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author | Matei Zaharia <matei@eecs.berkeley.edu> | 2013-08-31 17:40:33 -0700 |
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committer | Matei Zaharia <matei@eecs.berkeley.edu> | 2013-08-31 17:40:33 -0700 |
commit | 9ddad0dcb47e3326151a53e270448b5135805ae5 (patch) | |
tree | 76f17bcb3af42b67b2e0ee93e1367d8e6dff8398 /docs/quick-start.md | |
parent | 4819baa658a6c8a3e4c5c504af284ea6091e4c35 (diff) | |
download | spark-9ddad0dcb47e3326151a53e270448b5135805ae5.tar.gz spark-9ddad0dcb47e3326151a53e270448b5135805ae5.tar.bz2 spark-9ddad0dcb47e3326151a53e270448b5135805ae5.zip |
Fixes suggested by Patrick
Diffstat (limited to 'docs/quick-start.md')
-rw-r--r-- | docs/quick-start.md | 10 |
1 files changed, 2 insertions, 8 deletions
diff --git a/docs/quick-start.md b/docs/quick-start.md index bac5d690a6..11d4370a1d 100644 --- a/docs/quick-start.md +++ b/docs/quick-start.md @@ -126,7 +126,7 @@ object SimpleJob { This job simply 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 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 also adds two repositories which host Spark dependencies: +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: {% highlight scala %} name := "Simple Project" @@ -137,9 +137,7 @@ scalaVersion := "{{site.SCALA_VERSION}}" libraryDependencies += "org.spark-project" %% "spark-core" % "{{site.SPARK_VERSION}}" -resolvers ++= Seq( - "Akka Repository" at "http://repo.akka.io/releases/", - "Spray Repository" at "http://repo.spray.cc/") +resolvers += "Akka Repository" at "http://repo.akka.io/releases/" {% endhighlight %} If you also wish to read data from Hadoop's HDFS, you will also need to add a dependency on `hadoop-client` for your version of HDFS: @@ -211,10 +209,6 @@ To build the job, we also write a Maven `pom.xml` file that lists Spark as a dep <version>1.0</version> <repositories> <repository> - <id>Spray.cc repository</id> - <url>http://repo.spray.cc</url> - </repository> - <repository> <id>Akka repository</id> <url>http://repo.akka.io/releases</url> </repository> |