（转） hadoop 一个Job多个MAP与REDUCE的执行

http://blog.csdn.net/chaoping315/article/details/6221440

在hadoop 中一个Job中可以按顺序运行多个mapper对数据进行前期的处理，再进行reduce，经reduce后的结果可经个经多个按顺序执行的mapper进行后期的处理，这样的Job是不会保存中间结果的，并大大减少了I/O操作。

例如：在一个Job中，按顺序执行 MAP1->MAP2->REDUCE->MAP3->MAP4 在这种链式结构中，要将MAP2与REDUCE看成这个MAPREDUCE的核心部分(就像是单个中的MAP与REDUCE)，并且partitioning与shuffling在此处才会被应用到。所以MAP1作为前期处理，而MAP3与MAP4作为后期处理。

Configuration conf = getConf();
JobConf job = new JobConf(conf);


job.setJobName(“ChainJob”);
job.setInputFormat(TextInputFormat.class);
job.setOutputFormat(TextOutputFormat.class);


FileInputFormat.setInputPaths(job, in);
FileOutputFormat.setOutputPath(job, out);


JobConf map1Conf = new JobConf(false);

ChainMapper.addMapp(job,
                    Map1.class,
                    LongWritable.class,
                    Text.class,
                    Text.class,
                    Text.class,
                    true,
                    map1Conf);

//将map1加入到Job中


JobConf map2Conf = new JobConf(false);
ChainMapper.addMapper(job,
                      BMap.class,
                      Text.class,
                      Text.class,
                      LongWritable.class,
                      Text.class,
                      true,
                      map2Conf);

/将map2加入到Job中





JobConf reduceConf = new JobConf(false);
ChainReducer.setReducer(job,
                        Reduce.class,
                        LongWritable.class,
                        Text.class,
                        Text.class,
                        Text.class,
                        true,
                        reduceConf);

/将reduce加入到Job中





JobConf map3Conf = new JobConf(false);
ChainReducer.addMapper(job,
                       Map3.class,
                       Text.class,
                       Text.class,
                       LongWritable.class,
                       Text.class,
                       true,
                       map3Conf);

/将map3加入到Job中



JobConf map4Conf = new JobConf(false);
ChainReducer.addMapper(job,
                       Map4.class,
                       LongWritable.class,
                       Text.class,
                       LongWritable.class,
                       Text.class,
                       true,
                       map4Conf);

//将map4加入到Job中

JobClient.runJob(job);

注：上一个的输出是一下的输入，所以上一个的输出数据类型必须与下一个输入的数据类型一样

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addMapper中的参数

public static <K1,V1,K2,V2> void
addMapper(JobConf job,
                    Class<? extends Mapper<K1,V1,K2,V2>> klass,
                    Class<? extends K1> inputKeyClass,
                    Class<? extends V1> inputValueClass,
                    Class<? extends K2> outputKeyClass,
                    Class<? extends V2> outputValueClass,
                    boolean byValue,
                    JobConf mapperConf)