[SPARK-1824] Remove <master> from Python examples

docs/index.md

@@ -43,12 +43,15 @@ The `--master` option specifies the
 locally with one thread, or `local[N]` to run locally with N threads. You should start by using
 `local` for testing. For a full list of options, run Spark shell with the `--help` option.
 
-Spark also provides a Python interface. To run an example Spark application written in Python, use
-`bin/pyspark <program> [params]`. For example,
+Spark also provides a Python interface. To run Spark interactively in a Python interpreter, use
+`bin/pyspark`. As in Spark shell, you can also pass in the `--master` option to configure your
+master URL.
 
-    ./bin/pyspark examples/src/main/python/pi.py local[2] 10
+    ./bin/pyspark --master local[2]
 
-or simply `bin/pyspark` without any arguments to run Spark interactively in a python interpreter.
+Example applications are also provided in Python. For example,
+
+    ./bin/spark-submit examples/src/main/python/pi.py 10
 
 # Launching on a Cluster
 

docs/python-programming-guide.md

@@ -60,13 +60,9 @@ 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 `bin/spark-submit` script, which automatically
-configures the Java and Python environment for running Spark.
-
-
 # Interactive Use
 
-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:
+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:
 
 {% highlight bash %}
 $ sbt/sbt assembly
@@ -83,20 +79,24 @@ The Python shell can be used explore data interactively and is a simple way to l
 {% endhighlight %}
 
 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):
+your machine's logical cores. To connect to a non-local cluster, or to specify a number of cores,
+set the `--master` flag. For example, to use the `bin/pyspark` shell with a
+[standalone Spark cluster](spark-standalone.html):
 
 {% highlight bash %}
-$ MASTER=spark://IP:PORT ./bin/pyspark
+$ ./bin/pyspark --master spark://1.2.3.4:7077
 {% endhighlight %}
 
 Or, to use exactly four cores on the local machine:
 
 {% highlight bash %}
-$ MASTER=local[4] ./bin/pyspark
+$ ./bin/pyspark --master local[4]
 {% endhighlight %}
 
+Under the hood `bin/pyspark` is a wrapper around the
+[Spark submit script](cluster-overview.html#launching-applications-with-spark-submit), so these
+two scripts share the same list of options. For a complete list of options, run `bin/pyspark` with
+the `--help` option.
 
 ## IPython
 
@@ -115,13 +115,14 @@ the [IPython Notebook](http://ipython.org/notebook.html) with PyLab graphing sup
 $ 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.
+IPython also works on a cluster or on multiple cores if you set the `--master` flag.
 
 
 # Standalone Programs
 
-PySpark can also be used from standalone Python scripts by creating a SparkContext in your script and running the script using `bin/spark-submit`.
-The Quick Start guide includes a [complete example](quick-start.html#standalone-applications) of a standalone Python application.
+PySpark can also be used from standalone Python scripts by creating a SparkContext in your script
+and running the script using `bin/spark-submit`. The Quick Start guide includes a
+[complete example](quick-start.html#standalone-applications) of a standalone Python application.
 
 Code dependencies can be deployed by passing .zip or .egg files in the `--py-files` option of `spark-submit`:
 
@@ -138,6 +139,7 @@ You can set [configuration properties](configuration.html#spark-properties) by p
 {% highlight python %}
 from pyspark import SparkConf, SparkContext
 conf = (SparkConf()
+         .setMaster("local")
          .setAppName("My app")
          .set("spark.executor.memory", "1g"))
 sc = SparkContext(conf = conf)
@@ -164,6 +166,6 @@ some example applications.
 PySpark also includes several sample programs in the [`examples/src/main/python` folder](https://github.com/apache/spark/tree/master/examples/src/main/python).
 You can run them by passing the files to `pyspark`; e.g.:
 
-    ./bin/spark-submit examples/src/main/python/wordcount.py local[2] README.md
+    ./bin/spark-submit examples/src/main/python/wordcount.py README.md
 
-Each program prints usage help when run without arguments.
+Each program prints usage help when run without the sufficient arguments.

