Apache Spark UDF: Accessing Iceberg - apache-spark

Question

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

Answer 1

You cannot define a Spark Session or any other Spark API's in a UDF, that are instantiated, controlled by the Driver.