Stateful Network Word Count via apache spark - Java Big Data

Java examples for Big Data:apache spark

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Description

Stateful Network Word Count via apache spark

Demo Code



import java.util.Arrays;
import java.util.List;
import java.util.regex.Pattern;

import scala.Tuple2;

import com.google.common.base.Optional;
import com.google.common.collect.Lists;

import org.apache.spark.HashPartitioner;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.StorageLevels;
import org.apache.spark.api.java.function.FlatMapFunction;
import org.apache.spark.api.java.function.Function2;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.streaming.Durations;
import org.apache.spark.streaming.api.java.JavaDStream;
import org.apache.spark.streaming.api.java.JavaPairDStream;
import org.apache.spark.streaming.api.java.JavaReceiverInputDStream;
import org.apache.spark.streaming.api.java.JavaStreamingContext;

public class StatefulNetworkWordCount {
    private static final Pattern SPACE = Pattern.compile(" ");

    public static void main(String[] args) {

        final Function2<List<Integer>, Optional<Integer>, Optional<Integer>> updateFunction = new Function2<List<Integer>, Optional<Integer>, Optional<Integer>>() {
            @Override/* ww  w.  ja  v  a 2s .  com*/
            public Optional<Integer> call(List<Integer> values,
                    Optional<Integer> state) {
                Integer newSum = state.or(0);
                for (Integer value : values) {
                    newSum += value;
                }
                return Optional.of(newSum);
            }
        };

        SparkConf sparkConf = new SparkConf().setAppName(
                "JavaStatefulNetworkWordCount").setMaster("local[*]");
        JavaStreamingContext ssc = new JavaStreamingContext(sparkConf,
                Durations.seconds(1));
        ssc.checkpoint(".");

        List<Tuple2<String, Integer>> tuples = Arrays.asList(
                new Tuple2<String, Integer>("hello", 1),
                new Tuple2<String, Integer>("world", 1));
        JavaPairRDD<String, Integer> initialRDD = ssc.sparkContext()
                .parallelizePairs(tuples);

        JavaReceiverInputDStream<String> lines = ssc.socketTextStream(
                args[0], Integer.parseInt(args[1]),
                StorageLevels.MEMORY_AND_DISK_SER_2);

        JavaDStream<String> words = lines
                .flatMap(new FlatMapFunction<String, String>() {
                    @Override
                    public Iterable<String> call(String x) {
                        return Lists.newArrayList(SPACE.split(x));
                    }
                });

        JavaPairDStream<String, Integer> wordsDstream = words
                .mapToPair(new PairFunction<String, String, Integer>() {
                    @Override
                    public Tuple2<String, Integer> call(String s) {
                        return new Tuple2<String, Integer>(s, 1);
                    }
                });

        JavaPairDStream<String, Integer> stateDstream = wordsDstream
                .updateStateByKey(updateFunction, new HashPartitioner(ssc
                        .sparkContext().defaultParallelism()), initialRDD);

        stateDstream.print();
        ssc.start();
        ssc.awaitTermination();
    }
}

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