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Posted to issues@spark.apache.org by "Sean Owen (JIRA)" <ji...@apache.org> on 2016/08/30 08:08:20 UTC

[jira] [Updated] (SPARK-17307) Document what all access is needed on S3 bucket when trying to save a model

     [ https://issues.apache.org/jira/browse/SPARK-17307?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sean Owen updated SPARK-17307:
------------------------------
    Priority: Minor  (was: Major)

I think this is mostly not Spark-specific and is S3-specific, but if you can propose a short additional bit of doc in the right place I'll look at it.

> Document what all access is needed on S3 bucket when trying to save a model
> ---------------------------------------------------------------------------
>
>                 Key: SPARK-17307
>                 URL: https://issues.apache.org/jira/browse/SPARK-17307
>             Project: Spark
>          Issue Type: Documentation
>            Reporter: Aseem Bansal
>            Priority: Minor
>
> I faced this lack of documentation when I was trying to save a model to S3. Initially I thought it should be only write. Then I found it also needs delete to delete temporary files. Now I requested access for delete and tried again and I am get the error
> Exception in thread "main" org.apache.hadoop.fs.s3.S3Exception: org.jets3t.service.S3ServiceException: S3 PUT failed for '/dev-qa_%24folder%24' XML Error Message
> To reproduce this error the below can be used
> {code}
> SparkSession sparkSession = SparkSession
>                 .builder()
>                 .appName("my app")
>                 .master("local") 
>                 .getOrCreate();
>         JavaSparkContext jsc = new JavaSparkContext(sparkSession.sparkContext());
> jsc.hadoopConfiguration().set("fs.s3n.awsAccessKeyId", <ACCESS_KEY>);
>         jsc.hadoopConfiguration().set("fs.s3n.awsSecretAccessKey", <SECRET ACCESS KEY>);
> //Create a Pipelinemode
>         pipelineModel.write().overwrite().save("s3n://<BUCKET>/dev-qa/modelTest");
> {code}
> This back and forth could be avoided if it was clearly mentioned what all access spark needs to write to S3. Also would be great if why all of the access is needed.



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