本文为您介绍MapReduce的Sort示例。

测试准备

  1. 准备好测试程序的JAR包,假设名字为mapreduce-examples.jar,本地存放路径为data\resources
  2. 准备好Sort的测试表和资源。
    1. 创建测试表。
      create table ss_in(key bigint, value bigint);
      create table ss_out(key bigint, value bigint);
    2. 添加测试资源。
      add jar data\resources\mapreduce-examples.jar -f;
  3. 使用Tunnel导入数据。
    tunnel upload data ss_in;
    导入ss_in表的数据文件data的内容。
     2,1
     1,1
     3,1

测试步骤

在MaxCompute客户端中执行Sort。
jar -resources mapreduce-examples.jar -classpath data\resources\mapreduce-examples.jar
com.aliyun.odps.mapred.open.example.Sort ss_in ss_out;

预期结果

作业成功结束后,输出表ss_out中的内容如下。
+------------+------------+
| key        | value      |
+------------+------------+
| 1          | 1          |
| 2          | 1          |
| 3          | 1          |
+------------+------------+

代码示例

package com.aliyun.odps.mapred.open.example;
import java.io.IOException;
import java.util.Date;
import com.aliyun.odps.data.Record;
import com.aliyun.odps.data.TableInfo;
import com.aliyun.odps.mapred.JobClient;
import com.aliyun.odps.mapred.MapperBase;
import com.aliyun.odps.mapred.TaskContext;
import com.aliyun.odps.mapred.conf.JobConf;
import com.aliyun.odps.mapred.example.lib.IdentityReducer;
import com.aliyun.odps.mapred.utils.InputUtils;
import com.aliyun.odps.mapred.utils.OutputUtils;
import com.aliyun.odps.mapred.utils.SchemaUtils;
/**
     * This is the trivial map/reduce program that does absolutely nothing other
     * than use the framework to fragment and sort the input values.
     *
     **/
public class Sort {
    static int printUsage() {
        System.out.println("sort <input> <output>");
        return -1;
    }
    /**
       * Implements the identity function, mapping record's first two columns to
       * outputs.
       **/
    public static class IdentityMapper extends MapperBase {
        private Record key;
        private Record value;
        @Override
            public void setup(TaskContext context) throws IOException {
            key = context.createMapOutputKeyRecord();
            value = context.createMapOutputValueRecord();
        }
        @Override
            public void map(long recordNum, Record record, TaskContext context)
            throws IOException {
            key.set(new Object[] { (Long) record.get(0) });
            value.set(new Object[] { (Long) record.get(1) });
            context.write(key, value);
        }
    }
    /**
       * The main driver for sort program. Invoke this method to submit the
       * map/reduce job.
       *
       * @throws IOException
       *           When there is communication problems with the job tracker.
       **/
    public static void main(String[] args) throws Exception {
        JobConf jobConf = new JobConf();
        jobConf.setMapperClass(IdentityMapper.class);
        jobConf.setReducerClass(IdentityReducer.class);
        /**为了全局有序,这里设置了reducer的个数为1,所有的数据都会集中到一个reducer上面。*/
        /**只能用于小数据量,大数据量需要考虑其他的方式,比如TeraSort。*/
        jobConf.setNumReduceTasks(1);
        jobConf.setMapOutputKeySchema(SchemaUtils.fromString("key:bigint"));
        jobConf.setMapOutputValueSchema(SchemaUtils.fromString("value:bigint"));
        InputUtils.addTable(TableInfo.builder().tableName(args[0]).build(), jobConf);
        OutputUtils.addTable(TableInfo.builder().tableName(args[1]).build(), jobConf);
        Date startTime = new Date();
        System.out.println("Job started: " + startTime);
        JobClient.runJob(jobConf);
        Date end_time = new Date();
        System.out.println("Job ended: " + end_time);
        System.out.println("The job took " + (end_time.getTime() - startTime.getTime()) / 1000 + " seconds.");
    }
}