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https://issues.apache.org/jira/browse/HIVE-15104?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16388551#comment-16388551
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Sahil Takiar commented on HIVE-15104:
-------------------------------------
Hey [~lirui] I found some time to do some internal testing of this patch. I ran
a 1 TB Parquet TPC-DS benchmark (subset of 49 queries, run three times each) on
a physical cluster and found similar results to your TPC-DS benchmark.
|| ||Baseline Run (default configuration)||Optimized Serde||Optimized Serde +
No GroupBy||
||Shuffle Bytes Read|699.5 GB|530.7 GB|531.6 GB|
||Shuffle Bytes Written|690 GB|529.5 GB|530.3 GB|
||Total Latency (min)|202|191|190|
So about a 25% improvement on shuffle data and 5% performance improvement. I
think the improvement for the shuffle data is significant and is a good
improvement.
A few questions on the implementation.
* The {{HiveKryoRegistrator}} still seems to be serializing the {{hashCode}}
so where are the actual savings coming from?
* I'm not sure I understand why the performance should improve when
{{hive.spark.use.groupby.shuffle}} is set to {{false}}. It's still using the
same registrator right?
* You said that we need to serialize the {{hashCode}} because "{{The cached
RDDs will be serialized when stored to disk or transferred via network, then we
need the hash code after the data is deserialized"}} - why do we need the
{{hashCode}} after deserializing the data?
> Hive on Spark generate more shuffle data than hive on mr
> --------------------------------------------------------
>
> Key: HIVE-15104
> URL: https://issues.apache.org/jira/browse/HIVE-15104
> Project: Hive
> Issue Type: Bug
> Components: Spark
> Affects Versions: 1.2.1
> Reporter: wangwenli
> Assignee: Rui Li
> Priority: Major
> Fix For: 3.0.0
>
> Attachments: HIVE-15104.1.patch, HIVE-15104.10.patch,
> HIVE-15104.2.patch, HIVE-15104.3.patch, HIVE-15104.4.patch,
> HIVE-15104.5.patch, HIVE-15104.6.patch, HIVE-15104.7.patch,
> HIVE-15104.8.patch, HIVE-15104.9.patch, TPC-H 100G.xlsx
>
>
> the same sql, running on spark and mr engine, will generate different size
> of shuffle data.
> i think it is because of hive on mr just serialize part of HiveKey, but hive
> on spark which using kryo will serialize full of Hivekey object.
> what is your opionion?
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