Java SparkContext類代碼示例

import org.apache.spark.SparkContext; //導入依賴的package包/類
private RDD<Element> doOperation(final GetRDDOfElements operation,
                                 final Context context,
                                 final AccumuloStore accumuloStore)
        throws OperationException {
    final Configuration conf = getConfiguration(operation);
    final SparkContext sparkContext = SparkContextUtil.getSparkSession(context, accumuloStore.getProperties()).sparkContext();
    sparkContext.hadoopConfiguration().addResource(conf);
    // Use batch scan option when performing seeded operation
    InputConfigurator.setBatchScan(AccumuloInputFormat.class, conf, true);
    addIterators(accumuloStore, conf, context.getUser(), operation);
    addRanges(accumuloStore, conf, operation);
    final RDD<Tuple2<Element, NullWritable>> pairRDD = sparkContext.newAPIHadoopRDD(conf,
            ElementInputFormat.class,
            Element.class,
            NullWritable.class);
    return pairRDD.map(new FirstElement(), ClassTagConstants.ELEMENT_CLASS_TAG);
}
 

import org.apache.spark.SparkContext; //導入依賴的package包/類
@SuppressWarnings("unchecked")
public PredictWithModel(String modelName, String modelPath, String testFile, int numClasses, int minPartition, double threshold, SparkContext sc){
    this.numClasses = numClasses;
    this.threshold = threshold;

    if(modelName.equals("LogisticRegressionModel")){
      LogisticRegressionModel lrmodel = LogisticRegressionModel.load(sc, modelPath);
      this.model = (T)(Object) lrmodel;
    }
    else if(modelName.equals("SVMModel")){
      SVMModel svmmodel = SVMModel.load(sc, modelPath);
      this.model = (T)(Object) svmmodel;
    }
    else if(modelName.equals("NaiveBayesModel")){
      NaiveBayesModel bayesmodel = NaiveBayesModel.load(sc, modelPath);
      this.model = (T)(Object) bayesmodel;
    }
    
    //Load testing data
    LoadProcess loadProcess = new LoadProcess(sc, minPartition);    
    testingData = loadProcess.load(testFile, "Vector");  
    testingData.cache();    
}
 

import org.apache.spark.SparkContext; //導入依賴的package包/類
public void initializeVariables(SparkContext sc) {
    for (int i = 0; i < this.numberOfCluster; i++) {
        longAccumulator = sc.accumulator(0L, new LongAccumulatorParam());
        clusterCounterMaliciousList.add(longAccumulator);

        longAccumulator = sc.accumulator(0L, new LongAccumulatorParam());
        clusterCounterBenignList.add(longAccumulator);
    }


    longAccumulator = sc.accumulator(0L, new LongAccumulatorParam());

    totalBenign = sc.accumulator(0L, new LongAccumulatorParam());
    totalMalicious = sc.accumulator(0L, new LongAccumulatorParam());

    totalNanoSeconds = sc.accumulator(0L, "totalNanoSeconds", new LongAccumulatorParam());
    flowCounterMalicious = sc.accumulator(new HashMap<BigInteger, Boolean>(), new UniqueFlowAccumulatorParam());
    flowCounterBenign = sc.accumulator(new HashMap<BigInteger, Boolean>(), new UniqueFlowAccumulatorParam());
}
 

import org.apache.spark.SparkContext; //導入依賴的package包/類
/**
 * @param args
 * @throws SQLException
 */
public static void main(String[] args) throws SQLException {
    if (args.length == 0) {
        System.out.println("Usage: ImpalaSparkJDBC <url> <tableName>");
        System.out.println("       (secure)   jdbc:impala://impala-host:21050/;AuthMech=1;KrbRealm=realm;KrbHostFQDN=krbHost;KrbServiceName=impala");
        System.out.println("       (insecure) jdbc:hive2://impala-host:21050/;auth=noSasl");
        System.exit(1);
    }

