The insret statment from Java code:
INSERT INTO table1 PARTITION (part1,part2) SELECT * FROM TEMP_TABLE_APPEND TEMP_TABLE
The excetpion stack trace:
org.apache.spark.sql.AnalysisException: Can only write data to relations with a single path.
at org.apache.spark.sql.execution.datasources.DataSourceAnalysis$$anonfun$apply$1.applyOrElse(DataSourceStrategy.scala:202)
at org.apache.spark.sql.execution.datasources.DataSourceAnalysis$$anonfun$apply$1.applyOrElse(DataSourceStrategy.scala:150)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDown$2(AnalysisHelper.scala:108)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:74)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDown$1(AnalysisHelper.scala:108)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.allowInvokingTransformsInAnalyzer(AnalysisHelper.scala:221)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsDown(AnalysisHelper.scala:106)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsDown$(AnalysisHelper.scala:104)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsDown(LogicalPlan.scala:29)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperators(AnalysisHelper.scala:73)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperators$(AnalysisHelper.scala:72)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:29)
at org.apache.spark.sql.execution.datasources.DataSourceAnalysis$.apply(DataSourceStrategy.scala:150)
at org.apache.spark.sql.execution.datasources.DataSourceAnalysis$.apply(DataSourceStrategy.scala:58)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$2(RuleExecutor.scala:216)
at scala.collection.LinearSeqOptimized.foldLeft(LinearSeqOptimized.scala:126)
at scala.collection.LinearSeqOptimized.foldLeft$(LinearSeqOptimized.scala:122)
at scala.collection.immutable.List.foldLeft(List.scala:89)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1(RuleExecutor.scala:213)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1$adapted(RuleExecutor.scala:205)
at scala.collection.immutable.List.foreach(List.scala:392)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:205)
at org.apache.spark.sql.catalyst.analysis.Analyzer.org$apache$spark$sql$catalyst$analysis$Analyzer$$executeSameContext(Analyzer.scala:196)
at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:190)
at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:155)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$executeAndTrack$1(RuleExecutor.scala:183)
at org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:88)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.executeAndTrack(RuleExecutor.scala:183)
at org.apache.spark.sql.catalyst.analysis.Analyzer.$anonfun$executeAndCheck$1(Analyzer.scala:174)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.markInAnalyzer(AnalysisHelper.scala:228)
at org.apache.spark.sql.catalyst.analysis.Analyzer.executeAndCheck(Analyzer.scala:173)
at org.apache.spark.sql.execution.QueryExecution.$anonfun$analyzed$1(QueryExecution.scala:73)
at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111)
at org.apache.spark.sql.execution.QueryExecution.$anonfun$executePhase$1(QueryExecution.scala:143)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)
at org.apache.spark.sql.execution.QueryExecution.executePhase(QueryExecution.scala:143)
at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:73)
at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:71)
at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:63)
at org.apache.spark.sql.Dataset$.$anonfun$ofRows$2(Dataset.scala:98)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)
at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:96)
at org.apache.spark.sql.SparkSession.$anonfun$sql$1(SparkSession.scala:618)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:613)
In case anyone else has this problem....
The issue was a parameter in spark-defaults.conf:
spark.sql.hive.manageFilesourcePartitions=false
It was set to false for some reason (the default is true).
