Merge pull request #557 from ScrapCodes/style. Closes #557.

SPARK-1058, Fix Style Errors and Add Scala Style to Spark Build.

Author: Patrick Wendell <pwendell@gmail.com>
Author: Prashant Sharma <scrapcodes@gmail.com>

== Merge branch commits ==

commit 1a8bd1c059b842cb95cc246aaea74a79fec684f4
Author: Prashant Sharma <scrapcodes@gmail.com>
Date:   Sun Feb 9 17:39:07 2014 +0530

    scala style fixes

commit f91709887a8e0b608c5c2b282db19b8a44d53a43
Author: Patrick Wendell <pwendell@gmail.com>
Date:   Fri Jan 24 11:22:53 2014 -0800

    Adding scalastyle snapshot

4 files changed, 25 insertions, 14 deletions

diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/StatefulNetworkWordCount.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/StatefulNetworkWordCount.scala
index 88f1cef89b..c2d84a8e08 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/StatefulNetworkWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/streaming/examples/StatefulNetworkWordCount.scala
@@ -19,18 +19,21 @@ package org.apache.spark.streaming.examples
 
 import org.apache.spark.streaming._
 import org.apache.spark.streaming.StreamingContext._
-
+// scalastyle:off
 /**
- * Counts words cumulatively in UTF8 encoded, '\n' delimited text received from the network every second.
+ * Counts words cumulatively in UTF8 encoded, '\n' delimited text received from the network every
+ * second.
  * Usage: StatefulNetworkWordCount <master> <hostname> <port>
  *   <master> is the Spark master URL. In local mode, <master> should be 'local[n]' with n > 1.
- *   <hostname> and <port> describe the TCP server that Spark Streaming would connect to receive data.
+ *   <hostname> and <port> describe the TCP server that Spark Streaming would connect to receive
+ *   data.
  *
  * To run this on your local machine, you need to first run a Netcat server
  *    `$ nc -lk 9999`
  * and then run the example
  *    `$ ./bin/run-example org.apache.spark.streaming.examples.StatefulNetworkWordCount local[2] localhost 9999`
  */
+// scalastyle:on
 object StatefulNetworkWordCount {
   def main(args: Array[String]) {
     if (args.length < 3) {
@@ -50,8 +53,8 @@ object StatefulNetworkWordCount {
     }
 
     // Create the context with a 1 second batch size
-    val ssc = new StreamingContext(args(0), "NetworkWordCumulativeCountUpdateStateByKey", Seconds(1),
-      System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass))
+    val ssc = new StreamingContext(args(0), "NetworkWordCumulativeCountUpdateStateByKey",
+      Seconds(1), System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass))
     ssc.checkpoint(".")
 
     // Create a NetworkInputDStream on target ip:port and count the
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterAlgebirdHLL.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterAlgebirdHLL.scala
index a0094d460f..c6215fd0d7 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterAlgebirdHLL.scala
+++ b/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterAlgebirdHLL.scala
@@ -23,20 +23,24 @@ import com.twitter.algebird.HyperLogLog._
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming.{Seconds, StreamingContext}
 import org.apache.spark.streaming.twitter._
-
+// scalastyle:off
 /**
  * Illustrates the use of the HyperLogLog algorithm, from Twitter's Algebird library, to compute
  * a windowed and global estimate of the unique user IDs occurring in a Twitter stream.
  * <p>
  * <p>
- *   This <a href="http://highlyscalable.wordpress.com/2012/05/01/probabilistic-structures-web-analytics-data-mining/">
+ *   This <a href= "http://highlyscalable.wordpress.com/2012/05/01/probabilistic-structures-web-analytics-data
+ * -mining/">
  *   blog post</a> and this
- *   <a href="http://highscalability.com/blog/2012/4/5/big-data-counting-how-to-count-a-billion-distinct-objects-us.html">blog post</a>
- *   have good overviews of HyperLogLog (HLL). HLL is a memory-efficient datastructure for estimating
- *   the cardinality of a data stream, i.e. the number of unique elements.
+ *   <a href= "http://highscalability.com/blog/2012/4/5/big-data-counting-how-to-count-a-billion-distinct-objects-us.html">
+ *     blog post</a>
+ *   have good overviews of HyperLogLog (HLL). HLL is a memory-efficient datastructure for
+ *   estimating the cardinality of a data stream, i.e. the number of unique elements.
  * <p><p>
- *   Algebird's implementation is a monoid, so we can succinctly merge two HLL instances in the reduce operation.
+ *   Algebird's implementation is a monoid, so we can succinctly merge two HLL instances in the
+ *   reduce operation.
  */
+// scalastyle:on
 object TwitterAlgebirdHLL {
   def main(args: Array[String]) {
     if (args.length < 1) {
@@ -82,7 +86,8 @@ object TwitterAlgebirdHLL {
         userSet ++= partial
         println("Exact distinct users this batch: %d".format(partial.size))
         println("Exact distinct users overall: %d".format(userSet.size))
-        println("Error rate: %2.5f%%".format(((globalHll.estimatedSize / userSet.size.toDouble) - 1) * 100))
+        println("Error rate: %2.5f%%".format(((globalHll.estimatedSize / userSet.size.toDouble) - 1
+          ) * 100))
       }
     })
 
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/clickstream/PageViewGenerator.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/clickstream/PageViewGenerator.scala
index a2600989ca..0ac46c31c2 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/clickstream/PageViewGenerator.scala
+++ b/examples/src/main/scala/org/apache/spark/streaming/examples/clickstream/PageViewGenerator.scala
@@ -36,6 +36,7 @@ object PageView extends Serializable {
   }
 }
 
+// scalastyle:off
 /** Generates streaming events to simulate page views on a website.
   *
   * This should be used in tandem with PageViewStream.scala. Example:
@@ -44,7 +45,8 @@ object PageView extends Serializable {
   *
   * When running this, you may want to set the root logging level to ERROR in
   * conf/log4j.properties to reduce the verbosity of the output.
-  * */
+  */
+// scalastyle:on
 object PageViewGenerator {
   val pages = Map("http://foo.com/"        -> .7,
                   "http://foo.com/news"    -> 0.2,
diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/clickstream/PageViewStream.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/clickstream/PageViewStream.scala
index bb44bc3d06..2b130fb30e 100644
--- a/examples/src/main/scala/org/apache/spark/streaming/examples/clickstream/PageViewStream.scala
+++ b/examples/src/main/scala/org/apache/spark/streaming/examples/clickstream/PageViewStream.scala
@@ -21,7 +21,7 @@ import org.apache.spark.SparkContext._
 import org.apache.spark.streaming.{Seconds, StreamingContext}
 import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.streaming.examples.StreamingExamples
-
+// scalastyle:off
 /** Analyses a streaming dataset of web page views. This class demonstrates several types of
   * operators available in Spark streaming.
   *
@@ -29,6 +29,7 @@ import org.apache.spark.streaming.examples.StreamingExamples
   * $ ./bin/run-example org.apache.spark.streaming.examples.clickstream.PageViewGenerator 44444 10
   * $ ./bin/run-example org.apache.spark.streaming.examples.clickstream.PageViewStream errorRatePerZipCode localhost 44444
   */
+// scalastyle:on
 object PageViewStream {
   def main(args: Array[String]) {
     if (args.length != 3) {