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From "Gunther Hagleitner (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (HIVE-8168) With dynamic partition enabled fact table selectivity is not taken into account when generating the physical plan (Use CBO cardinality using physical plan generation)
Date Tue, 21 Oct 2014 21:13:34 GMT

     [ https://issues.apache.org/jira/browse/HIVE-8168?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Gunther Hagleitner updated HIVE-8168:
-------------------------------------
    Fix Version/s:     (was: vectorization-branch)

> With dynamic partition enabled fact table selectivity is not taken into account when
generating the physical plan (Use CBO cardinality using physical plan generation)
> ----------------------------------------------------------------------------------------------------------------------------------------------------------------------
>
>                 Key: HIVE-8168
>                 URL: https://issues.apache.org/jira/browse/HIVE-8168
>             Project: Hive
>          Issue Type: Bug
>          Components: Tez
>    Affects Versions: 0.14.0
>            Reporter: Mostafa Mokhtar
>            Assignee: Prasanth J
>            Priority: Critical
>              Labels: performance
>             Fix For: 0.14.0
>
>         Attachments: HIVE-8168.1.patch, HIVE-8168.2.patch, HIVE-8168.3.patch, HIVE-8168.4.patch,
HIVE-8168.5.patch
>
>
> When calculating estimate row counts & data size during physical plan generation
in StatsRulesProcFactory doesn't know that there will be dynamic partition pruning and it
is hard to know how many partitions will qualify at runtime, as a result with Dynamic partition
pruning enabled a query 32 can run with 570 compared to 70 tasks with dynamic partition pruning
disabled and actual partition filters on the fact table.
> The long term solution for this issue is to use the cardinality estimates from CBO as
it takes into account join selectivity and such, estimate from CBO won't address the number
of the tasks used for the partitioned table but they will address the incorrect number of
tasks used for the concequent reducers where the majority of the slowdown is coming from.
> Plan dynamic partition pruning on 
> {code}
>    Map 5 
>             Map Operator Tree:
>                 TableScan
>                   alias: ss
>                   filterExpr: ss_store_sk is not null (type: boolean)
>                   Statistics: Num rows: 550076554 Data size: 47370018896 Basic stats:
COMPLETE Column stats: NONE
>                   Filter Operator
>                     predicate: ss_store_sk is not null (type: boolean)
>                     Statistics: Num rows: 275038277 Data size: 23685009448 Basic stats:
COMPLETE Column stats: NONE
>                     Map Join Operator
>                       condition map:
>                            Inner Join 0 to 1
>                       condition expressions:
>                         0 {ss_store_sk} {ss_net_profit}
>                         1 
>                       keys:
>                         0 ss_sold_date_sk (type: int)
>                         1 d_date_sk (type: int)
>                       outputColumnNames: _col6, _col21
>                       input vertices:
>                         1 Map 1
>                       Statistics: Num rows: 302542112 Data size: 26053511168 Basic stats:
COMPLETE Column stats: NONE
>                       Map Join Operator
>                         condition map:
>                              Inner Join 0 to 1
>                         condition expressions:
>                           0 {_col21}
>                           1 {s_county} {s_state}
>                         keys:
>                           0 _col6 (type: int)
>                           1 s_store_sk (type: int)
>                         outputColumnNames: _col21, _col80, _col81
>                         input vertices:
>                           1 Map 2
>                         Statistics: Num rows: 332796320 Data size: 28658862080 Basic
stats: COMPLETE Column stats: NONE
>                         Map Join Operator
>                           condition map:
>                                Left Semi Join 0 to 1
>                           condition expressions:
>                             0 {_col21} {_col80} {_col81}
>                             1 
>                           keys:
>                             0 _col81 (type: string)
>                             1 _col0 (type: string)
>                           outputColumnNames: _col21, _col80, _col81
>                           input vertices:
>                             1 Reducer 11
>                           Statistics: Num rows: 366075968 Data size: 31524749312 Basic
