Sahil Takiar created HIVE-17178:
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Summary: 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: Sahil Takiar
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:
Map Reduce Local Work
Map 3
Map Operator Tree:
TableScan
alias: part_table_2
Statistics: Num rows: 8 Data size: 8 Basic stats: COMPLETE
Column stats: NONE
Select Operator
expressions: col (type: int), part_col (type: int)
outputColumnNames: _col0, _col1
Statistics: Num rows: 8 Data size: 8 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: _col1 (type: int)
outputColumnNames: _col0
Statistics: Num rows: 8 Data size: 8 Basic stats:
COMPLETE Column stats: NONE
Group By Operator
keys: _col0 (type: int)
mode: hash
outputColumnNames: _col0
Statistics: Num rows: 8 Data size: 8 Basic stats:
COMPLETE Column stats: NONE
Spark Partition Pruning Sink Operator
partition key expr: part_col
Statistics: Num rows: 8 Data size: 8 Basic stats:
COMPLETE Column stats: NONE
target column name: part_col
target work: Map 2
Local Work:
Map Reduce Local Work
Stage: Stage-1
Spark
#### A masked pattern was here ####
Vertices:
Map 2
Map Operator Tree:
TableScan
alias: part_table_1
Statistics: Num rows: 8 Data size: 8 Basic stats: COMPLETE
Column stats: NONE
Select Operator
expressions: col (type: int), part_col (type: int)
outputColumnNames: _col0, _col1
Statistics: Num rows: 8 Data size: 8 Basic stats: COMPLETE
Column stats: NONE
Map Join Operator
condition map:
Inner Join 0 to 1
Inner Join 0 to 2
keys:
0 _col0 (type: int)
1 _col1 (type: int)
2 _col1 (type: int)
outputColumnNames: _col0, _col1, _col2, _col3, _col4
input vertices:
0 Map 1
2 Map 3
Statistics: Num rows: 17 Data size: 17 Basic stats:
COMPLETE Column stats: NONE
File Output Operator
compressed: false
Statistics: Num rows: 17 Data size: 17 Basic stats:
COMPLETE Column stats: NONE
table:
input format:
org.apache.hadoop.mapred.SequenceFileInputFormat
output format:
org.apache.hadoop.hive.ql.io.HiveSequenceFileOutputFormat
serde:
org.apache.hadoop.hive.serde2.lazy.LazySimpleSerDe
Local Work:
Map Reduce Local Work
Stage: Stage-0
Fetch Operator
limit: -1
Processor Tree:
ListSink
{code}
The DPP subtrees on Map 1 are exactly the same. We should be able to combine
them, which avoids doing duplicate work.
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