本文为您介绍MapReduce的Sort示例。
测试准备
- 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。
- 准备好Sort的测试表和资源。
- 创建测试表。
create table ss_in(key bigint, value bigint); create table ss_out(key bigint, value bigint);
- 添加测试资源。
add jar data\resources\mapreduce-examples.jar -f;
- 创建测试表。
- 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
Pom依赖信息,请参见注意事项。
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.");
}
}