[SPARK-54028][SQL] Use empty schema when altering a view which is not Hive compatible #52730
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HyukjinKwon
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sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveExternalCatalog.scala
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sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveExternalCatalog.scala
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sql/hive/src/test/scala/org/apache/spark/sql/hive/HiveMetastoreCatalogSuite.scala
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…Catalog.scala Co-authored-by: Wenchen Fan <cloud0fan@gmail.com>
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sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveExternalCatalog.scala
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… Hive compatible ### What changes were proposed in this pull request? Spark attempts to save views in a Hive-compatible format and only sets the schema to empty if the save operation fails. However, due to certain Hive compatibility issues, the save operation may succeed while subsequent read operations fail. This issue arises after the change introduced in [SPARK-46934](https://issues.apache.org/jira/browse/SPARK-46934), which removed the verifyColumnDataType check during the ALTER TABLE operation. ### Why are the changes needed? to not save malformed views that no one can read. ### Does this PR introduce _any_ user-facing change? Yes, the malformed view will be saved in non hive compatible way so that Spark can read it. ### How was this patch tested? Updated Test case ### Was this patch authored or co-authored using generative AI tooling? No Closes #52730 from cravani/SPARK-54028. Authored-by: Chiran Ravani <chiran54321@gmail.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> (cherry picked from commit 3d292dc) Signed-off-by: Wenchen Fan <wenchen@databricks.com>
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… Hive compatible ### What changes were proposed in this pull request? Spark attempts to save views in a Hive-compatible format and only sets the schema to empty if the save operation fails. However, due to certain Hive compatibility issues, the save operation may succeed while subsequent read operations fail. This issue arises after the change introduced in [SPARK-46934](https://issues.apache.org/jira/browse/SPARK-46934), which removed the verifyColumnDataType check during the ALTER TABLE operation. ### Why are the changes needed? to not save malformed views that no one can read. ### Does this PR introduce _any_ user-facing change? Yes, the malformed view will be saved in non hive compatible way so that Spark can read it. ### How was this patch tested? Updated Test case ### Was this patch authored or co-authored using generative AI tooling? No Closes apache#52730 from cravani/SPARK-54028. Authored-by: Chiran Ravani <chiran54321@gmail.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com>
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What changes were proposed in this pull request?
Spark attempts to save views in a Hive-compatible format and only sets the schema to empty if the save operation fails.
However, due to certain Hive compatibility issues, the save operation may succeed while subsequent read operations fail. This issue arises after the change introduced in SPARK-46934, which removed the verifyColumnDataType check during the ALTER TABLE operation.
Why are the changes needed?
to not save malformed views that no one can read.
Does this PR introduce any user-facing change?
Yes, the malformed view will be saved in non hive compatible way so that Spark can read it.
How was this patch tested?
Updated Test case
Was this patch authored or co-authored using generative AI tooling?
No