From 44dd57fb66bb676d753ad8d9757f9f4c03364113 Mon Sep 17 00:00:00 2001
From: Prashant Sharma <prashant.s@imaginea.com>
Date: Thu, 8 May 2014 10:23:05 -0700
Subject: SPARK-1565, update examples to be used with spark-submit script.

Commit for initial feedback, basically I am curious if we should prompt user for providing args esp. when its mandatory. And can we skip if they are not ?

Also few other things that did not work like
`bin/spark-submit examples/target/scala-2.10/spark-examples-1.0.0-SNAPSHOT-hadoop1.0.4.jar --class org.apache.spark.examples.SparkALS --arg 100 500 10 5 2`

Not all the args get passed properly, may be I have messed up something will try to sort it out hopefully.

Author: Prashant Sharma <prashant.s@imaginea.com>

Closes #552 from ScrapCodes/SPARK-1565/update-examples and squashes the following commits:

669dd23 [Prashant Sharma] Review comments
2727e70 [Prashant Sharma] SPARK-1565, update examples to be used with spark-submit script.
---
 .../java/org/apache/spark/examples/JavaHdfsLR.java | 13 ++++----
 .../org/apache/spark/examples/JavaLogQuery.java    | 13 ++++----
 .../org/apache/spark/examples/JavaPageRank.java    | 15 +++++----
 .../org/apache/spark/examples/JavaSparkPi.java     | 18 +++++------
 .../java/org/apache/spark/examples/JavaTC.java     | 24 +++++++-------
 .../org/apache/spark/examples/JavaWordCount.java   | 12 ++++---
 .../org/apache/spark/examples/mllib/JavaALS.java   | 22 ++++++-------
 .../apache/spark/examples/mllib/JavaKMeans.java    | 22 ++++++-------
 .../org/apache/spark/examples/mllib/JavaLR.java    | 18 +++++------
 .../apache/spark/examples/sql/JavaSparkSQL.java    |  5 +--
 .../examples/streaming/JavaFlumeEventCount.java    | 19 +++++------
 .../examples/streaming/JavaKafkaWordCount.java     | 27 ++++++++--------
 .../examples/streaming/JavaNetworkWordCount.java   | 25 +++++++--------
 .../spark/examples/streaming/JavaQueueStream.java  | 22 ++++++-------
 .../org/apache/spark/examples/BroadcastTest.scala  | 22 ++++++-------
 .../apache/spark/examples/CassandraCQLTest.scala   | 19 ++++++-----
 .../org/apache/spark/examples/CassandraTest.scala  | 10 +++---
 .../spark/examples/ExceptionHandlingTest.scala     | 11 ++-----
 .../org/apache/spark/examples/GroupByTest.scala    | 25 +++++++--------
 .../org/apache/spark/examples/HBaseTest.scala      |  6 ++--
 .../scala/org/apache/spark/examples/HdfsTest.scala |  4 +--
 .../scala/org/apache/spark/examples/LogQuery.scala | 14 ++++----
 .../apache/spark/examples/MultiBroadcastTest.scala | 17 +++++-----
 .../spark/examples/SimpleSkewedGroupByTest.scala   | 24 +++++++-------
 .../apache/spark/examples/SkewedGroupByTest.scala  | 25 +++++++--------
 .../scala/org/apache/spark/examples/SparkALS.scala | 18 +++--------
 .../org/apache/spark/examples/SparkHdfsLR.scala    | 13 ++++----
 .../org/apache/spark/examples/SparkKMeans.scala    | 18 +++++------
 .../scala/org/apache/spark/examples/SparkLR.scala  | 11 +++----
 .../org/apache/spark/examples/SparkPageRank.scala  | 14 +++-----
 .../scala/org/apache/spark/examples/SparkPi.scala  | 10 ++----
 .../scala/org/apache/spark/examples/SparkTC.scala  | 12 +++----
 .../apache/spark/examples/SparkTachyonHdfsLR.scala | 12 +++----
 .../org/apache/spark/examples/SparkTachyonPi.scala | 10 ++----
 .../spark/examples/bagel/WikipediaPageRank.scala   | 10 +++---
 .../bagel/WikipediaPageRankStandalone.scala        | 10 +++---
 .../examples/graphx/LiveJournalPageRank.scala      |  6 ++--
 .../apache/spark/examples/sql/RDDRelation.scala    |  5 +--
 .../spark/examples/sql/hive/HiveFromSpark.scala    |  5 +--
 .../spark/examples/streaming/ActorWordCount.scala  | 21 ++++++------
 .../spark/examples/streaming/FlumeEventCount.scala | 14 ++++----
 .../spark/examples/streaming/HdfsWordCount.scala   | 18 +++++------
 .../spark/examples/streaming/KafkaWordCount.scala  | 21 ++++++------
 .../spark/examples/streaming/MQTTWordCount.scala   | 26 +++++++--------
 .../examples/streaming/NetworkWordCount.scala      | 23 ++++++--------
 .../spark/examples/streaming/QueueStream.scala     | 10 ++----
 .../spark/examples/streaming/RawNetworkGrep.scala  | 16 ++++------
 .../streaming/RecoverableNetworkWordCount.scala    | 37 +++++++++++-----------
 .../streaming/StatefulNetworkWordCount.scala       | 21 ++++++------
 .../examples/streaming/TwitterAlgebirdCMS.scala    | 15 +++------
 .../examples/streaming/TwitterAlgebirdHLL.scala    | 14 +++-----
 .../examples/streaming/TwitterPopularTags.scala    | 13 +++-----
 .../spark/examples/streaming/ZeroMQWordCount.scala | 23 +++++++-------
 53 files changed, 389 insertions(+), 469 deletions(-)

(limited to 'examples/src')

diff --git a/examples/src/main/java/org/apache/spark/examples/JavaHdfsLR.java b/examples/src/main/java/org/apache/spark/examples/JavaHdfsLR.java
index bd96274021..6c177de359 100644
--- a/examples/src/main/java/org/apache/spark/examples/JavaHdfsLR.java
+++ b/examples/src/main/java/org/apache/spark/examples/JavaHdfsLR.java
@@ -17,6 +17,7 @@
 
 package org.apache.spark.examples;
 
+import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.api.java.function.Function;
@@ -103,16 +104,16 @@ public final class JavaHdfsLR {
 
   public static void main(String[] args) {
 
-    if (args.length < 3) {
-      System.err.println("Usage: JavaHdfsLR <master> <file> <iters>");
+    if (args.length < 2) {
+      System.err.println("Usage: JavaHdfsLR <file> <iters>");
       System.exit(1);
     }
 
-    JavaSparkContext sc = new JavaSparkContext(args[0], "JavaHdfsLR",
-        System.getenv("SPARK_HOME"), JavaSparkContext.jarOfClass(JavaHdfsLR.class));
-    JavaRDD<String> lines = sc.textFile(args[1]);
+    SparkConf sparkConf = new SparkConf().setAppName("JavaHdfsLR");
+    JavaSparkContext sc = new JavaSparkContext(sparkConf);
+    JavaRDD<String> lines = sc.textFile(args[0]);
     JavaRDD<DataPoint> points = lines.map(new ParsePoint()).cache();
-    int ITERATIONS = Integer.parseInt(args[2]);
+    int ITERATIONS = Integer.parseInt(args[1]);
 
     // Initialize w to a random value
     double[] w = new double[D];
diff --git a/examples/src/main/java/org/apache/spark/examples/JavaLogQuery.java b/examples/src/main/java/org/apache/spark/examples/JavaLogQuery.java
index 3f7a879538..812e9d5580 100644
--- a/examples/src/main/java/org/apache/spark/examples/JavaLogQuery.java
+++ b/examples/src/main/java/org/apache/spark/examples/JavaLogQuery.java
@@ -20,6 +20,7 @@ package org.apache.spark.examples;
 import com.google.common.collect.Lists;
 import scala.Tuple2;
 import scala.Tuple3;
+import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaPairRDD;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
@@ -34,6 +35,8 @@ import java.util.regex.Pattern;
 
 /**
  * Executes a roll up-style query against Apache logs.
+ *  
+ * Usage: JavaLogQuery [logFile]
  */
 public final class JavaLogQuery {
 
@@ -97,15 +100,11 @@ public final class JavaLogQuery {
   }
 
   public static void main(String[] args) {
-    if (args.length == 0) {
-      System.err.println("Usage: JavaLogQuery <master> [logFile]");
-      System.exit(1);
-    }
 
-    JavaSparkContext jsc = new JavaSparkContext(args[0], "JavaLogQuery",
-      System.getenv("SPARK_HOME"), JavaSparkContext.jarOfClass(JavaLogQuery.class));
+    SparkConf sparkConf = new SparkConf().setAppName("JavaLogQuery");
+    JavaSparkContext jsc = new JavaSparkContext(sparkConf);
 
-    JavaRDD<String> dataSet = (args.length == 2) ? jsc.textFile(args[1]) : jsc.parallelize(exampleApacheLogs);
+    JavaRDD<String> dataSet = (args.length == 1) ? jsc.textFile(args[0]) : jsc.parallelize(exampleApacheLogs);
 
     JavaPairRDD<Tuple3<String, String, String>, Stats> extracted = dataSet.mapToPair(new PairFunction<String, Tuple3<String, String, String>, Stats>() {
       @Override
diff --git a/examples/src/main/java/org/apache/spark/examples/JavaPageRank.java b/examples/src/main/java/org/apache/spark/examples/JavaPageRank.java
index e31f676f5f..7ea6df9c17 100644
--- a/examples/src/main/java/org/apache/spark/examples/JavaPageRank.java
+++ b/examples/src/main/java/org/apache/spark/examples/JavaPageRank.java
@@ -18,9 +18,12 @@
 package org.apache.spark.examples;
 
 
+
 import scala.Tuple2;
 
 import com.google.common.collect.Iterables;
+
+import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaPairRDD;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
@@ -54,20 +57,20 @@ public final class JavaPageRank {
   }
 
   public static void main(String[] args) throws Exception {
-    if (args.length < 3) {
-      System.err.println("Usage: JavaPageRank <master> <file> <number_of_iterations>");
+    if (args.length < 2) {
+      System.err.println("Usage: JavaPageRank <file> <number_of_iterations>");
       System.exit(1);
     }
 
-    JavaSparkContext ctx = new JavaSparkContext(args[0], "JavaPageRank",
-      System.getenv("SPARK_HOME"), JavaSparkContext.jarOfClass(JavaPageRank.class));
+    SparkConf sparkConf = new SparkConf().setAppName("JavaPageRank");
+    JavaSparkContext ctx = new JavaSparkContext(sparkConf);
 
     // Loads in input file. It should be in format of:
     //     URL         neighbor URL
     //     URL         neighbor URL
     //     URL         neighbor URL
     //     ...
-    JavaRDD<String> lines = ctx.textFile(args[1], 1);
+    JavaRDD<String> lines = ctx.textFile(args[0], 1);
 
     // Loads all URLs from input file and initialize their neighbors.
     JavaPairRDD<String, Iterable<String>> links = lines.mapToPair(new PairFunction<String, String, String>() {
@@ -87,7 +90,7 @@ public final class JavaPageRank {
     });
 
     // Calculates and updates URL ranks continuously using PageRank algorithm.
-    for (int current = 0; current < Integer.parseInt(args[2]); current++) {
+    for (int current = 0; current < Integer.parseInt(args[1]); current++) {
       // Calculates URL contributions to the rank of other URLs.
       JavaPairRDD<String, Double> contribs = links.join(ranks).values()
         .flatMapToPair(new PairFlatMapFunction<Tuple2<Iterable<String>, Double>, String, Double>() {
diff --git a/examples/src/main/java/org/apache/spark/examples/JavaSparkPi.java b/examples/src/main/java/org/apache/spark/examples/JavaSparkPi.java
index ac8df02c46..11157d7573 100644
--- a/examples/src/main/java/org/apache/spark/examples/JavaSparkPi.java
+++ b/examples/src/main/java/org/apache/spark/examples/JavaSparkPi.java
@@ -17,6 +17,7 @@
 
 package org.apache.spark.examples;
 
+import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.api.java.function.Function;
@@ -25,19 +26,18 @@ import org.apache.spark.api.java.function.Function2;
 import java.util.ArrayList;
 import java.util.List;
 
