hadoop多文件格式输入「建议收藏」

版本号：

CDH5.0.0 （hdfs:2.3。mapreduce：2.3，yarn：2.3）

hadoop多文件格式输入，一般能够使用MultipleInputs类指定不同的输入文件路径以及输入文件格式。

比方如今有例如以下的需求：

现有两份数据：

phone：

123,good number
124,common number
125,bad number

user：

zhangsan,123
lisi,124
wangwu,125

如今须要把user和phone依照phone number连接起来，得到以下的结果：

zhangsan,123,good number
lisi,124,common number
wangwu,125,bad number

那么就能够使用MultipleInputs来操作，这里把user和phone上传到hdfs文件夹中，各自是/multiple/user/user , /multiple/phone/phone。

设计的MultipleDriver例如以下：

package multiple.input;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
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 org.slf4j.Logger;
//import org.slf4j.LoggerFactory;
/**
 * input1(/multiple/user/user):
 * username,user_phone
 *  
 * input2(/multiple/phone/phone):
 *  user_phone,description 
 *  
 * output: username,user_phone,description
 * 
 * @author fansy
 *
 */
public class MultipleDriver extends Configured implements Tool{
//	private  Logger log = LoggerFactory.getLogger(MultipleDriver.class);
	
	private String input1=null;
	private String input2=null;
	private String output=null;
	private String delimiter=null;
	
	public static void main(String[] args) throws Exception {
		Configuration conf=new Configuration();
//		conf.set("fs.defaultFS", "hdfs://node33:8020");  
//        conf.set("mapreduce.framework.name", "yarn");  
//        conf.set("yarn.resourcemanager.address", "node33:8032"); 
        
		ToolRunner.run(conf, new MultipleDriver(), args);
	}

	@Override
	public int run(String[] arg0) throws Exception {
		configureArgs(arg0);
		checkArgs();
		
		Configuration conf= getConf();
		conf.set("delimiter", delimiter);
		 @SuppressWarnings("deprecation")
		Job job = new Job(conf, "merge user and phone information ");
        job.setJarByClass(MultipleDriver.class);

        job.setReducerClass(MultipleReducer.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(FlagStringDataType.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(NullWritable.class);
        
        job.setNumReduceTasks(1);
        MultipleInputs.addInputPath(job, new Path(input1), TextInputFormat.class, Multiple1Mapper.class);
        MultipleInputs.addInputPath(job, new Path(input2), TextInputFormat.class, Multiple2Mapper.class);
        FileOutputFormat.setOutputPath(job, new Path(output));
        
        int res = job.waitForCompletion(true) ? 0 : 1;
        return res;
	}
	

	/**
	 * check the args 
	 */
	private void checkArgs() {
		if(input1==null||"".equals(input1)){
			System.out.println("no user input...");
			printUsage();
			System.exit(-1);
		}
		if(input2==null||"".equals(input2)){
			System.out.println("no phone input...");
			printUsage();
			System.exit(-1);
		}
		if(output==null||"".equals(output)){
			System.out.println("no output...");
			printUsage();
			System.exit(-1);
		}
		if(delimiter==null||"".equals(delimiter)){
			System.out.println("no delimiter...");
			printUsage();
			System.exit(-1);
		}
	
	}

	/**
	 * configuration the args
	 * @param args
	 */
	private void configureArgs(String[] args) {
    	for(int i=0;i<args.length;i++){
    		if("-i1".equals(args[i])){
    			input1=args[++i];
    		}
    		if("-i2".equals(args[i])){
    			input2=args[++i];
    		}
    		
    		if("-o".equals(args[i])){
    			output=args[++i];
    		}
    		
    		if("-delimiter".equals(args[i])){
    			delimiter=args[++i];
    		}
    		
    	}
	}
	public static void printUsage(){
    	System.err.println("Usage:");
    	System.err.println("-i1 input \t user data path.");
    	System.err.println("-i2 input \t phone data path.");
    	System.err.println("-o output \t output data path.");
    	System.err.println("-delimiter  data delimiter , default is comma  .");
    }
}

这里指定两个mapper和一个reducer，两个mapper分别相应处理user和phone的数据，分别例如以下：

mapper1（处理user数据）：

package multiple.input;

import java.io.IOException;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
/**
 * input :
 * username,phone
 * 
 * output:
 * <key,value>  --> <[phone],[0,username]>
 * @author fansy
 *
 */
public class Multiple1Mapper extends Mapper<LongWritable,Text,Text,FlagStringDataType>{
	private  Logger log = LoggerFactory.getLogger(Multiple1Mapper.class);
	private String delimiter=null; // default is comma
	@Override
	public void setup(Context cxt){
		delimiter= cxt.getConfiguration().get("delimiter", ",");
		log.info("This is the begin of Multiple1Mapper");
	} 
	
