MapReduce Tool 接口

package com.cakin.hadoop.mr;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import org.apache.hadoop.io.WritableComparable;
public class UserWritable implements WritableComparable<UserWritable> {
private Integer id;
private Integer income;
private Integer expenses;
private Integer sum;

public void write(DataOutput out) throws IOException {
// TODO Auto-generated method stub
out.writeInt(id);
out.writeInt(income);
out.writeInt(expenses);
out.writeInt(sum);
}
public void readFields(DataInput in) throws IOException {
// TODO Auto-generated method stub
this.id=in.readInt();
this.income=in.readInt();
this.expenses=in.readInt();
this.sum=in.readInt();
}

public Integer getId() {
return id;
}
public UserWritable setId(Integer id) {
this.id = id;
return this;
}
public Integer getIncome() {
return income;
}
public UserWritable setIncome(Integer income) {
this.income = income;
return this;
}
public Integer getExpenses() {
return expenses;
}
public UserWritable setExpenses(Integer expenses) {
this.expenses = expenses;
return this;
}
public Integer getSum() {
return sum;
}
public UserWritable setSum(Integer sum) {
this.sum = sum;
return this;
}
public int compareTo(UserWritable o) {
// TODO Auto-generated method stub
return this.id>o.getId()?1:-1;
}
@Override
public String toString() {
return id + "\t"+income+"\t"+expenses+"\t"+sum;
}

}

package com.cakin.hadoop.mr;
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
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.Tool;
import org.apache.hadoop.util.ToolRunner;
public class CountMapReduce2 implements Tool{
public static class CountMapper extends Mapper<LongWritable,Text,IntWritable,UserWritable>
{
private UserWritable userWritable =new UserWritable();
private IntWritable id =new IntWritable();
@Override
protected void map(LongWritable key,Text value,
Mapper<LongWritable,Text,IntWritable,UserWritable>.Context context) throws IOException, InterruptedException{
String line = value.toString();
String[] words = line.split("\t");
if(words.length ==3)
{
userWritable.setId(Integer.parseInt(words[0]))
.setIncome(Integer.parseInt(words[1]))
.setExpenses(Integer.parseInt(words[2]))
.setSum(Integer.parseInt(words[1])-Integer.parseInt(words[2]));
id.set(Integer.parseInt(words[0]));
}
context.write(id, userWritable);
}
}
public static class CountReducer extends Reducer<IntWritable,UserWritable,UserWritable,NullWritable>
{
/*
* 输入数据
* <1,{[1,1000,0,1000],[1,2000,1000,1000]}>
* <2,[2,500,300,200],[2,500,200,300]>
*
* */

private UserWritable userWritable = new UserWritable();
private NullWritable n = NullWritable.get();
protected void reduce(IntWritable key,Iterable<UserWritable> values,
Reducer<IntWritable,UserWritable,UserWritable,NullWritable>.Context context) throws IOException, InterruptedException{
Integer income=0;
Integer expenses = 0;
Integer sum =0;
for(UserWritable u:values)
{
income += u.getIncome();
expenses+=u.getExpenses();
}
sum = income - expenses;
userWritable.setId(key.get())
.setIncome(income)
.setExpenses(expenses)
.setSum(sum);
context.write(userWritable, n);
}
}
private Configuration conf;
public static void main(String[] args) throws Exception
{
int status = ToolRunner.run(new CountMapReduce2(), args);
if(status!=1){
System.err.println("任务失败------");
}
}

public void setConf(Configuration conf) {
this.conf = conf;

}
public Configuration getConf() {
return conf;
}
public int run(String[] args) throws Exception {
Configuration conf=new Configuration();
conf.setBoolean("mapreduce.job.ubertask.enable", true);
/*
* 集群中节点都有配置文件
conf.set("mapreduce.framework.name.", "yarn");
conf.set("yarn.resourcemanager.hostname", "mini1");
*/
Job job=Job.getInstance(conf,"countMR");
//jar包在哪里,现在在客户端，传递参数
//任意运行，类加载器知道这个类的路径，就可以知道jar包所在的本地路径
job.setJarByClass(CountMapReduce.class);
//指定本业务job要使用的mapper/Reducer业务类
job.setMapperClass(CountMapper.class);
job.setReducerClass(CountReducer.class);
//指定mapper输出数据的kv类型
job.setMapOutputKeyClass(IntWritable.class);
job.setMapOutputValueClass(UserWritable.class);
//指定最终输出的数据kv类型
job.setOutputKeyClass(UserWritable.class);
job.setOutputKeyClass(NullWritable.class);
//指定job的输入原始文件所在目录
FileInputFormat.setInputPaths(job, new Path(args[0]));
//指定job的输出结果所在目录
FileOutputFormat.setOutputPath(job, new Path(args[1]));
//将job中配置的相关参数及job所用的java类在的jar包，提交给yarn去运行
//提交之后，此时客户端代码就执行完毕，退出
//job.submit();
//等集群返回结果在退出
boolean res=job.waitForCompletion(true);
if(!res)
{
System.err.println("任务执行失败--------");
}
return res?1:-1;
}
}

