[
https://issues.apache.org/jira/browse/HIVE-17178?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16334874#comment-16334874
]
Hive QA commented on HIVE-17178:
--------------------------------
Here are the results of testing the latest attachment:
https://issues.apache.org/jira/secure/attachment/12907104/HIVE-17178.2.patch
{color:green}SUCCESS:{color} +1 due to 1 test(s) being added or modified.
{color:red}ERROR:{color} -1 due to 19 failed/errored test(s), 11600 tests
executed
*Failed tests:*
{noformat}
org.apache.hadoop.hive.cli.TestCliDriver.org.apache.hadoop.hive.cli.TestCliDriver
(batchId=16)
org.apache.hadoop.hive.cli.TestCliDriver.testCliDriver[mapjoin_hook]
(batchId=12)
org.apache.hadoop.hive.cli.TestCliDriver.testCliDriver[ppd_join5] (batchId=35)
org.apache.hadoop.hive.cli.TestEncryptedHDFSCliDriver.testCliDriver[encryption_move_tbl]
(batchId=173)
org.apache.hadoop.hive.cli.TestMiniLlapCliDriver.testCliDriver[llap_smb]
(batchId=151)
org.apache.hadoop.hive.cli.TestMiniLlapLocalCliDriver.testCliDriver[bucket_map_join_tez1]
(batchId=170)
org.apache.hadoop.hive.cli.TestMiniLlapLocalCliDriver.testCliDriver[insert_values_orig_table_use_metadata]
(batchId=165)
org.apache.hadoop.hive.cli.TestMiniLlapLocalCliDriver.testCliDriver[llap_acid]
(batchId=169)
org.apache.hadoop.hive.cli.TestMiniLlapLocalCliDriver.testCliDriver[llap_acid_fast]
(batchId=160)
org.apache.hadoop.hive.cli.TestMiniLlapLocalCliDriver.testCliDriver[sysdb]
(batchId=160)
org.apache.hadoop.hive.cli.TestMiniSparkOnYarnCliDriver.testCliDriver[spark_dynamic_partition_pruning_6]
(batchId=178)
org.apache.hadoop.hive.cli.TestNegativeCliDriver.testCliDriver[authorization_part]
(batchId=94)
org.apache.hadoop.hive.cli.TestSparkCliDriver.testCliDriver[ppd_join5]
(batchId=121)
org.apache.hadoop.hive.ql.io.TestDruidRecordWriter.testWrite (batchId=254)
org.apache.hive.jdbc.TestSSL.testConnectionMismatch (batchId=232)
org.apache.hive.jdbc.TestSSL.testConnectionWrongCertCN (batchId=232)
org.apache.hive.jdbc.TestSSL.testMetastoreConnectionWrongCertCN (batchId=232)
org.apache.hive.jdbc.TestTriggersMoveWorkloadManager.testTriggerMoveConflictKill
(batchId=233)
org.apache.hive.minikdc.TestJdbcWithDBTokenStoreNoDoAs.org.apache.hive.minikdc.TestJdbcWithDBTokenStoreNoDoAs
(batchId=247)
{noformat}
Test results: https://builds.apache.org/job/PreCommit-HIVE-Build/8759/testReport
Console output: https://builds.apache.org/job/PreCommit-HIVE-Build/8759/console
Test logs: http://104.198.109.242/logs/PreCommit-HIVE-Build-8759/
Messages:
{noformat}
Executing org.apache.hive.ptest.execution.TestCheckPhase
Executing org.apache.hive.ptest.execution.PrepPhase
Executing org.apache.hive.ptest.execution.YetusPhase
Executing org.apache.hive.ptest.execution.ExecutionPhase
Executing org.apache.hive.ptest.execution.ReportingPhase
Tests exited with: TestsFailedException: 19 tests failed
{noformat}
This message is automatically generated.
ATTACHMENT ID: 12907104 - PreCommit-HIVE-Build
> 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
> 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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