[jira] [Updated] (HIVE-17178) Spark Partition Pruning Sink Operator can't target multiple Works

2018-03-11 Thread Rui Li (JIRA) 

 [ 
https://issues.apache.org/jira/browse/HIVE-17178?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Rui Li updated HIVE-17178:
--
   Resolution: Fixed
Fix Version/s: 3.0.0
   Status: Resolved  (was: Patch Available)

Pushed to master. Thanks [~stakiar] for the review.

> Spark Partition Pruning Sink Operator can't target multiple Works
> -
>
> Key: HIVE-17178
> URL: https://issues.apache.org/jira/browse/HIVE-17178
> Project: Hive
>  Issue Type: Sub-task
>  Components: Spark
>Reporter: Sahil Takiar
>Assignee: Rui Li
>Priority: Major
> Fix For: 3.0.0
>
> Attachments: HIVE-17178.1.patch, HIVE-17178.2.patch, 
> HIVE-17178.3.patch, HIVE-17178.4.patch, HIVE-17178.5.patch, HIVE-17178.6.patch
>
>
> A Spark Partition Pruning Sink Operator cannot be used to target multiple Map 
> Work objects. The entire DPP subtree (SEL-GBY-SPARKPRUNINGSINK) is duplicated 
> if a single table needs to be used to target multiple Map Works.
> The following query shows the issue:
> {code}
> set hive.spark.dynamic.partition.pruning=true;
> set hive.auto.convert.join=true;
> create table part_table_1 (col int) partitioned by (part_col int);
> create table part_table_2 (col int) partitioned by (part_col int);
> create table regular_table (col int);
> insert into table regular_table values (1);
> alter table part_table_1 add partition (part_col=1);
> insert into table part_table_1 partition (part_col=1) values (1), (2), (3), 
> (4);
> alter table part_table_1 add partition (part_col=2);
> insert into table part_table_1 partition (part_col=2) values (1), (2), (3), 
> (4);
> alter table part_table_2 add partition (part_col=1);
> insert into table part_table_2 partition (part_col=1) values (1), (2), (3), 
> (4);
> alter table part_table_2 add partition (part_col=2);
> insert into table part_table_2 partition (part_col=2) values (1), (2), (3), 
> (4);
> explain select * from regular_table, part_table_1, part_table_2 where 
> regular_table.col = part_table_1.part_col and regular_table.col = 
> part_table_2.part_col;
> {code}
> The explain plan is
> {code}
> STAGE DEPENDENCIES:
>   Stage-2 is a root stage
>   Stage-1 depends on stages: Stage-2
>   Stage-0 depends on stages: Stage-1
> STAGE PLANS:
>   Stage: Stage-2
> Spark
>  A masked pattern was here 
>   Vertices:
> Map 1 
> Map Operator Tree:
> TableScan
>   alias: regular_table
>   Statistics: Num rows: 1 Data size: 1 Basic stats: COMPLETE 
> Column stats: NONE
>   Filter Operator
> predicate: col is not null (type: boolean)
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> Select Operator
>   expressions: col (type: int)
>   outputColumnNames: _col0
>   Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
>   Spark HashTable Sink Operator
> keys:
>   0 _col0 (type: int)
>   1 _col1 (type: int)
>   2 _col1 (type: int)
>   Select Operator
> expressions: _col0 (type: int)
> outputColumnNames: _col0
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> Group By Operator
>   keys: _col0 (type: int)
>   mode: hash
>   outputColumnNames: _col0
>   Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
>   Spark Partition Pruning Sink Operator
> partition key expr: part_col
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> target column name: part_col
> target work: Map 2
>   Select Operator
> expressions: _col0 (type: int)
> outputColumnNames: _col0
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> Group By Operator
>   keys: _col0 (type: int)
>   mode: hash
>   outputColumnNames: _col0
>   Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
>   Spark Partition Pruning Sink Operator
> partition key expr: part

[jira] [Updated] (HIVE-17178) Spark Partition Pruning Sink Operator can't target multiple Works

2018-03-07 Thread Rui Li (JIRA) 

 [ 
https://issues.apache.org/jira/browse/HIVE-17178?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Rui Li updated HIVE-17178:
--
Attachment: HIVE-17178.6.patch

