Add `pyspark` script to replace the other scripts.

Expand the PySpark programming guide.

docs/python-programming-guide.md

@@ -24,6 +24,35 @@ There are a few key differences between the Python and Scala APIs:
     - `sample`
     - `sort`
 
+In PySpark, RDDs support the same methods as their Scala counterparts but take Python functions and return Python collection types.
+Short functions can be passed to RDD methods using Python's [`lambda`](http://www.diveintopython.net/power_of_introspection/lambda_functions.html) syntax:
+
+{% highlight python %}
+logData = sc.textFile(logFile).cache()
+errors = logData.filter(lambda s: 'ERROR' in s.split())
+{% endhighlight %}
+
+You can also pass functions that are defined using the `def` keyword; this is useful for more complicated functions that cannot be expressed using `lambda`:
+
+{% highlight python %}
+def is_error(line):
+    return 'ERROR' in line.split()
+errors = logData.filter(is_error)
+{% endhighlight %}
+
+Functions can access objects in enclosing scopes, although modifications to those objects within RDD methods will not be propagated to other tasks:
+
+{% highlight python %}
+error_keywords = ["Exception", "Error"]
+def is_error(line):
+     words = line.split()
+     return any(keyword in words for keyword in error_keywords)
+errors = logData.filter(is_error)
+{% endhighlight %}
+
+PySpark will automatically ship these functions to workers, along with any objects that they reference.
+Instances of classes will be serialized and shipped to workers by PySpark, but classes themselves cannot be automatically distributed to workers.
+The [Standalone Use](#standalone-use) section describes how to ship code dependencies to workers.
 
 # Installing and Configuring PySpark
 
@@ -34,13 +63,14 @@ By default, PySpark's scripts will run programs using `python`; an alternate Pyt
 
 All of PySpark's library dependencies, including [Py4J](http://py4j.sourceforge.net/), are bundled with PySpark and automatically imported.
 
-Standalone PySpark jobs should be run using the `run-pyspark` script, which automatically configures the Java and Python environmnt using the settings in `conf/spark-env.sh`.
+Standalone PySpark jobs should be run using the `pyspark` script, which automatically configures the Java and Python environment using the settings in `conf/spark-env.sh`.
 The script automatically adds the `pyspark` package to the `PYTHONPATH`.
 
 
 # Interactive Use
 
-PySpark's `pyspark-shell` script provides a simple way to learn the API:
+The `pyspark` script launches a Python interpreter that is configured to run PySpark jobs.
+When run without any input files, `pyspark` launches a shell that can be used explore data interactively, which is a simple way to learn the API:
 
 {% highlight python %}
 >>> words = sc.textFile("/usr/share/dict/words")
@@ -48,9 +78,18 @@ PySpark's `pyspark-shell` script provides a simple way to learn the API:
 [u'spar', u'sparable', u'sparada', u'sparadrap', u'sparagrass']
 {% endhighlight %}
 
+By default, the `pyspark` shell creates SparkContext that runs jobs locally.
+To connect to a non-local cluster, set the `MASTER` environment variable.
+For example, to use the `pyspark` shell with a [standalone Spark cluster](spark-standalone.html):
+
+{% highlight shell %}
+$ MASTER=spark://IP:PORT ./pyspark
+{% endhighlight %}
+
+
 # Standalone Use
 
-PySpark can also be used from standalone Python scripts by creating a SparkContext in the script and running the script using the `run-pyspark` script in the `pyspark` directory.
+PySpark can also be used from standalone Python scripts by creating a SparkContext in your script and running the script using `pyspark`.
 The Quick Start guide includes a [complete example](quick-start.html#a-standalone-job-in-python) of a standalone Python job.
 
 Code dependencies can be deployed by listing them in the `pyFiles` option in the SparkContext constructor:
@@ -65,8 +104,8 @@ Code dependencies can be added to an existing SparkContext using its `addPyFile(
 
 # Where to Go from Here
 
-PySpark includes several sample programs using the Python API in `pyspark/examples`.
-You can run them by passing the files to the `pyspark-run` script included in PySpark -- for example `./pyspark-run examples/wordcount.py`.
+PySpark includes several sample programs using the Python API in `python/examples`.
+You can run them by passing the files to the `pyspark` script -- for example `./pyspark python/examples/wordcount.py`.
 Each example program prints usage help when run without any arguments.
 