	@Override
	public void map(LongWritable key,Text value,Context cxt) throws IOException,InterruptedException{
		String info= new String(value.getBytes(),"UTF-8");
		String[] values = info.split(delimiter);
		if(values.length!=2){
			return;
		}
		log.info("key-->"+values[1]+"=========value-->"+"[0,"+values[0]+"]");
		cxt.write(new Text(values[1]), new FlagStringDataType(0,values[0]));
	}
}

mapper2（处理phone数据）：

package multiple.input;

import java.io.IOException;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
/**
 * input :
 * phone,description
 * 
 * output:
 * <key,value>  --> <[phone],[1,description]>
 * @author fansy
 *
 */
public class Multiple2Mapper extends Mapper<LongWritable,Text,Text,FlagStringDataType>{
	private  Logger log = LoggerFactory.getLogger(Multiple2Mapper.class);
	private String delimiter=null; // default is comma
	@Override
	public void setup(Context cxt){
		delimiter= cxt.getConfiguration().get("delimiter", ",");
		log.info("This is the begin of Multiple2Mapper");
	} 
	
	@Override
	public void map(LongWritable key,Text value,Context cxt) throws IOException,InterruptedException{
		String[] values= value.toString().split(delimiter);
		if(values.length!=2){
			return;
		}
		log.info("key-->"+values[0]+"=========value-->"+"[1,"+values[1]+"]");
		cxt.write(new Text(values[0]), new FlagStringDataType(1,values[1]));
	}
}

这里的FlagStringDataType是自己定义的：

package multiple.input;

import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;

import org.apache.hadoop.io.WritableComparable;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import com.google.common.primitives.Ints;

public class FlagStringDataType implements WritableComparable<FlagStringDataType> {
	private  Logger log = LoggerFactory.getLogger(FlagStringDataType.class);
  private String value;
  private int flag;
  public FlagStringDataType() {
  }

  public FlagStringDataType(int flag,String value) {
    this.value = value;
    this.flag=flag;
  }

  public String get() {
    return value;
  }

  public void set(String value) {
    this.value = value;
  }

  @Override
  public boolean equals(Object other) {
    return other != null && getClass().equals(other.getClass()) 
    		&& ((FlagStringDataType) other).get() == value
    		&&((FlagStringDataType) other).getFlag()==flag;
  }

  @Override
  public int hashCode() {
    return Ints.hashCode(flag)+value.hashCode();
  }

  @Override
  public int compareTo(FlagStringDataType other) {
	 
    if (flag >= other.flag) {
      if (flag > other.flag) {
        return 1;
      }
    } else {
      return -1;
    }
    return value.compareTo(other.value);
  }

  @Override
  public void write(DataOutput out) throws IOException {
	log.info("in write()::"+"flag:"+flag+",vlaue:"+value);
    out.writeInt(flag);
    out.writeUTF(value);
  }

  @Override
  public void readFields(DataInput in) throws IOException {
	  log.info("in read()::"+"flag:"+flag+",vlaue:"+value);
	  flag=in.readInt();
	  value = in.readUTF();
	  log.info("in read()::"+"flag:"+flag+",vlaue:"+value);
  }

public int getFlag() {
	return flag;
}

public void setFlag(int flag) {
	this.flag = flag;
}

public String toString(){
	return flag+":"+value;
}

}

这个自己定义类，使用一个flag来指定是哪个数据。而value则相应是其值。

这样做的优点是在reduce端能够依据flag的值来推断其输出位置。这样的设计方式能够对多种输入的整合有非常大帮助，在mahout中也能够看到这样的设计。

reducer（汇总输出数据）：

package multiple.input;

import java.io.IOException;

import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

public class MultipleReducer extends Reducer<Text,FlagStringDataType,Text,NullWritable>{
	private  Logger log = LoggerFactory.getLogger(MultipleReducer.class);
	private String delimiter=null; // default is comma
	@Override
	public void setup(Context cxt){
		delimiter= cxt.getConfiguration().get("delimiter", ",");
	} 
	@Override
	public void reduce(Text key, Iterable<FlagStringDataType> values,Context cxt) throws IOException,InterruptedException{
		log.info("================");
		log.info("         =======");
		log.info("              ==");
		String[] value= new String[3];
		value[2]=key.toString();
		for(FlagStringDataType v:values){
			int index= v.getFlag();
			log.info("index:"+index+"-->value:"+v.get());
			value[index]= v.get();
		}
		log.info("              ==");
		log.info("         =======");
		log.info("================");
		cxt.write(new Text(value[2]+delimiter+value[0]+delimiter+value[1]),NullWritable.get());
	}
}