> Spark Partition Pruning Sink Operator can't target multiple Works
> -
>
> Key: HIVE-17178
> URL: https://issues.apache.org/jira/browse/HIVE-17178
> Project: Hive
>  Issue Type: Sub-task
>  Components: Spark
>Reporter: Sahil Takiar
>Assignee: Rui Li
>Priority: Major
> Attachments: HIVE-17178.1.patch, HIVE-17178.2.patch, 
> HIVE-17178.3.patch, HIVE-17178.4.patch, HIVE-17178.5.patch, HIVE-17178.6.patch
>
>
> A Spark Partition Pruning Sink Operator cannot be used to target multiple Map 
> Work objects. The entire DPP subtree (SEL-GBY-SPARKPRUNINGSINK) is duplicated 
> if a single table needs to be used to target multiple Map Works.
> The following query shows the issue:
> {code}
> set hive.spark.dynamic.partition.pruning=true;
> set hive.auto.convert.join=true;
> create table part_table_1 (col int) partitioned by (part_col int);
> create table part_table_2 (col int) partitioned by (part_col int);
> create table regular_table (col int);
> insert into table regular_table values (1);
> alter table part_table_1 add partition (part_col=1);
> insert into table part_table_1 partition (part_col=1) values (1), (2), (3), 
> (4);
> alter table part_table_1 add partition (part_col=2);
> insert into table part_table_1 partition (part_col=2) values (1), (2), (3), 
> (4);
> alter table part_table_2 add partition (part_col=1);
> insert into table part_table_2 partition (part_col=1) values (1), (2), (3), 
> (4);
> alter table part_table_2 add partition (part_col=2);
> insert into table part_table_2 partition (part_col=2) values (1), (2), (3), 
> (4);
> explain select * from regular_table, part_table_1, part_table_2 where 
> regular_table.col = part_table_1.part_col and regular_table.col = 
> part_table_2.part_col;
> {code}
> The explain plan is
> {code}
> STAGE DEPENDENCIES:
>   Stage-2 is a root stage
>   Stage-1 depends on stages: Stage-2
>   Stage-0 depends on stages: Stage-1
> STAGE PLANS:
>   Stage: Stage-2
> Spark
>  A masked pattern was here 
>   Vertices:
> Map 1 
> Map Operator Tree:
> TableScan
>   alias: regular_table
>   Statistics: Num rows: 1 Data size: 1 Basic stats: COMPLETE 
> Column stats: NONE
>   Filter Operator
> predicate: col is not null (type: boolean)
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> Select Operator
>   expressions: col (type: int)
>   outputColumnNames: _col0
>   Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
>   Spark HashTable Sink Operator
> keys:
>   0 _col0 (type: int)
>   1 _col1 (type: int)
>   2 _col1 (type: int)
>   Select Operator
> expressions: _col0 (type: int)
> outputColumnNames: _col0
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> Group By Operator
>   keys: _col0 (type: int)
>   mode: hash
>   outputColumnNames: _col0
>   Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
>   Spark Partition Pruning Sink Operator
> partition key expr: part_col
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> target column name: part_col
> target work: Map 2
>   Select Operator
> expressions: _col0 (type: int)
> outputColumnNames: _col0
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> Group By Operator
>   keys: _col0 (type: int)
>   mode: hash
>   outputColumnNames: _col0
>   Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
>   Spark Partition Pruning Sink Operator
> partition key expr: part_col
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> targe

[jira] [Updated] (HIVE-17178) Spark Partition Pruning Sink Operator can't target multiple Works

2018-02-26 Thread Rui Li (JIRA) 

 [ 
https://issues.apache.org/jira/browse/HIVE-17178?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Rui Li updated HIVE-17178:
--
Attachment: HIVE-17178.5.patch

