Hadoop基础---流量求和MapReduce程序及自定义数据类型
内容导读
互联网集市收集整理的这篇技术教程文章主要介绍了Hadoop基础---流量求和MapReduce程序及自定义数据类型，小编现在分享给大家，供广大互联网技能从业者学习和参考。文章包含7189字，纯文字阅读大概需要11分钟。
内容图文
一：测试数据
1363157985066 13726230503 00-FD-07-A4-72-B8:CMCC 120.196.100.82 i02.c.aliimg.com 24 27 2481 24681 200 1363157995052 13826544101 5C-0E-8B-C7-F1-E0:CMCC 120.197.40.4 4 0 264 0 200 1363157991076 13926435656 20-10-7A-28-CC-0A:CMCC 120.196.100.99 2 4 132 1512 200 1363154400022 13926251106 5C-0E-8B-8B-B1-50:CMCC 120.197.40.4 4 0 240 0 200 1363157993044 18211575961 94-71-AC-CD-E6-18:CMCC-EASY 120.196.100.99 iface.qiyi.com 视频网站 15 12 1527 2106 200 1363157995074 84138413 5C-0E-8B-8C-E8-20:7DaysInn 120.197.40.4 122.72.52.12 20 16 4116 1432 200 1363157993055 13560439658 C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18 15 1116 954 200 1363157995033 15920133257 5C-0E-8B-C7-BA-20:CMCC 120.197.40.4 sug.so.360.cn 信息安全 20 20 3156 2936 200 1363157983019 13719199419 68-A1-B7-03-07-B1:CMCC-EASY 120.196.100.82 4 0 240 0 200 1363157984041 13660577991 5C-0E-8B-92-5C-20:CMCC-EASY 120.197.40.4 s19.cnzz.com 站点统计 24 9 6960 690 200 1363157973098 15013685858 5C-0E-8B-C7-F7-90:CMCC 120.197.40.4 rank.ie.sogou.com 搜索引擎 28 27 3659 3538 200 1363157986029 15989002119 E8-99-C4-4E-93-E0:CMCC-EASY 120.196.100.99 www.umeng.com 站点统计 3 3 1938 180 200 1363157992093 13560439658 C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 15 9 918 4938 200 1363157986041 13480253104 5C-0E-8B-C7-FC-80:CMCC-EASY 120.197.40.4 3 3 180 180 200 1363157984040 13602846565 5C-0E-8B-8B-B6-00:CMCC 120.197.40.4 2052.flash2-http.qq.com 综合门户 15 12 1938 2910 200 1363157995093 13922314466 00-FD-07-A2-EC-BA:CMCC 120.196.100.82 img.qfc.cn 12 12 3008 3720 200 1363157982040 13502468823 5C-0A-5B-6A-0B-D4:CMCC-EASY 120.196.100.99 y0.ifengimg.com 综合门户 57 102 7335 110349 200 1363157986072 18320173382 84-25-DB-4F-10-1A:CMCC-EASY 120.196.100.99 input.shouji.sogou.com 搜索引擎 21 18 9531 2412 200 1363157990043 13925057413 00-1F-64-E1-E6-9A:CMCC 120.196.100.55 t3.baidu.com 搜索引擎 69 63 11058 48243 200 1363157988072 13760778710 00-FD-07-A4-7B-08:CMCC 120.196.100.82 2 2 120 120 200 1363157985079 13823070001 20-7C-8F-70-68-1F:CMCC 120.196.100.99 6 3 360 180 200 1363157985069 13600217502 00-1F-64-E2-E8-B1:CMCC 120.196.100.55 18 138 1080 186852 200
二：按照需求自定义数据类型
参考LongWritable进行改造：
package cn.hadoop.mr.wc;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import org.apache.hadoop.io.WritableComparable;
public class FlowBean implements WritableComparable<FlowBean> {
private String phoneNB;
private long up_flow;
private long down_flow;
private long sum_flow;
public FlowBean() {} //无参构造函数，用于反序列化时使用
public FlowBean(String phoneNB, long up_flow, long down_flow) {
this.phoneNB = phoneNB;
this.up_flow = up_flow;
this.down_flow = down_flow;
this.sum_flow = up_flow + down_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 getDown_flow() {
return down_flow;
}
public void setDown_flow(long down_flow) {
this.down_flow = down_flow;
}
public long getSum_flow() {
return up_flow + down_flow;
}
//用于序列化
@Override
public void write(DataOutput out) throws IOException {
// TODO Auto-generated method stub
