序列化
序列化：把内存中的对象转化为字节序列（或数据传输协议）以便于存储到硬盘（或网络传输）。
反序列化：把接收的字节序列（或数据传输协议）转化为内存中的对象。
在MapReduce中，默认采用的序列化机制是AVRO,为了方便使用，提供了接口Writable
需求
统计用户的上传流量和下载流量
文件
1 12344567895 2153 6185
2 13344567895 2253 6285
3 12344567895 2353 6385
4 13344567895 2453 6485
5 14344567895 2553 6585
6 14344567895 2653 6685
Bean类
import org.apache.hadoop.io.Writable;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
public class FlowBean implements Writable {
private Long upFlow;
private Long downFlow;
private Long sumFlow;
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;
}
public void set(Long upFlow, Long downFlow) {
this.upFlow = upFlow;
this.downFlow = downFlow;
this.sumFlow = upFlow + downFlow;
}
@Override
public String toString() {
return "FlowBean=[\t" +
"upFlow="+upFlow+"\t"+"downFlow="+downFlow+"\t"+"sumFlow="+sumFlow+"]";
}
@Override
public void write(DataOutput dataOutput) throws IOException {
dataOutput.writeLong(upFlow);
dataOutput.writeLong(downFlow);
dataOutput.writeLong(sumFlow);
}
@Override
public void readFields(DataInput dataInput) throws IOException {
upFlow = dataInput.readLong();
downFlow = dataInput.readLong();
sumFlow = dataInput.readLong();
}
}
Mapper类
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import java.io.IOException;
public class FlowMapper extends Mapper<LongWritable, Text, Text, FlowBean> {
private Text phone = new Text();
private FlowBean flowBean = new FlowBean();
@Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String[] datas = value.toString().split("\t");
phone.set(datas[1]);
long upFlow = Long.parseLong(datas[datas.length -2]);
long downFlow = Long.parseLong(datas[datas.length -1]);
flowBean.set(upFlow, downFlow);
context.write(phone, flowBean);
}
}
Reducer类
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import java.io.IOException;
public class FlowReducer extends Reducer<Text, FlowBean, Text, FlowBean> {
private FlowBean sumFlow = new FlowBean();
@Override
protected void reduce(Text key, Iterable<FlowBean> values, Context context) throws IOException, InterruptedException {
long sumUpFlow = 0;
long sumDownFlow = 0;
for (FlowBean value : values) {
sumUpFlow += value.getUpFlow();
sumDownFlow += value.getDownFlow();
}
sumFlow.set(sumUpFlow, sumDownFlow);
context.write(key, sumFlow);
}
}
Driver类
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;
import java.io.IOException;
public class FlowDriver {
public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
Job job = Job.getInstance(new Configuration());
job.setJarByClass(FlowDriver.class);
job.setMapperClass(FlowMapper.class);
job.setOutputKeyClass(FlowReducer.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(FlowBean.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(FlowBean.class);
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
boolean b = job.waitForCompletion(true);
System.exit(b ? 0 : 1);
}
}
输出结果
12344567895 FlowBean=[ upFlow=2353 downFlow=6385 sumFlow=8738]
12344567895 FlowBean=[ upFlow=2153 downFlow=6185 sumFlow=8338]
13344567895 FlowBean=[ upFlow=2453 downFlow=6485 sumFlow=8938]
13344567895 FlowBean=[ upFlow=2253 downFlow=6285 sumFlow=8538]
14344567895 FlowBean=[ upFlow=2653 downFlow=6685 sumFlow=9338]
14344567895 FlowBean=[ upFlow=2553 downFlow=6585 sumFlow=9138]
Processed:
0.015, SQL:
9