> Spark Partition Pruning Sink Operator can't target multiple Works
> -
>
> Key: HIVE-17178
> URL: https://issues.apache.org/jira/browse/HIVE-17178
> Project: Hive
>  Issue Type: Sub-task
>  Components: Spark
>Reporter: Sahil Takiar
>Assignee: Rui Li
>Priority: Major
> Attachments: HIVE-17178.1.patch, HIVE-17178.2.patch, 
> HIVE-17178.3.patch, HIVE-17178.4.patch, HIVE-17178.5.patch
>
>
> A Spark Partition Pruning Sink Operator cannot be used to target multiple Map 
> Work objects. The entire DPP subtree (SEL-GBY-SPARKPRUNINGSINK) is duplicated 
> if a single table needs to be used to target multiple Map Works.
> The following query shows the issue:
> {code}
> set hive.spark.dynamic.partition.pruning=true;
> set hive.auto.convert.join=true;
> create table part_table_1 (col int) partitioned by (part_col int);
> create table part_table_2 (col int) partitioned by (part_col int);
> create table regular_table (col int);
> insert into table regular_table values (1);
> alter table part_table_1 add partition (part_col=1);
> insert into table part_table_1 partition (part_col=1) values (1), (2), (3), 
> (4);
> alter table part_table_1 add partition (part_col=2);
> insert into table part_table_1 partition (part_col=2) values (1), (2), (3), 
> (4);
> alter table part_table_2 add partition (part_col=1);
> insert into table part_table_2 partition (part_col=1) values (1), (2), (3), 
> (4);
> alter table part_table_2 add partition (part_col=2);
> insert into table part_table_2 partition (part_col=2) values (1), (2), (3), 
> (4);
> explain select * from regular_table, part_table_1, part_table_2 where 
> regular_table.col = part_table_1.part_col and regular_table.col = 
> part_table_2.part_col;
> {code}
> The explain plan is
> {code}
> STAGE DEPENDENCIES:
>   Stage-2 is a root stage
>   Stage-1 depends on stages: Stage-2
>   Stage-0 depends on stages: Stage-1
> STAGE PLANS:
>   Stage: Stage-2
> Spark
>  A masked pattern was here 
>   Vertices:
> Map 1 
> Map Operator Tree:
> TableScan
>   alias: regular_table
>   Statistics: Num rows: 1 Data size: 1 Basic stats: COMPLETE 
> Column stats: NONE
>   Filter Operator
> predicate: col is not null (type: boolean)
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> Select Operator
>   expressions: col (type: int)
>   outputColumnNames: _col0
>   Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
>   Spark HashTable Sink Operator
> keys:
>   0 _col0 (type: int)
>   1 _col1 (type: int)
>   2 _col1 (type: int)
>   Select Operator
> expressions: _col0 (type: int)
> outputColumnNames: _col0
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> Group By Operator
>   keys: _col0 (type: int)
>   mode: hash
>   outputColumnNames: _col0
>   Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
>   Spark Partition Pruning Sink Operator
> partition key expr: part_col
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> target column name: part_col
> target work: Map 2
>   Select Operator
> expressions: _col0 (type: int)
> outputColumnNames: _col0
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> Group By Operator
>   keys: _col0 (type: int)
>   mode: hash
>   outputColumnNames: _col0
>   Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
>   Spark Partition Pruning Sink Operator
> partition key expr: part_col
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> target column name: part_

[jira] [Updated] (HIVE-17178) Spark Partition Pruning Sink Operator can't target multiple Works

2018-02-17 Thread Rui Li (JIRA) 

 [ 
https://issues.apache.org/jira/browse/HIVE-17178?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Rui Li updated HIVE-17178:
--
Attachment: HIVE-17178.4.patch

