[jira] [Resolved] (SPARK-23006) S3 sync issue

["Sean Owen (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-23006?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sean Owen resolved SPARK-23006.
-------------------------------
    Resolution: Invalid

> S3 sync issue
> -------------
>
>                 Key: SPARK-23006
>                 URL: https://issues.apache.org/jira/browse/SPARK-23006
>             Project: Spark
>          Issue Type: Brainstorming
>          Components: Spark Core
>    Affects Versions: 2.1.1
>         Environment: AWS EMR 5.7
>            Reporter: Dhruv sharma
>            Priority: Minor
>   Original Estimate: 24h
>  Remaining Estimate: 24h
>
> Using S3 to read and write for our various jobs.
> Facing some read and write inconsistencies, which are leading to the job failures.
> Details are as below:
> - WRITE ERRORS : 
>      While writing to s3 have seen the exception "File already exists". 
>      Understanding is while writing to s3 if some executors die then the same task is delegated to another executor, which then retries the write. 
>      But file being partially written gives the above exception.
>     Tried tuning the time multiplier so that WRITE tasks are not killed. Its working but still not a robust solution.
> - READ AFTER WRITE ERRORS
>     One of the jobs is deleting the S3 data and then writing the data to the location.
>     S3 doesn't guarantee immediate deletion.
>     The second job when tries to read the location using [ spark.read.json("bucket/key1/*") ] 
>     Gives an exception "FILE_NOT_FOUND".
>     Reason being it lists the data that is deleted but still that deletion is not synced.
> Is there a way we can tune our spark configuration, to remove such exceptions.
> Further what should be kept in mind while interacting with S3 for read and write.



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