第1关:成绩统计:
任务描述
相关知识
什么是MapReduce
如何使用MapReduce进行运算
代码解释
编程要求
测试说明
任务描述
本关任务:使用Map/Reduce计算班级中年龄最大的学生。
相关知识
为了完成本关任务,你需要掌握:1.什么是MapReduce,2.如何使用MapReduce进行运算。
什么是MapReduce
MapReduce是一种可用于数据处理的编程模型,我们现在设想一个场景,你接到一个任务,任务是:挖掘分析我国气象中心近年来的数据日志,该数据日志大小有3T,让你分析计算出每一年的最高气温,如果你现在只有一台计算机,如何处理呢?我想你应该会读取这些数据,并且将读取到的数据与目前的最大气温值进行比较。比较完所有的数据之后就可以得出最高气温了。不过以我们的经验都知道要处理这么多数据肯定是非常耗时的。
如果我现在给你三台机器,你会如何处理呢?看到下图你应该想到了:最好的处理方式是将这些数据切分成三块,然后分别计算处理这些数据(Map),处理完毕之后发送到一台机器上进行合并(merge),再计算合并之后的数据,归纳(reduce)并输出。
这就是一个比较完整的MapReduce的过程了。
开始你的任务吧,祝你成功!
答案代码--------------------------------------
import java.io.IOException;
import java.util.StringTokenizer;
import java.io.IOException;
import java.util.StringTokenizer;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.*;
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 WordCount {
public static class TokenizerMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
private int maxValue = 0;
public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString(),"\n");
while (itr.hasMoreTokens()) {
String[] str = itr.nextToken().split(" ");
String name = str[0];
one.set(Integer.parseInt(str[1]));
word.set(name);
context.write(word,one);
}
}
}
public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
private IntWritable result = new IntWritable();
public void reduce(Text key, Iterable<IntWritable> values, Context context)
throws IOException, InterruptedException {
int maxAge = 0;
int age = 0;
for (IntWritable intWritable : values) {
maxAge = Math.max(maxAge, intWritable.get());
}
result.set(maxAge);
context.write(key, result);
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
Job job = new Job(conf, "word count");
job.setJarByClass(WordCount.class);
job.setMapperClass(TokenizerMapper.class);
job.setCombinerClass(IntSumReducer.class);
job.setReducerClass(IntSumReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
String inputfile = "/user/test/input";
String outputFile = "/user/test/output/";
FileInputFormat.addInputPath(job, new Path(inputfile));
FileOutputFormat.setOutputPath(job, new Path(outputFile));
job.waitForCompletion(true);
}
}
命令行
touch file01
echo Hello World Bye World
cat file01
echo Hello World Bye World >file01
cat file01
touch file02
echo Hello Hadoop Goodbye Hadoop >file02
cat file02
start-dfs.sh
hadoop fs -mkdir /usr
hadoop fs -mkdir /usr/input
hadoop fs -ls /usr/output
hadoop fs -ls /
hadoop fs -ls /usr
hadoop fs -put file01 /usr/input
hadoop fs -put file02 /usr/input
hadoop fs -ls /usr/input
第2关:文件内容合并去重
任务描述
相关知识
map类
Reducer类
Job类
编程要求
测试说明
任务描述
本关任务:使用Map/Reduce编程实现文件合并和去重操作。
相关知识
通过上一小节的学习我们了解了MapReduce大致的使用方式,本关我们来了解一下Mapper类,Reducer类和Job类。
map类
首先我们来看看Mapper对象:
在编写MapReduce程序时,要编写一个类继承Mapper类,这个Mapper类是一个泛型类型,它有四个形参类型,分别指定了map()函数的输入键,输入值,和输出键,输出值的类型。就第一关的例子来说,输入键是一个长整型,输入值是一行文本,输出键是单词,输出值是单词出现的次数。
答案代码-------------------
import java.io.IOException;
import java.util.*;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.*;
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 Merge {
public static class Map extends Mapper<Object, Text, Text, Text>{
public void map(Object key, Text value, Context content)
throws IOException, InterruptedException {
Text text1 = new Text();
Text text2 = new Text();
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
text1.set(itr.nextToken());
text2.set(itr.nextToken());
content.write(text1, text2);
}
}
}
public static class Reduce extends Reducer<Text, Text, Text, Text> {
public void reduce(Text key, Iterable<Text> values, Context context)
throws IOException, InterruptedException {
Set<String> set = new TreeSet<String>();
for(Text tex : values){
set.add(tex.toString());
}
for(String tex : set){
context.write(key, new Text(tex));
}
}
}
public static void main(String[] args) throws Exception{
Configuration conf = new Configuration();
