Merge branch 'master' into streaming

Conflicts:
	.gitignore

41 files changed, 825 insertions, 167 deletions

diff --git a/examples/pom.xml b/examples/pom.xml
index 7d975875fa..3e5271ec2f 100644
--- a/examples/pom.xml
+++ b/examples/pom.xml
@@ -3,8 +3,8 @@
   <modelVersion>4.0.0</modelVersion>
   <parent>
     <groupId>org.spark-project</groupId>
-    <artifactId>parent</artifactId>
-    <version>0.7.0-SNAPSHOT</version>
+    <artifactId>spark-parent</artifactId>
+    <version>0.8.0-SNAPSHOT</version>
     <relativePath>../pom.xml</relativePath>
   </parent>
 
@@ -22,7 +22,7 @@
     <dependency>
       <groupId>com.twitter</groupId>
       <artifactId>algebird-core_2.9.2</artifactId>
-      <version>0.1.8</version>
+      <version>0.1.11</version>
     </dependency>
     <dependency>
       <groupId>org.scalatest</groupId>
@@ -34,6 +34,41 @@
       <artifactId>scalacheck_${scala.version}</artifactId>
       <scope>test</scope>
     </dependency>
+      <dependency>
+        <groupId>org.apache.cassandra</groupId>
+        <artifactId>cassandra-all</artifactId>
+        <version>1.2.5</version>
+        <exclusions>
+            <exclusion>
+                <groupId>com.google.guava</groupId>
+                <artifactId>guava</artifactId>
+            </exclusion>
+            <exclusion>
+                <groupId>com.googlecode.concurrentlinkedhashmap</groupId>
+                <artifactId>concurrentlinkedhashmap-lru</artifactId>
+            </exclusion>
+            <exclusion>
+                <groupId>com.ning</groupId>
+                <artifactId>compress-lzf</artifactId>
+            </exclusion>
+            <exclusion>
+                <groupId>io.netty</groupId>
+                <artifactId>netty</artifactId>
+            </exclusion>
+            <exclusion>
+                <groupId>jline</groupId>
+                <artifactId>jline</artifactId>
+            </exclusion>
+            <exclusion>
+                <groupId>log4j</groupId>
+                <artifactId>log4j</artifactId>
+            </exclusion>
+            <exclusion>
+                <groupId>org.apache.cassandra.deps</groupId>
+                <artifactId>avro</artifactId>
+            </exclusion>
+        </exclusions>
+      </dependency>
   </dependencies>
   <build>
     <outputDirectory>target/scala-${scala.version}/classes</outputDirectory>
@@ -67,6 +102,11 @@
           <artifactId>hadoop-core</artifactId>
           <scope>provided</scope>
         </dependency>
+        <dependency>
+          <groupId>org.apache.hbase</groupId>
+          <artifactId>hbase</artifactId>
+          <version>0.94.6</version>
+        </dependency>
       </dependencies>
       <build>
         <plugins>
@@ -105,6 +145,11 @@
           <artifactId>hadoop-client</artifactId>
           <scope>provided</scope>
         </dependency>
+        <dependency>
+          <groupId>org.apache.hbase</groupId>
+          <artifactId>hbase</artifactId>
+          <version>0.94.6</version>
+        </dependency>
       </dependencies>
       <build>
         <plugins>
@@ -118,5 +163,48 @@
         </plugins>
       </build>
     </profile>
+    <profile>
+      <id>hadoop2-yarn</id>
+      <dependencies>
+        <dependency>
+          <groupId>org.spark-project</groupId>
+          <artifactId>spark-core</artifactId>
+          <version>${project.version}</version>
+          <classifier>hadoop2-yarn</classifier>
+        </dependency>
+        <dependency>
+          <groupId>org.spark-project</groupId>
+          <artifactId>spark-streaming</artifactId>
+          <version>${project.version}</version>
+          <classifier>hadoop2-yarn</classifier>
+        </dependency>
+        <dependency>
+          <groupId>org.apache.hadoop</groupId>
+          <artifactId>hadoop-client</artifactId>
+          <scope>provided</scope>
+        </dependency>
+        <dependency>
+          <groupId>org.apache.hadoop</groupId>
+          <artifactId>hadoop-yarn-api</artifactId>
+          <scope>provided</scope>
+        </dependency>
+        <dependency>
+          <groupId>org.apache.hadoop</groupId>
+          <artifactId>hadoop-yarn-common</artifactId>
+          <scope>provided</scope>
+        </dependency>
+      </dependencies>
+      <build>
+        <plugins>
+          <plugin>
+            <groupId>org.apache.maven.plugins</groupId>
+            <artifactId>maven-jar-plugin</artifactId>
+            <configuration>
+              <classifier>hadoop2-yarn</classifier>
+            </configuration>
+          </plugin>
+        </plugins>
+      </build>
+    </profile>
   </profiles>
 </project>
diff --git a/examples/src/main/java/spark/examples/JavaHdfsLR.java b/examples/src/main/java/spark/examples/JavaHdfsLR.java
index 29839d5668..8b0a9b6808 100644
--- a/examples/src/main/java/spark/examples/JavaHdfsLR.java
+++ b/examples/src/main/java/spark/examples/JavaHdfsLR.java
@@ -10,6 +10,9 @@ import java.util.Arrays;
 import java.util.StringTokenizer;
 import java.util.Random;
 
+/**
+ * Logistic regression based classification.
+ */
 public class JavaHdfsLR {
 
   static int D = 10;   // Number of dimensions
@@ -85,7 +88,8 @@ public class JavaHdfsLR {
       System.exit(1);
     }
 
