[SPARK-11809] Switch the default Mesos mode to coarse-grained mode

Based on my conversions with people, I believe the consensus is that the coarse-grained mode is more stable and easier to reason about. It is best to use that as the default rather than the more flaky fine-grained mode.

Author: Reynold Xin <rxin@databricks.com>

Closes #9795 from rxin/SPARK-11809.

1 files changed, 17 insertions, 10 deletions

diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md
index 5be208cf34..a197d0e373 100644
--- a/docs/running-on-mesos.md
+++ b/docs/running-on-mesos.md
@@ -161,21 +161,15 @@ Note that jars or python files that are passed to spark-submit should be URIs re
 
 # Mesos Run Modes
 
-Spark can run over Mesos in two modes: "fine-grained" (default) and "coarse-grained".
+Spark can run over Mesos in two modes: "coarse-grained" (default) and "fine-grained".
 
-In "fine-grained" mode (default), each Spark task runs as a separate Mesos task. This allows
-multiple instances of Spark (and other frameworks) to share machines at a very fine granularity,
-where each application gets more or fewer machines as it ramps up and down, but it comes with an
-additional overhead in launching each task. This mode may be inappropriate for low-latency
-requirements like interactive queries or serving web requests.
-
-The "coarse-grained" mode will instead launch only *one* long-running Spark task on each Mesos
+The "coarse-grained" mode will launch only *one* long-running Spark task on each Mesos
 machine, and dynamically schedule its own "mini-tasks" within it. The benefit is much lower startup
 overhead, but at the cost of reserving the Mesos resources for the complete duration of the
 application.
 
-To run in coarse-grained mode, set the `spark.mesos.coarse` property in your
-[SparkConf](configuration.html#spark-properties):
+Coarse-grained is the default mode. You can also set `spark.mesos.coarse` property to true
+to turn it on explictly in [SparkConf](configuration.html#spark-properties):
 
 {% highlight scala %}
 conf.set("spark.mesos.coarse", "true")
@@ -186,6 +180,19 @@ acquire. By default, it will acquire *all* cores in the cluster (that get offere
 only makes sense if you run just one application at a time. You can cap the maximum number of cores
 using `conf.set("spark.cores.max", "10")` (for example).
 
+In "fine-grained" mode, each Spark task runs as a separate Mesos task. This allows
+multiple instances of Spark (and other frameworks) to share machines at a very fine granularity,
+where each application gets more or fewer machines as it ramps up and down, but it comes with an
+additional overhead in launching each task. This mode may be inappropriate for low-latency
+requirements like interactive queries or serving web requests.
+
+To run in coarse-grained mode, set the `spark.mesos.coarse` property to false in your
+[SparkConf](configuration.html#spark-properties):
+
+{% highlight scala %}
+conf.set("spark.mesos.coarse", "false")
+{% endhighlight %}
+
 You may also make use of `spark.mesos.constraints` to set attribute based constraints on mesos resource offers. By default, all resource offers will be accepted.
 
 {% highlight scala %}