使用MapReduce运行自定义bean案例

如果一个文件的内容不只是简单的单词，而是类似于一个对象那般，有多种属性值，如：

在这个文件中，每一行的内容分别代表：手机号、IP、访问网站、上行流量、下行流量、状态码，现在需要统计每个手机号访问网站的上行流量、下行流量以及它们的总和。由于mapper按照每行进行切片，不妨创建一个bean，封装这些属性。

FlowBean.java

public class FlowBean implements Writable{
	
	private long upFlow;//上行流量
	private long downFlow;//下行流量
	private long sumFlow;//流量总和
	
	public FlowBean() {
		
	}

	public long getUpFlow() {
		return upFlow;
	}

	public void setUpFlow(long upFlow) {
		this.upFlow = upFlow;
	}

	public long getDownFlow() {
		return downFlow;
	}

	public void setDownFlow(long downFlow) {
		this.downFlow = downFlow;
	}

	public long getSumFlow() {
		return sumFlow;
	}

	public void setSumFlow(long sumFlow) {
		this.sumFlow = sumFlow;
	}

	// 序列化   在写出属性时，如果为引用数据类型，属性不能为null
	@Override
	public void write(DataOutput out) throws IOException {
		out.writeLong(upFlow);
		out.writeLong(downFlow);
		out.writeLong(sumFlow);
	}

	//反序列化   序列化和反序列化的顺序要一致
	@Override
	public void readFields(DataInput in) throws IOException {
		upFlow=in.readLong();
		downFlow=in.readLong();
		sumFlow=in.readLong();
	}

	@Override
	public String toString() {
		return  upFlow + "\t" + downFlow + "\t" + sumFlow;
	}

}

FlowBeanMapper.java

/*
 * 1. 统计手机号(String)的上行(long,int)，下行(long,int)，总流量(long,int)
 * 
 * 手机号为key,Bean{上行(long,int)，下行(long,int)，总流量(long,int)}为value
 */
public class FlowBeanMapper extends Mapper<LongWritable, Text, Text, FlowBean>{
	
	private Text out_key=new Text();
	private FlowBean out_value=new FlowBean();
	
	// (0,1	13736230513	192.196.100.1	www.baidu.com	2481	24681	200)
	@Override
	protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, FlowBean>.Context context)
			throws IOException, InterruptedException {
		
		String[] words = value.toString().split("\t");
		
		//封装手机号
		out_key.set(words[1]);
		// 封装上行
		out_value.setUpFlow(Long.parseLong(words[words.length-3]));
		// 封装下行
		out_value.setDownFlow(Long.parseLong(words[words.length-2]));
		//写出
		context.write(out_key, out_value);
	
	}

}

FlowBeanReducer.java

public class FlowBeanReducer extends Reducer<Text, FlowBean, Text, FlowBean>{
	
	private FlowBean out_value=new FlowBean();
	
	@Override
	protected void reduce(Text key, Iterable<FlowBean> values, Reducer<Text, FlowBean, Text, FlowBean>.Context context)
			throws IOException, InterruptedException {
		
		long sumUpFlow=0;
		long sumDownFlow=0;
		
		for (FlowBean flowBean : values) {
			
			sumUpFlow+=flowBean.getUpFlow();
			sumDownFlow+=flowBean.getDownFlow();
			
		}
		
		out_value.setUpFlow(sumUpFlow);
		out_value.setDownFlow(sumDownFlow);
		out_value.setSumFlow(sumDownFlow+sumUpFlow);
		
		context.write(key, out_value);
		
	}

}

FlowBeanDriver.java

public class FlowBeanDriver {
	
	public static void main(String[] args) throws Exception {
		
		Path inputPath=new Path("e:/mrinput/flowbean");
		Path outputPath=new Path("e:/mroutput/flowbean");
		
		//作为整个Job的配置
		Configuration conf = new Configuration();
		
		
		FileSystem fs=FileSystem.get(conf);
		
		if (fs.exists(outputPath)) {		
			fs.delete(outputPath, true);//保证输出目录不存在
		}
		
		// ①创建Job
		Job job = Job.getInstance(conf);
		
		// ②设置Job
		// 设置Job运行的Mapper，Reducer类型，Mapper,Reducer输出的key-value类型
		job.setMapperClass(FlowBeanMapper.class);
		job.setReducerClass(FlowBeanReducer.class);
		
		// Job需要根据Mapper和Reducer输出的Key-value类型准备序列化器，通过序列化器对输出的key-value进行序列化和反序列化
		// 如果Mapper和Reducer输出的Key-value类型一致，直接设置Job最终的输出类型
		job.setOutputKeyClass(Text.class);
		job.setOutputValueClass(FlowBean.class);
		
		// 设置输入目录和输出目录
		FileInputFormat.setInputPaths(job, inputPath);
		FileOutputFormat.setOutputPath(job, outputPath);
		
		// ③运行Job
		job.waitForCompletion(true);
	}

}

统计结果：