    Properties prop = new Properties();
    prop.setProperty("driver","com.cloudera.impala.jdbc41.Driver");

    System.setProperty("java.security.auth.login.config", "jaas.conf");
    System.setProperty("sun.security.jgss.debug","true");
    System.setProperty("javax.security.auth.useSubjectCredsOnly","false");

    SparkConf sparkConf = new SparkConf().setAppName("ImpalaJDBC");
    SparkContext sc = new SparkContext(sparkConf);
    SQLContext sqlContext = SQLContext.getOrCreate(sc);
    sqlContext.read().jdbc(args[0], args[1], prop).show();
}
 

import org.apache.spark.SparkContext; //導入依賴的package包/類
static
public void checkVersion(){
	SparkContext sparkContext = SparkContext.getOrCreate();

	int[] version = parseVersion(sparkContext.version());

	if(!Arrays.equals(ConverterUtil.VERSION, version)){
		throw new IllegalArgumentException("Expected Apache Spark ML version " + formatVersion(ConverterUtil.VERSION) + ", got version " + formatVersion(version) + " (" + sparkContext.version() + ")");
	}
}
 

import org.apache.spark.SparkContext; //導入依賴的package包/類
public Bounded(
    SparkContext sc,
    BoundedSource<T> source,
    SerializablePipelineOptions options,
    String stepName) {
  super(sc, NIL, JavaSparkContext$.MODULE$.<WindowedValue<T>>fakeClassTag());
  this.source = source;
  this.options = options;
  // the input parallelism is determined by Spark's scheduler backend.
  // when running on YARN/SparkDeploy it's the result of max(totalCores, 2).
  // when running on Mesos it's 8.
  // when running local it's the total number of cores (local = 1, local[N] = N,
  // local[*] = estimation of the machine's cores).
  // ** the configuration "spark.default.parallelism" takes precedence over all of the above **
  this.numPartitions = sc.defaultParallelism();
  checkArgument(this.numPartitions > 0, "Number of partitions must be greater than zero.");
  this.stepName = stepName;
  this.metricsAccum = MetricsAccumulator.getInstance();
}
 

import org.apache.spark.SparkContext; //導入依賴的package包/類
public void run() {
  long microsLower = day * 1000;
  long microsUpper = (day * 1000) + TimeUnit.DAYS.toMicros(1) - 1;

  log.info("Running Dependencies job for {}: {} ≤ Span.timestamp {}", dateStamp, microsLower,
      microsUpper);

  SparkContext sc = new SparkContext(conf);

  List<DependencyLink> links = javaFunctions(sc).cassandraTable(keyspace, "traces")
      .spanBy(r -> r.getLong("trace_id"), Long.class)
      .flatMapValues(new CassandraRowsToDependencyLinks(logInitializer, microsLower, microsUpper))
      .values()
      .mapToPair(link -> new Tuple2<>(new Tuple2<>(link.parent, link.child), link))
      .reduceByKey((l, r) -> DependencyLink.builder()
          .parent(l.parent)
          .child(l.child)
          .callCount(l.callCount + r.callCount)
          .errorCount(l.errorCount + r.errorCount).build())
      .values().collect();

  sc.stop();

  saveToCassandra(links);
}
 

import org.apache.spark.SparkContext; //導入依賴的package包/類
public RFileReaderRDD(final SparkContext sparkContext,
                      final String instanceName,
                      final String zookeepers,
                      final String user,
                      final String password,
                      final String tableName,
                      final Set<String> auths,
                      final byte[] serialisedConfiguration) {
    super(sparkContext, JavaConversions.asScalaBuffer(new ArrayList<>()),
            ClassTag$.MODULE$.apply(Map.Entry.class));
    this.instanceName = instanceName;
    this.zookeepers = zookeepers;
    this.user = user;
    this.password = password;
    this.tableName = tableName;
    this.auths = auths;
    this.serialisedConfiguration = serialisedConfiguration;
}
 