Commneting this line solved the problem
Related
I have requirement where I am deleting duplicate records from delta file using databricks sql. Below is my query
%sql
delete from delta.`adls_delta_file_path` where code = 'XYZ '
but it gives below error
com.databricks.backend.common.rpc.DatabricksExceptions$SQLExecutionException: java.util.NoSuchElementException: None.get at scala.None$.get(Option.scala:529) at scala.None$.get(Option.scala:527) at com.privacera.spark.agent.bV.a(bV.java) at com.privacera.spark.agent.bV.a(bV.java) at com.privacera.spark.agent.bc.a(bc.java) at com.privacera.spark.agent.bc.apply(bc.java) at org.apache.spark.sql.catalyst.trees.TreeNode.foreach(TreeNode.scala:252) at com.privacera.spark.agent.bV.a(bV.java) at com.privacera.spark.base.interceptor.c.b(c.java) at com.privacera.spark.base.interceptor.c.a(c.java) at com.privacera.spark.agent.n.a(n.java) at com.privacera.spark.agent.n.apply(n.java) at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$3(RuleExecutor.scala:221) at com.databricks.spark.util.FrameProfiler$.record(FrameProfiler.scala:80) at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$2(RuleExecutor.scala:221) at scala.collection.LinearSeqOptimized.foldLeft(LinearSeqOptimized.scala:126) at scala.collection.LinearSeqOptimized.foldLeft$(LinearSeqOptimized.scala:122) at scala.collection.immutable.List.foldLeft(List.scala:89) at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1(RuleExecutor.scala:218) at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1$adapted(RuleExecutor.scala:210) at scala.collection.immutable.List.foreach(List.scala:392) at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:210) at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$executeAndTrack$1(RuleExecutor.scala:188) at org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:109) at org.apache.spark.sql.catalyst.rules.RuleExecutor.executeAndTrack(RuleExecutor.scala:188) at org.apache.spark.sql.execution.QueryExecution.$anonfun$optimizedPlan$1(QueryExecution.scala:112) at com.databricks.spark.util.FrameProfiler$.record(FrameProfiler.scala:80) at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:134) at org.apache.spark.sql.execution.QueryExecution.$anonfun$executePhase$1(QueryExecution.scala:180) at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:854) at org.apache.spark.sql.execution.QueryExecution.executePhase(QueryExecution.scala:180) at org.apache.spark.sql.execution.QueryExecution.optimizedPlan$lzycompute(QueryExecution.scala:109) at org.apache.spark.sql.execution.QueryExecution.optimizedPlan(QueryExecution.scala:109) at org.apache.spark.sql.execution.QueryExecution.assertOptimized(QueryExecution.scala:120) at org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:139) at org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:136) at org.apache.spark.sql.execution.QueryExecution.$anonfun$simpleString$2(QueryExecution.scala:199) at org.apache.spark.sql.execution.ExplainUtils$.processPlan(ExplainUtils.scala:115) at org.apache.spark.sql.execution.QueryExecution.simpleString(QueryExecution.scala:199) at org.apache.spark.sql.execution.QueryExecution.org$apache$spark$sql$execution$QueryExecution$$explainString(QueryExecution.scala:260) at org.apache.spark.sql.execution.QueryExecution.explainStringLocal(QueryExecution.scala:226) at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withCustomExecutionEnv$5(SQLExecution.scala:123) at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:273) at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withCustomExecutionEnv$1(SQLExecution.scala:104) at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:854) at org.apache.spark.sql.execution.SQLExecution$.withCustomExecutionEnv(SQLExecution.scala:77) at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:223) at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3823) at org.apache.spark.sql.Dataset.(Dataset.scala:235) at org.apache.spark.sql.Dataset$.$anonfun$ofRows$2(Dataset.scala:104) at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:854) at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:101) at org.apache.spark.sql.SparkSession.$anonfun$sql$1(SparkSession.scala:689) at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:854) at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:684) at org.apache.spark.sql.SQLContext.sql(SQLContext.scala:694) at com.databricks.backend.daemon.driver.SQLDriverLocal.$anonfun$executeSql$1(SQLDriverLocal.scala:91) at scala.collection.TraversableLike.