spark is case sensitive? Spark is not case sensitive by default. If you have same column name in different case (Name, name), if you try to select eit...

[ramravi]

spark is case sensitive?

Spark is not case sensitive by default. If you have same column name in different case (Name, name), if you try to select either "Name" or "name" column you will get column ambiguity error.

There is a way to handle this issue by adding spark config , using a SparkSession object named spark:

spark.conf.set('spark.sql.caseSensitive', True)

By default it is False.

[source2sea]

Hi, even though i set the conf to be true, on writing to disk it had exceptions complaining it has duplicate columns.

below is the error message

org.apache.spark.sql.AnalysisException: Found duplicate column(s) in the data to save: branchavailablity.element.salesleadtime
	at org.apache.spark.sql.delta.DeltaAnalysisException$.apply(DeltaSharedExceptions.scala:57)
	at org.apache.spark.sql.delta.schema.SchemaMergingUtils$.checkColumnNameDuplication(SchemaMergingUtils.scala:117)
	at org.apache.spark.sql.delta.schema.SchemaMergingUtils$.mergeSchemas(SchemaMergingUtils.scala:160)
	at org.apache.spark.sql.delta.schema.ImplicitMetadataOperation$.mergeSchema(ImplicitMetadataOperation.scala:161)
	at org.apache.spark.sql.delta.schema.ImplicitMetadataOperation.updateMetadata(ImplicitMetadataOperation.scala:64)
	at org.apache.spark.sql.delta.schema.ImplicitMetadataOperation.updateMetadata$(ImplicitMetadataOperation.scala:52)
	at org.apache.spark.sql.delta.commands.WriteIntoDelta.updateMetadata(WriteIntoDelta.scala:70)
	at org.apache.spark.sql.delta.commands.WriteIntoDelta.write(WriteIntoDelta.scala:137)
	at org.apache.spark.sql.delta.commands.WriteIntoDelta.$anonfun$run$1(WriteIntoDelta.scala:95)
	at org.apache.spark.sql.delta.commands.WriteIntoDelta.$anonfun$run$1$adapted(WriteIntoDelta.scala:90)
	at org.apache.spark.sql.delta.DeltaLog.withNewTransaction(DeltaLog.scala:255)
	at org.apache.spark.sql.delta.commands.WriteIntoDelta.run(WriteIntoDelta.scala:90)
	at org.apache.spark.sql.delta.sources.DeltaDataSource.createRelation(DeltaDataSource.scala:161)
	at org.apache.spark.sql.execution.datasources.SaveIntoDataSourceCommand.run(SaveIntoDataSourceCommand.scala:45)
	at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:75)
	at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:73)
	at org.apache.spark.sql.execution.command.ExecutedCommandExec.executeCollect(commands.scala:84)
	at org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.$anonfun$applyOrElse$1(QueryExecution.scala:97)
	at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:103)
	at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:163)
	at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:90)
	at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775)
	at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
	at org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.applyOrElse(QueryExecution.scala:97)
	at org.apache.spark.sql.execution.QueryExecution$$anonfun$eagerlyExecuteCommands$1.applyOrElse(QueryExecution.scala:93)
	at org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$transformDownWithPruning$1(TreeNode.scala:481)
	at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:82)
	at org.apache.spark.sql.catalyst.trees.TreeNode.transformDownWithPruning(TreeNode.scala:481)
	at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.org$apache$spark$sql$catalyst$plans$logical$AnalysisHelper$$super$transformDownWithPruning(LogicalPlan.scala:30)
	at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning(AnalysisHelper.scala:267)
	at org.apache.spark.sql.catalyst.plans.logical.AnalysisHelper.transformDownWithPruning$(AnalysisHelper.scala:263)
	at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:30)
	at org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.transformDownWithPruning(LogicalPlan.scala:30)
	at org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:457)
	at org.apache.spark.sql.execution.QueryExecution.eagerlyExecuteCommands(QueryExecution.scala:93)
	at org.apache.spark.sql.execution.QueryExecution.commandExecuted$lzycompute(QueryExecution.scala:80)
	at org.apache.spark.sql.execution.QueryExecution.commandExecuted(QueryExecution.scala:78)
	at org.apache.spark.sql.execution.QueryExecution.assertCommandExecuted(QueryExecution.scala:115)
	at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:848)
	at org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:382)
	at org.apache.spark.sql.DataFrameWriter.saveInternal(DataFrameWriter.scala:349)
	at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:239)
	at com.myCompany.myProject.myMethod(WriteToDisk.scala:51)

[zerospeed]

Hi I had similar issues with parquet files when trying to query athena, 

fix was i had to inspect the parquet file since it contained columns such as "Name", "name" which the aws crawler / athena would interpret as a duplicate column since it would see "name" and "name" or "Name" and "Name".