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https://issues.apache.org/jira/browse/SPARK-44339?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17741340#comment-17741340
]
Yuming Wang commented on SPARK-44339:
-------------------------------------
It seems it's cloudera spark issue.
> spark3-shell errors org.apache.hadoop.hive.ql.metadata.HiveException: Unable
> to fetch table <hive_table_name>. Permission denied: user [AD user] does not
> have [SELECT] privilege on [<database>/<hive table>] when reads hive view
> ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-44339
> URL: https://issues.apache.org/jira/browse/SPARK-44339
> Project: Spark
> Issue Type: Bug
> Components: Spark Shell, Spark Submit
> Affects Versions: 3.3.0
> Environment: CDP 7.1.7 Ranger, kerberized and hadoop impersonation
> enabled.
> Reporter: Amar Gurung
> Priority: Critical
>
> *Problem statement*
> A hive view is created using beeline to restrict the users from accessing the
> original hive table since the data contains sensitive information.
> For illustration purpose, let's consider a sensitive table as emp_db.employee
> with columns id, name, salary created through beeline by user '{*}userA{*}'
>
> {code:java}
> create external table emp_db.employee (id int, name string, salary double)
> location '<hdfs_path>'{code}
>
> A view is created using beeline by the same user '{*}userA{*}'
>
> {code:java}
> ate view empview_db.emp_v as select id,name from emp_db.employee' {code}
>
> From Ranger UI, we define a policy under Hadoop SQL Policies that will let
> '{*}userB{*}' to access database - empview_db and table - emp_v with SELECT
> permission.
>
> *Steps to replicate*
> # ssh to edge node where beeline is available using *userB*
> # Try executing following queries
> ## select * from emp_db.employee *;*
> ## desc formatted empview_db.emp_v;
> ## Above queries works fine without any issues.
> # Now, try using spark3-shell using *userB*
> {code:java}
> # spark3-shell --deploy-mode client
> Setting default log level to "WARN".
> To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use
> setLogLevel(newLevel).
> 23/07/08 01:24:09 WARN HiveConf: HiveConf of name hive.masking.algo does not
> exist
> Spark context Web UI available at http://xxxxxxx:4040
> Spark context available as 'sc' (master = yarn, app id = application_xxx_xxx).
> Spark session available as 'spark'.
> Welcome to
> ____ __
> / __/__ ___ _____/ /__
> _\ \/ _ \/ _ `/ __/ '_/
> /___/ .__/\_,_/_/ /_/\_\ version 3.3.0.3.3.7180.0-274
> /_/
>
> Using Scala version 2.12.15 (Java HotSpot(TM) 64-Bit Server VM, Java
> 1.8.0_181)
> Type in expressions to have them evaluated.
> Type :help for more information.scala> spark.table("empview_db.emp_v").schema
> 23/07/08 01:24:30 WARN HiveClientImpl: Detected HiveConf
> hive.execution.engine is 'tez' and will be reset to 'mr' to disable useless
> hive logic
> Hive Session ID = b1e3c813-aea9-40da-9012-949e82d4205e
> org.apache.spark.sql.AnalysisException:
> org.apache.hadoop.hive.ql.metadata.HiveException: Unable to fetch table
> employee. Permission denied: user [userB] does not have [SELECT] privilege on
> [emp_db/employee]
> at
> org.apache.spark.sql.hive.HiveExternalCatalog.withClient(HiveExternalCatalog.scala:110)
> at
> org.apache.spark.sql.hive.HiveExternalCatalog.tableExists(HiveExternalCatalog.scala:877)
> at
> org.apache.spark.sql.catalyst.catalog.ExternalCatalogWithListener.tableExists(ExternalCatalogWithListener.scala:146)
> at
> org.apache.spark.sql.catalyst.catalog.SessionCatalog.tableExists(SessionCatalog.scala:488)
> at
> org.apache.spark.sql.catalyst.catalog.SessionCatalog.requireTableExists(SessionCatalog.scala:224)
> at
> org.apache.spark.sql.catalyst.catalog.SessionCatalog.getTableRawMetadata(SessionCatalog.scala:514)
> at
> org.apache.spark.sql.catalyst.catalog.SessionCatalog.getTableMetadata(SessionCatalog.scala:500)
> at
> org.apache.spark.sql.execution.datasources.v2.V2SessionCatalog.loadTable(V2SessionCatalog.scala:66)
> at
> org.apache.spark.sql.connector.catalog.CatalogV2Util$.loadTable(CatalogV2Util.scala:311)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.$anonfun$lookupRelation$3(Analyzer.scala:1206)
