Repository: spark Updated Branches: refs/heads/branch-0.9 69fc97df0 -> 19cf2f73e
Fixed typo on Spark quick-start docs. Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/19cf2f73 Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/19cf2f73 Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/19cf2f73 Branch: refs/heads/branch-0.9 Commit: 19cf2f73e2e8c2dc50fb8b9ff5d5e1da9e9800a6 Parents: 69fc97d Author: Tathagata Das <tathagata.das1...@gmail.com> Authored: Mon Apr 7 18:27:46 2014 -0700 Committer: Tathagata Das <tathagata.das1...@gmail.com> Committed: Mon Apr 7 18:27:46 2014 -0700 ---------------------------------------------------------------------- docs/quick-start.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/19cf2f73/docs/quick-start.md ---------------------------------------------------------------------- diff --git a/docs/quick-start.md b/docs/quick-start.md index 13df6be..60e8b1b 100644 --- a/docs/quick-start.md +++ b/docs/quick-start.md @@ -124,7 +124,7 @@ object SimpleApp { } {% endhighlight %} -This program just counts the number of lines containing 'a' and the number containing 'b' in the Spark README. Note that you'll need to replace $YOUR_SPARK_HOME with the location where Spark is installed. Unlike the earlier examples with the Spark shell, which initializes its own SparkContext, we initialize a SparkContext as part of the proogram. We pass the SparkContext constructor four arguments, the type of scheduler we want to use (in this case, a local scheduler), a name for the application, the directory where Spark is installed, and a name for the jar file containing the application's code. The final two arguments are needed in a distributed setting, where Spark is running across several nodes, so we include them for completeness. Spark will automatically ship the jar files you list to slave nodes. +This program just counts the number of lines containing 'a' and the number containing 'b' in the Spark README. Note that you'll need to replace $YOUR_SPARK_HOME with the location where Spark is installed. Unlike the earlier examples with the Spark shell, which initializes its own SparkContext, we initialize a SparkContext as part of the program. We pass the SparkContext constructor four arguments, the type of scheduler we want to use (in this case, a local scheduler), a name for the application, the directory where Spark is installed, and a name for the jar file containing the application's code. The final two arguments are needed in a distributed setting, where Spark is running across several nodes, so we include them for completeness. Spark will automatically ship the jar files you list to slave nodes. This file depends on the Spark API, so we'll also include an sbt configuration file, `simple.sbt` which explains that Spark is a dependency. This file also adds a repository that Spark depends on:
