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https://issues.apache.org/jira/browse/HIVE-16793?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16058396#comment-16058396
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Vineet Garg commented on HIVE-16793:
------------------------------------
Latest patch has two known issues:
1. Plans for scalar queries will not have merged joins (since introduction of
project b/w them prevents merging). We plan to keep it as it is and will
probably enhance join merge code in future.
2. Join ordering for some queries (query6, query54 and query58) has changed. I
have verified that there is no regression so it is safe.
> Scalar sub-query: Scalar safety checks for explicit group-bys
> -------------------------------------------------------------
>
> Key: HIVE-16793
> URL: https://issues.apache.org/jira/browse/HIVE-16793
> Project: Hive
> Issue Type: Bug
> Components: SQL
> Affects Versions: 3.0.0
> Reporter: Gopal V
> Assignee: Vineet Garg
> Attachments: HIVE-16793.1.patch, HIVE-16793.2.patch
>
>
> This query has an sq_count check, though is useless on a constant key.
> {code}
> hive> explain select * from part where p_size > (select max(p_size) from part
> where p_type = '1' group by p_type);
> Warning: Map Join MAPJOIN[37][bigTable=?] in task 'Map 1' is a cross product
> Warning: Map Join MAPJOIN[36][bigTable=?] in task 'Map 1' is a cross product
> OK
> Plan optimized by CBO.
> Vertex dependency in root stage
> Map 1 <- Reducer 4 (BROADCAST_EDGE), Reducer 6 (BROADCAST_EDGE)
> Reducer 3 <- Map 2 (SIMPLE_EDGE)
> Reducer 4 <- Reducer 3 (CUSTOM_SIMPLE_EDGE)
> Reducer 6 <- Map 5 (SIMPLE_EDGE)
> Stage-0
> Fetch Operator
> limit:-1
> Stage-1
> Map 1 vectorized, llap
> File Output Operator [FS_64]
> Select Operator [SEL_63] (rows=66666666 width=621)
>
> Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8"]
> Filter Operator [FIL_62] (rows=66666666 width=625)
> predicate:(_col5 > _col10)
> Map Join Operator [MAPJOIN_61] (rows=200000000 width=625)
>
> Conds:(Inner),Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8","_col10"]
> <-Reducer 6 [BROADCAST_EDGE] vectorized, llap
> BROADCAST [RS_58]
> Select Operator [SEL_57] (rows=1 width=4)
> Output:["_col0"]
> Group By Operator [GBY_56] (rows=1 width=89)
>
> Output:["_col0","_col1"],aggregations:["max(VALUE._col0)"],keys:KEY._col0
> <-Map 5 [SIMPLE_EDGE] vectorized, llap
> SHUFFLE [RS_55]
> PartitionCols:_col0
> Group By Operator [GBY_54] (rows=86 width=89)
>
> Output:["_col0","_col1"],aggregations:["max(_col1)"],keys:'1'
> Select Operator [SEL_53] (rows=1212121 width=109)
> Output:["_col1"]
> Filter Operator [FIL_52] (rows=1212121 width=109)
> predicate:(p_type = '1')
> TableScan [TS_17] (rows=200000000 width=109)
>
> tpch_flat_orc_1000@part,part,Tbl:COMPLETE,Col:COMPLETE,Output:["p_type","p_size"]
> <-Map Join Operator [MAPJOIN_60] (rows=200000000 width=621)
>
> Conds:(Inner),Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8"]
> <-Reducer 4 [BROADCAST_EDGE] vectorized, llap
> BROADCAST [RS_51]
> Select Operator [SEL_50] (rows=1 width=8)
> Filter Operator [FIL_49] (rows=1 width=8)
> predicate:(sq_count_check(_col0) <= 1)
> Group By Operator [GBY_48] (rows=1 width=8)
> Output:["_col0"],aggregations:["count(VALUE._col0)"]
> <-Reducer 3 [CUSTOM_SIMPLE_EDGE] vectorized, llap
> PARTITION_ONLY_SHUFFLE [RS_47]
> Group By Operator [GBY_46] (rows=1 width=8)
> Output:["_col0"],aggregations:["count()"]
> Select Operator [SEL_45] (rows=1 width=85)
> Group By Operator [GBY_44] (rows=1 width=85)
> Output:["_col0"],keys:KEY._col0
> <-Map 2 [SIMPLE_EDGE] vectorized, llap
> SHUFFLE [RS_43]
> PartitionCols:_col0
> Group By Operator [GBY_42] (rows=83
> width=85)
> Output:["_col0"],keys:'1'
> Select Operator [SEL_41] (rows=1212121
> width=105)
> Filter Operator [FIL_40] (rows=1212121
> width=105)
> predicate:(p_type = '1')
> TableScan [TS_2] (rows=200000000
> width=105)
>
> tpch_flat_orc_1000@part,part,Tbl:COMPLETE,Col:COMPLETE,Output:["p_type"]
> <-Select Operator [SEL_59] (rows=200000000 width=621)
>
> Output:["_col0","_col1","_col2","_col3","_col4","_col5","_col6","_col7","_col8"]
> TableScan [TS_0] (rows=200000000 width=621)
>
> tpch_flat_orc_1000@part,part,Tbl:COMPLETE,Col:COMPLETE,Output:["p_partkey","p_name","p_mfgr","p_brand","p_type","p_size","p_container","p_retailprice","p_comment"]
> {code}
> -The other version without the filter is missing the check, though the
> compiler cannot assume the nDV of p_type.- Fixed by HIVE-16851
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