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https://issues.apache.org/jira/browse/SPARK-18949?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiao Li updated SPARK-18949:
----------------------------
    Description: 
Currently, we only have a SQL interface for recovering all the partitions in 
the directory of a table and update the catalog. `MSCK REPAIR TABLE` or `ALTER 
TABLE table RECOVER PARTITIONS`. (Actually, very hard for me to remember `MSCK` 
and have no clue what it means)

After the new "Scalable Partition Handling", the table repair becomes much more 
important for making visible the data in the created data source partitioned 
table.

It is desriable to add it into the Catalog interface so that users can repair 
the table by
{noformat}
spark.catalog.recoverPartitions("testTable")
{noformat}


  was:
Currently, we only have a SQL interface for recovering all the partitions in 
the directory of a table and update the catalog. `MSCK REPAIR TABLE`. 
(Actually, very hard for me to remember `MSCK` and have no clue what it means)

After the new "Scalable Partition Handling", the table repair becomes much more 
important for making visible the data in the created data source partitioned 
table.


> Add recoverPartitions API to Catalog
> ------------------------------------
>
>                 Key: SPARK-18949
>                 URL: https://issues.apache.org/jira/browse/SPARK-18949
>             Project: Spark
>          Issue Type: Improvement
>          Components: SQL
>    Affects Versions: 2.1.0
>            Reporter: Xiao Li
>            Assignee: Xiao Li
>
> Currently, we only have a SQL interface for recovering all the partitions in 
> the directory of a table and update the catalog. `MSCK REPAIR TABLE` or 
> `ALTER TABLE table RECOVER PARTITIONS`. (Actually, very hard for me to 
> remember `MSCK` and have no clue what it means)
> After the new "Scalable Partition Handling", the table repair becomes much 
> more important for making visible the data in the created data source 
> partitioned table.
> It is desriable to add it into the Catalog interface so that users can repair 
> the table by
> {noformat}
> spark.catalog.recoverPartitions("testTable")
> {noformat}



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