> Spark Partition Pruning Sink Operator can't target multiple Works
> -
>
> Key: HIVE-17178
> URL: https://issues.apache.org/jira/browse/HIVE-17178
> Project: Hive
>  Issue Type: Sub-task
>  Components: Spark
>Reporter: Sahil Takiar
>Assignee: Rui Li
>Priority: Major
> Attachments: HIVE-17178.1.patch, HIVE-17178.2.patch, 
> HIVE-17178.3.patch, HIVE-17178.4.patch
>
>
> A Spark Partition Pruning Sink Operator cannot be used to target multiple Map 
> Work objects. The entire DPP subtree (SEL-GBY-SPARKPRUNINGSINK) is duplicated 
> if a single table needs to be used to target multiple Map Works.
> The following query shows the issue:
> {code}
> set hive.spark.dynamic.partition.pruning=true;
> set hive.auto.convert.join=true;
> create table part_table_1 (col int) partitioned by (part_col int);
> create table part_table_2 (col int) partitioned by (part_col int);
> create table regular_table (col int);
> insert into table regular_table values (1);
> alter table part_table_1 add partition (part_col=1);
> insert into table part_table_1 partition (part_col=1) values (1), (2), (3), 
> (4);
> alter table part_table_1 add partition (part_col=2);
> insert into table part_table_1 partition (part_col=2) values (1), (2), (3), 
> (4);
> alter table part_table_2 add partition (part_col=1);
> insert into table part_table_2 partition (part_col=1) values (1), (2), (3), 
> (4);
> alter table part_table_2 add partition (part_col=2);
> insert into table part_table_2 partition (part_col=2) values (1), (2), (3), 
> (4);
> explain select * from regular_table, part_table_1, part_table_2 where 
> regular_table.col = part_table_1.part_col and regular_table.col = 
> part_table_2.part_col;
> {code}
> The explain plan is
> {code}
> STAGE DEPENDENCIES:
>   Stage-2 is a root stage
>   Stage-1 depends on stages: Stage-2
>   Stage-0 depends on stages: Stage-1
> STAGE PLANS:
>   Stage: Stage-2
> Spark
>  A masked pattern was here 
>   Vertices:
> Map 1 
> Map Operator Tree:
> TableScan
>   alias: regular_table
>   Statistics: Num rows: 1 Data size: 1 Basic stats: COMPLETE 
> Column stats: NONE
>   Filter Operator
> predicate: col is not null (type: boolean)
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> Select Operator
>   expressions: col (type: int)
>   outputColumnNames: _col0
>   Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
>   Spark HashTable Sink Operator
> keys:
>   0 _col0 (type: int)
>   1 _col1 (type: int)
>   2 _col1 (type: int)
>   Select Operator
> expressions: _col0 (type: int)
> outputColumnNames: _col0
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> Group By Operator
>   keys: _col0 (type: int)
>   mode: hash
>   outputColumnNames: _col0
>   Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
>   Spark Partition Pruning Sink Operator
> partition key expr: part_col
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> target column name: part_col
> target work: Map 2
>   Select Operator
> expressions: _col0 (type: int)
> outputColumnNames: _col0
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> Group By Operator
>   keys: _col0 (type: int)
>   mode: hash
>   outputColumnNames: _col0
>   Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
>   Spark Partition Pruning Sink Operator
> partition key expr: part_col
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> target column name: part_col
>

[jira] [Updated] (HIVE-17178) Spark Partition Pruning Sink Operator can't target multiple Works

2018-02-12 Thread Rui Li (JIRA) 

 [ 
https://issues.apache.org/jira/browse/HIVE-17178?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Rui Li updated HIVE-17178:
--
Attachment: HIVE-17178.3.patch

