Word Count V2
import java.io.BufferedReader; import java.io.FileReader; import java.io.IOException; import java.util.ArrayList; import java.util.HashSet; import java.util.Iterator; import java.util.List; import java.util.Set; import java.util.StringTokenizer; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.conf.Configured; 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.mapred.FileInputFormat; import org.apache.hadoop.mapred.FileOutputFormat; import org.apache.hadoop.mapred.JobClient; import org.apache.hadoop.mapred.JobConf; import org.apache.hadoop.mapred.MapReduceBase; import org.apache.hadoop.mapred.Mapper; import org.apache.hadoop.mapred.OutputCollector; import org.apache.hadoop.mapred.Reducer; import org.apache.hadoop.mapred.Reporter; import org.apache.hadoop.mapred.TextInputFormat; import org.apache.hadoop.mapred.TextOutputFormat; import org.apache.hadoop.util.GenericOptionsParser; import org.apache.hadoop.util.StringUtils; import org.apache.hadoop.util.Tool; import org.apache.hadoop.util.ToolRunner; //WordCountV2 //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 // //測試方法: // 將此程式運作在hadoop 0.20 平台上,執行: // --------------------------- // hadoop jar WordCountV2.jar -Dwordcount.case.sensitive=false \ // <input> <output> -skip patterns/patterns.txt // --------------------------- // //注意: //1. 在hdfs 上來源檔案的路徑為 你所指定的 <input> // 請注意必須先放資料到此hdfs上的資料夾內,且此資料夾內只能放檔案,不可再放資料夾 //2. 運算完後,程式將執行結果放在hdfs 的輸出路徑為 你所指定的 <output> //3. 請建立一個資料夾 pattern 並在裡面放置pattern.txt,內容如下(一行一個,前置提示符號\) // \. // \, // \! @SuppressWarnings("deprecation") public class WordCountV2 extends Configured implements Tool { public static class Map extends MapReduceBase implements 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 long numRecords = 0; private String inputFile; public void configure(JobConf job) { caseSensitive = job.getBoolean("wordcount.case.sensitive", true); inputFile = job.get("map.input.file"); if (job.getBoolean("wordcount.skip.patterns", false)) { Path[] patternsFiles = new Path[0]; try { patternsFiles = DistributedCache.getLocalCacheFiles(job); } catch (IOException ioe) { System.err .println("Caught exception while getting cached files: " + StringUtils.stringifyException(ioe)); } for (Path patternsFile : patternsFiles) { parseSkipFile(patternsFile); } } } 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)); } } public void map(LongWritable key, Text value, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException { 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()); output.collect(word, one); reporter.incrCounter(Counters.INPUT_WORDS, 1); } if ((++numRecords % 100) == 0) { reporter.setStatus("Finished processing " + numRecords + " records " + "from the input file: " + inputFile); } } } public static class Reduce extends MapReduceBase implements Reducer<Text, IntWritable, Text, IntWritable> { public void reduce(Text key, Iterator<IntWritable> values, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException { int sum = 0; while (values.hasNext()) { sum += values.next().get(); } output.collect(key, new IntWritable(sum)); } } public int run(String[] args) throws Exception { JobConf conf = new JobConf(getConf(), WordCount.class); conf.setJobName("wordcount"); String[] otherArgs = new GenericOptionsParser(conf, args) .getRemainingArgs(); if (otherArgs.length < 2) { System.out.println("WordCountV2 [-Dwordcount.case.sensitive=<false|true>] \\ "); System.out.println(" <inDir> <outDir> [-skip Pattern_file]"); return 0; } conf.setOutputKeyClass(Text.class); conf.setOutputValueClass(IntWritable.class); conf.setMapperClass(Map.class); conf.setCombinerClass(Reduce.class); conf.setReducerClass(Reduce.class); conf.setInputFormat(TextInputFormat.class); conf.setOutputFormat(TextOutputFormat.class); List<String> other_args = new ArrayList<String>(); 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); } else { other_args.add(args[i]); } } FileInputFormat.setInputPaths(conf, new Path(other_args.get(0))); FileOutputFormat.setOutputPath(conf, new Path(other_args.get(1))); CheckAndDelete.checkAndDelete(other_args.get(1), conf); JobClient.runJob(conf); return 0; } public static void main(String[] args) throws Exception { // String[] argv = { "-Dwordcount.case.sensitive=false", "input", // "output-wc2", "-skip", "patterns/patterns.txt" }; // args = argv; int res = ToolRunner.run(new Configuration(), new WordCountV2(), args); System.exit(res); } }
Last modified 15 years ago
Last modified on Jan 19, 2010, 7:49:55 PM