[jira] [Comment Edited] (SPARK-19713) saveAsTable

["Eric Maynard (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-19713?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15928778#comment-15928778 ] 

Eric Maynard edited comment on SPARK-19713 at 3/16/17 8:08 PM:
---------------------------------------------------------------

Not really relevant here, but to address:

>1. Hive will not be able to create the table as the folder already exists
You absolutely can construct a Hive external table on top of an existing folder.

>2. Hive cannot drop the table because the spark has not updated HiveMetaStore
The canonical solution to this is to run  `MSCK REPAIR TABLE myTable;` in Hive. 


was (Author: emaynard1121):
Not really relevant here, but to address:
>2. Hive cannot drop the table because the spark has not updated HiveMetaStore
The canonical solution to this is to run  `MSCK REPAIR TABLE myTable;` in Hive. 

> saveAsTable
> -----------
>
>                 Key: SPARK-19713
>                 URL: https://issues.apache.org/jira/browse/SPARK-19713
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 1.6.1
>            Reporter: Balaram R Gadiraju
>
> Hi,
> I just observed that when we use dataframe.saveAsTable("table") -- In oldversions
> and dataframe.write.saveAsTable("table") -- in the newer versions
>                 When using the method “df3.saveAsTable("brokentable")” in scale code. This creates a folder in hdfs and doesn’t update hive-metastore that it plans to create the table. So if anything goes wrong in between the folder still exists and hive is not aware of the folder creation. This will block the users from creating the table “brokentable” as the folder already exists, we can remove the folder using “hadoop fs –rmr /data/hive/databases/testdb.db/brokentable”.  So below is the workaround which will enable to you to continue the development work.
> Current Code:
> val df3 = sqlContext.sql("select * fromtesttable")
> df3.saveAsTable("brokentable")
> THE WORKAROUND:
> By registering the DataFrame as table and then using sql command to load the data will resolve the issue. EX:
> val df3 = sqlContext.sql("select * from testtable").registerTempTable("df3")
> sqlContext.sql("CREATE TABLE brokentable AS SELECT * FROM df3")



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