[jira] [Commented] (SPARK-17195) Dealing with JDBC column nullability when it is not reliable

["Hyukjin Kwon (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-17195?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15432103#comment-15432103 ] 

Hyukjin Kwon commented on SPARK-17195:
--------------------------------------

I think if Teradata JDBC gives an incorrect schema (about nullability), it should be fixed in Teradata. I mean.. JDBC is a protocol and we might not have to consider the cases where the implementation does not complies the protocol.
I see there are some cases for nullability in schema, `ResultSetMetaData.columnNullableUnknown`, `ResultSetMetaData.columnNullable` and `ResultSetMetaData.columnNoNulls`. If the nullability is "unknown", then it should set `ResultSetMetaData.columnNullableUnknown`.

Another thought is to make JDBC data source to accept user's defined schema. If the "inferred" schema is not reliable in many cases, I think it might be sensible to support this by implementing `SchemaRelationProvider`.


> Dealing with JDBC column nullability when it is not reliable
> ------------------------------------------------------------
>
>                 Key: SPARK-17195
>                 URL: https://issues.apache.org/jira/browse/SPARK-17195
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.0.0
>            Reporter: Jason Moore
>
> Starting with Spark 2.0.0, the column "nullable" property is important to have correct for the code generation to work properly.  Marking the column as nullable = false used to (<2.0.0) allow null values to be operated on, but now this will result in:
> {noformat}
> Caused by: java.lang.NullPointerException
>         at org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter.write(UnsafeRowWriter.java:210)
>         at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
>         at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
>         at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:370)
>         at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
> {noformat}
> I'm all for the change towards a more ridged behavior (enforcing correct input).  But the problem I'm facing now is that when I used JDBC to read from a Teradata server, the column nullability is often not correct (particularly when sub-queries are involved).
> This is the line in question:
> https://github.com/apache/spark/blob/v2.0.0/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCRDD.scala#L140
> I'm trying to work out what would be the way forward for me on this.  I know that it's really the fault of the Teradata database server not returning the correct schema, but I'll need to make Spark itself or my application resilient to this behavior.
> One of the Teradata JDBC Driver tech leads has told me that "when the rsmd.getSchemaName and rsmd.getTableName methods return an empty zero-length string, then the other metadata values may not be completely accurate" - so one option could be to treat the nullability (at least) the same way as the "unknown" case (as nullable = true).  For reference, see the rest of our discussion here: http://forums.teradata.com/forum/connectivity/teradata-jdbc-driver-returns-the-wrong-schema-column-nullability
> Any other thoughts?



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