Github user vanzin commented on a diff in the pull request:
https://github.com/apache/spark/pull/1159#discussion_r14038548
--- Diff: docs/running-on-yarn.md ---
@@ -95,6 +95,13 @@ Most of the configs are the same for Spark on YARN as
for other deployment modes
The amount of off heap memory (in megabytes) to be allocated per
driver. This is memory that accounts for things like VM overheads, interned
strings, other native overheads, etc.
</td>
</tr>
+<tr>
+ <td><code>spark.yarn.access.namenodes</code></td>
+ <td>(none)</td>
+ <td>
+ A list of secure HDFS namenodes your spark application is going to
access. For example,
spark.yarn.access.namenodes=hdfs://nn1.com:8032,hdfs://nn2.com:8032. Spark
acquires security Tokens for each of the namenodes so that the spark
application can access those remote HDFS clusters.
--- End diff --
Maybe it's sort of redundant, but we've seen enough people running
different HDFS services under different Kerberos realms that I think it should
be mentioned here that the user running the Spark job needs to be able to
access all the listed NNs (either by them being on the same realm or in a
trusted realm).
Also, nits: backquotes around the example, and capitalize "Spark" before
application.
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
