Add way to limit default # of cores used by applications on standalone mode

Also documents the spark.deploy.spreadOut option.

1 files changed, 2 insertions, 3 deletions

diff --git a/docs/job-scheduling.md b/docs/job-scheduling.md
index 5951155fe3..df2faa5e41 100644
--- a/docs/job-scheduling.md
+++ b/docs/job-scheduling.md
@@ -32,9 +32,8 @@ Resource allocation can be configured as follows, based on the cluster type:
 
 * **Standalone mode:** By default, applications submitted to the standalone mode cluster will run in
   FIFO (first-in-first-out) order, and each application will try to use all available nodes. You can limit
-  the number of nodes an application uses by setting the `spark.cores.max` configuration property in it. This
-  will allow multiple users/applications to run concurrently. For example, you might launch a long-running
-  server that uses 10 cores, and allow users to launch shells that use 20 cores each.
+  the number of nodes an application uses by setting the `spark.cores.max` configuration property in it,
+  or change the default for applications that don't set this setting through `spark.deploy.defaultCores`. 
   Finally, in addition to controlling cores, each application's `spark.executor.memory` setting controls
   its memory use.
 * **Mesos:** To use static partitioning on Mesos, set the `spark.mesos.coarse` configuration property to `true`,