-    JavaSparkContext sc = new JavaSparkContext(args[0], "JavaHdfsLR");
+    JavaSparkContext sc = new JavaSparkContext(args[0], "JavaHdfsLR",
+        System.getenv("SPARK_HOME"), System.getenv("SPARK_EXAMPLES_JAR"));
     JavaRDD<String> lines = sc.textFile(args[1]);
     JavaRDD<DataPoint> points = lines.map(new ParsePoint()).cache();
     int ITERATIONS = Integer.parseInt(args[2]);
diff --git a/examples/src/main/java/spark/examples/JavaKMeans.java b/examples/src/main/java/spark/examples/JavaKMeans.java
new file mode 100644
index 0000000000..626034eb0d
--- /dev/null
+++ b/examples/src/main/java/spark/examples/JavaKMeans.java
@@ -0,0 +1,114 @@
+package spark.examples;
+
+import scala.Tuple2;
+import spark.api.java.JavaPairRDD;
+import spark.api.java.JavaRDD;
+import spark.api.java.JavaSparkContext;
+import spark.api.java.function.Function;
+import spark.api.java.function.PairFunction;
+import spark.util.Vector;
+
+import java.util.List;
+import java.util.Map;
+
+/**
+ * K-means clustering using Java API.
+ */
+public class JavaKMeans {
+
+  /** Parses numbers split by whitespace to a vector */
+  static Vector parseVector(String line) {
+    String[] splits = line.split(" ");
+    double[] data = new double[splits.length];
+    int i = 0;
+    for (String s : splits)
+      data[i] = Double.parseDouble(splits[i++]);
+    return new Vector(data);
+  }
+
+  /** Computes the vector to which the input vector is closest using squared distance */
+  static int closestPoint(Vector p, List<Vector> centers) {
+    int bestIndex = 0;
+    double closest = Double.POSITIVE_INFINITY;
+    for (int i = 0; i < centers.size(); i++) {
+      double tempDist = p.squaredDist(centers.get(i));
+      if (tempDist < closest) {
+        closest = tempDist;
+        bestIndex = i;
+      }
+    }
+    return bestIndex;
+  }
+
+  /** Computes the mean across all vectors in the input set of vectors */
+  static Vector average(List<Vector> ps) {
+    int numVectors = ps.size();
+    Vector out = new Vector(ps.get(0).elements());
+    // start from i = 1 since we already copied index 0 above
+    for (int i = 1; i < numVectors; i++) {
+      out.addInPlace(ps.get(i));
+    }
+    return out.divide(numVectors);
+  }
+
+  public static void main(String[] args) throws Exception {
+    if (args.length < 4) {
+      System.err.println("Usage: JavaKMeans <master> <file> <k> <convergeDist>");
+      System.exit(1);
+    }
+    JavaSparkContext sc = new JavaSparkContext(args[0], "JavaKMeans",
+      System.getenv("SPARK_HOME"), System.getenv("SPARK_EXAMPLES_JAR"));
+    String path = args[1];
+    int K = Integer.parseInt(args[2]);
+    double convergeDist = Double.parseDouble(args[3]);
+
+    JavaRDD<Vector> data = sc.textFile(path).map(
+      new Function<String, Vector>() {
+        @Override
+        public Vector call(String line) throws Exception {
+          return parseVector(line);
+        }
+      }
+    ).cache();
+
+    final List<Vector> centroids = data.takeSample(false, K, 42);
+
+    double tempDist;
+    do {
+      // allocate each vector to closest centroid
+      JavaPairRDD<Integer, Vector> closest = data.map(
+        new PairFunction<Vector, Integer, Vector>() {
+          @Override
+          public Tuple2<Integer, Vector> call(Vector vector) throws Exception {
+            return new Tuple2<Integer, Vector>(
+              closestPoint(vector, centroids), vector);
+          }
+        }
+      );
+
+      // group by cluster id and average the vectors within each cluster to compute centroids
+      JavaPairRDD<Integer, List<Vector>> pointsGroup = closest.groupByKey();
+      Map<Integer, Vector> newCentroids = pointsGroup.mapValues(
+        new Function<List<Vector>, Vector>() {
+          public Vector call(List<Vector> ps) throws Exception {
+            return average(ps);
+          }
+        }).collectAsMap();
+      tempDist = 0.0;
+      for (int i = 0; i < K; i++) {
+        tempDist += centroids.get(i).squaredDist(newCentroids.get(i));
+      }
+      for (Map.Entry<Integer, Vector> t: newCentroids.entrySet()) {
+        centroids.set(t.getKey(), t.getValue());
+      }
+      System.out.println("Finished iteration (delta = " + tempDist + ")");
+    } while (tempDist > convergeDist);
+
+    System.out.println("Final centers:");
+    for (Vector c : centroids)
+      System.out.println(c);
+
+    System.exit(0);
+
+  }
+}
diff --git a/examples/src/main/java/spark/examples/JavaLogQuery.java b/examples/src/main/java/spark/examples/JavaLogQuery.java
new file mode 100644
index 0000000000..6b22e7120c
--- /dev/null
+++ b/examples/src/main/java/spark/examples/JavaLogQuery.java
@@ -0,0 +1,114 @@
+package spark.examples;
+
+import com.google.common.collect.Lists;
+import scala.Tuple2;
+import scala.Tuple3;
+import spark.api.java.JavaPairRDD;
+import spark.api.java.JavaRDD;
+import spark.api.java.JavaSparkContext;
+import spark.api.java.function.Function2;
+import spark.api.java.function.PairFunction;
+
+import java.io.Serializable;
+import java.util.Collections;
+import java.util.List;
+import java.util.regex.Matcher;
+import java.util.regex.Pattern;
+
+/**
+ * Executes a roll up-style query against Apache logs.
+ */
+public class JavaLogQuery {
+
+  public static List<String> exampleApacheLogs = Lists.newArrayList(
+    "10.10.10.10 - \"FRED\" [18/Jan/2013:17:56:07 +1100] \"GET http://images.com/2013/Generic.jpg " +
+      "HTTP/1.1\" 304 315 \"http://referall.com/\" \"Mozilla/4.0 (compatible; MSIE 7.0; " +
+      "Windows NT 5.1; GTB7.4; .NET CLR 2.0.50727; .NET CLR 3.0.04506.30; .NET CLR 3.0.04506.648; " +
+      ".NET CLR 3.5.21022; .NET CLR 3.0.4506.2152; .NET CLR 1.0.3705; .NET CLR 1.1.4322; .NET CLR " +
+      "3.5.30729; Release=ARP)\" \"UD-1\" - \"image/jpeg\" \"whatever\" 0.350 \"-\" - \"\" 265 923 934 \"\" " +
+      "62.24.11.25 images.com 1358492167 - Whatup",
+    "10.10.10.10 - \"FRED\" [18/Jan/2013:18:02:37 +1100] \"GET http://images.com/2013/Generic.jpg " +
+      "HTTP/1.1\" 304 306 \"http:/referall.com\" \"Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; " +
+      "GTB7.4; .NET CLR 2.0.50727; .NET CLR 3.0.04506.30; .NET CLR 3.0.04506.648; .NET CLR " +
+      "3.5.21022; .NET CLR 3.0.4506.2152; .NET CLR 1.0.3705; .NET CLR 1.1.4322; .NET CLR  " +
+      "3.5.30729; Release=ARP)\" \"UD-1\" - \"image/jpeg\" \"whatever\" 0.352 \"-\" - \"\" 256 977 988 \"\" " +
+      "0 73.23.2.15 images.com 1358492557 - Whatup");
+
+  public static Pattern apacheLogRegex = Pattern.compile(
+    "^([\\d.]+) (\\S+) (\\S+) \\[([\\w\\d:/]+\\s[+\\-]\\d{4})\\] \"(.+?)\" (\\d{3}) ([\\d\\-]+) \"([^\"]+)\" \"([^\"]+)\".*");
+
+  /** Tracks the total query count and number of aggregate bytes for a particular group. */
+  public static class Stats implements Serializable {
+
+    private int count;
+    private int numBytes;
+
+    public Stats(int count, int numBytes) {
+      this.count = count;
+      this.numBytes = numBytes;
+    }
+    public Stats merge(Stats other) {
+      return new Stats(count + other.count, numBytes + other.numBytes);
+    }
+
+    public String toString() {
+      return String.format("bytes=%s\tn=%s", numBytes, count);
+    }
+  }
+
+  public static Tuple3<String, String, String> extractKey(String line) {
+    Matcher m = apacheLogRegex.matcher(line);
+    List<String> key = Collections.emptyList();
+    if (m.find()) {
+      String ip = m.group(1);
+      String user = m.group(3);
+      String query = m.group(5);
+      if (!user.equalsIgnoreCase("-")) {
+        return new Tuple3<String, String, String>(ip, user, query);
+      }
+    }
+    return new Tuple3<String, String, String>(null, null, null);
+  }
+
+  public static Stats extractStats(String line) {
+    Matcher m = apacheLogRegex.matcher(line);
+    if (m.find()) {
+      int bytes = Integer.parseInt(m.group(7));
+      return new Stats(1, bytes);
+    }
+    else
+      return new Stats(1, 0);
+  }
+
+  public static void main(String[] args) throws Exception {
+    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"), System.getenv("SPARK_EXAMPLES_JAR"));
+
+    JavaRDD<String> dataSet = (args.length == 2) ? jsc.textFile(args[1]) : jsc.parallelize(exampleApacheLogs);
+
+    JavaPairRDD<Tuple3<String, String, String>, Stats> extracted = dataSet.map(new PairFunction<String, Tuple3<String, String, String>, Stats>() {
+      @Override
+      public Tuple2<Tuple3<String, String, String>, Stats> call(String s) throws Exception {
+        return new Tuple2<Tuple3<String, String, String>, Stats>(extractKey(s), extractStats(s));
+      }
+    });
+
+    JavaPairRDD<Tuple3<String, String, String>, Stats> counts = extracted.reduceByKey(new Function2<Stats, Stats, Stats>() {
+      @Override
+      public Stats call(Stats stats, Stats stats2) throws Exception {
+        return stats.merge(stats2);
+      }
+    });
+
+    List<Tuple2<Tuple3<String, String, String>, Stats>> output = counts.collect();
+    for (Tuple2 t : output) {
+      System.out.println(t._1 + "\t" + t._2);
+    }
+    System.exit(0);
+  }
+}
diff --git a/examples/src/main/java/spark/examples/JavaSparkPi.java b/examples/src/main/java/spark/examples/JavaSparkPi.java
new file mode 100644
index 0000000000..a15a967de8
--- /dev/null
+++ b/examples/src/main/java/spark/examples/JavaSparkPi.java
@@ -0,0 +1,48 @@
+package spark.examples;
+
+import spark.api.java.JavaRDD;
+import spark.api.java.JavaSparkContext;
+import spark.api.java.function.Function;
+import spark.api.java.function.Function2;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/** Computes an approximation to pi */
+public class JavaSparkPi {
+
+
+  public static void main(String[] args) throws Exception {
+    if (args.length == 0) {
+      System.err.println("Usage: JavaLogQuery <master> [slices]");
+      System.exit(1);
+    }
+
+    JavaSparkContext jsc = new JavaSparkContext(args[0], "JavaLogQuery",
+      System.getenv("SPARK_HOME"), System.getenv("SPARK_EXAMPLES_JAR"));
+
+    int slices = (args.length == 2) ? Integer.parseInt(args[1]) : 2;
+    int n = 100000 * slices;
+    List<Integer> l = new ArrayList<Integer>(n);
+    for (int i = 0; i < n; i++)
+      l.add(i);
+
+    JavaRDD<Integer> dataSet = jsc.parallelize(l, slices);
+
+    int count = dataSet.map(new Function<Integer, Integer>() {
+      @Override
+      public Integer call(Integer integer) throws Exception {
+        double x = Math.random() * 2 - 1;
+        double y = Math.random() * 2 - 1;
+        return (x * x + y * y < 1) ? 1 : 0;
+      }
+    }).reduce(new Function2<Integer, Integer, Integer>() {
+      @Override
+      public Integer call(Integer integer, Integer integer2) throws Exception {
+        return integer + integer2;
+      }
+    });
+
+    System.out.println("Pi is roughly " + 4.0 * count / n);
+  }
+}
diff --git a/examples/src/main/java/spark/examples/JavaTC.java b/examples/src/main/java/spark/examples/JavaTC.java
index e3bd881b8f..b319bdab44 100644
--- a/examples/src/main/java/spark/examples/JavaTC.java
+++ b/examples/src/main/java/spark/examples/JavaTC.java
@@ -28,7 +28,7 @@ public class JavaTC {
       Tuple2<Integer, Integer> e = new Tuple2<Integer, Integer>(from, to);
       if (from != to) edges.add(e);
     }
-    return new ArrayList(edges);
+    return new ArrayList<Tuple2<Integer, Integer>>(edges);
   }
 
