pyspark -> bin/pyspark

README.md

@@ -21,7 +21,7 @@ Once you've built Spark, the easiest way to start using it is the shell:
 
     ./bin/spark-shell
 
-Or, for the Python API, the Python shell (`./pyspark`).
+Or, for the Python API, the Python shell (`./bin/pyspark`).
 
 Spark also comes with several sample programs in the `examples` directory.
 To run one of them, use `./bin/run-example <class> <params>`. For example:

docs/index.md

@@ -35,7 +35,7 @@ or `local` to run locally with one thread, or `local[N]` to run locally with N t
 `local` for testing.
 
 Finally, you can run Spark interactively through modified versions of the Scala shell (`./bin/spark-shell`) or
-Python interpreter (`./pyspark`). These are a great way to learn the framework.
+Python interpreter (`./bin/pyspark`). These are a great way to learn the framework.
 
 # Launching on a Cluster
 

docs/python-programming-guide.md

@@ -47,7 +47,7 @@ PySpark will automatically ship these functions to workers, along with any objec
 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.
 
-In addition, PySpark fully supports interactive use---simply run `./pyspark` to launch an interactive shell.
+In addition, PySpark fully supports interactive use---simply run `./bin/pyspark` to launch an interactive shell.
 
 
 # Installing and Configuring PySpark
@@ -60,17 +60,17 @@ By default, PySpark requires `python` to be available on the system `PATH` and u
 
 All of PySpark's library dependencies, including [Py4J](http://py4j.sourceforge.net/), are bundled with PySpark and automatically imported.
 
-Standalone PySpark applications should be run using the `pyspark` script, which automatically configures the Java and Python environment using the settings in `conf/spark-env.sh` or `.cmd`.
-The script automatically adds the `pyspark` package to the `PYTHONPATH`.
+Standalone PySpark applications should be run using the `bin/pyspark` script, which automatically configures the Java and Python environment using the settings in `conf/spark-env.sh` or `.cmd`.
+The script automatically adds the `bin/pyspark` package to the `PYTHONPATH`.
 
 
 # Interactive Use
 
-The `pyspark` script launches a Python interpreter that is configured to run PySpark applications. To use `pyspark` interactively, first build Spark, then launch it directly from the command line without any options:
+The `bin/pyspark` script launches a Python interpreter that is configured to run PySpark applications. To use `pyspark` interactively, first build Spark, then launch it directly from the command line without any options:
 
 {% highlight bash %}
 $ sbt/sbt assembly
-$ ./pyspark
+$ ./bin/pyspark
 {% endhighlight %}
 
 The Python shell can be used explore data interactively and is a simple way to learn the API:
@@ -82,35 +82,35 @@ 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 `pyspark` shell creates SparkContext that runs applications locally on a single core.
+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.
-For example, to use the `pyspark` shell with a [standalone Spark cluster](spark-standalone.html):
+For example, to use the `bin/pyspark` shell with a [standalone Spark cluster](spark-standalone.html):
 
 {% highlight bash %}
-$ MASTER=spark://IP:PORT ./pyspark
+$ MASTER=spark://IP:PORT ./bin/pyspark
 {% endhighlight %}
 
 Or, to use four cores on the local machine:
 
 {% highlight bash %}
-$ MASTER=local[4] ./pyspark
+$ MASTER=local[4] ./bin/pyspark
 {% endhighlight %}
 
 
 ## IPython
 
 It is also possible to launch PySpark in [IPython](http://ipython.org), the enhanced Python interpreter.
-To do this, set the `IPYTHON` variable to `1` when running `pyspark`:
+To do this, set the `IPYTHON` variable to `1` when running `bin/pyspark`:
 
 {% highlight bash %}
-$ IPYTHON=1 ./pyspark
+$ IPYTHON=1 ./bin/pyspark
 {% endhighlight %}
 
 Alternatively, you can customize the `ipython` command by setting `IPYTHON_OPTS`. For example, to launch
 the [IPython Notebook](http://ipython.org/notebook.html) with PyLab graphing support:
 
 {% highlight bash %}
-$ IPYTHON_OPTS="notebook --pylab inline" ./pyspark
+$ IPYTHON_OPTS="notebook --pylab inline" ./bin/pyspark
 {% endhighlight %}
 
 IPython also works on a cluster or on multiple cores if you set the `MASTER` environment variable.
@@ -118,7 +118,7 @@ IPython also works on a cluster or on multiple cores if you set the `MASTER` env
 
 # Standalone Programs
 
-PySpark can also be used from standalone Python scripts by creating a SparkContext in your script and running the script using `pyspark`.
+PySpark can also be used from standalone Python scripts by creating a SparkContext in your script and running the script using `bin/pyspark`.
 The Quick Start guide includes a [complete example](quick-start.html#a-standalone-app-in-python) of a standalone Python application.
 
 Code dependencies can be deployed by listing them in the `pyFiles` option in the SparkContext constructor:
@@ -153,6 +153,6 @@ Many of the methods also contain [doctests](http://docs.python.org/2/library/doc
 PySpark also includes several sample programs in the [`python/examples` folder](https://github.com/apache/incubator-spark/tree/master/python/examples).
 You can run them by passing the files to `pyspark`; e.g.:
 
-    ./pyspark python/examples/wordcount.py
+    ./bin/pyspark python/examples/wordcount.py
 
 Each program prints usage help when run without arguments.

docs/quick-start.md

@@ -277,11 +277,11 @@ We can pass Python functions to Spark, which are automatically serialized along
 For applications 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.html).
 `SimpleApp` is simple enough that we do not need to specify any code dependencies.
 
-We can run this application using the `pyspark` script:
+We can run this application using the `bin/pyspark` script:
 
 {% highlight python %}
 $ cd $SPARK_HOME
-$ ./pyspark SimpleApp.py
+$ ./bin/pyspark SimpleApp.py
 ...
 Lines with a: 46, Lines with b: 23
 {% endhighlight python %}

python/pyspark/shell.py

@@ -47,7 +47,7 @@ print "Spark context available as sc."
 if add_files != None:
     print "Adding files: [%s]" % ", ".join(add_files)
 
-# The ./pyspark script stores the old PYTHONSTARTUP value in OLD_PYTHONSTARTUP,
+# The ./bin/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):