[jira] [Updated] (SPARK-16870) add "spark.sql.broadcastTimeout" into docs/sql-programming-guide.md to help people to how to fix this timeout error when it happenned

2016-08-03 Thread Liang Ke (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-16870?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Liang Ke updated SPARK-16870:
-
Description: 
here my workload and what I found 
I run a large number jobs with spark-sql at the same time. and meet the error 
that print timeout (some job contains the broadcast-join operator) : 
16/08/03 15:43:23 ERROR SparkExecuteStatementOperation: Error executing query, 
currentState RUNNING,
java.util.concurrent.TimeoutException: Futures timed out after [300 seconds]
at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:219)
at 
scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223)
at scala.concurrent.Await$$anonfun$result$1.apply(package.scala:107)
at 
scala.concurrent.BlockContext$DefaultBlockContext$.blockOn(BlockContext.scala:53)
at scala.concurrent.Await$.result(package.scala:107)
at 
org.apache.spark.sql.execution.joins.BroadcastHashOuterJoin.doExecute(BroadcastHashOuterJoin.scala:113)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at 
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at 
org.apache.spark.sql.execution.Filter.doExecute(basicOperators.scala:70)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at 
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at 
org.apache.spark.sql.execution.Project.doExecute(basicOperators.scala:46)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at 
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at 
org.apache.spark.sql.execution.ConvertToSafe.doExecute(rowFormatConverters.scala:56)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at 
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at 
org.apache.spark.sql.hive.execution.InsertIntoHiveTable.sideEffectResult$lzycompute(InsertIntoHiveTable.scala:201)
at 
org.apache.spark.sql.hive.execution.InsertIntoHiveTable.sideEffectResult(InsertIntoHiveTable.scala:127)
at 
org.apache.spark.sql.hive.execution.InsertIntoHiveTable.doExecute(InsertIntoHiveTable.scala:276)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at 
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at 
org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:55)
at 
org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:55)
at org.apache.spark.sql.DataFrame.(DataFrame.scala:145)
at org.apache.spark.sql.DataFrame.(DataFrame.scala:130)
at org.apache.spark.sql.DataFrame$.apply(DataFrame.scala:52)
at org.apache.spark.sql.SQLContext.sql(SQLContext.scala:817)
at 
org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation.org$apache$spark$sql$hive$thriftserver$SparkExecute
StatementOperation$$execute(SparkExecuteStatementOperation.scala:211)
at 
org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation$$anon$1$$anon$2.run(SparkExecuteStatementOperation.
scala:154)
at 
org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation$$anon$1$$anon$2.run(SparkExecuteStatementOperation.
scala:151)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:415)
at 
org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1793)
at 
org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation$$anon$1.run(SparkExecuteStatementOperation.scala:16
4)
at 
java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
at

[jira] [Updated] (SPARK-16870) add "spark.sql.broadcastTimeout" into docs/sql-programming-guide.md to help people to how to fix this timeout error when it happenned

2016-08-03 Thread Liang Ke (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-16870?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Liang Ke updated SPARK-16870:
-
Description: 
here my workload and what I found 
I run a large number jobs with spark-sql at the same time. and meet the error 
that print timeout (some job contains the broadcast-join operator) : 
16/08/03 15:43:23 ERROR SparkExecuteStatementOperation: Error executing query, 
currentState RUNNING,
java.util.concurrent.TimeoutException: Futures timed out after [300 seconds]
at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:219)
at 
scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223)
at scala.concurrent.Await$$anonfun$result$1.apply(package.scala:107)
at 
scala.concurrent.BlockContext$DefaultBlockContext$.blockOn(BlockContext.scala:53)
at scala.concurrent.Await$.result(package.scala:107)
at 
org.apache.spark.sql.execution.joins.BroadcastHashOuterJoin.doExecute(BroadcastHashOuterJoin.scala:113)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at 
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at 
org.apache.spark.sql.execution.Filter.doExecute(basicOperators.scala:70)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at 
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at 
org.apache.spark.sql.execution.Project.doExecute(basicOperators.scala:46)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at 
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at 
org.apache.spark.sql.execution.ConvertToSafe.doExecute(rowFormatConverters.scala:56)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at 
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at 
org.apache.spark.sql.hive.execution.InsertIntoHiveTable.sideEffectResult$lzycompute(InsertIntoHiveTable.scala:201)
at 
org.apache.spark.sql.hive.execution.InsertIntoHiveTable.sideEffectResult(InsertIntoHiveTable.scala:127)
at 
org.apache.spark.sql.hive.execution.InsertIntoHiveTable.doExecute(InsertIntoHiveTable.scala:276)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at 
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at 
org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:55)
at 
org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:55)
at org.apache.spark.sql.DataFrame.(DataFrame.scala:145)
at org.apache.spark.sql.DataFrame.(DataFrame.scala:130)
at org.apache.spark.sql.DataFrame$.apply(DataFrame.scala:52)
at org.apache.spark.sql.SQLContext.sql(SQLContext.scala:817)
at 
org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation.org$apache$spark$sql$hive$thriftserver$SparkExecute
StatementOperation$$execute(SparkExecuteStatementOperation.scala:211)
at 
org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation$$anon$1$$anon$2.run(SparkExecuteStatementOperation.
scala:154)
at 
org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation$$anon$1$$anon$2.run(SparkExecuteStatementOperation.
scala:151)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:415)
at 
org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1793)
at 
org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation$$anon$1.run(SparkExecuteStatementOperation.scala:16
4)
at 
java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
at

[jira] [Updated] (SPARK-16870) add "spark.sql.broadcastTimeout" into docs/sql-programming-guide.md to help people to how to fix this timeout error when it happenned