> at scala.Option.orElse(Option.scala:447)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.$anonfun$lookupRelation$1(Analyzer.scala:1205)
> at scala.Option.orElse(Option.scala:447)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.org$apache$spark$sql$catalyst$analysis$Analyzer$ResolveRelations$$lookupRelation(Analyzer.scala:1197)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$13.applyOrElse(Analyzer.scala:1068)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$13.applyOrElse(Analyzer.scala:1032)
> at
> org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsUpWithPruning$3(AnalysisHelper.scala:138)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:176)
> at
> org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsUpWithPruning$1(AnalysisHelper.scala:138)
> at
> org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.allowInvokingTransformsInAnalyzer(AnalysisHelper.scala:323)
> at
> org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsUpWithPruning(AnalysisHelper.scala:134)
> at
> org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsUpWithPruning$(AnalysisHelper.scala:130)
> at
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsUpWithPruning(LogicalPlan.scala:30)
> at
> org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsUpWithPruning$2(AnalysisHelper.scala:135)
> at
> org.apache.spark.sql.catalyst.trees.UnaryLike.mapChildren(TreeNode.scala:1228)
> at
> org.apache.spark.sql.catalyst.trees.UnaryLike.mapChildren$(TreeNode.scala:1227)
> at
> org.apache.spark.sql.catalyst.plans.logical.OrderPreservingUnaryNode.mapChildren(LogicalPlan.scala:208)
> at
> org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsUpWithPruning$1(AnalysisHelper.scala:135)
> at
> org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.allowInvokingTransformsInAnalyzer(AnalysisHelper.scala:323)
> at
> org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsUpWithPruning(AnalysisHelper.scala:134)
> at
> org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsUpWithPruning$(AnalysisHelper.scala:130)
> at
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsUpWithPruning(LogicalPlan.scala:30)
> at
> org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsUpWithPruning$2(AnalysisHelper.scala:135)
> at
> org.apache.spark.sql.catalyst.trees.UnaryLike.mapChildren(TreeNode.scala:1228)
> at
> org.apache.spark.sql.catalyst.trees.UnaryLike.mapChildren$(TreeNode.scala:1227)
> at
> org.apache.spark.sql.catalyst.plans.logical.OrderPreservingUnaryNode.mapChildren(LogicalPlan.scala:208)
> at
> org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsUpWithPruning$1(AnalysisHelper.scala:135)
> at
> org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.allowInvokingTransformsInAnalyzer(AnalysisHelper.scala:323)
> at
> org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsUpWithPruning(AnalysisHelper.scala:134)
> at
> org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsUpWithPruning$(AnalysisHelper.scala:130)
> at
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsUpWithPruning(LogicalPlan.scala:30)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:1032)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:991)
> at
> org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$2(RuleExecutor.scala:211)
> at
> scala.collection.LinearSeqOptimized.foldLeft(LinearSeqOptimized.scala:126)
> at
> scala.collection.LinearSeqOptimized.foldLeft$(LinearSeqOptimized.scala:122)
> at scala.collection.immutable.List.foldLeft(List.scala:91)
> at
> org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1(RuleExecutor.scala:208)
> at
> org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1$adapted(RuleExecutor.scala:200)
> at scala.collection.immutable.List.foreach(List.scala:431)
> at
> org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:200)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer.org$apache$spark$sql$catalyst$analysis$Analyzer$$executeSameContext(Analyzer.scala:227)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.$anonfun$resolveViews$2(Analyzer.scala:1012)
> at
> org.apache.spark.sql.internal.SQLConf$.withExistingConf(SQLConf.scala:158)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.$anonfun$resolveViews$1(Analyzer.scala:1012)
