MaxGekk commented on code in PR #44358:
URL: https://github.com/apache/spark/pull/44358#discussion_r1430105163
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sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala:
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@@ -1180,12 +1180,15 @@ object JdbcUtils extends Logging with SQLConfHelper {
}
}
- def classifyException[T](message: String, dialect: JdbcDialect)(f: => T): T
= {
+ def classifyException[T](
Review Comment:
> We have the well-defined FAILED_JDBC error now, what do we expect the
dialect to overwrite?
We know which operation from Spark side failed but we don't know the root
cause of the issue. A dialect can "understand" DBMS specific error better, and
map some cases to Spark exceptions. Here is a simple example:
- Spark creates an index, and there is the error class
`FAILED_JDBC.CREATE_INDEX` for the cases of any unclassified failures, but
- Postgres dialect knows that the SQL state `42P07` from `SQLException`
belongs to existence of the object or in our case it means the index exists
already. So, it can provide more precise exception
`IndexAlreadyExistsException`
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