hadoop多文件格式输入

时间：2023-03-08 23:11:27
hadoop多文件格式输入

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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