> at
> org.apache.spark.sql.catalyst.analysis.AnalysisContext$.withAnalysisContext(Analyzer.scala:166)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.org$apache$spark$sql$catalyst$analysis$Analyzer$ResolveRelations$$resolveViews(Analyzer.scala:1004)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.org$apache$spark$sql$catalyst$analysis$Analyzer$ResolveRelations$$resolveViews(Analyzer.scala:1020)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$13.$anonfun$applyOrElse$47(Analyzer.scala:1068)
> at scala.Option.map(Option.scala:230)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$13.applyOrElse(Analyzer.scala:1068)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$$anonfun$apply$13.applyOrElse(Analyzer.scala:1032)
> at
> org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsUpWithPruning$3(AnalysisHelper.scala:138)
> at
> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:176)
> at
> org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsUpWithPruning$1(AnalysisHelper.scala:138)
> at
> org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.allowInvokingTransformsInAnalyzer(AnalysisHelper.scala:323)
> at
> org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsUpWithPruning(AnalysisHelper.scala:134)
> at
> org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsUpWithPruning$(AnalysisHelper.scala:130)
> at
> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsUpWithPruning(LogicalPlan.scala:30)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:1032)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveRelations$.apply(Analyzer.scala:991)
> at
> org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$2(RuleExecutor.scala:211)
> at
> scala.collection.LinearSeqOptimized.foldLeft(LinearSeqOptimized.scala:126)
> at
> scala.collection.LinearSeqOptimized.foldLeft$(LinearSeqOptimized.scala:122)
> at scala.collection.immutable.List.foldLeft(List.scala:91)
> at
> org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1(RuleExecutor.scala:208)
> at
> org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1$adapted(RuleExecutor.scala:200)
> at scala.collection.immutable.List.foreach(List.scala:431)
> at
> org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:200)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer.org$apache$spark$sql$catalyst$analysis$Analyzer$$executeSameContext(Analyzer.scala:227)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer.$anonfun$execute$1(Analyzer.scala:223)
> at
> org.apache.spark.sql.catalyst.analysis.AnalysisContext$.withNewAnalysisContext(Analyzer.scala:172)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:223)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:187)
> at
> org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$executeAndTrack$1(RuleExecutor.scala:179)
> at
> org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:88)
> at
> org.apache.spark.sql.catalyst.rules.RuleExecutor.executeAndTrack(RuleExecutor.scala:179)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer.$anonfun$executeAndCheck$1(Analyzer.scala:208)
> at
> org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.markInAnalyzer(AnalysisHelper.scala:330)
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer.executeAndCheck(Analyzer.scala:207)
> at
> org.apache.spark.sql.execution.QueryExecution.$anonfun$analyzed$1(QueryExecution.scala:76)
> at
> org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111)
> at
> org.apache.spark.sql.execution.QueryExecution.$anonfun$executePhase$2(QueryExecution.scala:186)
> at
> org.apache.spark.sql.execution.QueryExecution$.withInternalError(QueryExecution.scala:511)
> at
> org.apache.spark.sql.execution.QueryExecution.$anonfun$executePhase$1(QueryExecution.scala:186)
> at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:779)
> at
> org.apache.spark.sql.execution.QueryExecution.executePhase(QueryExecution.scala:185)
> at
> org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:76)
> at
> org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:74)
> at
> org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:66)
