Java JavaRDD.unpersist方法代碼示例

import org.apache.spark.api.java.JavaRDD; //導入方法依賴的package包/類
/**
	 * @param args
	 */
	public static void main(String[] args) {
		//C:\Users\sumit.kumar\Downloads\bin\warehouse
		//System.setProperty("hadoop.home.dir", "C:\\Users\\sumit.kumar\\Downloads");
		String logFile = "src/main/resources/Apology_by_Plato.txt"; // Should be some file on your system
		Logger rootLogger = LogManager.getRootLogger();
		rootLogger.setLevel(Level.WARN);
		 SparkConf conf = new SparkConf().setMaster("local").setAppName("ActionExamples").set("spark.hadoop.validateOutputSpecs", "false");
			JavaSparkContext sparkContext = new JavaSparkContext(conf);
		    JavaRDD<Integer> rdd = sparkContext.parallelize(Arrays.asList(1, 2, 3,4,5),3).cache();	
		    JavaRDD<Integer> evenRDD= rdd.filter(new org.apache.spark.api.java.function.Function<Integer, Boolean>() {
			@Override
			public Boolean call(Integer v1) throws Exception {
			  return ((v1%2)==0)?true:false;
				}
			});
		    
		    evenRDD.persist(StorageLevel.MEMORY_AND_DISK());
		    evenRDD.foreach(new VoidFunction<Integer>() {
			@Override
			public void call(Integer t) throws Exception {
			System.out.println("The value of RDD are :"+t);
			 }
			});
		   //unpersisting the RDD 
		   evenRDD.unpersist();
		   rdd.unpersist();
		   
		   /* JavaRDD<String> lines = spark.read().textFile(logFile).javaRDD().cache();
		    System.out.println("DEBUG: \n"+ lines.toDebugString());
		   long word= lines.count();
		   JavaRDD<String> distinctLines=lines.distinct();
		   System.out.println("DEBUG: \n"+ distinctLines.toDebugString());
		   JavaRDD<String> finalRdd=lines.subtract(distinctLines);
		    
		   
		   System.out.println("DEBUG: \n"+ finalRdd.toDebugString());
		   System.out.println("The count is "+word);
		   System.out.println("The count is "+distinctLines.count());
		   System.out.println("The count is "+finalRdd.count());
		   
		   finalRdd.foreach(new VoidFunction<String>() {
			
			@Override
			public void call(String t) throws Exception {
				// TODO Auto-generated method stub
				System.out.println(t);
			}
		});
*/	    /*SparkConf conf = new SparkConf().setAppName("Simple Application");
	    JavaSparkContext sc = new JavaSparkContext(conf);
	    StorageLevel newLevel;
		JavaRDD<String> logData = sc.textFile(logFile).cache();

	    long numAs = logData.filter(new Function(logFile, logFile, logFile, logFile, false) {
	      public Boolean call(String s) { return s.contains("a"); }
	    }).count();

	    long numBs = logData.filter(new Function(logFile, logFile, logFile, logFile, false) {
	      public Boolean call(String s) { return s.contains("b"); }
	    }).count();

	    System.out.println("Lines with a: " + numAs + ", lines with b: " + numBs);
	    
	    sc.stop();*/

	}
 

import org.apache.spark.api.java.JavaRDD; //導入方法依賴的package包/類
@Override
public PMML buildModel(JavaSparkContext sparkContext,
                       JavaRDD<String> trainData,
                       List<?> hyperParameters,
                       Path candidatePath) {
  int features = (Integer) hyperParameters.get(0);
  double lambda = (Double) hyperParameters.get(1);
  double alpha = (Double) hyperParameters.get(2);
  double epsilon = Double.NaN;
  if (logStrength) {
    epsilon = (Double) hyperParameters.get(3);
  }
  Preconditions.checkArgument(features > 0);
  Preconditions.checkArgument(lambda >= 0.0);
  Preconditions.checkArgument(alpha > 0.0);
  if (logStrength) {
    Preconditions.checkArgument(epsilon > 0.0);
  }

  JavaRDD<String[]> parsedRDD = trainData.map(MLFunctions.PARSE_FN);
  parsedRDD.cache();