examples/src/main/python/als.py

@@ -46,15 +46,15 @@ def update(i, vec, mat, ratings):
     return np.linalg.solve(XtX, Xty)
 
 if __name__ == "__main__":
-    if len(sys.argv) < 2:
-        print >> sys.stderr, "Usage: als <master> <M> <U> <F> <iters> <slices>"
-        exit(-1)
-    sc = SparkContext(sys.argv[1], "PythonALS", pyFiles=[realpath(__file__)])
-    M = int(sys.argv[2]) if len(sys.argv) > 2 else 100
-    U = int(sys.argv[3]) if len(sys.argv) > 3 else 500
-    F = int(sys.argv[4]) if len(sys.argv) > 4 else 10
-    ITERATIONS = int(sys.argv[5]) if len(sys.argv) > 5 else 5
-    slices = int(sys.argv[6]) if len(sys.argv) > 6 else 2
+    """
+    Usage: als [M] [U] [F] [iterations] [slices]"
+    """
+    sc = SparkContext(appName="PythonALS")
+    M = int(sys.argv[1]) if len(sys.argv) > 1 else 100
+    U = int(sys.argv[2]) if len(sys.argv) > 2 else 500
+    F = int(sys.argv[3]) if len(sys.argv) > 3 else 10
+    ITERATIONS = int(sys.argv[4]) if len(sys.argv) > 4 else 5
+    slices = int(sys.argv[5]) if len(sys.argv) > 5 else 2
 
     print "Running ALS with M=%d, U=%d, F=%d, iters=%d, slices=%d\n" % \
             (M, U, F, ITERATIONS, slices)

examples/src/main/python/kmeans.py

@@ -45,14 +45,14 @@ def closestPoint(p, centers):
 
 
 if __name__ == "__main__":
-    if len(sys.argv) < 5:
-        print >> sys.stderr, "Usage: kmeans <master> <file> <k> <convergeDist>"
+    if len(sys.argv) != 4:
+        print >> sys.stderr, "Usage: kmeans <file> <k> <convergeDist>"
         exit(-1)
-    sc = SparkContext(sys.argv[1], "PythonKMeans")
-    lines = sc.textFile(sys.argv[2])
+    sc = SparkContext(appName="PythonKMeans")
+    lines = sc.textFile(sys.argv[1])
     data = lines.map(parseVector).cache()
-    K = int(sys.argv[3])
-    convergeDist = float(sys.argv[4])
+    K = int(sys.argv[2])
+    convergeDist = float(sys.argv[3])
 
     kPoints = data.takeSample(False, K, 1)
     tempDist = 1.0

examples/src/main/python/logistic_regression.py

@@ -47,12 +47,12 @@ def readPointBatch(iterator):
     return [matrix]
 
 if __name__ == "__main__":
-    if len(sys.argv) != 4:
-        print >> sys.stderr, "Usage: logistic_regression <master> <file> <iters>"
+    if len(sys.argv) != 3:
+        print >> sys.stderr, "Usage: logistic_regression <file> <iterations>"
         exit(-1)
-    sc = SparkContext(sys.argv[1], "PythonLR", pyFiles=[realpath(__file__)])
-    points = sc.textFile(sys.argv[2]).mapPartitions(readPointBatch).cache()
-    iterations = int(sys.argv[3])
+    sc = SparkContext(appName="PythonLR")
+    points = sc.textFile(sys.argv[1]).mapPartitions(readPointBatch).cache()
+    iterations = int(sys.argv[2])
 
     # Initialize w to a random value
     w = 2 * np.random.ranf(size=D) - 1

examples/src/main/python/mllib/kmeans.py

@@ -33,12 +33,12 @@ def parseVector(line):
 
 
 if __name__ == "__main__":
-    if len(sys.argv) < 4:
-        print >> sys.stderr, "Usage: kmeans <master> <file> <k>"
+    if len(sys.argv) != 3:
+        print >> sys.stderr, "Usage: kmeans <file> <k>"
         exit(-1)
-    sc = SparkContext(sys.argv[1], "KMeans")
-    lines = sc.textFile(sys.argv[2])
+    sc = SparkContext(appName="KMeans")
+    lines = sc.textFile(sys.argv[1])
     data = lines.map(parseVector)
-    k = int(sys.argv[3])
+    k = int(sys.argv[2])
     model = KMeans.train(data, k)
     print "Final centers: " + str(model.clusterCenters)

examples/src/main/python/mllib/logistic_regression.py

@@ -39,12 +39,12 @@ def parsePoint(line):
 
 
 if __name__ == "__main__":
-    if len(sys.argv) != 4:
-        print >> sys.stderr, "Usage: logistic_regression <master> <file> <iters>"
+    if len(sys.argv) != 3:
+        print >> sys.stderr, "Usage: logistic_regression <file> <iterations>"
         exit(-1)
-    sc = SparkContext(sys.argv[1], "PythonLR")
-    points = sc.textFile(sys.argv[2]).map(parsePoint)
-    iterations = int(sys.argv[3])
+    sc = SparkContext(appName="PythonLR")
+    points = sc.textFile(sys.argv[1]).map(parsePoint)
+    iterations = int(sys.argv[2])
     model = LogisticRegressionWithSGD.train(points, iterations)
     print "Final weights: " + str(model.weights)
     print "Final intercept: " + str(model.intercept)

examples/src/main/python/pagerank.py

@@ -36,19 +36,19 @@ def parseNeighbors(urls):
 
 
 if __name__ == "__main__":
-    if len(sys.argv) < 3:
-        print >> sys.stderr, "Usage: pagerank <master> <file> <number_of_iterations>"
+    if len(sys.argv) != 3:
+        print >> sys.stderr, "Usage: pagerank <file> <iterations>"
         exit(-1)
 
