第一个MapReduce任务

    前两天在公司内网上搭了个2个节点hadoop集群，暂时没有多大实际意义，仅用作自己的测试。遇到的问题在阿里巴巴这位仁兄的《Hadoop集群配置和使用技巧 》都有提到的。也遇到了reduce任务卡住的问题，只需要在每个节点的/etc/hosts将集群中的机器都配置上即可解决。
   今天将一个日志统计任务用Hadoop MapReduce框架重新实现了一次，数据量并不大，每天分析一个2G多的日志文件罢了。先前是用Ruby配合cat、grep命令搞定，运行一次在 50多秒左右，如果纯粹采用Ruby的话CPU占用率非常高而且慢的无法忍受，利用IO.popen调用linux的cat、grep命令先期处理就好多 了。看看这个MapReduce任务：

public class GameCount extends Configured implements
        org.apache.hadoop.util.Tool {
    public static class MapClass extends MapReduceBase implements
            Mapper<LongWritable, Text, Text, IntWritable> {

        private Pattern pattern;

        public void configure(JobConf job) {
            String gameName = job.get("mapred.mapper.game");
            pattern = Pattern.compile("play\\sgame\\s" + gameName
                    + ".*uid=(\\d+),score=(-?\\d+),money=(-?\\d+)");
        }

        @Override
        public void map(LongWritable key, Text value,
                OutputCollector<Text, IntWritable> output, Reporter reporter)
                throws IOException {
            String text = value.toString();
            Matcher matcher = pattern.matcher(text);
            int total = 0; // 总次数
            while (matcher.find()) {
                int record = Integer.parseInt(matcher.group(2));
                output.collect(new Text(matcher.group(1)), new IntWritable(
                        record));
                total += 1;
            }
            output.collect(new Text("total"), new IntWritable(total));
        }
    }

    public static class ReduceClass extends MapReduceBase implements
            Reducer<Text, IntWritable, Text, IntWritable> {

        @Override
        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));
        }

    }

    static int printUsage() {
        System.out
                .println("gamecount [-m <maps>] [-r <reduces>] <input> <output> <gamename>");
        ToolRunner.printGenericCommandUsage(System.out);
        return -1;
    }

    public int run(String[] args) throws Exception {
        JobConf conf = new JobConf(getConf(), GameCount.class);
        conf.setJobName("gamecount");

       conf.setOutputKeyClass(Text.class);
        conf.setOutputValueClass(IntWritable.class);

        conf.setMapperClass(MapClass.class);
        conf.setCombinerClass(ReduceClass.class);
        conf.setReducerClass(ReduceClass.class);

        List<String> other_args = new ArrayList<String>();
        for (int i = 0; i < args.length; ++i) {
            try {
                if ("-m".equals(args[i])) {
                    conf.setNumMapTasks(Integer.parseInt(args[++i]));
                } else if ("-r".equals(args[i])) {
                    conf.setNumReduceTasks(Integer.parseInt(args[++i]));
                } else {
                    other_args.add(args[i]);
                }
            } catch (NumberFormatException except) {
                System.out.println("ERROR: Integer expected instead of "
                        + args[i]);
                return printUsage();
            } catch (ArrayIndexOutOfBoundsException except) {
                System.out.println("ERROR: Required parameter missing from "
                        + args[i - 1]);
                return printUsage();
            }
        }
        // Make sure there are exactly 2 parameters left.
        if (other_args.size() != 3) {
            System.out.println("ERROR: Wrong number of parameters: "
                    + other_args.size() + " instead of 2.");
            return printUsage();
        }
        FileInputFormat.setInputPaths(conf, other_args.get(0));
        FileOutputFormat.setOutputPath(conf, new Path(other_args.get(1)));
        conf.set("mapred.mapper.game", args[2]);
        JobClient.runJob(conf);
        return 0;
    }

    public static void main(String[] args) throws Exception {
        long start = System.nanoTime();
        int res = ToolRunner.run(new Configuration(), new GameCount(), args);
        System.out.println("running time:" + (System.nanoTime() - start)
                / 1000000 + " ms");
        System.exit(res);
    }

}

 

    代码没啥好解释的，就是分析类似"play game DouDiZhu result:uid=1871653,score=-720,money=0"这样的字符串，分析每天玩家玩游戏的次数、分数等。打包成GameCount.jar，执行：

 

hadoop jar GameCount.jar test.GameCount /usr/logs/test.log /usr/output DouDiZhu

 

   统计的运行时间在100多秒，适当增加map和reduce任务个数没有多大改善，不过CPU占用率还是挺高的。