2016-08-03 Thread Liang Ke (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-16870?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Liang Ke updated SPARK-16870:
-
Description: 
here my workload and what I found 
I run a large number jobs with spark-sql at the same time. and meet the error 
that print timeout (some job contains the broadcast-join operator) : 
16/08/03 15:43:23 ERROR SparkExecuteStatementOperation: Error executing query, 
currentState RUNNING,
java.util.concurrent.TimeoutException: Futures timed out after [300 seconds]
at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:219)
at 
scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223)
at scala.concurrent.Await$$anonfun$result$1.apply(package.scala:107)
at 
scala.concurrent.BlockContext$DefaultBlockContext$.blockOn(BlockContext.scala:53)
at scala.concurrent.Await$.result(package.scala:107)
at 
org.apache.spark.sql.execution.joins.BroadcastHashOuterJoin.doExecute(BroadcastHashOuterJoin.scala:113)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at 
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at 
org.apache.spark.sql.execution.Filter.doExecute(basicOperators.scala:70)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at 
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at 
org.apache.spark.sql.execution.Project.doExecute(basicOperators.scala:46)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at 
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at 
org.apache.spark.sql.execution.ConvertToSafe.doExecute(rowFormatConverters.scala:56)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at 
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at 
org.apache.spark.sql.hive.execution.InsertIntoHiveTable.sideEffectResult$lzycompute(InsertIntoHiveTable.scala:201)
at 
org.apache.spark.sql.hive.execution.InsertIntoHiveTable.sideEffectResult(InsertIntoHiveTable.scala:127)
at 
org.apache.spark.sql.hive.execution.InsertIntoHiveTable.doExecute(InsertIntoHiveTable.scala:276)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at 
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at 
org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:55)
at 
org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:55)
at org.apache.spark.sql.DataFrame.(DataFrame.scala:145)
at org.apache.spark.sql.DataFrame.(DataFrame.scala:130)
at org.apache.spark.sql.DataFrame$.apply(DataFrame.scala:52)
at org.apache.spark.sql.SQLContext.sql(SQLContext.scala:817)
at 
org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation.org$apache$spark$sql$hive$thriftserver$SparkExecute
StatementOperation$$execute(SparkExecuteStatementOperation.scala:211)
at 
org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation$$anon$1$$anon$2.run(SparkExecuteStatementOperation.
scala:154)
at 
org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation$$anon$1$$anon$2.run(SparkExecuteStatementOperation.
scala:151)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:415)
at 
org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1793)
at 
org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation$$anon$1.run(SparkExecuteStatementOperation.scala:16
4)
at 
java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
at

[jira] [Commented] (SPARK-16870) add "spark.sql.broadcastTimeout" into docs/sql-programming-guide.md to help people to how to fix this timeout error when it happenned

2016-08-03 Thread Liang Ke (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-16870?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15406834#comment-15406834
 ] 

Liang Ke commented on SPARK-16870:
--

thx :) I have update it

> add "spark.sql.broadcastTimeout" into docs/sql-programming-guide.md to help 
> people to how to fix this timeout error when it happenned
> -
>
> Key: SPARK-16870
> URL: https://issues.apache.org/jira/browse/SPARK-16870
> Project: Spark
>  Issue Type: Improvement
>Reporter: Liang Ke
>Priority: Trivial
>
> here my workload and what I found 
> I run a large number jobs with spark-sql at the same time. and meet the error 
> that print timeout (some job contains the broadcast-join operator) : 
> 16/08/03 15:43:23 ERROR SparkExecuteStatementOperation: Error executing 
> query, currentState RUNNING,
> java.util.concurrent.TimeoutException: Futures timed out after [300 seconds]
> at 
> scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:219)
> at 
> scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223)
> at scala.concurrent.Await$$anonfun$result$1.apply(package.scala:107)
> at 
> scala.concurrent.BlockContext$DefaultBlockContext$.blockOn(BlockContext.scala:53)
> at scala.concurrent.Await$.result(package.scala:107)
> at 
> org.apache.spark.sql.execution.joins.BroadcastHashOuterJoin.doExecute(BroadcastHashOuterJoin.scala:113)
> at 
> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
> at 
> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
> at 
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
> at 
> org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
> at 
> org.apache.spark.sql.execution.Filter.doExecute(basicOperators.scala:70)
> at 
> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
> at 
> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
> at 
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
> at 
> org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
> at 
> org.apache.spark.sql.execution.Project.doExecute(basicOperators.scala:46)
> at 
> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
> at 
> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
> at 
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
> at 
> org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
> at 
> org.apache.spark.sql.execution.ConvertToSafe.doExecute(rowFormatConverters.scala:56)
> at 
> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
> at 
> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
> at 
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
> at 
> org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
> at 
> org.apache.spark.sql.hive.execution.InsertIntoHiveTable.sideEffectResult$lzycompute(InsertIntoHiveTable.scala:201)
> at 
> org.apache.spark.sql.hive.execution.InsertIntoHiveTable.sideEffectResult(InsertIntoHiveTable.scala:127)
> at 
> org.apache.spark.sql.hive.execution.InsertIntoHiveTable.doExecute(InsertIntoHiveTable.scala:276)
> at 
> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
> at 
> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
> at 
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
> at 
> org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
> at 
> org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:55)
> at 
> org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:55)
> at org.apache.spark.sql.DataFrame.(DataFrame.scala:145)
> at org.apache.spark.sql.DataFrame.(DataFrame.scala:130)
> at org.apache.spark.sql.DataFrame$.apply(DataFrame.scala:52)
> at org.apache.spark.sql.SQLContext.sql(SQLContext.scala:817)
> at 
> org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation.org$apache$spark$sql$hive$thriftserver$SparkExecute
> StatementOperation$$execute(SparkExecuteStatementOperation.scala:211)
> at 
>

[jira] [Updated] (SPARK-16870) add "spark.sql.broadcastTimeout" into docs/sql-programming-guide.md to help people to how to fix this timeout error when it happenned