> at org.apache.spark.sql.Dataset$.$anonfun$ofRows$1(Dataset.scala:91)
> at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:779)
> at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:89)
> at org.apache.spark.sql.DataFrameReader.table(DataFrameReader.scala:607)
> at org.apache.spark.sql.SparkSession.table(SparkSession.scala:600)
> ... 47 elided
> Caused by: org.apache.hadoop.hive.ql.metadata.HiveException: Unable to fetch
> table employee. Permission denied: user [userB] does not have [SELECT]
> privilege on [emp_db/employee]
> at org.apache.hadoop.hive.ql.metadata.Hive.getTable(Hive.java:1462)
> at org.apache.hadoop.hive.ql.metadata.Hive.getTable(Hive.java:1411)
> at org.apache.hadoop.hive.ql.metadata.Hive.getTable(Hive.java:1391)
> at org.apache.spark.sql.hive.client.Shim_v0_12.getTable(HiveShim.scala:639)
> at
> org.apache.spark.sql.hive.client.HiveClientImpl.getRawTableOption(HiveClientImpl.scala:429)
> at
> org.apache.spark.sql.hive.client.HiveClientImpl.$anonfun$tableExists$1(HiveClientImpl.scala:444)
> at scala.runtime.java8.JFunction0$mcZ$sp.apply(JFunction0$mcZ$sp.java:23)
> at
> org.apache.spark.sql.hive.client.HiveClientImpl.$anonfun$withHiveState$1(HiveClientImpl.scala:321)
> at
> org.apache.spark.sql.hive.client.HiveClientImpl.liftedTree1$1(HiveClientImpl.scala:248)
> at
> org.apache.spark.sql.hive.client.HiveClientImpl.retryLocked(HiveClientImpl.scala:247)
> at
> org.apache.spark.sql.hive.client.HiveClientImpl.withHiveState(HiveClientImpl.scala:301)
> at
> org.apache.spark.sql.hive.client.HiveClientImpl.tableExists(HiveClientImpl.scala:444)
> at
> org.apache.spark.sql.hive.HiveExternalCatalog.$anonfun$tableExists$1(HiveExternalCatalog.scala:877)
> at scala.runtime.java8.JFunction0$mcZ$sp.apply(JFunction0$mcZ$sp.java:23)
> at
> org.apache.spark.sql.hive.HiveExternalCatalog.withClient(HiveExternalCatalog.scala:101)
> ... 151 more
> Caused by: org.apache.hadoop.hive.metastore.api.MetaException: Permission
> denied: user [userB] does not have [SELECT] privilege on [emp_db/employee]
> at
> org.apache.hadoop.hive.metastore.api.ThriftHiveMetastore$get_table_req_result$get_table_req_resultStandardScheme.read(ThriftHiveMetastore.java)
> at
> org.apache.hadoop.hive.metastore.api.ThriftHiveMetastore$get_table_req_result$get_table_req_resultStandardScheme.read(ThriftHiveMetastore.java)
> at
> org.apache.hadoop.hive.metastore.api.ThriftHiveMetastore$get_table_req_result.read(ThriftHiveMetastore.java)
> at org.apache.thrift.TServiceClient.receiveBase(TServiceClient.java:88)
> at
> org.apache.hadoop.hive.metastore.api.ThriftHiveMetastore$Client.recv_get_table_req(ThriftHiveMetastore.java:2378)
> at
> org.apache.hadoop.hive.metastore.api.ThriftHiveMetastore$Client.get_table_req(ThriftHiveMetastore.java:2365)
> at
> org.apache.hadoop.hive.metastore.HiveMetaStoreClient.getTable(HiveMetaStoreClient.java:2047)
> at
> org.apache.hadoop.hive.ql.metadata.SessionHiveMetaStoreClient.getTable(SessionHiveMetaStoreClient.java:206)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:498)
> at
> org.apache.hadoop.hive.metastore.RetryingMetaStoreClient.invoke(RetryingMetaStoreClient.java:213)
> at com.sun.proxy.$Proxy48.getTable(Unknown Source)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:498)
> at
> org.apache.hadoop.hive.metastore.HiveMetaStoreClient$SynchronizedHandler.invoke(HiveMetaStoreClient.java:3514)
> at com.sun.proxy.$Proxy48.getTable(Unknown Source)
> at org.apache.hadoop.hive.ql.metadata.Hive.getTable(Hive.java:1453)
> ... 165 more
> {code}
> *Expected behavior* - we want spark to behave just like beeline where SELECT
> * from <view-name> and DESC formatted <view-name> on view works fine without
> any errors.
> The CDP 7.1.7 documentation link
> [https://docs.cloudera.com/cdp-private-cloud-base/7.1.7/developing-spark-applications/topics/spark-interaction-with-hive-views.html?]
> describes 'Interacting Hive Views'. However, the explanation doesn't fit
> well with the behavior we see from spark3-shell for hive views.
> Looking forward for feedback and inputs that may unblock my use case. Please
> let me know if you need any further information.
>
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