content: /**
   * Licensed to the Apache Software Foundation (ASF) under one or more contributor license
   * agreements. See the NOTICE file distributed with this work for additional information regarding
   * copyright ownership. The ASF licenses this file to you under the Apache License, Version 2.0 (the
   * "License"); you may not use this file except in compliance with the License. You may obtain a
   * copy of the License at
   * 
   * http://www.apache.org/licenses/LICENSE-2.0
   * 
   * Unless required by applicable law or agreed to in writing, software distributed under the License
   * is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express
   * or implied. See the License for the specific language governing permissions and limitations under
   * the License.
   */
  
  package com.aliyun.odps.mapred.open.example;
  
  import java.io.IOException;
  import java.util.Date;
  import java.util.Iterator;
  
  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.ReducerBase;
  import com.aliyun.odps.mapred.TaskContext;
  import com.aliyun.odps.mapred.conf.JobConf;
  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);
      }
  
    }
  
    public static class IdentityReducer extends ReducerBase {
  
      private Record result = null;
  
      @Override
      public void setup(TaskContext context) throws IOException {
        result = context.createOutputRecord();
      }
  
      /**
       * Writes all keys and values directly to output.
       */
      @Override
      public void reduce(Record key, Iterator<Record> values, TaskContext context) throws IOException {
        result.set(0, key.get(0));
  
        while (values.hasNext()) {
          Record val = values.next();
          result.set(1, val.get(0));
          context.write(result);
        }
      }
  
    }
  
    /**
     * 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.");
    }
  }
  