stats: COMPLETE Column stats: NONE
>                           Select Operator
>                             expressions: _col81 (type: string), _col80 (type: string),
_col21 (type: float)
>                             outputColumnNames: _col81, _col80, _col21
>                             Statistics: Num rows: 366075968 Data size: 31524749312 Basic
stats: COMPLETE Column stats: NONE
>                             Group By Operator
>                               aggregations: sum(_col21)
>                               keys: _col81 (type: string), _col80 (type: string), '0'
(type: string)
>                               mode: hash
>                               outputColumnNames: _col0, _col1, _col2, _col3
>                               Statistics: Num rows: 1098227904 Data size: 94574247936
Basic stats: COMPLETE Column stats: NONE
>                               Reduce Output Operator
>                                 key expressions: _col0 (type: string), _col1 (type: string),
_col2 (type: string)
>                                 sort order: +++
>                                 Map-reduce partition columns: _col0 (type: string), _col1
(type: string), _col2 (type: string)
>                                 Statistics: Num rows: 1098227904 Data size: 94574247936
Basic stats: COMPLETE Column stats: NONE
>                                 value expressions: _col3 (type: double)
> {code}
> Plan snippet with partition pruning off and explicit partition filters
> {code}
>     Map 5 
>             Map Operator Tree:
>                 TableScan
>                   alias: ss
>                   filterExpr: ((ss_sold_date_sk is not null and ss_store_sk is not null)
and ss_sold_date BETWEEN '1999-06-01' AND '2000-05-31') (type: boolean)
>                   Statistics: Num rows: 110339135 Data size: 4817453454 Basic stats:
COMPLETE Column stats: NONE
>                   Filter Operator
>                     predicate: (ss_sold_date_sk is not null and ss_store_sk is not null)
(type: boolean)
>                     Statistics: Num rows: 27584784 Data size: 1204363374 Basic stats:
COMPLETE Column stats: NONE
>                     Map Join Operator
>                       condition map:
>                            Inner Join 0 to 1
>                       condition expressions:
>                         0 {ss_store_sk} {ss_net_profit}
>                         1 
>                       keys:
>                         0 ss_sold_date_sk (type: int)
>                         1 d_date_sk (type: int)
>                       outputColumnNames: _col7, _col22
>                       input vertices:
>                         1 Map 1
>                       Statistics: Num rows: 30343264 Data size: 1324799744 Basic stats:
COMPLETE Column stats: NONE
>                       Map Join Operator
>                         condition map:
>                              Inner Join 0 to 1
>                         condition expressions:
>                           0 {_col22}
>                           1 {s_county} {s_state}
>                         keys:
>                           0 _col7 (type: int)
>                           1 s_store_sk (type: int)
>                         outputColumnNames: _col22, _col81, _col82
>                         input vertices:
>                           1 Map 2
>                         Statistics: Num rows: 33377592 Data size: 1457279744 Basic stats:
COMPLETE Column stats: NONE
>                         Map Join Operator
>                           condition map:
>                                Left Semi Join 0 to 1
>                           condition expressions:
>                             0 {_col22} {_col81} {_col82}
>                             1 
>                           keys:
>                             0 _col82 (type: string)
>                             1 _col0 (type: string)
>                           outputColumnNames: _col22, _col81, _col82
>                           input vertices:
>                             1 Reducer 11
>                           Statistics: Num rows: 36715352 Data size: 1603007744 Basic
stats: COMPLETE Column stats: NONE
>                           Select Operator
>                             expressions: _col82 (type: string), _col81 (type: string),
_col22 (type: float)
>                             outputColumnNames: _col82, _col81, _col22
>                             Statistics: Num rows: 36715352 Data size: 1603007744 Basic
stats: COMPLETE Column stats: NONE
>                             Group By Operator
>                               aggregations: sum(_col22)
>                               keys: _col82 (type: string), _col81 (type: string), '0'
(type: string)
>                               mode: hash
>                               outputColumnNames: _col0, _col1, _col2, _col3