2016-08-03 Thread Liang Ke (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-16870?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Liang Ke updated SPARK-16870:
-
Description: 
here my workload and what I found 
I run a large number jobs with spark-sql at the same time. and meet the error 
that print timeout (some job contains the broadcast-join operator) : 
16/08/03 15:43:23 ERROR SparkExecuteStatementOperation: Error executing query, 
currentState RUNNING,
java.util.concurrent.TimeoutException: Futures timed out after [300 seconds]
at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:219)
at 
scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223)
at scala.concurrent.Await$$anonfun$result$1.apply(package.scala:107)
at 
scala.concurrent.BlockContext$DefaultBlockContext$.blockOn(BlockContext.scala:53)
at scala.concurrent.Await$.result(package.scala:107)
at 
org.apache.spark.sql.execution.joins.BroadcastHashOuterJoin.doExecute(BroadcastHashOuterJoin.scala:113)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at 
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at 
org.apache.spark.sql.execution.Filter.doExecute(basicOperators.scala:70)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at 
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at 
org.apache.spark.sql.execution.Project.doExecute(basicOperators.scala:46)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at 
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at 
org.apache.spark.sql.execution.ConvertToSafe.doExecute(rowFormatConverters.scala:56)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at 
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at 
org.apache.spark.sql.hive.execution.InsertIntoHiveTable.sideEffectResult$lzycompute(InsertIntoHiveTable.scala:201)
at 
org.apache.spark.sql.hive.execution.InsertIntoHiveTable.sideEffectResult(InsertIntoHiveTable.scala:127)
at 
org.apache.spark.sql.hive.execution.InsertIntoHiveTable.doExecute(InsertIntoHiveTable.scala:276)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at 
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at 
org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:55)
at 
org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:55)
at org.apache.spark.sql.DataFrame.(DataFrame.scala:145)
at org.apache.spark.sql.DataFrame.(DataFrame.scala:130)
at org.apache.spark.sql.DataFrame$.apply(DataFrame.scala:52)
at org.apache.spark.sql.SQLContext.sql(SQLContext.scala:817)
at 
org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation.org$apache$spark$sql$hive$thriftserver$SparkExecute
StatementOperation$$execute(SparkExecuteStatementOperation.scala:211)
at 
org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation$$anon$1$$anon$2.run(SparkExecuteStatementOperation.
scala:154)
at 
org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation$$anon$1$$anon$2.run(SparkExecuteStatementOperation.
scala:151)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:415)
at 
org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1793)
at 
org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation$$anon$1.run(SparkExecuteStatementOperation.scala:16
4)
at 
java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
at

[jira] [Created] (SPARK-16870) Timeout in seconds for the broadcast wait time in broadcast joins

2016-08-03 Thread Liang Ke (JIRA) 
Liang Ke created SPARK-16870:


 Summary: Timeout in seconds for the broadcast wait time in 
broadcast joins 
 Key: SPARK-16870
 URL: https://issues.apache.org/jira/browse/SPARK-16870
 Project: Spark
  Issue Type: Improvement
Reporter: Liang Ke
Priority: Trivial


16/08/03 15:43:23 ERROR SparkExecuteStatementOperation: Error executing query, 
currentState RUNNING,
java.util.concurrent.TimeoutException: Futures timed out after [300 seconds]
at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:219)
at 
scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223)
at scala.concurrent.Await$$anonfun$result$1.apply(package.scala:107)
at 
scala.concurrent.BlockContext$DefaultBlockContext$.blockOn(BlockContext.scala:53)
at scala.concurrent.Await$.result(package.scala:107)
at 
org.apache.spark.sql.execution.joins.BroadcastHashOuterJoin.doExecute(BroadcastHashOuterJoin.scala:113)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at 
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at 
org.apache.spark.sql.execution.Filter.doExecute(basicOperators.scala:70)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at 
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at 
org.apache.spark.sql.execution.Project.doExecute(basicOperators.scala:46)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at 
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at 
org.apache.spark.sql.execution.ConvertToSafe.doExecute(rowFormatConverters.scala:56)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at 
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at 
org.apache.spark.sql.hive.execution.InsertIntoHiveTable.sideEffectResult$lzycompute(InsertIntoHiveTable.scala:201)
at 
org.apache.spark.sql.hive.execution.InsertIntoHiveTable.sideEffectResult(InsertIntoHiveTable.scala:127)
at 
org.apache.spark.sql.hive.execution.InsertIntoHiveTable.doExecute(InsertIntoHiveTable.scala:276)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
at 
org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
at 
org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
at 
org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:55)
at 
org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:55)
at org.apache.spark.sql.DataFrame.(DataFrame.scala:145)
at org.apache.spark.sql.DataFrame.(DataFrame.scala:130)
at org.apache.spark.sql.DataFrame$.apply(DataFrame.scala:52)
at org.apache.spark.sql.SQLContext.sql(SQLContext.scala:817)
at 
org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation.org$apache$spark$sql$hive$thriftserver$SparkExecute
StatementOperation$$execute(SparkExecuteStatementOperation.scala:211)
at 
org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation$$anon$1$$anon$2.run(SparkExecuteStatementOperation.
scala:154)
at 
org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation$$anon$1$$anon$2.run(SparkExecuteStatementOperation.
scala:151)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:415)
at 
org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1793)
at 
org.apache.spark.sql.hive.thriftserver.SparkExecuteStatementOperation$$anon$1.run(SparkExecuteStatementOperation.scala:16
4)
at 
java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:471)
at

[jira] [Commented] (SPARK-16735) Fail to create a map contains decimal type with literals having different inferred precessions and scales

2016-07-26 Thread Liang Ke (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-16735?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15394864#comment-15394864
 ] 

Liang Ke commented on SPARK-16735:
--

https://github.com/apache/spark/pull/14374

> Fail to create a map contains decimal type with literals having different 
> inferred precessions and scales
> -
>
> Key: SPARK-16735
> URL: https://issues.apache.org/jira/browse/SPARK-16735
> Project: Spark
>  Issue Type: Sub-task
>Affects Versions: 2.0.0, 2.0.1
>Reporter: Liang Ke
>
> In Spark 2.0, we will parse float literals as decimals. However, it 
> introduces a side-effect, which is described below.
> spark-sql> select map(0.1,0.01, 0.2,0.033);
> Error in query: cannot resolve 'map(CAST(0.1 AS DECIMAL(1,1)), CAST(0.01 AS 
> DECIMAL(2,2)), CAST(0.2 AS DECIMAL(1,1)), CAST(0.033 AS DECIMAL(3,3)))' due 
> to data type mismatch: The given values of function map should all be the 
> same type, but they are [decimal(2,2), decimal(3,3)]; line 1 pos 7



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[jira] [Commented] (SPARK-16715) Fix a potential ExprId conflict for SubexpressionEliminationSuite."Semantic equals and hash"

2016-07-26 Thread Liang Ke (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-16715?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15394860#comment-15394860
 ] 

Liang Ke commented on SPARK-16715:
--

Sorry, mistake the jira id.