out.writeUTF(phoneNB);
out.writeLong(up_flow);
out.writeLong(down_flow);
out.writeLong(up_flow+down_flow);
}
//用于反序列化
@Override
public void readFields(DataInput in) throws IOException {
// TODO Auto-generated method stub
phoneNB = in.readUTF();
up_flow = in.readLong();
down_flow = in.readLong();
sum_flow = in.readLong();
}
@Override
public int compareTo(FlowBean o) {
// TODO Auto-generated method stub
return 0;
}
@Override
public String toString() {
return "" + phoneNB + "\t" + up_flow + "\t" + down_flow + "\t"+ sum_flow;
}
}
三：实现Map程序
package cn.hadoop.fs;
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;
import cn.hadoop.mr.wc.FlowBean;
public class FlowSumMapper extends Mapper<LongWritable, Text, Text, FlowBean>{
@Override
protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, FlowBean>.Context context)
throws IOException, InterruptedException {
//获取一行数据
String line = value.toString();
//进行切分
String[] fields = StringUtils.split(line, "\t");
//获取我们需要的数据
String phoneNB = fields[1];
long up_flow = Long.parseLong(fields[7]);
long down_flow = Long.parseLong(fields[8]);
//封装数据为KV并输出
context.write(new Text(phoneNB), new FlowBean(phoneNB,up_flow,down_flow));
}
}
四：实现Reduce程序
package cn.hadoop.fs;
import java.io.IOException;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import cn.hadoop.mr.wc.FlowBean;
public class FlowSumReducer extends Reducer<Text, FlowBean, Text, FlowBean>{
@Override
protected void reduce(Text key, Iterable<FlowBean> values, Reducer<Text, FlowBean, Text, FlowBean>.Context context)
throws IOException, InterruptedException {
long up_flow_c = 0;
long down_flow_c = 0;
for(FlowBean bean: values) {
up_flow_c += bean.getUp_flow();
down_flow_c += bean.getDown_flow();
}
context.write(key, new FlowBean(key.toString(),up_flow_c,down_flow_c));
}
}
五：实现主函数调用
package cn.hadoop.fs;
import java.io.IOException;
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.Job;
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 cn.hadoop.mr.wc.FlowBean;
public class FlowSumRunner extends Configured implements Tool{
@Override
public int run(String[] args) throws Exception {
Configuration conf = new Configuration();
Job job = Job.getInstance(conf);
job.setJarByClass(FlowSumRunner.class);
job.setMapperClass(FlowSumMapper.class);
job.setReducerClass(FlowSumReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(FlowBean.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(FlowBean.class);
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
return job.waitForCompletion(true)?0:1;
}
public static void main(String[] args) throws Exception {
int res = ToolRunner.run(new Configuration(), new FlowSumRunner(), args);
System.exit(res);
}
}
六：测试结果
hadoop jar fs.jar cn.hadoop.fs.FlowSumRunner /fs/input/ /fs/output
内容总结
以上是互联网集市为您收集整理的Hadoop基础---流量求和MapReduce程序及自定义数据类型全部内容，希望文章能够帮你解决Hadoop基础---流量求和MapReduce程序及自定义数据类型所遇到的程序开发问题。 如果觉得互联网集市技术教程内容还不错，欢迎将互联网集市网站推荐给程序员好友。
内容备注
版权声明：本文内容由互联网用户自发贡献，该文观点与技术仅代表作者本人。本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。如发现本站有涉嫌侵权/违法违规的内容， 请发送邮件至 gblab@vip.qq.com 举报，一经查实，本站将立刻删除。
内容手机端
扫描二维码推送至手机访问。
来源：【匿名】