java - Spark Java 中的移动平均线

Question

我有实时流数据进入 spark，我想对该时间序列数据进行移动平均预测。有什么方法可以在 Java 中使用 spark 来实现吗？

我已经提到:https://gist.github.com/samklr/27411098f04fc46dcd05/revisions和 Apache Spark Moving Average但是这两个代码都是用 Scala 编写的。由于我不熟悉 Scala，我无法判断我是否会发现它有用，甚至无法将代码转换为 Java。Spark Java 中是否有直接实现预测的方法？

Answer 1

为了将 Scala 代码翻译成 Java，我接受了您提到的问题并努力了几个小时:

// Read a file containing the Stock Quotations
// You can also paralelize a collection of objects to create a RDD
JavaRDD<String> linesRDD = sc.textFile("some sample file containing stock prices");

// Convert the lines into our business objects
JavaRDD<StockQuotation> quotationsRDD = linesRDD.flatMap(new ConvertLineToStockQuotation());

// We need these two objects in order to use the MLLib RDDFunctions object
ClassTag<StockQuotation> classTag = scala.reflect.ClassManifestFactory.fromClass(StockQuotation.class);
RDD<StockQuotation> rdd = JavaRDD.toRDD(quotationsRDD);

// Instantiate a RDDFunctions object to work with
RDDFunctions<StockQuotation> rddFs = RDDFunctions.fromRDD(rdd, classTag);

// This applies the sliding function and return the (DATE,SMA) tuple
JavaPairRDD<Date, Double> smaPerDate =     rddFs.sliding(slidingWindow).toJavaRDD().mapToPair(new MovingAvgByDateFunction());
List<Tuple2<Date, Double>> smaPerDateList = smaPerDate.collect();

然后你必须使用一个新的函数类来对每个数据窗口进行实际计算:

public class MovingAvgByDateFunction implements PairFunction<Object,Date,Double> {

/**
 * 
 */
private static final long serialVersionUID = 9220435667459839141L;

@Override
public Tuple2<Date, Double> call(Object t) throws Exception {

    StockQuotation[] stocks = (StockQuotation[]) t;
    List<StockQuotation> stockList = Arrays.asList(stocks);

    Double result = stockList.stream().collect(Collectors.summingDouble(new ToDoubleFunction<StockQuotation>() {

        @Override
        public double applyAsDouble(StockQuotation value) {
            return value.getValue();
        }
    }));

    result = result / stockList.size();

    return new Tuple2<Date, Double>(stockList.get(0).getTimestamp(),result);
}
}

如果您想了解更多详细信息，请参阅我在此处撰写的关于简单移动平均线的文章: https://t.co/gmWltdANd3