7.Spark Streaming：输入DStream之基础数据源以及基于HDFS的实时wordcount程序

输入DStream之基础数据源

HDFS文件

基于HDFS文件的实时计算，其实就是，监控一个HDFS目录，只要其中有新文件出现，就实时处理。相当于处理实时的文件流。

streamingContext.fileStream<KeyClass, ValueClass, InputFormatClass>(dataDirectory)streamingContext.fileStream[KeyClass, ValueClass, InputFormatClass](dataDirectory)

Spark Streaming会监视指定的HDFS目录，并且处理出现在目录中的文件。要注意的是，所有放入HDFS目录中的文件，都必须有相同的格式；必须使用移动或者重命名的方式，将文件移入目录；一旦处理之后，文件的内容即使改变，也不会再处理了；基于HDFS文件的数据源是没有Receiver的，因此不会占用一个cpu core。

 

java版本

package cn.spark.study.streaming;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;@Overridepublic 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;@Overridepublic 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;@Overridepublic Integer call(Integer v1, Integer v2) throws Exception {return v1 + v2;}});wordCounts.print();jssc.start();jssc.awaitTermination();jssc.close();}}

scala版本

package cn.spark.study.streaming import org.apache.spark.SparkConfimport org.apache.spark.streaming.StreamingContextimport org.apache.spark.streaming.Seconds /** * @author Administrator */object HDFSWordCount {    def main(args: Array[String]): Unit = {    val conf = new SparkConf()        .setMaster("local[2]")          .setAppName("HDFSWordCount")    val ssc = new StreamingContext(conf, Seconds(5))        val lines = ssc.textFileStream("hdfs://spark1:9000/wordcount_dir")      val words = lines.flatMap { _.split(" ") }      val pairs = words.map { word => (word, 1) }      val wordCounts = pairs.reduceByKey(_ + _)          wordCounts.print()          ssc.start()    ssc.awaitTermination()  }  }

运行步骤：

打包，上传到linux中；编写spark-submit脚本；运行脚本；上传文件到hdfs://spark1:9000/wordcount_dir/下。

hadoop fs -put t1.txt /wordcount_dir/tt1.txt

 

 

运行结果：