> Fix a potential ExprId conflict for SubexpressionEliminationSuite."Semantic 
> equals and hash"
> 
>
> Key: SPARK-16715
> URL: https://issues.apache.org/jira/browse/SPARK-16715
> Project: Spark
>  Issue Type: Bug
>  Components: Tests
>Reporter: Shixiong Zhu
>Assignee: Shixiong Zhu
> Fix For: 2.0.1, 2.1.0
>
>
> SubexpressionEliminationSuite."Semantic equals and hash" assumes the default 
> AttributeReference's exprId wont' be "ExprId(1)". However, that depends on 
> when this test runs. It may happen to use "ExprId(1)".



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[jira] [Issue Comment Deleted] (SPARK-16735) Fail to create a map contains decimal type with literals having different inferred precessions and scales

2016-07-26 Thread Liang Ke (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-16735?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Liang Ke updated SPARK-16735:
-
Comment: was deleted

(was: hi, if some one can help me to push my patch to github ? 
thx a lot : ))

> Fail to create a map contains decimal type with literals having different 
> inferred precessions and scales
> -
>
> Key: SPARK-16735
> URL: https://issues.apache.org/jira/browse/SPARK-16735
> Project: Spark
>  Issue Type: Sub-task
>Affects Versions: 2.0.0, 2.0.1
>Reporter: Liang Ke
>
> In Spark 2.0, we will parse float literals as decimals. However, it 
> introduces a side-effect, which is described below.
> spark-sql> select map(0.1,0.01, 0.2,0.033);
> Error in query: cannot resolve 'map(CAST(0.1 AS DECIMAL(1,1)), CAST(0.01 AS 
> DECIMAL(2,2)), CAST(0.2 AS DECIMAL(1,1)), CAST(0.033 AS DECIMAL(3,3)))' due 
> to data type mismatch: The given values of function map should all be the 
> same type, but they are [decimal(2,2), decimal(3,3)]; line 1 pos 7



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[jira] [Updated] (SPARK-16735) Fail to create a map contains decimal type with literals having different inferred precessions and scales

2016-07-26 Thread Liang Ke (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-16735?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Liang Ke updated SPARK-16735:
-
Attachment: (was: SPARK-16735.patch)

> Fail to create a map contains decimal type with literals having different 
> inferred precessions and scales
> -
>
> Key: SPARK-16735
> URL: https://issues.apache.org/jira/browse/SPARK-16735
> Project: Spark
>  Issue Type: Sub-task
>Affects Versions: 2.0.0, 2.0.1
>Reporter: Liang Ke
>
> In Spark 2.0, we will parse float literals as decimals. However, it 
> introduces a side-effect, which is described below.
> spark-sql> select map(0.1,0.01, 0.2,0.033);
> Error in query: cannot resolve 'map(CAST(0.1 AS DECIMAL(1,1)), CAST(0.01 AS 
> DECIMAL(2,2)), CAST(0.2 AS DECIMAL(1,1)), CAST(0.033 AS DECIMAL(3,3)))' due 
> to data type mismatch: The given values of function map should all be the 
> same type, but they are [decimal(2,2), decimal(3,3)]; line 1 pos 7



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[jira] [Comment Edited] (SPARK-16735) Fail to create a map contains decimal type with literals having different inferred precessions and scales

2016-07-26 Thread Liang Ke (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-16735?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15393639#comment-15393639
 ] 

Liang Ke edited comment on SPARK-16735 at 7/26/16 12:00 PM:


hi, if some one can help me to push my patch to github ? 
thx a lot : )


was (Author: biglobster):
hi, if some one can help me to push my patch to github? thx alot:)

> Fail to create a map contains decimal type with literals having different 
> inferred precessions and scales
> -
>
> Key: SPARK-16735
> URL: https://issues.apache.org/jira/browse/SPARK-16735
> Project: Spark
>  Issue Type: Sub-task
>Affects Versions: 2.0.0, 2.0.1
>Reporter: Liang Ke
> Attachments: SPARK-16735.patch
>
>
> In Spark 2.0, we will parse float literals as decimals. However, it 
> introduces a side-effect, which is described below.
> spark-sql> select map(0.1,0.01, 0.2,0.033);
> Error in query: cannot resolve 'map(CAST(0.1 AS DECIMAL(1,1)), CAST(0.01 AS 
> DECIMAL(2,2)), CAST(0.2 AS DECIMAL(1,1)), CAST(0.033 AS DECIMAL(3,3)))' due 
> to data type mismatch: The given values of function map should all be the 
> same type, but they are [decimal(2,2), decimal(3,3)]; line 1 pos 7



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[jira] [Updated] (SPARK-16735) Fail to create a map contains decimal type with literals having different inferred precessions and scales

2016-07-26 Thread Liang Ke (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-16735?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Liang Ke updated SPARK-16735:
-
Attachment: SPARK-16735.patch

hi, if some one can help me to push my patch to github? thx alot:)

> Fail to create a map contains decimal type with literals having different 
> inferred precessions and scales
> -
>
> Key: SPARK-16735
> URL: https://issues.apache.org/jira/browse/SPARK-16735
> Project: Spark
>  Issue Type: Sub-task
>Affects Versions: 2.0.0, 2.0.1
>Reporter: Liang Ke
> Attachments: SPARK-16735.patch
>
>
> In Spark 2.0, we will parse float literals as decimals. However, it 
> introduces a side-effect, which is described below.
> spark-sql> select map(0.1,0.01, 0.2,0.033);
> Error in query: cannot resolve 'map(CAST(0.1 AS DECIMAL(1,1)), CAST(0.01 AS 
> DECIMAL(2,2)), CAST(0.2 AS DECIMAL(1,1)), CAST(0.033 AS DECIMAL(3,3)))' due 
> to data type mismatch: The given values of function map should all be the 
> same type, but they are [decimal(2,2), decimal(3,3)]; line 1 pos 7



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[jira] [Created] (SPARK-16735) Fail to create a map contains decimal type with literals having different inferred precessions and scales

2016-07-26 Thread Liang Ke (JIRA) 
Liang Ke created SPARK-16735:


 Summary: Fail to create a map contains decimal type with literals 
having different inferred precessions and scales
 Key: SPARK-16735
 URL: https://issues.apache.org/jira/browse/SPARK-16735
 Project: Spark
  Issue Type: Sub-task
Affects Versions: 2.0.0, 2.0.1
Reporter: Liang Ke


