From 6fbff0ec62ad3571704f63a7e2a1f92844276ca9 Mon Sep 17 00:00:00 2001
From: Aaron Kimball <aaron@magnify.io>
Date: Sun, 2 Mar 2014 23:13:05 -0800
Subject: [PATCH 1/2] SPARK-1173. Improve scala streaming docs.

Clarify imports to add implicit conversions to DStream and
fix other small typos in the streaming intro documentation.
---
 docs/streaming-programming-guide.md | 38 +++++++++++++++++++++++++----
 1 file changed, 33 insertions(+), 5 deletions(-)

diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index 57e88581616a2..0cc572d1fde36 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -58,11 +58,21 @@ do is as follows.
 
 <div class="codetabs">
 <div data-lang="scala"  markdown="1" >
+First, we import the names of the Spark Streaming classes, and some implicit
+conversions from StreamingContext into our environment, to add useful methods to
+other classes we need (like DStream).
 
-First, we create a
-[StreamingContext](api/streaming/index.html#org.apache.spark.streaming.StreamingContext) object,
-which is the main entry point for all streaming
-functionality. Besides Spark's configuration, we specify that any DStream will be processed
+[StreamingContext](api/streaming/index.html#org.apache.spark.streaming.StreamingContext) is the
+main entry point for all streaming functionality.
+
+{% highlight scala %}
+import org.apache.spark.streaming._
+import org.apache.spark.streaming.StreamingContext._
+{% endhighlight %}
+
+Then we create a
+[StreamingContext](api/streaming/index.html#org.apache.spark.streaming.StreamingContext) object.
+Besides Spark's configuration, we specify that any DStream will be processed
 in 1 second batches.
 
 {% highlight scala %}
@@ -98,7 +108,7 @@ val pairs = words.map(word => (word, 1))
 val wordCounts = pairs.reduceByKey(_ + _)
 
 // Print a few of the counts to the console
-wordCount.print()
+wordCounts.print()
 {% endhighlight %}
 
 The `words` DStream is further mapped (one-to-one transformation) to a DStream of `(word,
@@ -262,6 +272,24 @@ Time: 1357008430000 ms
     </td>
 </table>
 
+If you plan to run the Scala code for Spark Streaming-based use cases in the Spark
+shell, you should start the shell with the SparkConfiguration pre-configured to
+discard old batches periodically:
+
+{% highlight bash %}
+$ SPARK_JAVA_OPTS=-Dspark.cleaner.ttl=10000 bin/spark-shell
+{% endhighlight %}
+
+... and create your StreamingContext by wrapping the existing interactive shell
+SparkContext object, `sc`:
+
+{% highlight scala %}
+val ssc = new StreamingContext(sc, Seconds(1))
+{% endhighlight %}
+
+When working with the shell, you may also need to send a `^D` to your netcat session
+to force the pipeline to print the word counts to the console at the sink.
+
 ***************************************************************************************************  
 
 # Basics

From 152e7422b83b6043dc9b10190ccf7569830f12b3 Mon Sep 17 00:00:00 2001
From: Aaron Kimball <aaron@magnify.io>
Date: Sun, 2 Mar 2014 23:40:50 -0800
Subject: [PATCH 2/2] SPARK-1173. Fix typo in Java streaming example.

---
 docs/streaming-programming-guide.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md
index 0cc572d1fde36..2a56cf07d0cfc 100644
--- a/docs/streaming-programming-guide.md
+++ b/docs/streaming-programming-guide.md
@@ -188,7 +188,7 @@ JavaPairDStream<String, Integer> wordCounts = pairs.reduceByKey(
       return i1 + i2;
     }
   });
-wordCount.print();     // Print a few of the counts to the console
+wordCounts.print();     // Print a few of the counts to the console
 {% endhighlight %}
 
 The `words` DStream is further mapped (one-to-one transformation) to a DStream of `(word,