  Map<String,Integer> userIDIndexMap = buildIDIndexMapping(parsedRDD, true);
  Map<String,Integer> itemIDIndexMap = buildIDIndexMapping(parsedRDD, false);

  log.info("Broadcasting ID-index mappings for {} users, {} items",
           userIDIndexMap.size(), itemIDIndexMap.size());

  Broadcast<Map<String,Integer>> bUserIDToIndex = sparkContext.broadcast(userIDIndexMap);
  Broadcast<Map<String,Integer>> bItemIDToIndex = sparkContext.broadcast(itemIDIndexMap);

  JavaRDD<Rating> trainRatingData = parsedToRatingRDD(parsedRDD, bUserIDToIndex, bItemIDToIndex);
  trainRatingData = aggregateScores(trainRatingData, epsilon);
  ALS als = new ALS()
      .setRank(features)
      .setIterations(iterations)
      .setLambda(lambda)
      .setCheckpointInterval(5);
  if (implicit) {
    als = als.setImplicitPrefs(true).setAlpha(alpha);
  }

  RDD<Rating> trainingRatingDataRDD = trainRatingData.rdd();
  trainingRatingDataRDD.cache();
  MatrixFactorizationModel model = als.run(trainingRatingDataRDD);
  trainingRatingDataRDD.unpersist(false);

  bUserIDToIndex.unpersist();
  bItemIDToIndex.unpersist();

  parsedRDD.unpersist();

  Broadcast<Map<Integer,String>> bUserIndexToID = sparkContext.broadcast(invertMap(userIDIndexMap));
  Broadcast<Map<Integer,String>> bItemIndexToID = sparkContext.broadcast(invertMap(itemIDIndexMap));

  PMML pmml = mfModelToPMML(model,
                            features,
                            lambda,
                            alpha,
                            epsilon,
                            implicit,
                            logStrength,
                            candidatePath,
                            bUserIndexToID,
                            bItemIndexToID);
  unpersist(model);

  bUserIndexToID.unpersist();
  bItemIndexToID.unpersist();

  return pmml;
}
 

import org.apache.spark.api.java.JavaRDD; //導入方法依賴的package包/類
@Override
public double evaluate(JavaSparkContext sparkContext,
                       PMML model,
                       Path modelParentPath,
                       JavaRDD<String> testData,
                       JavaRDD<String> trainData) {

  JavaRDD<String[]> parsedTestRDD = testData.map(MLFunctions.PARSE_FN);
  parsedTestRDD.cache();

  Map<String,Integer> userIDToIndex = buildIDIndexOneWayMap(model, parsedTestRDD, true);
  Map<String,Integer> itemIDToIndex = buildIDIndexOneWayMap(model, parsedTestRDD, false);

  log.info("Broadcasting ID-index mappings for {} users, {} items",
           userIDToIndex.size(), itemIDToIndex.size());

  Broadcast<Map<String,Integer>> bUserIDToIndex = sparkContext.broadcast(userIDToIndex);
  Broadcast<Map<String,Integer>> bItemIDToIndex = sparkContext.broadcast(itemIDToIndex);

  JavaRDD<Rating> testRatingData = parsedToRatingRDD(parsedTestRDD, bUserIDToIndex, bItemIDToIndex);
  double epsilon = Double.NaN;
  if (logStrength) {
    epsilon = Double.parseDouble(AppPMMLUtils.getExtensionValue(model, "epsilon"));
  }
  testRatingData = aggregateScores(testRatingData, epsilon);

  MatrixFactorizationModel mfModel =
      pmmlToMFModel(sparkContext, model, modelParentPath, bUserIDToIndex, bItemIDToIndex);

  parsedTestRDD.unpersist();

  double eval;
  if (implicit) {
    double auc = Evaluation.areaUnderCurve(sparkContext, mfModel, testRatingData);
    log.info("AUC: {}", auc);
    eval = auc;
  } else {
    double rmse = Evaluation.rmse(mfModel, testRatingData);
    log.info("RMSE: {}", rmse);
    eval = -rmse;
  }
  unpersist(mfModel);

  bUserIDToIndex.unpersist();
  bItemIDToIndex.unpersist();

  return eval;
}