剖析美国平均气温项目

第一步：数据集导入HDFS

使用命令行访问刚刚上传至HDFS的数据集

第一步：在 Map 阶段，提取气象站和气温数据。

public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

            String line = value.toString(); //读取每行数据
            int temperature = Integer.parseInt(line.substring(14, 19).trim());//气温

            if (temperature != -9999) { //过滤无效数据
                String weatherStationId = "weatherStationId";//真实的气象站id是从文件名字中提取，为了便于单元测试，这里key设置为常量weatherStationId
                context.write(new Text(weatherStationId), new IntWritable(temperature));
            }
        }

第二步：在 Reduce 阶段，统计每个气象站的平均气温。

public static class TemperatureReducer extends Reducer< Text, IntWritable, Text, IntWritable> {
		private IntWritable result = new IntWritable();

		public void reduce(Text key, Iterable< IntWritable> values,Context context) throws IOException, InterruptedException {
			int sum = 0;
			int count = 0;
			for (IntWritable val : values) {
				sum += val.get();
				count++;
			}
			result.set(sum / count);
			context.write(key, result);
		}
	}

第三步：对代码进行单元测试及debug调试

/**
 * Mapper 端的单元测试
 */
@SuppressWarnings("all")
public class TemperatureMapperTest {
	private Mapper mapper;//定义一个Mapper对象
	private MapDriver driver;//定义一个MapDriver 对象

	@Before
	public void init() {
		mapper = new Temperature.TemperatureMapper();//实例化一个Temperature中的TemperatureMapper对象
		driver = new MapDriver(mapper);//实例化MapDriver对象
	}

	@Test
	public void test() throws IOException {
		//输入一行测试数据
		String line = "1985 07 31 02   200    94 10137   220    26     1     0 -9999";
		driver.withInput(new LongWritable(), new Text(line))//跟TemperatureMapper输入类型一致
				.withOutput(new Text("weatherStationId"), new IntWritable(200))//跟TemperatureMapper输出类型一致
				.runTest();
	}
}

/**
 * Reducer 单元测试
 */
@SuppressWarnings("all")
public class TemperatureReduceTest {
	private Reducer reducer;//定义一个Reducer对象
	private ReduceDriver driver;//定义一个ReduceDriver对象

	@Before
	public void init() {
		reducer = new Temperature.TemperatureReducer();//实例化一个Temperature中的TemperatureReducer对象
		driver = new ReduceDriver(reducer);//实例化ReduceDriver对象
	}

	@Test
	public void test() throws IOException {
		String key = "weatherStationId";//声明一个key值
		List values = new ArrayList();
		values.add(new IntWritable(200));//添加第一个value值
		values.add(new IntWritable(100));//添加第二个value值
		driver.withInput(new Text("weatherStationId"), values)//跟TemperatureReducer输入类型一致
			  .withOutput(new Text("weatherStationId"), new IntWritable(150))//跟TemperatureReducer输出类型一致
			  .runTest();
	}
}

/**
 * Mapper 和 Reducer 集成起来测试
 */
@SuppressWarnings("all")
public class TemperatureTest {
	private Mapper mapper;//定义一个Mapper对象
	private Reducer reducer;//定义一个Reducer对象
	private MapReduceDriver driver;//定义一个MapReduceDriver 对象

	@Before
	public void init() {
		mapper = new Temperature.TemperatureMapper();//实例化一个Temperature中的TemperatureMapper对象
		reducer = new Temperature.TemperatureReducer();//实例化一个Temperature中的TemperatureReducer对象
		driver = new MapReduceDriver(mapper, reducer);//实例化MapReduceDriver对象
	}

	@Test
	public void test() throws RuntimeException, IOException {
		//输入两行行测试数据
		String line = "1985 07 31 02   200    94 10137   220    26     1     0 -9999";
		String line2 = "1985 07 31 11   100    56 -9999    50     5 -9999     0 -9999";
		driver.withInput(new LongWritable(), new Text(line))//跟TemperatureMapper输入类型一致
			  .withInput(new LongWritable(), new Text(line2))
			  .withOutput(new Text("weatherStationId"), new IntWritable(150))//跟TemperatureReducer输出类型一致
			  .runTest();
	}
}

第四步：将项目编译和打包为Temperature.jar，使用客户端将 Temperature.jar上传至hadoop的/home/hadoop/data目录下。

第五步：使用cd /home/hadoop/data 切换到当前目录，通过

export HADOOP_CLASSPATH=/home/hadoop/data/Temperature.jar

hadoop com.hadoop.base.Temperature

命令行执行任务。

第六步：任务的最终结果输出到 HDFS ，使用hadoop fs -cat /weather/output/part-r-00000命令查看结果。

原文地址：https://www.cnblogs.com/zhoupp/p/10914302.html