-/** Computes an approximation to pi */
+/** 
+ * Computes an approximation to pi
+ * Usage: JavaSparkPi [slices]
+ */
 public final class JavaSparkPi {
+  
 
   public static void main(String[] args) throws Exception {
-    if (args.length == 0) {
-      System.err.println("Usage: JavaSparkPi <master> [slices]");
-      System.exit(1);
-    }
-
-    JavaSparkContext jsc = new JavaSparkContext(args[0], "JavaSparkPi",
-      System.getenv("SPARK_HOME"), JavaSparkContext.jarOfClass(JavaSparkPi.class));
+    SparkConf sparkConf = new SparkConf().setAppName("JavaSparkPi");
+    JavaSparkContext jsc = new JavaSparkContext(sparkConf);
 
-    int slices = (args.length == 2) ? Integer.parseInt(args[1]) : 2;
+    int slices = (args.length == 1) ? Integer.parseInt(args[0]) : 2;
     int n = 100000 * slices;
     List<Integer> l = new ArrayList<Integer>(n);
     for (int i = 0; i < n; i++) {
diff --git a/examples/src/main/java/org/apache/spark/examples/JavaTC.java b/examples/src/main/java/org/apache/spark/examples/JavaTC.java
index d66b9ba265..2563fcdd23 100644
--- a/examples/src/main/java/org/apache/spark/examples/JavaTC.java
+++ b/examples/src/main/java/org/apache/spark/examples/JavaTC.java
@@ -17,19 +17,22 @@
 
 package org.apache.spark.examples;
 
-import scala.Tuple2;
-import org.apache.spark.api.java.JavaPairRDD;
-import org.apache.spark.api.java.JavaSparkContext;
-import org.apache.spark.api.java.function.PairFunction;
-
 import java.util.ArrayList;
 import java.util.HashSet;
 import java.util.List;
 import java.util.Random;
 import java.util.Set;
 
+import scala.Tuple2;
+
+import org.apache.spark.SparkConf;
+import org.apache.spark.api.java.JavaPairRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.api.java.function.PairFunction;
+
 /**
  * Transitive closure on a graph, implemented in Java.
+ * Usage: JavaTC [slices]
  */
 public final class JavaTC {
 
@@ -61,14 +64,9 @@ public final class JavaTC {
   }
 
   public static void main(String[] args) {
-    if (args.length == 0) {
-      System.err.println("Usage: JavaTC <host> [<slices>]");
-      System.exit(1);
-    }
-
-    JavaSparkContext sc = new JavaSparkContext(args[0], "JavaTC",
-        System.getenv("SPARK_HOME"), JavaSparkContext.jarOfClass(JavaTC.class));
-    Integer slices = (args.length > 1) ? Integer.parseInt(args[1]): 2;
+    SparkConf sparkConf = new SparkConf().setAppName("JavaHdfsLR");
+    JavaSparkContext sc = new JavaSparkContext(sparkConf);
+    Integer slices = (args.length > 0) ? Integer.parseInt(args[0]): 2;
     JavaPairRDD<Integer, Integer> tc = sc.parallelizePairs(generateGraph(), slices).cache();
 
     // Linear transitive closure: each round grows paths by one edge,
diff --git a/examples/src/main/java/org/apache/spark/examples/JavaWordCount.java b/examples/src/main/java/org/apache/spark/examples/JavaWordCount.java
index 87c1b80981..9a6a944f7e 100644
--- a/examples/src/main/java/org/apache/spark/examples/JavaWordCount.java
+++ b/examples/src/main/java/org/apache/spark/examples/JavaWordCount.java
@@ -18,6 +18,7 @@
 package org.apache.spark.examples;
 
 import scala.Tuple2;
+import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaPairRDD;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
@@ -33,14 +34,15 @@ public final class JavaWordCount {
   private static final Pattern SPACE = Pattern.compile(" ");
 
   public static void main(String[] args) throws Exception {
-    if (args.length < 2) {
-      System.err.println("Usage: JavaWordCount <master> <file>");
+
+    if (args.length < 1) {
+      System.err.println("Usage: JavaWordCount <file>");
       System.exit(1);
     }
 
-    JavaSparkContext ctx = new JavaSparkContext(args[0], "JavaWordCount",
-        System.getenv("SPARK_HOME"), JavaSparkContext.jarOfClass(JavaWordCount.class));
-    JavaRDD<String> lines = ctx.textFile(args[1], 1);
+    SparkConf sparkConf = new SparkConf().setAppName("JavaWordCount");
+    JavaSparkContext ctx = new JavaSparkContext(sparkConf);
+    JavaRDD<String> lines = ctx.textFile(args[0], 1);
 
     JavaRDD<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
       @Override
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaALS.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaALS.java
index 4533c4c5f2..8d381d4e0a 100644
--- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaALS.java
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaALS.java
@@ -17,6 +17,7 @@
 
 package org.apache.spark.examples.mllib;
 
+import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.api.java.function.Function;
@@ -57,23 +58,22 @@ public final class JavaALS {
 
   public static void main(String[] args) {
 
-    if (args.length != 5 && args.length != 6) {
+    if (args.length < 4) {
       System.err.println(
-          "Usage: JavaALS <master> <ratings_file> <rank> <iterations> <output_dir> [<blocks>]");
+        "Usage: JavaALS <ratings_file> <rank> <iterations> <output_dir> [<blocks>]");
       System.exit(1);
     }
-
-    int rank = Integer.parseInt(args[2]);
-    int iterations = Integer.parseInt(args[3]);
-    String outputDir = args[4];
+    SparkConf sparkConf = new SparkConf().setAppName("JavaALS");
+    int rank = Integer.parseInt(args[1]);
+    int iterations = Integer.parseInt(args[2]);
+    String outputDir = args[3];
     int blocks = -1;
-    if (args.length == 6) {
-      blocks = Integer.parseInt(args[5]);
+    if (args.length == 5) {
+      blocks = Integer.parseInt(args[4]);
     }
 
-    JavaSparkContext sc = new JavaSparkContext(args[0], "JavaALS",
-        System.getenv("SPARK_HOME"), JavaSparkContext.jarOfClass(JavaALS.class));
-    JavaRDD<String> lines = sc.textFile(args[1]);
+    JavaSparkContext sc = new JavaSparkContext(sparkConf);
+    JavaRDD<String> lines = sc.textFile(args[0]);
 
     JavaRDD<Rating> ratings = lines.map(new ParseRating());
 
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaKMeans.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaKMeans.java
index 0cfb8e69ed..f796123a25 100644
--- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaKMeans.java
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaKMeans.java
@@ -19,6 +19,7 @@ package org.apache.spark.examples.mllib;
 
 import java.util.regex.Pattern;
 
+import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.api.java.function.Function;
@@ -48,24 +49,21 @@ public final class JavaKMeans {
   }
 
   public static void main(String[] args) {
-
-    if (args.length < 4) {
+    if (args.length < 3) {
       System.err.println(
-          "Usage: JavaKMeans <master> <input_file> <k> <max_iterations> [<runs>]");
+        "Usage: JavaKMeans <input_file> <k> <max_iterations> [<runs>]");
       System.exit(1);
     }
-
-    String inputFile = args[1];
-    int k = Integer.parseInt(args[2]);
-    int iterations = Integer.parseInt(args[3]);
+    String inputFile = args[0];
+    int k = Integer.parseInt(args[1]);
+    int iterations = Integer.parseInt(args[2]);
     int runs = 1;
 
-    if (args.length >= 5) {
-      runs = Integer.parseInt(args[4]);
+    if (args.length >= 4) {
+      runs = Integer.parseInt(args[3]);
     }
-
-    JavaSparkContext sc = new JavaSparkContext(args[0], "JavaKMeans",
-        System.getenv("SPARK_HOME"), JavaSparkContext.jarOfClass(JavaKMeans.class));
+    SparkConf sparkConf = new SparkConf().setAppName("JavaKMeans");
+    JavaSparkContext sc = new JavaSparkContext(sparkConf);
     JavaRDD<String> lines = sc.textFile(inputFile);
 
     JavaRDD<Vector> points = lines.map(new ParsePoint());
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaLR.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaLR.java
index f6e48b4987..eceb6927d5 100644
--- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaLR.java
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaLR.java
@@ -19,6 +19,7 @@ package org.apache.spark.examples.mllib;
 
 import java.util.regex.Pattern;
 
+import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.api.java.function.Function;
@@ -51,17 +52,16 @@ public final class JavaLR {
   }
 
   public static void main(String[] args) {
-    if (args.length != 4) {
-      System.err.println("Usage: JavaLR <master> <input_dir> <step_size> <niters>");
+    if (args.length != 3) {
+      System.err.println("Usage: JavaLR <input_dir> <step_size> <niters>");
       System.exit(1);
     }
-
-    JavaSparkContext sc = new JavaSparkContext(args[0], "JavaLR",
-        System.getenv("SPARK_HOME"), JavaSparkContext.jarOfClass(JavaLR.class));
-    JavaRDD<String> lines = sc.textFile(args[1]);
+    SparkConf sparkConf = new SparkConf().setAppName("JavaLR");
+    JavaSparkContext sc = new JavaSparkContext(sparkConf);
+    JavaRDD<String> lines = sc.textFile(args[0]);
     JavaRDD<LabeledPoint> points = lines.map(new ParsePoint()).cache();
-    double stepSize = Double.parseDouble(args[2]);
-    int iterations = Integer.parseInt(args[3]);
+    double stepSize = Double.parseDouble(args[1]);
+    int iterations = Integer.parseInt(args[2]);
 
     // Another way to configure LogisticRegression
     //
@@ -73,7 +73,7 @@ public final class JavaLR {
     // LogisticRegressionModel model = lr.train(points.rdd());
 
     LogisticRegressionModel model = LogisticRegressionWithSGD.train(points.rdd(),
-        iterations, stepSize);
+      iterations, stepSize);
 
     System.out.print("Final w: " + model.weights());
 
diff --git a/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java b/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java
index d62a72f534..ad5ec84b71 100644
--- a/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java
+++ b/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java
@@ -20,6 +20,7 @@ package org.apache.spark.examples.sql;
 import java.io.Serializable;
 import java.util.List;
 
+import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.api.java.function.Function;
@@ -51,8 +52,8 @@ public class JavaSparkSQL {
   }
 
   public static void main(String[] args) throws Exception {
-    JavaSparkContext ctx = new JavaSparkContext("local", "JavaSparkSQL",
-        System.getenv("SPARK_HOME"), JavaSparkContext.jarOfClass(JavaSparkSQL.class));
+    SparkConf sparkConf = new SparkConf().setAppName("JavaSparkSQL");
+    JavaSparkContext ctx = new JavaSparkContext(sparkConf);
     JavaSQLContext sqlCtx = new JavaSQLContext(ctx);
 
     // Load a text file and convert each line to a Java Bean.
diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaFlumeEventCount.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaFlumeEventCount.java
index a5ece68cef..400b68c221 100644
--- a/examples/src/main/java/org/apache/spark/examples/streaming/JavaFlumeEventCount.java
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaFlumeEventCount.java
@@ -17,6 +17,7 @@
 
 package org.apache.spark.examples.streaming;
 
+import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.function.Function;
 import org.apache.spark.examples.streaming.StreamingExamples;
 import org.apache.spark.streaming.*;
@@ -31,9 +32,8 @@ import org.apache.spark.streaming.flume.SparkFlumeEvent;
  *  an Avro server on at the request host:port address and listen for requests.
  *  Your Flume AvroSink should be pointed to this address.
  *
- *  Usage: JavaFlumeEventCount <master> <host> <port>
+ *  Usage: JavaFlumeEventCount <host> <port>
  *
- *    <master> is a Spark master URL
  *    <host> is the host the Flume receiver will be started on - a receiver
  *           creates a server and listens for flume events.
  *    <port> is the port the Flume receiver will listen on.
@@ -43,22 +43,19 @@ public final class JavaFlumeEventCount {
   }
 
   public static void main(String[] args) {
-    if (args.length != 3) {
-      System.err.println("Usage: JavaFlumeEventCount <master> <host> <port>");
+    if (args.length != 2) {
+      System.err.println("Usage: JavaFlumeEventCount <host> <port>");
       System.exit(1);
     }
 
