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author | felixcheung <felixcheung_m@hotmail.com> | 2015-11-03 11:53:10 -0800 |
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committer | Shivaram Venkataraman <shivaram@cs.berkeley.edu> | 2015-11-03 11:53:10 -0800 |
commit | a9676cc7107c5df6c62a58668c4d95ced1238370 (patch) | |
tree | 3098730205bb53400a7c4647380926465b464c48 /docs | |
parent | ebf8b0b48deaad64f7ca27051caee763451e2623 (diff) | |
download | spark-a9676cc7107c5df6c62a58668c4d95ced1238370.tar.gz spark-a9676cc7107c5df6c62a58668c4d95ced1238370.tar.bz2 spark-a9676cc7107c5df6c62a58668c4d95ced1238370.zip |
[SPARK-11407][SPARKR] Add doc for running from RStudio
![image](https://cloud.githubusercontent.com/assets/8969467/10871746/612ba44a-80a4-11e5-99a0-40b9931dee52.png)
(This is without css, but you get the idea)
shivaram
Author: felixcheung <felixcheung_m@hotmail.com>
Closes #9401 from felixcheung/rstudioprogrammingguide.
Diffstat (limited to 'docs')
-rw-r--r-- | docs/sparkr.md | 46 |
1 files changed, 43 insertions, 3 deletions
diff --git a/docs/sparkr.md b/docs/sparkr.md index 497a276679..437bd4756c 100644 --- a/docs/sparkr.md +++ b/docs/sparkr.md @@ -30,14 +30,22 @@ The entry point into SparkR is the `SparkContext` which connects your R program You can create a `SparkContext` using `sparkR.init` and pass in options such as the application name , any spark packages depended on, etc. Further, to work with DataFrames we will need a `SQLContext`, which can be created from the SparkContext. If you are working from the `sparkR` shell, the -`SQLContext` and `SparkContext` should already be created for you. +`SQLContext` and `SparkContext` should already be created for you, and you would not need to call +`sparkR.init`. +<div data-lang="r" markdown="1"> {% highlight r %} sc <- sparkR.init() sqlContext <- sparkRSQL.init(sc) {% endhighlight %} +</div> + +## Starting Up from RStudio -In the event you are creating `SparkContext` instead of using `sparkR` shell or `spark-submit`, you +You can also start SparkR from RStudio. You can connect your R program to a Spark cluster from +RStudio, R shell, Rscript or other R IDEs. To start, make sure SPARK_HOME is set in environment +(you can check [Sys.getenv](https://stat.ethz.ch/R-manual/R-devel/library/base/html/Sys.getenv.html)), +load the SparkR package, and call `sparkR.init` as below. In addition to calling `sparkR.init`, you could also specify certain Spark driver properties. Normally these [Application properties](configuration.html#application-properties) and [Runtime Environment](configuration.html#runtime-environment) cannot be set programmatically, as the @@ -45,9 +53,41 @@ driver JVM process would have been started, in this case SparkR takes care of th them, pass them as you would other configuration properties in the `sparkEnvir` argument to `sparkR.init()`. +<div data-lang="r" markdown="1"> {% highlight r %} -sc <- sparkR.init("local[*]", "SparkR", "/home/spark", list(spark.driver.memory="2g")) +if (nchar(Sys.getenv("SPARK_HOME")) < 1) { + Sys.setenv(SPARK_HOME = "/home/spark") +} +library(SparkR, lib.loc = c(file.path(Sys.getenv("SPARK_HOME"), "R", "lib"))) +sc <- sparkR.init(master = "local[*]", sparkEnvir = list(spark.driver.memory="2g")) {% endhighlight %} +</div> + +The following options can be set in `sparkEnvir` with `sparkR.init` from RStudio: + +<table class="table"> + <tr><th>Property Name</th><th>Property group</th><th><code>spark-submit</code> equivalent</th></tr> + <tr> + <td><code>spark.driver.memory</code></td> + <td>Application Properties</td> + <td><code>--driver-memory</code></td> + </tr> + <tr> + <td><code>spark.driver.extraClassPath</code></td> + <td>Runtime Environment</td> + <td><code>--driver-class-path</code></td> + </tr> + <tr> + <td><code>spark.driver.extraJavaOptions</code></td> + <td>Runtime Environment</td> + <td><code>--driver-java-options</code></td> + </tr> + <tr> + <td><code>spark.driver.extraLibraryPath</code></td> + <td>Runtime Environment</td> + <td><code>--driver-library-path</code></td> + </tr> +</table> </div> |