Spark Streaming的样本demo统计

废话不多说，直接上代码

package com.demo;

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

import org.apache.log4j.Level;
import org.apache.log4j.Logger;
import org.apache.spark.SparkConf;
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;

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

import scala.Tuple2;

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

    public static void main(String[] args) {
        //屏蔽日志
        Logger.getLogger("org.apache.spark").setLevel(Level.OFF);

        // Create the context with a 1 second batch size
        SparkConf sparkConf = new SparkConf().setAppName("NetworkWordCount").setMaster("local[2]");
        JavaStreamingContext ssc = new JavaStreamingContext(sparkConf, Durations.seconds(1));

        // Create a JavaReceiverInputDStream on target ip:port and count the
        // words in input stream of \n delimited text (eg. generated by 'nc')
        // Note that no duplication in storage level only for running locally.
        // Replication necessary in distributed scenario for fault tolerance.
        JavaReceiverInputDStream<String> lines = ssc.socketTextStream("192.168.49.151",9999, StorageLevels.MEMORY_AND_DISK_SER);
        //增加checkpoint
        ssc.checkpoint("/home/dinpay/stream/checkpoint");
        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> wordCounts = words.mapToPair(
          new PairFunction<String, String, Integer>() {
            @Override
            public Tuple2<String, Integer> call(String s) {
              return new Tuple2<String, Integer>(s, 1);
            }
          });
        //无状态统计计算
        JavaPairDStream<String, Integer> nostat =  wordCounts.reduceByKey(new Function2<Integer, Integer, Integer>() {
            @Override
            public Integer call(Integer i1, Integer i2) {
              return i1 + i2;
            }
          });

        //有状态统计计算
        JavaPairDStream<String, Integer> stat =  wordCounts.updateStateByKey(new Function2<List<Integer>, Optional<Integer>,
                Optional<Integer>>() {
            @Override
            public Optional<Integer> call(List<Integer> values, Optional<Integer> state){
                Integer updateValue = 0;
                if(state.isPresent()){
                    updateValue = state.get();
                }
                for (Integer value : values) {
                    updateValue += value;
                }
                return Optional.of(updateValue);
            }
        });

        //窗口计算 滑动10秒 统计窗口长度是15秒
        JavaPairDStream<String, Integer> windowstat = wordCounts
                .reduceByKeyAndWindow(new Function2<Integer, Integer, Integer>() {
                      @Override
                      public Integer call(Integer i1, Integer i2) {
                        return i1 + i2;
                      }
                }, Durations.seconds(15), Durations.seconds(30));

        //nostat.print();
        //stat.print();
        windowstat.print();

        ssc.start();
        ssc.awaitTermination();
        ssc.close();
    }

}