输入DStream之基础数据源

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;

/**
* 基于HDFS文件的实时wordcount程序
* @author Administrator
*
*/
public class HDFSWordCount {

​public static void main(String[] args) {
​​SparkConf conf = new SparkConf()​​​​.setMaster("local[2]")​​​​.setAppName("HDFSWordCount");
JavaStreamingContext jssc = new JavaStreamingContext(conf, Durations.seconds(5));
​​// 首先，使用JavaStreamingContext的textFileStream()方法，针对HDFS目录创建输入数据流
​​JavaDStream<String> lines = jssc.textFileStream("hdfs://spark1:9000/wordcount_dir");
​​​​// 执行wordcount操作
​​JavaDStream<String> words = lines.flatMap(new FlatMapFunction<String, String>() {

​​​private static final long serialVersionUID = 1L;

​​​@Override
​​​public Iterable<String> call(String line) throws Exception {
​​​​return Arrays.asList(line.split(" "));
​​​}
​​});
JavaPairDStream<String, Integer> pairs = words.mapToPair(
new PairFunction<String, String, Integer>() {
​​​​​private static final long serialVersionUID = 1L;

​​​​​@Override
​​​​​public Tuple2<String, Integer> call(String word) ​​​​​​​throws Exception {
​​​​​​return new Tuple2<String, Integer>(word, 1);
​​​​​}
​​​​});
​​JavaPairDStream<String, Integer> wordCounts = pairs.reduceByKey(

​​​​new Function2<Integer, Integer, Integer>() {

private static final long serialVersionUID = 1L;

@Override
​​​​​public Integer call(Integer v1, Integer v2) throws Exception {
​​​​​​return v1 + v2;
​​​​​}​​​​
​​​​});
wordCounts.print();
​​jssc.start();
​​jssc.awaitTermination();
​​jssc.close();
​}
}