     StreamingExamples.setStreamingLogLevels();
 
-    String master = args[0];
-    String host = args[1];
-    int port = Integer.parseInt(args[2]);
+    String host = args[0];
+    int port = Integer.parseInt(args[1]);
 
     Duration batchInterval = new Duration(2000);
-
-    JavaStreamingContext ssc = new JavaStreamingContext(master, "FlumeEventCount", batchInterval,
-            System.getenv("SPARK_HOME"),
-            JavaStreamingContext.jarOfClass(JavaFlumeEventCount.class));
+    SparkConf sparkConf = new SparkConf().setAppName("JavaFlumeEventCount");
+    JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, batchInterval);
     JavaReceiverInputDStream<SparkFlumeEvent> flumeStream = FlumeUtils.createStream(ssc, "localhost", port);
 
     flumeStream.count();
diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java
index da51eb189a..6a74cc50d1 100644
--- a/examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaKafkaWordCount.java
@@ -21,7 +21,11 @@ import java.util.Map;
 import java.util.HashMap;
 import java.util.regex.Pattern;
 
+
+import scala.Tuple2;
+
 import com.google.common.collect.Lists;
+import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.function.FlatMapFunction;
 import org.apache.spark.api.java.function.Function;
 import org.apache.spark.api.java.function.Function2;
@@ -33,19 +37,18 @@ import org.apache.spark.streaming.api.java.JavaPairDStream;
 import org.apache.spark.streaming.api.java.JavaPairReceiverInputDStream;
 import org.apache.spark.streaming.api.java.JavaStreamingContext;
 import org.apache.spark.streaming.kafka.KafkaUtils;
-import scala.Tuple2;
 
 /**
  * Consumes messages from one or more topics in Kafka and does wordcount.
- * Usage: JavaKafkaWordCount <master> <zkQuorum> <group> <topics> <numThreads>
- *   <master> is the Spark master URL. In local mode, <master> should be 'local[n]' with n > 1.
+ * Usage: JavaKafkaWordCount <zkQuorum> <group> <topics> <numThreads>
  *   <zkQuorum> is a list of one or more zookeeper servers that make quorum
  *   <group> is the name of kafka consumer group
  *   <topics> is a list of one or more kafka topics to consume from
  *   <numThreads> is the number of threads the kafka consumer should use
  *
  * Example:
- *    `./bin/run-example org.apache.spark.examples.streaming.JavaKafkaWordCount local[2] zoo01,zoo02,
+ *    `./bin/spark-submit examples.jar \
+ *    --class org.apache.spark.examples.streaming.JavaKafkaWordCount zoo01,zoo02, \
  *    zoo03 my-consumer-group topic1,topic2 1`
  */
 
@@ -56,27 +59,25 @@ public final class JavaKafkaWordCount {
   }
 
   public static void main(String[] args) {
-    if (args.length < 5) {
-      System.err.println("Usage: KafkaWordCount <master> <zkQuorum> <group> <topics> <numThreads>");
+    if (args.length < 4) {
+      System.err.println("Usage: JavaKafkaWordCount <zkQuorum> <group> <topics> <numThreads>");
       System.exit(1);
     }
 
     StreamingExamples.setStreamingLogLevels();
-
+    SparkConf sparkConf = new SparkConf().setAppName("JavaKafkaWordCount");
     // Create the context with a 1 second batch size
-    JavaStreamingContext jssc = new JavaStreamingContext(args[0], "KafkaWordCount",
-            new Duration(2000), System.getenv("SPARK_HOME"),
-            JavaStreamingContext.jarOfClass(JavaKafkaWordCount.class));
+    JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, new Duration(2000));
 
-    int numThreads = Integer.parseInt(args[4]);
+    int numThreads = Integer.parseInt(args[3]);
     Map<String, Integer> topicMap = new HashMap<String, Integer>();
-    String[] topics = args[3].split(",");
+    String[] topics = args[2].split(",");
     for (String topic: topics) {
       topicMap.put(topic, numThreads);
     }
 
     JavaPairReceiverInputDStream<String, String> messages =
-            KafkaUtils.createStream(jssc, args[1], args[2], topicMap);
+            KafkaUtils.createStream(jssc, args[0], args[1], topicMap);
 
     JavaDStream<String> lines = messages.map(new Function<Tuple2<String, String>, String>() {
       @Override
diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaNetworkWordCount.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaNetworkWordCount.java
index ac84991d87..e5cbd39f43 100644
--- a/examples/src/main/java/org/apache/spark/examples/streaming/JavaNetworkWordCount.java
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaNetworkWordCount.java
@@ -17,9 +17,10 @@
 
 package org.apache.spark.examples.streaming;
 
-import com.google.common.collect.Lists;
-import org.apache.spark.streaming.api.java.JavaReceiverInputDStream;
 import scala.Tuple2;
+import com.google.common.collect.Lists;
+
+import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.function.FlatMapFunction;
 import org.apache.spark.api.java.function.Function2;
 import org.apache.spark.api.java.function.PairFunction;
@@ -27,41 +28,39 @@ import org.apache.spark.examples.streaming.StreamingExamples;
 import org.apache.spark.streaming.Duration;
 import org.apache.spark.streaming.api.java.JavaDStream;
 import org.apache.spark.streaming.api.java.JavaPairDStream;
+import org.apache.spark.streaming.api.java.JavaReceiverInputDStream;
 import org.apache.spark.streaming.api.java.JavaStreamingContext;
 
 import java.util.regex.Pattern;
 
 /**
  * Counts words in UTF8 encoded, '\n' delimited text received from the network every second.
- * Usage: JavaNetworkWordCount <master> <hostname> <port>
- *   <master> is the Spark master URL. In local mode, <master> should be 'local[n]' with n > 1.
+ * Usage: JavaNetworkWordCount <hostname> <port>
  *   <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
- *    `$ ./run org.apache.spark.examples.streaming.JavaNetworkWordCount local[2] localhost 9999`
+ *    `$ ./bin/spark-submit examples.jar \
+ *    --class org.apache.spark.examples.streaming.JavaNetworkWordCount localhost 9999`
  */
 public final class JavaNetworkWordCount {
   private static final Pattern SPACE = Pattern.compile(" ");
 
   public static void main(String[] args) {
-    if (args.length < 3) {
-      System.err.println("Usage: JavaNetworkWordCount <master> <hostname> <port>\n" +
-          "In local mode, <master> should be 'local[n]' with n > 1");
+    if (args.length < 2) {
+      System.err.println("Usage: JavaNetworkWordCount <hostname> <port>");
       System.exit(1);
     }
 
     StreamingExamples.setStreamingLogLevels();
-
+    SparkConf sparkConf = new SparkConf().setAppName("JavaNetworkWordCount");
     // Create the context with a 1 second batch size
-    JavaStreamingContext ssc = new JavaStreamingContext(args[0], "JavaNetworkWordCount",
-            new Duration(1000), System.getenv("SPARK_HOME"),
-            JavaStreamingContext.jarOfClass(JavaNetworkWordCount.class));
+    JavaStreamingContext ssc = new JavaStreamingContext(sparkConf,  new Duration(1000));
 
     // Create a JavaReceiverInputDStream on target ip:port and count the
     // words in input stream of \n delimited text (eg. generated by 'nc')
-    JavaReceiverInputDStream<String> lines = ssc.socketTextStream(args[1], Integer.parseInt(args[2]));
+    JavaReceiverInputDStream<String> lines = ssc.socketTextStream(args[0], Integer.parseInt(args[1]));
     JavaDStream<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
       @Override
       public Iterable<String> call(String x) {
diff --git a/examples/src/main/java/org/apache/spark/examples/streaming/JavaQueueStream.java b/examples/src/main/java/org/apache/spark/examples/streaming/JavaQueueStream.java
index 819311968f..4ce8437f82 100644
--- a/examples/src/main/java/org/apache/spark/examples/streaming/JavaQueueStream.java
+++ b/examples/src/main/java/org/apache/spark/examples/streaming/JavaQueueStream.java
@@ -17,8 +17,16 @@
 
 package org.apache.spark.examples.streaming;
 
-import com.google.common.collect.Lists;
+
+import java.util.LinkedList;
+import java.util.List;
+import java.util.Queue;
+
 import scala.Tuple2;
+
+import com.google.common.collect.Lists;
+
+import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.function.Function2;
 import org.apache.spark.api.java.function.PairFunction;
@@ -28,25 +36,17 @@ import org.apache.spark.streaming.api.java.JavaDStream;
 import org.apache.spark.streaming.api.java.JavaPairDStream;
 import org.apache.spark.streaming.api.java.JavaStreamingContext;
 
-import java.util.LinkedList;
-import java.util.List;
-import java.util.Queue;
-
 public final class JavaQueueStream {
   private JavaQueueStream() {
   }
 
   public static void main(String[] args) throws Exception {
-    if (args.length < 1) {
-      System.err.println("Usage: JavaQueueStream <master>");
-      System.exit(1);
-    }
 
     StreamingExamples.setStreamingLogLevels();
+    SparkConf sparkConf = new SparkConf().setAppName("JavaQueueStream");
 
     // Create the context
-    JavaStreamingContext ssc = new JavaStreamingContext(args[0], "QueueStream", new Duration(1000),
-            System.getenv("SPARK_HOME"), JavaStreamingContext.jarOfClass(JavaQueueStream.class));
+    JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, new Duration(1000));
 
     // Create the queue through which RDDs can be pushed to
     // a QueueInputDStream
diff --git a/examples/src/main/scala/org/apache/spark/examples/BroadcastTest.scala b/examples/src/main/scala/org/apache/spark/examples/BroadcastTest.scala
index f6dfd2c4c6..973049b95a 100644
--- a/examples/src/main/scala/org/apache/spark/examples/BroadcastTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/BroadcastTest.scala
@@ -17,28 +17,26 @@
 
 package org.apache.spark.examples
 
-import org.apache.spark.SparkContext
+import org.apache.spark.{SparkConf, SparkContext}
 
+/**
+  * Usage: BroadcastTest [slices] [numElem] [broadcastAlgo] [blockSize]
+  */
 object BroadcastTest {
   def main(args: Array[String]) {
-    if (args.length == 0) {
-      System.err.println("Usage: BroadcastTest <master> [slices] [numElem] [broadcastAlgo]" +
-        " [blockSize]")
-      System.exit(1)
-    }
 
-    val bcName = if (args.length > 3) args(3) else "Http"
-    val blockSize = if (args.length > 4) args(4) else "4096"
+    val bcName = if (args.length > 2) args(2) else "Http"
+    val blockSize = if (args.length > 3) args(3) else "4096"
 
     System.setProperty("spark.broadcast.factory", "org.apache.spark.broadcast." + bcName +
       "BroadcastFactory")
     System.setProperty("spark.broadcast.blockSize", blockSize)
+    val sparkConf = new SparkConf().setAppName("Broadcast Test")
 
-    val sc = new SparkContext(args(0), "Broadcast Test",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq)
+    val sc = new SparkContext(sparkConf)
 
-    val slices = if (args.length > 1) args(1).toInt else 2
-    val num = if (args.length > 2) args(2).toInt else 1000000
+    val slices = if (args.length > 0) args(0).toInt else 2
+    val num = if (args.length > 1) args(1).toInt else 1000000
 
     val arr1 = new Array[Int](num)
     for (i <- 0 until arr1.length) {
diff --git a/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala b/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala
index 3798329fc2..9a00701f98 100644
--- a/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/CassandraCQLTest.scala
@@ -30,7 +30,7 @@ import org.apache.cassandra.hadoop.cql3.CqlOutputFormat
 import org.apache.cassandra.utils.ByteBufferUtil
 import org.apache.hadoop.mapreduce.Job
 
-import org.apache.spark.SparkContext
+import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.SparkContext._
 
