Hadoop序列化的例子

女爷i 2022-04-10 11:29 237阅读 0赞

### Hadoop序列化的例子 ###

*  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
 *  2.数据准备
 *  3.图解流程
 *  4.代码实现
 *   *  4.1编写Bean对象
     *  4.2 编写Mapper
     *  4.3 编写Reducer
     *  4.4 编写Driver
 *  5.结果

前面讲解了有关序列化方面的东西，为了更好的理解，所以举个栗子。

# 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 #

# 2.数据准备 #

1	13736230513	192.196.100.1	www.w3cschool.com	2481	24681	200
    2	13846544121	192.196.100.2			264	0	200
    3 	13956435636	192.196.100.3			132	1512	200
    4 	13966251146	192.168.100.1			240	0	404
    5 	18271575951	192.168.100.2	www.lsl.com	1527	2106	200
    6 	12384188413	192.168.100.3	www.w3cschool.com	4116	1432	200
    7 	13590439668	192.168.100.4			1116	954	200
    8 	15910133277	192.168.100.5	www.hao123.com	3156	2936	200
    9 	13729199489	192.168.100.6			240	0	200
    10 	13630577991	192.168.100.7	www.shouhu.com	6960	690	200
    11 	15043685818	192.168.100.8	www.baidu.com	3659	3538	200
    12 	15959002129	192.168.100.9	www.lsl.com	1938	180	500
    13 	13560439638	192.168.100.10			918	4938	200
    14 	13470253144	192.168.100.11			180	180	200
    15 	13682846555	192.168.100.12	www.qq.com	1938	2910	200
    16 	13992314666	192.168.100.13	www.gaga.com	3008	3720	200
    17 	13509468723	192.168.100.14	www.qinghua.com	7335	110349	404

# 3.图解流程 #

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5NjU3OTA5_size_16_color_FFFFFF_t_70]

# 4.代码实现 #

## 4.1编写Bean对象 ##

package sumflown;
    
    import org.apache.hadoop.io.Writable;
    
    import java.io.DataInput;
    import java.io.DataOutput;
    import java.io.IOException;
    
    //1 实现writable接口
    public class FlowBean implements Writable { 
    
        //上行流量
        private long upFlow;
    
        //下行流量
        private long downFlow;
    
        //总流量
        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;
            sumFlow=upFlow+downFlow;
        }
    
        //3 序列化的方法
        @Override
        public void write(DataOutput out) throws IOException { 
            //写序列化数据
            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();
        }
    
        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;
        }
    
        // 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;
        }
    
        //根据需求快速赋值方法，也可不要但其他类的写法要发生改变
        public void set(long upFlow2, long downFlow2) { 
            downFlow=downFlow2;
            upFlow=upFlow2;
            sumFlow=downFlow2+upFlow2;
        }
    
    }

## 4.2 编写Mapper ##

package sumflown;
    
    import org.apache.hadoop.io.LongWritable;
    import org.apache.hadoop.io.Text;
    import org.apache.hadoop.mapreduce.Mapper;
    
    import java.io.IOException;
    
    public class FlowCountMapper extends Mapper<LongWritable, Text, Text, FlowBean> { 
    
        private FlowBean flowBean = new FlowBean();
        Text t =new Text();
        @Override
        protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, FlowBean>.Context context)
                throws IOException, InterruptedException { 
            // 1.获取一行
            String line = value.toString();
    
            // 2.切割
            String[] fields = line.split("\t");
    
            // 3.封装对象
            String phoneNum = fields[1];// 获取手机号
            long upFlow = Long.parseLong(fields[fields.length - 3]);// 上行流量
            long downFlow = Long.parseLong(fields[fields.length - 2]);// 下行流量
    
            flowBean.set(upFlow, downFlow);
    
            t.set(phoneNum);
    
            // 4.写出
            context.write(t, flowBean);
    
    
        }
    }

## 4.3 编写Reducer ##

package sumflown;
    
    import org.apache.hadoop.io.Text;
    import org.apache.hadoop.mapreduce.Reducer;
    
    import java.io.IOException;
    
    public class FlowCountReducer extends Reducer<Text, FlowBean, Text, FlowBean>{ 
    
    
        private FlowBean flowBean = new FlowBean();
        @Override
        protected void reduce(Text key, Iterable<FlowBean> values, Reducer<Text, FlowBean, Text, FlowBean>.Context context)
                throws IOException, InterruptedException { 
    
            long sum_downFlow =0;
            long sum_upFlow =0;
    
            //1.累加求和
            for (FlowBean flowBean : values) { 
                sum_downFlow+=flowBean.getDownFlow();
                sum_upFlow+=flowBean.getUpFlow();
            }
    
            flowBean.set(sum_upFlow, sum_downFlow);
    
            //2.写出
            context.write(key, flowBean);
        }
    }

## 4.4 编写Driver ##

package sumflown;
    
    import org.apache.hadoop.conf.Configuration;
    import org.apache.hadoop.fs.FileSystem;
    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;
    
    import java.io.IOException;
    
    public class FlowsumDriver { 
        public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException { 
    
            //1.获取job
            Configuration conf = new Configuration();
            Job job = Job.getInstance(conf);
    
            FileSystem fs = FileSystem.get(conf);
            Path input = new Path("D:/input/one.txt");
            Path output = new Path("D:/output");
            if (fs.exists(output)) { 
                fs.delete(output, true);
            }
    
            //2.设置jar路径
            job.setJarByClass(FlowsumDriver.class);
    
            //3.设置mapper和reducer类
            job.setMapperClass(FlowCountMapper.class);
            job.setReducerClass(FlowCountReducer.class);
    
            //4.设置mapper的key和value输出的类型
            job.setMapOutputKeyClass(Text.class);
            job.setMapOutputValueClass(FlowBean.class);
    
            //5.设置最终输出的key和value输出类型
            job.setOutputKeyClass(Text.class);
            job.setOutputValueClass(FlowBean.class);
    
            //6.设置输入输出路径
            FileInputFormat.setInputPaths(job, input);
            FileOutputFormat.setOutputPath(job, output);
    
            //7.提交job
            boolean result=job.waitForCompletion(true);
    
            System.exit(result?0:1);
    
        }
    }

# 5.结果 #

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM5NjU3OTA5_size_16_color_FFFFFF_t_70 1]

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