[ 
https://issues.apache.org/jira/browse/SPARK-25717?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Jinhua Fu updated SPARK-25717:
------------------------------
    Description: 
Consider the following scenario:
{code:java}
spark.range(100).createTempView("temp")
(0 until 3).foreach { _ =>
  spark.sql("drop table if exists tableA")
  spark.sql("create table if not exists tableA(a int) partitioned by (p int) 
location 'file:/e:/study/warehouse/tableA'")
  spark.sql("insert overwrite table tableA partition(p=1) select * from temp")
  spark.sql("select count(1) from tableA where p=1").show
}
{code}
We expect the count always be 100, but the actual results are as follows:
{code:java}
+--------+
|count(1)|
+--------+
|     100|
+--------+

+--------+
|count(1)|
+--------+
|   200|
+--------+

+--------+
|count(1)|
+--------+
|   300|
+--------+
{code}
when spark executes an `insert overwrite` command,  it gets the historical 
partition first, and then delete it from fileSystem.

But for recreated external and partitioned table, the partitions were all 
deleted by the `drop  table` command with data unremoved. So the historical 
data is preserved which lead to the query results incorrect.

 

  was:
Consider the following scenario:
{code:java}
spark.range(100).createTempView("temp")
(0 until 3).foreach { _ =>
  spark.sql("drop table if exists tableA")
  spark.sql("create table if not exists tableA(a int) partitioned by (p int) 
location 'file:/e:/study/warehouse/tableA'")
  spark.sql("insert overwrite table tableA partition(p=1) select * from temp")
  spark.sql("select count(1) from tableA where p=1").show
}
{code}
We expect the count always be 100, but the actual results are as follows:
{code:java}
+--------+
|count(1)|
+--------+
|     100|
+--------+

+--------+
|count(1)|
+--------+
|   200|
+--------+

+--------+
|count(1)|
+--------+
|   300|
+--------+
{code}
when spark executes an `insert overwrite` command,  it gets the historical 
partition first, and then delete it from fileSystem.

But for recreated external and partitioned table, the partitions were all 
deleted by the `drop  table` command. So the historical data is preserved which 
lead to the query results incorrect.

 


> Insert overwrite a recreated external and partitioned table may result in 
> incorrect query results
> -------------------------------------------------------------------------------------------------
>
>                 Key: SPARK-25717
>                 URL: https://issues.apache.org/jira/browse/SPARK-25717
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.3.2
>            Reporter: Jinhua Fu
>            Priority: Major
>
> Consider the following scenario:
> {code:java}
> spark.range(100).createTempView("temp")
> (0 until 3).foreach { _ =>
>   spark.sql("drop table if exists tableA")
>   spark.sql("create table if not exists tableA(a int) partitioned by (p int) 
> location 'file:/e:/study/warehouse/tableA'")
>   spark.sql("insert overwrite table tableA partition(p=1) select * from temp")
>   spark.sql("select count(1) from tableA where p=1").show
> }
> {code}
> We expect the count always be 100, but the actual results are as follows:
> {code:java}
> +--------+
> |count(1)|
> +--------+
> |     100|
> +--------+
> +--------+
> |count(1)|
> +--------+
> |   200|
> +--------+
> +--------+
> |count(1)|
> +--------+
> |   300|
> +--------+
> {code}
> when spark executes an `insert overwrite` command,  it gets the historical 
> partition first, and then delete it from fileSystem.
> But for recreated external and partitioned table, the partitions were all 
> deleted by the `drop  table` command with data unremoved. So the historical 
> data is preserved which lead to the query results incorrect.
>  



--
This message was sent by Atlassian JIRA
(v7.6.3#76005)

---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org

Reply via email to