 /*
@@ -65,19 +65,18 @@ import org.apache.spark.SparkContext._
 /**
  * This example demonstrates how to read and write to cassandra column family created using CQL3
  * using Spark.
- * Parameters : <spark_master> <cassandra_node> <cassandra_port>
- * Usage: ./bin/run-example org.apache.spark.examples.CassandraCQLTest local[2] localhost 9160
- *
+ * Parameters : <cassandra_node> <cassandra_port>
+ * Usage: ./bin/spark-submit examples.jar \
+ *  --class org.apache.spark.examples.CassandraCQLTest localhost 9160
  */
 object CassandraCQLTest {
 
   def main(args: Array[String]) {
-    val sc = new SparkContext(args(0),
-               "CQLTestApp",
-               System.getenv("SPARK_HOME"),
-               SparkContext.jarOfClass(this.getClass).toSeq)
-    val cHost: String = args(1)
-    val cPort: String = args(2)
+    val sparkConf = new SparkConf().setAppName("CQLTestApp")
+
+    val sc = new SparkContext(sparkConf)
+    val cHost: String = args(0)
+    val cPort: String = args(1)
     val KeySpace = "retail"
     val InputColumnFamily = "ordercf"
     val OutputColumnFamily = "salecount"
diff --git a/examples/src/main/scala/org/apache/spark/examples/CassandraTest.scala b/examples/src/main/scala/org/apache/spark/examples/CassandraTest.scala
index ed5d2f9e46..91ba364a34 100644
--- a/examples/src/main/scala/org/apache/spark/examples/CassandraTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/CassandraTest.scala
@@ -30,7 +30,7 @@ import org.apache.cassandra.thrift._
 import org.apache.cassandra.utils.ByteBufferUtil
 import org.apache.hadoop.mapreduce.Job
 
-import org.apache.spark.SparkContext
+import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.SparkContext._
 
 /*
@@ -38,10 +38,10 @@ import org.apache.spark.SparkContext._
  * support for Hadoop.
  *
  * To run this example, run this file with the following command params -
- * <spark_master> <cassandra_node> <cassandra_port>
+ * <cassandra_node> <cassandra_port>
  *
  * So if you want to run this on localhost this will be,
- * local[3] localhost 9160
+ * localhost 9160
  *
  * The example makes some assumptions:
  * 1. You have already created a keyspace called casDemo and it has a column family named Words
@@ -54,9 +54,9 @@ import org.apache.spark.SparkContext._
 object CassandraTest {
 
   def main(args: Array[String]) {
-
+    val sparkConf = new SparkConf().setAppName("casDemo")
     // Get a SparkContext
-    val sc = new SparkContext(args(0), "casDemo")
+    val sc = new SparkContext(sparkConf)
 
     // Build the job configuration with ConfigHelper provided by Cassandra
     val job = new Job()
diff --git a/examples/src/main/scala/org/apache/spark/examples/ExceptionHandlingTest.scala b/examples/src/main/scala/org/apache/spark/examples/ExceptionHandlingTest.scala
index f0dcef431b..d42f63e870 100644
--- a/examples/src/main/scala/org/apache/spark/examples/ExceptionHandlingTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/ExceptionHandlingTest.scala
@@ -17,17 +17,12 @@
 
 package org.apache.spark.examples
 
-import org.apache.spark.SparkContext
+import org.apache.spark.{SparkConf, SparkContext}
 
 object ExceptionHandlingTest {
   def main(args: Array[String]) {
-    if (args.length == 0) {
-      System.err.println("Usage: ExceptionHandlingTest <master>")
-      System.exit(1)
-    }
-
-    val sc = new SparkContext(args(0), "ExceptionHandlingTest",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq)
+    val sparkConf = new SparkConf().setAppName("ExceptionHandlingTest")
+    val sc = new SparkContext(sparkConf)
     sc.parallelize(0 until sc.defaultParallelism).foreach { i =>
       if (math.random > 0.75) {
         throw new Exception("Testing exception handling")
diff --git a/examples/src/main/scala/org/apache/spark/examples/GroupByTest.scala b/examples/src/main/scala/org/apache/spark/examples/GroupByTest.scala
index e67bb29a49..efd91bb054 100644
--- a/examples/src/main/scala/org/apache/spark/examples/GroupByTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/GroupByTest.scala
@@ -19,24 +19,21 @@ package org.apache.spark.examples
 
 import java.util.Random
 
-import org.apache.spark.SparkContext
+import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.SparkContext._
 
+/**
+  * Usage: GroupByTest [numMappers] [numKVPairs] [KeySize] [numReducers]
+  */
 object GroupByTest {
   def main(args: Array[String]) {
-    if (args.length == 0) {
-      System.err.println(
-        "Usage: GroupByTest <master> [numMappers] [numKVPairs] [KeySize] [numReducers]")
-      System.exit(1)
-    }
-
-    var numMappers = if (args.length > 1) args(1).toInt else 2
-    var numKVPairs = if (args.length > 2) args(2).toInt else 1000
-    var valSize = if (args.length > 3) args(3).toInt else 1000
-    var numReducers = if (args.length > 4) args(4).toInt else numMappers
-
-    val sc = new SparkContext(args(0), "GroupBy Test",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq)
+    val sparkConf = new SparkConf().setAppName("GroupBy Test")
+    var numMappers = if (args.length > 0) args(0).toInt else 2
+    var numKVPairs = if (args.length > 1) args(1).toInt else 1000
+    var valSize = if (args.length > 2) args(2).toInt else 1000
+    var numReducers = if (args.length > 3) args(3).toInt else numMappers
+
+    val sc = new SparkContext(sparkConf)
 
     val pairs1 = sc.parallelize(0 until numMappers, numMappers).flatMap { p =>
       val ranGen = new Random
diff --git a/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala b/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala
index adbd1c02fa..a8c338480e 100644
--- a/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/HBaseTest.scala
@@ -26,11 +26,9 @@ import org.apache.spark.rdd.NewHadoopRDD
 
 object HBaseTest {
   def main(args: Array[String]) {
-    val sc = new SparkContext(args(0), "HBaseTest",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq)
-
+    val sparkConf = new SparkConf().setAppName("HBaseTest")
+    val sc = new SparkContext(sparkConf)
     val conf = HBaseConfiguration.create()
-
     // Other options for configuring scan behavior are available. More information available at
     // http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/mapreduce/TableInputFormat.html
     conf.set(TableInputFormat.INPUT_TABLE, args(1))
diff --git a/examples/src/main/scala/org/apache/spark/examples/HdfsTest.scala b/examples/src/main/scala/org/apache/spark/examples/HdfsTest.scala
index c7a4884af1..331de3ad1e 100644
--- a/examples/src/main/scala/org/apache/spark/examples/HdfsTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/HdfsTest.scala
@@ -21,8 +21,8 @@ import org.apache.spark._
 
 object HdfsTest {
   def main(args: Array[String]) {
-    val sc = new SparkContext(args(0), "HdfsTest",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq)
+    val sparkConf = new SparkConf().setAppName("HdfsTest")
+    val sc = new SparkContext(sparkConf)
     val file = sc.textFile(args(1))
     val mapped = file.map(s => s.length).cache()
     for (iter <- 1 to 10) {
diff --git a/examples/src/main/scala/org/apache/spark/examples/LogQuery.scala b/examples/src/main/scala/org/apache/spark/examples/LogQuery.scala
index f77a444ff7..4c655b84fd 100644
--- a/examples/src/main/scala/org/apache/spark/examples/LogQuery.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/LogQuery.scala
@@ -17,11 +17,13 @@
 
 package org.apache.spark.examples
 
-import org.apache.spark.SparkContext
+import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.SparkContext._
 
 /**
  * Executes a roll up-style query against Apache logs.
+ *  
+ * Usage: LogQuery [logFile]
  */
 object LogQuery {
   val exampleApacheLogs = List(
@@ -40,16 +42,12 @@ object LogQuery {
   )
 
   def main(args: Array[String]) {
-    if (args.length == 0) {
-      System.err.println("Usage: LogQuery <master> [logFile]")
-      System.exit(1)
-    }
 
-    val sc = new SparkContext(args(0), "Log Query",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq)
+    val sparkConf = new SparkConf().setAppName("Log Query")
+    val sc = new SparkContext(sparkConf)
 
     val dataSet =
-      if (args.length == 2) sc.textFile(args(1)) else sc.parallelize(exampleApacheLogs)
+      if (args.length == 1) sc.textFile(args(0)) else sc.parallelize(exampleApacheLogs)
     // scalastyle:off
     val apacheLogRegex =
       """^([\d.]+) (\S+) (\S+) \[([\w\d:/]+\s[+\-]\d{4})\] "(.+?)" (\d{3}) ([\d\-]+) "([^"]+)" "([^"]+)".*""".r
diff --git a/examples/src/main/scala/org/apache/spark/examples/MultiBroadcastTest.scala b/examples/src/main/scala/org/apache/spark/examples/MultiBroadcastTest.scala
index c8985eae33..2a5c0c0def 100644
--- a/examples/src/main/scala/org/apache/spark/examples/MultiBroadcastTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/MultiBroadcastTest.scala
@@ -18,20 +18,19 @@
 package org.apache.spark.examples
 
 import org.apache.spark.rdd.RDD
-import org.apache.spark.SparkContext
+import org.apache.spark.{SparkConf, SparkContext}
 
+/**
+  * Usage: MultiBroadcastTest [slices] [numElem]
+  */
 object MultiBroadcastTest {
   def main(args: Array[String]) {
-    if (args.length == 0) {
-      System.err.println("Usage: MultiBroadcastTest <master> [<slices>] [numElem]")
-      System.exit(1)
-    }
 
-    val sc = new SparkContext(args(0), "Multi-Broadcast Test",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq)
+    val sparkConf = new SparkConf().setAppName("Multi-Broadcast Test")
+    val sc = new SparkContext(sparkConf)
 
-    val slices = if (args.length > 1) args(1).toInt else 2
-    val num = if (args.length > 2) args(2).toInt else 1000000
+    val slices = if (args.length > 0) args(0).toInt else 2
+    val num = if (args.length > 1) args(1).toInt else 1000000
 
     val arr1 = new Array[Int](num)
     for (i <- 0 until arr1.length) {
diff --git a/examples/src/main/scala/org/apache/spark/examples/SimpleSkewedGroupByTest.scala b/examples/src/main/scala/org/apache/spark/examples/SimpleSkewedGroupByTest.scala
index 54e8503711..5291ab81f4 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SimpleSkewedGroupByTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SimpleSkewedGroupByTest.scala
@@ -19,25 +19,23 @@ package org.apache.spark.examples
 
 import java.util.Random
 
-import org.apache.spark.SparkContext
+import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.SparkContext._
 
+/**
+  * Usage: SimpleSkewedGroupByTest [numMappers] [numKVPairs] [valSize] [numReducers] [ratio]
+  */
 object SimpleSkewedGroupByTest {
   def main(args: Array[String]) {
-    if (args.length == 0) {
-      System.err.println("Usage: SimpleSkewedGroupByTest <master> " +
-        "[numMappers] [numKVPairs] [valSize] [numReducers] [ratio]")
-      System.exit(1)
-    }
 
-    var numMappers = if (args.length > 1) args(1).toInt else 2
-    var numKVPairs = if (args.length > 2) args(2).toInt else 1000
-    var valSize = if (args.length > 3) args(3).toInt else 1000
-    var numReducers = if (args.length > 4) args(4).toInt else numMappers
-    var ratio = if (args.length > 5) args(5).toInt else 5.0
+    val sparkConf = new SparkConf().setAppName("SimpleSkewedGroupByTest")
+    var numMappers = if (args.length > 0) args(0).toInt else 2
+    var numKVPairs = if (args.length > 1) args(1).toInt else 1000
+    var valSize = if (args.length > 2) args(2).toInt else 1000
+    var numReducers = if (args.length > 3) args(3).toInt else numMappers
+    var ratio = if (args.length > 4) args(4).toInt else 5.0
 
-    val sc = new SparkContext(args(0), "GroupBy Test",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq)
+    val sc = new SparkContext(sparkConf)
 
     val pairs1 = sc.parallelize(0 until numMappers, numMappers).flatMap { p =>
       val ranGen = new Random
diff --git a/examples/src/main/scala/org/apache/spark/examples/SkewedGroupByTest.scala b/examples/src/main/scala/org/apache/spark/examples/SkewedGroupByTest.scala
index 1c5f22e1c0..017d4e1e5c 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SkewedGroupByTest.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SkewedGroupByTest.scala
@@ -19,24 +19,21 @@ package org.apache.spark.examples
 
 import java.util.Random
 
-import org.apache.spark.SparkContext
+import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.SparkContext._
 
