Repository: spark Updated Branches: refs/heads/branch-2.0 88be66b93 -> 2c2b8f121
[MESOS] expand coarse-grained mode docs ## What changes were proposed in this pull request? docs ## How was this patch tested? viewed the docs in github Author: Michael Gummelt <mgumm...@mesosphere.io> Closes #14059 from mgummelt/coarse-grained. (cherry picked from commit 9c041990cf4d0138d9104207b5c2e7a319b42615) Signed-off-by: Reynold Xin <r...@databricks.com> Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/2c2b8f12 Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/2c2b8f12 Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/2c2b8f12 Branch: refs/heads/branch-2.0 Commit: 2c2b8f121a213618ef47cb030d17b9bd323f0d9e Parents: 88be66b Author: Michael Gummelt <mgumm...@mesosphere.io> Authored: Wed Jul 6 15:02:45 2016 -0700 Committer: Reynold Xin <r...@databricks.com> Committed: Wed Jul 6 15:02:50 2016 -0700 ---------------------------------------------------------------------- docs/running-on-mesos.md | 77 ++++++++++++++++++++++++++++--------------- 1 file changed, 51 insertions(+), 26 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/2c2b8f12/docs/running-on-mesos.md ---------------------------------------------------------------------- diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md index 4a0ab62..8ab5f30 100644 --- a/docs/running-on-mesos.md +++ b/docs/running-on-mesos.md @@ -180,30 +180,53 @@ 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: "coarse-grained" (default) and "fine-grained". - -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. - -Coarse-grained is the default mode. You can also set `spark.mesos.coarse` property to true -to turn it on explicitly in [SparkConf](configuration.html#spark-properties): - -{% highlight scala %} -conf.set("spark.mesos.coarse", "true") -{% endhighlight %} - -In addition, for coarse-grained mode, you can control the maximum number of resources Spark will -acquire. By default, it will acquire *all* cores in the cluster (that get offered by Mesos), which -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. +Spark can run over Mesos in two modes: "coarse-grained" (default) and +"fine-grained". + +## Coarse-Grained + +In "coarse-grained" mode, each Spark executor runs as a single Mesos +task. Spark executors are sized according to the following +configuration variables: + +* Executor memory: `spark.executor.memory` +* Executor cores: `spark.executor.cores` +* Number of executors: `spark.cores.max`/`spark.executor.cores` + +Please see the [Spark Configuration](configuration.html) page for +details and default values. + +Executors are brought up eagerly when the application starts, until +`spark.cores.max` is reached. If you don't set `spark.cores.max`, the +Spark application will reserve all resources offered to it by Mesos, +so we of course urge you to set this variable in any sort of +multi-tenant cluster, including one which runs multiple concurrent +Spark applications. + +The scheduler will start executors round-robin on the offers Mesos +gives it, but there are no spread guarantees, as Mesos does not +provide such guarantees on the offer stream. + +The benefit of coarse-grained mode is much lower startup overhead, but +at the cost of reserving Mesos resources for the complete duration of +the application. To configure your job to dynamically adjust to its +resource requirements, look into +[Dynamic Allocation](#dynamic-resource-allocation-with-mesos). + +## Fine-Grained + +In "fine-grained" mode, each Spark task inside the Spark executor runs +as a separate Mesos task. This allows multiple instances of Spark (and +other frameworks) to share cores at a very fine granularity, where +each application gets more or fewer cores 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. + +Note that while Spark tasks in fine-grained will relinquish cores as +they terminate, they will not relinquish memory, as the JVM does not +give memory back to the Operating System. Neither will executors +terminate when they're idle. To run in fine-grained mode, set the `spark.mesos.coarse` property to false in your [SparkConf](configuration.html#spark-properties): @@ -212,7 +235,9 @@ To run in fine-grained mode, set the `spark.mesos.coarse` property to false in y 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. +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 %} conf.set("spark.mesos.constraints", "os:centos7;us-east-1:false") @@ -246,7 +271,7 @@ In either case, HDFS runs separately from Hadoop MapReduce, without being schedu # Dynamic Resource Allocation with Mesos -Mesos supports dynamic allocation only with coarse-grain mode, which can resize the number of +Mesos supports dynamic allocation only with coarse-grained mode, which can resize the number of executors based on statistics of the application. For general information, see [Dynamic Resource Allocation](job-scheduling.html#dynamic-resource-allocation). --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org
