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https://issues.apache.org/jira/browse/SPARK-18591?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15733855#comment-15733855
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Takeshi Yamamuro commented on SPARK-18591:
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yea, If we can, it's the best. But IIUC it's difficult to do that in the fist
place because spark replaces logical nodes with physical ones in top-down way
and we can't check the output ordering of children in between planning.
> Replace hash-based aggregates with sort-based ones if inputs already sorted
> ---------------------------------------------------------------------------
>
> Key: SPARK-18591
> URL: https://issues.apache.org/jira/browse/SPARK-18591
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 2.0.2
> Reporter: Takeshi Yamamuro
>
> Spark currently uses sort-based aggregates only in limited condition; the
> cases where spark cannot use partial aggregates and hash-based ones.
> However, if input ordering has already satisfied the requirements of
> sort-based aggregates, it seems sort-based ones are faster than the other.
> {code}
> ./bin/spark-shell --conf spark.sql.shuffle.partitions=1
> val df = spark.range(10000000).selectExpr("id AS key", "id % 10 AS
> value").sort($"key").cache
> def timer[R](block: => R): R = {
> val t0 = System.nanoTime()
> val result = block
> val t1 = System.nanoTime()
> println("Elapsed time: " + ((t1 - t0 + 0.0) / 1000000000.0)+ "s")
> result
> }
> timer {
> df.groupBy("key").count().count
> }
> // codegen'd hash aggregate
> Elapsed time: 7.116962977s
> // non-codegen'd sort aggregarte
> Elapsed time: 3.088816662s
> {code}
> If codegen'd sort-based aggregates are supported in SPARK-16844, this seems
> to make the performance gap bigger;
> {code}
> - codegen'd sort aggregate
> Elapsed time: 1.645234684s
> {code}
> Therefore, it'd be better to use sort-based ones in this case.
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