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https://issues.apache.org/jira/browse/SPARK-22306?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Dongjoon Hyun updated SPARK-22306:
----------------------------------
Description:
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)]
{code}
was:
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}
> 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
>
> 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)]
> {code}
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