalSchema.fields(index).dataType)
     }
-    
+
     val freqItems = df.select(cols.map(Column(_)) : _*).rdd.aggregate(countMaps)(
       seqOp = (counts, row) => {
         var i = 0

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

@@ -25,7 +25,7 @@ import org.apache.spark.sql.functions._
 import org.apache.spark.sql.types._
 
 private[sql] object StatFunctions extends Logging {
-  
+
   /** Calculate the Pearson Correlation Coefficient for the given columns */
   private[sql] def pearsonCorrelation(df: DataFrame, cols: Seq[String]): Double = {
     val counts = collectStatisticalData(df, cols)

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

@@ -1299,7 +1299,7 @@ object functions {
    * @since 1.4.0
    */
   def toRadians(columnName: String): Column = toRadians(Column(columnName))
-    
+
 
   //////////////////////////////////////////////////////////////////////////////////////////////
   //////////////////////////////////////////////////////////////////////////////////////////////

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

@@ -304,7 +304,7 @@ private[sql] class JDBCRDD(
 
   // Each JDBC-to-Catalyst conversion corresponds to a tag defined here so that
   // we don't have to potentially poke around in the Metadata once for every
-  // row.  
+  // row.
   // Is there a better way to do this?  I'd rather be using a type that
   // contains only the tags I define.
   abstract class JDBCConversion

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

@@ -54,7 +54,7 @@ private[sql] object JDBCRelation {
     if (numPartitions == 1) return Array[Partition](JDBCPartition(null, 0))
     // Overflow and silliness can happen if you subtract then divide.
     // Here we get a little roundoff, but that's (hopefully) OK.
-    val stride: Long = (partitioning.upperBound / numPartitions 
+    val stride: Long = (partitioning.upperBound / numPartitions
                       - partitioning.lowerBound / numPartitions)
     var i: Int = 0
     var currentValue: Long = partitioning.lowerBound
@@ -140,10 +140,10 @@ private[sql] case class JDBCRelation(
       filters,
       parts)
   }
-  
+
   override def insert(data: DataFrame, overwrite: Boolean): Unit = {
     data.write
       .mode(if (overwrite) SaveMode.Overwrite else SaveMode.Append)
       .jdbc(url, table, properties)
-  }  
+  }
 }