参考文献：https://blog.csdn.net/markcheney/article/details/53998796 前言： MapReduce是一个高性能的批处理分布式计算框架，用于对海量数据进行并行分析和处理。与传统方法相比较，MapReduce更倾向于蛮力去解决问题，通过简单、粗暴、有效的方式去处理海量的数据。通过对数据的输入、拆分与组合（核心），将任务分配到多个节点服务器上，进行分布式计算，这样可以有效地提高数据管理的安全性，同时也能够很好地范围被管理的数据。 mapreduce概念+实例  mapreduce核心就是map+shuffle+reducer，首先通过读取文件，进行分片，通过map获取文件的key-value映射关系，用作reducer的输入，在作为reducer输入之前，要先对map的key进行一个shuffle，也就是排个序，然后将排完序的key-value作为reducer的输入进行reduce操作，当然一个mapreduce任务可以不要有reduce，只用一个map，接下来就来讲解一个mapreduce界的“hello world”。 ``` 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.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<Object, Text, Text, IntWritable>{ private final static IntWritable one = new IntWritable(1); private Text word = new Text(); public void map(Object key, Text value, Context context ) throws IOException, InterruptedException { System.out.println("key=:"+key.toString()); System.out.println("value=:"+value.toString()); StringTokenizer itr = new StringTokenizer(value.toString()); while (itr.hasMoreTokens()) { word.set(itr.nextToken()); context.write(word, one); } System.out.println("context=:"+context.toString()); } } 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 { System.setProperty("hadoop.home.dir", "D:\\hadoop2.7.6"); Configuration conf = new Configuration(); Job job = new Job(conf); job.setJarByClass(WordCount.class); job.setMapperClass(TokenizerMapper.class); job.setReducerClass(IntSumReducer.class); job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(IntWritable.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); FileInputFormat.addInputPath(job, new Path("d:/input4/WordCount.java")); FileOutputFormat.setOutputPath(job, new Path("d:/output4")); job.waitForCompletion(true); } } ```