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[SQL][CATALYST][MINOR] update some error comments #22694

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CarolinePeng wants to merge 3 commits into from
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[SQL][CATALYST][MINOR] update some error comments #22694

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d915482
update comment about RewriteDistinctAggregates
Oct 11, 2018
ded2c17
update another comment
Oct 12, 2018
64c0c86
update a new file
Oct 16, 2018
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4 changes: 2 additions & 2 deletions ...st/src/main/scala/org/apache/spark/sql/catalyst/optimizer/RewriteDistinctAggregates.scala
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Expand Up @@ -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.
*/
Expand Down Expand Up @@ -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(
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2 changes: 1 addition & 1 deletion sql/hive/src/main/scala/org/apache/spark/sql/hive/execution/HiveTableScanExec.scala
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Expand Up @@ -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)
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