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Diffstat (limited to 'docs/running-on-mesos.md')
| -rw-r--r-- | docs/running-on-mesos.md | 19 |
1 files changed, 9 insertions, 10 deletions
diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md index 322ff585f1..68259f0cb8 100644 --- a/docs/running-on-mesos.md +++ b/docs/running-on-mesos.md @@ -15,15 +15,16 @@ Spark can run on clusters managed by [Apache Mesos](http://mesos.apache.org/). F * `export MESOS_NATIVE_LIBRARY=<path to libmesos.so>`. This path is usually `<prefix>/lib/libmesos.so` (where the prefix is `/usr/local` by default, see above). Also, on Mac OS X, the library is called `libmesos.dylib` instead of `libmesos.so`. * `export SPARK_EXECUTOR_URI=<path to spark-{{site.SPARK_VERSION}}.tar.gz uploaded above>`. * `export MASTER=mesos://HOST:PORT` where HOST:PORT is the host and port (default: 5050) of your Mesos master (or `zk://...` if using Mesos with ZooKeeper). -8. To run a Spark application against the cluster, when you create your `SparkContext`, pass the string `mesos://HOST:PORT` as the first parameter. In addition, you'll need to set the `spark.executor.uri` property. For example: +8. To run a Spark application against the cluster, when you create your `SparkContext`, pass the string `mesos://HOST:PORT` as the master URL. In addition, you'll need to set the `spark.executor.uri` property. For example: {% highlight scala %} -System.setProperty("spark.executor.uri", "<path to spark-{{site.SPARK_VERSION}}.tar.gz uploaded above>") -val sc = new SparkContext("mesos://HOST:5050", "App Name", ...) +val conf = new SparkConf() + .setMaster("mesos://HOST:5050") + .setAppName("My app") + .set("spark.executor.uri", "<path to spark-{{site.SPARK_VERSION}}.tar.gz uploaded above>") +val sc = new SparkContext(conf) {% endhighlight %} -If you want to run Spark on Amazon EC2, you can use the Spark [EC2 launch scripts](ec2-scripts.html), which provide an easy way to launch a cluster with Mesos, Spark, and HDFS pre-configured. This will get you a cluster in about five minutes without any configuration on your part. - # Mesos Run Modes Spark can run over Mesos in two modes: "fine-grained" and "coarse-grained". In fine-grained mode, which is the default, @@ -34,17 +35,15 @@ launch only *one* long-running Spark task on each Mesos machine, and dynamically 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` system property to true *before* creating your SparkContext: +To run in coarse-grained mode, set the `spark.mesos.coarse` property in your [SparkConf](configuration.html#spark-properties): {% highlight scala %} -System.setProperty("spark.mesos.coarse", "true") -val sc = new SparkContext("mesos://HOST:5050", "App Name", ...) +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 `System.setProperty("spark.cores.max", "10")` (for example). -Again, this must be done *before* initializing a SparkContext. +application at a time. You can cap the maximum number of cores using `conf.set("spark.cores.max", "10")` (for example). # Running Alongside Hadoop |