Changed networkStream to socketStream and pluggableNetworkStream to become networkStream as a way to create streams from arbitrary network receiver.

1 files changed, 5 insertions, 5 deletions

diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index d408e80359..71e1bd4aab 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -34,16 +34,16 @@ The StreamingContext is used to creating InputDStreams from input sources:
 
 {% highlight scala %}
 // Assuming ssc is the StreamingContext
-ssc.networkStream(hostname, port)   // Creates a stream that uses a TCP socket to read data from hostname:port
-ssc.textFileStream(directory)       // Creates a stream by monitoring and processing new files in a HDFS directory
+ssc.textFileStream(directory)      // Creates a stream by monitoring and processing new files in a HDFS directory
+ssc.socketStream(hostname, port)   // Creates a stream that uses a TCP socket to read data from hostname:port
 {% endhighlight %}
 
-A complete list of input sources is available in the [StreamingContext API documentation](api/streaming/index.html#spark.streaming.StreamingContext). Data received from these sources can be processed using DStream operations, which are explained next.
+We also provide a input streams for Kafka, Flume, Akka actor, etc. For a complete list of input streams, take a look at the [StreamingContext API documentation](api/streaming/index.html#spark.streaming.StreamingContext).
 
 
 
 # DStream Operations
-Once an input DStream has been created, you can transform it using _DStream operators_. Most of these operators return new DStreams which you can further transform. Eventually, you'll need to call an _output operator_, which forces evaluation of the DStream by writing data out to an external source.
+Data received from the input streams can be processed using _DStream operations_. There are two kinds of operations - _transformations_ and _output operations_. Similar to RDD transformations, DStream transformations operate on one or more DStreams to create new DStreams with transformed data. After applying a sequence of transformations to the input streams, you'll need to call the output operations, which writies data out to an external source. 
 
 ## Transformations
 
@@ -452,4 +452,4 @@ If the driver had crashed in the middle of the processing of time 3, then it wil
 
 # Where to Go from Here
 * Documentation - [Scala and Java](api/streaming/index.html)
-* More examples - [Scala](https://github.com/mesos/spark/tree/master/examples/src/main/scala/spark/streaming/examples) and [Java](https://github.com/mesos/spark/tree/master/examples/src/main/java/spark/streaming/examples)
\ No newline at end of file
+* More examples - [Scala](https://github.com/mesos/spark/tree/master/examples/src/main/scala/spark/streaming/examples) and [Java](https://github.com/mesos/spark/tree/master/examples/src/main/java/spark/streaming/examples)