需求：1.统计每一个用户（手机号）所耗费的总上行流量、下行流量，总流量
1.数据如下：保存为.dat文件(因为以\t切分数据，文件格式必须合适)
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
1363157985066 13726238888 00-FD-07-A4-72-B8:CMCC 120.196.100.82 i02.c.aliimg.com 24 27 2481 24681 200
1363157993055 13560436666 C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18 15 1116 954 200
2.技术实现过程：
1.首先将Map输入中的手机号，上行流量，下行流量数据抽取出来(每一行输入数据调用一次自定义map方法处理数据)，
然后根据相同的key进行数据分发，以便于相同key会到同一个ReduceTask
2.Map输出为<手机号,bean>，自定义javaBean来封装流量信息，并将javaBean充当Map输出的Value来传输，javaBean
要实现Writable序列化接口，实现两个方法
3.Reduce在获得<手机号,list>后进行累积，然后输出结果即可(框架每传递进来一个kv组，reduce方法被调用一次)
3.代码：FlowCount.java
package cn.bigdata.hdfs.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);
}
}
FlowBean.java
如果想在Reducer的输出结果中使用自定义的数据类型，重写FlowBean的toString()方法即可。
package cn.bigdata.hdfs.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;
}
}
4.执行程序：
4.1.创建HDFS文件存放目录:hadoop fs -mkdir -p /wordcount/phoneFlum
4.2.运行MapReduce程序jar包：
hadoop jar flowsum.jar cn.bigdata.hdfs.flowsum.FlowCount /wordcount/phoneFlum /wordcount/phoneFlumOut
5.查看执行结果：
需求：2.将流量统计结果按照手机归属地省份不同输出到不同文件中(ReduceTask并行度控制，自定义Partitioner)
2.技术实现过程：
1.Mapreduce中会将map输出的kv对，按照相同key分组(调用getPartition)，然后分发给不同的reducetask
2.Map输出结果的时候调用了Partitioner组件(返回分区号)，由它决定将数据放到哪个区中，默认的分组规则为
：根据key的hashcode%reducetask数来分发，源代码如下：
public class HashPartitioner<K, V> extends Partitioner<K, V> {
/** Use {@link Object#hashCode()} to partition. */
public int getPartition(K key, V value,int numReduceTasks) {
return (key.hashCode() & Integer.MAX_VALUE) % numReduceTasks;
}
}
3.所以：如果要按照我们自己的需求进行分组，则需要改写数据分发（分组）组件Partitioner，自定义一个
CustomPartitioner继承抽象类：Partitioner，来返回一个分区编号
4.然后在job对象中，设置自定义partitioner： job.setPartitionerClass(CustomPartitioner.class)
5.自定义partition后，要根据自定义partitioner的逻辑设置相应数量的ReduceTask
3.代码实现自定义partitioner数据分区规则：
package cn.bigdata.hdfs.flowsum;
import java.util.HashMap;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Partitioner;
/**
* Partitioner<Text, FlowBean>中分别 对应的是map输出kv的类型
*/
public class ProvincePartitioner extends Partitioner<Text, FlowBean>{
public static HashMap<String, Integer> proviceDict = new HashMap<String, Integer>();
static{//分为5个区
proviceDict.put("136", 0);
proviceDict.put("137", 1);
proviceDict.put("138", 2);
proviceDict.put("139", 3);
} @Override
public int getPartition(Text key, FlowBean value, int numPartitions) {
String prefix = key.toString().substring(0, 3);
Integer provinceId = proviceDict.get(prefix);
return provinceId==null?4:provinceId;
}
}
//指定我们自定义的数据分区器
job.setPartitionerClass(ProvincePartitioner.class);
//同时指定相应“分区”数量的reducetask
job.setNumReduceTasks(5);
运行程序：hadoop jar flowsum.jar cn.bigdata.hdfs.flowsum.FlowCount /wordcount/phoneFlum /wordcount/phoneFlumOut1
此时生成了五个分区文件：
注意：如果reduceTask的数量>= getPartition的结果数 ，则会多产生几个空的输出文件part-r-000xx
如果1<reduceTask的数量<getPartition的结果数 ，则有一部分分区数据无处安放，会Exception
如果 reduceTask的数量=1，则不管mapTask端输出多少个分区文件，最终结果都交给这一个reduceTask，
最终也就只会产生一个结果文件 part-r-00000
需求：3.将统计结果按照总流量倒序排序
//指定我们自定义的数据分区器
job.setPartitionerClass(ProvincePartitioner.class);
//同时指定相应“分区”数量的reducetask
job.setNumReduceTasks(5);