CSE 512 Project Phase 2.scala

    /*---------------------------- Start an example Spatial Join Query using Cartesian Product algorithm ----------------------------*/
    /* Please see http://www.public.asu.edu/~jiayu2/geospark/javadoc/latest/ for function documentation */
    val objectRDD = new PointRDD(sc, "/home/SparkUser/Downloads/GeoSpark/src/test/resources/arealm.csv", 0, FileDataSplitter.CSV, false, 10); /* The O means spatial attribute starts at Column 0 and the 10 means 10 RDD partitions */
    val rectangleRDD = new RectangleRDD(sc, "/home/SparkUser/Downloads/GeoSpark/src/test/resources/zcta510.csv", 0, FileDataSplitter.CSV, false); /* The O means spatial attribute starts at Column 0. You might need to "collect" all rectangles into a list and do the Carteian Product join. */
    val resultSize = JoinQuery.SpatialJoinQuery(objectRDD, rectangleRDD, true).count();
    /*---------------------------- End an example Spatial Join Query using Cartesian Product algorithm ----------------------------*/
    

    CSE 512 Naive Spatial Join Query (Should be written in Java)

    1.Create a PointRDD objectRDD;
    2.Create a RectangleRDD queryWindowRDD;
    3.Collect rectangles from queryWindowRDD to one Java List L;
    4. For each rectangle R in L
        do RangeQuery.SpatialRangeQuery(objectRDD, queryEnvelope, 0, true);
    End;
    5.Collect all results;
    6.Parallelize the results to generate a RDD in this format: JavaPairRDD<Envelope, HashSet<Point>>.;
    7.Return the result RDD;