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前言：这里给出的是hadoop1版本的代码，如果你把前面的知识都掌握了的话，我相信你自己也可以写出hadoop2版本的代码来。
一、Pv
[hadoop@h71 q1]$ vi ip.txt
192.168.1.1
192.168.2.2
192.168.3.3
192.168.2.2
1.1.1.1
[hadoop@h71 q1]$ hadoop fs -put ip.txt /input
java代码：
import java.io.IOException;
import java.util.Iterator;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.TextInputFormat;
import org.apache.hadoop.mapred.TextOutputFormat;
public class IpPv {
public static class IpPvUvMap extends MapReduceBase implements Mapper<LongWritable, Text, Text, Text> {
@Override
public void map(LongWritable longWritable, Text text, OutputCollector<Text, Text>
outputCollector, Reporter reporter) throws IOException {
String ip = text.toString().split(" ", 5)[0];
outputCollector.collect(new Text("pv"), new Text("1"));
}
}
public static class IpPvUvReduce extends MapReduceBase implements Reducer<Text, Text, Text, Text> {
@Override
public void reduce(Text key, Iterator<Text> iterator, OutputCollector<Text, Text>
outputCollector, Reporter reporter) throws IOException {
long sum = 0;
while(iterator.hasNext()){
sum = sum + Long.parseLong(iterator.next().toString());
}
outputCollector.collect(new Text("pv"), new Text(String.valueOf(sum)));
}
}
public static void main(String [] args) throws IOException {
System.out.println(args.length);
if(args.length < 2){
System.out.println("args not right!");
return ;
}
JobConf conf = new JobConf(IpPv.class);
//set output key class
conf.setOutputKeyClass(Text.class);
conf.setOutputValueClass(Text.class);
//set mapper & reducer class
conf.setMapperClass(IpPvUvMap.class);
conf.setCombinerClass(IpPvUvReduce.class);
conf.setReducerClass(IpPvUvReduce.class);
// set format
conf.setInputFormat(TextInputFormat.class);
conf.setOutputFormat(TextOutputFormat.class);
String inputDir = args[0];
String outputDir = args[1];
// FileInputFormat.setInputPaths(conf, "/user/hadoop/input/");
FileInputFormat.setInputPaths(conf, inputDir);
FileOutputFormat.setOutputPath(conf, new Path(outputDir));
boolean flag = JobClient.runJob(conf).isSuccessful();
}
}
执行：
[hadoop@h71 q1]$ /usr/jdk1.7.0_25/bin/javac IpPv.java
[hadoop@h71 q1]$ /usr/jdk1.7.0_25/bin/jar cvf xx.jar IpPv*class
[hadoop@h71 q1]$ hadoop jar xx.jar IpPv /input/ip.txt /output
查看结果： [hadoop@h71 q1]$ hadoop fs -cat /output/part-00000
pv 5
************
OutputCollector和Reporter是Hadoop-0.19以前版本里面的API，在Hadoop-0.20.2以后就换成Context，Context的功能包含了OutputCollector和Reporter的功能
我们还可以在代码中进行设置来自定义 key/value 输出分隔符：在主函数中添加如下一行代码：
conf.set("mapred.textoutputformat.separator", ";"); //此处以”;“作为分割符
输出结果： pv;5
************
二、Uv
[hadoop@h71 q1]$ vi ii.txt
1.1.1.1 a
2.2.2.2 a
1.1.1.1 b
1.1.1.1 c
2.2.2.2 f
3.3.3.3 gg
[hadoop@h71 q1]$ hadoop fs -put ii.txt /input
java代码：
import java.io.IOException;
import java.util.Iterator;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.TextInputFormat;
import org.apache.hadoop.mapred.TextOutputFormat;
/**
* 在reduce端直接计算Uv
*/
public class IpUv {
public static class IpUvMapper1 extends MapReduceBase implements Mapper<LongWritable, Text, Text, Text> {
@Override
public void map(LongWritable longWritable, Text text,
