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Posted to issues@spark.apache.org by "kalyan s (Jira)" <ji...@apache.org> on 2023/04/28 05:04:00 UTC
[jira] [Comment Edited] (SPARK-43106) Data lost from the table if the INSERT OVERWRITE query fails
[ https://issues.apache.org/jira/browse/SPARK-43106?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17717461#comment-17717461 ]
kalyan s edited comment on SPARK-43106 at 4/28/23 5:03 AM:
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[~cloud_fan] , [~dongjoon] , [~gurwls223]
Appreciate input on this!
Myself(kalyan) and [~vaibhavb] are from Uber and we see our workloads getting into this problem more regularly.
While we are willing to work on it, we would like to know if there were any gotchas in taking forward the idea proposed in SPARK-19183.
was (Author: itskals):
[~cloud_fan] , [~dongjoon] , [~gurwls223]
Input from you folks will help us.
Myself(kalyan) and [~vaibhavb] are from Uber and we see our workloads getting into this problem more regularly.
While we are willing to work on it, we would like to know if there were any gotchas in taking forward the idea proposed in SPARK-19183.
> Data lost from the table if the INSERT OVERWRITE query fails
> ------------------------------------------------------------
>
> Key: SPARK-43106
> URL: https://issues.apache.org/jira/browse/SPARK-43106
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 3.3.2
> Reporter: vaibhav beriwala
> Priority: Major
>
> When we run an INSERT OVERWRITE query for an unpartitioned table on Spark-3, Spark has the following behavior:
> 1) It will first clean up all the data from the actual table path.
> 2) It will then launch a job that performs the actual insert.
>
> There are 2 major issues with this approach:
> 1) If the insert job launched in step 2 above fails for any reason, the data from the original table is lost.
> 2) If the insert job in step 2 above takes a huge time to complete, then table data is unavailable to other readers for the entire duration the job takes.
> This behavior is the same even for the partitioned tables when using static partitioning. For dynamic partitioning, we do not delete the table data before the job launch.
>
> Is there a reason as to why we perform this delete before the job launch and not as part of the Job commit operation? This issue is not there with Hive - where the data is cleaned up as part of the Job commit operation probably. As part of SPARK-19183, we did add a new hook in the commit protocol for this exact same purpose, but seems like its default behavior is still to delete the table data before the job launch.
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