In Spark 2.0, we will parse float literals as decimals. However, it introduces 
a side-effect, which is described below.

spark-sql> select map(0.1,0.01, 0.2,0.033);
Error in query: cannot resolve 'map(CAST(0.1 AS DECIMAL(1,1)), CAST(0.01 AS 
DECIMAL(2,2)), CAST(0.2 AS DECIMAL(1,1)), CAST(0.033 AS DECIMAL(3,3)))' due to 
data type mismatch: The given values of function map should all be the same 
type, but they are [decimal(2,2), decimal(3,3)]; line 1 pos 7



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[jira] [Commented] (SPARK-16603) Spark2.0 fail in executing the sql statement which field name begins with number,like "d.30_day_loss_user" while spark1.6 supports

2016-07-25 Thread Liang Ke (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-16603?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15391588#comment-15391588
 ] 

Liang Ke commented on SPARK-16603:
--

[~marymwu] this is not a bug

> Spark2.0 fail in executing the sql statement which field name begins with 
> number,like "d.30_day_loss_user" while spark1.6 supports
> --
>
> Key: SPARK-16603
> URL: https://issues.apache.org/jira/browse/SPARK-16603
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 2.0.0
>Reporter: marymwu
>Priority: Minor
>
> Spark2.0 fail in executing the sql statement which field name begins with 
> number,like "d.30_day_loss_user" while spark1.6 supports
> Error: org.apache.spark.sql.catalyst.parser.ParseException: mismatched input 
> '.30' expecting
> {')', ','}



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[jira] [Comment Edited] (SPARK-16603) Spark2.0 fail in executing the sql statement which field name begins with number,like "d.30_day_loss_user" while spark1.6 supports

2016-07-25 Thread Liang Ke (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-16603?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15391566#comment-15391566
 ] 

Liang Ke edited comment on SPARK-16603 at 7/25/16 9:20 AM:
---

Sorry. I read the spark sourcecode again, find it's a usage error:
right usage is quoted this column name. and query again without error.

 > select * from tsp where 'tsp.20_user_addr' <10;
16/07/25 18:05:34 INFO SparkSqlParser: Parsing command: select * from tsp where 
'tsp.20_user_addr' <10
16/07/25 18:05:35 INFO HiveMetaStore: 0: create_database: 
Database(name:default, description:default database, 
locationUri:hdfs://ht-chen-slave1:8020/apps/root/warehouse, parameters:{})
16/07/25 18:05:35 INFO audit: ugi=root  ip=unknown-ip-addr  
cmd=create_database: Database(name:default, description:default database, 
locationUri:hdfs://ht-chen-slave1:8020/apps/root/warehouse, parameters:{})
16/07/25 18:05:35 INFO HiveMetaStore: 0: get_table : db=default tbl=tsp
16/07/25 18:05:35 INFO audit: ugi=root  ip=unknown-ip-addr  cmd=get_table : 
db=default tbl=tsp
16/07/25 18:05:35 INFO CatalystSqlParser: Parsing command: int
16/07/25 18:05:35 INFO CatalystSqlParser: Parsing command: string
16/07/25 18:05:35 INFO HiveMetaStore: 0: get_table : db=default tbl=tsp
16/07/25 18:05:35 INFO audit: ugi=root  ip=unknown-ip-addr  cmd=get_table : 
db=default tbl=tsp
16/07/25 18:05:35 INFO CatalystSqlParser: Parsing command: int
16/07/25 18:05:35 INFO CatalystSqlParser: Parsing command: string
16/07/25 18:05:36 INFO CodeGenerator: Code generated in 300.833934 ms
Time taken: 1.793 seconds
16/07/25 18:05:36 INFO CliDriver: Time taken: 1.793 seconds

so, [~marymwu] this is not a bug



was (Author: biglobster):
Sorry. I read the spark sourcecode again, find it's a usage error:
right usage is quoted this column name. and query again without error.

 > select * from tsp where 'tsp.20_user_addr' <10;
16/07/25 18:05:34 INFO SparkSqlParser: Parsing command: select * from tsp where 
'tsp.20_user_addr' <10
16/07/25 18:05:35 INFO HiveMetaStore: 0: create_database: 
Database(name:default, description:default database, 
locationUri:hdfs://ht-chen-slave1:8020/apps/root/warehouse, parameters:{})
16/07/25 18:05:35 INFO audit: ugi=root  ip=unknown-ip-addr  
cmd=create_database: Database(name:default, description:default database, 
locationUri:hdfs://ht-chen-slave1:8020/apps/root/warehouse, parameters:{})
16/07/25 18:05:35 INFO HiveMetaStore: 0: get_table : db=default tbl=tsp
16/07/25 18:05:35 INFO audit: ugi=root  ip=unknown-ip-addr  cmd=get_table : 
db=default tbl=tsp
16/07/25 18:05:35 INFO CatalystSqlParser: Parsing command: int
16/07/25 18:05:35 INFO CatalystSqlParser: Parsing command: string
16/07/25 18:05:35 INFO HiveMetaStore: 0: get_table : db=default tbl=tsp
16/07/25 18:05:35 INFO audit: ugi=root  ip=unknown-ip-addr  cmd=get_table : 
db=default tbl=tsp
16/07/25 18:05:35 INFO CatalystSqlParser: Parsing command: int
16/07/25 18:05:35 INFO CatalystSqlParser: Parsing command: string
16/07/25 18:05:36 INFO CodeGenerator: Code generated in 300.833934 ms
Time taken: 1.793 seconds
16/07/25 18:05:36 INFO CliDriver: Time taken: 1.793 seconds

so, this is not a bug


> Spark2.0 fail in executing the sql statement which field name begins with 
> number,like "d.30_day_loss_user" while spark1.6 supports
> --
>
> Key: SPARK-16603
> URL: https://issues.apache.org/jira/browse/SPARK-16603
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 2.0.0
>Reporter: marymwu
>Priority: Minor
>
> Spark2.0 fail in executing the sql statement which field name begins with 
> number,like "d.30_day_loss_user" while spark1.6 supports
> Error: org.apache.spark.sql.catalyst.parser.ParseException: mismatched input 
> '.30' expecting
> {')', ','}



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[jira] [Issue Comment Deleted] (SPARK-16603) Spark2.0 fail in executing the sql statement which field name begins with number,like "d.30_day_loss_user" while spark1.6 supports