   static class ProjectFn extends PairFunction<Tuple2<Integer, Tuple2<Integer, Integer>>,
@@ -46,7 +46,8 @@ public class JavaTC {
       System.exit(1);
     }
 
-    JavaSparkContext sc = new JavaSparkContext(args[0], "JavaTC");
+    JavaSparkContext sc = new JavaSparkContext(args[0], "JavaTC",
+        System.getenv("SPARK_HOME"), System.getenv("SPARK_EXAMPLES_JAR"));
     Integer slices = (args.length > 1) ? Integer.parseInt(args[1]): 2;
     JavaPairRDD<Integer, Integer> tc = sc.parallelizePairs(generateGraph(), slices).cache();
 
diff --git a/examples/src/main/java/spark/examples/JavaWordCount.java b/examples/src/main/java/spark/examples/JavaWordCount.java
index a44cf8a120..9d4c7a252d 100644
--- a/examples/src/main/java/spark/examples/JavaWordCount.java
+++ b/examples/src/main/java/spark/examples/JavaWordCount.java
@@ -18,7 +18,8 @@ public class JavaWordCount {
       System.exit(1);
     }
 
-    JavaSparkContext ctx = new JavaSparkContext(args[0], "JavaWordCount");
+    JavaSparkContext ctx = new JavaSparkContext(args[0], "JavaWordCount",
+        System.getenv("SPARK_HOME"), System.getenv("SPARK_EXAMPLES_JAR"));
     JavaRDD<String> lines = ctx.textFile(args[1], 1);
 
     JavaRDD<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
@@ -29,7 +30,7 @@ public class JavaWordCount {
     
     JavaPairRDD<String, Integer> ones = words.map(new PairFunction<String, String, Integer>() {
       public Tuple2<String, Integer> call(String s) {
-        return new Tuple2(s, 1);
+        return new Tuple2<String, Integer>(s, 1);
       }
     });
     
diff --git a/examples/src/main/java/spark/streaming/examples/JavaFlumeEventCount.java b/examples/src/main/java/spark/streaming/examples/JavaFlumeEventCount.java
index cddce16e39..e24c6ddaa7 100644
--- a/examples/src/main/java/spark/streaming/examples/JavaFlumeEventCount.java
+++ b/examples/src/main/java/spark/streaming/examples/JavaFlumeEventCount.java
@@ -32,7 +32,8 @@ public class JavaFlumeEventCount {
 
     Duration batchInterval = new Duration(2000);
 
-    JavaStreamingContext sc = new JavaStreamingContext(master, "FlumeEventCount", batchInterval);
+    JavaStreamingContext sc = new JavaStreamingContext(master, "FlumeEventCount", batchInterval,
+            System.getenv("SPARK_HOME"), System.getenv("SPARK_EXAMPLES_JAR"));
 
     JavaDStream<SparkFlumeEvent> flumeStream = sc.flumeStream("localhost", port);
 
diff --git a/examples/src/main/java/spark/streaming/examples/JavaNetworkWordCount.java b/examples/src/main/java/spark/streaming/examples/JavaNetworkWordCount.java
index 0e9eadd01b..3e57580fd4 100644
--- a/examples/src/main/java/spark/streaming/examples/JavaNetworkWordCount.java
+++ b/examples/src/main/java/spark/streaming/examples/JavaNetworkWordCount.java
@@ -30,8 +30,8 @@ public class JavaNetworkWordCount {
     }
 
     // Create the context with a 1 second batch size
-    JavaStreamingContext ssc = new JavaStreamingContext(
-        args[0], "NetworkWordCount", new Duration(1000));
+    JavaStreamingContext ssc = new JavaStreamingContext(args[0], "NetworkWordCount",
+            new Duration(1000), System.getenv("SPARK_HOME"), System.getenv("SPARK_EXAMPLES_JAR"));
 
     // Create a NetworkInputDStream on target ip:port and count the
     // words in input stream of \n delimited test (eg. generated by 'nc')
diff --git a/examples/src/main/java/spark/streaming/examples/JavaQueueStream.java b/examples/src/main/java/spark/streaming/examples/JavaQueueStream.java
index 43c3cd4dfa..15b82c8da1 100644
--- a/examples/src/main/java/spark/streaming/examples/JavaQueueStream.java
+++ b/examples/src/main/java/spark/streaming/examples/JavaQueueStream.java
@@ -22,7 +22,8 @@ public class JavaQueueStream {
     }
 
     // Create the context
-    JavaStreamingContext ssc = new JavaStreamingContext(args[0], "QueueStream", new Duration(1000));
+    JavaStreamingContext ssc = new JavaStreamingContext(args[0], "QueueStream", new Duration(1000),
+            System.getenv("SPARK_HOME"), System.getenv("SPARK_EXAMPLES_JAR"));
 
     // Create the queue through which RDDs can be pushed to
     // a QueueInputDStream
diff --git a/examples/src/main/scala/spark/examples/BroadcastTest.scala b/examples/src/main/scala/spark/examples/BroadcastTest.scala
index 230097c7db..ba59be1687 100644
--- a/examples/src/main/scala/spark/examples/BroadcastTest.scala
+++ b/examples/src/main/scala/spark/examples/BroadcastTest.scala
@@ -9,19 +9,21 @@ object BroadcastTest {
       System.exit(1)
     }  
     
-    val spark = new SparkContext(args(0), "Broadcast Test")
+    val sc = new SparkContext(args(0), "Broadcast Test",
+      System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
     val slices = if (args.length > 1) args(1).toInt else 2
     val num = if (args.length > 2) args(2).toInt else 1000000
 
     var arr1 = new Array[Int](num)
-    for (i <- 0 until arr1.length) 
+    for (i <- 0 until arr1.length) {
       arr1(i) = i
+    }
     