[root@centos hadoop-2.7.4]# bin/hdfs dfs -cat /input/data
1    1000    0
2    500    300
1    2000    1000
2    500    200

[root@centos hadoop-2.7.4]# bin/yarn jar /root/jar/mapreduce2.jar /input/data /output4
17/12/21 22:11:35 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
17/12/21 22:11:36 WARN mapreduce.JobResourceUploader: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
17/12/21 22:11:38 INFO input.FileInputFormat: Total input paths to process : 1
17/12/21 22:11:38 INFO mapreduce.JobSubmitter: number of splits:1
17/12/21 22:11:39 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1513865426338_0001
17/12/21 22:11:40 INFO impl.YarnClientImpl: Submitted application application_1513865426338_0001
17/12/21 22:11:41 INFO mapreduce.Job: The url to track the job: http://centos:8088/proxy/application_1513865426338_0001/ 17/12/21 22:11:41 INFO mapreduce.Job: Running job: job_1513865426338_0001
17/12/21 22:12:07 INFO mapreduce.Job: Job job_1513865426338_0001 running in uber mode : true
17/12/21 22:12:07 INFO mapreduce.Job:  map 0% reduce 0%
17/12/21 22:12:12 INFO mapreduce.Job:  map 100% reduce 0%
17/12/21 22:12:15 INFO mapreduce.Job:  map 100% reduce 100%
17/12/21 22:12:15 INFO mapreduce.Job: Job job_1513865426338_0001 completed successfully
17/12/21 22:12:16 INFO mapreduce.Job: Counters: 52
File System Counters
FILE: Number of bytes read=220
FILE: Number of bytes written=346
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=310
HDFS: Number of bytes written=261779
HDFS: Number of read operations=35
HDFS: Number of large read operations=0
HDFS: Number of write operations=8
Job Counters
Launched map tasks=1
Launched reduce tasks=1
Other local map tasks=1
Total time spent by all maps in occupied slots (ms)=4379
Total time spent by all reduces in occupied slots (ms)=2899
TOTAL_LAUNCHED_UBERTASKS=2
NUM_UBER_SUBMAPS=1
NUM_UBER_SUBREDUCES=1
Total time spent by all map tasks (ms)=4379
Total time spent by all reduce tasks (ms)=2899
Total vcore-milliseconds taken by all map tasks=4379
Total vcore-milliseconds taken by all reduce tasks=2899
Total megabyte-milliseconds taken by all map tasks=4484096
Total megabyte-milliseconds taken by all reduce tasks=2968576
Map-Reduce Framework
Map input records=4
Map output records=4
Map output bytes=80
Map output materialized bytes=94
Input split bytes=94
Combine input records=0
Combine output records=0
Reduce input groups=2
Reduce shuffle bytes=94
Reduce input records=4
Reduce output records=2
Spilled Records=8
Shuffled Maps =1
Failed Shuffles=0
Merged Map outputs=1
GC time elapsed (ms)=103
CPU time spent (ms)=1630
Physical memory (bytes) snapshot=519172096
Virtual memory (bytes) snapshot=6041153536
Total committed heap usage (bytes)=273686528
Shuffle Errors
BAD_ID=0
CONNECTION=0
IO_ERROR=0
WRONG_LENGTH=0
WRONG_MAP=0
WRONG_REDUCE=0
File Input Format Counters
Bytes Read=41
File Output Format Counters
Bytes Written=32

[root@centos hadoop-2.7.4]# bin/hdfs dfs -cat /output4/part-r-00000
1    3000    1000    2000
2    1000    500    500