2016-07-25 Thread Liang Ke (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-16603?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Liang Ke updated SPARK-16603:
-
Comment: was deleted

(was: [~marymwu] this is not a bug)

> Spark2.0 fail in executing the sql statement which field name begins with 
> number,like "d.30_day_loss_user" while spark1.6 supports
> --
>
> Key: SPARK-16603
> URL: https://issues.apache.org/jira/browse/SPARK-16603
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 2.0.0
>Reporter: marymwu
>Priority: Minor
>
> Spark2.0 fail in executing the sql statement which field name begins with 
> number,like "d.30_day_loss_user" while spark1.6 supports
> Error: org.apache.spark.sql.catalyst.parser.ParseException: mismatched input 
> '.30' expecting
> {')', ','}



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[jira] [Comment Edited] (SPARK-16603) Spark2.0 fail in executing the sql statement which field name begins with number,like "d.30_day_loss_user" while spark1.6 supports

2016-07-25 Thread Liang Ke (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-16603?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15391566#comment-15391566
 ] 

Liang Ke edited comment on SPARK-16603 at 7/25/16 9:13 AM:
---

Sorry. I read the spark sourcecode again, find it's a usage error:
right usage is quoted this column name. and query again without error.

 > select * from tsp where 'tsp.20_user_addr' <10;
16/07/25 18:05:34 INFO SparkSqlParser: Parsing command: select * from tsp where 
'tsp.20_user_addr' <10
16/07/25 18:05:35 INFO HiveMetaStore: 0: create_database: 
Database(name:default, description:default database, 
locationUri:hdfs://ht-chen-slave1:8020/apps/root/warehouse, parameters:{})
16/07/25 18:05:35 INFO audit: ugi=root  ip=unknown-ip-addr  
cmd=create_database: Database(name:default, description:default database, 
locationUri:hdfs://ht-chen-slave1:8020/apps/root/warehouse, parameters:{})
16/07/25 18:05:35 INFO HiveMetaStore: 0: get_table : db=default tbl=tsp
16/07/25 18:05:35 INFO audit: ugi=root  ip=unknown-ip-addr  cmd=get_table : 
db=default tbl=tsp
16/07/25 18:05:35 INFO CatalystSqlParser: Parsing command: int
16/07/25 18:05:35 INFO CatalystSqlParser: Parsing command: string
16/07/25 18:05:35 INFO HiveMetaStore: 0: get_table : db=default tbl=tsp
16/07/25 18:05:35 INFO audit: ugi=root  ip=unknown-ip-addr  cmd=get_table : 
db=default tbl=tsp
16/07/25 18:05:35 INFO CatalystSqlParser: Parsing command: int
16/07/25 18:05:35 INFO CatalystSqlParser: Parsing command: string
16/07/25 18:05:36 INFO CodeGenerator: Code generated in 300.833934 ms
Time taken: 1.793 seconds
16/07/25 18:05:36 INFO CliDriver: Time taken: 1.793 seconds

so, this is not a bug @marymwu



was (Author: biglobster):
Sorry. I read the spark sourcecode again, find it's a usage error:
right usage is quoted this column name. and query again without error.

 > select * from tsp where 'tsp.20_user_addr' <10;
16/07/25 18:05:34 INFO SparkSqlParser: Parsing command: select * from tsp where 
'tsp.20_user_addr' <10
16/07/25 18:05:35 INFO HiveMetaStore: 0: create_database: 
Database(name:default, description:default database, 
locationUri:hdfs://ht-chen-slave1:8020/apps/root/warehouse, parameters:{})
16/07/25 18:05:35 INFO audit: ugi=root  ip=unknown-ip-addr  
cmd=create_database: Database(name:default, description:default database, 
locationUri:hdfs://ht-chen-slave1:8020/apps/root/warehouse, parameters:{})
16/07/25 18:05:35 INFO HiveMetaStore: 0: get_table : db=default tbl=tsp
16/07/25 18:05:35 INFO audit: ugi=root  ip=unknown-ip-addr  cmd=get_table : 
db=default tbl=tsp
16/07/25 18:05:35 INFO CatalystSqlParser: Parsing command: int
16/07/25 18:05:35 INFO CatalystSqlParser: Parsing command: string
16/07/25 18:05:35 INFO HiveMetaStore: 0: get_table : db=default tbl=tsp
16/07/25 18:05:35 INFO audit: ugi=root  ip=unknown-ip-addr  cmd=get_table : 
db=default tbl=tsp
16/07/25 18:05:35 INFO CatalystSqlParser: Parsing command: int
16/07/25 18:05:35 INFO CatalystSqlParser: Parsing command: string
16/07/25 18:05:36 INFO CodeGenerator: Code generated in 300.833934 ms
Time taken: 1.793 seconds
16/07/25 18:05:36 INFO CliDriver: Time taken: 1.793 seconds

so, this is not a bug


> Spark2.0 fail in executing the sql statement which field name begins with 
> number,like "d.30_day_loss_user" while spark1.6 supports
> --
>
> Key: SPARK-16603
> URL: https://issues.apache.org/jira/browse/SPARK-16603
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 2.0.0
>Reporter: marymwu
>Priority: Minor
>
> Spark2.0 fail in executing the sql statement which field name begins with 
> number,like "d.30_day_loss_user" while spark1.6 supports
> Error: org.apache.spark.sql.catalyst.parser.ParseException: mismatched input 
> '.30' expecting
> {')', ','}



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[jira] [Comment Edited] (SPARK-16603) Spark2.0 fail in executing the sql statement which field name begins with number,like "d.30_day_loss_user" while spark1.6 supports

2016-07-25 Thread Liang Ke (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-16603?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15391566#comment-15391566
 ] 

Liang Ke edited comment on SPARK-16603 at 7/25/16 9:13 AM:
---

Sorry. I read the spark sourcecode again, find it's a usage error:
right usage is quoted this column name. and query again without error.