+/**
+  * Usage: GroupByTest [numMappers] [numKVPairs] [KeySize] [numReducers]
+  */
 object SkewedGroupByTest {
   def main(args: Array[String]) {
-    if (args.length == 0) {
-      System.err.println(
-        "Usage: GroupByTest <master> [numMappers] [numKVPairs] [KeySize] [numReducers]")
-      System.exit(1)
-    }
-
-    var numMappers = if (args.length > 1) args(1).toInt else 2
-    var numKVPairs = if (args.length > 2) args(2).toInt else 1000
-    var valSize = if (args.length > 3) args(3).toInt else 1000
-    var numReducers = if (args.length > 4) args(4).toInt else numMappers
-
-    val sc = new SparkContext(args(0), "GroupBy Test",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq)
+    val sparkConf = new SparkConf().setAppName("GroupBy Test")
+    var numMappers = if (args.length > 0) args(0).toInt else 2
+    var numKVPairs = if (args.length > 1) args(1).toInt else 1000
+    var valSize = if (args.length > 2) args(2).toInt else 1000
+    var numReducers = if (args.length > 3) args(3).toInt else numMappers
+
+    val sc = new SparkContext(sparkConf)
 
     val pairs1 = sc.parallelize(0 until numMappers, numMappers).flatMap { p =>
       val ranGen = new Random
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkALS.scala b/examples/src/main/scala/org/apache/spark/examples/SparkALS.scala
index 0dc726aecd..5cbc966bf0 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkALS.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkALS.scala
@@ -88,32 +88,24 @@ object SparkALS {
   }
 
   def main(args: Array[String]) {
-    if (args.length == 0) {
-      System.err.println("Usage: SparkALS <master> [<M> <U> <F> <iters> <slices>]")
-      System.exit(1)
-    }
-
-    var host = ""
     var slices = 0
 
-    val options = (0 to 5).map(i => if (i < args.length) Some(args(i)) else None)
+    val options = (0 to 4).map(i => if (i < args.length) Some(args(i)) else None)
 
     options.toArray match {
-      case Array(host_, m, u, f, iters, slices_) =>
-        host = host_.get
+      case Array(m, u, f, iters, slices_) =>
         M = m.getOrElse("100").toInt
         U = u.getOrElse("500").toInt
         F = f.getOrElse("10").toInt
         ITERATIONS = iters.getOrElse("5").toInt
         slices = slices_.getOrElse("2").toInt
       case _ =>
-        System.err.println("Usage: SparkALS <master> [<M> <U> <F> <iters> <slices>]")
+        System.err.println("Usage: SparkALS [M] [U] [F] [iters] [slices]")
         System.exit(1)
     }
     printf("Running with M=%d, U=%d, F=%d, iters=%d\n", M, U, F, ITERATIONS)
-
-    val sc = new SparkContext(host, "SparkALS",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq)
+    val sparkConf = new SparkConf().setAppName("SparkALS")
+    val sc = new SparkContext(sparkConf)
 
     val R = generateR()
 
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala b/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala
index 3a6f18c33e..4906a696e9 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkHdfsLR.scala
@@ -49,20 +49,21 @@ object SparkHdfsLR {
   }
 
   def main(args: Array[String]) {
-    if (args.length < 3) {
-      System.err.println("Usage: SparkHdfsLR <master> <file> <iters>")
+    if (args.length < 2) {
+      System.err.println("Usage: SparkHdfsLR <file> <iters>")
       System.exit(1)
     }
-    val inputPath = args(1)
+
+    val sparkConf = new SparkConf().setAppName("SparkHdfsLR")
+    val inputPath = args(0)
     val conf = SparkHadoopUtil.get.newConfiguration()
-    val sc = new SparkContext(args(0), "SparkHdfsLR",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq, Map(),
+    val sc = new SparkContext(sparkConf,
       InputFormatInfo.computePreferredLocations(
         Seq(new InputFormatInfo(conf, classOf[org.apache.hadoop.mapred.TextInputFormat], inputPath))
       ))
     val lines = sc.textFile(inputPath)
     val points = lines.map(parsePoint _).cache()
-    val ITERATIONS = args(2).toInt
+    val ITERATIONS = args(1).toInt
 
     // Initialize w to a random value
     var w = DenseVector.fill(D){2 * rand.nextDouble - 1}
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala b/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala
index dcae9591b0..4d28e0aad6 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkKMeans.scala
@@ -21,7 +21,7 @@ import java.util.Random
 
 import breeze.linalg.{Vector, DenseVector, squaredDistance}
 
-import org.apache.spark.SparkContext
+import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.SparkContext._
 
 /**
@@ -52,16 +52,16 @@ object SparkKMeans {
   }
 
   def main(args: Array[String]) {
-    if (args.length < 4) {
-        System.err.println("Usage: SparkLocalKMeans <master> <file> <k> <convergeDist>")
-        System.exit(1)
+    if (args.length < 3) {
+      System.err.println("Usage: SparkKMeans <file> <k> <convergeDist>")
+      System.exit(1)
     }
-    val sc = new SparkContext(args(0), "SparkLocalKMeans",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq)
-    val lines = sc.textFile(args(1))
+    val sparkConf = new SparkConf().setAppName("SparkKMeans")
+    val sc = new SparkContext(sparkConf)
+    val lines = sc.textFile(args(0))
     val data = lines.map(parseVector _).cache()
-    val K = args(2).toInt
-    val convergeDist = args(3).toDouble
+    val K = args(1).toInt
+    val convergeDist = args(2).toDouble
 
     val kPoints = data.takeSample(withReplacement = false, K, 42).toArray
     var tempDist = 1.0
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala b/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala
index 4f74882ccb..99ceb3089e 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkLR.scala
@@ -27,6 +27,7 @@ import org.apache.spark._
 
 /**
  * Logistic regression based classification.
+ * Usage: SparkLR [slices]
  */
 object SparkLR {
   val N = 10000  // Number of data points
@@ -47,13 +48,9 @@ object SparkLR {
   }
 
   def main(args: Array[String]) {
-    if (args.length == 0) {
-      System.err.println("Usage: SparkLR <master> [<slices>]")
-      System.exit(1)
-    }
-    val sc = new SparkContext(args(0), "SparkLR",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq)
-    val numSlices = if (args.length > 1) args(1).toInt else 2
+    val sparkConf = new SparkConf().setAppName("SparkLR")
+    val sc = new SparkContext(sparkConf)
+    val numSlices = if (args.length > 0) args(0).toInt else 2
     val points = sc.parallelize(generateData, numSlices).cache()
 
     // Initialize w to a random value
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkPageRank.scala b/examples/src/main/scala/org/apache/spark/examples/SparkPageRank.scala
index fa41c5c560..40b36c779a 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkPageRank.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkPageRank.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.examples
 
 import org.apache.spark.SparkContext._
-import org.apache.spark.SparkContext
+import org.apache.spark.{SparkConf, SparkContext}
 
 /**
  * Computes the PageRank of URLs from an input file. Input file should
@@ -31,14 +31,10 @@ import org.apache.spark.SparkContext
  */
 object SparkPageRank {
   def main(args: Array[String]) {
-    if (args.length < 3) {
-      System.err.println("Usage: PageRank <master> <file> <number_of_iterations>")
-      System.exit(1)
-    }
-    var iters = args(2).toInt
-    val ctx = new SparkContext(args(0), "PageRank",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq)
-    val lines = ctx.textFile(args(1), 1)
+    val sparkConf = new SparkConf().setAppName("PageRank")
+    var iters = args(1).toInt
+    val ctx = new SparkContext(sparkConf)
+    val lines = ctx.textFile(args(0), 1)
     val links = lines.map{ s =>
       val parts = s.split("\\s+")
       (parts(0), parts(1))
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkPi.scala b/examples/src/main/scala/org/apache/spark/examples/SparkPi.scala
index d8f5720504..9fbb0a800d 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkPi.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkPi.scala
@@ -24,13 +24,9 @@ import org.apache.spark._
 /** Computes an approximation to pi */
 object SparkPi {
   def main(args: Array[String]) {
-    if (args.length == 0) {
-      System.err.println("Usage: SparkPi <master> [<slices>]")
-      System.exit(1)
-    }
-    val spark = new SparkContext(args(0), "SparkPi",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq)
-    val slices = if (args.length > 1) args(1).toInt else 2
+    val conf = new SparkConf().setAppName("Spark Pi")
+    val spark = new SparkContext(conf)
+    val slices = if (args.length > 0) args(0).toInt else 2
     val n = 100000 * slices
     val count = spark.parallelize(1 to n, slices).map { i =>
       val x = random * 2 - 1
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkTC.scala b/examples/src/main/scala/org/apache/spark/examples/SparkTC.scala
index 17d983cd87..f7f83086df 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkTC.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkTC.scala
@@ -20,7 +20,7 @@ package org.apache.spark.examples
 import scala.util.Random
 import scala.collection.mutable
 
-import org.apache.spark.SparkContext
+import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.SparkContext._
 
 /**
@@ -42,13 +42,9 @@ object SparkTC {
   }
 
   def main(args: Array[String]) {
-    if (args.length == 0) {
-      System.err.println("Usage: SparkTC <master> [<slices>]")
-      System.exit(1)
-    }
-    val spark = new SparkContext(args(0), "SparkTC",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq)
-    val slices = if (args.length > 1) args(1).toInt else 2
+    val sparkConf = new SparkConf().setAppName("SparkTC")
+    val spark = new SparkContext(sparkConf)
+    val slices = if (args.length > 0) args(0).toInt else 2
     var tc = spark.parallelize(generateGraph, slices).cache()
 
     // Linear transitive closure: each round grows paths by one edge,
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkTachyonHdfsLR.scala b/examples/src/main/scala/org/apache/spark/examples/SparkTachyonHdfsLR.scala
index 7e43c384bd..2212762186 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkTachyonHdfsLR.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkTachyonHdfsLR.scala
@@ -51,20 +51,16 @@ object SparkTachyonHdfsLR {
   }
 
   def main(args: Array[String]) {
-    if (args.length < 3) {
-      System.err.println("Usage: SparkTachyonHdfsLR <master> <file> <iters>")
-      System.exit(1)
-    }
-    val inputPath = args(1)
+    val inputPath = args(0)
     val conf = SparkHadoopUtil.get.newConfiguration()
-    val sc = new SparkContext(args(0), "SparkTachyonHdfsLR",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq, Map(),
+    val sparkConf = new SparkConf().setAppName("SparkTachyonHdfsLR")
+    val sc = new SparkContext(sparkConf,
       InputFormatInfo.computePreferredLocations(
         Seq(new InputFormatInfo(conf, classOf[org.apache.hadoop.mapred.TextInputFormat], inputPath))
       ))
     val lines = sc.textFile(inputPath)
     val points = lines.map(parsePoint _).persist(StorageLevel.OFF_HEAP)
-    val ITERATIONS = args(2).toInt
+    val ITERATIONS = args(1).toInt
 
     // Initialize w to a random value
     var w = DenseVector.fill(D){2 * rand.nextDouble - 1}
diff --git a/examples/src/main/scala/org/apache/spark/examples/SparkTachyonPi.scala b/examples/src/main/scala/org/apache/spark/examples/SparkTachyonPi.scala
index 93459110e4..7743f7968b 100644
--- a/examples/src/main/scala/org/apache/spark/examples/SparkTachyonPi.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/SparkTachyonPi.scala
@@ -28,14 +28,10 @@ import org.apache.spark.storage.StorageLevel
  */
 object SparkTachyonPi {
   def main(args: Array[String]) {
-    if (args.length == 0) {
-      System.err.println("Usage: SparkTachyonPi <master> [<slices>]")
-      System.exit(1)
-    }
-    val spark = new SparkContext(args(0), "SparkTachyonPi",
-      System.getenv("SPARK_HOME"), SparkContext.jarOfClass(this.getClass).toSeq)
+    val sparkConf = new SparkConf().setAppName("SparkTachyonPi")
+    val spark = new SparkContext(sparkConf)
 
