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| author | Matei Zaharia <matei@eecs.berkeley.edu> | 2013-09-07 00:34:12 -0400 |
|---|---|---|
| committer | Matei Zaharia <matei@eecs.berkeley.edu> | 2013-09-08 00:29:11 -0700 |
| commit | 651a96adf7b53085bd810e153f8eabf52eed1994 (patch) | |
| tree | 70e9c70470c93c4630de0f958eaed4b98706d2ba /docs/scala-programming-guide.md | |
| parent | 98fb69822cf780160bca51abeaab7c82e49fab54 (diff) | |
| download | spark-651a96adf7b53085bd810e153f8eabf52eed1994.tar.gz spark-651a96adf7b53085bd810e153f8eabf52eed1994.tar.bz2 spark-651a96adf7b53085bd810e153f8eabf52eed1994.zip | |
More fair scheduler docs and property names.
Also changed uses of "job" terminology to "application" when they
referred to an entire Spark program, to avoid confusion.
Diffstat (limited to 'docs/scala-programming-guide.md')
| -rw-r--r-- | docs/scala-programming-guide.md | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/docs/scala-programming-guide.md b/docs/scala-programming-guide.md index f7768e55fc..03647a2ad2 100644 --- a/docs/scala-programming-guide.md +++ b/docs/scala-programming-guide.md @@ -87,10 +87,10 @@ For running on YARN, Spark launches an instance of the standalone deploy cluster ### Deploying Code on a Cluster -If you want to run your job on a cluster, you will need to specify the two optional parameters to `SparkContext` to let it find your code: +If you want to run your application on a cluster, you will need to specify the two optional parameters to `SparkContext` to let it find your code: * `sparkHome`: The path at which Spark is installed on your worker machines (it should be the same on all of them). -* `jars`: A list of JAR files on the local machine containing your job's code and any dependencies, which Spark will deploy to all the worker nodes. You'll need to package your job into a set of JARs using your build system. For example, if you're using SBT, the [sbt-assembly](https://github.com/sbt/sbt-assembly) plugin is a good way to make a single JAR with your code and dependencies. +* `jars`: A list of JAR files on the local machine containing your application's code and any dependencies, which Spark will deploy to all the worker nodes. You'll need to package your application into a set of JARs using your build system. For example, if you're using SBT, the [sbt-assembly](https://github.com/sbt/sbt-assembly) plugin is a good way to make a single JAR with your code and dependencies. If you run `spark-shell` on a cluster, you can add JARs to it by specifying the `ADD_JARS` environment variable before you launch it. This variable should contain a comma-separated list of JARs. For example, `ADD_JARS=a.jar,b.jar ./spark-shell` will launch a shell with `a.jar` and `b.jar` on its classpath. In addition, any new classes you define in the shell will automatically be distributed. |