7 files changed, 25 insertions, 12 deletions
diff --git a/bin/spark-shell b/bin/spark-shell
index 535ee3ccd8..ea12d256b2 100755
--- a/bin/spark-shell
+++ b/bin/spark-shell
@@ -34,7 +34,7 @@ set -o posix
 FWDIR="$(cd `dirname $0`/..; pwd)"
 
 SPARK_REPL_OPTS="${SPARK_REPL_OPTS:-""}"
-DEFAULT_MASTER="local"
+DEFAULT_MASTER="local[*]"
 MASTER=${MASTER:-""}
 
 info_log=0
@@ -64,7 +64,7 @@ ${txtbld}OPTIONS${txtrst}:
                               is followed by m for megabytes or g for gigabytes, e.g. "1g".
     -dm --driver-memory     : The memory used by the Spark Shell, the number is followed 
                               by m for megabytes or g for gigabytes, e.g. "1g".
-    -m  --master            : A full string that describes the Spark Master, defaults to "local"
+    -m  --master            : A full string that describes the Spark Master, defaults to "local[*]"
                               e.g. "spark://localhost:7077".
     --log-conf              : Enables logging of the supplied SparkConf as INFO at start of the
                               Spark Context.
diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala
index 8382dd44f3..e5ebd350ee 100644
--- a/core/src/main/scala/org/apache/spark/SparkContext.scala
+++ b/core/src/main/scala/org/apache/spark/SparkContext.scala
@@ -1285,8 +1285,8 @@ object SparkContext extends Logging {
 
   /** Creates a task scheduler based on a given master URL. Extracted for testing. */
   private def createTaskScheduler(sc: SparkContext, master: String): TaskScheduler = {
-    // Regular expression used for local[N] master format
-    val LOCAL_N_REGEX = """local\[([0-9]+)\]""".r
+    // Regular expression used for local[N] and local[*] master formats
+    val LOCAL_N_REGEX = """local\[([0-9\*]+)\]""".r
     // Regular expression for local[N, maxRetries], used in tests with failing tasks
     val LOCAL_N_FAILURES_REGEX = """local\[([0-9]+)\s*,\s*([0-9]+)\]""".r
     // Regular expression for simulating a Spark cluster of [N, cores, memory] locally
@@ -1309,8 +1309,11 @@ object SparkContext extends Logging {
         scheduler
 
       case LOCAL_N_REGEX(threads) =>
+        def localCpuCount = Runtime.getRuntime.availableProcessors()
+        // local[*] estimates the number of cores on the machine; local[N] uses exactly N threads.
+        val threadCount = if (threads == "*") localCpuCount else threads.toInt
         val scheduler = new TaskSchedulerImpl(sc, MAX_LOCAL_TASK_FAILURES, isLocal = true)
-        val backend = new LocalBackend(scheduler, threads.toInt)
+        val backend = new LocalBackend(scheduler, threadCount)
         scheduler.initialize(backend)
         scheduler
 
diff --git a/core/src/test/scala/org/apache/spark/SparkContextSchedulerCreationSuite.scala b/core/src/test/scala/org/apache/spark/SparkContextSchedulerCreationSuite.scala
index b543471a5d..94fba10286 100644
--- a/core/src/test/scala/org/apache/spark/SparkContextSchedulerCreationSuite.scala
+++ b/core/src/test/scala/org/apache/spark/SparkContextSchedulerCreationSuite.scala
@@ -51,6 +51,14 @@ class SparkContextSchedulerCreationSuite
     }
   }
 
+  test("local-*") {
+    val sched = createTaskScheduler("local[*]")
+    sched.backend match {
+      case s: LocalBackend => assert(s.totalCores === Runtime.getRuntime.availableProcessors())
+      case _ => fail()
+    }
+  }
+
   test("local-n") {
     val sched = createTaskScheduler("local[5]")
     assert(sched.maxTaskFailures === 1)
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.
 
diff --git a/python/pyspark/shell.py b/python/pyspark/shell.py
index 3d779faf1f..35e48276e3 100644
--- a/python/pyspark/shell.py
+++ b/python/pyspark/shell.py
@@ -29,7 +29,7 @@ from pyspark.storagelevel import StorageLevel
 # this is the equivalent of ADD_JARS
 add_files = os.environ.get("ADD_FILES").split(',') if os.environ.get("ADD_FILES") != None else None
 
-sc = SparkContext(os.environ.get("MASTER", "local"), "PySparkShell", pyFiles=add_files)
+sc = SparkContext(os.environ.get("MASTER", "local[*]"), "PySparkShell", pyFiles=add_files)
 
 print """Welcome to
       ____              __
diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala b/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala
index 9b1da19500..5a367b6bb7 100644
--- a/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala
+++ b/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala
@@ -963,7 +963,7 @@ class SparkILoop(in0: Option[BufferedReader], protected val out: JPrintWriter,
       case Some(m) => m
       case None => {
         val prop = System.getenv("MASTER")
-        if (prop != null) prop else "local"
+        if (prop != null) prop else "local[*]"
       }
     }
     master