OutputCollector<Text, Text> outputCollector, Reporter reporter) throws IOException {
String ip = text.toString().split(" ", 5)[0];
outputCollector.collect(new Text(ip.trim()), new Text("1"));
}
}
public static class IpUvReducer1 extends MapReduceBase implements Reducer<Text, Text, Text, Text> {
@Override
public void reduce(Text text, Iterator<Text> iterator,
OutputCollector<Text, Text> outputCollector, Reporter reporter) throws IOException {
outputCollector.collect(text, new Text("1"));
}
}
public static class IpUvMapper2 extends MapReduceBase implements Mapper<LongWritable, Text, Text, Text>{
public void map(LongWritable longWritable, Text text,
OutputCollector<Text, Text> outputCollector, Reporter reporter) throws IOException {
String ip = text.toString().split("\t")[0];
outputCollector.collect(new Text("uv"), new Text("1"));
}
}
public static class IpUvReducer2 extends MapReduceBase implements Reducer<Text, Text, Text, Text>{
@Override
public void reduce(Text text, Iterator<Text> iterator,
OutputCollector<Text, Text> outputCollector, Reporter reporter) throws IOException {
long sum = 0;
/**
* uv, [1,1,1,1,1,1]
*/
while(iterator.hasNext()){
sum = sum + Long.parseLong(iterator.next().toString());
}
outputCollector.collect(new Text("uv"), new Text(String.valueOf(sum)));
}
}
public static void main(String [] args) throws IOException {
if(args.length < 2){
System.out.println("args not right!");
return ;
}
JobConf conf = new JobConf(IpUv.class);
conf.setOutputKeyClass(Text.class);
conf.setOutputValueClass(Text.class);
//set mapper & reducer class
conf.setMapperClass(IpUvMapper1.class);
conf.setReducerClass(IpUvReducer1.class);
// set format
conf.setInputFormat(TextInputFormat.class);
conf.setOutputFormat(TextOutputFormat.class);
String inputDir = args[0];
String outputDir = args[1];
// FileInputFormat.setInputPaths(conf, "/user/hadoop/rongxin/locationinput/");
FileInputFormat.setInputPaths(conf, inputDir);
FileOutputFormat.setOutputPath(conf, new Path(outputDir));
boolean flag = JobClient.runJob(conf).isSuccessful();
if(flag){
JobConf conf1 = new JobConf(IpUv.class);
conf1.setOutputKeyClass(Text.class);
conf1.setOutputValueClass(Text.class);
//set mapper & reducer class
conf1.setMapperClass(IpUvMapper2.class);
conf1.setReducerClass(IpUvReducer2.class);
// set format
conf1.setInputFormat(TextInputFormat.class);
conf1.setOutputFormat(TextOutputFormat.class);
// FileInputFormat.setInputPaths(conf, "/user/hadoop/rongxin/locationinput/");
FileInputFormat.setInputPaths(conf1, outputDir);
FileOutputFormat.setOutputPath(conf1, new Path(outputDir + "-2"));
boolean flag1 = JobClient.runJob(conf1).isSuccessful();
System.out.println(flag1);
}
}
}
执行：
[hadoop@h71 q1]$ /usr/jdk1.7.0_25/bin/javac IpUv.java
[hadoop@h71 q1]$ /usr/jdk1.7.0_25/bin/jar cvf xx.jar IpUv*class
[hadoop@h71 q1]$ hadoop jar xx.jar IpUv /input/ii.txt /output
查看结果： [hadoop@h71 q1]$ hadoop fs -cat /output/part-00000
1.1.1.1 1
2.2.2.2 1
3.3.3.3 1
[hadoop@h71 q1]$ hadoop fs -cat /output-2/part-00000
uv 3
【转载】 https://blog.csdn.net/m0_37739193/article/details/76566866
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