Java开发的mapreduce如何在hadoop中运行

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最近在学习hadoop，安装的版本是hadoop2.7.3。

思考着如何把编写好的mapreduce内容部署到hadoop中并运行这个程序，下面记录了这部分实践内容。上面代码打包 hadoop-test.jar，打包方式任选。

package com.ksy.hadoop;import java.io.IOException;import java.util.StringTokenizer;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.Mapper;import org.apache.hadoop.mapreduce.Reducer;import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;import org.apache.hadoop.util.GenericOptionsParser;/** * 该例子为网上经典例子统计单词出现次数 * */public class WordCount {    public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable> {        private final static IntWritable one = new IntWritable(1);        private Text word = new Text();        /**         * key 偏移量包括了回车所占的字符数(Windows和Linux环境会不同)         * value 一行数据         * context存储新Map的对象         */        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);            }        }    }    public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable> {        private IntWritable result = new IntWritable();        /**         * key 为Map中的key，hadoop会把相同key的内容合并为一个list，该list就为values。         * context为存放结果的对象         */        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);        }    }    public static void main(String[] args) throws Exception {        Configuration conf = new Configuration();        String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();        if (otherArgs.length != 2) {            System.err.println("Usage: wordcount <in> <out>");            System.exit(2);        }        Job job = new Job(conf, "word count");        job.setJarByClass(WordCount.class);        job.setMapperClass(TokenizerMapper.class);        job.setCombinerClass(IntSumReducer.class);        job.setReducerClass(IntSumReducer.class);        job.setOutputKeyClass(Text.class);        job.setOutputValueClass(IntWritable.class);        FileInputFormat.addInputPath(job, new Path(otherArgs[0]));        FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));        System.exit(job.waitForCompletion(true) ? 0 : 1);    }}

上传包到部署有hadoop的机器上，本例子上传到/home/hadoop目录。
用工具putty/SecureCRT登录到系统，进入hadoop/bin目录下。
运行命令./hadoop jar ~/hadoop-test.jar com.ksy.hadoop.WordCount /user/hadoopfile output，这样就把该例子运行了，通过./hdfs dfs -ls /user/hadoop/output/可以查看到运行后生成了两个文件
```
hadoop@ubuntu-114:/usr/local/hadoop/bin$ ./hdfs dfs -ls /user/hadoop/output/Found 2 items-rw-r--r--   1 hadoop supergroup          0 2017-07-25 19:00 /user/hadoop/output/_SUCCESS-rw-r--r--   1 hadoop supergroup      57649 2017-07-25 19:00 /user/hadoop/output/part-r-00000
```
其中/user/hadoopfile是需要分析的hdfs文件，该文件可以通过shell命令上传到hdfs中，output是输出目录。

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