这样设计的优点是，能够针对不同的输入数据採取不同的逻辑处理。并且不同的输入数据能够是序列文件的格式。

以下介绍一种方式和上面的比。略有不足。可是能够借鉴。

首先是Driver：

package multiple.input;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.NullWritable;
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;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
//import org.slf4j.Logger;
//import org.slf4j.LoggerFactory;
/**
 * input1(/multiple/user/user):
 * username,user_phone
 *  
 * input2(/multiple/phone/phone):
 *  user_phone,description 
 *  
 * output: username,user_phone,description
 * 
 * @author fansy
 *
 */
public class MultipleDriver2 extends Configured implements Tool{
//	private  Logger log = LoggerFactory.getLogger(MultipleDriver.class);
	
	private String input1=null;
	private String input2=null;
	private String output=null;
	private String delimiter=null;
	
	public static void main(String[] args) throws Exception {
		Configuration conf=new Configuration();
//		conf.set("fs.defaultFS", "hdfs://node33:8020");  
//        conf.set("mapreduce.framework.name", "yarn");  
//        conf.set("yarn.resourcemanager.address", "node33:8032"); 
        
		ToolRunner.run(conf, new MultipleDriver2(), args);
	}

	@Override
	public int run(String[] arg0) throws Exception {
		configureArgs(arg0);
		checkArgs();
		
		Configuration conf= getConf();
		conf.set("delimiter", delimiter);
		 @SuppressWarnings("deprecation")
		Job job = new Job(conf, "merge user and phone information ");
        job.setJarByClass(MultipleDriver2.class);
        job.setMapperClass(MultipleMapper.class);
        job.setReducerClass(MultipleReducer.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(FlagStringDataType.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(NullWritable.class);
        
        job.setNumReduceTasks(1);
        FileInputFormat.addInputPath(job, new Path(input1));
        FileInputFormat.addInputPath(job, new Path(input2));
        FileOutputFormat.setOutputPath(job, new Path(output));
        
        int res = job.waitForCompletion(true) ? 0 : 1;
        return res;
	}
	

	/**
	 * check the args 
	 */
	private void checkArgs() {
		if(input1==null||"".equals(input1)){
			System.out.println("no user input...");
			printUsage();
			System.exit(-1);
		}
		if(input2==null||"".equals(input2)){
			System.out.println("no phone input...");
			printUsage();
			System.exit(-1);
		}
		if(output==null||"".equals(output)){
			System.out.println("no output...");
			printUsage();
			System.exit(-1);
		}
		if(delimiter==null||"".equals(delimiter)){
			System.out.println("no delimiter...");
			printUsage();
			System.exit(-1);
		}
	
	}

	/**
	 * configuration the args
	 * @param args
	 */
	private void configureArgs(String[] args) {
    	for(int i=0;i<args.length;i++){
    		if("-i1".equals(args[i])){
    			input1=args[++i];
    		}
    		if("-i2".equals(args[i])){
    			input2=args[++i];
    		}
    		
    		if("-o".equals(args[i])){
    			output=args[++i];
    		}
    		
    		if("-delimiter".equals(args[i])){
    			delimiter=args[++i];
    		}
    		
    	}
	}
	public static void printUsage(){
    	System.err.println("Usage:");
    	System.err.println("-i1 input \t user data path.");
    	System.err.println("-i2 input \t phone data path.");
    	System.err.println("-o output \t output data path.");
    	System.err.println("-delimiter  data delimiter , default is comma  .");
    }
}

这里加入路径直接使用FileInputFormat加入输入路径，这样的话，针对不同的输入数据的不同业务逻辑能够在mapper中先推断眼下正在处理的是那个数据。然后依据其路径来进行相应的业务逻辑处理：

package multiple.input;

import java.io.IOException;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.InputSplit;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.input.FileSplit;
/**
 * input1 :
 * username,phone
 * 
 * input2
 * phone,description
 * 
 * output:
 * <key,value>  --> <[phone],[0,username]>
 * <key,value>  --> <[phone],[1,description]>
 * @author fansy
 *
 */
public class MultipleMapper extends Mapper<LongWritable,Text,Text,FlagStringDataType>{
	
	private String delimiter=null; // default is comma
	private boolean flag=false;
	@Override
	public void setup(Context cxt){
		delimiter= cxt.getConfiguration().get("delimiter", ",");
		InputSplit input=cxt.getInputSplit();  
	    String filename=((FileSplit) input).getPath().getParent().getName();
	    if("user".equals(filename)){
	    	flag=true;
	    }
	} 
	
	@Override
	public void map(LongWritable key,Text value,Context cxt) throws IOException,InterruptedException{
		String[] values= value.toString().split(delimiter);
		if(values.length!=2){
			return;
		}
		if(flag){
			cxt.write(new Text(values[1]), new FlagStringDataType(0,values[0]));
		}else{
			cxt.write(new Text(values[0]), new FlagStringDataType(1,values[1]));
		}
	}
}

整体来说。这样的处理方式事实上是不如第一种的，在每一个map函数中都须要进行推断。比第一种多了非常多操作；同一时候。针对不同的序列文件，这样的方式处理不了（Key、value的类型不一样的情况下）。

所以针对多文件格式的输入，不妨使用第一种方式。

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