Job执行可以分为本地执行或者集群执行。hadoop集群安装部署在远程centos系统中。使用经典的WordCount代码为例。

1. 本地执行模式（本地为MacOS环境），无需启动远程的hadoop集群，本地job会提交给本地执行器LocalJobRunner去执行。

1）输入输出数据存放在本地路径下：

首先，MapReduce代码如下：

• Mapper

package com.nasuf.hadoop.mr;import java.io.IOException;import org.apache.commons.lang.StringUtils;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;public class WCMapper extends Mapper<LongWritable, Text, Text, LongWritable> {@Overrideprotected void map(LongWritable key, Text value, Context context)throws IOException, InterruptedException {String line = value.toString();String[] words = StringUtils.split(line, " ");for (String word: words) {context.write(new Text(word), new LongWritable(1));}}}

• Reducer

package com.nasuf.hadoop.mr;import java.io.IOException;import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;public class WCReducer extends Reducer<Text, LongWritable, Text, LongWritable>{@Overrideprotected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {long count = 0;for (LongWritable value: values) {count += value.get();}context.write(key, new LongWritable(count));}}

• Runner

package com.nasuf.hadoop.mr;import java.io.IOException;import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
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;public class WCRunner {public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {Configuration conf = new Configuration();Job job = Job.getInstance(conf);// 设置整个job所用的类在哪个jar包job.setJarByClass(WCRunner.class);// 本job实用的mapper和reducer的类job.setMapperClass(WCMapper.class);job.setReducerClass(WCReducer.class);// 指定reducer的输出数据kv类型（若不指定下面mapper的输出类型，此处可以同时表明mapper和reducer的输出类型）job.setOutputKeyClass(Text.class);job.setOutputValueClass(LongWritable.class);// 指定mapper的输出数据kv类型job.setMapOutputKeyClass(Text.class);job.setMapOutputValueClass(LongWritable.class);// 指定原始数据存放位置FileInputFormat.setInputPaths(job, new Path("/Users/nasuf/Desktop/wc/srcdata"));// 处理结果的输出数据存放路径FileOutputFormat.setOutputPath(job, new Path("/Users/nasuf/Desktop/wc/output"));// 将job提交给集群运行job.waitForCompletion(true);}}

在本地模式中，可以将测试数据存放在"/Users/nasuf/Desktop/wc/srcdata"路径下，注意输出路径不能是已经存在的路径，不然会抛出异常。
2） 输入输出数据存放在hdfs中，需要启动远程的hdfs（无需启动yarn）
修改Runner代码如下：

        // 指定原始数据存放位置FileInputFormat.setInputPaths(job, new Path("hdfs://hdcluster01:9000/wc/srcdata"));// 处理结果的输出数据存放路径FileOutputFormat.setOutputPath(job, new Path("hdfs://hdcluster01:9000/wc/output1"));

如果出现如下错误：

org.apache.hadoop.security.AccessControlException: Permission denied: user=nasuf, access=WRITE, inode="/wc":parallels:supergroup:drwxr-xr-x

显然是权限问题。hadoop的用户目录是parallels，权限是rwxr-xr-x，而本地操作使用的用户是nasuf。解决方法如下：在vm启动参数中加入如下参数：-DHADOOP_USER_NAME=parallels即可。

2. 集群执行模式（首先需要启动yarn，job会提交到yarn框架中去执行。访问http://hdcluster01:8088可以查看job执行状态。）

1）使用命令直接执行jar

 hadoop jar wc.jar com.nasuf.hadoop.mr.WCRunner

查看http://hdcluster01:8088中job执行状态

2) 通过main方法直接在本地提交job到yarn集群中执行
将$HADOOP_HOME/etc/hadoop/mapred-site.xml 和 yarn-site.xml拷贝到工程的classpath下，直接执行上述代码，即可提交job到yarn集群中执行。
或者直接在代码中配置如下参数，与拷贝上述两个配置文件相同的作用：

conf.set("mapreduce.framework.name", "yarn");
conf.set("yarn.resourcemanager.hostname", "hdcluster01");
conf.set("yarn.nodemanager.aux-services", "mapreduce_shuffle");

如果出现如下错误信息：

2018-08-26 10:25:37,544 INFO  [main] mapreduce.Job (Job.java:monitorAndPrintJob(1375)) - Job job_1535213323614_0010 failed with state FAILED due to: Application application_1535213323614_0010 failed 2 times due to AM Container for appattempt_1535213323614_0010_000002 exited with  exitCode: -1000 due to: File file:/tmp/hadoop-yarn/staging/nasuf/.staging/job_1535213323614_0010/job.jar does not exist
.Failing this attempt.. Failing the application.

可以将core-site.xml配置文件同时拷贝到classpath中，或者同样配置如下参数：

conf.set("hadoop.tmp.dir", "/home/parallels/app/hadoop-2.4.1/data/");

即可解决问题。