引言
1.本文不描述MapReduce入门知识，这类知识网上很多，请自行查阅
2.本文的实例代码来自官网
http://hadoop.apache.org/docs/current/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html
最后的WordCount v2.0，该代码相比源码中的org.apache.Hadoop.examples.WordCount要复杂和完整，更适合作为MapReduce模板代码
3.本文的目的就是为开发MapReduce的同学提供一个详细注释了的模板，可以基于该模板做开发。
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官网实例代码（略有改动）
WordCount2.java
import java.io.BufferedReader; import java.io.FileReader; import java.io.IOException; import java.net.URI; import java.util.ArrayList; import java.util.HashSet; import java.util.List; import java.util.Set; 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.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.mapreduce.Counter; import org.apache.hadoop.util.GenericOptionsParser; import org.apache.hadoop.util.StringUtils; public class WordCount2 { // 日志组名MapCounters，日志名INPUT_WORDS static enum MapCounters { INPUT_WORDS } static enum ReduceCounters { OUTPUT_WORDS } // static enum CountersEnum { INPUT_WORDS,OUTPUT_WORDS } // 日志组名CountersEnum，日志名INPUT_WORDS和OUTPUT_WORDS public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable> { private final static IntWritable one = new IntWritable(1); // map输出的value private Text word = new Text(); // map输出的key private boolean caseSensitive; // 是否大小写敏感，从配置文件中读出赋值 private Set<String> patternsToSkip = new HashSet<String>(); // 用来保存需过滤的关键词，从配置文件中读出赋值 private Configuration conf; private BufferedReader fis; // 保存文件输入流 /** * 整个setup就做了两件事： 1.读取配置文件中的wordcount.case.sensitive，赋值给caseSensitive变量 * 2.读取配置文件中的wordcount.skip.patterns，如果为true，将CacheFiles的文件都加入过滤范围 */ @Override public void setup(Context context) throws IOException, InterruptedException { conf = context.getConfiguration(); // getBoolean(String name, boolean defaultValue) // 获取name指定属性的值，如果属性没有指定，或者指定的值无效，就用defaultValue返回。 // 属性可以在命令行中通过-Dpropretyname指定，例如 -Dwordcount.case.sensitive=true // 属性也可以在main函数中通过job.getConfiguration().setBoolean("wordcount.case.sensitive", // true)指定 caseSensitive = conf.getBoolean("wordcount.case.sensitive", true); // 配置文件中的wordcount.case.sensitive功能是否打开 // wordcount.skip.patterns属性的值取决于命令行参数是否有-skip，具体逻辑在main方法中 if (conf.getBoolean("wordcount.skip.patterns", false)) { // 配置文件中的wordcount.skip.patterns功能是否打开 URI[] patternsURIs = Job.getInstance(conf).getCacheFiles(); // getCacheFiles()方法可以取出缓存的本地化文件，本例中在main设置 for (URI patternsURI : patternsURIs) { // 每一个patternsURI都代表一个文件 Path patternsPath = new Path(patternsURI.getPath()); String patternsFileName = patternsPath.getName().toString(); parseSkipFile(patternsFileName); // 将文件加入过滤范围，具体逻辑参见parseSkipFile(String // fileName) } } } /** * 将指定文件的内容加入过滤范围 * * @param fileName */ private void parseSkipFile(String fileName) { try { fis = new BufferedReader(new FileReader(fileName)); String pattern = null; while ((pattern = fis.readLine()) != null) { // SkipFile的每一行都是一个需要过滤的pattern，例如! patternsToSkip.add(pattern); } } catch (IOException ioe) { System.err .println("Caught exception while parsing the cached file '" StringUtils.stringifyException(ioe)); } } @Override public void map(Object key, Text value, Context context) throws IOException, InterruptedException { // 这里的caseSensitive在setup()方法中赋值 String line = (caseSensitive) ? value.toString() : value.toString() .toLowerCase(); // 如果设置了大小写敏感，就保留原样，否则全转换成小写 for (String pattern : patternsToSkip) { // 将数据中所有满足patternsToSkip的pattern都过滤掉 line = line.replaceAll(pattern, ""); } StringTokenizer itr = new StringTokenizer(line); // 将line以tnrf为分隔符进行分隔 while (itr.hasMoreTokens()) { word.set(itr.nextToken()); context.write(word, one); // getCounter(String groupName, String counterName)计数器 // 枚举类型的名称即为组的名称，枚举类型的字段就是计数器名称 Counter counter = context.getCounter( MapCounters.class.getName(), MapCounters.INPUT_WORDS.toString()); counter.increment(1); } } } /** * Reducer没什么特别的升级特性 * * @author Administrator */ public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable> { private IntWritable result = new IntWritable(); 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); Counter counter = context.getCounter( ReduceCounters.class.getName(), ReduceCounters.OUTPUT_WORDS.toString()); counter.increment(1); } } public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); GenericOptionsParser optionParser = new GenericOptionsParser(conf, args); /** * 命令行语法是：hadoop command [genericOptions] [application-specific * arguments] getRemainingArgs()取到的只是[application-specific arguments] * 比如：$ bin/hadoop jar wc.jar WordCount2 -Dwordcount.case.sensitive=true * /user/joe/wordcount/input /user/joe/wordcount/output -skip * /user/joe/wordcount/patterns.txt * getRemainingArgs()取到的是/user/joe/wordcount/input * /user/joe/wordcount/output -skip /user/joe/wordcount/patterns.txt */ String[] remainingArgs = optionParser.getRemainingArgs(); // remainingArgs.length == 2时，包括输入输出路径： ///user/joe/wordcount/input /user/joe/wordcount/output // remainingArgs.length == 4时，包括输入输出路径和跳过文件： ///user/joe/wordcount/input /user/joe/wordcount/output -skip /user/joe/wordcount/patterns.txt if (!(remainingArgs.length != 2 || remainingArgs.length != 4)) { System.err .println("Usage: wordcount <in> <out> [-skip skipPatternFile]"); System.exit(2); } Job job = Job.getInstance(conf, "word count"); job.setJarByClass(WordCount2.class); job.setMapperClass(TokenizerMapper.class); job.setCombinerClass(IntSumReducer.class); job.setReducerClass(IntSumReducer.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); List<String> otherArgs = new ArrayList<String>(); // 除了 -skip 以外的其它参数 for (int i = 0; i < remainingArgs.length; i) { if ("-skip".equals(remainingArgs[i])) { job.addCacheFile(new Path(remainingArgs[ i]).toUri()); // 将 // -skip // 后面的参数，即skip模式文件的url，加入本地化缓存中 job.getConfiguration().setBoolean("wordcount.skip.patterns", true); // 这里设置的wordcount.skip.patterns属性，在mapper中使用 } else { otherArgs.add(remainingArgs[i]); // 将除了 -skip // 以外的其它参数加入otherArgs中 } } FileInputFormat.addInputPath(job, new Path(otherArgs.get(0))); // otherArgs的第一个参数是输入路径 FileOutputFormat.setOutputPath(job, new Path(otherArgs.get(1))); // otherArgs的第二个参数是输出路径 System.exit(job.waitForCompletion(true) ? 0 : 1); } }