import org.apache.spark.SparkContext; //導入依賴的package包/類
public void doOperation(final ImportJavaRDDOfElements operation, final Context context, final AccumuloStore store) throws OperationException {
    final String outputPath = operation.getOption(OUTPUT_PATH);
    if (null == outputPath || outputPath.isEmpty()) {
        throw new OperationException("Option outputPath must be set for this option to be run against the accumulostore");
    }
    final String failurePath = operation.getOption(FAILURE_PATH);
    if (null == failurePath || failurePath.isEmpty()) {
        throw new OperationException("Option failurePath must be set for this option to be run against the accumulostore");
    }

    final SparkContext sparkContext = SparkContextUtil.getSparkSession(context, store.getProperties()).sparkContext();
    final Broadcast<AccumuloElementConverter> broadcast = JavaSparkContext.fromSparkContext(sparkContext).broadcast(store.getKeyPackage().getKeyConverter());
    final ElementConverterFunction func = new ElementConverterFunction(broadcast);
    final JavaPairRDD<Key, Value> rdd = operation.getInput().flatMapToPair(func);
    final ImportKeyValueJavaPairRDDToAccumulo op =
            new ImportKeyValueJavaPairRDDToAccumulo.Builder()
                    .input(rdd)
                    .failurePath(failurePath)
                    .outputPath(outputPath)
                    .build();
    store.execute(new OperationChain(op), context);
}
 

import org.apache.spark.SparkContext; //導入依賴的package包/類
/**
 * Helper method for creating the spark context from the given cognition configuration
 * @return a new configured spark context
 */
public SparkContext createSparkContext() {
  SparkConf conf = new SparkConf();

  Configuration config = cognition.getProperties();

  conf.set("spark.serializer", KryoSerializer.class.getName());
  conf.setAppName(config.getString("app.name"));
  conf.setMaster(config.getString("master"));

  Iterator<String> iterator = config.getKeys("spark");
  while (iterator.hasNext()) {
    String key = iterator.next();
    conf.set(key, config.getString(key));
  }

  SparkContext sc = new SparkContext(conf);
  for (String jar : config.getStringArray("jars")) {
    sc.addJar(jar);
  }

  return sc;
}
 

import org.apache.spark.SparkContext; //導入依賴的package包/類
@Override
public void exportStatFiles(String outputPath, SparkContext sc) throws IOException {
    String d = DEFAULT_DELIMITER;


    //Total time stats (includes total example counts)
    String totalTimeStatsPath = FilenameUtils.concat(outputPath, FILENAME_TOTAL_TIME_STATS);
    StatsUtils.exportStats(workerFlatMapTotalTimeMs, totalTimeStatsPath, d, sc);

    //"Get initial model" stats:
    String getInitialModelStatsPath = FilenameUtils.concat(outputPath, FILENAME_GET_INITIAL_MODEL_STATS);
    StatsUtils.exportStats(workerFlatMapGetInitialModelTimeMs, getInitialModelStatsPath, d, sc);

    //"DataSet get time" stats:
    String getDataSetStatsPath = FilenameUtils.concat(outputPath, FILENAME_DATASET_GET_TIME_STATS);
    StatsUtils.exportStats(workerFlatMapDataSetGetTimesMs, getDataSetStatsPath, d, sc);

    //Process minibatch time stats:
    String processMiniBatchStatsPath = FilenameUtils.concat(outputPath, FILENAME_PROCESS_MINIBATCH_TIME_STATS);
    StatsUtils.exportStats(workerFlatMapProcessMiniBatchTimesMs, processMiniBatchStatsPath, d, sc);

    if (trainingWorkerSpecificStats != null)
        trainingWorkerSpecificStats.exportStatFiles(outputPath, sc);
}
 

