When creating a Hive table with Druid storage handler CREATE TABLE MyDruidTable ( `__time` timestamp , `dim1` string , `meas` int) STORED BY 'org.apache.hadoop.hive.druid.DruidStorageHandler' and executing this Spark code import org.apache.spark.sql.SaveMode val my_df = spark.read.parquet("<source>").withColumn("__time", (col("crdt")/1000).cast("timestamp")).limit(2) my_df .write.mode(SaveMode.Overwrite).saveAsTable("MyDruidTable") I get following error stack trace. java.util.NoSuchElementException: None.get at scala.None$.get(Option.scala:347) at scala.None$.get(Option.scala:345) at org.apache.spark.sql.execution.datasources.PreprocessTableCreation$$anonfun$apply$2.applyOrElse(rules.scala:110) at org.apache.spark.sql.execution.datasources.PreprocessTableCreation$$anonfun$apply$2.applyOrElse(rules.scala:75) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267) at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267) at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70) at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266) at org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:256) at org.apache.spark.sql.execution.datasources.PreprocessTableCreation.apply(rules.scala:75) at org.apache.spark.sql.execution.datasources.PreprocessTableCreation.apply(rules.scala:71) at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:85) at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:82) at scala.collection.IndexedSeqOptimized$class.foldl(IndexedSeqOptimized.scala:57) at scala.collection.IndexedSeqOptimized$class.foldLeft(IndexedSeqOptimized.scala:66) at scala.collection.mutable.ArrayBuffer.foldLeft(ArrayBuffer.scala:48) at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:82) at org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:74) at scala.collection.immutable.List.foreach(List.scala:381) at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:74) at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:69) at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:67) at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:50) at org.apache.spark.sql.execution.QueryExecution.withCachedData$lzycompute(QueryExecution.scala:73) at org.apache.spark.sql.execution.QueryExecution.withCachedData(QueryExecution.scala:72) at org.apache.spark.sql.execution.QueryExecution.optimizedPlan$lzycompute(QueryExecution.scala:78) at org.apache.spark.sql.execution.QueryExecution.optimizedPlan(QueryExecution.scala:78) at org.apache.spark.sql.execution.QueryExecution.sparkPlan$lzycompute(QueryExecution.scala:84) at org.apache.spark.sql.execution.QueryExecution.sparkPlan(QueryExecution.scala:80) at org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:89) at org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:89) at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:92) at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:92) at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:609) at org.apache.spark.sql.DataFrameWriter.createTable(DataFrameWriter.scala:419) at org.apache.spark.sql.DataFrameWriter.saveAsTable(DataFrameWriter.scala:398) at org.apache.spark.sql.DataFrameWriter.saveAsTable(DataFrameWriter.scala:354) ... 50 elided Doesn't Spark support Druid? Or is there a way to troubleshoot? We're using Spark with LLAP based on https://community.hortonworks.com/articles/101181/rowcolumn-level-security-in-sql-for-apache-spark-2.html ... View more