使用MapReduce求出各年销售笔数、各年销售总额
1）将上面的数据文件上传到hdfs
hdfs dfs -put sales.csv /input/
2）采用Eclipse/IDEA创建一个Maven工程，同时修改pom.xml文件，增加dependencies，/dependencies、build，/build节点，内容如下：
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-common</artifactId>
<version>2.7.7</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-hdfs</artifactId>
<version>2.7.7</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-mapreduce-client-core</artifactId>
<version>2.7.7</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>2.7.7</version>
</dependency>
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-jar-plugin</artifactId>
<version>2.6</version>
<configuration>
<archive>
<manifest>
<!-- main()所在的类，注意修改 -->
<mainClass>org.example.SoldMain</mainClass>
</manifest>
</archive>
</configuration>
</plugin>
</plugins>
3）开始开发java代码，需要4个类：
首先是主输出类SoldMain(代码如下)：
package org.example;
import org.apache.hadoop.conf.Configuration;
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;
public class SoldMain {
public static void main(String[] args) throws Exception {
//1. 创建一个job和任务入口(指定主类)
Job job = Job.getInstance(new Configuration());
job.setJarByClass(SoldMain.class);
//2. 指定job的mapper和输出的类型然后是SoldMapper类(代码如下)：
package org.example;
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 java.io.IOException;
public class SoldMapper extends Mapper<LongWritable, Text, Text, Sold> {
@Override
protected void map(LongWritable k1, Text v1,
Context context)
throws IOException, InterruptedException {
//字段名 prod_id,cust_id,time,channel_id,promo_id,quantity_sold,amount_sold
//数据类型：Int，Int，Date， Int，Int ，Int ,float(10,2),
//数据: 13,987,1998-01-10,3,999,1,1232.16
String data = v1.toString();
String[] words = data.split(",");
//数据: t1=987,1998-01-10,3,999,1,1232.16
String t1 = StringUtils.substringAfter(data, ",");
//数据: t2=1998-01-10,3,999,1,1232.16
String t2 = StringUtils.substringAfter(t1, ",");
//取年份为偏移量，数据: words2[0]=1998，words2[1]=01，words2[2]=10,3,999,1,1232.16
String[] words2 = t2.split("-");
// StringUtils.substringAfter("dskeabcedeh", "e");
// /*结果是：abcedeh*/
Sold sold = new Sold();
sold.setTime(words[2]);//数组word[]
sold.setQuantity_sold(Integer.parseInt(words[5]));
sold.setAmount_sold(Float.valueOf(words[6]));
context.write(new Text(words2[0]), sold);//数组word2[],word2[0]代表年份作为k2
}
}
接着是SoldReduce类(代码如下)：
package org.example;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import java.io.IOException;
public class SoldReduce extends Reducer<Text, Sold, Text, Text> {
protected void reduce(Text k3, Iterable<Sold> v3, Context context) throws IOException, InterruptedException {
int total1 = 0;
float total2 = 0;
for (Sold sold : v3) {
total1 = total1 + sold.getQuantity_sold();
total2 = total2 + sold.getAmount_sold();
}
String total = "销售笔数:" + Integer.toString(total1) + "," + "销售总额:" + Float.toString(total2);
context.write(k3, new Text(total));
}
}
最后是Sold类(代码如下)：
package org.example;
import org.apache.hadoop.io.Writable;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
public class Sold implements Writable {
//字段名 prod_id,cust_id,time,channel_id,promo_id,quantity_sold,amount_sold
//数据类型：Int，Int，Date， Int，Int ，Int ,float(10,2),
//数据: 13, 987, 1998/1/10, 3, 999,1, 1232.16
//由以上定义变量
private int prod_id;
private int cust_id;
private String time;
private int channel_id;
private int promo_id;
private int quantity_sold;
private float amount_sold;//奖金
//序列化方法：将java对象转化为可跨机器传输数据流（二进制串/字节）的一种技术
public void write(DataOutput out) throws IOException {
out.writeInt(this.prod_id);
out.writeInt(this.cust_id);
out.writeUTF(this.time);
out.writeInt(this.channel_id);
out.writeInt(this.promo_id);
out.writeInt(this.quantity_sold);
out.writeFloat(this.amount_sold);
}
//反序列化方法：将可跨机器传输数据流（二进制串）转化为java对象的一种技术
public void readFields(DataInput in) throws IOException {
this.prod_id = in.readInt();
this.cust_id = in.readInt();
this.time = in.readUTF();
this.channel_id = in.readInt();
this.promo_id = in.readInt();
this.quantity_sold = in.readInt();
this.amount_sold = in.readFloat();
}
public int getProd_id() {
return prod_id;
}
public void setProd_id(int prod_id) {
this.prod_id = prod_id;
}
public int getCust_id() {
return cust_id;
}
public void setCust_id(int cust_id) {
this.cust_id = cust_id;
}
public String getTime() {
return time;
}
public void setTime(String time) {
this.time = time;
}
public int getChannel_id() {
return channel_id;
}
public void setChannel_id(int channel_id) {
this.channel_id = channel_id;
}
public int getPromo_id() {
return promo_id;
}
public void setPromo_id(int promo_id) {
this.promo_id = promo_id;
}
public int getQuantity_sold() {
return quantity_sold;
}
public void setQuantity_sold(int quantity_sold) {
this.quantity_sold = quantity_sold;
}
public float getAmount_sold() {
return amount_sold;
}
public void setAmount_sold(float amount_sold) {
this.amount_sold = amount_sold;
}
}
4）使用命令(如下)打包:
mvn clean package
5）将jar包通过xftp传输到linux下，在hadoop环境运行jar包，命令如下：
hadoop jar annualTotal-0.0.1-SNAPSHOT.jar /input/sales.csv /output/sales
jar包名和输入输出名请自行修改
6）查看执行结果(命令如下)：
hdfs dfs -cat /output/sales/part-r-00000
输出路径请自行查看