> Spark Partition Pruning Sink Operator can't target multiple Works
> -
>
> Key: HIVE-17178
> URL: https://issues.apache.org/jira/browse/HIVE-17178
> Project: Hive
>  Issue Type: Sub-task
>  Components: Spark
>Reporter: Sahil Takiar
>Assignee: Rui Li
>Priority: Major
> Attachments: HIVE-17178.1.patch, HIVE-17178.2.patch, 
> HIVE-17178.3.patch
>
>
> A Spark Partition Pruning Sink Operator cannot be used to target multiple Map 
> Work objects. The entire DPP subtree (SEL-GBY-SPARKPRUNINGSINK) is duplicated 
> if a single table needs to be used to target multiple Map Works.
> The following query shows the issue:
> {code}
> set hive.spark.dynamic.partition.pruning=true;
> set hive.auto.convert.join=true;
> create table part_table_1 (col int) partitioned by (part_col int);
> create table part_table_2 (col int) partitioned by (part_col int);
> create table regular_table (col int);
> insert into table regular_table values (1);
> alter table part_table_1 add partition (part_col=1);
> insert into table part_table_1 partition (part_col=1) values (1), (2), (3), 
> (4);
> alter table part_table_1 add partition (part_col=2);
> insert into table part_table_1 partition (part_col=2) values (1), (2), (3), 
> (4);
> alter table part_table_2 add partition (part_col=1);
> insert into table part_table_2 partition (part_col=1) values (1), (2), (3), 
> (4);
> alter table part_table_2 add partition (part_col=2);
> insert into table part_table_2 partition (part_col=2) values (1), (2), (3), 
> (4);
> explain select * from regular_table, part_table_1, part_table_2 where 
> regular_table.col = part_table_1.part_col and regular_table.col = 
> part_table_2.part_col;
> {code}
> The explain plan is
> {code}
> STAGE DEPENDENCIES:
>   Stage-2 is a root stage
>   Stage-1 depends on stages: Stage-2
>   Stage-0 depends on stages: Stage-1
> STAGE PLANS:
>   Stage: Stage-2
> Spark
>  A masked pattern was here 
>   Vertices:
> Map 1 
> Map Operator Tree:
> TableScan
>   alias: regular_table
>   Statistics: Num rows: 1 Data size: 1 Basic stats: COMPLETE 
> Column stats: NONE
>   Filter Operator
> predicate: col is not null (type: boolean)
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> Select Operator
>   expressions: col (type: int)
>   outputColumnNames: _col0
>   Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
>   Spark HashTable Sink Operator
> keys:
>   0 _col0 (type: int)
>   1 _col1 (type: int)
>   2 _col1 (type: int)
>   Select Operator
> expressions: _col0 (type: int)
> outputColumnNames: _col0
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> Group By Operator
>   keys: _col0 (type: int)
>   mode: hash
>   outputColumnNames: _col0
>   Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
>   Spark Partition Pruning Sink Operator
> partition key expr: part_col
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> target column name: part_col
> target work: Map 2
>   Select Operator
> expressions: _col0 (type: int)
> outputColumnNames: _col0
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> Group By Operator
>   keys: _col0 (type: int)
>   mode: hash
>   outputColumnNames: _col0
>   Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
>   Spark Partition Pruning Sink Operator
> partition key expr: part_col
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> target column name: part_col
> target

[jira] [Updated] (HIVE-17178) Spark Partition Pruning Sink Operator can't target multiple Works

2018-01-22 Thread Rui Li (JIRA) 

 [ 
https://issues.apache.org/jira/browse/HIVE-17178?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Rui Li updated HIVE-17178:
--
Attachment: HIVE-17178.2.patch

> Spark Partition Pruning Sink Operator can't target multiple Works
> -
>
> Key: HIVE-17178
> URL: https://issues.apache.org/jira/browse/HIVE-17178
> Project: Hive
>  Issue Type: Sub-task
>  Components: Spark
>Reporter: Sahil Takiar
>Assignee: Rui Li
>Priority: Major
> Attachments: HIVE-17178.1.patch, HIVE-17178.2.patch
>
>
> A Spark Partition Pruning Sink Operator cannot be used to target multiple Map 
> Work objects. The entire DPP subtree (SEL-GBY-SPARKPRUNINGSINK) is duplicated 
> if a single table needs to be used to target multiple Map Works.
> The following query shows the issue:
> {code}
> set hive.spark.dynamic.partition.pruning=true;
> set hive.auto.convert.join=true;
> create table part_table_1 (col int) partitioned by (part_col int);
> create table part_table_2 (col int) partitioned by (part_col int);
> create table regular_table (col int);
> insert into table regular_table values (1);
> alter table part_table_1 add partition (part_col=1);
> insert into table part_table_1 partition (part_col=1) values (1), (2), (3), 
> (4);
> alter table part_table_1 add partition (part_col=2);
> insert into table part_table_1 partition (part_col=2) values (1), (2), (3), 
> (4);
> alter table part_table_2 add partition (part_col=1);
> insert into table part_table_2 partition (part_col=1) values (1), (2), (3), 
> (4);
> alter table part_table_2 add partition (part_col=2);
> insert into table part_table_2 partition (part_col=2) values (1), (2), (3), 
> (4);
> explain select * from regular_table, part_table_1, part_table_2 where 
> regular_table.col = part_table_1.part_col and regular_table.col = 
> part_table_2.part_col;
> {code}
> The explain plan is
> {code}
> STAGE DEPENDENCIES:
>   Stage-2 is a root stage
>   Stage-1 depends on stages: Stage-2
>   Stage-0 depends on stages: Stage-1
> STAGE PLANS:
>   Stage: Stage-2
> Spark
>  A masked pattern was here 
>   Vertices:
> Map 1 
> Map Operator Tree:
> TableScan
>   alias: regular_table
>   Statistics: Num rows: 1 Data size: 1 Basic stats: COMPLETE 
> Column stats: NONE
>   Filter Operator
> predicate: col is not null (type: boolean)
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> Select Operator
>   expressions: col (type: int)
>   outputColumnNames: _col0
>   Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
>   Spark HashTable Sink Operator
> keys:
>   0 _col0 (type: int)
>   1 _col1 (type: int)
>   2 _col1 (type: int)
>   Select Operator
> expressions: _col0 (type: int)
> outputColumnNames: _col0
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> Group By Operator
>   keys: _col0 (type: int)
>   mode: hash
>   outputColumnNames: _col0
>   Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
>   Spark Partition Pruning Sink Operator
> partition key expr: part_col
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> target column name: part_col
> target work: Map 2
>   Select Operator
> expressions: _col0 (type: int)
> outputColumnNames: _col0
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> Group By Operator
>   keys: _col0 (type: int)
>   mode: hash
>   outputColumnNames: _col0
>   Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
>   Spark Partition Pruning Sink Operator
> partition key expr: part_col
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> target column name: part_col
> target work: Map 3
>

