1） MapReduce中的基類Mapper類，自定義Mapper類的父類。
2） MapReduce中的基類Reducer類，自定義Reducer類的父類。
1、Mapper類
API文檔
1） InputSplit輸入分片，InputFormat輸入格式化
2） 對Mapper輸出結果進行Sorted排序和Group分組
3） 對Mapper輸出結果依據Reducer個數進行分區Patition
4） 對Mapper輸出數據進行Combiner
- 在Hadoop官方文檔的Mapper類說明：
Maps input key/value pairs to a set of intermediate key/value pairs.
Maps are the individual tasks which transform input records into a intermediate records. The transformed intermediate records need not be of the same type as the input records. A given input pair may map to zero or many output pairs.
The Hadoop Map-Reduce framework spawns one map task for each InputSplit generated by the InputFormat for the job. Mapper implementations can access the Configuration for the job via the JobContext.getConfiguration().
The framework first calls setup(org.apache.hadoop.mapreduce.Mapper.Context), followed by map(Object, Object, Context) for each key/value pair in the InputSplit. Finally cleanup(Context) is called.
All intermediate values associated with a given output key are subsequently grouped by the framework, and passed to a Reducer to determine the final output. Users can control the sorting and grouping by specifying two key RawComparator classes.
The Mapper outputs are partitioned per Reducer. Users can control which keys (and hence records) go to which Reducer by implementing a custom Partitioner.
Users can optionally specify a combiner, via Job.setCombinerClass(Class), to perform local aggregation of the intermediate outputs, which helps to cut down the amount of data transferred from the Mapper to the Reducer.
Applications can specify if and how the intermediate outputs are to be compressed and which CompressionCodecs are to be used via the Configuration.
If the job has zero reduces then the output of the Mapper is directly written to the OutputFormat without sorting by keys.
- Mapper類的結構：
- 方法如下：
第一類：protected類型，用戶根據實際需要進行覆寫。
1） setup：每個任務執行前調用一次。
2） map：每個Key/Value對調用一次。
3） clearup：每個任務執行結束前調用一次。
第二類，運行的方法
run()方法，是Mapper類的入口，方法內部調用了setup()、map()、clearup()三個方法。