Github user maropu commented on the issue:
https://github.com/apache/spark/pull/14876
I found that we need to push-down partial aggregation below exchange
operators instead of merging them? For example, in the spark v2.0 branch,
```
(0 to 1000).map(x => (x % 2, x.toString)).toDF("a",
"b").repartition($"a").createOrReplaceTempView("t")
spark.sql("select max(b) from t group by a").explain
```
This prints like:
```
== Physical Plan ==
SortAggregate(key=[a#5], functions=[max(b#6)], output=[max(b)#20])
+- SortAggregate(key=[a#5], functions=[partial_max(b#6)],
output=[a#5,max#22])
+- *Sort [a#5 ASC], false, 0
+- Exchange hashpartitioning(a#5, 4)
+- LocalTableScan [a#5, b#6]
```
In this case, I think it is more natural to push-down the partial
aggregation below the exchange.
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