CSE 512 Project Phase 2.scala

/*---------------------------- Start an example Spatial Join Query using Cartesian Product algorithm ----------------------------*/
val objectRDD = new PointRDD(sc, "/home/SparkUser/Downloads/GeoSpark/src/test/resources/arealm.csv", 0, "csv", 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, "csv"); /* 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.SpatialJoinQueryUsingCartesianProduct(objectRDD, rectangleRDD).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);
   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;