$anonfun$map$1(TraversableLike.scala:238) at scala.collection.immutable.List.foreach(List.scala:392) at scala.collection.TraversableLike.map(TraversableLike.scala:238) at scala.collection.TraversableLike.map$(TraversableLike.scala:231) at scala.collection.immutable.List.map(List.scala:298) at com.databricks.backend.daemon.driver.SQLDriverLocal.executeSql(SQLDriverLocal.scala:37) at com.databricks.backend.daemon.driver.SQLDriverLocal.repl(SQLDriverLocal.scala:145) at com.databricks.backend.daemon.driver.DriverLocal.$anonfun$execute$11(DriverLocal.scala:529) at com.databricks.logging.UsageLogging.$anonfun$withAttributionContext$1(UsageLogging.scala:266) at scala.util.DynamicVariable.withValue(DynamicVariable.scala:62) at com.databricks.logging.UsageLogging.withAttributionContext(UsageLogging.scala:261) at com.databricks.logging.UsageLogging.withAttributionContext$(UsageLogging.scala:258) at com.databricks.backend.daemon.driver.DriverLocal.withAttributionContext(DriverLocal.scala:50) at com.databricks.logging.UsageLogging.withAttributionTags(UsageLogging.scala:305) at com.databricks.logging.UsageLogging.withAttributionTags$(UsageLogging.scala:297) at com.databricks.backend.daemon.driver.DriverLocal.withAttributionTags(DriverLocal.scala:50) at com.databricks.backend.daemon.driver.DriverLocal.execute(DriverLocal.scala:506) at com.databricks.backend.daemon.driver.DriverWrapper.$anonfun$tryExecutingCommand$1(DriverWrapper.scala:611) at scala.util.Try$.apply(Try.scala:213) at com.databricks.backend.daemon.driver.DriverWrapper.tryExecutingCommand(DriverWrapper.scala:603) at com.databricks.backend.daemon.driver.DriverWrapper.executeCommandAndGetError(DriverWrapper.scala:522) at com.databricks.backend.daemon.driver.DriverWrapper.executeCommand(DriverWrapper.scala:557) at com.databricks.backend.daemon.driver.DriverWrapper.runInnerLoop(DriverWrapper.scala:427) at com.databricks.backend.daemon.driver.DriverWrapper.runInner(DriverWrapper.scala:370) at com.databricks.backend.daemon.driver.DriverWrapper.run(DriverWrapper.scala:221) at java.lang.Thread.run(Thread.java:748) at com.databricks.backend.daemon.driver.SQLDriverLocal.executeSql(SQLDriverLocal.scala:130) at com.databricks.backend.daemon.driver.SQLDriverLocal.repl(SQLDriverLocal.scala:145) at com.databricks.backend.daemon.driver.DriverLocal.$anonfun$execute$11(DriverLocal.scala:529) at com.databricks.logging.UsageLogging.$anonfun$withAttributionContext$1(UsageLogging.scala:266) at scala.util.DynamicVariable.withValue(DynamicVariable.scala:62) at com.databricks.logging.UsageLogging.withAttributionContext(UsageLogging.scala:261) at com.databricks.logging.UsageLogging.withAttributionContext$(UsageLogging.scala:258) at com.databricks.backend.daemon.driver.DriverLocal.withAttributionContext(DriverLocal.scala:50) at com.databricks.logging.UsageLogging.withAttributionTags(UsageLogging.scala:305) at com.databricks.logging.UsageLogging.withAttributionTags$(UsageLogging.scala:297) at com.databricks.backend.daemon.driver.DriverLocal.withAttributionTags(DriverLocal.scala:50) at com.databricks.backend.daemon.driver.DriverLocal.execute(DriverLocal.scala:506) at com.databricks.backend.daemon.driver.DriverWrapper.$anonfun$tryExecutingCommand$1(DriverWrapper.scala:611) at scala.util.Try$.apply(Try.scala:213) at com.databricks.backend.daemon.driver.DriverWrapper.tryExecutingCommand(DriverWrapper.scala:603) at com.databricks.backend.daemon.driver.DriverWrapper.executeCommandAndGetError(DriverWrapper.scala:522) at com.databricks.backend.daemon.driver.DriverWrapper.executeCommand(DriverWrapper.scala:557) at com.databricks.backend.daemon.driver.DriverWrapper.runInnerLoop(DriverWrapper.scala:427) at com.databricks.backend.daemon.driver.DriverWrapper.runInner(DriverWrapper.scala:370) at com.databricks.backend.daemon.driver.DriverWrapper.run(DriverWrapper.scala:221) at java.lang.Thread.run(Thread.java:748)
Any suggestion here .
com.databricks.backend.common.rpc.DatabricksExceptionsSQLExecutionException: java.util.NoSuchElementException: None.get at scala.None$.get(Option.scala:529)
And firstly, convert your delta file to delta table in databricks and enable the support for SQL commands by Configuring SparkSession then delete the duplicate records from delta table
For more understanding on conversion of file to delta table refer this document by Microsoft Delta Lake quickstart
This issue was related to cluster configuration .We have databricks cluster managed by Privecera.There are certain configuration in cluster that privecera blocks.We tried running on cluster without privecera and it worked.Raised a request with Privecera to find out actual cause.
Thanks for your suggestion
I am trying to access an Iceberg table from within a Spark Java UDF, but I am getting an error when running the first SQL statement in the UDF. Here is how I create the Spark session in the UDF:
SparkSession spark =
SparkSession.builder()
.master(...)
.appName("app")
.config(...)
...
