序列化案例实操

1.    需求

统计每一个手机号耗费的总上行流量、下行流量、总流量

（1）输入数据

（2）输入数据格式：

7      13560436666     120.196.100.99           1116          954                   200 id      手机号码           网络ip                        上行流量  下行流量     网络状态码

（3）期望输出数据格式

13560436666             1116                954                          2070 手机号码               上行流量        下行流量                  总流量

2．需求分析

3．编写MapReduce程序

（1）编写流量统计的Bean对象

package com.atguigu.mapreduce.flowsum; import java.io.DataInput; import java.io.DataOutput; import java.io.IOException; import org.apache.hadoop.io.Writable;   // 1 实现writable接口 public class FlowBean implements Writable{     private long upFlow;   private long downFlow;   private long sumFlow;     //2  反序列化时，需要反射调用空参构造函数，所以必须有   public FlowBean() {       super();   }     public FlowBean(long upFlow, long downFlow) {       super();       this.upFlow = upFlow;       this.downFlow = downFlow;       this.sumFlow = upFlow + downFlow;   }     //3  写序列化方法   @Override   public void write(DataOutput out) throws IOException {       out.writeLong(upFlow);       out.writeLong(downFlow);       out.writeLong(sumFlow);   }     //4 反序列化方法   //5 反序列化方法读顺序必须和写序列化方法的写顺序必须一致   @Override   public void readFields(DataInput in) throws IOException {       this.upFlow  = in.readLong();       this.downFlow = in.readLong();       this.sumFlow = in.readLong();   }     // 6 编写toString方法，方便后续打印到文本   @Override   public String toString() {       return upFlow + “\t” + downFlow + “\t” + sumFlow;   }     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;   } }

（2）编写Mapper类

package com.atguigu.mapreduce.flowsum; import java.io.IOException; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Mapper;   public class FlowCountMapper extends Mapper<LongWritable, Text, Text, FlowBean>{     FlowBean v = new FlowBean();   Text k = new Text();     @Override   protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {             // 1 获取一行       String line = value.toString();             // 2 切割字段       String[] fields = line.split(“\t”);             // 3 封装对象       // 取出手机号码       String phoneNum = fields[1];         // 取出上行流量和下行流量       long upFlow = Long.parseLong(fields[fields.length – 3]);       long downFlow = Long.parseLong(fields[fields.length – 2]);         k.set(phoneNum);       v.set(downFlow, upFlow);             // 4 写出       context.write(k, v);   } }

（3）编写Reducer类

package com.atguigu.mapreduce.flowsum; import java.io.IOException; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Reducer;   public class FlowCountReducer extends Reducer<Text, FlowBean, Text, FlowBean> {     @Override   protected void reduce(Text key, Iterable<FlowBean> values, Context context)throws IOException, InterruptedException {         long sum_upFlow = 0;       long sum_downFlow = 0;         // 1 遍历所用bean，将其中的上行流量，下行流量分别累加       for (FlowBean flowBean : values) {          sum_upFlow += flowBean.getUpFlow();          sum_downFlow += flowBean.getDownFlow();       }         // 2 封装对象       FlowBean resultBean = new FlowBean(sum_upFlow, sum_downFlow);             // 3 写出       context.write(key, resultBean);   } }

（4）编写Driver驱动类

package com.atguigu.mapreduce.flowsum; import java.io.IOException; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;   public class FlowsumDriver {     public static void main(String[] args) throws IllegalArgumentException, IOException, ClassNotFoundException, InterruptedException {       // 输入输出路径需要根据自己电脑上实际的输入输出路径设置 args = new String[] { “e:/input/inputflow”, “e:/output1” };         // 1 获取配置信息，或者job对象实例       Configuration configuration = new Configuration();       Job job = Job.getInstance(configuration);         // 6 指定本程序的jar包所在的本地路径       job.setJarByClass(FlowsumDriver.class);         // 2 指定本业务job要使用的mapper/Reducer业务类       job.setMapperClass(FlowCountMapper.class);       job.setReducerClass(FlowCountReducer.class);         // 3 指定mapper输出数据的kv类型       job.setMapOutputKeyClass(Text.class);       job.setMapOutputValueClass(FlowBean.class);         // 4 指定最终输出的数据的kv类型       job.setOutputKeyClass(Text.class);       job.setOutputValueClass(FlowBean.class);             // 5 指定job的输入原始文件所在目录       FileInputFormat.setInputPaths(job, new Path(args[0]));       FileOutputFormat.setOutputPath(job, new Path(args[1]));         // 7 将job中配置的相关参数，以及job所用的java类所在的jar包， 提交给yarn去运行       boolean result = job.waitForCompletion(true);       System.exit(result ? 0 : 1);   } }