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https://issues.apache.org/jira/browse/SPARK-14703?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15247592#comment-15247592
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Ceki Gulcu commented on SPARK-14703:
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@srowen Being able to configure loggers has been a oft-requested feature for
SLF4J. Can you briefly describe the *essential* configuration primitives you
would like SLF4J support?
> Spark uses SLF4J, but actually relies quite heavily on Log4J
> ------------------------------------------------------------
>
> Key: SPARK-14703
> URL: https://issues.apache.org/jira/browse/SPARK-14703
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core, YARN
> Affects Versions: 1.6.0
> Environment: 1.6.0-cdh5.7.0, logback 1.1.3, yarn
> Reporter: Matthew Byng-Maddick
> Priority: Minor
> Labels: log4j, logback, logging, slf4j
> Attachments: spark-logback.patch
>
>
> We've built a version of Hadoop CDH-5.7.0 in house with logback as the SLF4J
> provider, in order to send hadoop logs straight to logstash (to handle with
> logstash/elasticsearch), on top of our existing use of the logback backend.
> In trying to start spark-shell I discovered several points where the fact
> that we weren't quite using a real L4J caused the sc not to be created or the
> YARN module not to exist. There are many more places where we should probably
> be wrapping the logging more sensibly, but I have a basic patch that fixes
> some of the worst offenders (at least the ones that stop the sparkContext
> being created properly).
> I'm prepared to accept that this is not a good solution and there probably
> needs to be some sort of better wrapper, perhaps in the Logging.scala class
> which handles this properly.
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