林子雨编著《大数据基础编程、实验和案例教程》(教材官网)教材中的代码，在纸质教材中的印刷效果不是很好，可能会影响读者对代码的理解，为了方便读者正确理解代码或者直接拷贝代码用于上机实验，这里提供全书配套的所有代码。
查看教材所有章节的代码
第7章 MapReduce基础编程
教材第140页
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I love Spark
I love Hadoop
Hadoop is good
Spark is fast
教材第141页
public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable> {
private static final IntWritable one = new IntWritable(1);
private Text word = new Text();
public TokenizerMapper() {
}
public void map(Object key, Text value, Mapper<Object, Text, Text, IntWritable>.Context context) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
while(itr.hasMoreTokens()) {
this.word.set(itr.nextToken());
context.write(this.word, one);
}
}
}
教材第142页
public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
private IntWritable result = new IntWritable();
public IntSumReducer() {
}
public void reduce(Text key, Iterable<IntWritable> values, Reducer<Text, IntWritable, Text, IntWritable>.Context context) throws IOException, InterruptedException {
int sum = 0;
IntWritable val;
for(Iterator i$ = values.iterator(); i$.hasNext(); sum += val.get()) {
val = (IntWritable)i$.next();
}
this.result.set(sum);
context.write(key, this.result);
}
}
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: wordcount <in> [<in>...] <out>");
System.exit(2);
}
Job job = Job.getInstance(conf, "word count"); //设置环境参数
job.setJarByClass(WordCount.class); //设置整个程序的类名
job.setMapperClass(WordCount.TokenizerMapper.class); //添加Mapper类
job.setReducerClass(WordCount.IntSumReducer.class); //添加Reducer类
job.setOutputKeyClass(Text.class); //设置输出类型
job.setOutputValueClass(IntWritable.class); //设置输出类型
for(int i = 0; i < otherArgs.length - 1; ++i) {
FileInputFormat.addInputPath(job, new Path(otherArgs[i])); //设置输入文件
}
FileOutputFormat.setOutputPath(job, new Path(otherArgs[otherArgs.length - 1]));//设置输出文件
System.exit(job.waitForCompletion(true)?0:1);
}
教材第143页
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.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 WordCount() {
}
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: wordcount <in> [<in>...] <out>");
System.exit(2);
}
Job job = Job.getInstance(conf, "word count");
job.setJarByClass(WordCount.class);
job.setMapperClass(WordCount.TokenizerMapper.class);
job.setCombinerClass(WordCount.IntSumReducer.class);
job.setReducerClass(WordCount.IntSumReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
for(int i = 0; i < otherArgs.length - 1; ++i) {
FileInputFormat.addInputPath(job, new Path(otherArgs[i]));
}
FileOutputFormat.setOutputPath(job, new Path(otherArgs[otherArgs.length - 1]));
System.exit(job.waitForCompletion(true)?0:1);
}
public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable> {
private static final IntWritable one = new IntWritable(1);
private Text word = new Text();
public TokenizerMapper() {
}
public void map(Object key, Text value, Mapper<Object, Text, Text, IntWritable>.Context context) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
while(itr.hasMoreTokens()) {
this.word.set(itr.nextToken());
context.write(this.word, one);
}
}
}
public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
private IntWritable result = new IntWritable();
public IntSumReducer() {
}
public void reduce(Text key, Iterable<IntWritable> values, Reducer<Text, IntWritable, Text, IntWritable>.Context context) throws IOException, InterruptedException {
int sum = 0;
IntWritable val;
for(Iterator i$ = values.iterator(); i$.hasNext(); sum += val.get()) {
val = (IntWritable)i$.next();
}
this.result.set(sum);
context.write(key, this.result);
}
}
}
教材第145页
cd /usr/local/hadoop
export CLASSPATH="/usr/local/hadoop/share/hadoop/common/hadoop-common-2.7.1.jar:/usr/local/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-client-core-2.7.1.jar:/usr/local/hadoop/share/hadoop/common/lib/commons-cli-1.2.jar:$CLASSPATH"
javac WordCount.java
jar -cvf WordCount.jar *.class
教材第154页
cd /usr/local/hadoop/myapp
ls
cd /usr/local/hadoop
./sbin/start-dfs.sh
cd /usr/local/hadoop
./bin/hdfs dfs –rm –r input
./bin/hdfs dfs –rm –r output
cd /usr/local/hadoop
./bin/hdfs dfs –mkdir input
cd /usr/local/hadoop
./bin/hdfs dfs –put ./wordfile1.txt input
./bin/hdfs dfs –put ./wordfile2.txt input
cd /usr/local/hadoop
./bin/hadoop jar ./myapp/WordCount.jar input output
教材第156页
cd /usr/local/hadoop
./bin/hdfs dfs –cat output/*