Github user maropu commented on the issue:
https://github.com/apache/spark/pull/14876
On the other hand, when caching the already-partitioned input table, we
cannot push-down them;
```
(0 to 1000).map(x => (x % 2, x.toString)).toDF("a",
"b").repartition($"a").cache.createOrReplaceTempView("t")
spark.sql("select max(b) from t group by a").explain
```
```
== Physical Plan ==
SortAggregate(key=[a#40], functions=[max(b#41)])
+- SortAggregate(key=[a#40], functions=[partial_max(b#41)])
+- *Sort [a#40 ASC], false, 0
+- InMemoryTableScan [a#40, b#41]
: +- InMemoryRelation [a#40, b#41], true, 10000,
StorageLevel(disk, memory, deserialized, 1 replicas)
: : +- Exchange hashpartitioning(a#40, 4)
: : +- LocalTableScan [a#40, b#41]
```
ISTM all we can do is merge the aggregations into one in this case.
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