     for (i <- 0 until 2) {
       println("Iteration " + i)
       println("===========")
-      val barr1 = spark.broadcast(arr1)
-      spark.parallelize(1 to 10, slices).foreach {
+      val barr1 = sc.broadcast(arr1)
+      sc.parallelize(1 to 10, slices).foreach {
         i => println(barr1.value.size)
       }
     }
diff --git a/examples/src/main/scala/spark/examples/CassandraTest.scala b/examples/src/main/scala/spark/examples/CassandraTest.scala
new file mode 100644
index 0000000000..0fe1833e83
--- /dev/null
+++ b/examples/src/main/scala/spark/examples/CassandraTest.scala
@@ -0,0 +1,196 @@
+package spark.examples
+
+import org.apache.hadoop.mapreduce.Job
+import org.apache.cassandra.hadoop.ColumnFamilyOutputFormat
+import org.apache.cassandra.hadoop.ConfigHelper
+import org.apache.cassandra.hadoop.ColumnFamilyInputFormat
+import org.apache.cassandra.thrift._
+import spark.SparkContext
+import spark.SparkContext._
+import java.nio.ByteBuffer
+import java.util.SortedMap
+import org.apache.cassandra.db.IColumn
+import org.apache.cassandra.utils.ByteBufferUtil
+import scala.collection.JavaConversions._
+
+
+/*
+ * This example demonstrates using Spark with Cassandra with the New Hadoop API and Cassandra
+ * support for Hadoop.
+ *
+ * To run this example, run this file with the following command params -
+ * <spark_master> <cassandra_node> <cassandra_port>
+ *
+ * So if you want to run this on localhost this will be,
+ * local[3] localhost 9160
+ *
+ * The example makes some assumptions:
+ * 1. You have already created a keyspace called casDemo and it has a column family named Words
+ * 2. There are column family has a column named "para" which has test content.
+ *
+ * You can create the content by running the following script at the bottom of this file with
+ * cassandra-cli.
+ *
+ */
+object CassandraTest {
+
+  def main(args: Array[String]) {
+
+    // Get a SparkContext
+    val sc = new SparkContext(args(0), "casDemo")
+
+    // Build the job configuration with ConfigHelper provided by Cassandra
+    val job = new Job()
+    job.setInputFormatClass(classOf[ColumnFamilyInputFormat])
+
+    val host: String = args(1)
+    val port: String = args(2)
+
+    ConfigHelper.setInputInitialAddress(job.getConfiguration(), host)
+    ConfigHelper.setInputRpcPort(job.getConfiguration(), port)
+    ConfigHelper.setOutputInitialAddress(job.getConfiguration(), host)
+    ConfigHelper.setOutputRpcPort(job.getConfiguration(), port)
+    ConfigHelper.setInputColumnFamily(job.getConfiguration(), "casDemo", "Words")
+    ConfigHelper.setOutputColumnFamily(job.getConfiguration(), "casDemo", "WordCount")
+
+    val predicate = new SlicePredicate()
+    val sliceRange = new SliceRange()
+    sliceRange.setStart(Array.empty[Byte])
+    sliceRange.setFinish(Array.empty[Byte])
+    predicate.setSlice_range(sliceRange)
+    ConfigHelper.setInputSlicePredicate(job.getConfiguration(), predicate)
+
+    ConfigHelper.setInputPartitioner(job.getConfiguration(), "Murmur3Partitioner")
+    ConfigHelper.setOutputPartitioner(job.getConfiguration(), "Murmur3Partitioner")
+
+    // Make a new Hadoop RDD
+    val casRdd = sc.newAPIHadoopRDD(
+      job.getConfiguration(),
+      classOf[ColumnFamilyInputFormat],
+      classOf[ByteBuffer],
+      classOf[SortedMap[ByteBuffer, IColumn]])
+
+    // Let us first get all the paragraphs from the retrieved rows
+    val paraRdd = casRdd.map {
+      case (key, value) => {
+        ByteBufferUtil.string(value.get(ByteBufferUtil.bytes("para")).value())
+      }
+    }
+
+    // Lets get the word count in paras
+    val counts = paraRdd.flatMap(p => p.split(" ")).map(word => (word, 1)).reduceByKey(_ + _)
+
+    counts.collect().foreach {
+      case (word, count) => println(word + ":" + count)
+    }
+
+    counts.map {
+      case (word, count) => {
+        val colWord = new org.apache.cassandra.thrift.Column()
+        colWord.setName(ByteBufferUtil.bytes("word"))
+        colWord.setValue(ByteBufferUtil.bytes(word))
+        colWord.setTimestamp(System.currentTimeMillis)
+
+        val colCount = new org.apache.cassandra.thrift.Column()
+        colCount.setName(ByteBufferUtil.bytes("wcount"))
+        colCount.setValue(ByteBufferUtil.bytes(count.toLong))
+        colCount.setTimestamp(System.currentTimeMillis)
+
+        val outputkey = ByteBufferUtil.bytes(word + "-COUNT-" + System.currentTimeMillis)
+
+        val mutations: java.util.List[Mutation] = new Mutation() :: new Mutation() :: Nil
+        mutations.get(0).setColumn_or_supercolumn(new ColumnOrSuperColumn())
+        mutations.get(0).column_or_supercolumn.setColumn(colWord)
+        mutations.get(1).setColumn_or_supercolumn(new ColumnOrSuperColumn())
+        mutations.get(1).column_or_supercolumn.setColumn(colCount)
+        (outputkey, mutations)
+      }
+    }.saveAsNewAPIHadoopFile("casDemo", classOf[ByteBuffer], classOf[List[Mutation]],
+      classOf[ColumnFamilyOutputFormat], job.getConfiguration)
+  }
+}
+
+/*
+create keyspace casDemo;
+use casDemo;
+
+create column family WordCount with comparator = UTF8Type;
+update column family WordCount with column_metadata =
+  [{column_name: word, validation_class: UTF8Type},
+    {column_name: wcount, validation_class: LongType}];
+
+create column family Words with comparator = UTF8Type;
+update column family Words with column_metadata =
+  [{column_name: book, validation_class: UTF8Type},
+    {column_name: para, validation_class: UTF8Type}];
+
+assume Words keys as utf8;
+
+set Words['3musk001']['book'] = 'The Three Musketeers';
+set Words['3musk001']['para'] = 'On the first Monday of the month of April, 1625, the market
+  town of Meung, in which the author of ROMANCE OF THE ROSE was born, appeared to
+	be in as perfect a state of revolution as if the Huguenots had just made
+	a second La Rochelle of it. Many citizens, seeing the women flying
+	toward the High Street, leaving their children crying at the open doors,
+	hastened to don the cuirass, and supporting their somewhat uncertain
+	courage with a musket or a partisan, directed their steps toward the
+	hostelry of the Jolly Miller, before which was gathered, increasing
+	every minute, a compact group, vociferous and full of curiosity.';
+
+set Words['3musk002']['book'] = 'The Three Musketeers';
+set Words['3musk002']['para'] = 'In those times panics were common, and few days passed without
+  some city or other registering in its archives an event of this kind. There were
+	nobles, who made war against each other; there was the king, who made
+	war against the cardinal; there was Spain, which made war against the
+	king. Then, in addition to these concealed or public, secret or open
+	wars, there were robbers, mendicants, Huguenots, wolves, and scoundrels,
+	who made war upon everybody. The citizens always took up arms readily
+	against thieves, wolves or scoundrels, often against nobles or
+	Huguenots, sometimes against the king, but never against cardinal or
+	Spain. It resulted, then, from this habit that on the said first Monday
+	of April, 1625, the citizens, on hearing the clamor, and seeing neither
+	the red-and-yellow standard nor the livery of the Duc de Richelieu,
+	rushed toward the hostel of the Jolly Miller. When arrived there, the
+	cause of the hubbub was apparent to all';
+
+set Words['3musk003']['book'] = 'The Three Musketeers';
+set Words['3musk003']['para'] = 'You ought, I say, then, to husband the means you have, however
+  large the sum may be; but you ought also to endeavor to perfect yourself in
+	the exercises becoming a gentleman. I will write a letter today to the
+	Director of the Royal Academy, and tomorrow he will admit you without
+	any expense to yourself. Do not refuse this little service. Our
+	best-born and richest gentlemen sometimes solicit it without being able
+	to obtain it. You will learn horsemanship, swordsmanship in all its
+	branches, and dancing. You will make some desirable acquaintances; and
+	from time to time you can call upon me, just to tell me how you are
+	getting on, and to say whether I can be of further service to you.';
+
+
+set Words['thelostworld001']['book'] = 'The Lost World';
+set Words['thelostworld001']['para'] = 'She sat with that proud, delicate profile of hers outlined
+  against the red curtain.  How beautiful she was!  And yet how aloof!  We had been
+	friends, quite good friends; but never could I get beyond the same
+	comradeship which I might have established with one of my
+	fellow-reporters upon the Gazette,--perfectly frank, perfectly kindly,
+	and perfectly unsexual.  My instincts are all against a woman being too
+	frank and at her ease with me.  It is no compliment to a man.  Where
+	the real sex feeling begins, timidity and distrust are its companions,
+	heritage from old wicked days when love and violence went often hand in
+	hand.  The bent head, the averted eye, the faltering voice, the wincing
+	figure--these, and not the unshrinking gaze and frank reply, are the
+	true signals of passion.  Even in my short life I had learned as much
+	as that--or had inherited it in that race memory which we call instinct.';
+
+set Words['thelostworld002']['book'] = 'The Lost World';
+set Words['thelostworld002']['para'] = 'I always liked McArdle, the crabbed, old, round-backed,
+  red-headed news editor, and I rather hoped that he liked me.  Of course, Beaumont was
+	the real boss; but he lived in the rarefied atmosphere of some Olympian
+	height from which he could distinguish nothing smaller than an
+	international crisis or a split in the Cabinet.  Sometimes we saw him
+	passing in lonely majesty to his inner sanctum, with his eyes staring
+	vaguely and his mind hovering over the Balkans or the Persian Gulf.  He
+	was above and beyond us.  But McArdle was his first lieutenant, and it
+	was he that we knew.  The old man nodded as I entered the room, and he
+	pushed his spectacles far up on his bald forehead.';
+
+*/
diff --git a/examples/src/main/scala/spark/examples/ExceptionHandlingTest.scala b/examples/src/main/scala/spark/examples/ExceptionHandlingTest.scala
index c89f3dac0c..21a90f2e5a 100644
--- a/examples/src/main/scala/spark/examples/ExceptionHandlingTest.scala
+++ b/examples/src/main/scala/spark/examples/ExceptionHandlingTest.scala
@@ -9,9 +9,10 @@ object ExceptionHandlingTest {
       System.exit(1)
     }
 
