不多说，直接上代码。
对流量原始日志进行流量统计，将不同省份的用户统计结果输出到不同文件。
代码
package zhouls.bigdata.myMapReduce.flowsum;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.io.WritableComparable;
public class FlowBean implements WritableComparable<FlowBean>{
private String phoneNB;
private long up_flow;
private long d_flow;
private long s_flow;
//在反序列化时，反射机制需要调用空参构造函数，所以显示定义了一个空参构造函数
public FlowBean(){}
//为了对象数据的初始化方便，加入一个带参的构造函数
public FlowBean(String phoneNB, long up_flow, long d_flow) {
this.phoneNB = phoneNB;
this.up_flow = up_flow;
this.d_flow = d_flow;
this.s_flow = up_flow + d_flow;
}
public String getPhoneNB() {
return phoneNB;
}
public void setPhoneNB(String phoneNB) {
this.phoneNB = phoneNB;
}
public long getUp_flow() {
return up_flow;
}
public void setUp_flow(long up_flow) {
this.up_flow = up_flow;
}
public long getD_flow() {
return d_flow;
}
public void setD_flow(long d_flow) {
this.d_flow = d_flow;
}
public long getS_flow() {
return s_flow;
}
public void setS_flow(long s_flow) {
this.s_flow = s_flow;
}
//将对象数据序列化到流中
public void write(DataOutput out) throws IOException {
out.writeUTF(phoneNB);
out.writeLong(up_flow);
out.writeLong(d_flow);
out.writeLong(s_flow);
}
//从数据流中反序列出对象的数据
//从数据流中读出对象字段时，必须跟序列化时的顺序保持一致
public void readFields(DataInput in) throws IOException {
phoneNB = in.readUTF();
up_flow = in.readLong();
d_flow = in.readLong();
s_flow = in.readLong();
}
@Override
public String toString() {
return "" + up_flow + "\t" +d_flow + "\t" + s_flow;
}
public int compareTo(FlowBean o) {
return s_flow>o.getS_flow()?-1:1;
}
}
package zhouls.bigdata.myMapReduce.flowsum;
import java.io.IOException;
import org.apache.commons.lang.StringUtils;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
/**
* FlowBean 是我们自定义的一种数据类型，要在hadoop的各个节点之间传输，应该遵循hadoop的序列化机制
* 就必须实现hadoop相应的序列化接口
*
*
*/
public class FlowSumMapper extends Mapper<LongWritable, Text, Text, FlowBean>{
//拿到日志中的一行数据，切分各个字段，抽取出我们需要的字段：手机号，上行流量，下行流量，然后封装成kv发送出去
@Override
protected void map(LongWritable key, Text value,Context context)
throws IOException, InterruptedException {
//拿一行数据
String line = value.toString();
//切分成各个字段
String[] fields = StringUtils.split(line, "\t");
//拿到我们需要的字段
String phoneNB = fields[1];
long u_flow = Long.parseLong(fields[7]);
long d_flow = Long.parseLong(fields[8]);
//封装数据为kv并输出
context.write(new Text(phoneNB), new FlowBean(phoneNB,u_flow,d_flow));
}
}
package zhouls.bigdata.myMapReduce.flowsum;
import java.io.IOException;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
public class FlowSumReducer extends Reducer<Text, FlowBean, Text, FlowBean>{
//框架每传递一组数据<1387788654,{flowbean,flowbean,flowbean,flowbean.....}>调用一次我们的reduce方法
//reduce中的业务逻辑就是遍历values，然后进行累加求和再输出
@Override
protected void reduce(Text key, Iterable<FlowBean> values,Context context)
throws IOException, InterruptedException {
long up_flow_counter = 0;
long d_flow_counter = 0;
for(FlowBean bean : values){
up_flow_counter += bean.getUp_flow();
d_flow_counter += bean.getD_flow();
}
context.write(key, new FlowBean(key.toString(), up_flow_counter, d_flow_counter));
}
}
package zhouls.bigdata.myMapReduce.flowsum;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.InputFormat;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.OutputFormat;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
import zhouls.bigdata.myMapReduce.Anagram.Anagram;
//这是job描述和提交类的规范写法
public class FlowSumRunner extends Configured implements Tool{
public int run(String[] arg0) throws Exception {
Configuration conf = new Configuration();
Job job = Job.getInstance(conf);
job.setJarByClass(FlowSumRunner.class);
job.setMapperClass(FlowSumMapper.class);
job.setReducerClass(FlowSumReducer.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(FlowBean.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(FlowBean.class);
FileInputFormat.addInputPath(job, new Path(arg0[0]));// 文件输入路径
FileOutputFormat.setOutputPath(job, new Path(arg0[1]));// 文件输出路径
job.waitForCompletion(true);
return 0;
}
public static void main(String[] args) throws Exception {
//集群路径
// String[] args0 = { "hdfs://HadoopMaster:9000/flowSum/HTTP_20130313143750.dat",
// "hdfs://HadoopMaster:9000/out/flowSum"};
//本地路径
String[] args0 = { "./data/flowSum/HTTP_20130313143750.dat",
"./out/flowSum/"};
int ec = ToolRunner.run( new Configuration(), new FlowSumRunner(), args0);
System. exit(ec);
}
}
Hadoop MapReduce编程 API入门系列之网页流量版本1（二十二）
原文：http://www.cnblogs.com/zlslch/p/6165938.html