1.日志源文件 (各个列分别是: 账户,营业额,花费,日期)
zhangsan@163.com 6000 0 2014-02-20
lisi@163.com 2000 0 2014-02-20
lisi@163.com 0 100 2014-02-20
zhangsan@163.com 3000 0 2014-02-20
wangwu@126.com 9000 0 2014-02-20
wangwu@126.com 0 200 2014-02-20
想要的结果: (计算出每个账户的总营业额和总花费,要求营业额排序降序,如果营业额相同则花费少的在上面)
zhangsan@163.com 9000 0 9000
wangwu@126.com 9000 200 8800
lisi@163.com 2000 100 1900
2.写代码:
InfoBean.java 对账户的后三个字段封装成一个Bean对象
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1 import java.io.DataInput;
2 import java.io.DataOutput;
3 import java.io.IOException;
4
5 import org.apache.hadoop.io.WritableComparable;
6
7 //要和其他的InfoBean类型进行比较,所以此处泛型T为InfoBean
8 public class InfoBean implements WritableComparable<InfoBean> {
9
10 private String account;
11 private double income;
12 private double expenses;
13 private double surplus;
14
15 /*
16 *如果不写这个方法,封装InfoBean对象的时候就要分别set这个对象的各个属性.
17 */
18 public void set(String account,double income,double expenses){
19 this.account = account;
20 this.income = income;
21 this.expenses = expenses;
22 this.surplus = income -expenses;
23 }
24 @Override
25 public void write(DataOutput out) throws IOException {
26 out.writeUTF(account);
27 out.writeDouble(income);
28 out.writeDouble(expenses);
29 out.writeDouble(surplus);
30 }
31
32 @Override
33 public void readFields(DataInput in) throws IOException {
34 this.account = in.readUTF();
35 this.income = in.readDouble();
36 this.expenses = in.readDouble();
37 this.surplus = in.readDouble();
38 }
39
40 @Override
41 public int compareTo(InfoBean o) {
42 if(this.income == o.getIncome()){
43 return this.expenses > o.getExpenses() ? 1 : -1;
44 } else {
45 return this.income > o.getIncome() ? -1 : 1;
46 }
47 }
48
49 @Override
50 //toString()方法输出的格式最好和源文件trade_info.txt中的格式一样, 字段通过Tab键分隔.
51 //而且在SumReducer类输出k3,v3的时候会输出k3(context.write(key, v);) 所以这个地方没有必要再输出k3(account)
52 public String toString() {
53 // return "InfoBean [account=" + account + ", income=" + income
54 // + ", expenses=" + expenses + ", surplus=" + surplus + "]";
55 return this.income + "\t" + this.expenses+"\t" + this.surplus;
56 }
57 public double getIncome() {
58 return income;
59 }
60
61 public void setIncome(double income) {
62 this.income = income;
63 }
64
65 public double getExpenses() {
66 return expenses;
67 }
68
69 public void setExpenses(double expenses) {
70 this.expenses = expenses;
71 }
72
73 public double getSurplus() {
74 return surplus;
75 }
76
77 public void setSurplus(double surplus) {
78 this.surplus = surplus;
79 }
80
81 public String getAccount() {
82 return account;
83 }
84
85 public void setAccount(String account) {
86 this.account = account;
87 }
88
89 }
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SumStep.java
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1 import java.io.IOException;
2
3 import org.apache.hadoop.conf.Configuration;
4 import org.apache.hadoop.fs.Path;
5 import org.apache.hadoop.io.LongWritable;
6 import org.apache.hadoop.io.Text;
7 import org.apache.hadoop.mapreduce.Job;
8 import org.apache.hadoop.mapreduce.Mapper;
9 import org.apache.hadoop.mapreduce.Reducer;
10 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
11 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
12
13 public class SumStep {
14
15 public static class SumMapper extends Mapper<LongWritable, Text, Text, InfoBean>{
16 private Text k = new Text();
17 private InfoBean bean = new InfoBean();
18
19 @Override
20 protected void map(LongWritable key, Text value,Mapper<LongWritable, Text, Text, InfoBean>.Context context)
21 throws IOException, InterruptedException {
22
23 String line = value.toString();
24 String [] fields = line.split("\t");
25 String account = fields[0];
26 double income = Double.parseDouble(fields[1]);
27 double expenses = Double.parseDouble(fields[2]);
28 k.set(account);
29 bean.set(account, income, expenses);
30 context.write(k, bean);
31 }
32 }
33 public static class SumReducer extends Reducer<Text, InfoBean, Text, InfoBean>{
34 private InfoBean v = new InfoBean();
35 @Override
36 protected void reduce(Text key, Iterable<InfoBean> values,Reducer<Text, InfoBean, Text, InfoBean>.Context context)
37 throws IOException, InterruptedException {
38 double sum_in = 0;
39 double sum_out = 0;
40 for(InfoBean bean : values){
41 sum_in += bean.getIncome();
42 sum_out += bean.getExpenses();
43 }
44 /*
45 * 在crxy的流量统计的案例中 是如下的方式写出k3和v3的 在reduce方法中new这个封装好的对象.
46 * 但是如果数据量比较大的情况下 是可能会造成内存溢出的.
47 * TrafficWritable v3 = new TrafficWritable(t1, t2, t3, t4);
48 * context.write(k2, v3);
49 *
50 * 所以建议把这个封装的对象写在"脑袋顶上" 如上所示....private InfoBean v = new InfoBean();
51 * 但是如果你Java基础比较好的话可能会说 在Java中是引用传递...所以后面的v会覆盖前面的v,造成最后只有最有一个v
52 * 其实这里是不会产生问题的,因为context.write()方法会直接把v3对应的InfoBean对象序列化.