-    val sc = new SparkContext(args(0), "ExceptionHandlingTest")
+    val sc = new SparkContext(args(0), "ExceptionHandlingTest",
+      System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
     sc.parallelize(0 until sc.defaultParallelism).foreach { i =>
-      if (Math.random > 0.75)
+      if (math.random > 0.75)
         throw new Exception("Testing exception handling")
     }
 
diff --git a/examples/src/main/scala/spark/examples/GroupByTest.scala b/examples/src/main/scala/spark/examples/GroupByTest.scala
index 86dfba3a40..a6603653f1 100644
--- a/examples/src/main/scala/spark/examples/GroupByTest.scala
+++ b/examples/src/main/scala/spark/examples/GroupByTest.scala
@@ -9,14 +9,15 @@ object GroupByTest {
     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")
+    val sc = new SparkContext(args(0), "GroupBy Test",
+      System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
     
     val pairs1 = sc.parallelize(0 until numMappers, numMappers).flatMap { p =>
       val ranGen = new Random
diff --git a/examples/src/main/scala/spark/examples/HBaseTest.scala b/examples/src/main/scala/spark/examples/HBaseTest.scala
new file mode 100644
index 0000000000..6e910154d4
--- /dev/null
+++ b/examples/src/main/scala/spark/examples/HBaseTest.scala
@@ -0,0 +1,35 @@
+package spark.examples
+
+import spark._
+import spark.rdd.NewHadoopRDD
+import org.apache.hadoop.hbase.{HBaseConfiguration, HTableDescriptor}
+import org.apache.hadoop.hbase.client.HBaseAdmin
+import org.apache.hadoop.hbase.mapreduce.TableInputFormat
+
+object HBaseTest {
+  def main(args: Array[String]) {
+    val sc = new SparkContext(args(0), "HBaseTest",
+      System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
+
+    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))
+
+    // Initialize hBase table if necessary
+    val admin = new HBaseAdmin(conf)
+    if(!admin.isTableAvailable(args(1))) {
+      val tableDesc = new HTableDescriptor(args(1))
+      admin.createTable(tableDesc)
+    }
+
+    val hBaseRDD = sc.newAPIHadoopRDD(conf, classOf[TableInputFormat], 
+      classOf[org.apache.hadoop.hbase.io.ImmutableBytesWritable],
+      classOf[org.apache.hadoop.hbase.client.Result])
+
+    hBaseRDD.count()
+
+    System.exit(0)
+  }
+}
\ No newline at end of file
diff --git a/examples/src/main/scala/spark/examples/HdfsTest.scala b/examples/src/main/scala/spark/examples/HdfsTest.scala
index 7a4530609d..dd61c467f7 100644
--- a/examples/src/main/scala/spark/examples/HdfsTest.scala
+++ b/examples/src/main/scala/spark/examples/HdfsTest.scala
@@ -4,7 +4,8 @@ import spark._
 
 object HdfsTest {
   def main(args: Array[String]) {
-    val sc = new SparkContext(args(0), "HdfsTest")
+    val sc = new SparkContext(args(0), "HdfsTest",
+      System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
     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/spark/examples/LocalALS.scala b/examples/src/main/scala/spark/examples/LocalALS.scala
index 10e03359c9..2de810e062 100644
--- a/examples/src/main/scala/spark/examples/LocalALS.scala
+++ b/examples/src/main/scala/spark/examples/LocalALS.scala
@@ -1,11 +1,13 @@
 package spark.examples
 
-import java.util.Random
 import scala.math.sqrt
 import cern.jet.math._
 import cern.colt.matrix._
 import cern.colt.matrix.linalg._
 
+/**
+ * Alternating least squares matrix factorization.
+ */
 object LocalALS {
   // Parameters set through command line arguments
   var M = 0 // Number of movies
diff --git a/examples/src/main/scala/spark/examples/LocalKMeans.scala b/examples/src/main/scala/spark/examples/LocalKMeans.scala
index b442c604cd..4849f216fb 100644
--- a/examples/src/main/scala/spark/examples/LocalKMeans.scala
+++ b/examples/src/main/scala/spark/examples/LocalKMeans.scala
@@ -6,74 +6,77 @@ import spark.SparkContext._
 import scala.collection.mutable.HashMap
 import scala.collection.mutable.HashSet
 
+/**
+ * K-means clustering.
+ */
 object LocalKMeans {
-	val N = 1000
-	val R = 1000   	// Scaling factor
-	val D = 10
-	val K = 10
-	val convergeDist = 0.001
-	val rand = new Random(42)
-  	
-	def generateData = {
-	    def generatePoint(i: Int) = {
-	      Vector(D, _ => rand.nextDouble * R)
-	    }
-	    Array.tabulate(N)(generatePoint)
-	  }
-	
-	def closestPoint(p: Vector, centers: HashMap[Int, Vector]): Int = {
-		var index = 0
-		var bestIndex = 0
-		var closest = Double.PositiveInfinity
-	
-		for (i <- 1 to centers.size) {
-			val vCurr = centers.get(i).get
-			val tempDist = p.squaredDist(vCurr)
-			if (tempDist < closest) {
-				closest = tempDist
-				bestIndex = i
-			}
-		}
-	
-		return bestIndex
-	}
-
-	def main(args: Array[String]) {
-	  val data = generateData
-		var points = new HashSet[Vector]
-		var kPoints = new HashMap[Int, Vector]
-		var tempDist = 1.0
-		
-		while (points.size < K) {
-			points.add(data(rand.nextInt(N)))
-		}
-		
-		val iter = points.iterator
-		for (i <- 1 to points.size) {
-			kPoints.put(i, iter.next())
-		}
-
-		println("Initial centers: " + kPoints)
-
-		while(tempDist > convergeDist) {
-			var closest = data.map (p => (closestPoint(p, kPoints), (p, 1)))
-			
-			var mappings = closest.groupBy[Int] (x => x._1)
-			
-			var pointStats = mappings.map(pair => pair._2.reduceLeft [(Int, (Vector, Int))] {case ((id1, (x1, y1)), (id2, (x2, y2))) => (id1, (x1 + x2, y1+y2))})
-			
-			var newPoints = pointStats.map {mapping => (mapping._1, mapping._2._1/mapping._2._2)}
-			
-			tempDist = 0.0
-			for (mapping <- newPoints) {
-				tempDist += kPoints.get(mapping._1).get.squaredDist(mapping._2)
-			}
-			
-			for (newP <- newPoints) {
-				kPoints.put(newP._1, newP._2)
-			}
-		}
-
-		println("Final centers: " + kPoints)
-	}
+  val N = 1000
+  val R = 1000    // Scaling factor
+  val D = 10
+  val K = 10
+  val convergeDist = 0.001
+  val rand = new Random(42)
+
+  def generateData = {
+    def generatePoint(i: Int) = {
+      Vector(D, _ => rand.nextDouble * R)
+    }
+    Array.tabulate(N)(generatePoint)
+  }
+
+  def closestPoint(p: Vector, centers: HashMap[Int, Vector]): Int = {
+    var index = 0
+    var bestIndex = 0
+    var closest = Double.PositiveInfinity
+
+    for (i <- 1 to centers.size) {
+      val vCurr = centers.get(i).get
+      val tempDist = p.squaredDist(vCurr)
+      if (tempDist < closest) {
+        closest = tempDist
+        bestIndex = i
+      }
+    }
+
+    return bestIndex
+  }
+
+  def main(args: Array[String]) {
+    val data = generateData
+    var points = new HashSet[Vector]
+    var kPoints = new HashMap[Int, Vector]
+    var tempDist = 1.0
+
+    while (points.size < K) {
+      points.add(data(rand.nextInt(N)))
+    }
+
+    val iter = points.iterator
+    for (i <- 1 to points.size) {
+      kPoints.put(i, iter.next())
+    }
+
+    println("Initial centers: " + kPoints)
+
+    while(tempDist > convergeDist) {
+      var closest = data.map (p => (closestPoint(p, kPoints), (p, 1)))
+
+      var mappings = closest.groupBy[Int] (x => x._1)
+
+      var pointStats = mappings.map(pair => pair._2.reduceLeft [(Int, (Vector, Int))] {case ((id1, (x1, y1)), (id2, (x2, y2))) => (id1, (x1 + x2, y1+y2))})
+
+      var newPoints = pointStats.map {mapping => (mapping._1, mapping._2._1/mapping._2._2)}
+
+      tempDist = 0.0
+      for (mapping <- newPoints) {
+        tempDist += kPoints.get(mapping._1).get.squaredDist(mapping._2)
+      }
+
+      for (newP <- newPoints) {
+        kPoints.put(newP._1, newP._2)
+      }
+    }
+
+    println("Final centers: " + kPoints)
+  }
 }
diff --git a/examples/src/main/scala/spark/examples/LocalLR.scala b/examples/src/main/scala/spark/examples/LocalLR.scala
index 9553162004..cd73f553d6 100644
--- a/examples/src/main/scala/spark/examples/LocalLR.scala
+++ b/examples/src/main/scala/spark/examples/LocalLR.scala
@@ -3,6 +3,9 @@ package spark.examples
 import java.util.Random
 import spark.util.Vector
 
