Moving Average in Spark Java

Question

I have real time streaming data coming into spark and I would like to do a moving average forecasting on that time-series data. Is there any way to implement this using spar

Answer 1

I took the question you were referring and struggled for a couple of hours in order to translate the Scala code into Java:

// 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();

Then you have to use a new Function Class to do the actual calculation of each data window:

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);
}
}

If you want more detail on this, I wrote about Simple Moving Averages here: https://t.co/gmWltdANd3