Github user rxin commented on a diff in the pull request:
https://github.com/apache/spark/pull/5933#discussion_r29734022
--- Diff: docs/running-on-yarn.md ---
@@ -305,3 +305,4 @@ If you need a reference to the proper location to put
log files in the YARN so t
- In `yarn-cluster` mode, the local directories used by the Spark
executors and the Spark driver will be the local directories configured for
YARN (Hadoop YARN config `yarn.nodemanager.local-dirs`). If the user specifies
`spark.local.dir`, it will be ignored. In `yarn-client` mode, the Spark
executors will use the local directories configured for YARN while the Spark
driver will use those defined in `spark.local.dir`. This is because the Spark
driver does not run on the YARN cluster in `yarn-client` mode, only the Spark
executors do.
- The `--files` and `--archives` options support specifying file names
with the # similar to Hadoop. For example you can specify: `--files
localtest.txt#appSees.txt` and this will upload the file you have locally named
localtest.txt into HDFS but this will be linked to by the name `appSees.txt`,
and your application should use the name as `appSees.txt` to reference it when
running on YARN.
- The `--jars` option allows the `SparkContext.addJar` function to work if
you are using it with local files and running in `yarn-cluster` mode. It does
not need to be used if you are using it with HDFS, HTTP, HTTPS, or FTP files.
+- In order to make PySpark work on Yarn, please build spark with Jave 6.
--- End diff --
actually we should really fix this ...
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