写一个统计单词的MapReduce程序
时间: 2023-05-17 15:07:57 浏览: 84
好的,这是一个很好的编程问题。MapReduce是一种用于大规模数据处理的编程模型,它将数据分成小块并在多个计算机上并行处理。下面是一个简单的统计单词的MapReduce程序的伪代码:
Map函数:
输入:一行文本
输出:(单词, 1)
对于每一行文本:
将文本分割成单词列表
对于每个单词:
输出 (单词, 1)
Reduce函数:
输入:(单词, [1, 1, 1, ...])
输出:(单词, 单词出现的次数)
对于每个单词和对应的值列表:
将值列表相加得到单词出现的次数
输出 (单词, 单词出现的次数)
这个程序可以在Hadoop等分布式计算框架上运行。
相关问题
写一个统计单词的mapreduce程序
好的,以下是一个简单的MapReduce程序,用于统计输入文件中各个单词出现的次数:
Mapper 函数:
```
public class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
private Text word = new Text();
private final static IntWritable ONE = new IntWritable(1);
public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String line = value.toString();
StringTokenizer tokenizer = new StringTokenizer(line);
while (tokenizer.hasMoreTokens()) {
word.set(tokenizer.nextToken());
context.write(word, ONE);
}
}
}
```
Reducer 函数:
```
public class WordCountReducer 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 sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
result.set(sum);
context.write(key, result);
}
}
```
MapReduce 驱动程序:
```
public class WordCount {
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
Job job = Job.getInstance(conf, "word count");
job.setJarByClass(WordCount.class);
job.setMapperClass(WordCountMapper.class);
job.setCombinerClass(WordCountReducer.class);
job.setReducerClass(WordCountReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
```
以上程序的作用是:读取输入文件,将每一行拆分为单词,然后输出每个单词以及它们出现的次数。其中,Mapper 函数读取每个单词,然后以 (单词,1) 的形式输出;Reducer 函数接收 Mapper 输出的键值对,对每个键进行求和,并输出最终结果;MapReduce 驱动程序则配置 Mapper、Reducer 函数和输入输出路径,启动 MapReduce 作业。
使用mapreduce写一个词频统计
好的,这里是一个使用 MapReduce 统计单词频率的示例程序:
```java
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.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.output.FileOutputFormat;
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();
public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
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 sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
result.set(sum);
context.write(key, result);
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
Job job = Job.getInstance(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);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
```
该程序包含两个 MapReduce 阶段,第一个 MapReduce 阶段是 Mapper,它将输入数据中的每个单词作为键,值设置为 1。Reducer 将相同键的值相加,并将结果输出为键值对。该程序还使用了 Combiner,这是一个可选的优化步骤,它会在 Map 阶段之后,在每个 Map 节点上对中间结果进行一次局部合并,以减少数据传输量和计算量。