[SPARK-27573] Skip partial aggregation when data is already partitioned (or collapse adjacent partial and final aggregates)

• Type: Improvement
• Status: Open
• Priority: Major
• Resolution: Unresolved
• Affects Version/s: 3.1.0
• Component/s: Optimizer, SQL
• Assignee: Unassigned
• Reporter: Josh Rosen
• Created: 26/Apr/19 02:09
• Updated: 16/Mar/20 22:54

Description

When an aggregation requires a shuffle, Spark SQL performs separate partial and final aggregations:

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)

However, consider what happens if the dataset being aggregated is already pre-partitioned by the aggregate's grouping columns:

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) 

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 (since we already have all of the data co-located)!

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 wanted to somehow coerce Spark into skipping the ineffective partial aggregation and jumping directly to total aggregation. I thought that pre-partitioning would accomplish this, but doing so didn't achieve my goal due to the missing aggregation-collapsing (or partial-aggregate skipping) optimization.

Issue Links

• relates to: SPARK-1412 Disable partial aggregation automatically when reduction factor is low