hadoop實現自訂的資料類型

hadoop實現自訂的HTTP://www.aliyun.com/zixun/aggregation/18278.html">資料類型 博客分類： hadoop hadoopmrunit自訂資料類型 

關於自訂資料類型，HTTP://book.douban.com/annotation/17067489/ 一文中給出了一個比較清晰的說明和解釋。

以wordCount為例子

定義自己的資料類型Http類

import java.io.DataInput;import java.io.DataOutput;import java.io.IOException;import org.apache.hadoop.io.WritableComparable;public class Http implements WritableComparable<Http>{ public Http(){ } private String value; public Http(String value) { setValue(value); } public String getValue() { return value; } public void setValue(String valu e) { this.value = value; } public void readFields(DataInput in) throws IOException { value = in.readUTF(); } public void w rite(DataOutput out) throws IOException { out.writeUTF(value); } public int compareTo(Http HTTP) { return (value.compareTo (HTTP.value)); } @Override public int hashCode() { final int prime = 31; int result = 1; result = prime * result + ((value == null) ? 0 : value.hashCode()); return result; } @Override public boolean equals(Object obj) { if (!( obj instanceof Http)) return false; Http other = (Http)obj; return this.value.equals(other.value); } @Override public String toString() { return value; }}

 編寫wordcount程式

import java.io.IOException;import java.util.StringTokenizer;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.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.GenericOptionsParser; public class WordCountEntry{ public static class TokenizerMapper extends Mapper<LongWritable, Http, Http, IntWritable& gt; { private final static IntWritable one = new IntWritable(1); private Http word = new Http(); public void map(LongWritable key, Http value, CoNtext coNtext) throws IOException, InterruptedException { StringTokenizer itr = new StringTokenizer(val ue.toString()); while (itr.hasMoreTokens()) { word.setValue(itr.nextToken()); coNtext.write(word, one); } } } public static class IntSumReducer extends Reducer<Http, IntWritable, Http, IntWritable> { private IntWritable result = n ew IntWritable(); public void reduce(Http key, Iterable<IntWritable> values, CoNtext coNtext) throws IOException, InterruptedException { int sum = 0; for (IntWritable val : values) { sum += val.get(); } result.set(sum); coNtext.write(ke y, result); } } public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException { Configuratio n conf = new Configuration(); String[] otherArgs = new GenericOptionsParser(conf, args) .getRemainingArgs(); if (otherArgs.length != 2) { System.err.println("Usage: wordcount <in> <out>"); System.exit(2); } Path input = new Path(args[0]); Path output = new Path(args[1]); Job job = new Job(conf, "word count"); job.setJarByClass(WordCountEntry.class); job.setMapperClass(TokenizerMapper.class); job.setCombinerClass(IntSumReducer.class); job.setReducerClass(IntSumReducer.class); job.setOutputKeyClass(Http.class); job.setOutputValueClass(IntWritable.class); FileInputFormat.addInputPath(job, input); FileOutputFormat.setOutputPath(job, output); System.exit(job.waitForCompletion(true) ? 0 : 1); }}

 編寫mrUnit測試案例進行mapreduce程式測試

import java.util.ArrayList;import java.util.List;import org.apache.hadoop.io.IntWritable;import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.mrunit.mapreduce.MapDriver;import org.apache.hadoop.mrunit.mapreduce.ReduceDriver;import org.junit.Before;import org.junit.Test;import com.geo.dmp.WordCountEntry.IntSumReducer;import com.geo.dmp.WordCountEntry.TokenizerMapper;public class WordCountEntryTest{ private MapDriver<LongWritable, Http, Http, IntWritable> mapDriver; private ReduceDriver< Http, IntWritable, Http, IntWritable> reduceDriver; @Before public void setUpBeforeClass() throws Exception { TokenizerMapper tm = new TokenizerMapper(); mapDriver = MapDrive r.newMapDriver(tm); IntSumReducer isr = new IntSumReducer(); reduceDriver = ReduceDriver.newReduceDriver(isr); } @Test public void TokenizerMapperTest() { mapDriver.withInput(new LongWritable(), new Http("01a55\tablsd")); mapDriver .withOutput(new Http("01a55"), new IntWritable(1)); mapDriver.withOutput(new Http("ablsd"), new IntWritable(1)); mapDriver.runTest(); } @Test public void IntSumReducerTest() { List<IntWritable> values = new ArrayList<IntWritable>(); values.add (new IntWritable(1)); values.add(new IntWritable(1)); reduceDriver.withInput(new Http("01a55"), values); reduceDriver.withOutput(new Http("01a55"), new IntWritable(2)); reduceDriver.runTest(); }}