import org.apache.spark.SparkContext; //導入依賴的package包/類
private void submitBatch(int batch, int batchSize, int numPartitions, final JavaRDD<String> streamed, final Properties properties) {
    final List<Integer> list = new ArrayList<>();
    for (int j = batch*batchSize; j < numPartitions && j < (batch+1)*batchSize; j++) {
        list.add(j);
    }
    if (LOG.isTraceEnabled())
        LOG.trace("Submitting batch " + batch + " with partitions " + list);
    final Seq objects = JavaConversions.asScalaBuffer(list).toList();
    completionService.submit(new Callable<Object>() {
        @Override
        public Object call() {
            SparkContext sc = SpliceSpark.getContextUnsafe().sc();
            sc.setLocalProperties(properties);
            String[] results = (String[]) sc.runJob(streamed.rdd(), new FunctionAdapter(), objects, tag);
            for (String o2: results) {
                if ("STOP".equals(o2)) {
                    return "STOP";
                }
            }
            return "CONTINUE";
        }
    });
}
 

import org.apache.spark.SparkContext; //導入依賴的package包/類
public TrainModel(SparkContext sc, String trainFile, String validFile, int numClasses, String modelName, int colIdx){
    this.trainFile = trainFile;
    this.validFile = validFile;
    this.numClasses = numClasses;
    this.modelName = modelName;

    // Load training/validate data
    LoadProcess loadProcess = new LoadProcess(sc, this.minPartition);   
    trainingData = loadProcess.load(trainFile, "LabeledPoint", colIdx);
    trainingData.cache();
    validData = loadProcess.load(validFile, "LabeledPoint", colIdx);      
    validData.cache();
}
 

import org.apache.spark.SparkContext; //導入依賴的package包/類
private JavaRDD<VideoViewEvent> prepareVideoRDD(String localFilePath, SparkContext sparkContext) {
	JavaRDD<VideoViewEvent> videoEventRDD = sparkContext.textFile(localFilePath, 2).toJavaRDD()
			.map(new Function<String, VideoViewEvent>() {
				private static final long serialVersionUID = 1L;

				@Override
				public VideoViewEvent call(String line) throws Exception {
					return new Gson().fromJson(line, VideoViewEvent.class);
				}
			});
	return videoEventRDD;
}
 

import org.apache.spark.SparkContext; //導入依賴的package包/類
public void initializeVariables(SparkContext sc) {
    for (int i = 0; i < this.numberOfLabels; i++) {
        longAccumulator = sc.accumulator(0L, new LongAccumulatorParam());
        classificationCounterValidatedList.add(longAccumulator);

        longAccumulator = sc.accumulator(0L, new LongAccumulatorParam());
        classificationCounterOriginList.add(longAccumulator);

        //unique entries
        flowCounter = sc.accumulator(new HashMap<BigInteger, Boolean>(), new UniqueFlowAccumulatorParam());
        uniqueOriginEntires.add(flowCounter);

        flowCounter = sc.accumulator(new HashMap<BigInteger, Boolean>(), new UniqueFlowAccumulatorParam());
        uniqueValidatedEntires.add(flowCounter);
    }


    longAccumulator = sc.accumulator(0L, new LongAccumulatorParam());

    totalBenign = sc.accumulator(0L, new LongAccumulatorParam());
    totalMalicious = sc.accumulator(0L, new LongAccumulatorParam());

    truePositive = sc.accumulator(0L, new LongAccumulatorParam());
    falseNegative = sc.accumulator(0L, new LongAccumulatorParam());
    falsePositive = sc.accumulator(0L, new LongAccumulatorParam());
    trueNegative = sc.accumulator(0L, new LongAccumulatorParam());

    totalNanoSeconds = sc.accumulator(0L, "totalNanoSeconds", new LongAccumulatorParam());
    flowCounterMalicious = sc.accumulator(new HashMap<BigInteger, Boolean>(), new UniqueFlowAccumulatorParam());
    flowCounterBenign = sc.accumulator(new HashMap<BigInteger, Boolean>(), new UniqueFlowAccumulatorParam());
}