CR feedback from Matei
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@ -21,9 +21,6 @@ Spark uses [Simple Build Tool](http://www.scala-sbt.org), which is bundled with
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For its Scala API, Spark {{site.SPARK_VERSION}} depends on Scala {{site.SCALA_VERSION}}. If you write applications in Scala, you will need to use this same version of Scala in your own program -- newer major versions may not work. You can get the right version of Scala from [scala-lang.org](http://www.scala-lang.org/download/).
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Note: if you are building a binary distribution using `./make-distribution.sh`, you will not need to run
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`sbt/sbt assembly`.
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# Testing the Build
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Spark comes with several sample programs in the `examples` directory.
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@ -5,7 +5,7 @@ title: Spark Standalone Mode
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In addition to running on the Mesos or YARN cluster managers, Spark also provides a simple standalone deploy mode. You can launch a standalone cluster either manually, by starting a master and workers by hand, or use our provided [launch scripts](#cluster-launch-scripts). It is also possible to run these daemons on a single machine for testing.
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# Deploying Spark Standalone to a Cluster
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# Installing Spark Standalone to a Cluster
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The easiest way to deploy Spark is by running the `./make-distribution.sh` script to create a binary distribution.
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This distribution can be deployed to any machine with the Java runtime installed; there is no need to install Scala.
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@ -13,9 +13,6 @@ This distribution can be deployed to any machine with the Java runtime installed
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The recommended procedure is to deploy and start the master on one node first, get the master spark URL,
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then modify `conf/spark-env.sh` in the `dist/` directory before deploying to all the other nodes.
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It is also possible to deploy the source directory once you have built it with `sbt assembly`. Scala 2.9.3
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will need to be deployed on all the machines as well, and SCALA_HOME will need to point to the Scala installation.
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# Starting a Cluster Manually
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You can start a standalone master server by executing:
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