Spark之Streaming实时监听Hdfs文件目录

应用场景：我们使用Streaming实时监听指定的Hdfs目录，当该目录有新的文件增加会读取它，并完成单词计数的操作。
这里和上一篇的差别就是：上一篇用的是socketTextStream而这里用的是：textFileStream。
其他没有不同。
代码展示：

import java.util.Arrays;
import org.apache.spark.SparkConf;
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.JavaStreamingContext;
import scala.Tuple2;
public class SparkStreamDfs002 {
    public static void main(String[] args) {
        SparkConf conf = new SparkConf().setMaster("local[4]").setAppName("NetworkWordCount").set("spark.testing.memory",
                "2147480000");
        JavaStreamingContext jssc = new JavaStreamingContext(conf, Durations.seconds(1));
        System.out.println(jssc);
        //创建监听文件流
        JavaDStream lines=jssc.textFileStream("hdfs://192.168.61.128:9000/sparkStreaminput001/");

        JavaDStream words = lines.flatMap(new FlatMapFunction() {
            public Iterable call(String x) {
                System.out.println(Arrays.asList(x.split(" ")).get(0));
                return Arrays.asList(x.split(" "));
            }
        });

        JavaPairDStream pairs = words.mapToPair(new PairFunction() {
            public Tuple2 call(String s) {
                return new Tuple2(s, 1);
           }
        });

        JavaPairDStream wordCounts = pairs.reduceByKey(new Function2() {
            public Integer call(Integer i1, Integer i2) {
                return i1 + i2;
            }
        });

        wordCounts.print();

        wordCounts.dstream().saveAsTextFiles("hdfs://192.168.61.128:9000/sparkStream001/wordCount/", "spark");

        jssc.start(); 
        jssc.awaitTermination();



    }

}