官网MapReduce实例代码详细批注

来源：互联网发布：蓝牙鼠标怎么连接mac 编辑：程序博客网时间：2024/05/15 07:38
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。
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\t\n\r\f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);    }

来源：http://blog.csdn.net/u010967382/article/details/40152495
0 0