Repository: spark Updated Branches: refs/heads/master 31921e0f0 -> a416e41e2
[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 <r...@databricks.com> Closes #9795 from rxin/SPARK-11809. Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/a416e41e Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/a416e41e Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/a416e41e Branch: refs/heads/master Commit: a416e41e285700f861559d710dbf857405bfddf6 Parents: 31921e0 Author: Reynold Xin <r...@databricks.com> Authored: Wed Nov 18 12:50:29 2015 -0800 Committer: Reynold Xin <r...@databricks.com> Committed: Wed Nov 18 12:50:29 2015 -0800 ---------------------------------------------------------------------- .../scala/org/apache/spark/SparkContext.scala | 2 +- docs/job-scheduling.md | 2 +- docs/running-on-mesos.md | 27 ++++++++++++-------- 3 files changed, 19 insertions(+), 12 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/a416e41e/core/src/main/scala/org/apache/spark/SparkContext.scala ---------------------------------------------------------------------- diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index b5645b0..ab374cb 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -2710,7 +2710,7 @@ object SparkContext extends Logging { case mesosUrl @ MESOS_REGEX(_) => MesosNativeLibrary.load() val scheduler = new TaskSchedulerImpl(sc) - val coarseGrained = sc.conf.getBoolean("spark.mesos.coarse", false) + val coarseGrained = sc.conf.getBoolean("spark.mesos.coarse", defaultValue = true) val url = mesosUrl.stripPrefix("mesos://") // strip scheme from raw Mesos URLs val backend = if (coarseGrained) { new CoarseMesosSchedulerBackend(scheduler, sc, url, sc.env.securityManager) http://git-wip-us.apache.org/repos/asf/spark/blob/a416e41e/docs/job-scheduling.md ---------------------------------------------------------------------- diff --git a/docs/job-scheduling.md b/docs/job-scheduling.md index a3c34cb..36327c6 100644 --- a/docs/job-scheduling.md +++ b/docs/job-scheduling.md @@ -47,7 +47,7 @@ application is not running tasks on a machine, other applications may run tasks is useful when you expect large numbers of not overly active applications, such as shell sessions from separate users. However, it comes with a risk of less predictable latency, because it may take a while for an application to gain back cores on one node when it has work to do. To use this mode, simply use a -`mesos://` URL without setting `spark.mesos.coarse` to true. +`mesos://` URL and set `spark.mesos.coarse` to false. Note that none of the modes currently provide memory sharing across applications. If you would like to share data this way, we recommend running a single server application that can serve multiple requests by querying http://git-wip-us.apache.org/repos/asf/spark/blob/a416e41e/docs/running-on-mesos.md ---------------------------------------------------------------------- diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md index 5be208c..a197d0e 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 %} --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org