conf.set("fs.default.name","hdfs://localhost:9000");
Job job = Job.getInstance(conf,"Merge and duplicate removal");
job.setJarByClass(Merge.class);
job.setMapperClass(Map.class);
job.setCombinerClass(Reduce.class);
job.setReducerClass(Reduce.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
String inputPath = "/user/tmp/input/";
String outputPath = "/user/tmp/output/";
FileInputFormat.addInputPath(job, new Path(inputPath));
FileOutputFormat.setOutputPath(job, new Path(outputPath));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
第3关:信息挖掘 - 挖掘父子关系
任务描述
编程要求
测试说明
任务描述
本关任务:对给定的表格进行信息挖掘。
编程要求
你编写的程序要能挖掘父子辈关系,给出祖孙辈关系的表格。规则如下:
孙子在前,祖父在后;
输入文件路径:/user/reduce/input;
输出文件路径:/user/reduce/output。
测试说明
程序会对你编写的代码进行测试:
下面给出一个child-parent的表格,要求挖掘其中的父子辈关系,给出祖孙辈关系的表格。
输入文件内容如下:
child parent
Steven Lucy
Steven Jack
Jone Lucy
Jone Jack
Lucy Mary
Lucy Frank
Jack Alice
Jack Jesse
David Alice
David Jesse
Philip David
Philip Alma
Mark David
Mark Alma
输出文件内容如下:
grand_child grand_parent
Mark Jesse
Mark Alice
Philip Jesse
Philip Alice
Jone Jesse
Jone Alice
Steven Jesse
Steven Alice
Steven Frank
Steven Mary
Jone Frank
Jone Mary
开始你的任务吧,祝你成功!
答案代码------------------------
import java.io.IOException;
import java.util.*;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
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.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
public class simple_data_mining {
public static int time = 0;
public static class Map extends Mapper<Object, Text, Text, Text>{
public void map(Object key, Text value, Context context) throws IOException,InterruptedException{
String line = value.toString();
String[] childAndParent = line.split(" ");
List<String> list = new ArrayList<>(2);
for (String childOrParent : childAndParent) {
if (!"".equals(childOrParent)) {
list.add(childOrParent);
}
}
if (!"child".equals(list.get(0))) {
String childName = list.get(0);
String parentName = list.get(1);
String relationType = "1";
context.write(new Text(parentName), new Text(relationType + "+"
+ childName + "+" + parentName));
relationType = "2";
context.write(new Text(childName), new Text(relationType + "+"
+ childName + "+" + parentName));
}
}
}
public static class Reduce extends Reducer<Text, Text, Text, Text>{
public void reduce(Text key, Iterable<Text> values,Context context) throws IOException,InterruptedException{
if (time == 0) {
context.write(new Text("grand_child"), new Text("grand_parent"));
time++;
}
List<String> grandChild = new ArrayList<>();
List<String> grandParent = new ArrayList<>();
for (Text text : values) {
String s = text.toString();
String[] relation = s.split("\\+");
String relationType = relation[0];
String childName = relation[1];
String parentName = relation[2];
if ("1".equals(relationType)) {
grandChild.add(childName);
} else {
grandParent.add(parentName);
}
}
int grandParentNum = grandParent.size();
int grandChildNum = grandChild.size();
if (grandParentNum != 0 && grandChildNum != 0) {
for (int m = 0; m < grandChildNum; m++) {
for (int n = 0; n < grandParentNum; n++) {
context.write(new Text(grandChild.get(m)), new Text(
grandParent.get(n)));
}
}
}
}
}
public static void main(String[] args) throws Exception{
Configuration conf = new Configuration();
Job job = Job.getInstance(conf,"Single table join");
job.setJarByClass(simple_data_mining.class);
job.setMapperClass(Map.class);
job.setReducerClass(Reduce.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
String inputPath = "/user/reduce/input";
String outputPath = "/user/reduce/output";
FileInputFormat.addInputPath(job, new Path(inputPath));
FileOutputFormat.setOutputPath(job, new Path(outputPath));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
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