     # Initialize the spark context.
-    sc = SparkContext(sys.argv[1], "PythonPageRank")
+    sc = SparkContext(appName="PythonPageRank")
 
     # Loads in input file. It should be in format of:
     #     URL         neighbor URL
     #     URL         neighbor URL
     #     URL         neighbor URL
     #     ...
-    lines = sc.textFile(sys.argv[2], 1)
+    lines = sc.textFile(sys.argv[1], 1)
 
     # Loads all URLs from input file and initialize their neighbors.
     links = lines.map(lambda urls: parseNeighbors(urls)).distinct().groupByKey().cache()
@@ -57,7 +57,7 @@ def parseNeighbors(urls):
     ranks = links.map(lambda (url, neighbors): (url, 1.0))
 
     # Calculates and updates URL ranks continuously using PageRank algorithm.
-    for iteration in xrange(int(sys.argv[3])):
+    for iteration in xrange(int(sys.argv[2])):
         # Calculates URL contributions to the rank of other URLs.
         contribs = links.join(ranks).flatMap(lambda (url, (urls, rank)):
             computeContribs(urls, rank))

examples/src/main/python/pi.py

@@ -23,11 +23,11 @@
 
 
 if __name__ == "__main__":
-    if len(sys.argv) == 1:
-        print >> sys.stderr, "Usage: pi <master> [<slices>]"
-        exit(-1)
-    sc = SparkContext(sys.argv[1], "PythonPi")
-    slices = int(sys.argv[2]) if len(sys.argv) > 2 else 2
+    """
+        Usage: pi [slices]
+    """
+    sc = SparkContext(appName="PythonPi")
+    slices = int(sys.argv[1]) if len(sys.argv) > 1 else 2
     n = 100000 * slices
     def f(_):
         x = random() * 2 - 1

examples/src/main/python/sort.py

@@ -21,11 +21,11 @@
 
 
 if __name__ == "__main__":
-    if len(sys.argv) < 3:
-        print >> sys.stderr, "Usage: sort <master> <file>"
+    if len(sys.argv) != 2:
+        print >> sys.stderr, "Usage: sort <file>"
         exit(-1)
-    sc = SparkContext(sys.argv[1], "PythonSort")
-    lines = sc.textFile(sys.argv[2], 1)
+    sc = SparkContext(appName="PythonSort")
+    lines = sc.textFile(sys.argv[1], 1)
     sortedCount = lines.flatMap(lambda x: x.split(' ')) \
                   .map(lambda x: (int(x), 1)) \
                   .sortByKey(lambda x: x)

examples/src/main/python/transitive_closure.py

@@ -36,11 +36,11 @@ def generateGraph():
 
 
 if __name__ == "__main__":
-    if len(sys.argv) == 1:
-        print >> sys.stderr, "Usage: transitive_closure <master> [<slices>]"
-        exit(-1)
-    sc = SparkContext(sys.argv[1], "PythonTransitiveClosure")
-    slices = int(sys.argv[2]) if len(sys.argv) > 2 else 2
+    """
+    Usage: transitive_closure [slices]
+    """
+    sc = SparkContext(appName="PythonTransitiveClosure")
+    slices = int(sys.argv[1]) if len(sys.argv) > 1 else 2
     tc = sc.parallelize(generateGraph(), slices).cache()
 
     # Linear transitive closure: each round grows paths by one edge,

examples/src/main/python/wordcount.py

@@ -22,11 +22,11 @@
 
 
 if __name__ == "__main__":
-    if len(sys.argv) < 3:
-        print >> sys.stderr, "Usage: wordcount <master> <file>"
+    if len(sys.argv) != 2:
+        print >> sys.stderr, "Usage: wordcount <file>"
         exit(-1)
-    sc = SparkContext(sys.argv[1], "PythonWordCount")
-    lines = sc.textFile(sys.argv[2], 1)
+    sc = SparkContext(appName="PythonWordCount")
+    lines = sc.textFile(sys.argv[1], 1)
     counts = lines.flatMap(lambda x: x.split(' ')) \
                   .map(lambda x: (x, 1)) \
                   .reduceByKey(add)