Github user ash211 commented on a diff in the pull request:
https://github.com/apache/spark/pull/3349#discussion_r20548296
--- Diff: docs/running-on-mesos.md ---
@@ -183,6 +183,47 @@ node. Please refer to [Hadoop on
Mesos](https://github.com/mesos/hadoop).
In either case, HDFS runs separately from Hadoop MapReduce, without being
scheduled through Mesos.
+# Configuration
+
+See the [configuration page](configuration.html) for information on Spark
configurations. The following configs are specific for Spark on Mesos.
+
+#### Spark Properties
+
+<table class="table">
+<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
+<tr>
+ <td><code>spark.mesos.coarse</code></td>
+ <td>false</td>
+ <td>
+ Set the run mode for Spark on Mesos. For more information about the
run mode, refer to #Mesos Run Mode section above.
+ </td>
+</tr>
+<tr>
+ <td><code>spark.mesos.extra.cores</code></td>
+ <td>0</td>
+ <td>
+ Set the extra amount of cpus to request per task.
--- End diff --
Is this setting for both coarse and fine grained modes?
Also can you provide a formula that produces the total number of cores
requested? From what you have now I'm thinking something like:
`totalCoresPerExecutor = numTasks + extraCores`
This would be similar to the formula for memoryOverhead below
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