HADOOP之HDFS使用idea操作MapReduce(七)

使用idea操作mapreduce。进行计算

在文章: HADOOP之HDFS用idea操作(五) 基础之上进行

引入mapred-site.xml、yarn-site.xml

因是root启动，所以需要修改hdfs-site.xml

        <property>
          <name>dfs.ha.fencing.ssh.private-key-files</name>
          <value>/root/.ssh/id_dsa</value>
        </property>

pom增加

        <!-- https://mvnrepository.com/artifact/org.apache.hadoop/hadoop-client -->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>2.6.5</version>
        </dependency>

编写类MyWordCount

package com.xiaoke.mapreduce.wc;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

public class MyWordCount {

    public static void main(String[] args) throws Exception {
        Configuration configuration = new Configuration(true);

//点击Job进去，按照example写
        Job job = Job.getInstance(configuration);


        job.setJarByClass(MyWordCount.class);

        // Specify various job-specific parameters
        job.setJobName("xiaokeke1");

        Path inputPath = new Path("/data/wc/input");
        TextInputFormat.setInputPaths(job, inputPath);

        Path outputPath = new Path("/data/wc/output");
        if (outputPath.getFileSystem(configuration).exists(outputPath))
            outputPath.getFileSystem(configuration).delete(outputPath, true);
        TextOutputFormat.setOutputPath(job, outputPath);

        job.setMapperClass(MyMapper.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);
        job.setReducerClass(MyReducer.class);

        job.waitForCompletion(true);

    }
}

MyMapper类：

package com.xiaoke.mapreduce.wc;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;
import java.util.StringTokenizer;

public class MyMapper extends Mapper<Object, Text, Text, IntWritable> {


   private final static IntWritable one = new IntWritable(1);
   private Text word = new Text();

   /*
   hello hadoop 1
   hello hadoop 2
   hello hadoop 3
    */
    public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
        StringTokenizer itr = new StringTokenizer(value.toString());
        while (itr.hasMoreTokens()) {
            word.set(itr.nextToken());
            context.write(word, one);
        }
    }

}

MyReducer类:

package com.xiaoke.mapreduce.wc;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

import java.io.IOException;

public class MyReducer extends Reducer<Text, IntWritable, Text, IntWritable> {

    private IntWritable result = new IntWritable();

    /*
    hello  1
    hello  1
    hello  1

    hadoop 1
    hadoop 1
    hadoop 1

    1      1
    2      1
    以组为单位
     */
    public void reduce(Text key, Iterable<IntWritable> values,
                       Context context) throws IOException, InterruptedException {
        int sum = 0;
        for (IntWritable val : values) {
            sum += val.get();
        }
        result.set(sum);
        context.write(key, result);
    }

}

打包:

maven ->> clean & package

上传：

   hadoop-hdfs-1.0-SNAPSHOT.jar

运行程序：需要指定包名

hadoop jar hadoop-hdfs-1.0-SNAPSHOT.jar com.xiaoke.mapreduce.wc.MyWordCount

运行结果：

查看计算结果:

hdfs dfs -cat /data/wc/output/part-r-00000

注意点: 

• 当windows环境变量修改了之后，需要重新启动idea

以上的为线上环境发布方式 

-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

本地启动需要配置:

                1，在win的系统中部署hadoop：
                    C:usrhadoop-2.6.5hadoop-2.6.5
                2，将hadoop资料中hadoop-installsoftin  文件覆盖到部署到bin目录下
                    还要将hadoop.dll  复制到  c:windwossystem32
                3，设置环境变量：HADOOP_HOME  C:usrhadoop-2.6.5hadoop-2.6.5 
                4. 重启idea

------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

本地idea集群测试方式

        //让框架知道是windows异构平台运行
        configuration.set("mapreduce.app-submission.cross-platform","true");
        //需要先打包
job.setJar("D:\code\mayun_hadoop\test\hadoop\target\hadoop-hdfs-1.0-SNAPSHOT.jar");

本地idea单机测试方式,跑的最快，hdfs和上直接有结果

1.注掉setJar
2. //让框架知道是windows异构平台运行
        configuration.set("mapreduce.app-submission.cross-platform","true");
3. configuration.set("mapreduce.framework.name", "local");

动态参数设置进conf中:

        //工具类帮我们把-D 等等的属性直接set到conf
        GenericOptionsParser parser = new GenericOptionsParser(configuration, args);  
        String[] othargs = parser.getRemainingArgs();

代码:  https://gitee.com/Xiaokeworksveryhard/big-data.git