MapReduce高阶训练
一、上网流量统计
数据格式如下:
二、需求：统计求和
统计每个手机号的上行数据包数总和，下行数据包数总和，上行总流量之和，下行总流量之和
分析：以手机号码作为key值，上行数据包，下行数据包，上行总流量，下行总流量四个字段作为value值，然后以这个key和value作为map阶段的输出，reduce阶段的输入。
1、思路分析
2、代码实现
第一步：自定义map的输出value对象FlowBean
import org.apache.hadoop.io.Writable;import org.apache.hadoop.io.WritableComparable;import java.io.DataInput;import java.io.DataOutput;import java.io.IOException;//如果MR中的JavaBean不是作为K2，则只需要实现Writable接口即可public class FlowBean implements Writable{
private Integer upFlow; //上行包数 private Integer downFlow; //下行包数 private Integer upCountFlow; //上行流量和 private Integer downCountFlow; //下行流量和 public FlowBean() {
}
public FlowBean(Integer upFlow, Integer downFlow, Integer upCountFlow, Integer downCountFlow) {
this.upFlow = upFlow;
this.downFlow = downFlow;
this.upCountFlow = upCountFlow;
this.downCountFlow = downCountFlow;
}
public Integer getUpFlow() {
return upFlow;
}
@Override public String toString() {
return upFlow +
"\t" + downFlow +
"\t" + upCountFlow +
"\t" + downCountFlow;
}
public void setUpFlow(Integer upFlow) {
this.upFlow = upFlow;
}
public Integer getDownFlow() {
return downFlow;
}
public void setDownFlow(Integer downFlow) {
this.downFlow = downFlow;
}
public Integer getUpCountFlow() {
return upCountFlow;
}
public void setUpCountFlow(Integer upCountFlow) {
this.upCountFlow = upCountFlow;
}
public Integer getDownCountFlow() {
return downCountFlow;
}
public void setDownCountFlow(Integer downCountFlow) {
this.downCountFlow = downCountFlow;
}
//序列化 @Override public void write(DataOutput out) throws IOException {
out.writeInt(upFlow);
out.writeInt(downFlow);
out.writeInt(upCountFlow);
out.writeInt(downCountFlow);
}
//反序列化 @Override public void readFields(DataInput in) throws IOException {
this.upFlow = in.readInt();
this.downFlow = in.readInt();
this.upCountFlow = in.readInt();
this.downCountFlow = in.readInt();
}
}第二步：定义FlowMapper类
import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Mapper;import java.io.IOException;public class FlowCountMapper extends Mapper<LongWritable, Text,Text,FlowBean> {
@Override protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
//1:拆分行文本数据（拆分v1） String[] split = value.toString().split("\t");
//2:从拆分数组中得到手机号，得到K2 String phoneNum = split[1];
//3:从拆分数组中得到4个流量字段，并封装到FlowBean,得到V2 FlowBean flowBean = new FlowBean();
flowBean.setUpFlow(Integer.parseInt(split[6]));
flowBean.setDownFlow(Integer.parseInt(split[7]));
flowBean.setUpCountFlow(Integer.parseInt(split[8]));
flowBean.setDownCountFlow(Integer.parseInt(split[9]));
//4:将K2和V2写入上下文中 context.write(new Text(phoneNum), flowBean);
}
}第三步：定义FlowCountReducer类
import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Reducer;import java.io.IOException;public class FlowCountReducer extends Reducer<Text,FlowBean,Text,FlowBean> {
@Override protected void reduce(Text key, Iterable<FlowBean> values, Context context) throws IOException, InterruptedException {
//1:定义四个变量，分别来存储上行包数，下行包数，上行流量总和，下行流量总和 int upFlow = 0;
int downFlow = 0;
int upCountFlow = 0;
int downCountFlow = 0;
//2:遍历集合，将集合中每一个FlowBean的四个流量字段相加 for (FlowBean flowBean : values) {
upFlow += flowBean.getUpFlow();
downFlow += flowBean.getDownFlow();
upCountFlow += flowBean.getUpCountFlow();
downCountFlow += flowBean.getDownCountFlow();
}
//3:K3就是原来的K2，V3就是新的FlowBean FlowBean flowBean = new FlowBean(upFlow, downFlow, upCountFlow, downCountFlow);
//4:将K3和V3写入上下文中 context.write(key, flowBean);
}
}第四步：程序main函数入口FlowCountRunner
import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.NullWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Job;import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;import java.io.IOException;public class FlowCountRunner {
public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
//1、创建建一个job任务对象 Configuration configuration = new Configuration();
Job job = Job.getInstance(configuration, "flowcount_demo");
//2、指定job所在的jar包 job.setJarByClass(FlowCountRunner.class);
//3、指定源文件的读取方式类和源文件的读取路径 job.setInputFormatClass(TextInputFormat.class); //按照行读取 //TextInputFormat.addInputPath(job, new Path("hdfs://node1:8020/input/wordcount")); //只需要指定源文件所在的目录即可 TextInputFormat.addInputPath(job, new Path("file:///E:\\input\\flowcount")); //只需要指定源文件所在的目录即可 //4、指定自定义的Mapper类和K2、V2类型 job.setMapperClass(FlowCountMapper.class); //指定Mapper类 job.setMapOutputKeyClass(Text.class); //K2类型 job.setMapOutputValueClass(FlowBean.class);//V2类型 //5、指定自定义分区类（如果有的话） //6、指定自定义分组类（如果有的话） //7、指定自定义Combiner类(如果有的话) //设置ReduceTask个数 //8、指定自定义的Reducer类和K3、V3的数据类型 job.setReducerClass(FlowCountReducer.class); //指定Reducer类 job.setOutputKeyClass(Text.class); //K3类型 job.setOutputValueClass(FlowBean.class); //V3类型 //9、指定输出方式类和结果输出路径 job.setOutputFormatClass(TextOutputFormat.class);
//TextOutputFormat.setOutputPath(job, new Path("hdfs://node1:8020/output/wordcount")); //目标目录不能存在，否则报错 TextOutputFormat.setOutputPath(job, new Path("file:///E:\\output\\flowcount")); //目标目录不能存在，否则报错 //10、将job提交到yarn集群 boolean bl = job.waitForCompletion(true); //true表示可以看到任务的执行进度 //11.退出执行进程 System.exit(bl?0:1);
}
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