Sanchay Javeria created LIVY-770:
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Summary: Livy sql session doesn't return the correct errors
Key: LIVY-770
URL: https://issues.apache.org/jira/browse/LIVY-770
Project: Livy
Issue Type: Bug
Components: Server
Environment: Ubuntu18
Reporter: Sanchay Javeria
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$Worker.run(ThreadPoolExecutor.java:624)',
'java.lang.Thread.run(Thread.java:748)']
{code}
However, the real stack trace in the driver logs will be something like:
{code:java}
20/05/08 19:19:56 WARN repl.SQLInterpreter: Fail to execute query select * from
default.partitioned_table where limit 1
org.apache.spark.SparkException: Job aborted.
at
org.apache.spark.sql.execution.datasources.FileFormatWriter$.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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