.enableHiveSupport()
.getOrCreate();
Here is the statement that raises the exception:
spark.sql("USE db");
I have noticed that the environment variables in the Spark config (RuntimeConfig config = spark.conf();) are not the same in the Spark session created in the UDF as opposed to the value defined in the Jupyter notebook from which I am calling the UDF. I wonder why.
Here is the exception I see in the log:
21/05/11 11:41:45 ERROR Executor: Exception in task 0.0 in stage 2.0 (TID 2)
org.apache.spark.SparkException: Failed to execute user defined function(UDFRegistration$$Lambda$888/1578405895: (string) => string)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.project_doConsume_0$(Unknown Source)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anon$1.hasNext(WholeStageCodegenExec.scala:729)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$getByteArrayRdd$1(SparkPlan.scala:340)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2(RDD.scala:872)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsInternal$2$adapted(RDD.scala:872)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:127)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:446)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:449)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Caused by: java.lang.IllegalStateException: No active or default Spark session found
at org.apache.spark.sql.SparkSession$.$anonfun$active$2(SparkSession.scala:1055)
at scala.Option.getOrElse(Option.scala:189)
at org.apache.spark.sql.SparkSession$.$anonfun$active$1(SparkSession.scala:1055)
at scala.Option.getOrElse(Option.scala:189)
at org.apache.spark.sql.SparkSession$.active(SparkSession.scala:1054)
at org.apache.spark.sql.SparkSession.active(SparkSession.scala)
at org.apache.iceberg.spark.SparkCatalog.buildIcebergCatalog(SparkCatalog.java:97)
at org.apache.iceberg.spark.SparkCatalog.initialize(SparkCatalog.java:380)
at org.apache.spark.sql.connector.catalog.Catalogs$.load(Catalogs.scala:61)
at org.apache.spark.sql.connector.catalog.CatalogManager.$anonfun$catalog$1(CatalogManager.scala:52)
at scala.collection.mutable.HashMap.getOrElseUpdate(HashMap.scala:86)
at org.apache.spark.sql.connector.catalog.CatalogManager.catalog(CatalogManager.scala:52)
at org.apache.spark.sql.connector.catalog.LookupCatalog$CatalogAndNamespace$.unapply(LookupCatalog.scala:92)
at org.apache.spark.sql.catalyst.analysis.ResolveCatalogs$$anonfun$apply$1.applyOrElse(ResolveCatalogs.scala:191)
at org.apache.spark.sql.catalyst.analysis.ResolveCatalogs$$anonfun$apply$1.applyOrElse(ResolveCatalogs.scala:34)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDown$2(AnalysisHelper.scala:108)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:72)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.$anonfun$resolveOperatorsDown$1(AnalysisHelper.scala:108)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.allowInvokingTransformsInAnalyzer(AnalysisHelper.scala:194)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsDown(AnalysisHelper.scala:106)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperatorsDown$(AnalysisHelper.scala:104)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsDown(LogicalPlan.scala:29)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperators(AnalysisHelper.scala:73)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.resolveOperators$(AnalysisHelper.scala:72)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperators(LogicalPlan.scala:29)
at org.apache.spark.sql.catalyst.analysis.ResolveCatalogs.apply(ResolveCatalogs.scala:34)
at org.apache.spark.sql.catalyst.analysis.ResolveCatalogs.apply(ResolveCatalogs.scala:29)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$2(RuleExecutor.scala:149)
at scala.collection.LinearSeqOptimized.foldLeft(LinearSeqOptimized.scala:126)
at scala.collection.LinearSeqOptimized.foldLeft$(LinearSeqOptimized.scala:122)
at scala.collection.immutable.List.foldLeft(List.scala:89)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1(RuleExecutor.scala:146)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1$adapted(RuleExecutor.scala:138)
at scala.collection.immutable.List.foreach(List.scala:392)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:138)
at org.apache.spark.sql.catalyst.analysis.Analyzer.org$apache$spark$sql$catalyst$analysis$Analyzer$$executeSameContext(Analyzer.scala:176)
at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:170)
at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:130)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$executeAndTrack$1(RuleExecutor.scala:116)
at org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:88)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.executeAndTrack(RuleExecutor.scala:116)
at org.apache.spark.sql.catalyst.analysis.Analyzer.$anonfun$executeAndCheck$1(Analyzer.scala:154)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.markInAnalyzer(AnalysisHelper.scala:201)
at org.apache.spark.sql.catalyst.analysis.Analyzer.executeAndCheck(Analyzer.scala:153)
at org.apache.spark.sql.execution.QueryExecution.$anonfun$analyzed$1(QueryExecution.scala:68)
at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111)
at org.apache.spark.sql.execution.QueryExecution.$anonfun$executePhase$1(QueryExecution.scala:133)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:764)
at org.apache.spark.sql.execution.QueryExecution.executePhase(QueryExecution.scala:133)
at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:68)
at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:66)
at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:58)
at org.apache.spark.sql.Dataset$.$anonfun$ofRows$2(Dataset.scala:99)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:764)
at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:97)
at org.apache.spark.sql.SparkSession.$anonfun$sql$1(SparkSession.scala:607)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:764)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:602)
at app.spark.udf.IcebergLoader.load(IcebergLoader.java:87)
at app.spark.udf.ServiceProvider.get(ServiceProvider.java:28)
at app.spark.udf.UdfHelper.get(UdfHelper.java:96)
at app.spark.udf.Udf.call(Udf.java:27)
at app.spark.udf.Udf.call(Udf.java:12)
at org.apache.spark.sql.UDFRegistration.$anonfun$register$283(UDFRegistration.scala:747)
... 18 more
I am not sure if it is valid to create a Spark session inside a UDF. Is there a way for the Spark session in the UDF to be the same as the Spark session that would be created in the Jupyter notebook from which the UDF is invoked?
