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https://issues.apache.org/jira/browse/SPARK-14139?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15211381#comment-15211381
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koert kuipers commented on SPARK-14139:
---------------------------------------
i believe the difference is in the definition of schema in Dataset.
before it was:
{noformat}
override def schema: StructType = resolvedTEncoder.schema
{noformat}
now it is:
{noformat}
def schema: StructType = queryExecution.analyzed.schema
{noformat}
but queryExecution.analyzed (which is a LogicalPlan) does not respect
nullability in multiple places. In this particular case it is in
RowEncoder.extractorsFor, where for a StructType for the fields nullable is
ignored.
> Dataset loses nullability in operations with RowEncoder
> -------------------------------------------------------
>
> Key: SPARK-14139
> URL: https://issues.apache.org/jira/browse/SPARK-14139
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Reporter: koert kuipers
> Priority: Minor
>
> When i do
> {noformat}
> val df1 = sc.makeRDD(1 to 3).toDF
> val df2 = df1.map(row => Row(row(0).asInstanceOf[Int] +
> 1))(RowEncoder(df1.schema))
> println(s"schema before ${df1.schema} and after ${df2.schema}")
> {noformat}
> I get:
> {noformat}
> schema before StructType(StructField(value,IntegerType,false)) and after
> StructType(StructField(value,IntegerType,true))
> {noformat}
> The change in field nullable is unexpected and i consider it a bug.
> This bug was introduced in:
> [SPARK-13244][SQL] Migrates DataFrame to Dataset
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