[jira] [Commented] (SPARK-22306) INFER_AND_SAVE overwrites important metadata in Parquet Metastore table

["Dongjoon Hyun (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-22306?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16211518#comment-16211518 ] 

Dongjoon Hyun commented on SPARK-22306:
---------------------------------------

When we release 2.2.1, we had better give a warning about this regression.

> INFER_AND_SAVE overwrites important metadata in Parquet Metastore table
> -----------------------------------------------------------------------
>
>                 Key: SPARK-22306
>                 URL: https://issues.apache.org/jira/browse/SPARK-22306
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.2.0
>         Environment: Hive 2.3.0 (PostgresQL metastore, stored as Parquet)
> Spark 2.2.0
>            Reporter: David Malinge
>            Priority: Critical
>
> I noticed some critical changes on my hive tables and realized that they were caused by a simple select on SparkSQL. Looking at the logs, I found out that this select was actually performing an update on the database "Saving case-sensitive schema for table". 
> I then found out that Spark 2.2.0 introduces a new default value for spark.sql.hive.caseSensitiveInferenceMode (see SPARK-20888): INFER_AND_SAVE
> The issue is that this update changes critical metadata of the table, in particular:
> - changes the owner to the current user
> - removes bucketing metadata (BUCKETING_COLS, SDS)
> - removes sorting metadata (SORT_COLS)
> Switching the property to: NEVER_INFER prevents the issue.
> Also, note that the damage can be fix manually in Hive with e.g.:
> {code:sql}
> alter table [table_name] 
> clustered by ([col1], [col2]) 
> sorted by ([colA], [colB])
> into [n] buckets
> {code}
> *REPRODUCE (branch-2.2)*
> Spark 2.3 (master) branch is good. This is a regression on Spark 2.2 only. By default, Parquet Hive table is affected and only Hive may suffer from this.
> {code}
> hive> CREATE TABLE t(a string, b string) CLUSTERED BY (a, b) SORTED BY (a, b) INTO 10 BUCKETS STORED AS PARQUET;
> hive> INSERT INTO t VALUES('a','b');
> hive> DESC FORMATTED t;
> ...
> Num Buckets:        	10
> Bucket Columns:     	[a, b]
> Sort Columns:       	[Order(col:a, order:1), Order(col:b, order:1)]
> scala> sql("DESC FORMATTED t").show(false)
> // no information about buckets.
> hive> DESC FORMATTED t;
> Num Buckets:        	-1
> Bucket Columns:     	[]
> Sort Columns:       	[]
> {code}



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