Mapreduce的分区—Partitioner

1． 需求
将流量汇总统计结果按照手机归属地不同省份输出到不同文件中。
2． 分析
Mapreduce中会将map输出的kv对，按照相同key分组，然后分发给不同的reducetask。
默认的分发规则为：根据key的hashcode%reducetask数来分发
所以：如果要按照我们自己的需求进行分组，则需要改写数据分发（分组）组件Partitioner，自定义一个CustomPartitioner继承抽象类：Partitioner，然后在job对象中，设置自定义partitioner： job.setPartitionerClass(CustomPartitioner.class
3． 实现

public class ProvincePartitioner extends Partitioner<Text, FlowBean>

    public static HashMap<String, Integer>  provinceMap = new HashMap<String, Integer>();

    static{

        provinceMap.put("134", 0);

        provinceMap.put("135", 1);

        provinceMap.put("136", 2);

        provinceMap.put("137", 3);

        provinceMap.put("138", 4);

    }

    @Override

    public int getPartition(Text key, FlowBean value, int numPartitions) {

        Integer code = provinceMap.get(key.toString().substring(0, 3));

        if (code != null) {

            return code;

        }

        return 5;

    }

}

public class FlowSumProvince {

 public static class FlowSumProvinceMapper extends Mapper<LongWritable, Text, Text, FlowBean>{

     Text k = new Text();

     FlowBean  v = new FlowBean();

     @Override

     protected void map(LongWritable key, Text value,Context context)

             throws IOException, InterruptedException {

             //拿取一行文本转为String

             String line = value.toString();

             //按照分隔符\t进行分割

             String[] fileds = line.split("\t");

             //获取用户手机号

             String phoneNum = fileds[1];

             long upFlow = Long.parseLong(fileds[fileds.length-3]);

             long downFlow = Long.parseLong(fileds[fileds.length-2]);

             k.set(phoneNum);

             v.set(upFlow, downFlow);

             context.write(k,v);

        }

    }

    public static class FlowSumProvinceReducer extends Reducer<Text, FlowBean, Text, FlowBean>{

        FlowBean  v  = new FlowBean();

        @Override

        protected void reduce(Text key, Iterable<FlowBean> flowBeans,Context context) throws IOException, InterruptedException {

            long upFlowCount = 0;

            long downFlowCount = 0;

            for (FlowBean flowBean : flowBeans) {

                upFlowCount += flowBean.getUpFlow();

                downFlowCount += flowBean.getDownFlow();

            }

            v.set(upFlowCount, downFlowCount);

            context.write(key, v);

    }

    public static void main(String[] args) throws Exception{

        Configuration conf = new Configuration();

        conf.set("mapreduce.framework.name", "local");

        Job job = Job.getInstance(conf);

        //指定我这个 job 所在的 jar包位置

        job.setJarByClass(FlowSumProvince.class);

        //指定我们使用的Mapper是那个类  reducer是哪个类

        job.setMapperClass(FlowSumProvinceMapper.class);

        job.setReducerClass(FlowSumProvinceReducer.class);

//        job.setCombinerClass(FlowSumProvinceReducer.class);

        // 设置我们的业务逻辑 Mapper 类的输出 key 和 value 的数据类型

        job.setMapOutputKeyClass(Text.class);

        job.setMapOutputValueClass(FlowBean.class);

        // 设置我们的业务逻辑 Reducer 类的输出 key 和 value 的数据类型

        job.setOutputKeyClass(Text.class);

        job.setOutputValueClass(FlowBean.class);

        //这里设置运行reduceTask的个数

        //getPartition 返回的分区个数 = NumReduceTasks   正常执行

        //getPartition 返回的分区个数 > NumReduceTasks   报错：Illegal partition

        //getPartition 返回的分区个数 < NumReduceTasks   可以执行 ，多出空白文件

        job.setNumReduceTasks(10);

        //这里指定使用我们自定义的分区组件

        job.setPartitionerClass(ProvincePartitioner.class);

        FileInputFormat.setInputPaths(job, new Path("D:\\flowsum\\input"));

        // 指定处理完成之后的结果所保存的位置

        FileOutputFormat.setOutputPath(job, new Path("D:\\flowsum\\outputProvince"));

        boolean res = job.waitForCompletion(true);

        System.exit(res ? 0 : 1);

    }

 }

}