From 9baac56ccd57d3890a9b6439d4e13bbe9381822b Mon Sep 17 00:00:00 2001
From: Reynold Xin <rxin@databricks.com>
Date: Mon, 16 Feb 2015 10:09:55 -0800
Subject: Minor fixes for commit https://github.com/apache/spark/pull/4592.

---
 sql/core/src/main/scala/org/apache/spark/sql/DataFrameImpl.scala | 6 +++---
 sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala   | 9 ++++-----
 2 files changed, 7 insertions(+), 8 deletions(-)

(limited to 'sql')

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameImpl.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameImpl.scala
index 9eb0c13140..500e3c90fd 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrameImpl.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrameImpl.scala
@@ -83,17 +83,17 @@ private[sql] class DataFrameImpl protected[sql](
 
   protected[sql] def resolve(colName: String): NamedExpression = {
     queryExecution.analyzed.resolve(colName, sqlContext.analyzer.resolver).getOrElse {
-      throw new RuntimeException(
+      throw new AnalysisException(
         s"""Cannot resolve column name "$colName" among (${schema.fieldNames.mkString(", ")})""")
     }
   }
 
-  protected[sql] def numericColumns(): Seq[Expression] = {
+  protected[sql] def numericColumns: Seq[Expression] = {
     schema.fields.filter(_.dataType.isInstanceOf[NumericType]).map { n =>
       queryExecution.analyzed.resolve(n.name, sqlContext.analyzer.resolver).get
     }
   }
- 
+
   override def toDF(colNames: String*): DataFrame = {
     require(schema.size == colNames.size,
       "The number of columns doesn't match.\n" +
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala b/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala
index a5a677b688..2ecf086de9 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/GroupedData.scala
@@ -17,8 +17,8 @@
 
 package org.apache.spark.sql
 
-import scala.language.implicitConversions
 import scala.collection.JavaConversions._
+import scala.language.implicitConversions
 
 import org.apache.spark.sql.catalyst.analysis.Star
 import org.apache.spark.sql.catalyst.expressions._
@@ -26,7 +26,6 @@ import org.apache.spark.sql.catalyst.plans.logical.Aggregate
 import org.apache.spark.sql.types.NumericType
 
 
-
 /**
  * A set of methods for aggregations on a [[DataFrame]], created by [[DataFrame.groupBy]].
  */
@@ -48,13 +47,13 @@ class GroupedData protected[sql](df: DataFrameImpl, groupingExprs: Seq[Expressio
       // No columns specified. Use all numeric columns.
       df.numericColumns
     } else {
-      // Make sure all specified columns are numeric
+      // Make sure all specified columns are numeric.
       colNames.map { colName =>
         val namedExpr = df.resolve(colName)
         if (!namedExpr.dataType.isInstanceOf[NumericType]) {
           throw new AnalysisException(
             s""""$colName" is not a numeric column. """ +
-            "Aggregation function can only be performed on a numeric column.")
+            "Aggregation function can only be applied on a numeric column.")
         }
         namedExpr
       }
@@ -64,7 +63,7 @@ class GroupedData protected[sql](df: DataFrameImpl, groupingExprs: Seq[Expressio
       Alias(a, a.toString)()
     }
   }
- 
+
   private[this] def strToExpr(expr: String): (Expression => Expression) = {
     expr.toLowerCase match {
       case "avg" | "average" | "mean" => Average
-- 
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