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一、最大值（Max） 情况1：
[hadoop@h71 q1]$ vi ql.txt
aa 111
22 555
[hadoop@h71 q1]$ hadoop fs -put ql.txt /input
java代码：
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.IntWritable;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
public class MaxValue extends Configured implements Tool {
public static class MapClass extends Mapper<LongWritable, Text, Text, IntWritable> {
private int maxNum = 0;
public void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
String[] str = value.toString().split(" ");
try {// 对于非数字字符我们忽略掉
for(int i=0;i<str.length;i++){
int temp = Integer.parseInt(str);
if (temp > maxNum) {
maxNum = temp;
}
}
} catch (NumberFormatException e) {
}
}
@Override
protected void cleanup(Context context) throws IOException,
InterruptedException {
context.write(new Text("Max"), new IntWritable(maxNum));
}
}
public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable> {
private int maxNum = 0;
private Text one = new Text();
public void reduce(Text key, Iterable<IntWritable> values, Context context)
throws IOException, InterruptedException {
for (IntWritable val : values) {
if ( val.get() > maxNum) {
maxNum = val.get();
}
}
one = key;
}
@Override
protected void cleanup(Context context) throws IOException,
InterruptedException {
context.write(one, new IntWritable(maxNum));
}
}
public int run(String[] args) throws Exception {
Configuration conf = getConf();
conf.set("mapred.jar","mv.jar");
Job job = new Job(conf, "MaxNum");
job.setJarByClass(MaxValue.class);
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
job.setMapperClass(MapClass.class);
job.setCombinerClass(Reduce.class);
job.setReducerClass(Reduce.class);
job.setInputFormatClass(TextInputFormat.class);
job.setOutputFormatClass(TextOutputFormat.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
System.exit(job.waitForCompletion(true) ? 0 : 1);
return 0;
}
public static void main(String[] args) throws Exception {
long start = System.nanoTime();
int res = ToolRunner.run(new Configuration(), new MaxValue(), args);
System.out.println(System.nanoTime()-start);
System.exit(res);
}
}
[hadoop@h71 q1]$ /usr/jdk1.7.0_25/bin/javac MaxValue.java
[hadoop@h71 q1]$ /usr/jdk1.7.0_25/bin/jar cvf xx.jar MaxValue*class
[hadoop@h71 q1]$ hadoop jar xx.jar MaxValue /input/ql.txt /output
[hadoop@h71 q1]$ hadoop fs -cat /user/hadoop/output/part-r-00000
Max 555
*************
setup()，此方法被MapReduce框架仅且执行一次，在执行Map任务前，进行相关变量或者资源的集中初始化工作。若是将资源初始化工作放在方法map()中，导致Mapper任务在解析每一行输入时都会进行资源初始化工作，导致重复，程序运行效率不高！
cleanup(),此方法被MapReduce框架仅且执行一次，在执行完毕Map任务后，进行相关变量或资源的释放工作。若是将释放资源工作放入方法map()中，也会导致Mapper任务在解析、处理每一行文本后释放资源，而且在下一行文本解析前还要重复初始化，导致反复重复，程序运行效率不高！
*************
情况2：
[hadoop@h71 q1]$ vi ceshi.txt
2
8
8
3
2
3
5
3
0
2
7
[hadoop@h71 q1]$ hadoop fs -put ceshi.txt /input
java代码：
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.NullWritable;
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;
import org.apache.hadoop.util.GenericOptionsParser;
public class Max {
public static class MaxMapper extends Mapper<LongWritable, Text, LongWritable, NullWritable> {
public long max = Long.MIN_VALUE;
public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
max = Math.max(Long.parseLong(value.toString()), max);
}
protected void cleanup(Mapper.Context context) throws IOException, InterruptedException {
context.write(new LongWritable(max), NullWritable.get());
}
}
public static class MaxReducer extends Reducer<LongWritable, NullWritable, LongWritable, NullWritable> {
public long max = Long.MIN_VALUE;
public void reduce(LongWritable key, Iterable<NullWritable> values, Context context) throws IOException, InterruptedException {
max = Math.max(max, key.get());
}
protected void cleanup(Reducer.Context context) throws IOException, InterruptedException {
context.write(new LongWritable(max), NullWritable.get());
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
if (otherArgs.length < 2) {
System.err.println("Usage: Max <in> [<in>...] <out>");
System.exit(2);
}
Job job = Job.getInstance(conf, "Max");
job.setJarByClass(Max.class);
job.setMapperClass(MaxMapper.class);
job.setCombinerClass(MaxReducer.class);