-    val slices = if (args.length > 1) args(1).toInt else 2
+    val slices = if (args.length > 0) args(0).toInt else 2
     val n = 100000 * slices
 
     val rdd = spark.parallelize(1 to n, slices)
diff --git a/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRank.scala b/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRank.scala
index 25bd55ca88..235c3bf820 100644
--- a/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRank.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRank.scala
@@ -32,22 +32,22 @@ import scala.xml.{XML,NodeSeq}
  */
 object WikipediaPageRank {
   def main(args: Array[String]) {
-    if (args.length < 5) {
+    if (args.length < 4) {
       System.err.println(
-        "Usage: WikipediaPageRank <inputFile> <threshold> <numPartitions> <host> <usePartitioner>")
+        "Usage: WikipediaPageRank <inputFile> <threshold> <numPartitions> <usePartitioner>")
       System.exit(-1)
     }
     val sparkConf = new SparkConf()
+    sparkConf.setAppName("WikipediaPageRank")
     sparkConf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
     sparkConf.set("spark.kryo.registrator",  classOf[PRKryoRegistrator].getName)
 
     val inputFile = args(0)
     val threshold = args(1).toDouble
     val numPartitions = args(2).toInt
-    val host = args(3)
-    val usePartitioner = args(4).toBoolean
+    val usePartitioner = args(3).toBoolean
 
-    sparkConf.setMaster(host).setAppName("WikipediaPageRank")
+    sparkConf.setAppName("WikipediaPageRank")
     val sc = new SparkContext(sparkConf)
 
     // Parse the Wikipedia page data into a graph
diff --git a/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRankStandalone.scala b/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRankStandalone.scala
index dee3cb6c0a..a197dac87d 100644
--- a/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRankStandalone.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRankStandalone.scala
@@ -30,22 +30,20 @@ import org.apache.spark.rdd.RDD
 
 object WikipediaPageRankStandalone {
   def main(args: Array[String]) {
-    if (args.length < 5) {
+    if (args.length < 4) {
       System.err.println("Usage: WikipediaPageRankStandalone <inputFile> <threshold> " +
-        "<numIterations> <host> <usePartitioner>")
+        "<numIterations> <usePartitioner>")
       System.exit(-1)
     }
     val sparkConf = new SparkConf()
     sparkConf.set("spark.serializer", "spark.bagel.examples.WPRSerializer")
 
-
     val inputFile = args(0)
     val threshold = args(1).toDouble
     val numIterations = args(2).toInt
-    val host = args(3)
-    val usePartitioner = args(4).toBoolean
+    val usePartitioner = args(3).toBoolean
 
-    sparkConf.setMaster(host).setAppName("WikipediaPageRankStandalone")
+    sparkConf.setAppName("WikipediaPageRankStandalone")
 
     val sc = new SparkContext(sparkConf)
 
diff --git a/examples/src/main/scala/org/apache/spark/examples/graphx/LiveJournalPageRank.scala b/examples/src/main/scala/org/apache/spark/examples/graphx/LiveJournalPageRank.scala
index d58fddff2b..6ef3b62dcb 100644
--- a/examples/src/main/scala/org/apache/spark/examples/graphx/LiveJournalPageRank.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/graphx/LiveJournalPageRank.scala
@@ -28,9 +28,9 @@ import org.apache.spark.graphx.lib.Analytics
  */
 object LiveJournalPageRank {
   def main(args: Array[String]) {
-    if (args.length < 2) {
+    if (args.length < 1) {
       System.err.println(
-        "Usage: LiveJournalPageRank <master> <edge_list_file>\n" +
+        "Usage: LiveJournalPageRank <edge_list_file>\n" +
           "    [--tol=<tolerance>]\n" +
           "        The tolerance allowed at convergence (smaller => more accurate). Default is " +
           "0.001.\n" +
@@ -44,6 +44,6 @@ object LiveJournalPageRank {
       System.exit(-1)
     }
 
-    Analytics.main(args.patch(1, List("pagerank"), 0))
+    Analytics.main(args.patch(0, List("pagerank"), 0))
   }
 }
diff --git a/examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala b/examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala
index ff9254b044..61c460c6b1 100644
--- a/examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.examples.sql
 
-import org.apache.spark.SparkContext
+import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.sql.SQLContext
 
 // One method for defining the schema of an RDD is to make a case class with the desired column
@@ -26,7 +26,8 @@ case class Record(key: Int, value: String)
 
 object RDDRelation {
   def main(args: Array[String]) {
-    val sc = new SparkContext("local", "RDDRelation")
+    val sparkConf = new SparkConf().setAppName("RDDRelation")
+    val sc = new SparkContext(sparkConf)
     val sqlContext = new SQLContext(sc)
 
     // Importing the SQL context gives access to all the SQL functions and implicit conversions.
diff --git a/examples/src/main/scala/org/apache/spark/examples/sql/hive/HiveFromSpark.scala b/examples/src/main/scala/org/apache/spark/examples/sql/hive/HiveFromSpark.scala
index 66ce93a26e..b262fabbe0 100644
--- a/examples/src/main/scala/org/apache/spark/examples/sql/hive/HiveFromSpark.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/sql/hive/HiveFromSpark.scala
@@ -17,7 +17,7 @@
 
 package org.apache.spark.examples.sql.hive
 
-import org.apache.spark.SparkContext
+import org.apache.spark.{SparkConf, SparkContext}
 import org.apache.spark.sql._
 import org.apache.spark.sql.hive.LocalHiveContext
 
@@ -25,7 +25,8 @@ object HiveFromSpark {
   case class Record(key: Int, value: String)
 
   def main(args: Array[String]) {
-    val sc = new SparkContext("local", "HiveFromSpark")
+    val sparkConf = new SparkConf().setAppName("HiveFromSpark")
+    val sc = new SparkContext(sparkConf)
 
     // A local hive context creates an instance of the Hive Metastore in process, storing the
     // the warehouse data in the current directory.  This location can be overridden by
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala
index 84cf43df0f..e29e16a9c1 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/ActorWordCount.scala
@@ -126,31 +126,30 @@ object FeederActor {
 /**
  * A sample word count program demonstrating the use of plugging in
  * Actor as Receiver
- * Usage: ActorWordCount <master> <hostname> <port>
- *   <master> is the Spark master URL. In local mode, <master> should be 'local[n]' with n > 1.
+ * Usage: ActorWordCount <hostname> <port>
  *   <hostname> and <port> describe the AkkaSystem that Spark Sample feeder is running on.
  *
  * To run this example locally, you may run Feeder Actor as
- *    `$ ./bin/run-example org.apache.spark.examples.streaming.FeederActor 127.0.1.1 9999`
+ *    `./bin/spark-submit examples.jar \
+ *    --class org.apache.spark.examples.streaming.FeederActor 127.0.1.1 9999`
  * and then run the example
- *    `./bin/run-example org.apache.spark.examples.streaming.ActorWordCount local[2] 127.0.1.1 9999`
+ *    `./bin/spark-submit examples.jar --class org.apache.spark.examples.streaming.ActorWordCount \
+ *     127.0.1.1 9999`
  */
 object ActorWordCount {
   def main(args: Array[String]) {
-    if (args.length < 3) {
+    if (args.length < 2) {
       System.err.println(
-        "Usage: ActorWordCount <master> <hostname> <port>" +
-        "In local mode, <master> should be 'local[n]' with n > 1")
+        "Usage: ActorWordCount <hostname> <port>")
       System.exit(1)
     }
 
     StreamingExamples.setStreamingLogLevels()
 
-    val Seq(master, host, port) = args.toSeq
-
+    val Seq(host, port) = args.toSeq
+    val sparkConf = new SparkConf().setAppName("ActorWordCount")
     // Create the context and set the batch size
-    val ssc = new StreamingContext(master, "ActorWordCount", Seconds(2),
-      System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass).toSeq)
+    val ssc = new StreamingContext(sparkConf, Seconds(2))
 
     /*
      * Following is the use of actorStream to plug in custom actor as receiver
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/FlumeEventCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/FlumeEventCount.scala
index 5b2a1035fc..38362edac2 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/FlumeEventCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/FlumeEventCount.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.examples.streaming
 
+import org.apache.spark.SparkConf
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming._
 import org.apache.spark.streaming.flume._
@@ -29,9 +30,8 @@ import org.apache.spark.util.IntParam
  *  an Avro server on at the request host:port address and listen for requests.
  *  Your Flume AvroSink should be pointed to this address.
  *
- *  Usage: FlumeEventCount <master> <host> <port>
+ *  Usage: FlumeEventCount <host> <port>
  *
- *    <master> is a Spark master URL
  *    <host> is the host the Flume receiver will be started on - a receiver
  *           creates a server and listens for flume events.
  *    <port> is the port the Flume receiver will listen on.
@@ -40,21 +40,21 @@ object FlumeEventCount {
   def main(args: Array[String]) {
     if (args.length != 3) {
       System.err.println(
-        "Usage: FlumeEventCount <master> <host> <port>")
+        "Usage: FlumeEventCount <host> <port>")
       System.exit(1)
     }
 
     StreamingExamples.setStreamingLogLevels()
 
-    val Array(master, host, IntParam(port)) = args
+    val Array(host, IntParam(port)) = args
 
     val batchInterval = Milliseconds(2000)
+    val sparkConf = new SparkConf().setAppName("FlumeEventCount")
     // Create the context and set the batch size
-    val ssc = new StreamingContext(master, "FlumeEventCount", batchInterval,
-      System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass).toSeq)
+    val ssc = new StreamingContext(sparkConf, batchInterval)
 
     // Create a flume stream
-    val stream = FlumeUtils.createStream(ssc, host,port,StorageLevel.MEMORY_ONLY_SER_2)
+    val stream = FlumeUtils.createStream(ssc, host, port, StorageLevel.MEMORY_ONLY_SER_2)
 
     // Print out the count of events received from this server in each batch
     stream.count().map(cnt => "Received " + cnt + " flume events." ).print()
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/HdfsWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/HdfsWordCount.scala
index b440956ba3..55ac48cfb6 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/HdfsWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/HdfsWordCount.scala
@@ -17,35 +17,35 @@
 
 package org.apache.spark.examples.streaming
 
+import org.apache.spark.SparkConf
 import org.apache.spark.streaming.{Seconds, StreamingContext}
 import org.apache.spark.streaming.StreamingContext._
 
 /**
  * Counts words in new text files created in the given directory
- * Usage: HdfsWordCount <master> <directory>
- *   <master> is the Spark master URL.
+ * Usage: HdfsWordCount <directory>
  *   <directory> is the directory that Spark Streaming will use to find and read new text files.
  *
  * To run this on your local machine on directory `localdir`, run this example
- *    `$ ./bin/run-example org.apache.spark.examples.streaming.HdfsWordCount local[2] localdir`
+ *    `$ ./bin/spark-submit examples.jar \
+ *    --class org.apache.spark.examples.streaming.HdfsWordCount localdir`
  * Then create a text file in `localdir` and the words in the file will get counted.
  */
 object HdfsWordCount {
   def main(args: Array[String]) {
-    if (args.length < 2) {
-      System.err.println("Usage: HdfsWordCount <master> <directory>")
+    if (args.length < 1) {
+      System.err.println("Usage: HdfsWordCount <directory>")
       System.exit(1)
     }
 
     StreamingExamples.setStreamingLogLevels()
-
+    val sparkConf = new SparkConf().setAppName("HdfsWordCount")
     // Create the context
-    val ssc = new StreamingContext(args(0), "HdfsWordCount", Seconds(2),
-      System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass).toSeq)
+    val ssc = new StreamingContext(sparkConf, Seconds(2))
 
     // Create the FileInputDStream on the directory and use the
     // stream to count words in new files created
-    val lines = ssc.textFileStream(args(1))
+    val lines = ssc.textFileStream(args(0))
     val words = lines.flatMap(_.split(" "))
     val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _)
     wordCounts.print()
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala
index c3aae5af05..3af806981f 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/KafkaWordCount.scala
@@ -24,34 +24,33 @@ import kafka.producer._
 import org.apache.spark.streaming._
 import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.streaming.kafka._
+import org.apache.spark.SparkConf
 