Martin
You cannot define a Spark Session or any other Spark API's in a UDF, that are instantiated, controlled by the Driver.
I have used spark.sql.parser.quotedRegexColumnNames=true to use regex operation in spark SQL
The same query consist of reserved key word timestamp.
Here is the query
SELECT cast(delta.day as string) as day,
`timestamp` as `timestamp`,
delta.`(p_[0-9]+)`
FROM test_table delta
Executing the query is failing with following exception
Caused by: org.apache.spark.sql.AnalysisException: Invalid usage of '*' in expression 'alias';
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.failAnalysis(CheckAnalysis.scala:42)
at org.apache.spark.sql.catalyst.analysis.Analyzer.failAnalysis(Analyzer.scala:95)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$expandStarExpression$1.applyOrElse(Analyzer.scala:1021)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$expandStarExpression$1.applyOrElse(Analyzer.scala:997)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:278)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:278)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:277)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$.expandStarExpression(Analyzer.scala:997)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$org$apache$spark$sql$catalyst$analysis$Analyzer$ResolveReferences$$buildExpandedProjectList$1.apply(Analyzer.scala:982)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$org$apache$spark$sql$catalyst$analysis$Analyzer$ResolveReferences$$buildExpandedProjectList$1.apply(Analyzer.scala:977)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:241)
at scala.collection.immutable.List.foreach(List.scala:392)
at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:241)
at scala.collection.immutable.List.flatMap(List.scala:355)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$.org$apache$spark$sql$catalyst$analysis$Analyzer$ResolveReferences$$buildExpandedProjectList(Analyzer.scala:977)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$apply$9.applyOrElse(Analyzer.scala:905)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$apply$9.applyOrElse(Analyzer.scala:900)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$$anonfun$resolveOperatorsUp$1$$anonfun$apply$1.apply(AnalysisHelper.scala:90)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$$anonfun$resolveOperatorsUp$1$$anonfun$apply$1.apply(AnalysisHelper.scala:90)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$$anonfun$resolveOperatorsUp$1.apply(AnalysisHelper.scala:89)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$$anonfun$resolveOperatorsUp$1.apply(AnalysisHelper.scala:86)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.allowInvokingTransformsInAnalyzer(AnalysisHelper.scala:194)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$class.resolveOperatorsUp(AnalysisHelper.scala:86)
at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveOperatorsUp(LogicalPlan.scala:29)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$.apply(Analyzer.scala:900)
at org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$.apply(Analyzer.scala:758)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:87)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:84)
at scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:124)
at scala.collection.immutable.List.foldLeft(List.scala:84)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:84)
at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:76)
at scala.collection.immutable.List.foreach(List.scala:392)
at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:76)
at org.apache.spark.sql.catalyst.analysis.Analyzer.org$apache$spark$sql$catalyst$analysis$Analyzer$$executeSameContext(Analyzer.scala:127)
at org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:121)
at org.apache.spark.sql.catalyst.analysis.Analyzer$$anonfun$executeAndCheck$1.apply(Analyzer.scala:106)
at org.apache.spark.sql.catalyst.analysis.Analyzer$$anonfun$executeAndCheck$1.apply(Analyzer.scala:105)
at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper$.markInAnalyzer(AnalysisHelper.scala:201)
at org.apache.spark.sql.catalyst.analysis.Analyzer.executeAndCheck(Analyzer.scala:105)
at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:57)
at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:55)
at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:47)
at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:78)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:651)
removing alias
as `timestamp`
from select query executed successfully. I believe there is an ambiguity to resolve the difference between regex and reserved keywords.
