Hadoop0.20.2 Bloom filter应用示例

1. 简介

参见《Hadoop in Action》P102 以及 《Hadoop实战（第2版）》（陆嘉恒）P69

2. 案例

网上大部分的说明仅仅是按照《Hadoop in Action》中的示例代码给出，这里是Hadoop0.20.2版本，在该版本中已经实现了BloomFilter。

案例文件如下：

customers.txt

1,Stephanie Leung,555-555-5555

2,Edward Kim,123-456-7890

3,Jose Madriz,281-330-8004

4,David Stork,408-555-0000

-----------------------------------------------------------------

orders.txt

3,A,12.95,02-Jun-2008

1,B,88.25,20-May-2008

2,C,32.00,30-Nov-2007

3,D,25.02,22-Jan-2009

5,E,34.59,05-Jan-2010

6,F,28.67,16-Jan-2008

7,G,49.82,24-Jan-2009

两个文件通过customer ID关联。

3. 代码

import java.io.BufferedReader;
import java.io.IOException;
import java.io.InputStreamReader;
import java.util.ArrayList;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FSDataInputStream;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
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.input.FileSplit;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
import org.apache.hadoop.util.bloom.BloomFilter;
import org.apache.hadoop.util.bloom.Key;
import org.apache.hadoop.util.hash.Hash;

public class BloomMRMain {
	public static class BloomMapper extends Mapper<Object, Text, Text, Text> {
		BloomFilter bloomFilter = new BloomFilter(10000, 6, Hash.MURMUR_HASH);

		protected void setup(Context context) throws IOException ,InterruptedException {
			Configuration conf = context.getConfiguration();

			String path = "hdfs://localhost:9000/user/hezhixue/input/customers.txt";
			Path file = new Path(path);

			FileSystem hdfs = FileSystem.get(conf);
			FSDataInputStream dis = hdfs.open(file);
			BufferedReader reader = new BufferedReader(new InputStreamReader(dis));
			String temp;
			while ((temp = reader.readLine()) != null) {
//				System.out.println("bloom filter temp:" + temp);
				String[] tokens = temp.split(",");
				if (tokens.length > 0) {
					bloomFilter.add(new Key(tokens[0].getBytes()));
				}
			}
		}

		protected void map(Object key, Text value, Context context) throws IOException ,InterruptedException {
			//获得文件输入路径
            String pathName = ((FileSplit) context.getInputSplit()).getPath().toString();
            if (pathName.contains("customers")) {
            	String data = value.toString();
            	String[] tokens = data.split(",");
            	if (tokens.length == 3) {
            		String outKey = tokens[0];
            		String outVal = "0" + ":" + tokens[1] + "," + tokens[2];
            		context.write(new Text(outKey), new Text(outVal));
            	}
            } else if (pathName.contains("orders")) {
            	String data = value.toString();
            	String[] tokens = data.split(",");
            	if (tokens.length == 4) {
            		String outKey = tokens[0];
            		System.out.println("in map and outKey:" + outKey);
            		if (bloomFilter.membershipTest(new Key(outKey.getBytes()))) {
            			String outVal = "1" + ":" + tokens[1] + "," + tokens[2]+ "," + tokens[3];
            			context.write(new Text(outKey), new Text(outVal));
            		}
            	}
            }
		}
	}

	public static class BloomReducer extends Reducer<Text, Text, Text, Text> {
		ArrayList<Text> leftTable = new ArrayList<Text>();
		ArrayList<Text> rightTable = new ArrayList<Text>();

		protected void reduce(Text key, Iterable<Text> values, Context context) throws IOException ,InterruptedException {

			 leftTable.clear();
	         rightTable.clear();

			for (Text val : values) {
				String outVal = val.toString();
				System.out.println("key: " + key.toString() + " : " + outVal);
				int index = outVal.indexOf(":");
				String flag = outVal.substring(0, index);
				if ("0".equals(flag)) {
					leftTable.add(new Text(outVal.substring(index+1)));
				} else if ("1".equals(flag)) {
					rightTable.add(new Text(outVal.substring(index + 1)));
				}
			}

			if (leftTable.size() > 0 && rightTable.size() > 0) {
				for(Text left : leftTable) {
					for (Text right : rightTable) {
						context.write(key, new Text(left.toString() + "," + right.toString()));
					}
				}
			}
		}
	}

	public static void main(String[] args) throws Exception {
		Configuration conf = new Configuration();

		String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();

	    if (otherArgs.length != 2) {
	      System.err.println("Usage: BloomMRMain <in> <out>");
	      System.exit(2);
	    }	    

	    Job job = new Job(conf, "BloomMRMain");
	    job.setJarByClass(BloomMRMain.class);

	    job.setMapperClass(BloomMapper.class);
	    job.setReducerClass(BloomReducer.class);

	    job.setInputFormatClass(TextInputFormat.class);
	    job.setOutputFormatClass(TextOutputFormat.class);

	    job.setMapOutputKeyClass(Text.class);
	    job.setMapOutputValueClass(Text.class);

	    job.setOutputKeyClass(Text.class);
	    job.setOutputValueClass(Text.class);	

	    FileInputFormat.addInputPath(job, new Path(otherArgs[0]));
	    FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));

	    System.exit(job.waitForCompletion(true) ? 0 : 1);
	}
}

Hadoop0.20.2 Bloom filter应用示例