-// scalastyle:off
 /**
  * Consumes messages from one or more topics in Kafka and does wordcount.
- * Usage: KafkaWordCount <master> <zkQuorum> <group> <topics> <numThreads>
- *   <master> is the Spark master URL. In local mode, <master> should be 'local[n]' with n > 1.
+ * Usage: KafkaWordCount <zkQuorum> <group> <topics> <numThreads>
  *   <zkQuorum> is a list of one or more zookeeper servers that make quorum
  *   <group> is the name of kafka consumer group
  *   <topics> is a list of one or more kafka topics to consume from
  *   <numThreads> is the number of threads the kafka consumer should use
  *
  * Example:
- *    `./bin/run-example org.apache.spark.examples.streaming.KafkaWordCount local[2] zoo01,zoo02,zoo03 my-consumer-group topic1,topic2 1`
+ *    `./bin/spark-submit examples.jar \
+ *    --class org.apache.spark.examples.streaming.KafkaWordCount local[2] zoo01,zoo02,zoo03 \
+ *    my-consumer-group topic1,topic2 1`
  */
-// scalastyle:on
 object KafkaWordCount {
   def main(args: Array[String]) {
-    if (args.length < 5) {
-      System.err.println("Usage: KafkaWordCount <master> <zkQuorum> <group> <topics> <numThreads>")
+    if (args.length < 4) {
+      System.err.println("Usage: KafkaWordCount <zkQuorum> <group> <topics> <numThreads>")
       System.exit(1)
     }
 
     StreamingExamples.setStreamingLogLevels()
 
-    val Array(master, zkQuorum, group, topics, numThreads) = args
-
-    val ssc =  new StreamingContext(master, "KafkaWordCount", Seconds(2),
-      System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass).toSeq)
+    val Array(zkQuorum, group, topics, numThreads) = args
+    val sparkConf = new SparkConf().setAppName("KafkaWordCount")
+    val ssc =  new StreamingContext(sparkConf, Seconds(2))
     ssc.checkpoint("checkpoint")
 
     val topicpMap = topics.split(",").map((_,numThreads.toInt)).toMap
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala
index 47bf1e5a06..3a10daa9ab 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/MQTTWordCount.scala
@@ -24,6 +24,7 @@ import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming.{Seconds, StreamingContext}
 import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.streaming.mqtt._
+import org.apache.spark.SparkConf
 
 /**
  * A simple Mqtt publisher for demonstration purposes, repeatedly publishes
@@ -64,7 +65,6 @@ object MQTTPublisher {
   }
 }
 
-// scalastyle:off
 /**
  * A sample wordcount with MqttStream stream
  *
@@ -74,30 +74,28 @@ object MQTTPublisher {
  * Eclipse paho project provides Java library for Mqtt Client http://www.eclipse.org/paho/
  * Example Java code for Mqtt Publisher and Subscriber can be found here
  * https://bitbucket.org/mkjinesh/mqttclient
- * Usage: MQTTWordCount <master> <MqttbrokerUrl> <topic>
- * In local mode, <master> should be 'local[n]' with n > 1
- *   <MqttbrokerUrl> and <topic> describe where Mqtt publisher is running.
+ * Usage: MQTTWordCount <MqttbrokerUrl> <topic>
+\ *   <MqttbrokerUrl> and <topic> describe where Mqtt publisher is running.
  *
  * To run this example locally, you may run publisher as
- *    `$ ./bin/run-example org.apache.spark.examples.streaming.MQTTPublisher tcp://localhost:1883 foo`
+ *    `$ ./bin/spark-submit examples.jar \
+ *    --class org.apache.spark.examples.streaming.MQTTPublisher tcp://localhost:1883 foo`
  * and run the example as
- *    `$ ./bin/run-example org.apache.spark.examples.streaming.MQTTWordCount local[2] tcp://localhost:1883 foo`
+ *    `$ ./bin/spark-submit examples.jar \
+ *    --class org.apache.spark.examples.streaming.MQTTWordCount tcp://localhost:1883 foo`
  */
-// scalastyle:on
 object MQTTWordCount {
 
   def main(args: Array[String]) {
-    if (args.length < 3) {
+    if (args.length < 2) {
       System.err.println(
-        "Usage: MQTTWordCount <master> <MqttbrokerUrl> <topic>" +
-          " In local mode, <master> should be 'local[n]' with n > 1")
+        "Usage: MQTTWordCount <MqttbrokerUrl> <topic>")
       System.exit(1)
     }
 
-    val Seq(master, brokerUrl, topic) = args.toSeq
-
-    val ssc = new StreamingContext(master, "MqttWordCount", Seconds(2), System.getenv("SPARK_HOME"),
-    StreamingContext.jarOfClass(this.getClass).toSeq)
+    val Seq(brokerUrl, topic) = args.toSeq
+    val sparkConf = new SparkConf().setAppName("MQTTWordCount")
+    val ssc = new StreamingContext(sparkConf, Seconds(2))
     val lines = MQTTUtils.createStream(ssc, brokerUrl, topic, StorageLevel.MEMORY_ONLY_SER_2)
 
     val words = lines.flatMap(x => x.toString.split(" "))
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/NetworkWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/NetworkWordCount.scala
index acfe9a4da3..ad7a199b2c 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/NetworkWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/NetworkWordCount.scala
@@ -17,41 +17,38 @@
 
 package org.apache.spark.examples.streaming
 
+import org.apache.spark.SparkConf
 import org.apache.spark.streaming.{Seconds, StreamingContext}
 import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.storage.StorageLevel
 
-// scalastyle:off
 /**
  * Counts words in text encoded with UTF8 received from the network every second.
  *
- * Usage: NetworkWordCount <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.
+ * Usage: NetworkWordCount <hostname> <port>
+ * <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.examples.streaming.NetworkWordCount local[2] localhost 9999`
+ *    `$ ./bin/spark-submit examples.jar \
+ *    --class org.apache.spark.examples.streaming.NetworkWordCount localhost 9999`
  */
-// scalastyle:on
 object NetworkWordCount {
   def main(args: Array[String]) {
-    if (args.length < 3) {
-      System.err.println("Usage: NetworkWordCount <master> <hostname> <port>\n" +
-        "In local mode, <master> should be 'local[n]' with n > 1")
+    if (args.length < 2) {
+      System.err.println("Usage: NetworkWordCount <hostname> <port>")
       System.exit(1)
     }
 
     StreamingExamples.setStreamingLogLevels()
-
+    val sparkConf = new SparkConf().setAppName("NetworkWordCount");
     // Create the context with a 1 second batch size
-    val ssc = new StreamingContext(args(0), "NetworkWordCount", Seconds(1),
-      System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass).toSeq)
+    val ssc = new StreamingContext(sparkConf, Seconds(1))
 
     // Create a NetworkInputDStream on target ip:port and count the
     // words in input stream of \n delimited text (eg. generated by 'nc')
-    val lines = ssc.socketTextStream(args(1), args(2).toInt, StorageLevel.MEMORY_ONLY_SER)
+    val lines = ssc.socketTextStream(args(0), args(1).toInt, StorageLevel.MEMORY_ONLY_SER)
     val words = lines.flatMap(_.split(" "))
     val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _)
     wordCounts.print()
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/QueueStream.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/QueueStream.scala
index f92f72f2de..4caa906591 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/QueueStream.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/QueueStream.scala
@@ -19,6 +19,7 @@ package org.apache.spark.examples.streaming
 
 import scala.collection.mutable.SynchronizedQueue
 
+import org.apache.spark.SparkConf
 import org.apache.spark.rdd.RDD
 import org.apache.spark.streaming.{Seconds, StreamingContext}
 import org.apache.spark.streaming.StreamingContext._
@@ -26,16 +27,11 @@ import org.apache.spark.streaming.StreamingContext._
 object QueueStream {
 
   def main(args: Array[String]) {
-    if (args.length < 1) {
-      System.err.println("Usage: QueueStream <master>")
-      System.exit(1)
-    }
 
     StreamingExamples.setStreamingLogLevels()
-
+    val sparkConf = new SparkConf().setAppName("QueueStream")
     // Create the context
-    val ssc = new StreamingContext(args(0), "QueueStream", Seconds(1),
-      System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass).toSeq)
+    val ssc = new StreamingContext(sparkConf, Seconds(1))
 
     // Create the queue through which RDDs can be pushed to
     // a QueueInputDStream
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/RawNetworkGrep.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/RawNetworkGrep.scala
index 1b0319a046..a9aaa445bc 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/RawNetworkGrep.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/RawNetworkGrep.scala
@@ -17,6 +17,7 @@
 
 package org.apache.spark.examples.streaming
 
+import org.apache.spark.SparkConf
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming._
 import org.apache.spark.util.IntParam
@@ -27,29 +28,26 @@ import org.apache.spark.util.IntParam
  * will only work with spark.streaming.util.RawTextSender running on all worker nodes
  * and with Spark using Kryo serialization (set Java property "spark.serializer" to
  * "org.apache.spark.serializer.KryoSerializer").
- * Usage: RawNetworkGrep <master> <numStreams> <host> <port> <batchMillis>
- *   <master> is the Spark master URL
+ * Usage: RawNetworkGrep <numStreams> <host> <port> <batchMillis>
  *   <numStream> is the number rawNetworkStreams, which should be same as number
  *               of work nodes in the cluster
  *   <host> is "localhost".
  *   <port> is the port on which RawTextSender is running in the worker nodes.
  *   <batchMillise> is the Spark Streaming batch duration in milliseconds.
  */
-
 object RawNetworkGrep {
   def main(args: Array[String]) {
-    if (args.length != 5) {
-      System.err.println("Usage: RawNetworkGrep <master> <numStreams> <host> <port> <batchMillis>")
+    if (args.length != 4) {
+      System.err.println("Usage: RawNetworkGrep <numStreams> <host> <port> <batchMillis>")
       System.exit(1)
     }
 
     StreamingExamples.setStreamingLogLevels()
 
-    val Array(master, IntParam(numStreams), host, IntParam(port), IntParam(batchMillis)) = args
-
+    val Array(IntParam(numStreams), host, IntParam(port), IntParam(batchMillis)) = args
+    val sparkConf = new SparkConf().setAppName("RawNetworkGrep")
     // Create the context
-    val ssc = new StreamingContext(master, "RawNetworkGrep", Milliseconds(batchMillis),
-      System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass).toSeq)
+    val ssc = new StreamingContext(sparkConf, Duration(batchMillis))
 
     val rawStreams = (1 to numStreams).map(_ =>
       ssc.rawSocketStream[String](host, port, StorageLevel.MEMORY_ONLY_SER_2)).toArray
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala
index b0bc31cc66..ace785d9fe 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/RecoverableNetworkWordCount.scala
@@ -17,19 +17,21 @@
 
 package org.apache.spark.examples.streaming
 
+import java.io.File
+import java.nio.charset.Charset
+
+import com.google.common.io.Files
+
+import org.apache.spark.SparkConf
+import org.apache.spark.rdd.RDD
 import org.apache.spark.streaming.{Time, Seconds, StreamingContext}
 import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.util.IntParam
-import java.io.File
-import org.apache.spark.rdd.RDD
-import com.google.common.io.Files
-import java.nio.charset.Charset
 
 /**
  * Counts words in text encoded with UTF8 received from the network every second.
  *
- * Usage: NetworkWordCount <master> <hostname> <port> <checkpoint-directory> <output-file>
- *   <master> is the Spark master URL. In local mode, <master> should be 'local[n]' with n > 1.
+ * Usage: NetworkWordCount <hostname> <port> <checkpoint-directory> <output-file>
  *   <hostname> and <port> describe the TCP server that Spark Streaming would connect to receive
  *   data. <checkpoint-directory> directory to HDFS-compatible file system which checkpoint data
  *   <output-file> file to which the word counts will be appended
@@ -44,8 +46,9 @@ import java.nio.charset.Charset
  *
  * and run the example as
  *
- *      `$ ./run-example org.apache.spark.examples.streaming.RecoverableNetworkWordCount \
- *              local[2] localhost 9999 ~/checkpoint/ ~/out`
+ *      `$ ./bin/spark-submit examples.jar \
+ *      --class org.apache.spark.examples.streaming.RecoverableNetworkWordCount \
+ *              localhost 9999 ~/checkpoint/ ~/out`
  *
  * If the directory ~/checkpoint/ does not exist (e.g. running for the first time), it will create
  * a new StreamingContext (will print "Creating new context" to the console). Otherwise, if
@@ -67,17 +70,16 @@ import java.nio.charset.Charset
 
 object RecoverableNetworkWordCount {
 
-  def createContext(master: String, ip: String, port: Int, outputPath: String) = {
+  def createContext(ip: String, port: Int, outputPath: String) = {
 
