amoghmargoor commented on a change in pull request #28804:
URL: https://github.com/apache/spark/pull/28804#discussion_r447158417
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File path:
sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
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@@ -2196,6 +2196,13 @@ object SQLConf {
.checkValue(bit => bit >= 10 && bit <= 30, "The bit value must be in
[10, 30].")
.createWithDefault(16)
+ val SKIP_PARTIAL_AGGREGATE_ENABLED =
+ buildConf("spark.sql.aggregate.partialaggregate.skip.enabled")
+ .internal()
+ .doc("Avoid sort/spill to disk during partial aggregation")
+ .booleanConf
+ .createWithDefault(true)
Review comment:
@maropu This is very useful suggestion. One issue is columns stats are
rarely computed. We came across this work in HIVE
https://issues.apache.org/jira/browse/HIVE-291. They turn off map side
aggregate (i.e., partial aggregate will be pass through) in Physical operator
(i.e., Group-By operator) if map-side aggregation reduce the entries by at
least half and they look at 100000 rows to do that (ref: patch
https://issues.apache.org/jira/secure/attachment/12400257/291.1.txt). Should we
do something similar in HashAggregateExec here ? Any thoughts on this ?
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