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Posted to issues@spark.apache.org by "Dongjoon Hyun (JIRA)" <ji...@apache.org> on 2017/01/22 02:49:26 UTC
[jira] [Commented] (SPARK-19308) Unable to write to Hive table
where column names contains period (.)
[ https://issues.apache.org/jira/browse/SPARK-19308?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15833246#comment-15833246 ]
Dongjoon Hyun commented on SPARK-19308:
---------------------------------------
Hi, [~vapira].
Thank you for reporting. This issue seems to be fixed in SPARK-18123.
{code}
scala> spark.version
res0: String = 2.2.0-SNAPSHOT
scala> val test = sc.parallelize(Array("{\"a\":1,\"b.b\":2}"))
test: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[0] at parallelize at <console>:24
scala> val j = spark.read.json(test)
j: org.apache.spark.sql.DataFrame = [a: bigint, b.b: bigint]
scala> j.write.mode("append").saveAsTable("test")
scala> j.write.mode("append").saveAsTable("test")
{code}
> Unable to write to Hive table where column names contains period (.)
> --------------------------------------------------------------------
>
> Key: SPARK-19308
> URL: https://issues.apache.org/jira/browse/SPARK-19308
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.0.2, 2.1.0
> Reporter: Maria Rebelka
> Labels: hive
>
> When saving DataFrame which contains columns with dots to Hive in append mode, it only succeeds when the table doesn't exists yet.
> {noformat}
> scala> spark.sql("drop table test")
> res0: org.apache.spark.sql.DataFrame = []
> scala> val test = sc.parallelize(Array("{\"a\":1,\"b.b\":2}"))
> test: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[1] at parallelize at <console>:24
> scala> val j = spark.read.json(test)
> j: org.apache.spark.sql.DataFrame = [a: bigint, b.b: bigint]
> scala> j.write.mode("append").saveAsTable("test")
> // succeeds
> scala> j.write.mode("append").saveAsTable("test")
> org.apache.spark.sql.AnalysisException: cannot resolve '`b.b`' given input columns: [a, b.b]; line 1 pos 0;
> 'Project [a#6L, 'b.b]
> +- LogicalRDD [a#6L, b.b#7L]
> at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
> at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:77)
> at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:74)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:308)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:308)
> at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:69)
> at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:307)
> at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionUp$1(QueryPlan.scala:269)
> at org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:279)
> at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2$1.apply(QueryPlan.scala:283)
> at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
> at scala.collection.AbstractTraversable.map(Traversable.scala:104)
> at org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$2(QueryPlan.scala:283)
> at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$8.apply(QueryPlan.scala:288)
> at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:186)
> at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsUp(QueryPlan.scala:288)
> at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:74)
> at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:67)
> at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:126)
> at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:67)
> at org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:58)
> at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:49)
> at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:64)
> at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$withPlan(Dataset.scala:2603)
> at org.apache.spark.sql.Dataset.select(Dataset.scala:969)
> at org.apache.spark.sql.Dataset.selectExpr(Dataset.scala:1004)
> at org.apache.spark.sql.execution.command.CreateDataSourceTableAsSelectCommand.run(createDataSourceTables.scala:236)
> at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:58)
> at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:56)
> at org.apache.spark.sql.execution.command.ExecutedCommandExec.doExecute(commands.scala:74)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:115)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:115)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:136)
> at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
> at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:133)
> at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:114)
> at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:86)
> at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:86)
> at org.apache.spark.sql.DataFrameWriter.saveAsTable(DataFrameWriter.scala:378)
> at org.apache.spark.sql.DataFrameWriter.saveAsTable(DataFrameWriter.scala:354)
> ... 48 elided
> scala> spark.sql("drop table test")
> res3: org.apache.spark.sql.DataFrame = []
> scala> j.write.mode("append").saveAsTable("test")
> // succeeds again
> {noformat}
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