Hadoop 進階課程

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說明

WordCountV20
說明： 
  用於字數統計，並且增加略過大小寫辨識、符號篩除等功能 [已全改為 hadoop 0.20 API ]

測試方法：
  將此程式運作在hadoop 0.20 平台上，執行：
  ---------------------------
  hadoop jar WordCountV2.jar "/home/nchc/input" "/home/nchc/output-wc2" "-c"  "-skip" "/home/nchc/patterns/patterns.txt"
  ---------------------------

注意：
1. 以在程式內設定<input> <output> 路徑為local 的 "/home/nchc/input" "/home/nchc/output-wc2"
2. 若要測試 skip功能，請建立一個"/home/nchc/patterns/patterns.txt" 檔，內容如下（一行一個，前置提示符號\）
        \.
        \,
        \!
3. 若要測試過濾大小寫功能，請加入 -c 參數（有-c 代表 "不考慮大小寫" ）
4. 注意 DistributedCache , setup() , conf 參數傳遞於 main, mapper, setup 中

WordCountV2.java

package org.nchc.hadoop;

import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;
import java.util.HashSet;
import java.util.Set;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.filecache.DistributedCache;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.StringUtils;

public class WordCountV20 {

  public static class Map extends
      Mapper<LongWritable, Text, Text, IntWritable> {
    static enum Counters {
      INPUT_WORDS
    }

    private final static IntWritable one = new IntWritable(1);
    private Text word = new Text();

    private boolean caseSensitive = true;
    private Set<String> patternsToSkip = new HashSet<String>();

    private void parseSkipFile(Path patternsFile) {
      try {
        BufferedReader fis = new BufferedReader(new FileReader(
            patternsFile.toString()));
        String pattern = null;
        while ((pattern = fis.readLine()) != null) {
          patternsToSkip.add(pattern);
        }
      } catch (IOException ioe) {
        System.err
            .println("Caught exception while parsing the cached file '"
                + patternsFile
                + "' : "
                + StringUtils.stringifyException(ioe));
      }
    }

    @Override
    public void setup(Context context) {
      Configuration conf = context.getConfiguration();
      caseSensitive = conf.getBoolean("wordcount.case.sensitive", true);

      if (conf.getBoolean("wordcount.skip.patterns", false)) {
        Path[] patternsFiles = new Path[0];
        try {
          patternsFiles = DistributedCache.getLocalCacheFiles(conf);
        } catch (IOException ioe) {
          System.err
              .println("Caught exception while getting cached files: "
                  + StringUtils.stringifyException(ioe));
        }
        for (Path patternsFile : patternsFiles) {
          parseSkipFile(patternsFile);
        }
      }
    }

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

      String line = (caseSensitive) ? value.toString() : value.toString()
          .toLowerCase();

      for (String pattern : patternsToSkip) {
        line = line.replaceAll(pattern, "");
      }

      StringTokenizer tokenizer = new StringTokenizer(line);
      while (tokenizer.hasMoreTokens()) {
        word.set(tokenizer.nextToken());
        context.write(word, one);

      }

    }
  }

  public static class Reduce extends
      Reducer<Text, IntWritable, Text, IntWritable> {
    private IntWritable result = new IntWritable();

    @Override
    public void reduce(Text key, Iterable<IntWritable> values,
        Context context) throws IOException, InterruptedException {
      int sum = 0;
      for (IntWritable val : values) {
        sum += val.get();
      }
      result.set(sum);
      context.write(key, result);
    }
  }

  public static void main(String[] args) throws Exception {
    String[] argv = { "/home/nchc/input", "/home/nchc/output-wc2", "-c",  "-skip",
        "/home/nchc/patterns/patterns.txt" };

    args = argv;
    Configuration conf = new Configuration();
    conf.set("mapred.job.tracker", "local"); // for single
    conf.set("fs.default.name", "file:///"); // for single

    if (args.length < 2) {
      System.err
          .println("Usage: hadoop jar WordCount.jar <input> <output> [-c] [-skip <path>]");
      System.exit(2);
    }

    for (int i = 0; i < args.length; ++i) {
      if ("-skip".equals(args[i])) {
        DistributedCache
            .addCacheFile(new Path(args[++i]).toUri(), conf);
        conf.setBoolean("wordcount.skip.patterns", true);
      }
      if ("-c".equals(args[i])){
        conf.setBoolean("wordcount.case.sensitive", false);
      }
    }

    CheckAndDelete.checkAndDelete(args[1], conf);
    Job job = new Job(conf, "Word Count");
    job.setJarByClass(WordCountV20.class);
    job.setMapperClass(Map.class);
    job.setCombinerClass(Reduce.class);
    job.setReducerClass(Reduce.class);
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(IntWritable.class);
    FileInputFormat.addInputPath(job, new Path(args[0]));
    FileOutputFormat.setOutputPath(job, new Path(args[1]));

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