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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