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 0000000000000000000000000000000000000000..626034eb0d4d3a59f49a45e0a71d90896a095da1
--- /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 0000000000000000000000000000000000000000..6b22e7120c9174ccc602482aac062383d748ebe2
--- /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 0000000000000000000000000000000000000000..a15a967de85dfc505877f5a9d79ec30a481a505e
--- /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/scala/spark/examples/SparkKMeans.scala b/examples/src/main/scala/spark/examples/SparkKMeans.scala
index 7c21ea12fb72430089d0a4166c8b74fac7677277..4161c59fead2046851428f799f1ecbc07b1eedf8 100644
--- a/examples/src/main/scala/spark/examples/SparkKMeans.scala
+++ b/examples/src/main/scala/spark/examples/SparkKMeans.scala
@@ -64,6 +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/SparkPi.scala b/examples/src/main/scala/spark/examples/SparkPi.scala
index 5a31d74444f1c637b835c907c2337d09724f59e3..f598d2ff9c7cdf0565594eb36965fbdea5b27b25 100644
--- a/examples/src/main/scala/spark/examples/SparkPi.scala
+++ b/examples/src/main/scala/spark/examples/SparkPi.scala
@@ -4,6 +4,7 @@ import scala.math.random
import spark._
import SparkContext._
+/** Computes an approximation to pi */
object SparkPi {
def main(args: Array[String]) {
if (args.length == 0) {