[jira] [Created] (SPARK-43106) Data lost from the table if the INSERT OVERWRITE query fails

["vaibhav beriwala (Jira)" <ji...@apache.org>]

vaibhav beriwala created SPARK-43106:
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             Summary: 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: Improvement
          Components: SQL
    Affects Versions: 3.3.2
            Reporter: vaibhav beriwala


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.



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