Any help to resolve this is much appreciated
easiest way to reproduce the error is executing following queries in spark-shell
scala> spark.sql("SET spark.sql.parser.quotedRegexColumnNames=true").show(false)
+---------------------------------------+-----+
|key |value|
+---------------------------------------+-----+
|spark.sql.parser.quotedRegexColumnNames|true |
+---------------------------------------+-----+
scala> spark.sql("select `timestamp` as `timestamp` from test_table").show();
Got in touch with the Cloudera team and got to know that this feature is being worked upon currently.
https://issues.apache.org/jira/browse/SPARK-12139?focusedCommentId=17346581&page=com.atlassian.jira.plugin.system.issuetabpanels%3Acomment-tabpanel#comment-17346581
I want to schedule the Databricks notebook which merges small ORC files to one bigger ORC file on a daily basis for a particular hive table. I'm looking to implement this using Spark, but currently stuck with the error as shown below.
My databricks runtime: 6.3 (includes Apache Spark 2.4.4, Scala 2.11)
Any pointers would be great.
import org.apache.spark.sql.{DataFrame, SaveMode, SparkSession}
val spark = SparkSession.builder().appName("Hive small ORC files merge").enableHiveSupport().getOrCreate()
spark.sql("ALTER TABLE [TABLE_NAME] CONCATENATE")
Error:
`Operation not allowed: ALTER TABLE CONCATENATE(line 1, pos 0)
== SQL ==ALTER TABLE CONCATENATE^^^
at org.apache.spark.sql.catalyst.parser.ParserUtils$.operationNotAllowed(ParserUtils.scala:43)
at org.apache.spark.sql.execution.SparkSqlAstBuilder$$anonfun$visitFailNativeCommand$1.apply(SparkSqlParser.scala:1135)
at org.apache.spark.sql.execution.SparkSqlAstBuilder$$anonfun$visitFailNativeCommand$1.apply(SparkSqlParser.scala:1126)
at org.apache.spark.sql.catalyst.parser.ParserUtils$.withOrigin(ParserUtils.scala:110)
at org.apache.spark.sql.execution.SparkSqlAstBuilder.visitFailNativeCommand(SparkSqlParser.scala:1126)
at org.apache.spark.sql.execution.SparkSqlAstBuilder.visitFailNativeCommand(SparkSqlParser.scala:62)
at org.apache.spark.sql.catalyst.parser.SqlBaseParser$FailNativeCommandContext.accept(SqlBaseParser.java:831)
at org.antlr.v4.runtime.tree.AbstractParseTreeVisitor.visit(AbstractParseTreeVisitor.java:18)
at org.apache.spark.sql.catalyst.parser.AstBuilder$$anonfun$visitSingleStatement$1.apply(AstBuilder.scala:74)
at org.apache.spark.sql.catalyst.parser.AstBuilder$$anonfun$visitSingleStatement$1.apply(AstBuilder.scala:74)
at org.apache.spark.sql.catalyst.parser.ParserUtils$.withOrigin(ParserUtils.scala:110)
at org.apache.spark.sql.catalyst.parser.AstBuilder.visitSingleStatement(AstBuilder.scala:73)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser$$anonfun$parsePlan$1.apply(ParseDriver.scala:70)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser$$anonfun$parsePlan$1.apply(ParseDriver.scala:69)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parse(ParseDriver.scala:100)
at org.apache.spark.sql.execution.SparkSqlParser.parse(SparkSqlParser.scala:55)
at org.apache.spark.sql.catalyst.parser.AbstractSqlParser.parsePlan(ParseDriver.scala:69)
at com.databricks.sql.parser.DatabricksSqlParser$$anonfun$parsePlan$1.apply(DatabricksSqlParser.scala:64)
at com.databricks.sql.parser.DatabricksSqlParser$$anonfun$parsePlan$1.apply(DatabricksSqlParser.scala:61)
at com.databricks.sql.parser.DatabricksSqlParser.parse(DatabricksSqlParser.scala:84)
at com.databricks.sql.parser.DatabricksSqlParser.parsePlan(DatabricksSqlParser.scala:61)
at org.apache.spark.sql.SparkSession$$anonfun$6.apply(SparkSession.scala:694)
at org.apache.spark.sql.SparkSession$$anonfun$6.apply(SparkSession.scala:694)
at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111)
at org.apache.spark.sql.SparkSession.sql(SparkSession.scala:693)
at org.apache.spark.sql.SQLContext.sql(SQLContext.scala:716)