+/**
+ * Logistic regression based classification.
+ */
 object LocalLR {
   val N = 10000  // Number of data points
   val D = 10   // Number of dimensions
diff --git a/examples/src/main/scala/spark/examples/LogQuery.scala b/examples/src/main/scala/spark/examples/LogQuery.scala
index 5330b8da94..6497596d35 100644
--- a/examples/src/main/scala/spark/examples/LogQuery.scala
+++ b/examples/src/main/scala/spark/examples/LogQuery.scala
@@ -26,7 +26,9 @@ object LogQuery {
       System.err.println("Usage: LogQuery <master> [logFile]")
       System.exit(1)
     }
-    val sc = new SparkContext(args(0), "Log Query")
+
+    val sc = new SparkContext(args(0), "Log Query",
+      System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
 
     val dataSet =
       if (args.length == 2) sc.textFile(args(1))
diff --git a/examples/src/main/scala/spark/examples/MultiBroadcastTest.scala b/examples/src/main/scala/spark/examples/MultiBroadcastTest.scala
index 83ae014e94..a0aaf60918 100644
--- a/examples/src/main/scala/spark/examples/MultiBroadcastTest.scala
+++ b/examples/src/main/scala/spark/examples/MultiBroadcastTest.scala
@@ -8,25 +8,29 @@ object MultiBroadcastTest {
       System.err.println("Usage: BroadcastTest <master> [<slices>] [numElem]")
       System.exit(1)
     }
-    
-    val spark = new SparkContext(args(0), "Broadcast Test")
+
+    val sc = new SparkContext(args(0), "Broadcast Test",
+      System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
+
     val slices = if (args.length > 1) args(1).toInt else 2
     val num = if (args.length > 2) args(2).toInt else 1000000
 
     var arr1 = new Array[Int](num)
-    for (i <- 0 until arr1.length) 
+    for (i <- 0 until arr1.length) {
       arr1(i) = i
-    
+    }
+
     var arr2 = new Array[Int](num)
-    for (i <- 0 until arr2.length)
+    for (i <- 0 until arr2.length) {
       arr2(i) = i
+    }
 
-    val barr1 = spark.broadcast(arr1)
-    val barr2 = spark.broadcast(arr2)
-    spark.parallelize(1 to 10, slices).foreach {
+    val barr1 = sc.broadcast(arr1)
+    val barr2 = sc.broadcast(arr2)
+    sc.parallelize(1 to 10, slices).foreach {
       i => println(barr1.value.size + barr2.value.size)
     }
-    
+
     System.exit(0)
   }
 }
diff --git a/examples/src/main/scala/spark/examples/SimpleSkewedGroupByTest.scala b/examples/src/main/scala/spark/examples/SimpleSkewedGroupByTest.scala
index 50b3a263b4..461b84a2c6 100644
--- a/examples/src/main/scala/spark/examples/SimpleSkewedGroupByTest.scala
+++ b/examples/src/main/scala/spark/examples/SimpleSkewedGroupByTest.scala
@@ -11,15 +11,16 @@ object SimpleSkewedGroupByTest {
         "[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 sc = new SparkContext(args(0), "GroupBy Test")
-    
+    val sc = new SparkContext(args(0), "GroupBy Test",
+      System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
+
     val pairs1 = sc.parallelize(0 until numMappers, numMappers).flatMap { p =>
       val ranGen = new Random
       var result = new Array[(Int, Array[Byte])](numKVPairs)
diff --git a/examples/src/main/scala/spark/examples/SkewedGroupByTest.scala b/examples/src/main/scala/spark/examples/SkewedGroupByTest.scala
index d2117a263e..435675f9de 100644
--- a/examples/src/main/scala/spark/examples/SkewedGroupByTest.scala
+++ b/examples/src/main/scala/spark/examples/SkewedGroupByTest.scala
@@ -10,19 +10,20 @@ object SkewedGroupByTest {
       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")
-    
+    val sc = new SparkContext(args(0), "GroupBy Test",
+      System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
+
     val pairs1 = sc.parallelize(0 until numMappers, numMappers).flatMap { p =>
       val ranGen = new Random
 
       // map output sizes lineraly increase from the 1st to the last
-      numKVPairs = (1. * (p + 1) / numMappers * numKVPairs).toInt
+      numKVPairs = (1.0 * (p + 1) / numMappers * numKVPairs).toInt
 
       var arr1 = new Array[(Int, Array[Byte])](numKVPairs)
       for (i <- 0 until numKVPairs) {
@@ -31,11 +32,11 @@ object SkewedGroupByTest {
         arr1(i) = (ranGen.nextInt(Int.MaxValue), byteArr)
       }
       arr1
-    }.cache
+    }.cache()
     // Enforce that everything has been calculated and in cache
-    pairs1.count
+    pairs1.count()
     
-    println(pairs1.groupByKey(numReducers).count)
+    println(pairs1.groupByKey(numReducers).count())
 
     System.exit(0)
   }
diff --git a/examples/src/main/scala/spark/examples/SparkALS.scala b/examples/src/main/scala/spark/examples/SparkALS.scala
index 5e01885dbb..8fb3b0fb2a 100644
--- a/examples/src/main/scala/spark/examples/SparkALS.scala
+++ b/examples/src/main/scala/spark/examples/SparkALS.scala
@@ -1,14 +1,14 @@
 package spark.examples
 
-import java.io.Serializable
-import java.util.Random
 import scala.math.sqrt
 import cern.jet.math._
 import cern.colt.matrix._
 import cern.colt.matrix.linalg._
 import spark._
-import scala.Option
 
+/**
+ * Alternating least squares matrix factorization.
+ */
 object SparkALS {
   // Parameters set through command line arguments
   var M = 0 // Number of movies
@@ -70,30 +70,32 @@ 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
 
-    (0 to 5).map(i => {
-      i match {
-        case a if a < args.length => Some(args(a))
-        case _ => None
-      }
-    }).toArray match {
-      case Array(host_, m, u, f, iters, slices_) => {
-        host = host_ getOrElse "local"
-        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>]")
+    val options = (0 to 5).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
+        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.exit(1)
-      }
     }
     printf("Running with M=%d, U=%d, F=%d, iters=%d\n", M, U, F, ITERATIONS)
-    val spark = new SparkContext(host, "SparkALS")
+
+    val sc = new SparkContext(host, "SparkALS",
+      System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
     
     val R = generateR()
 
@@ -102,19 +104,19 @@ object SparkALS {
     var us = Array.fill(U)(factory1D.random(F))
 
     // Iteratively update movies then users
-    val Rc  = spark.broadcast(R)
-    var msc = spark.broadcast(ms)
-    var usc = spark.broadcast(us)
+    val Rc  = sc.broadcast(R)
+    var msb = sc.broadcast(ms)
+    var usb = sc.broadcast(us)
     for (iter <- 1 to ITERATIONS) {
       println("Iteration " + iter + ":")
-      ms = spark.parallelize(0 until M, slices)
-                .map(i => update(i, msc.value(i), usc.value, Rc.value))
+      ms = sc.parallelize(0 until M, slices)
+                .map(i => update(i, msb.value(i), usb.value, Rc.value))
                 .toArray
-      msc = spark.broadcast(ms) // Re-broadcast ms because it was updated
-      us = spark.parallelize(0 until U, slices)
-                .map(i => update(i, usc.value(i), msc.value, algebra.transpose(Rc.value)))
+      msb = sc.broadcast(ms) // Re-broadcast ms because it was updated
+      us = sc.parallelize(0 until U, slices)
+                .map(i => update(i, usb.value(i), msb.value, algebra.transpose(Rc.value)))
                 .toArray
-      usc = spark.broadcast(us) // Re-broadcast us because it was updated
+      usb = sc.broadcast(us) // Re-broadcast us because it was updated
       println("RMSE = " + rmse(R, ms, us))
       println()
     }
diff --git a/examples/src/main/scala/spark/examples/SparkHdfsLR.scala b/examples/src/main/scala/spark/examples/SparkHdfsLR.scala
index 5b2bc84d69..3d080a0257 100644
--- a/examples/src/main/scala/spark/examples/SparkHdfsLR.scala
+++ b/examples/src/main/scala/spark/examples/SparkHdfsLR.scala
@@ -4,7 +4,12 @@ import java.util.Random
 import scala.math.exp
 import spark.util.Vector
 import spark._
+import spark.deploy.SparkHadoopUtil
+import spark.scheduler.InputFormatInfo
 
