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-rw-r--r--docs/python-programming-guide.md7
-rw-r--r--docs/scala-programming-guide.md5
2 files changed, 7 insertions, 5 deletions
diff --git a/docs/python-programming-guide.md b/docs/python-programming-guide.md
index c2e5327324..888631e702 100644
--- a/docs/python-programming-guide.md
+++ b/docs/python-programming-guide.md
@@ -82,15 +82,16 @@ The Python shell can be used explore data interactively and is a simple way to l
>>> help(pyspark) # Show all pyspark functions
{% endhighlight %}
-By default, the `bin/pyspark` shell creates SparkContext that runs applications locally on a single core.
-To connect to a non-local cluster, or use multiple cores, set the `MASTER` environment variable.
+By default, the `bin/pyspark` shell creates SparkContext that runs applications locally on all of
+your machine's logical cores.
+To connect to a non-local cluster, or to specify a number of cores, set the `MASTER` environment variable.
For example, to use the `bin/pyspark` shell with a [standalone Spark cluster](spark-standalone.html):
{% highlight bash %}
$ MASTER=spark://IP:PORT ./bin/pyspark
{% endhighlight %}
-Or, to use four cores on the local machine:
+Or, to use exactly four cores on the local machine:
{% highlight bash %}
$ MASTER=local[4] ./bin/pyspark
diff --git a/docs/scala-programming-guide.md b/docs/scala-programming-guide.md
index 77373890ee..a07cd2e0a3 100644
--- a/docs/scala-programming-guide.md
+++ b/docs/scala-programming-guide.md
@@ -54,7 +54,7 @@ object for more advanced configuration.
The `master` parameter is a string specifying a [Spark or Mesos cluster URL](#master-urls) to connect to, or a special "local" string to run in local mode, as described below. `appName` is a name for your application, which will be shown in the cluster web UI. Finally, the last two parameters are needed to deploy your code to a cluster if running in distributed mode, as described later.
-In the Spark shell, a special interpreter-aware SparkContext is already created for you, in the variable called `sc`. Making your own SparkContext will not work. You can set which master the context connects to using the `MASTER` environment variable, and you can add JARs to the classpath with the `ADD_JARS` variable. For example, to run `bin/spark-shell` on four cores, use
+In the Spark shell, a special interpreter-aware SparkContext is already created for you, in the variable called `sc`. Making your own SparkContext will not work. You can set which master the context connects to using the `MASTER` environment variable, and you can add JARs to the classpath with the `ADD_JARS` variable. For example, to run `bin/spark-shell` on exactly four cores, use
{% highlight bash %}
$ MASTER=local[4] ./bin/spark-shell
@@ -74,6 +74,7 @@ The master URL passed to Spark can be in one of the following formats:
<tr><th>Master URL</th><th>Meaning</th></tr>
<tr><td> local </td><td> Run Spark locally with one worker thread (i.e. no parallelism at all). </td></tr>
<tr><td> local[K] </td><td> Run Spark locally with K worker threads (ideally, set this to the number of cores on your machine).
+<tr><td> local[*] </td><td> Run Spark locally with as many worker threads as logical cores on your machine.</td></tr>
</td></tr>
<tr><td> spark://HOST:PORT </td><td> Connect to the given <a href="spark-standalone.html">Spark standalone
cluster</a> master. The port must be whichever one your master is configured to use, which is 7077 by default.
@@ -84,7 +85,7 @@ The master URL passed to Spark can be in one of the following formats:
</td></tr>
</table>
-If no master URL is specified, the spark shell defaults to "local".
+If no master URL is specified, the spark shell defaults to "local[*]".
For running on YARN, Spark launches an instance of the standalone deploy cluster within YARN; see [running on YARN](running-on-yarn.html) for details.