 > select * from tsp where 'tsp.20_user_addr' <10;
16/07/25 18:05:34 INFO SparkSqlParser: Parsing command: select * from tsp where 
'tsp.20_user_addr' <10
16/07/25 18:05:35 INFO HiveMetaStore: 0: create_database: 
Database(name:default, description:default database, 
locationUri:hdfs://ht-chen-slave1:8020/apps/root/warehouse, parameters:{})
16/07/25 18:05:35 INFO audit: ugi=root  ip=unknown-ip-addr  
cmd=create_database: Database(name:default, description:default database, 
locationUri:hdfs://ht-chen-slave1:8020/apps/root/warehouse, parameters:{})
16/07/25 18:05:35 INFO HiveMetaStore: 0: get_table : db=default tbl=tsp
16/07/25 18:05:35 INFO audit: ugi=root  ip=unknown-ip-addr  cmd=get_table : 
db=default tbl=tsp
16/07/25 18:05:35 INFO CatalystSqlParser: Parsing command: int
16/07/25 18:05:35 INFO CatalystSqlParser: Parsing command: string
16/07/25 18:05:35 INFO HiveMetaStore: 0: get_table : db=default tbl=tsp
16/07/25 18:05:35 INFO audit: ugi=root  ip=unknown-ip-addr  cmd=get_table : 
db=default tbl=tsp
16/07/25 18:05:35 INFO CatalystSqlParser: Parsing command: int
16/07/25 18:05:35 INFO CatalystSqlParser: Parsing command: string
16/07/25 18:05:36 INFO CodeGenerator: Code generated in 300.833934 ms
Time taken: 1.793 seconds
16/07/25 18:05:36 INFO CliDriver: Time taken: 1.793 seconds

so, this is not a bug



was (Author: biglobster):
Sorry. I read the spark sourcecode again, find it's a usage error:
right usage is quoted this column name. and query again without error.

 > select * from tsp where 'tsp.20_user_addr' <10;
16/07/25 18:05:34 INFO SparkSqlParser: Parsing command: select * from tsp where 
'tsp.20_user_addr' <10
16/07/25 18:05:35 INFO HiveMetaStore: 0: create_database: 
Database(name:default, description:default database, 
locationUri:hdfs://ht-chen-slave1:8020/apps/root/warehouse, parameters:{})
16/07/25 18:05:35 INFO audit: ugi=root  ip=unknown-ip-addr  
cmd=create_database: Database(name:default, description:default database, 
locationUri:hdfs://ht-chen-slave1:8020/apps/root/warehouse, parameters:{})
16/07/25 18:05:35 INFO HiveMetaStore: 0: get_table : db=default tbl=tsp
16/07/25 18:05:35 INFO audit: ugi=root  ip=unknown-ip-addr  cmd=get_table : 
db=default tbl=tsp
16/07/25 18:05:35 INFO CatalystSqlParser: Parsing command: int
16/07/25 18:05:35 INFO CatalystSqlParser: Parsing command: string
16/07/25 18:05:35 INFO HiveMetaStore: 0: get_table : db=default tbl=tsp
16/07/25 18:05:35 INFO audit: ugi=root  ip=unknown-ip-addr  cmd=get_table : 
db=default tbl=tsp
16/07/25 18:05:35 INFO CatalystSqlParser: Parsing command: int
16/07/25 18:05:35 INFO CatalystSqlParser: Parsing command: string
16/07/25 18:05:36 INFO CodeGenerator: Code generated in 300.833934 ms
Time taken: 1.793 seconds
16/07/25 18:05:36 INFO CliDriver: Time taken: 1.793 seconds

so, this is not a bug @marymwu


> Spark2.0 fail in executing the sql statement which field name begins with 
> number,like "d.30_day_loss_user" while spark1.6 supports
> --
>
> Key: SPARK-16603
> URL: https://issues.apache.org/jira/browse/SPARK-16603
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 2.0.0
>Reporter: marymwu
>Priority: Minor
>
> Spark2.0 fail in executing the sql statement which field name begins with 
> number,like "d.30_day_loss_user" while spark1.6 supports
> Error: org.apache.spark.sql.catalyst.parser.ParseException: mismatched input 
> '.30' expecting
> {')', ','}



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[jira] [Comment Edited] (SPARK-16603) Spark2.0 fail in executing the sql statement which field name begins with number,like "d.30_day_loss_user" while spark1.6 supports

2016-07-25 Thread Liang Ke (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-16603?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15391566#comment-15391566
 ] 

Liang Ke edited comment on SPARK-16603 at 7/25/16 9:00 AM:
---

Sorry. I read the spark sourcecode again, find it's a usage error:
right usage is quoted this column name. and query again without error.

 > select * from tsp where 'tsp.20_user_addr' <10;
16/07/25 18:05:34 INFO SparkSqlParser: Parsing command: select * from tsp where 
'tsp.20_user_addr' <10
16/07/25 18:05:35 INFO HiveMetaStore: 0: create_database: 
Database(name:default, description:default database, 
locationUri:hdfs://ht-chen-slave1:8020/apps/root/warehouse, parameters:{})
16/07/25 18:05:35 INFO audit: ugi=root  ip=unknown-ip-addr  
cmd=create_database: Database(name:default, description:default database, 
locationUri:hdfs://ht-chen-slave1:8020/apps/root/warehouse, parameters:{})
16/07/25 18:05:35 INFO HiveMetaStore: 0: get_table : db=default tbl=tsp
16/07/25 18:05:35 INFO audit: ugi=root  ip=unknown-ip-addr  cmd=get_table : 
db=default tbl=tsp
16/07/25 18:05:35 INFO CatalystSqlParser: Parsing command: int
16/07/25 18:05:35 INFO CatalystSqlParser: Parsing command: string
16/07/25 18:05:35 INFO HiveMetaStore: 0: get_table : db=default tbl=tsp
16/07/25 18:05:35 INFO audit: ugi=root  ip=unknown-ip-addr  cmd=get_table : 
db=default tbl=tsp
16/07/25 18:05:35 INFO CatalystSqlParser: Parsing command: int
16/07/25 18:05:35 INFO CatalystSqlParser: Parsing command: string
16/07/25 18:05:36 INFO CodeGenerator: Code generated in 300.833934 ms
Time taken: 1.793 seconds
16/07/25 18:05:36 INFO CliDriver: Time taken: 1.793 seconds

so, this is not a bug



was (Author: biglobster):
Sorry. I read the spark sourcecode again, find it's a usage error:
right usage is quoted this column name. and query again without error.

