[jira] [Comment Edited] (SPARK-13979) Killed executor is respawned without AWS keys in standalone spark cluster

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

    [ https://issues.apache.org/jira/browse/SPARK-13979?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15366459#comment-15366459 ] 

Mitesh edited comment on SPARK-13979 at 7/7/16 5:36 PM:
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Just to be clear, you dont kill the worker process, you kill the executor process {{CoarseGrainedExecutorBackend}}. You killed that process and then master brought another one up, and then that process can successfully read from S3 bucket with the original keys given by driver? 


was (Author: masterddt):
Just to be clear, you dont kill the worker process, you kill the executor process `CoarseGrainedExecutorBackend`. You killed that process and then master brought another one up, and then that process can successfully read from S3 bucket with the original keys given by driver? 

> Killed executor is respawned without AWS keys in standalone spark cluster
> -------------------------------------------------------------------------
>
>                 Key: SPARK-13979
>                 URL: https://issues.apache.org/jira/browse/SPARK-13979
>             Project: Spark
>          Issue Type: Bug
>          Components: Spark Core
>    Affects Versions: 1.5.2
>         Environment: I'm using Spark 1.5.2 with Hadoop 2.7 and running experiments on a simple standalone cluster:
> 1 master
> 2 workers
> All ubuntu 14.04 with Java 8/Scala 2.10
>            Reporter: Allen George
>
> I'm having a problem where respawning a failed executor during a job that reads/writes parquet on S3 causes subsequent tasks to fail because of missing AWS keys.
> h4. Setup:
> I'm using Spark 1.5.2 with Hadoop 2.7 and running experiments on a simple standalone cluster:
> 1 master
> 2 workers
> My application is co-located on the master machine, while the two workers are on two other machines (one worker per machine). All machines are running in EC2. I've configured my setup so that my application executes its task on two executors (one executor per worker).
> h4. Application:
> My application reads and writes parquet files on S3. I set the AWS keys on the SparkContext by doing:
> val sc = new SparkContext()
> val hadoopConf = sc.hadoopConfiguration
> hadoopConf.set("fs.s3n.awsAccessKeyId", "SOME_KEY")
> hadoopConf.set("fs.s3n.awsSecretAccessKey", "SOME_SECRET")
> At this point I'm done, and I go ahead and use "sc".
> h4. Issue:
> I can read and write parquet files without a problem with this setup. *BUT* if an executor dies during a job and is respawned by a worker, tasks fail with the following error:
> "Caused by: java.lang.IllegalArgumentException: AWS Access Key ID and Secret Access Key must be specified as the username or password (respectively) of a s3n URL, or by setting the {{fs.s3n.awsAccessKeyId}} or {{fs.s3n.awsSecretAccessKey}} properties (respectively)."
> h4. Basic analysis
> I think I've traced this down to the following:
> SparkHadoopUtil is initialized with an empty {{SparkConf}}. Later, classes like {{DataSourceStrategy}} simply call {{SparkHadoopUtil.get.conf}} and access the (now invalid; missing various properties) {{HadoopConfiguration}} that's built from this empty {{SparkConf}} object. It's unclear to me why this is done, and it seems that the code as written would cause broken results anytime callers use {{SparkHadoopUtil.get.conf}} directly.



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