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https://issues.apache.org/jira/browse/HIVE-17486?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16308995#comment-16308995
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liyunzhang commented on HIVE-17486:
-----------------------------------
[~stakiar]:
the original purpose to change M->R to M->M->R is to let
CombineEquivalentWorkResolver combine same Maps. Like
logical plan
{code}
TS[0]-FIL[52]-SEL[2]-GBY[3]-RS[4]-GBY[5]-RS[42]-JOIN[48]-SEL[49]-LIM[50]-FS[51]
TS[1] -FIL[53]-SEL[9]-GBY[10]-RS[11]-GBY[12]-RS[43]-JOIN[48]
{code}
physical plan
{code}
Map1:TS[0]
Map2:TS[1]
Map3:FIL[52]-SEL[2]-GBY[3]-RS[4]
Map4:FIL[53]-SEL[9]-GBY[10]-RS[11]
Reducer1:GBY[5]-RS[42]-JOIN[48]-SEL[49]-LIM[50]-FS[51]
Reducer2:GBY[12]-RS[43]
{code}
For {{CombineEquivalentWorkResolver}}, it will combine same Maps. In above
case, Map2 will be removed because TS\[0\] is same as TS\[1\].
But when I finish the code, I found that there is no necessary to use this way
to combine TS\[0\] and TS\[1\]. {{MapInput}} is responsible for TS and I only
need generate same MapInput for TS\[0\] and TS\[1\]. More detail see
HIVE-17486.5.patch.
> Enable SharedWorkOptimizer in tez on HOS
> ----------------------------------------
>
> Key: HIVE-17486
> URL: https://issues.apache.org/jira/browse/HIVE-17486
> Project: Hive
> Issue Type: Bug
> Reporter: liyunzhang
> Assignee: liyunzhang
> Attachments: HIVE-17486.1.patch, HIVE-17486.2.patch,
> HIVE-17486.3.patch, HIVE-17486.4.patch, explain.28.share.false,
> explain.28.share.true, scanshare.after.svg, scanshare.before.svg
>
>
> in HIVE-16602, Implement shared scans with Tez.
> Given a query plan, the goal is to identify scans on input tables that can be
> merged so the data is read only once. Optimization will be carried out at the
> physical level. In Hive on Spark, it caches the result of spark work if the
> spark work is used by more than 1 child spark work. After sharedWorkOptimizer
> is enabled in physical plan in HoS, the identical table scans are merged to 1
> table scan. This result of table scan will be used by more 1 child spark
> work. Thus we need not do the same computation because of cache mechanism.
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