在hadoop 中一個Job中可以按順序執行多個mapper對資料進行前期的處理，再進行reduce，經reduce後的結果可經個經多個按順序執行的mapper進行後期的處理，這樣的Job是不會儲存中間結果的，並大大減少了I/O操作。

例如：在一個Job中，按順序執行 MAP1->MAP2->REDUCE->MAP3->MAP4 在這種鏈式結構中，要將MAP2與REDUCE看成這個MAPREDUCE的核心部分(就像是單箇中的MAP與REDUCE)，並且partitioning與shuffling在此處才會被應用到。所以MAP1作為前期處理，而MAP3與MAP4作為後期處理。

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); //將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); /將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); /將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); /將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); //將map4加入到Job中 JobClient.runJob(job); 注：上一個的輸出是一下的輸入，所以上一個的輸出資料型別必須與下一個輸入的資料型別一樣

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addMapper中的引數

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)