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https://issues.apache.org/jira/browse/HIVE-15682?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15855133#comment-15855133
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Xuefu Zhang commented on HIVE-15682:
------------------------------------
I did some performance measurement query order by queries using our
non-dedicated cluster, and here is the perf diff w/ vs w/o HIVE-15580:
{code}
select count(*) from (select request_lat from dwh.fact_trip where datestr >
'2017-01-27' order by request_lat) x;
Origin: 246.56, 342.78, 216.40, 216.587, 270.805, 449.232, 233.406 AVG: 282.25
patch: 125.21, 123.22, 166.31, 168.30, 120.428, 119.21, 120.385 AVG: 134.72
(code}
I used static allocation to avoid further env variations in the test. The perf
numbers are in seconds. The inconclusive (due to the nature of our cluster)
conclusion is that HIVE-15580 actually boosts the performance by 2.1X.
> Eliminate the dummy iterator and optimize the per row based reducer-side
> processing
> -----------------------------------------------------------------------------------
>
> Key: HIVE-15682
> URL: https://issues.apache.org/jira/browse/HIVE-15682
> Project: Hive
> Issue Type: Improvement
> Components: Spark
> Affects Versions: 2.2.0
> Reporter: Xuefu Zhang
> Assignee: Xuefu Zhang
>
> HIVE-15580 introduced a dummy iterator per input row which can be eliminated.
> This is because {{SparkReduceRecordHandler}} is able to handle single key
> value pairs. We can refactor this part of code 1. to remove the need for a
> iterator and 2. to optimize the code path for per (key, value) based (instead
> of (key, value iterator)) processing. It would be also great if we can
> measure the performance after the optimizations and compare to performance
> prior to HIVE-15580.
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