[
https://issues.apache.org/jira/browse/LIVY-770?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Gyorgy Gal updated LIVY-770:
----------------------------
Fix Version/s: 0.10.0
(was: 0.9.0)
This issue has been moved to the 0.10.0 release as part of a bulk update. If
you feel this is moved out inappropriately, feel free to provide justification
and reset the Fix Version to 0.9.0.
> Livy sql session doesn't return the correct error stack trace
> -------------------------------------------------------------
>
> Key: LIVY-770
> URL: https://issues.apache.org/jira/browse/LIVY-770
> Project: Livy
> Issue Type: Bug
> Components: Server
> Environment: Ubuntu18
> Reporter: RightBitShift
> Priority: Major
> Fix For: 0.10.0
>
>
> Livy session with Kind "sql" doesn't always return the correct error message
> for failed SQL queries.
> For example, run any query in a SQL session on a partitioned table without
> specifying a partition predicate -
> 1)
> {code:java}
> curl --location --request POST 'http://<livy_instance>:8088/sessions'
> --header 'Content-Type: application/json' --data-raw '{"kind": "sql",
> "conf":{"livy.spark.master":"yarn"}}'{code}
>
> 2)
> {code:java}
> curl --location --request POST
> 'http://<livy_instance>:8088/sessions/0/statements' --header 'Content-Type:
> application/json' --data-raw '{"code": "select * from
> default.partitioned_table limit 1"}'{code}
>
> Livy will have this stack trace:
>
> {code:java}
> Traceback:
> ['org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:198)',
>
> 'org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:159)',
>
> 'org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult$lzycompute(commands.scala:104)',
>
> 'org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult(commands.scala:102)',
>
> 'org.apache.spark.sql.execution.command.DataWritingCommandExec.doExecute(commands.scala:122)',
>
> 'org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)',
>
> 'org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)',
>
> 'org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)',
>
> 'org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)',
>
> 'org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)',
> 'org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)',
> 'org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:80)',
>
> 'org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:80)',
>
> 'org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:676)',
>
> 'org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:676)',
>
> 'org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78)',
>
> 'org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125)',
>
> 'org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73)',
>
> 'org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:676)',
> 'org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:285)',
> 'org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:271)',
> 'org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:229)',
> 'org.apache.spark.sql.DataFrameWriter.json(DataFrameWriter.scala:545)',
> 'org.apache.livy.repl.SQLInterpreter.execute(SQLInterpreter.scala:104)',
> 'org.apache.livy.repl.Session$$anonfun$7.apply(Session.scala:274)',
> 'org.apache.livy.repl.Session$$anonfun$7.apply(Session.scala:272)',
> 'scala.Option.map(Option.scala:146)',
> 'org.apache.livy.repl.Session.org$apache$livy$repl$Session$$executeCode(Session.scala:272)',
>
> 'org.apache.livy.repl.Session$$anonfun$execute$1.apply$mcV$sp(Session.scala:168)',
> 'org.apache.livy.repl.Session$$anonfun$execute$1.apply(Session.scala:163)',
> 'org.apache.livy.repl.Session$$anonfun$execute$1.apply(Session.scala:163)',
> 'scala.concurrent.impl.Future$PromiseCompletingRunnable.liftedTree1$1(Future.scala:24)',
>
> 'scala.concurrent.impl.Future$PromiseCompletingRunnable.run(Future.scala:24)',
>
> 'java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)',
>
> 'java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)',
> 'java.lang.Thread.run(Thread.java:748)']
> {code}
>
> However, the real stack trace in the driver logs will be something like:
>
> {code:java}
> 20/05/08 19:19:56 WARN repl.SQLInterpreter: Fail to execute query select *
> from default.partitioned_table limit 1
> org.apache.spark.SparkException: Job aborted.
> at
> org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:198)
> at
> org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand.run(InsertIntoHadoopFsRelationCommand.scala:159)
> at
> org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult$lzycompute(commands.scala:104)
> at
> org.apache.spark.sql.execution.command.DataWritingCommandExec.sideEffectResult(commands.scala:102)
> at
> org.apache.spark.sql.execution.command.DataWritingCommandExec.doExecute(commands.scala:122)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
> at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)
> at
> org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:80)
> at
> org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:80)
> at
> org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:676)
> at
> org.apache.spark.sql.DataFrameWriter$$anonfun$runCommand$1.apply(DataFrameWriter.scala:676)
> at
> org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78)
> at
> org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125)
> at
> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73)
> at
> org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:676)
> at
> org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:285)
> at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:271)
> at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:229)
> at org.apache.spark.sql.DataFrameWriter.json(DataFrameWriter.scala:545)
> at org.apache.livy.repl.SQLInterpreter.execute(SQLInterpreter.scala:104)
> at org.apache.livy.repl.Session$$anonfun$7.apply(Session.scala:274)
> at org.apache.livy.repl.Session$$anonfun$7.apply(Session.scala:272)
> at scala.Option.map(Option.scala:146)
> at
> org.apache.livy.repl.Session.org$apache$livy$repl$Session$$executeCode(Session.scala:272)
> at
> org.apache.livy.repl.Session$$anonfun$execute$1.apply$mcV$sp(Session.scala:168)
> at
> org.apache.livy.repl.Session$$anonfun$execute$1.apply(Session.scala:163)
> at
> org.apache.livy.repl.Session$$anonfun$execute$1.apply(Session.scala:163)
> at
> scala.concurrent.impl.Future$PromiseCompletingRunnable.liftedTree1$1(Future.scala:24)
> at
> scala.concurrent.impl.Future$PromiseCompletingRunnable.run(Future.scala:24)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
> at java.lang.Thread.run(Thread.java:748)
> Caused by: org.apache.spark.sql.catalyst.errors.package$TreeNodeException:
> execute, tree:
> Exchange SinglePartition
> +- *(1) LocalLimit 1
> +- Scan hive default.unique_actions_sa [userid#5L, action_type#6, count#7,
> count_sa#8, dt#9], HiveTableRelation `default`.`unique_actions_sa`,
> org.apache.hadoop.hive.ql.io.orc.OrcSerde, [userid#5L, action_type#6,
> count#7, count_sa#8], [dt#9]
> at
> org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:56)
> at
> org.apache.spark.sql.execution.exchange.ShuffleExchangeExec.doExecute(ShuffleExchangeExec.scala:119)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
> at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)
> at
> org.apache.spark.sql.execution.InputAdapter.inputRDDs(WholeStageCodegenExec.scala:391)
> at
> org.apache.spark.sql.execution.BaseLimitExec$class.inputRDDs(limit.scala:62)
> at
> org.apache.spark.sql.execution.GlobalLimitExec.inputRDDs(limit.scala:108)
> at
> org.apache.spark.sql.execution.WholeStageCodegenExec.doExecute(WholeStageCodegenExec.scala:627)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:131)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:127)
> at
> org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:155)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at
> org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:152)
> at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:127)
> at
> org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:143)
> ... 35 more
> Caused by: org.apache.hadoop.hive.ql.parse.SemanticException: No partition
> predicate found for partitioned table
> default.partitioned_table.
> {code}
>
> Notice the last line *Caused by:
> org.apache.hadoop.hive.ql.parse.SemanticException: No partition predicate
> found for partitioned table default.partitioned_table.*
> Is there any way we can fetch this in Livy to return to the user without
> having to dig in the driver logs?
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