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https://issues.apache.org/jira/browse/HIVE-15682?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15857172#comment-15857172
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Xuefu Zhang commented on HIVE-15682:
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Hi [~Ferd], when I ran the query, I had two day's data which is about 25m rows.
I just ran the query again, with about 10 day's data, the runtime is about 600s
with 130m rows. I have 32 executors, each having 4 cores. The query spends most
of the time on the second stage where sorting via a single reducer occurs.
I don't think the scale matters much as long as the query runs for sometime (in
minutes at least). Thus, you should be able to use TPC-DS data for this
exercise.
> Eliminate per-row based dummy iterator creation
> -----------------------------------------------
>
> 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
> Fix For: 2.2.0
>
> Attachments: HIVE-15682.patch
>
>
> 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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