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Diffstat (limited to 'docs/quick-start.md')
| -rw-r--r-- | docs/quick-start.md | 6 |
1 files changed, 5 insertions, 1 deletions
diff --git a/docs/quick-start.md b/docs/quick-start.md index 5625fc2ddf..defdb34836 100644 --- a/docs/quick-start.md +++ b/docs/quick-start.md @@ -123,7 +123,7 @@ object SimpleJob extends Application { This job simply counts the number of lines containing 'a' and the number containing 'b' in a system log file. Unlike the earlier examples with the Spark shell, which initializes its own SparkContext, we initialize a SparkContext as part of the job. 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 job, the directory where Spark is installed, and a name for the jar file containing the job's sources. 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 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 two repositories which host Spark dependencies: {% highlight scala %} name := "Simple Project" @@ -133,6 +133,10 @@ version := "1.0" scalaVersion := "{{site.SCALA_VERSION}}" libraryDependencies += "org.spark-project" %% "spark-core" % "{{site.SPARK_VERSION}}" + +resolvers ++= Seq( + "Typesafe Repository" at "http://repo.typesafe.com/typesafe/releases/", + "Spray Repository" at "http://repo.spray.cc/") {% endhighlight %} Of course, for sbt to work correctly, we'll need to layout `SimpleJob.scala` and `simple.sbt` according to the typical directory structure. Once that is in place, we can create a JAR package containing the job's code, then use `sbt run` to execute our example job. |