     // If you do not see this printed, that means the StreamingContext has been loaded
     // from the new checkpoint
     println("Creating new context")
     val outputFile = new File(outputPath)
     if (outputFile.exists()) outputFile.delete()
-
+    val sparkConf = new SparkConf().setAppName("RecoverableNetworkWordCount")
     // Create the context with a 1 second batch size
-    val ssc = new StreamingContext(master, "RecoverableNetworkWordCount", Seconds(1),
-      System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass).toSeq)
+    val ssc = new StreamingContext(sparkConf, Seconds(1))
 
     // Create a NetworkInputDStream on target ip:port and count the
     // words in input stream of \n delimited text (eg. generated by 'nc')
@@ -94,13 +96,12 @@ object RecoverableNetworkWordCount {
   }
 
   def main(args: Array[String]) {
-    if (args.length != 5) {
+    if (args.length != 4) {
       System.err.println("You arguments were " + args.mkString("[", ", ", "]"))
       System.err.println(
         """
-          |Usage: RecoverableNetworkWordCount <master> <hostname> <port> <checkpoint-directory>
-          |     <output-file> <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
+          |Usage: RecoverableNetworkWordCount <hostname> <port> <checkpoint-directory>
+          |     <output-file>. <hostname> and <port> describe the TCP server that Spark
           |     Streaming would connect to receive data. <checkpoint-directory> directory to
           |     HDFS-compatible file system which checkpoint data <output-file> file to which the
           |     word counts will be appended
@@ -111,10 +112,10 @@ object RecoverableNetworkWordCount {
       )
       System.exit(1)
     }
-    val Array(master, ip, IntParam(port), checkpointDirectory, outputPath) = args
+    val Array(ip, IntParam(port), checkpointDirectory, outputPath) = args
     val ssc = StreamingContext.getOrCreate(checkpointDirectory,
       () => {
-        createContext(master, ip, port, outputPath)
+        createContext(ip, port, outputPath)
       })
     ssc.start()
     ssc.awaitTermination()
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala
index 8001d56c98..5e1415f3cc 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/StatefulNetworkWordCount.scala
@@ -17,28 +17,27 @@
 
 package org.apache.spark.examples.streaming
 
+import org.apache.spark.SparkConf
 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.
- * Usage: StatefulNetworkWordCount <master> <hostname> <port>
- *   <master> is the Spark master URL. In local mode, <master> should be 'local[n]' with n > 1.
+ * Usage: StatefulNetworkWordCount <hostname> <port>
  *   <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.examples.streaming.StatefulNetworkWordCount local[2] localhost 9999`
+ *    `$ ./bin/spark-submit examples.jar
+ *    --class org.apache.spark.examples.streaming.StatefulNetworkWordCount localhost 9999`
  */
-// scalastyle:on
 object StatefulNetworkWordCount {
   def main(args: Array[String]) {
-    if (args.length < 3) {
-      System.err.println("Usage: StatefulNetworkWordCount <master> <hostname> <port>\n" +
-        "In local mode, <master> should be 'local[n]' with n > 1")
+    if (args.length < 2) {
+      System.err.println("Usage: StatefulNetworkWordCount <hostname> <port>")
       System.exit(1)
     }
 
@@ -52,14 +51,14 @@ object StatefulNetworkWordCount {
       Some(currentCount + previousCount)
     }
 
+    val sparkConf = new SparkConf().setAppName("NetworkWordCumulativeCountUpdateStateByKey")
     // 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).toSeq)
+    val ssc = new StreamingContext(sparkConf, Seconds(1))
     ssc.checkpoint(".")
 
     // Create a NetworkInputDStream on target ip:port and count the
     // words in input stream of \n delimited test (eg. generated by 'nc')
-    val lines = ssc.socketTextStream(args(1), args(2).toInt)
+    val lines = ssc.socketTextStream(args(0), args(1).toInt)
     val words = lines.flatMap(_.split(" "))
     val wordDstream = words.map(x => (x, 1))
 
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdCMS.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdCMS.scala
index b12617d881..683752ac96 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdCMS.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdCMS.scala
@@ -19,11 +19,13 @@ package org.apache.spark.examples.streaming
 
 import com.twitter.algebird._
 
+import org.apache.spark.SparkConf
 import org.apache.spark.SparkContext._
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming.{Seconds, StreamingContext}
 import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.streaming.twitter._
+
 // scalastyle:off
 /**
  * Illustrates the use of the Count-Min Sketch, from Twitter's Algebird library, to compute
@@ -49,12 +51,6 @@ import org.apache.spark.streaming.twitter._
 // scalastyle:on
 object TwitterAlgebirdCMS {
   def main(args: Array[String]) {
-    if (args.length < 1) {
-      System.err.println("Usage: TwitterAlgebirdCMS <master>" +
-        " [filter1] [filter2] ... [filter n]")
-      System.exit(1)
-    }
-
     StreamingExamples.setStreamingLogLevels()
 
     // CMS parameters
@@ -65,10 +61,9 @@ object TwitterAlgebirdCMS {
     // K highest frequency elements to take
     val TOPK = 10
 
-    val (master, filters) = (args.head, args.tail)
-
-    val ssc = new StreamingContext(master, "TwitterAlgebirdCMS", Seconds(10),
-      System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass).toSeq)
+    val filters = args
+    val sparkConf = new SparkConf().setAppName("TwitterAlgebirdCMS")
+    val ssc = new StreamingContext(sparkConf, Seconds(10))
     val stream = TwitterUtils.createStream(ssc, None, filters, StorageLevel.MEMORY_ONLY_SER_2)
 
     val users = stream.map(status => status.getUser.getId)
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdHLL.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdHLL.scala
index 22f232c725..62db5e663b 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdHLL.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterAlgebirdHLL.scala
@@ -23,6 +23,8 @@ import com.twitter.algebird.HyperLogLog._
 import org.apache.spark.storage.StorageLevel
 import org.apache.spark.streaming.{Seconds, StreamingContext}
 import org.apache.spark.streaming.twitter._
+import org.apache.spark.SparkConf
+
 // scalastyle:off
 /**
  * Illustrates the use of the HyperLogLog algorithm, from Twitter's Algebird library, to compute
@@ -42,20 +44,14 @@ import org.apache.spark.streaming.twitter._
 // scalastyle:on
 object TwitterAlgebirdHLL {
   def main(args: Array[String]) {
-    if (args.length < 1) {
-      System.err.println("Usage: TwitterAlgebirdHLL <master>" +
-        " [filter1] [filter2] ... [filter n]")
-      System.exit(1)
-    }
 
     StreamingExamples.setStreamingLogLevels()
 
     /** Bit size parameter for HyperLogLog, trades off accuracy vs size */
     val BIT_SIZE = 12
-    val (master, filters) = (args.head, args.tail)
-
-    val ssc = new StreamingContext(master, "TwitterAlgebirdHLL", Seconds(5),
-      System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass).toSeq)
+    val filters = args
+    val sparkConf = new SparkConf().setAppName("TwitterAlgebirdHLL")
+    val ssc = new StreamingContext(sparkConf, Seconds(5))
     val stream = TwitterUtils.createStream(ssc, None, filters, StorageLevel.MEMORY_ONLY_SER)
 
     val users = stream.map(status => status.getUser.getId)
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterPopularTags.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterPopularTags.scala
index 5b58e94600..1ddff22cb8 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterPopularTags.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/TwitterPopularTags.scala
@@ -21,6 +21,7 @@ import org.apache.spark.streaming.{Seconds, StreamingContext}
 import StreamingContext._
 import org.apache.spark.SparkContext._
 import org.apache.spark.streaming.twitter._
+import org.apache.spark.SparkConf
 
 /**
  * Calculates popular hashtags (topics) over sliding 10 and 60 second windows from a Twitter
@@ -30,18 +31,12 @@ import org.apache.spark.streaming.twitter._
  */
 object TwitterPopularTags {
   def main(args: Array[String]) {
-    if (args.length < 1) {
-      System.err.println("Usage: TwitterPopularTags <master>" +
-        " [filter1] [filter2] ... [filter n]")
-      System.exit(1)
-    }
 
     StreamingExamples.setStreamingLogLevels()
 
-    val (master, filters) = (args.head, args.tail)
-
-    val ssc = new StreamingContext(master, "TwitterPopularTags", Seconds(2),
-      System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass).toSeq)
+    val filters = args
+    val sparkConf = new SparkConf().setAppName("TwitterPopularTags")
+    val ssc = new StreamingContext(sparkConf, Seconds(2))
     val stream = TwitterUtils.createStream(ssc, None, filters)
 
     val hashTags = stream.flatMap(status => status.getText.split(" ").filter(_.startsWith("#")))
diff --git a/examples/src/main/scala/org/apache/spark/examples/streaming/ZeroMQWordCount.scala b/examples/src/main/scala/org/apache/spark/examples/streaming/ZeroMQWordCount.scala
index de46e5f5b1..7ade3f1018 100644
--- a/examples/src/main/scala/org/apache/spark/examples/streaming/ZeroMQWordCount.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/streaming/ZeroMQWordCount.scala
@@ -28,6 +28,7 @@ import org.apache.spark.streaming.StreamingContext._
 import org.apache.spark.streaming.zeromq._
 
 import scala.language.implicitConversions
+import org.apache.spark.SparkConf
 
 /**
  * A simple publisher for demonstration purposes, repeatedly publishes random Messages
@@ -63,30 +64,28 @@ object SimpleZeroMQPublisher {
  * Install zeroMQ (release 2.1) core libraries. [ZeroMQ Install guide]
  * (http://www.zeromq.org/intro:get-the-software)
  *
- * Usage: ZeroMQWordCount <master> <zeroMQurl> <topic>
- * In local mode, <master> should be 'local[n]' with n > 1
+ * Usage: ZeroMQWordCount <zeroMQurl> <topic>
  *   <zeroMQurl> and <topic> describe where zeroMq publisher is running.
  *
  * To run this example locally, you may run publisher as
- *    `$ ./bin/run-example org.apache.spark.examples.streaming.SimpleZeroMQPublisher tcp://127.0.1.1:1234 foo.bar`
+ *    `$ ./bin/spark-submit examples.jar \
+ *    --class org.apache.spark.examples.streaming.SimpleZeroMQPublisher tcp://127.0.1.1:1234 foo.bar`
  * and run the example as
- *    `$ ./bin/run-example org.apache.spark.examples.streaming.ZeroMQWordCount local[2] tcp://127.0.1.1:1234 foo`
+ *    `$ ./bin/spark-submit examples.jar \
+ *    --class org.apache.spark.examples.streaming.ZeroMQWordCount tcp://127.0.1.1:1234 foo`
  */
 // scalastyle:on
 object ZeroMQWordCount {
   def main(args: Array[String]) {
-    if (args.length < 3) {
-      System.err.println(
-        "Usage: ZeroMQWordCount <master> <zeroMQurl> <topic>" +
-          "In local mode, <master> should be 'local[n]' with n > 1")
+    if (args.length < 2) {
+      System.err.println("Usage: ZeroMQWordCount <zeroMQurl> <topic>")
       System.exit(1)
     }
     StreamingExamples.setStreamingLogLevels()
-    val Seq(master, url, topic) = args.toSeq
-
+    val Seq(url, topic) = args.toSeq
+    val sparkConf = new SparkConf().setAppName("ZeroMQWordCount")
     // Create the context and set the batch size
-    val ssc = new StreamingContext(master, "ZeroMQWordCount", Seconds(2),
-      System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass).toSeq)
+    val ssc = new StreamingContext(sparkConf, Seconds(2))
 
     def bytesToStringIterator(x: Seq[ByteString]) = (x.map(_.utf8String)).iterator
 
-- 
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