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1. 启动hadoop sh start-dfs.sh sh start-yarn.sh 2. 启动spark cd /appl/spark-1.4.0/ sbin/start-all.sh 3. 准备数据 hadoop fs -put /mk/test/kmeans_data.txt /test/ 4. 编写程序 包

Java

import org.apache.spark.api.java.*;import org.apache.spark.api.java.function.Function;import org.apache.spark.mllib.clustering.KMeans;import org.apache.spark.mllib.clustering.KMeansModel;import org.apache.spark.mllib.linalg.Vector;import org.apache.spark.mllib.linalg.Vectors;import org.apache.spark.SparkConf;/* Test: * sh start-dfs.sh * sh start-yarn.sh * cd /appl/spark-1.4.0/ * sbin/start-all.sh * hadoop fs -put /mk/test/kmeans_data.txt /test/ * ./bin/spark-submit /mk/test/KMeansSim.jar */public class KMeansSim {  public static void main(String[] args) {	// environment initialization    SparkConf conf = new SparkConf().setAppName("K-means Example");    JavaSparkContext sc = new JavaSparkContext(conf);    // Load and parse data (${SPARK_HOME}/data/mllib/kmeans_data.txt)    String path = "/test/kmeans_data.txt";    JavaRDD
   
     data = sc.textFile(path);    JavaRDD
    
      parsedData = data.map(      new Function
     
      () {        public Vector call(String s) {          return Vectors.dense(toDoubleArray(s));        }      }    );    parsedData.cache();    // Cluster the data into two classes using KMeans    int numClusters = 2;    int numIterations = 20;    KMeansModel clusters = KMeans.train(parsedData.rdd(), numClusters, numIterations);    // Evaluate clustering by computing Within Set Sum of Squared Errors    double WSSSE = clusters.computeCost(parsedData.rdd());    System.out.println("Within Set Sum of Squared Errors = " + WSSSE);    // Save and load model    clusters.save(sc.sc(), "myModelPath");    KMeansModel sameModel = KMeansModel.load(sc.sc(), "myModelPath");        // predict test    System.out.println("~~~predict:" + clusters.predict(Vectors.dense(    		toDoubleArray("1.0 2.1 3.8"))));        // ending    sc.stop();  }    // String to double[]  public static double[] toDoubleArray(String s) {	  String[] sarray = s.split(" ");      double[] values = new double[sarray.length];      for (int i = 0; i < sarray.length; i++)        values[i] = Double.parseDouble(sarray[i]);      return values;  }}
     
    
   

5. 运行

./bin/spark-submit /mk/test/KMeansSim.jar