at line341589a136f246f788b6b288061c96ae31.$read$$iw$$iw$$iw$$iw$$iw$$iw.<init>(command-4297307810790143:4)
at line341589a136f246f788b6b288061c96ae31.$read$$iw$$iw$$iw$$iw$$iw.<init>(command-4297307810790143:50)
at line341589a136f246f788b6b288061c96ae31.$read$$iw$$iw$$iw$$iw.<init>(command-4297307810790143:52)
at line341589a136f246f788b6b288061c96ae31.$read$$iw$$iw$$iw.<init>(command-4297307810790143:54)
at line341589a136f246f788b6b288061c96ae31.$read$$iw$$iw.<init>(command-4297307810790143:56)
at line341589a136f246f788b6b288061c96ae31.$read$$iw.<init>(command-4297307810790143:58)
at line341589a136f246f788b6b288061c96ae31.$read.<init>(command-4297307810790143:60)
at line341589a136f246f788b6b288061c96ae31.$read$.<init>(command-4297307810790143:64)
at line341589a136f246f788b6b288061c96ae31.$read$.<clinit>(command-4297307810790143)
at line341589a136f246f788b6b288061c96ae31.$eval$.$print$lzycompute(<notebook>:7)
at line341589a136f246f788b6b288061c96ae31.$eval$.$print(<notebook>:6)
at line341589a136f246f788b6b288061c96ae31.$eval.$print(<notebook>)
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 scala.tools.nsc.interpreter.IMain$ReadEvalPrint.call(IMain.scala:793)
at scala.tools.nsc.interpreter.IMain$Request.loadAndRun(IMain.scala:1054)
at scala.tools.nsc.interpreter.IMain$WrappedRequest$$anonfun$loadAndRunReq$1.apply(IMain.scala:645)
at scala.tools.nsc.interpreter.IMain$WrappedRequest$$anonfun$loadAndRunReq$1.apply(IMain.scala:644)
at scala.reflect.internal.util.ScalaClassLoader$class.asContext(ScalaClassLoader.scala:31)
at scala.reflect.internal.util.AbstractFileClassLoader.asContext(AbstractFileClassLoader.scala:19)
at scala.tools.nsc.interpreter.IMain$WrappedRequest.loadAndRunReq(IMain.scala:644)
at scala.tools.nsc.interpreter.IMain.interpret(IMain.scala:576)
at scala.tools.nsc.interpreter.IMain.interpret(IMain.scala:572)
at com.databricks.backend.daemon.driver.DriverILoop.execute(DriverILoop.scala:215)
at com.databricks.backend.daemon.driver.ScalaDriverLocal$$anonfun$repl$1.apply$mcV$sp(ScalaDriverLocal.scala:202)
at com.databricks.backend.daemon.driver.ScalaDriverLocal$$anonfun$repl$1.apply(ScalaDriverLocal.scala:202)
at com.databricks.backend.daemon.driver.ScalaDriverLocal$$anonfun$repl$1.apply(ScalaDriverLocal.scala:202)
at com.databricks.backend.daemon.driver.DriverLocal$TrapExitInternal$.trapExit(DriverLocal.scala:699)
at com.databricks.backend.daemon.driver.DriverLocal$TrapExit$.apply(DriverLocal.scala:652)
at com.databricks.backend.daemon.driver.ScalaDriverLocal.repl(ScalaDriverLocal.scala:202)
at com.databricks.backend.daemon.driver.DriverLocal$$anonfun$execute$9.apply(DriverLocal.scala:385)
at com.databricks.backend.daemon.driver.DriverLocal$$anonfun$execute$9.apply(DriverLocal.scala:362)
at com.databricks.logging.UsageLogging$$anonfun$withAttributionContext$1.apply(UsageLogging.scala:251)
at scala.util.DynamicVariable.withValue(DynamicVariable.scala:58)
at com.databricks.logging.UsageLogging$class.withAttributionContext(UsageLogging.scala:246)
at com.databricks.backend.daemon.driver.DriverLocal.withAttributionContext(DriverLocal.scala:49)
at com.databricks.logging.UsageLogging$class.withAttributionTags(UsageLogging.scala:288)
at com.databricks.backend.daemon.driver.DriverLocal.withAttributionTags(DriverLocal.scala:49)
at com.databricks.backend.daemon.driver.DriverLocal.execute(DriverLocal.scala:362)
at com.databricks.backend.daemon.driver.DriverWrapper$$anonfun$tryExecutingCommand$2.apply(DriverWrapper.scala:644)
at com.databricks.backend.daemon.driver.DriverWrapper$$anonfun$tryExecutingCommand$2.apply(DriverWrapper.scala:644)
at scala.util.Try$.apply(Try.scala:192)
at com.databricks.backend.daemon.driver.DriverWrapper.tryExecutingCommand(DriverWrapper.scala:639)
at com.databricks.backend.daemon.driver.DriverWrapper.getCommandOutputAndError(DriverWrapper.scala:485)
