Josh Rosen created SPARK-27573:
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Summary: Collapse adjacent aggregate physical operators when
possible
Key: SPARK-27573
URL: https://issues.apache.org/jira/browse/SPARK-27573
Project: Spark
Issue Type: Improvement
Components: Optimizer, SQL
Affects Versions: 2.4.0
Reporter: Josh Rosen
When an aggregation requires a shuffle, Spark SQL performs separate partial and
final aggregations:
{code:java}
sql("select id % 100 as k, id as v from range(100000)")
.groupBy("k")
.sum("v")
.explain
== Physical Plan ==
*(2) HashAggregate(keys=[k#64L], functions=[sum(v#65L)])
+- Exchange(coordinator id: 2031684357) hashpartitioning(k#64L, 5340),
coordinator[target post-shuffle partition size: 67108864]
+- *(1) HashAggregate(keys=[k#64L], functions=[partial_sum(v#65L)])
+- *(1) Project [(id#66L % 100) AS k#64L, id#66L AS v#65L]
+- *(1) Range (0, 100000, step=1, splits=10)
{code}
However, consider what happens if the dataset being aggregated is already
pre-partitioned by the aggregate's grouping columns:
{code:java}
sql("select id % 100 as k, id as v from range(100000)")
.repartition(10, $"k")
.groupBy("k")
.sum("v")
.explain
== Physical Plan ==
*(2) HashAggregate(keys=[k#50L], functions=[sum(v#51L)], output=[k#50L,
sum(v)#58L])
+- *(2) HashAggregate(keys=[k#50L], functions=[partial_sum(v#51L)],
output=[k#50L, sum#63L])
+- Exchange(coordinator id: 39015877) hashpartitioning(k#50L, 10),
coordinator[target post-shuffle partition size: 67108864]
+- *(1) Project [(id#52L % 100) AS k#50L, id#52L AS v#51L]
+- *(1) Range (0, 100000, step=1, splits=10)
{code}
Here, we end up with back-to-back HashAggregate operators which are performed
as part of the same stage.
For certain aggregates (e.g. _sum_, _count_), this duplication is unnecessary:
we could have just performed a total aggregation instead!
The duplicate aggregate is problematic in cases where the aggregate inputs and
outputs are the same order of magnitude (e.g.counting the number of duplicate
records in a dataset where duplicates are extremely rare).
My motivation for this optimization is similar to SPARK-1412: I know that
partial aggregation doesn't help for my workload, so I want to somehow coerce
Spark into skipping the ineffective partial aggregation and jumping directly to
total aggregation.
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