spark-graphx

问题 In Java (not Scala!) Spark 3.0.1 have a JavaRDD instance object neighborIdsRDD which its type is JavaRDD<Tuple2<Object, long[]>> . Part of my code related to the generation of the JavaRDD is the following: GraphOps<String, String> graphOps = new GraphOps<>(graph, stringTag, stringTag); JavaRDD<Tuple2<Object, long[]>> neighborIdsRDD = graphOps.collectNeighborIds(EdgeDirection.Either()).toJavaRDD(); I have had to get a JavaRDD using toJavaRDD() because collectNeighborIds returns a org.apache

问题 I've a table containing the following two columns: Device-Id Account-Id d1 a1 d2 a1 d1 a2 d2 a3 d3 a4 d3 a5 d4 a6 d1 a4 Device-Id is the unique Id of the device on which my app is installed and Account-Id is the id of a user account. A user can have multiple devices and can create multiple accounts on the same device(eg. d1 device has a1, a2 and a3 accounts set up). I want to find unique actual users(should be represented as a new column with some unique UUID in the generated table) and the

问题 I've a table containing the following two columns: Device-Id Account-Id d1 a1 d2 a1 d1 a2 d2 a3 d3 a4 d3 a5 d4 a6 d1 a4 Device-Id is the unique Id of the device on which my app is installed and Account-Id is the id of a user account. A user can have multiple devices and can create multiple accounts on the same device(eg. d1 device has a1, a2 and a3 accounts set up). I want to find unique actual users(should be represented as a new column with some unique UUID in the generated table) and the

问题 I've a table containing the following two columns: Device-Id Account-Id d1 a1 d2 a1 d1 a2 d2 a3 d3 a4 d3 a5 d4 a6 d1 a4 Device-Id is the unique Id of the device on which my app is installed and Account-Id is the id of a user account. A user can have multiple devices and can create multiple accounts on the same device(eg. d1 device has a1, a2 and a3 accounts set up). I want to find unique actual users(should be represented as a new column with some unique UUID in the generated table) and the

来源： https://stackoverflow.com/questions/63428029/how-to-handle-error-of-key-not-found-in-spark

来源： https://stackoverflow.com/questions/30830382/subtract-an-rdd-from-another-rdd-doesnt-work-correctly

问题 there is my program： static class Vprog extends AbstractFunction3< Object, OddRange, OddRange, OddRange> implements Serializable { @Override public OddRange apply(Object l, OddRange self, OddRange sumOdd) { System.out.println(self.getS()+self.getI()+" ---> "+sumOdd.getS()+sumOdd.getI()); self.setS(sumOdd.getS() + self.getS()); self.setI(self.getI() + sumOdd.getI()); return new OddRange(self.getS(), self.getI()); } } the question is if I use return new OddRange like above in class Vprog，I can

问题 I'm working with GraphX and Pregel with the Java API. I'm trying to implement a MaxValue Algorithm(Given a weighted graph and output is the max weight). But my implementation is not working: public class Main { public static void main(String[] args){ SparkConf conf = new SparkConf().setAppName("MaxValue").setMaster("spark://home:7077"); JavaSparkContext sc = new JavaSparkContext(conf); JavaRDD<String> text_file = sc.textFile(args[0]); JavaRDD<String[]> text_file_arr = text_file.map(l -> l

问题 I'm working with GraphX and Pregel with the Java API. I'm trying to implement a MaxValue Algorithm(Given a weighted graph and output is the max weight). But my implementation is not working: public class Main { public static void main(String[] args){ SparkConf conf = new SparkConf().setAppName("MaxValue").setMaster("spark://home:7077"); JavaSparkContext sc = new JavaSparkContext(conf); JavaRDD<String> text_file = sc.textFile(args[0]); JavaRDD<String[]> text_file_arr = text_file.map(l -> l

问题 I'm working with GraphX and Pregel with the Java API. I'm trying to implement a MaxValue Algorithm(Given a weighted graph and output is the max weight). But my implementation is not working: public class Main { public static void main(String[] args){ SparkConf conf = new SparkConf().setAppName("MaxValue").setMaster("spark://home:7077"); JavaSparkContext sc = new JavaSparkContext(conf); JavaRDD<String> text_file = sc.textFile(args[0]); JavaRDD<String[]> text_file_arr = text_file.map(l -> l