+/**
+ * Logistic regression based classification.
+ */
 object SparkHdfsLR {
   val D = 10   // Numer of dimensions
   val rand = new Random(42)
@@ -29,8 +34,13 @@ object SparkHdfsLR {
       System.err.println("Usage: SparkHdfsLR <master> <file> <iters>")
       System.exit(1)
     }
-    val sc = new SparkContext(args(0), "SparkHdfsLR")
-    val lines = sc.textFile(args(1))
+    val inputPath = args(1)
+    val conf = SparkHadoopUtil.newConfiguration()
+    val sc = new SparkContext(args(0), "SparkHdfsLR",
+      System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")), Map(), 
+      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
 
diff --git a/examples/src/main/scala/spark/examples/SparkKMeans.scala b/examples/src/main/scala/spark/examples/SparkKMeans.scala
index 6375961390..4161c59fea 100644
--- a/examples/src/main/scala/spark/examples/SparkKMeans.scala
+++ b/examples/src/main/scala/spark/examples/SparkKMeans.scala
@@ -7,6 +7,9 @@ import spark.SparkContext._
 import scala.collection.mutable.HashMap
 import scala.collection.mutable.HashSet
 
+/**
+ * K-means clustering.
+ */
 object SparkKMeans {
   val R = 1000     // Scaling factor
   val rand = new Random(42)
@@ -36,7 +39,8 @@ object SparkKMeans {
         System.err.println("Usage: SparkLocalKMeans <master> <file> <k> <convergeDist>")
         System.exit(1)
     }
-    val sc = new SparkContext(args(0), "SparkLocalKMeans")
+    val sc = new SparkContext(args(0), "SparkLocalKMeans",
+      System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
     val lines = sc.textFile(args(1))
     val data = lines.map(parseVector _).cache()
     val K = args(2).toInt
@@ -60,6 +64,7 @@ object SparkKMeans {
       for (newP <- newPoints) {
         kPoints(newP._1) = newP._2
       }
+      println("Finished iteration (delta = " + tempDist + ")")
     }
 
     println("Final centers:")
diff --git a/examples/src/main/scala/spark/examples/SparkLR.scala b/examples/src/main/scala/spark/examples/SparkLR.scala
index aaaf062c8f..2f41aeb376 100644
--- a/examples/src/main/scala/spark/examples/SparkLR.scala
+++ b/examples/src/main/scala/spark/examples/SparkLR.scala
@@ -5,6 +5,9 @@ import scala.math.exp
 import spark.util.Vector
 import spark._
 
+/**
+ * Logistic regression based classification.
+ */
 object SparkLR {
   val N = 10000  // Number of data points
   val D = 10   // Numer of dimensions
@@ -28,7 +31,8 @@ object SparkLR {
       System.err.println("Usage: SparkLR <master> [<slices>]")
       System.exit(1)
     }
-    val sc = new SparkContext(args(0), "SparkLR")
+    val sc = new SparkContext(args(0), "SparkLR",
+      System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
     val numSlices = if (args.length > 1) args(1).toInt else 2
     val points = sc.parallelize(generateData, numSlices).cache()
 
diff --git a/examples/src/main/scala/spark/examples/SparkPi.scala b/examples/src/main/scala/spark/examples/SparkPi.scala
index 2f226f1380..f598d2ff9c 100644
--- a/examples/src/main/scala/spark/examples/SparkPi.scala
+++ b/examples/src/main/scala/spark/examples/SparkPi.scala
@@ -4,13 +4,15 @@ import scala.math.random
 import spark._
 import SparkContext._
 
+/** 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")
+    val spark = new SparkContext(args(0), "SparkPi",
+      System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
     val slices = if (args.length > 1) args(1).toInt else 2
     val n = 100000 * slices
     val count = spark.parallelize(1 to n, slices).map { i =>
diff --git a/examples/src/main/scala/spark/examples/SparkTC.scala b/examples/src/main/scala/spark/examples/SparkTC.scala
index 90bae011ad..911ae8f168 100644
--- a/examples/src/main/scala/spark/examples/SparkTC.scala
+++ b/examples/src/main/scala/spark/examples/SparkTC.scala
@@ -9,7 +9,6 @@ import scala.collection.mutable
  * Transitive closure on a graph.
  */
 object SparkTC {
-
   val numEdges = 200
   val numVertices = 100
   val rand = new Random(42)
@@ -29,7 +28,8 @@ object SparkTC {
       System.err.println("Usage: SparkTC <master> [<slices>]")
       System.exit(1)
     }
-    val spark = new SparkContext(args(0), "SparkTC")
+    val spark = new SparkContext(args(0), "SparkTC",
+      System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
     val slices = if (args.length > 1) args(1).toInt else 2
     var tc = spark.parallelize(generateGraph, slices).cache()
 
diff --git a/examples/src/main/scala/spark/streaming/examples/ActorWordCount.scala b/examples/src/main/scala/spark/streaming/examples/ActorWordCount.scala
index 76293fbb96..3b847fe603 100644
--- a/examples/src/main/scala/spark/streaming/examples/ActorWordCount.scala
+++ b/examples/src/main/scala/spark/streaming/examples/ActorWordCount.scala
@@ -131,7 +131,8 @@ object ActorWordCount {
     val Seq(master, host, port) = args.toSeq
 
     // Create the context and set the batch size
-    val ssc = new StreamingContext(master, "ActorWordCount", Seconds(2))
+    val ssc = new StreamingContext(master, "ActorWordCount", Seconds(2),
+      System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
 
     /*
      * Following is the use of actorStream to plug in custom actor as receiver
diff --git a/examples/src/main/scala/spark/streaming/examples/FlumeEventCount.scala b/examples/src/main/scala/spark/streaming/examples/FlumeEventCount.scala
index 461929fba2..39c76fd98a 100644
--- a/examples/src/main/scala/spark/streaming/examples/FlumeEventCount.scala
+++ b/examples/src/main/scala/spark/streaming/examples/FlumeEventCount.scala
@@ -30,7 +30,8 @@ object FlumeEventCount {
 
     val batchInterval = Milliseconds(2000)
     // Create the context and set the batch size
-    val ssc = new StreamingContext(master, "FlumeEventCount", batchInterval)
+    val ssc = new StreamingContext(master, "FlumeEventCount", batchInterval,
+      System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
 
     // Create a flume stream
     val stream = ssc.flumeStream(host,port,StorageLevel.MEMORY_ONLY)
diff --git a/examples/src/main/scala/spark/streaming/examples/HdfsWordCount.scala b/examples/src/main/scala/spark/streaming/examples/HdfsWordCount.scala
index 8530f5c175..9389f8a38d 100644
--- a/examples/src/main/scala/spark/streaming/examples/HdfsWordCount.scala
+++ b/examples/src/main/scala/spark/streaming/examples/HdfsWordCount.scala
@@ -22,7 +22,8 @@ object HdfsWordCount {
     }
 
     // Create the context
-    val ssc = new StreamingContext(args(0), "HdfsWordCount", Seconds(2))
+    val ssc = new StreamingContext(args(0), "HdfsWordCount", Seconds(2),
+      System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
 
     // Create the FileInputDStream on the directory and use the
     // stream to count words in new files created
diff --git a/examples/src/main/scala/spark/streaming/examples/KafkaWordCount.scala b/examples/src/main/scala/spark/streaming/examples/KafkaWordCount.scala
index e0c3555f21..9202e65e09 100644
--- a/examples/src/main/scala/spark/streaming/examples/KafkaWordCount.scala
+++ b/examples/src/main/scala/spark/streaming/examples/KafkaWordCount.scala
@@ -32,8 +32,8 @@ object KafkaWordCount {
 
     val Array(master, zkQuorum, group, topics, numThreads) = args
 
-    val sc = new SparkContext(master, "KafkaWordCount")
-    val ssc =  new StreamingContext(sc, Seconds(2))
+    val ssc =  new StreamingContext(master, "KafkaWordCount", Seconds(2),
+      System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
     ssc.checkpoint("checkpoint")
 
     val topicpMap = topics.split(",").map((_,numThreads.toInt)).toMap
diff --git a/examples/src/main/scala/spark/streaming/examples/NetworkWordCount.scala b/examples/src/main/scala/spark/streaming/examples/NetworkWordCount.scala
index 5ac6d19b34..704540c2bf 100644
--- a/examples/src/main/scala/spark/streaming/examples/NetworkWordCount.scala
+++ b/examples/src/main/scala/spark/streaming/examples/NetworkWordCount.scala
@@ -23,7 +23,8 @@ object NetworkWordCount {
     }
 
