From 2b53447f325fa7adcfb9c69fd824467bf420af04 Mon Sep 17 00:00:00 2001
From: Aaron Kimball <aaron@magnify.io>
Date: Sun, 2 Mar 2014 23:26:47 -0800
Subject: [PATCH] SPARK-1173. Improve scala streaming docs.
Clarify imports to add implicit conversions to DStream and
fix other small typos in the streaming intro documentation.
Tested by inspecting output via a local jekyll server, c&p'ing the scala commands into a spark terminal.
Author: Aaron Kimball <aaron@magnify.io>
Closes #64 from kimballa/spark-1173-streaming-docs and squashes the following commits:
6fbff0e [Aaron Kimball] SPARK-1173. Improve scala streaming docs.
---
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 57e8858161..0cc572d1fd 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
--
GitLab