Github user JoshRosen commented on a diff in the pull request:
https://github.com/apache/spark/pull/2808#discussion_r18999350
--- Diff: docs/streaming-programming-guide.md ---
@@ -398,6 +498,30 @@ JavaSparkContext sc = ... //existing JavaSparkContext
JavaStreamingContext ssc = new JavaStreamingContext(sc, new
Duration(1000));
{% endhighlight %}
</div>
+<div data-lang="python" markdown="1">
+
+A
[StreamingContext](api/python/pyspark.streaming.html#pyspark.streaming.StreamingContext)
object can be created from a
[SparkContext](api/python/pyspark.html#pyspark.SparkContext) object.
+
+{% highlight python %}
+from pyspark import SparkContext
+from pyspark.streaming import StreamingContext
+
+sc = SparkContext(master, appName)
+ssc = StreamingContext(sc, 1)
+{% endhighlight %}
+
+The `appName` parameter is a name for your application to show on the
cluster UI.
+`master` is a [Spark, Mesos or YARN cluster
URL](submitting-applications.html#master-urls),
+or a special __"local[\*]"__ string to run in local mode. In practice,
when running on a cluster,
+you will not want to hardcode `master` in the program,
+but rather [launch the application with
`spark-submit`](submitting-applications.html) and
+receive it there. However, for local testing and unit tests, you can pass
"local[\*]" to run Spark Streaming
+in-process (detects the number of cores in the local system).
+
+The batch interval must be set based on the latency requirements of your
application
+and available cluster resources. See the [Performance
Tuning](#setting-the-right-batch-size)
+section for more details.
+</div>
</div>
After a context is defined, you have to do the follow steps.
--- End diff --
As long as you're editing this file, it would be nice to fix the incorrect
formatting of the list here; the current markdown renders as
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