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author | Reza Zadeh <rizlar@gmail.com> | 2014-01-09 22:45:32 -0800 |
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committer | Reza Zadeh <rizlar@gmail.com> | 2014-01-09 22:45:32 -0800 |
commit | 21c8a54c08354f8934fd8ec58b43879c1686ccad (patch) | |
tree | 51426328d9f0eafdeec7fb46ef99c86f27f86dd2 /docs/index.md | |
parent | cf5bd4ab2e9db72d3d9164053523e9e872d85b94 (diff) | |
parent | 300eaa994c399a0c991c1e39b4dd864a7aa4bdc6 (diff) | |
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Merge remote-tracking branch 'upstream/master' into sparsesvd
Conflicts:
docs/mllib-guide.md
Diffstat (limited to 'docs/index.md')
-rw-r--r-- | docs/index.md | 10 |
1 files changed, 5 insertions, 5 deletions
diff --git a/docs/index.md b/docs/index.md index d3ac696d1e..86d574daaa 100644 --- a/docs/index.md +++ b/docs/index.md @@ -24,9 +24,9 @@ For its Scala API, Spark {{site.SPARK_VERSION}} depends on Scala {{site.SCALA_VE # Running the Examples and Shell Spark comes with several sample programs in the `examples` directory. -To run one of the samples, use `./run-example <class> <params>` in the top-level Spark directory -(the `run-example` script sets up the appropriate paths and launches that program). -For example, try `./run-example org.apache.spark.examples.SparkPi local`. +To run one of the samples, use `./bin/run-example <class> <params>` in the top-level Spark directory +(the `bin/run-example` script sets up the appropriate paths and launches that program). +For example, try `./bin/run-example org.apache.spark.examples.SparkPi local`. Each example prints usage help when run with no parameters. Note that all of the sample programs take a `<master>` parameter specifying the cluster URL @@ -34,8 +34,8 @@ to connect to. This can be a [URL for a distributed cluster](scala-programming-g or `local` to run locally with one thread, or `local[N]` to run locally with N threads. You should start by using `local` for testing. -Finally, you can run Spark interactively through modified versions of the Scala shell (`./spark-shell`) or -Python interpreter (`./pyspark`). These are a great way to learn the framework. +Finally, you can run Spark interactively through modified versions of the Scala shell (`./bin/spark-shell`) or +Python interpreter (`./bin/pyspark`). These are a great way to learn the framework. # Launching on a Cluster |