[jira] [Updated] (HIVE-17178) Spark Partition Pruning Sink Operator can't target multiple Works

2018-01-18 Thread Rui Li (JIRA) 

 [ 
https://issues.apache.org/jira/browse/HIVE-17178?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Rui Li updated HIVE-17178:
--
Status: Patch Available  (was: Open)

> Spark Partition Pruning Sink Operator can't target multiple Works
> -
>
> Key: HIVE-17178
> URL: https://issues.apache.org/jira/browse/HIVE-17178
> Project: Hive
>  Issue Type: Sub-task
>  Components: Spark
>Reporter: Sahil Takiar
>Assignee: Rui Li
>Priority: Major
> Attachments: HIVE-17178.1.patch
>
>
> A Spark Partition Pruning Sink Operator cannot be used to target multiple Map 
> Work objects. The entire DPP subtree (SEL-GBY-SPARKPRUNINGSINK) is duplicated 
> if a single table needs to be used to target multiple Map Works.
> The following query shows the issue:
> {code}
> set hive.spark.dynamic.partition.pruning=true;
> set hive.auto.convert.join=true;
> create table part_table_1 (col int) partitioned by (part_col int);
> create table part_table_2 (col int) partitioned by (part_col int);
> create table regular_table (col int);
> insert into table regular_table values (1);
> alter table part_table_1 add partition (part_col=1);
> insert into table part_table_1 partition (part_col=1) values (1), (2), (3), 
> (4);
> alter table part_table_1 add partition (part_col=2);
> insert into table part_table_1 partition (part_col=2) values (1), (2), (3), 
> (4);
> alter table part_table_2 add partition (part_col=1);
> insert into table part_table_2 partition (part_col=1) values (1), (2), (3), 
> (4);
> alter table part_table_2 add partition (part_col=2);
> insert into table part_table_2 partition (part_col=2) values (1), (2), (3), 
> (4);
> explain select * from regular_table, part_table_1, part_table_2 where 
> regular_table.col = part_table_1.part_col and regular_table.col = 
> part_table_2.part_col;
> {code}
> The explain plan is
> {code}
> STAGE DEPENDENCIES:
>   Stage-2 is a root stage
>   Stage-1 depends on stages: Stage-2
>   Stage-0 depends on stages: Stage-1
> STAGE PLANS:
>   Stage: Stage-2
> Spark
>  A masked pattern was here 
>   Vertices:
> Map 1 
> Map Operator Tree:
> TableScan
>   alias: regular_table
>   Statistics: Num rows: 1 Data size: 1 Basic stats: COMPLETE 
> Column stats: NONE
>   Filter Operator
> predicate: col is not null (type: boolean)
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> Select Operator
>   expressions: col (type: int)
>   outputColumnNames: _col0
>   Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
>   Spark HashTable Sink Operator
> keys:
>   0 _col0 (type: int)
>   1 _col1 (type: int)
>   2 _col1 (type: int)
>   Select Operator
> expressions: _col0 (type: int)
> outputColumnNames: _col0
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> Group By Operator
>   keys: _col0 (type: int)
>   mode: hash
>   outputColumnNames: _col0
>   Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
>   Spark Partition Pruning Sink Operator
> partition key expr: part_col
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> target column name: part_col
> target work: Map 2
>   Select Operator
> expressions: _col0 (type: int)
> outputColumnNames: _col0
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> Group By Operator
>   keys: _col0 (type: int)
>   mode: hash
>   outputColumnNames: _col0
>   Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
>   Spark Partition Pruning Sink Operator
> partition key expr: part_col
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> target column name: part_col
> target work: Map 3
> Local Work