>                               Statistics: Num rows: 36715352 Data size: 1603007744 Basic
stats: COMPLETE Column stats: NONE
>                               Reduce Output Operator
>                                 key expressions: _col0 (type: string), _col1 (type: string),
_col2 (type: string)
>                                 sort order: +++
>                                 Map-reduce partition columns: _col0 (type: string), _col1
(type: string), _col2 (type: string)
>                                 Statistics: Num rows: 36715352 Data size: 1603007744
Basic stats: COMPLETE Column stats: NONE
>                                 value expressions: _col3 (type: double)
> {code}
> Plan snippet with partition pruning on and fetch column stats also on , in this case
row estimate a
> {code}
>       Map 5 
>             Map Operator Tree:
>                 TableScan
>                   alias: ss
>                   filterExpr: ss_store_sk is not null (type: boolean)
>                   Statistics: Num rows: 550076554 Data size: 47370018896 Basic stats:
COMPLETE Column stats: COMPLETE
>                   Filter Operator
>                     predicate: ss_store_sk is not null (type: boolean)
>                     Statistics: Num rows: 537120379 Data size: 4195767284 Basic stats:
COMPLETE Column stats: COMPLETE
>                     Map Join Operator
>                       condition map:
>                            Inner Join 0 to 1
>                       condition expressions:
>                         0 {ss_store_sk} {ss_net_profit}
>                         1 
>                       keys:
>                         0 ss_sold_date_sk (type: int)
>                         1 d_date_sk (type: int)
>                       outputColumnNames: _col6, _col21
>                       input vertices:
>                         1 Map 1
>                       Statistics: Num rows: 36524 Data size: 292192 Basic stats: COMPLETE
Column stats: COMPLETE
>                       Map Join Operator
>                         condition map:
>                              Inner Join 0 to 1
>                         condition expressions:
>                           0 {_col21}
>                           1 {s_county} {s_state}
>                         keys:
>                           0 _col6 (type: int)
>                           1 s_store_sk (type: int)
>                         outputColumnNames: _col21, _col80, _col81
>                         input vertices:
>                           1 Map 2
>                         Statistics: Num rows: 45017 Data size: 8508213 Basic stats: COMPLETE
Column stats: COMPLETE
>                         Map Join Operator
>                           condition map:
>                                Left Semi Join 0 to 1
>                           condition expressions:
>                             0 {_col21} {_col80} {_col81}
>                             1 
>                           keys:
>                             0 _col81 (type: string)
>                             1 _col0 (type: string)
>                           outputColumnNames: _col21, _col80, _col81
>                           input vertices:
>                             1 Reducer 11
>                           Statistics: Num rows: 49518 Data size: 9359035 Basic stats:
COMPLETE Column stats: NONE
>                           Select Operator
>                             expressions: _col81 (type: string), _col80 (type: string),
_col21 (type: float)
>                             outputColumnNames: _col81, _col80, _col21
>                             Statistics: Num rows: 49518 Data size: 9359035 Basic stats:
COMPLETE Column stats: NONE
>                             Group By Operator
>                               aggregations: sum(_col21)
>                               keys: _col81 (type: string), _col80 (type: string), '0'
(type: string)
>                               mode: hash
>                               outputColumnNames: _col0, _col1, _col2, _col3
>                               Statistics: Num rows: 148554 Data size: 28077105 Basic
stats: COMPLETE Column stats: NONE
>                               Reduce Output Operator
>                                 key expressions: _col0 (type: string), _col1 (type: string),
_col2 (type: string)
>                                 sort order: +++
>                                 Map-reduce partition columns: _col0 (type: string), _col1
(type: string), _col2 (type: string)
>                                 Statistics: Num rows: 148554 Data size: 28077105 Basic
stats: COMPLETE Column stats: NONE
>                                 value expressions: _col3 (type: double)
> {code}



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