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https://issues.apache.org/jira/browse/SPARK-20328?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15968388#comment-15968388
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Michael Gummelt commented on SPARK-20328:
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Hey [~vanzin], thanks for the response.
Everything you said is correct, but I want to clarify one thing:
> You just need to make the Mesos backend in Spark do that automatically for
> the submitting user.
The problem can't be solved in the Mesos backend. When I fetch delegation
tokens for transmission to Executors in the Mesos backend, there's no problem.
I can set whatever renewer I want.
The problem is that there's a second location where delegation tokens are
fetched: {{HadoopRDD}}. This is entirely separate from the fetching that the
scheduler backends do (either Mesos or YARN). {{HadoopRDD}} tries to fetch
split data, and ultimately calls into {{TokenCache}} in the hadoop library,
which fetches delegation tokens with the renewer set to the YARN
ResourceManager's principal:
https://github.com/apache/hadoop/blob/trunk/hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-core/src/main/java/org/apache/hadoop/mapred/FileInputFormat.java#L213
The big question I have, which I suppose is more for the {{hadoop}} team, is
why in the world is {{FileInputFormat}} fetching delegation tokens. AFAICT,
they're not sending those tokens to any other process. They're just fetching
split data directly from the Name Nodes, and there should be no delegation
required.
> HadoopRDDs create a MapReduce JobConf, but are not MapReduce jobs
> -----------------------------------------------------------------
>
> Key: SPARK-20328
> URL: https://issues.apache.org/jira/browse/SPARK-20328
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 2.1.0, 2.1.1, 2.1.2
> Reporter: Michael Gummelt
>
> In order to obtain {{InputSplit}} information, {{HadoopRDD}} creates a
> MapReduce {{JobConf}} out of the Hadoop {{Configuration}}:
> https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala#L138
> Semantically, this is a problem because a HadoopRDD does not represent a
> Hadoop MapReduce job. Practically, this is a problem because this line:
> https://github.com/apache/spark/blob/master/core/src/main/scala/org/apache/spark/rdd/HadoopRDD.scala#L194
> results in this MapReduce-specific security code being called:
> https://github.com/apache/hadoop/blob/trunk/hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-core/src/main/java/org/apache/hadoop/mapreduce/security/TokenCache.java#L130,
> which assumes the MapReduce master is configured (e.g. via
> {{yarn.resourcemanager.*}}). If it isn't, an exception is thrown.
> So I'm seeing this exception thrown as I'm trying to add Kerberos support for
> the Spark Mesos scheduler:
> {code}
> Exception in thread "main" java.io.IOException: Can't get Master Kerberos
> principal for use as renewer
> at
> org.apache.hadoop.mapreduce.security.TokenCache.obtainTokensForNamenodesInternal(TokenCache.java:116)
> at
> org.apache.hadoop.mapreduce.security.TokenCache.obtainTokensForNamenodesInternal(TokenCache.java:100)
> at
> org.apache.hadoop.mapreduce.security.TokenCache.obtainTokensForNamenodes(TokenCache.java:80)
> at
> org.apache.hadoop.mapred.FileInputFormat.listStatus(FileInputFormat.java:205)
> at
> org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:313)
> at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:202)
> {code}
> I have a workaround where I set a YARN-specific configuration variable to
> trick {{TokenCache}} into thinking YARN is configured, but this is obviously
> suboptimal.
> The proper fix to this would likely require significant {{hadoop}}
> refactoring to make split information available without going through
> {{JobConf}}, so I'm not yet sure what the best course of action is.
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