Hadoop工作原理图-WordCount示例

一个Mapper对应一个碎片段。

import org.apache.hadoop.io.IntWritable;import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Mapper;import org.apache.hadoop.util.StringUtils;import java.io.IOException;/** * author: test * date: 2015/1/25. */public class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable> {    /**     * 输入：     * 行所在的下标为key，类型为LongWritable     * 行的内容为value，类型为Text     *     * 输出：     * key: Text     * value: IntWritable     */    //此方法循环调用，从文件的split中，读取每行调用一次，把该行所在的下标为key，以该行的值（内容）为value，    protected void map(LongWritable key, Text value,                       Context context) throws IOException, InterruptedException {        String[] words = StringUtils.split(value.toString(), ' ');        for (String word : words) {            context.write(new Text(word), new IntWritable(1));        }    }}

import org.apache.hadoop.io.IntWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Reducer;import java.io.IOException;/** * author: test * date: 2015/1/25. */public class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable> {    /**     * 此方法循环调用，每组调用一次     * 这组的特点是：key相同，value可能有多个     */    protected void reduce(Text key, Iterable<IntWritable> values, Context context)            throws IOException, InterruptedException {        int sum = 0;        for (IntWritable value : values) {            sum += value.get();        }        context.write(new Text(key), new IntWritable(sum));    }}

import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.fs.FileSystem;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.FileInputFormat;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;/** * author: test * date: 2015/1/25. */public class RunJob {    public static void main(String[] args) {        Configuration conf = new Configuration();//装在src或者classPath下的所有配置文件        try {            Job job = Job.getInstance();            job.setJarByClass(RunJob.class);            job.setJobName("WordCount");            job.setMapperClass(WordCountMapper.class);            job.setReducerClass(WordCountReducer.class);            job.setMapOutputKeyClass(Text.class);            job.setMapOutputValueClass(IntWritable.class);            FileSystem fs = FileSystem.get(conf);            FileInputFormat.addInputPath(job, new Path("D:/hadoop/input/input"));            Path output = new Path("D:/hadoop/output/wc");            if (fs.exists(output)) {                fs.delete(output, true);//递归删除            }            FileOutputFormat.setOutputPath(job, output);            if (job.waitForCompletion(true)) {                System.out.println("Job Done!");            }        } catch (Exception e) {            e.printStackTrace();        }    }}

执行：
1.打jar包，名字为wc.jar

2.hadoop jar wc.jar com.xxx.RunJob(入口类)

how to kill a MapReduce job

Depending on the version, do:

version <2.3.0

Kill a hadoop job:

hadoop job -kill $jobId

You can get a list of all jobId's doing:

hadoop job -list

---

version >=2.3.0

Kill a hadoop job:

yarn application -kill $ApplicationId

You can get a list of all ApplicationId's doing:

yarn application -list

hadoop与job相关的命令：
1.查看 Job 信息：
hadoop job -list 
2.杀掉 Job： 
hadoop  job –kill  job_id
3.指定路径下查看历史日志汇总：
hadoop job -history output-dir 
4.作业的更多细节： 
hadoop job -history all output-dir 
5.打印map和reduce完成百分比和所有计数器：
hadoop job –status job_id 
6.杀死任务。被杀死的任务不会不利于失败尝试：
hadoop jab -kill-task <task-id> 
7.使任务失败。被失败的任务会对失败尝试不利：
hadoop job  -fail-task <task-id>