[jira] [Updated] (SPARK-20240) SparkSQL support limitations of max dynamic partitions when inserting hive table

["Hyukjin Kwon (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-20240?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Hyukjin Kwon updated SPARK-20240:
---------------------------------
    Labels: bulk-closed  (was: )

> SparkSQL support limitations of max dynamic partitions when inserting hive table
> --------------------------------------------------------------------------------
>
>                 Key: SPARK-20240
>                 URL: https://issues.apache.org/jira/browse/SPARK-20240
>             Project: Spark
>          Issue Type: Improvement
>          Components: SQL
>    Affects Versions: 1.6.2, 1.6.3
>            Reporter: zenglinxi
>            Priority: Major
>              Labels: bulk-closed
>
> We found that HDFS problem occurs sometimes when user have a typo in their code  while using SparkSQL inserting data into a partition table.
> For Example:
> create table:
>  {quote}
>  create table test_tb (
>    price double,
> ) PARTITIONED BY (day_partition string ,hour_partition string)
> {quote}
> normal sql for inserting table:
>  {quote}
> insert overwrite table test_tb partition(day_partition, hour_partition) select price, day_partition, hour_partition from other_table;
>  {quote}
> sql with typo:
>  {quote}
> insert overwrite table test_tb partition(day_partition, hour_partition) select hour_partition, day_partition, price from other_table;
>  {quote}
> This typo makes SparkSQL take column "price" as "hour_partition",  which may create million HDFS files in short time if the "other_table" has large data with a wide range of "price" and give rise to awful performance of NameNode RPC.
> We think it's a good idea to limit the maximum number of files allowed to be create by each task for protecting HDFS NameNode from unconscious error.



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

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