[jira] [Updated] (HIVE-17178) Spark Partition Pruning Sink Operator can't target multiple Works

2018-01-18 Thread Rui Li (JIRA) 

 [ 
https://issues.apache.org/jira/browse/HIVE-17178?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Rui Li updated HIVE-17178:
--
Attachment: HIVE-17178.1.patch

> Spark Partition Pruning Sink Operator can't target multiple Works
> -
>
> Key: HIVE-17178
> URL: https://issues.apache.org/jira/browse/HIVE-17178
> Project: Hive
>  Issue Type: Sub-task
>  Components: Spark
>Reporter: Sahil Takiar
>Assignee: Rui Li
>Priority: Major
> Attachments: HIVE-17178.1.patch
>
>
> A Spark Partition Pruning Sink Operator cannot be used to target multiple Map 
> Work objects. The entire DPP subtree (SEL-GBY-SPARKPRUNINGSINK) is duplicated 
> if a single table needs to be used to target multiple Map Works.
> The following query shows the issue:
> {code}
> set hive.spark.dynamic.partition.pruning=true;
> set hive.auto.convert.join=true;
> create table part_table_1 (col int) partitioned by (part_col int);
> create table part_table_2 (col int) partitioned by (part_col int);
> create table regular_table (col int);
> insert into table regular_table values (1);
> alter table part_table_1 add partition (part_col=1);
> insert into table part_table_1 partition (part_col=1) values (1), (2), (3), 
> (4);
> alter table part_table_1 add partition (part_col=2);
> insert into table part_table_1 partition (part_col=2) values (1), (2), (3), 
> (4);
> alter table part_table_2 add partition (part_col=1);
> insert into table part_table_2 partition (part_col=1) values (1), (2), (3), 
> (4);
> alter table part_table_2 add partition (part_col=2);
> insert into table part_table_2 partition (part_col=2) values (1), (2), (3), 
> (4);
> explain select * from regular_table, part_table_1, part_table_2 where 
> regular_table.col = part_table_1.part_col and regular_table.col = 
> part_table_2.part_col;
> {code}
> The explain plan is
> {code}
> STAGE DEPENDENCIES:
>   Stage-2 is a root stage
>   Stage-1 depends on stages: Stage-2
>   Stage-0 depends on stages: Stage-1
> STAGE PLANS:
>   Stage: Stage-2
> Spark
>  A masked pattern was here 
>   Vertices:
> Map 1 
> Map Operator Tree:
> TableScan
>   alias: regular_table
>   Statistics: Num rows: 1 Data size: 1 Basic stats: COMPLETE 
> Column stats: NONE
>   Filter Operator
> predicate: col is not null (type: boolean)
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> Select Operator
>   expressions: col (type: int)
>   outputColumnNames: _col0
>   Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
>   Spark HashTable Sink Operator
> keys:
>   0 _col0 (type: int)
>   1 _col1 (type: int)
>   2 _col1 (type: int)
>   Select Operator
> expressions: _col0 (type: int)
> outputColumnNames: _col0
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> Group By Operator
>   keys: _col0 (type: int)
>   mode: hash
>   outputColumnNames: _col0
>   Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
>   Spark Partition Pruning Sink Operator
> partition key expr: part_col
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> target column name: part_col
> target work: Map 2
>   Select Operator
> expressions: _col0 (type: int)
> outputColumnNames: _col0
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> Group By Operator
>   keys: _col0 (type: int)
>   mode: hash
>   outputColumnNames: _col0
>   Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
>   Spark Partition Pruning Sink Operator
> partition key expr: part_col
> Statistics: Num rows: 1 Data size: 1 Basic stats: 
> COMPLETE Column stats: NONE
> target column name: part_col
> target work: Map 3
> Local Work:
>