select * from tsp where 'tsp.20_user_addr' <10

so, this is not a bug


> Spark2.0 fail in executing the sql statement which field name begins with 
> number,like "d.30_day_loss_user" while spark1.6 supports
> --
>
> Key: SPARK-16603
> URL: https://issues.apache.org/jira/browse/SPARK-16603
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 2.0.0
>Reporter: marymwu
>Priority: Minor
>
> Spark2.0 fail in executing the sql statement which field name begins with 
> number,like "d.30_day_loss_user" while spark1.6 supports
> Error: org.apache.spark.sql.catalyst.parser.ParseException: mismatched input 
> '.30' expecting
> {')', ','}



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[jira] [Issue Comment Deleted] (SPARK-16603) Spark2.0 fail in executing the sql statement which field name begins with number,like "d.30_day_loss_user" while spark1.6 supports

2016-07-25 Thread Liang Ke (JIRA) 

 [ 
https://issues.apache.org/jira/browse/SPARK-16603?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Liang Ke updated SPARK-16603:
-
Comment: was deleted

(was: so, this is not a bug)

> Spark2.0 fail in executing the sql statement which field name begins with 
> number,like "d.30_day_loss_user" while spark1.6 supports
> --
>
> Key: SPARK-16603
> URL: https://issues.apache.org/jira/browse/SPARK-16603
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 2.0.0
>Reporter: marymwu
>Priority: Minor
>
> Spark2.0 fail in executing the sql statement which field name begins with 
> number,like "d.30_day_loss_user" while spark1.6 supports
> Error: org.apache.spark.sql.catalyst.parser.ParseException: mismatched input 
> '.30' expecting
> {')', ','}



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[jira] [Comment Edited] (SPARK-16603) Spark2.0 fail in executing the sql statement which field name begins with number,like "d.30_day_loss_user" while spark1.6 supports

2016-07-25 Thread Liang Ke (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-16603?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15391566#comment-15391566
 ] 

Liang Ke edited comment on SPARK-16603 at 7/25/16 9:00 AM:
---

Sorry. I read the spark sourcecode again, find it's a usage error:
right usage is quoted this column name. and query again without error.

select * from tsp where 'tsp.20_user_addr' <10

so, this is not a bug



was (Author: biglobster):
Sorry. I read the spark sourcecode again, find it's a usage error:
right usage is quoted this column name. and query again without error.

select * from tsp where 'tsp.20_user_addr' <10


> Spark2.0 fail in executing the sql statement which field name begins with 
> number,like "d.30_day_loss_user" while spark1.6 supports
> --
>
> Key: SPARK-16603
> URL: https://issues.apache.org/jira/browse/SPARK-16603
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 2.0.0
>Reporter: marymwu
>Priority: Minor
>
> Spark2.0 fail in executing the sql statement which field name begins with 
> number,like "d.30_day_loss_user" while spark1.6 supports
> Error: org.apache.spark.sql.catalyst.parser.ParseException: mismatched input 
> '.30' expecting
> {')', ','}



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[jira] [Commented] (SPARK-16603) Spark2.0 fail in executing the sql statement which field name begins with number,like "d.30_day_loss_user" while spark1.6 supports

2016-07-25 Thread Liang Ke (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-16603?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15391568#comment-15391568
 ] 

Liang Ke commented on SPARK-16603:
--

so, this is not a bug

> Spark2.0 fail in executing the sql statement which field name begins with 
> number,like "d.30_day_loss_user" while spark1.6 supports
> --
>
> Key: SPARK-16603
> URL: https://issues.apache.org/jira/browse/SPARK-16603
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 2.0.0
>Reporter: marymwu
>Priority: Minor
>
> Spark2.0 fail in executing the sql statement which field name begins with 
> number,like "d.30_day_loss_user" while spark1.6 supports
> Error: org.apache.spark.sql.catalyst.parser.ParseException: mismatched input 
> '.30' expecting
> {')', ','}



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[jira] [Comment Edited] (SPARK-16603) Spark2.0 fail in executing the sql statement which field name begins with number,like "d.30_day_loss_user" while spark1.6 supports

2016-07-25 Thread Liang Ke (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-16603?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15391566#comment-15391566
 ] 

Liang Ke edited comment on SPARK-16603 at 7/25/16 8:58 AM:
---

Sorry. I read the spark sourcecode again, find it's a usage error:
right usage is quoted this column name. and query again without error.

select * from tsp where 'tsp.20_user_addr' <10



was (Author: biglobster):
Sorry. I read the spark sourcecode again, find it's a usage error:
right usage is adding quota between this column name. and query again without 
error.

select * from tsp where 'tsp.20_user_addr' <10


> Spark2.0 fail in executing the sql statement which field name begins with 
> number,like "d.30_day_loss_user" while spark1.6 supports
> --
>
> Key: SPARK-16603
> URL: https://issues.apache.org/jira/browse/SPARK-16603
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 2.0.0
>Reporter: marymwu
>Priority: Minor
>
> Spark2.0 fail in executing the sql statement which field name begins with 
> number,like "d.30_day_loss_user" while spark1.6 supports
> Error: org.apache.spark.sql.catalyst.parser.ParseException: mismatched input 
> '.30' expecting
> {')', ','}



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[jira] [Commented] (SPARK-16603) Spark2.0 fail in executing the sql statement which field name begins with number,like "d.30_day_loss_user" while spark1.6 supports

2016-07-25 Thread Liang Ke (JIRA) 

[ 
https://issues.apache.org/jira/browse/SPARK-16603?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15391566#comment-15391566
 ] 

Liang Ke commented on SPARK-16603:
--

Sorry. I read the spark sourcecode again, find it's a usage error:
right usage is adding quota between this column name. and query again without 
error.

select * from tsp where 'tsp.20_user_addr' <10


> Spark2.0 fail in executing the sql statement which field name begins with 
> number,like "d.30_day_loss_user" while spark1.6 supports
> --
>
> Key: SPARK-16603
> URL: https://issues.apache.org/jira/browse/SPARK-16603
> Project: Spark
>  Issue Type: Bug
>  Components: SQL
>Affects Versions: 2.0.0
>Reporter: marymwu
>Priority: Minor
>
> Spark2.0 fail in executing the sql statement which field name begins with 
> number,like "d.30_day_loss_user" while spark1.6 supports
> Error: org.apache.spark.sql.catalyst.parser.ParseException: mismatched input 
> '.30' expecting
> {')', ','}



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