diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/RewriteDistinctAggregates.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/RewriteDistinctAggregates.scala
index 4448ace7105a..b9468007cac6 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/RewriteDistinctAggregates.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/RewriteDistinctAggregates.scala
@@ -95,7 +95,7 @@ import org.apache.spark.sql.types.IntegerType
  *
  * This rule duplicates the input data by two or more times (# distinct groups + an optional
  * non-distinct group). This will put quite a bit of memory pressure of the used aggregate and
- * exchange operators. Keeping the number of distinct groups as low a possible should be priority,
+ * exchange operators. Keeping the number of distinct groups as low as possible should be priority,
  * we could improve this in the current rule by applying more advanced expression canonicalization
  * techniques.
  */
@@ -241,7 +241,7 @@ object RewriteDistinctAggregates extends Rule[LogicalPlan] {
         groupByAttrs ++ distinctAggChildAttrs ++ Seq(gid) ++ regularAggChildAttrMap.map(_._2),
         a.child)
 
-      // Construct the first aggregate operator. This de-duplicates the all the children of
+      // Construct the first aggregate operator. This de-duplicates all the children of
       // distinct operators, and applies the regular aggregate operators.
       val firstAggregateGroupBy = groupByAttrs ++ distinctAggChildAttrs :+ gid
       val firstAggregate = Aggregate(
diff --git a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScanExec.scala b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScanExec.scala
index b3795b443040..92c6632ad786 100644
--- a/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScanExec.scala
+++ b/sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScanExec.scala
@@ -182,7 +182,7 @@ case class HiveTableScanExec(
 
   protected override def doExecute(): RDD[InternalRow] = {
     // Using dummyCallSite, as getCallSite can turn out to be expensive with
-    // with multiple partitions.
+    // multiple partitions.
     val rdd = if (!relation.isPartitioned) {
       Utils.withDummyCallSite(sqlContext.sparkContext) {
         hadoopReader.makeRDDForTable(hiveQlTable)