[jira] [Resolved] (SPARK-18591) Replace hash-based aggregates with sort-based ones if inputs already sorted

["Hyukjin Kwon (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-18591?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Hyukjin Kwon resolved SPARK-18591.
----------------------------------
    Resolution: Incomplete

> 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
>            Priority: Major
>              Labels: bulk-closed
>
> 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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