     // Create the context with a 1 second batch size
-    val ssc = new StreamingContext(args(0), "NetworkWordCount", Seconds(1))
+    val ssc = new StreamingContext(args(0), "NetworkWordCount", Seconds(1),
+      System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
 
     // Create a NetworkInputDStream on target ip:port and count the
     // words in input stream of \n delimited test (eg. generated by 'nc') 
diff --git a/examples/src/main/scala/spark/streaming/examples/QueueStream.scala b/examples/src/main/scala/spark/streaming/examples/QueueStream.scala
index 2a265d021d..f450e21040 100644
--- a/examples/src/main/scala/spark/streaming/examples/QueueStream.scala
+++ b/examples/src/main/scala/spark/streaming/examples/QueueStream.scala
@@ -15,7 +15,8 @@ object QueueStream {
     }
     
     // Create the context
-    val ssc = new StreamingContext(args(0), "QueueStream", Seconds(1))
+    val ssc = new StreamingContext(args(0), "QueueStream", Seconds(1),
+      System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
 
     // Create the queue through which RDDs can be pushed to 
     // a QueueInputDStream
@@ -30,10 +31,10 @@ object QueueStream {
     
     // Create and push some RDDs into
     for (i <- 1 to 30) {
-      rddQueue += ssc.sc.makeRDD(1 to 1000, 10)
+      rddQueue += ssc.sparkContext.makeRDD(1 to 1000, 10)
       Thread.sleep(1000)
     }
     ssc.stop()
     System.exit(0)
   }
-}
\ No newline at end of file
+}
diff --git a/examples/src/main/scala/spark/streaming/examples/RawNetworkGrep.scala b/examples/src/main/scala/spark/streaming/examples/RawNetworkGrep.scala
index 66e709b7a3..175281e095 100644
--- a/examples/src/main/scala/spark/streaming/examples/RawNetworkGrep.scala
+++ b/examples/src/main/scala/spark/streaming/examples/RawNetworkGrep.scala
@@ -31,10 +31,11 @@ object RawNetworkGrep {
     val Array(master, IntParam(numStreams), host, IntParam(port), IntParam(batchMillis)) = args
 
     // Create the context
-    val ssc = new StreamingContext(master, "RawNetworkGrep", Milliseconds(batchMillis))
+    val ssc = new StreamingContext(master, "RawNetworkGrep", Milliseconds(batchMillis),
+      System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
 
     // Warm up the JVMs on master and slave for JIT compilation to kick in
-    RawTextHelper.warmUp(ssc.sc)
+    RawTextHelper.warmUp(ssc.sparkContext)
 
     val rawStreams = (1 to numStreams).map(_ =>
       ssc.rawSocketStream[String](host, port, StorageLevel.MEMORY_ONLY_SER_2)).toArray
diff --git a/examples/src/main/scala/spark/streaming/examples/TwitterAlgebirdCMS.scala b/examples/src/main/scala/spark/streaming/examples/TwitterAlgebirdCMS.scala
index 39a1a702ee..a9642100e3 100644
--- a/examples/src/main/scala/spark/streaming/examples/TwitterAlgebirdCMS.scala
+++ b/examples/src/main/scala/spark/streaming/examples/TwitterAlgebirdCMS.scala
@@ -43,12 +43,13 @@ object TwitterAlgebirdCMS {
     val Array(master, username, password) = args.slice(0, 3)
     val filters = args.slice(3, args.length)
 
-    val ssc = new StreamingContext(master, "TwitterAlgebirdCMS", Seconds(10))
+    val ssc = new StreamingContext(master, "TwitterAlgebirdCMS", Seconds(10),
+      System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
     val stream = ssc.twitterStream(username, password, filters, StorageLevel.MEMORY_ONLY_SER)
 
     val users = stream.map(status => status.getUser.getId)
 
-    val cms = new CountMinSketchMonoid(DELTA, EPS, SEED, PERC)
+    val cms = new CountMinSketchMonoid(EPS, DELTA, SEED, PERC)
     var globalCMS = cms.zero
     val mm = new MapMonoid[Long, Int]()
     var globalExact = Map[Long, Int]()
diff --git a/examples/src/main/scala/spark/streaming/examples/TwitterAlgebirdHLL.scala b/examples/src/main/scala/spark/streaming/examples/TwitterAlgebirdHLL.scala
index 914fba4ca2..f3288bfb85 100644
--- a/examples/src/main/scala/spark/streaming/examples/TwitterAlgebirdHLL.scala
+++ b/examples/src/main/scala/spark/streaming/examples/TwitterAlgebirdHLL.scala
@@ -32,7 +32,8 @@ object TwitterAlgebirdHLL {
     val Array(master, username, password) = args.slice(0, 3)
     val filters = args.slice(3, args.length)
 
-    val ssc = new StreamingContext(master, "TwitterAlgebirdHLL", Seconds(5))
+    val ssc = new StreamingContext(master, "TwitterAlgebirdHLL", Seconds(5),
+      System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
     val stream = ssc.twitterStream(username, password, filters, StorageLevel.MEMORY_ONLY_SER)
 
     val users = stream.map(status => status.getUser.getId)
diff --git a/examples/src/main/scala/spark/streaming/examples/TwitterPopularTags.scala b/examples/src/main/scala/spark/streaming/examples/TwitterPopularTags.scala
index fdb3a4c73c..9d4494c6f2 100644
--- a/examples/src/main/scala/spark/streaming/examples/TwitterPopularTags.scala
+++ b/examples/src/main/scala/spark/streaming/examples/TwitterPopularTags.scala
@@ -21,7 +21,8 @@ object TwitterPopularTags {
     val Array(master, username, password) = args.slice(0, 3)
     val filters = args.slice(3, args.length)
 
-    val ssc = new StreamingContext(master, "TwitterPopularTags", Seconds(2))
+    val ssc = new StreamingContext(master, "TwitterPopularTags", Seconds(2),
+      System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
     val stream = ssc.twitterStream(username, password, filters)
 
     val hashTags = stream.flatMap(status => status.getText.split(" ").filter(_.startsWith("#")))
diff --git a/examples/src/main/scala/spark/streaming/examples/ZeroMQWordCount.scala b/examples/src/main/scala/spark/streaming/examples/ZeroMQWordCount.scala
index 5ed9b7cb76..74d0d338b7 100644
--- a/examples/src/main/scala/spark/streaming/examples/ZeroMQWordCount.scala
+++ b/examples/src/main/scala/spark/streaming/examples/ZeroMQWordCount.scala
@@ -58,7 +58,8 @@ object ZeroMQWordCount {
     val Seq(master, url, topic) = args.toSeq
 
     // Create the context and set the batch size
-    val ssc = new StreamingContext(master, "ZeroMQWordCount", Seconds(2))
+    val ssc = new StreamingContext(master, "ZeroMQWordCount", Seconds(2),
+      System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
 
     def bytesToStringIterator(x: Seq[Seq[Byte]]) = (x.map(x => new String(x.toArray))).iterator
 
@@ -70,4 +71,4 @@ object ZeroMQWordCount {
     ssc.start()
   }
 
-}
\ No newline at end of file
+}
diff --git a/examples/src/main/scala/spark/streaming/examples/clickstream/PageViewStream.scala b/examples/src/main/scala/spark/streaming/examples/clickstream/PageViewStream.scala
index fba72519a9..e226a4a73a 100644
--- a/examples/src/main/scala/spark/streaming/examples/clickstream/PageViewStream.scala
+++ b/examples/src/main/scala/spark/streaming/examples/clickstream/PageViewStream.scala
@@ -24,7 +24,8 @@ object PageViewStream {
     val port = args(2).toInt
 
     // Create the context
-    val ssc = new StreamingContext("local[2]", "PageViewStream", Seconds(1))
+    val ssc = new StreamingContext("local[2]", "PageViewStream", Seconds(1),
+      System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
 
     // Create a NetworkInputDStream on target host:port and convert each line to a PageView
     val pageViews = ssc.socketTextStream(host, port)
@@ -60,7 +61,7 @@ object PageViewStream {
                                    .map("Unique active users: " + _)
 
     // An external dataset we want to join to this stream
-    val userList = ssc.sc.parallelize(
+    val userList = ssc.sparkContext.parallelize(
        Map(1 -> "Patrick Wendell", 2->"Reynold Xin", 3->"Matei Zaharia").toSeq)
 
     metric match {