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;

@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();
}
}
scala版本

package cn.spark.study.streaming

import org.apache.spark.SparkConf
import org.apache.spark.streaming.StreamingContext
import 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：