job.setReducerClass(MaxReducer.class);
job.setOutputKeyClass(LongWritable.class);
job.setOutputValueClass(NullWritable.class);
for (int i = 0; i < otherArgs.length - 1; ++i) {
FileInputFormat.addInputPath(job, new Path(otherArgs));
}
FileOutputFormat.setOutputPath(job,
new Path(otherArgs[otherArgs.length - 1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
[hadoop@h71 q1]$ /usr/jdk1.7.0_25/bin/javac Max.java
[hadoop@h71 q1]$ /usr/jdk1.7.0_25/bin/jar cvf xx.jar Max*class
[hadoop@h71 q1]$ hadoop jar xx.jar Max /input/ceshi.txt /output
[hadoop@h71 q1]$ hadoop fs -cat /output/part-r-00000
8
二、求和（Sum） [hadoop@h71 q1]$ vi ceshi.txt
2
8
8
3
2
3
5
3
0
2
7
[hadoop@h71 q1]$ hadoop fs -put ceshi.txt /input
java代码：
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.NullWritable;
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;
import org.apache.hadoop.util.GenericOptionsParser;
public class Sum {
public static class SumMapper extends Mapper<LongWritable, Text, LongWritable, NullWritable> {
public long sum = 0;
public void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
sum += Long.parseLong(value.toString());
}
protected void cleanup(Context context) throws IOException, InterruptedException {
context.write(new LongWritable(sum), NullWritable.get());
}
}
public static class SumReducer extends Reducer<LongWritable, NullWritable, LongWritable, NullWritable> {
public long sum = 0;
public void reduce(LongWritable key, Iterable<NullWritable> values, Context context)
throws IOException, InterruptedException {
sum += key.get();
}
protected void cleanup(Context context) throws IOException, InterruptedException {
context.write(new LongWritable(sum), NullWritable.get());
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
if (otherArgs.length < 2) {
System.err.println("Usage: Sum <in> [<in>...] <out>");
System.exit(2);
}
Job job = Job.getInstance(conf, "Sum");
job.setJarByClass(Sum.class);
job.setMapperClass(SumMapper.class);
job.setCombinerClass(SumReducer.class);
job.setReducerClass(SumReducer.class);
job.setOutputKeyClass(LongWritable.class);
job.setOutputValueClass(NullWritable.class);
for (int i = 0; i < otherArgs.length - 1; ++i) {
FileInputFormat.addInputPath(job, new Path(otherArgs));
}
FileOutputFormat.setOutputPath(job,
new Path(otherArgs[otherArgs.length - 1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
[hadoop@h71 q1]$ hadoop fs -cat /output/part-r-00000
43
三、平均值（Avg）
情况1：
[hadoop@h71 q1]$ vi math.txt
zs 80
ls 90
ww 95
[hadoop@h71 q1]$ vi china.txt
zs 60
ls 65
ww 90
[hadoop@h71 q1]$ hadoop fs -put math.txt /input
[hadoop@h71 q1]$ hadoop fs -put china.txt /input
java代码：
import java.io.IOException;
import java.util.Iterator;
import java.util.StringTokenizer;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
public class Score {
public static class Map extends Mapper<LongWritable, Text, Text, IntWritable> {
// 实现map函数
public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
// 将输入的纯文本文件的数据转化成String
String line = value.toString();
// 将输入的数据首先按行进行分割
StringTokenizer tokenizerArticle = new StringTokenizer(line, "\n");
// 分别对每一行进行处理
while (tokenizerArticle.hasMoreElements()) {
// 每行按空格划分
StringTokenizer tokenizerLine = new StringTokenizer(tokenizerArticle.nextToken());
String strName = tokenizerLine.nextToken();// 学生姓名部分
String strScore = tokenizerLine.nextToken();// 成绩部分
Text name = new Text(strName);
int scoreInt = Integer.parseInt(strScore);
// 输出姓名和成绩
context.write(name, new IntWritable(scoreInt));
}
}
}
public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable> {
// 实现reduce函数
public void reduce(Text key, Iterable<IntWritable> values,
Context context) throws IOException, InterruptedException {
int sum = 0;
int count = 0;
Iterator<IntWritable> iterator = values.iterator();
while (iterator.hasNext()) {
sum += iterator.next().get();// 计算总分
count++;// 统计总的科目数
}
int average = (int) sum / count;// 计算平均成绩
context.write(key, new IntWritable(average));
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
conf.set("mapred.jar","Score.jar");
Job job = new Job(conf, "Score Average");
job.setJarByClass(Score.class);
// 设置Map、Combine和Reduce处理类
job.setMapperClass(Map.class);
