52、Spark Streaming之输入DStream之基础数据源以及基于HDFS的实时wordcount程序

一、概述

1、Socket：之前的wordcount例子，已经演示过了，StreamingContext.socketTextStream()

2、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。

二、代码实现

1、java实现

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;

public class HDFSWordCount {
    public static void main(String[] args) {
        SparkConf conf = new SparkConf()
                .setMaster("local[2]")
                .setAppName("WordCount");  

        JavaStreamingContext jssc = new JavaStreamingContext(conf, Durations.seconds(3));

        // 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();
    }
}

###运行脚本
[root@spark1 streaming]# cat hdfswordcount.sh
/usr/local/spark-1.5.1-bin-hadoop2.4/bin/spark-submit \
--class cn.spark.study.streaming.HDFSWordCount \
--num-executors 3 \
--driver-memory 100m \
--executor-memory 100m \
--executor-cores 3 \
--files /usr/local/hive/conf/hive-site.xml \
--driver-class-path /usr/local/hive/lib/mysql-connector-java-5.1.17.jar \
/usr/local/spark-study/java/streaming/saprk-study-java-0.0.1-SNAPSHOT-jar-with-dependencies.jar \

##此时打包上传，启动运行脚本，他就会一直监视hdfs的指定目录

##把准备好的文件上传到hdfs，程序会马上读取到，并统计出来
hdfs dfs -mkdir /wordcount_dir
hdfs dfs -put t1.txt /wordcount_dir/t1.txt

2、scala实现

package cn.spark.study.streaming

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

object HDFSWordCount {
  def main(args: Array[String]): Unit = {
    val conf = new SparkConf().setMaster("local[2]").setAppName("HDFSWordCount")
    val ssc = new StreamingContext(conf, Seconds(3))

    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()

  }
}

##运行脚本
[root@spark1 streaming]# cat hdfswordcount.sh
/usr/local/spark-1.5.1-bin-hadoop2.4/bin/spark-submit \
--class cn.spark.study.streaming.HDFSWordCount \
--num-executors 3 \
--driver-memory 100m \
--executor-memory 100m \
--executor-cores 3 \
--files /usr/local/hive/conf/hive-site.xml \
--driver-class-path /usr/local/hive/lib/mysql-connector-java-5.1.17.jar \
/usr/local/spark-study/scala/streaming/spark-study-scala.jar \

##打包--上传，运行脚本

##程序会监控着hdfs目录，此时上传一个新文件到hdfs，程序会马上读取到并统计出来
hdfs dfs -put t2.txt /wordcount_dir/t2.txt