at com.databricks.backend.daemon.driver.DriverWrapper.executeCommand(DriverWrapper.scala:597)
at com.databricks.backend.daemon.driver.DriverWrapper.runInnerLoop(DriverWrapper.scala:390)
at com.databricks.backend.daemon.driver.DriverWrapper.runInner(DriverWrapper.scala:337)
at com.databricks.backend.daemon.driver.DriverWrapper.run(DriverWrapper.scala:219)
at java.lang.Thread.run(Thread.java:748)`
As per spark source code, ALTER TABLE <> CONCATENATE option is not implemented or not supported as of now. Please check below code for more information.
Spark SQL Parser
Spark Un Supported Hive Native Commands
This command works from Hive only:
ALTER TABLE <table_name> CONCATENATE;
Does not work from Spark, yet.
I am executing query on SparkSession which throws Object is not an instance of declaring class, below is the code after which
Dataset<Row> results = spark.sql("SELECT t1.someCol FROM table1 t1 join table2 t2 on t1.someCol=t2.someCol");
results.count();
The exception is during method count()
I have also observed if the query is simple select col from table1, that runs fine but join query above causes error.
I am using Spark 2.1 and to create SparkSession I do below
SparkSession.builder().appName("Spark SQL").config(mysparkConf).getOrCreate();
More stack trace below :-
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1435)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1423)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1422)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1422)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:802)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1650)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1605)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1594)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:628)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1918)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1931)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1944)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1958)
at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:935)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
at org.apache.spark.rdd.RDD.collect(RDD.scala:934)
at org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:275)
at org.apache.spark.sql.Dataset$$anonfun$org$apache$spark$sql$Dataset$$execute$1$1.apply(Dataset.scala:2371)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:57)
at org.apache.spark.sql.Dataset.withNewExecutionId(Dataset.scala:2765)
at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$execute$1(Dataset.scala:2370)
at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$collect(Dataset.scala:2377)
at org.apache.spark.sql.Dataset$$anonfun$count$1.apply(Dataset.scala:2405)
at org.apache.spark.sql.Dataset$$anonfun$count$1.apply(Dataset.scala:2404)
at org.apache.spark.sql.Dataset.withCallback(Dataset.scala:2778)
at org.apache.spark.sql.Dataset.count(Dataset.scala:2404)
at com.citi.eq.ioi.engine.OrderMatchingEngine.main(OrderMatchingEngine.java:85)
Caused by: java.lang.IllegalArgumentException: object is not an instance of declaring class
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.spark.sql.SQLContext$$anonfun$beansToRows$1$$anonfun$apply$1.apply(SQLContext.scala:1113)
at org.apache.spark.sql.SQLContext$$anonfun$beansToRows$1$$anonfun$apply$1.apply(SQLContext.scala:1113)
When you do not have an domain specific Type, do not use Dataset. Instead use DataFrame i.e. untyped view of Dataset.
A Dataset is a strongly typed collection of domain-specific objects that can be transformed in parallel using functional or relational operations. Each Dataset also has an untyped view called a DataFrame, which is a Dataset of Row.
You can refer this:
https://spark.apache.org/docs/latest/api/scala/index.html#org.apache.spark.sql.Dataset