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https://issues.apache.org/jira/browse/SPARK-23006?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Sean Owen updated SPARK-23006:
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Flags: (was: Important)
Target Version/s: (was: 2.1.1)
Labels: (was: aws emr hadoop read s3 spark write)
Priority: Minor (was: Blocker)
Fix Version/s: (was: 2.1.1)
See your other JIRA; this isn't suitable here as it's open-ended questions.
> 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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