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package cn.itcast.bigdata.mr.flowsum;
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class FlowCount {
static class FlowCountMapper extends Mapper<LongWritable, Text, Text, FlowBean>{
@Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
//将一行内容转成string
String line = value.toString();
//切分字段
String[] fields = line.split("\t");
//取出手机号
String phoneNbr = fields[1];
//取出上行流量下行流量
long upFlow = Long.parseLong(fields[fields.length-3]);
long dFlow = Long.parseLong(fields[fields.length-2]);
context.write(new Text(phoneNbr), new FlowBean(upFlow, dFlow));
}
}
static class FlowCountReducer extends Reducer<Text, FlowBean, Text, FlowBean>{
//<183323,bean1><183323,bean2><183323,bean3><183323,bean4>.......
@Override
protected void reduce(Text key, Iterable<FlowBean> values, Context context) throws IOException, InterruptedException {
long sum_upFlow = 0;
long sum_dFlow = 0;
//遍历所有bean，将其中的上行流量，下行流量分别累加
for(FlowBean bean: values){
sum_upFlow += bean.getUpFlow();
sum_dFlow += bean.getdFlow();
}
FlowBean resultBean = new FlowBean(sum_upFlow, sum_dFlow);
context.write(key, resultBean);
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
/*conf.set("mapreduce.framework.name", "yarn");
conf.set("yarn.resoucemanager.hostname", "mini1");*/
Job job = Job.getInstance(conf);
/*job.setJar("/home/hadoop/wc.jar");*/
//指定本程序的jar包所在的本地路径
job.setJarByClass(FlowCount.class);
//指定本业务job要使用的mapper/Reducer业务类
job.setMapperClass(FlowCountMapper.class);
job.setReducerClass(FlowCountReducer.class);
//指定mapper输出数据的kv类型
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(FlowBean.class);
//指定最终输出的数据的kv类型
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(FlowBean.class);
//指定job的输入原始文件所在目录
FileInputFormat.setInputPaths(job, new Path(args[0]));
//指定job的输出结果所在目录
FileOutputFormat.setOutputPath(job, new Path(args[1]));
//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
/*job.submit();*/
boolean res = job.waitForCompletion(true);
System.exit(res?0:1);
}
}
package cn.itcast.bigdata.mr.flowsum;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import org.apache.hadoop.io.Writable;
public class FlowBean implements Writable{
private long upFlow;
private long dFlow;
private long sumFlow;
//反序列化时，需要反射调用空参构造函数，所以要显示定义一个
public FlowBean(){}
public FlowBean(long upFlow, long dFlow) {
this.upFlow = upFlow;
this.dFlow = dFlow;
this.sumFlow = upFlow + dFlow;
}
public long getUpFlow() {
return upFlow;
}
public void setUpFlow(long upFlow) {
this.upFlow = upFlow;
}
public long getdFlow() {
return dFlow;
}
public void setdFlow(long dFlow) {
this.dFlow = dFlow;
}
public long getSumFlow() {
return sumFlow;
}
public void setSumFlow(long sumFlow) {
this.sumFlow = sumFlow;
}
/**
* 序列化方法
*/
@Override
public void write(DataOutput out) throws IOException {
out.writeLong(upFlow);
out.writeLong(dFlow);
out.writeLong(sumFlow);
}
/**
* 反序列化方法
* 注意：反序列化的顺序跟序列化的顺序完全一致
*/
@Override
public void readFields(DataInput in) throws IOException {
upFlow = in.readLong();
dFlow = in.readLong();
sumFlow = in.readLong();
}
@Override
public String toString() {
return upFlow + "\t" + dFlow + "\t" + sumFlow;
}
}