hadoop 之 MultipleInputs--为多个输入指定不同的InputFormat和Mapper

MultipleInputs 介绍

默认情况下，MapReduce作业的输入可以包含多个输入文件，但是所有的文件都由同一个InputFormat 和 同一个Mapper 来处理，这是的多个文件应该是格式相同，内容可以使用同一个Mapper处理。

但是，有可能这多个文件的数据格式不同，这是使用同一个Mapper来处理就显得不合适了。

对于上述问题，MultipleInputs可以妥善处理，他允许对每条输入路径指定InputFormat和Mapper。

对于Reducer来说，是聚合后的map输出，并不知道是由不同的mapper产生的。

实例

1.要处理的文件：

trade_info1.txt

zhangsan@163.com    6000    0   2014-02-20
lisi@163.com    2000    0   2014-02-20
lisi@163.com    0   100 2014-02-20
zhangsan@163.com    3000    0   2014-02-20
wangwu@126.com  9000    0   2014-02-20
wangwu@126.com  0   200     2014-02-20

trade_info.txt

zhangsan@163.com,6000,0,2014-02-20
lisi@163.com,2000,0,2014-02-20
lisi@163.com,0,100,2014-02-20
zhangsan@163.com,3000,0,2014-02-20
wangwu@126.com,9000,0,2014-02-20
wangwu@126.com,0,200,2014-02-20

2.代码：

处理多个不同输入的重要代码

MultipleInputs.addInputPath(job, new Path(args[0]), TextInputFormat.class, SumStepByToolMapper.class);
MultipleInputs.addInputPath(job, new Path(args[1]), TextInputFormat.class, SumStepByToolWithCommaMapper.class);

两个不同的Mapper在针对每行记录时，使用了不同的分隔符将记录分成不同的内容，这是两个Mapper唯一的不同。

String line = value.toString();
String[] fields = line.split("\t");

String line = value.toString();
String[] fields = line.split(",");

package mapreduce.mr;

import java.io.IOException;
import java.util.HashMap;
import java.util.Map;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
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.Mapper;
import org.apache.hadoop.mapreduce.Partitioner;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.MultipleInputs;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

import mapreduce.bean.InfoBeanMy;

public class SumStepByTool extends Configured implements Tool{

public static class SumStepByToolMapper extends Mapper<LongWritable, Text, Text, InfoBeanMy>{

private InfoBeanMy outBean = new InfoBeanMy();
private Text k = new Text();

@Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException{

String line = value.toString();
String[] fields = line.split("\t");

String account = fields[0];
double income = Double.parseDouble(fields[1]);
double expense = Double.parseDouble(fields[2]);

outBean.setFields(account, income, expense);
k.set(account);

context.write(k, outBean);
}
}

public static class SumStepByToolWithCommaMapper extends Mapper<LongWritable, Text, Text, InfoBeanMy>{

private InfoBeanMy outBean = new InfoBeanMy();
private Text k = new Text();

@Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException{

String line = value.toString();
String[] fields = line.split(",");

String account = fields[0];
double income = Double.parseDouble(fields[1]);
double expense = Double.parseDouble(fields[2]);

outBean.setFields(account, income, expense);
k.set(account);

context.write(k, outBean);
}
}

public static class SumStepByToolReducer extends Reducer<Text, InfoBeanMy, Text, InfoBeanMy>{

private InfoBeanMy outBean = new InfoBeanMy();
@Override
protected void reduce(Text key, Iterable<InfoBeanMy> values, Context context) throws IOException, InterruptedException{
double income_sum = 0;
double expense_sum = 0;

for(InfoBeanMy infoBeanMy : values)
{
income_sum += infoBeanMy.getIncome();
expense_sum += infoBeanMy.getExpense();
}
outBean.setFields("", income_sum, expense_sum);
context.write(key, outBean);
}

}

public static class SumStepByToolPartitioner extends Partitioner<Text, InfoBeanMy>{

private static Map<String, Integer> accountMap = new HashMap<String, Integer>();

static {
accountMap.put("zhangsan", 1);
accountMap.put("lisi", 2);
accountMap.put("wangwu", 3);
}

@Override
public int getPartition(Text key, InfoBeanMy value, int numPartitions) {
String keyString = key.toString();
String name = keyString.substring(0, keyString.indexOf("@"));
Integer part = accountMap.get(name);
if (part == null )
{
part = 0;
}
return part;
}

}

public int run(String[] args) throws Exception {
Configuration conf = getConf();
//conf.setInt("mapreduce.input.lineinputformat.linespermap", 2);
Job job = Job.getInstance(conf);
job.setJarByClass(this.getClass());
job.setJobName("SumStepByTool");

//job.setInputFormatClass(TextInputFormat.class); //这个是默认的输入格式
//job.setInputFormatClass(KeyValueTextInputFormat.class); //这个把一行记录的第一个区域当做key，其他区域作为value
//job.setInputFormatClass(NLineInputFormat.class);

//      job.setMapperClass(SumStepByToolMapper.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(InfoBeanMy.class);

job.setReducerClass(SumStepByToolReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(InfoBeanMy.class);
job.setNumReduceTasks(3);

MultipleInputs.addInputPath(job, new Path(args[0]), TextInputFormat.class, SumStepByToolMapper.class);
MultipleInputs.addInputPath(job, new Path(args[1]), TextInputFormat.class, SumStepByToolWithCommaMapper.class);
//      FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[2]));

return job.waitForCompletion(true) ? 0:-1;
}

public static void main(String[] args) throws Exception {
int exitCode = ToolRunner.run(new SumStepByTool(),args);
System.exit(exitCode);
}
}

运行时：

有三个参数，前两个为输入路径，最后一个为输出路径，

[root@hadoop1 tmp]# hadoop jar sortscore.jar mapreduce.mr.SumStepByTool /tradeinfoIn/trade_info1.txt /tradeinfoIn/trade_info.txt /tradeinfoOut/

注意

没有使用MultipleInputs时，是使用FileInputFormat来指定输入路径的，时候后，MultipleInputs替代了其工作，但是仍用FileOutputFormat指定输出路径；