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https://issues.apache.org/jira/browse/SPARK-21725?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16226337#comment-16226337
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xinzhang commented on SPARK-21725:
----------------------------------

Now I try with the master branch.
The problem is still here.
Steps:
1.download . install . exec hivesql  (hive-1.2.1 . Here prove my hive is OK)
!https://user-images.githubusercontent.com/8244097/32210043-7554300e-be46-11e7-8ce0-f61bc0bfa998.png!

2.download . install . exec spark-sql  (spark-master I build it with master the 
lastest commit 44c4003155c1d243ffe0f73d5537b4c8b3f3b564)
First time . Spark-sql  result: GOOD
!https://user-images.githubusercontent.com/8244097/32210200-5b02de20-be47-11e7-8eac-e0228a7cf7f5.png!

Second time . Spark-sql  result: GOOD
!https://user-images.githubusercontent.com/8244097/32210320-f518aa12-be47-11e7-9a86-a16819583748.png!

3.use spark-sql thriftserver
First time . Spark-sql  result: GOOD
Second time .Spark-sql result: BAD
!https://user-images.githubusercontent.com/8244097/32210560-47d431da-be49-11e7-8279-7dd88dda42a6.png!



> spark thriftserver insert overwrite table partition select 
> -----------------------------------------------------------
>
>                 Key: SPARK-21725
>                 URL: https://issues.apache.org/jira/browse/SPARK-21725
>             Project: Spark
>          Issue Type: Bug
>          Components: Spark Core
>    Affects Versions: 2.1.0
>         Environment: centos 6.7 spark 2.1  jdk8
>            Reporter: xinzhang
>              Labels: spark-sql
>
> use thriftserver create table with partitions.
> session 1:
>  SET hive.default.fileformat=Parquet;create table tmp_10(count bigint) 
> partitioned by (pt string) stored as parquet;
> --ok
>  !exit
> session 2:
>  SET hive.default.fileformat=Parquet;create table tmp_11(count bigint) 
> partitioned by (pt string) stored as parquet; 
> --ok
>  !exit
> session 3:
> --connect the thriftserver
> SET hive.default.fileformat=Parquet;insert overwrite table tmp_10 
> partition(pt='1') select count(1) count from tmp_11;
> --ok
>  !exit
> session 4(do it again):
> --connect the thriftserver
> SET hive.default.fileformat=Parquet;insert overwrite table tmp_10 
> partition(pt='1') select count(1) count from tmp_11;
> --error
>  !exit
> -------------------------------------------------------------------------------------
> 17/08/14 18:13:42 ERROR SparkExecuteStatementOperation: Error executing 
> query, currentState RUNNING, 
> java.lang.reflect.InvocationTargetException
> ......
> ......
> Caused by: org.apache.hadoop.hive.ql.metadata.HiveException: Unable to move 
> source 
> hdfs://dc-hadoop54:50001/group/user/user1/meta/hive-temp-table/user1.db/tmp_11/.hive-staging_hive_2017-08-14_18-13-39_035_6303339779053
> 512282-2/-ext-10000/part-00000 to destination 
> hdfs://dc-hadoop54:50001/group/user/user1/meta/hive-temp-table/user1.db/tmp_11/pt=1/part-00000
>         at org.apache.hadoop.hive.ql.metadata.Hive.moveFile(Hive.java:2644)
>         at org.apache.hadoop.hive.ql.metadata.Hive.copyFiles(Hive.java:2711)
>         at 
> org.apache.hadoop.hive.ql.metadata.Hive.loadPartition(Hive.java:1403)
>         at 
> org.apache.hadoop.hive.ql.metadata.Hive.loadPartition(Hive.java:1324)
>         ... 45 more
> Caused by: java.io.IOException: Filesystem closed
> ....
> -------------------------------------------------------------------------------------
> the doc about the parquet table desc here 
> http://spark.apache.org/docs/latest/sql-programming-guide.html#parquet-files
> Hive metastore Parquet table conversion
> When reading from and writing to Hive metastore Parquet tables, Spark SQL 
> will try to use its own Parquet support instead of Hive SerDe for better 
> performance. This behavior is controlled by the 
> spark.sql.hive.convertMetastoreParquet configuration, and is turned on by 
> default.
> I am confused the problem appear in the table(partitions)  but it is ok with 
> table(with out partitions) . It means spark do not use its own parquet ?
> Maybe someone give any suggest how could I avoid the issue?



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