job.setCombinerClass(Reduce.class);
job.setReducerClass(Reduce.class);
// 设置输出类型
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
// 将输入的数据集分割成小数据块splites，提供一个RecordReder的实现
job.setInputFormatClass(TextInputFormat.class);
// 提供一个RecordWriter的实现，负责数据输出
job.setOutputFormatClass(TextOutputFormat.class);
// 设置输入和输出目录
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
[hadoop@h71 q1]$ /usr/jdk1.7.0_25/bin/javac Score.java
[hadoop@h71 q1]$ /usr/jdk1.7.0_25/bin/jar cvf xx.jar Score*class
[hadoop@h71 q1]$ hadoop jar xx.jar Score /input/* /output
[hadoop@h71 q1]$ hadoop fs -cat /output/part-r-00000
ls 77
ww 92
zs 70
补充：迭代器（Iterator）
迭代器是一种设计模式，它是一个对象，它可以遍历并选择序列中的对象，而开发人员不需要了解该序列的底层结构。迭代器通常被称为“轻量级”对象，因为创建它的代价小。
Java中的Iterator功能比较简单，并且只能单向移动：
(1) 使用方法iterator()要求容器返回一个Iterator。第一次调用Iterator的next()方法时，它返回序列的第一个元素。注意：iterator()方法是java.lang.Iterable接口,被Collection继承。
(2) 使用next()获得序列中的下一个元素。
(3) 使用hasNext()检查序列中是否还有元素。
(4) 使用remove()将迭代器新返回的元素删除。
Iterator是Java迭代器最简单的实现，为List设计的ListIterator具有更多的功能，它可以从两个方向遍历List，也可以从List中插入和删除元素。
1.创建集合：
Collection c = new ArrayList<String>();
2.添加元素：
c.add("hehehe");
c.add("huhuhu");
c.add("wawawa");
3.获取集合的迭代器：
Iterator iterator = c.iterator();
4.进行遍历：
while(iterator.hasNext())//如果仍有元素可以迭代，则返回 true
{
System.out.println(iterator.next());//返回迭代的下一个元素。
}
情况2： [hadoop@h71 q1]$ vi ceshi.txt
2
8
8
3
2
3
5
3
0
2
7
[hadoop@h71 q1]$ hadoop fs -put ceshi.txt /input
java代码：
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
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;
import org.apache.hadoop.util.GenericOptionsParser;
public class Average {
public static class AvgMapper extends Mapper<LongWritable, Text, LongWritable, LongWritable> {
public long sum = 0;
public long count = 0;
public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
sum += Long.parseLong(value.toString());
count += 1;
}
protected void cleanup(Context context) throws IOException, InterruptedException {
context.write(new LongWritable(sum), new LongWritable(count));
}
}
public static class AvgCombiner extends Reducer<LongWritable, LongWritable, LongWritable, LongWritable> {
public long sum = 0;
public long count = 0;
public void reduce(LongWritable key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {
sum += key.get();
for (LongWritable v : values) {
count += v.get();
}
}
protected void cleanup(Context context) throws IOException, InterruptedException {
context.write(new LongWritable(sum), new LongWritable(count));
}
}
public static class AvgReducer extends Reducer<LongWritable, LongWritable, DoubleWritable, NullWritable> {
public long sum = 0;
public long count = 0;
public void reduce(LongWritable key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {
sum += key.get();
for (LongWritable v : values) {
count += v.get();
}
}
protected void cleanup(Context context) throws IOException, InterruptedException {
context.write(new DoubleWritable(new Double(sum)/count), NullWritable.get());
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
if (otherArgs.length < 2) {
System.err.println("Usage: Avg <in> [<in>...] <out>");
System.exit(2);
}
Job job = Job.getInstance(conf, "Avg");
job.setJarByClass(Average.class);
job.setMapperClass(AvgMapper.class);
job.setCombinerClass(AvgCombiner.class);
job.setReducerClass(AvgReducer.class);
//注意这里:由于Mapper与Reducer的输出Key,Value类型不同,所以要单独为Mapper设置类型
job.setMapOutputKeyClass(LongWritable.class);
job.setMapOutputValueClass(LongWritable.class);
job.setOutputKeyClass(DoubleWritable.class);
job.setOutputValueClass(NullWritable.class);
for (int i = 0; i < otherArgs.length - 1; ++i) {
FileInputFormat.addInputPath(job, new Path(otherArgs));
}
FileOutputFormat.setOutputPath(job,
new Path(otherArgs[otherArgs.length - 1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
[hadoop@h71 q1]$ /usr/jdk1.7.0_25/bin/javac Average.java
[hadoop@h71 q1]$ /usr/jdk1.7.0_25/bin/jar cvf xx.jar Average*class
[hadoop@h71 q1]$ hadoop jar xx.jar Average /input/ceshi.txt /output
[hadoop@h71 q1]$ hadoop fs -cat /output/part-r-00000
3.909090909090909
【转】 https://blog.csdn.net/m0_37739193/article/details/76169108
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