Github user vanzin commented on a diff in the pull request:
https://github.com/apache/spark/pull/18849#discussion_r134029245
--- Diff:
sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveExternalCatalog.scala ---
@@ -1175,6 +1205,27 @@ private[spark] class HiveExternalCatalog(conf:
SparkConf, hadoopConf: Configurat
client.listFunctions(db, pattern)
}
+ /** Detect whether a table is stored with Hive-compatible metadata. */
+ private def isHiveCompatible(table: CatalogTable): Boolean = {
--- End diff --
> So far, the safest way to check the compatibility is to compare the
schema.
That is not enough, as I've tried to explain several times.
`alterTableSchema` is broken in older Spark versions and can end up creating
corrupt tables (where the schema does not match the storage descriptor, for
example). So you need a reliable way of detecting compatibility, and the schema
is not it.
The closest we have currently is the storage descriptor (the fallback case
in my code).
Let me think about what happens when an older Spark touches the tables with
the new property set. `alterTableSchema` even in older Spark versions will
maintain that information, but maybe other code paths won't.
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