53 * 虽然之前对象的引用确实覆盖了,但是之前对象的值等都保存了下来.是可以放在这个类的"脑袋顶上"的.
54 * 让这个类公用这个InfoBean对象.
55 */
56
57 v.set(key.toString(),sum_in,sum_out);
58 context.write(key, v);
59 }
60 }
61 public static void main(String[] args) throws Exception {
62 Configuration conf = new Configuration();
63 Job job = Job.getInstance(conf);
64 job.setJarByClass(SumStep.class);
65
66 job.setMapperClass(SumMapper.class);
67 //以下两行可以在满足一定条件的时候省略掉.
68 //在满足k2和k3,v2和v3一一对应的时候就可以省略掉. 看SumReducer类所在行的泛型.
69 job.setMapOutputKeyClass(Text.class);
70 job.setMapOutputValueClass(InfoBean.class);
71
72 FileInputFormat.setInputPaths(job, new Path(args[0]));
73
74 job.setReducerClass(SumReducer.class);
75 job.setOutputKeyClass(Text.class);
76 job.setOutputValueClass(InfoBean.class);
77 FileOutputFormat.setOutputPath(job, new Path(args[1]));
78 job.waitForCompletion(true);
79 }
80 }
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项目打成jar包放到Linux中,日志源文件上传到HDFS上.运行结果如下:
hadoop jar /root/itcastmr.jar itcastmr.SumStep /user/root/trade_info.txt /tradeout
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但是这个结果并没有排序.还是按照账号的字典排序.
以这个MR的输出当做输入对其根据InfoBean对象进行排序.....
上代码SortStep.java:
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1 import java.io.IOException;
2
3 import org.apache.hadoop.conf.Configuration;
4 import org.apache.hadoop.fs.Path;
5 import org.apache.hadoop.io.LongWritable;
6 import org.apache.hadoop.io.NullWritable;
7 import org.apache.hadoop.io.Text;
8 import org.apache.hadoop.mapreduce.Job;
9 import org.apache.hadoop.mapreduce.Mapper;
10 import org.apache.hadoop.mapreduce.Reducer;
11 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
12 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
13
14 public class SortStep {
15 //这个Mapper读取的HDFS文件是SumStep Reduce计算输出的文件.
16 public static class SortMapper extends Mapper<LongWritable, Text, InfoBean, NullWritable>{
17 private InfoBean k = new InfoBean();
18 @Override
19 protected void map(LongWritable key,Text value,Mapper<LongWritable, Text, InfoBean, NullWritable>.Context context)
20 throws IOException, InterruptedException {
21 String line = value.toString();
22 String [] fields = line.split("\t");
23 String account = fields[0];
24 double income = Double.parseDouble(fields[1]);
25 double expenses = Double.parseDouble(fields[2]);
26 k.set(account, income, expenses);
27 //现在是要求按照InfoBean对象中的规则排序(InfoBean中有compareTo方法)...所以InfoBean对象当做k2...
28 context.write(k,NullWritable.get());//不能传null,NullWritable.get() 是获得的this对象.
29 }
30 }
31 public static class SortReducer extends Reducer<InfoBean, NullWritable, Text, InfoBean>{
32 private Text k = new Text();
33 @Override
34 protected void reduce(InfoBean bean, Iterable<NullWritable> values,Reducer<InfoBean, NullWritable, Text, InfoBean>.Context context)
35 throws IOException, InterruptedException {
36 String account = bean.getAccount();
37 k.set(account);
38 context.write(k, bean);
39 }
40 }
41
42 public static void main(String[] args) throws Exception {
43 Configuration conf = new Configuration();
44 Job job = Job.getInstance(conf);
45 job.setJarByClass(SortStep.class);
46
47 job.setMapperClass(SortMapper.class);
48 //以下两行可以在满足一定条件的时候省略掉.
49 //在满足k2和k3,v2和v3一一对应的时候就可以省略掉. 看SumReducer类所在行的泛型.
50 job.setMapOutputKeyClass(InfoBean.class);
51 job.setMapOutputValueClass(NullWritable.class);
52
53 FileInputFormat.setInputPaths(job, new Path(args[0]));
54
55 job.setReducerClass(SortReducer.class);
56 job.setOutputKeyClass(Text.class);
57 job.setOutputValueClass(InfoBean.class);
58 FileOutputFormat.setOutputPath(job, new Path(args[1]));
59 job.waitForCompletion(true);
60 }
61 }
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打成jar包,然后运行命令....输入为上面SumStep.java的输出
hadoop jar /root/itcastmr.jar itcastmr.SortStep /tradeout /trade_sort_out
排序之后的结果:
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在MapReduce读取输入数据的时候,如果这个文件是以下划线开始的话,那么会不会读取这个文件中的内容...."_SUCCESS"文件就不会读取....
如果想对某个类进行排序,
1.这个类要实现WritableComparable接口,
2.还要重写compareTo方法. 根据自己的业务逻辑自定义排序.
只需要把要排序的类当做k2 就可以了...框架自动排序.
要排序对象的compareTo方法是框架调用的,框架在shuffle这个阶段会调用排序.
shuffle后面会讲,shuffle由很多很多的阶段组成,分区,排序,分组,combiner等等...把这些小的细节都讲完了之后再讲shuffle.
本文转自SummerChill博客园博客，原文链接：http://www.cnblogs.com/DreamDrive/p/7398455.html，如需转载请自行联系原作者
