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https://issues.apache.org/jira/browse/SPARK-31071?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Wenchen Fan resolved SPARK-31071.
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Fix Version/s: 3.1.0
Resolution: Fixed
Issue resolved by pull request 27851
[https://github.com/apache/spark/pull/27851]
> Spark Encoders.bean() should allow marking non-null fields in its Spark schema
> ------------------------------------------------------------------------------
>
> Key: SPARK-31071
> URL: https://issues.apache.org/jira/browse/SPARK-31071
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 2.4.4
> Reporter: Kyrill Alyoshin
> Assignee: L. C. Hsieh
> Priority: Major
> Fix For: 3.1.0
>
>
> Spark _Encoders.bean()_ method should allow the generated StructType schema
> fields be *non-nullable*.
> Currently, any non-primitive type is automatically _nullable_. It is
> hard-coded in the _org.apache.spark.sql.catalyst.JavaTypeReference_ class.
> This can lead to rather interesting situations... For example, let's say I
> want to save a dataframe using an Avro format with my own non-spark generated
> Avro schema. Let's also say that my Avro schema has a field that is non-null
> (i.e., not a union type). Well, it appears *impossible* to store a dataframe
> using such an Avro schema since Spark would assume that the field is nullable
> (as it is in its own schema) which would conflict with Avro schema semantics
> and throw an exception.
> I propose making a change to the _JavaTypeReference_ class to observe the
> JSR-305 _Nonnull_ annotation (and its children) on the provided bean class
> during StructType schema generation. This would allow bean creators to
> control the resulting Spark schema so much better.
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