Github user andrewor14 commented on a diff in the pull request:
https://github.com/apache/spark/pull/9795#discussion_r45173391
--- Diff: docs/running-on-mesos.md ---
@@ -161,20 +161,14 @@ 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
+To run in coarse-grained mode, set the `spark.mesos.coarse` property to
true in your
--- End diff --
that or say `set the property to true (already the default)` or something
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