 We currently provide [API documentation](api/pyspark/index.html) for the Python API as Epydoc.

docs/quick-start.md

@@ -258,11 +258,11 @@ We can pass Python functions to Spark, which are automatically serialized along
 For jobs that use custom classes or third-party libraries, we can add those code dependencies to SparkContext to ensure that they will be available on remote machines; this is described in more detail in the [Python programming guide](python-programming-guide).
 `SimpleJob` is simple enough that we do not need to specify any code dependencies.
 
-We can run this job using the `run-pyspark` script in `$SPARK_HOME/pyspark`:
+We can run this job using the `pyspark` script:
 
 {% highlight python %}
 $ cd $SPARK_HOME
-$ ./pyspark/run-pyspark SimpleJob.py
+$ ./pyspark SimpleJob.py
 ...
 Lines with a: 8422, Lines with b: 1836
 {% endhighlight python %}

run-pyspark → pyspark

@@ -20,6 +20,10 @@ export PYSPARK_PYTHON
 # Add the PySpark classes to the Python path:
 export PYTHONPATH=$SPARK_HOME/python/:$PYTHONPATH
 
+# Load the PySpark shell.py script when ./pyspark is used interactively:
+export OLD_PYTHONSTARTUP=$PYTHONSTARTUP
+export PYTHONSTARTUP=$FWDIR/python/pyspark/shell.py
+
 # Launch with `scala` by default:
 if [[ "$SPARK_LAUNCH_WITH_SCALA" != "0" ]] ; then
     export SPARK_LAUNCH_WITH_SCALA=1

pyspark-shell

-#!/usr/bin/env bash
-FWDIR="`dirname $0`"
-exec $FWDIR/run-pyspark $FWDIR/python/pyspark/shell.py "$@"

python/pyspark/shell.py

 """
 An interactive shell.
-"""
-import optparse  # I prefer argparse, but it's not included with Python < 2.7
-import code
-import sys
 
+This fle is designed to be launched as a PYTHONSTARTUP script.
+"""
+import os
 from pyspark.context import SparkContext
 
 
-def main(master='local', ipython=False):
-    sc = SparkContext(master, 'PySparkShell')
-    user_ns = {'sc' : sc}
-    banner = "Spark context avaiable as sc."
-    if ipython:
-        import IPython
-        IPython.embed(user_ns=user_ns, banner2=banner)
-    else:
-        print banner
-        code.interact(local=user_ns)
-
+sc = SparkContext(os.environ.get("MASTER", "local"), "PySparkShell")
+print "Spark context avaiable as sc."
 
-if __name__ == '__main__':
-    usage = "usage: %prog [options] master"
-    parser = optparse.OptionParser(usage=usage)
-    parser.add_option("-i", "--ipython", help="Run IPython shell",
-                      action="store_true")
-    (options, args) = parser.parse_args()
-    if len(sys.argv) > 1:
-        master = args[0]
-    else:
-        master = 'local'
-    main(master, options.ipython)
+# The ./pyspark script stores the old PYTHONSTARTUP value in OLD_PYTHONSTARTUP,
+# which allows us to execute the user's PYTHONSTARTUP file:
+_pythonstartup = os.environ.get('OLD_PYTHONSTARTUP')
+if _pythonstartup and os.path.isfile(_pythonstartup):
+        execfile(_pythonstartup)

python/run-tests

+#!/usr/bin/env bash
+
+# Figure out where the Scala framework is installed
+FWDIR="$(cd `dirname $0`; cd ../; pwd)"
+
+$FWDIR/pyspark pyspark/rdd.py
+$FWDIR/pyspark -m doctest pyspark/broadcast.py
+
+# TODO: in the long-run, it would be nice to use a test runner like `nose`.