MapReduce是一种编程模型,用于处理大规模数据集。它通过将作业分成两个阶段—映射(Map)和归约(Reduce)—来并行处理数据。在映射阶段,输入数据被分成小块并独立处理;归约阶段则汇总这些结果以得到最终输出。
MapReduce是一种编程模型,用于处理和生成大数据集的并行算法,它由两个主要步骤组成:Map(映射)和Reduce(归约),以下是一个简单的MapReduce作业示例,以及如何在Hadoop环境中运行它。
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1、编写一个Mapper类:
import java.io.IOException; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Mapper; public class WordCountMapper 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 { String[] words = value.toString().split("\s+"); for (String w : words) { word.set(w); context.write(word, one); } } }
2、编写一个Reducer类:
import java.io.IOException; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Reducer; public class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable> { public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException { int sum = 0; for (IntWritable val : values) { sum += val.get(); } context.write(key, new IntWritable(sum)); } }
3、编写一个驱动程序来运行MapReduce作业:
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.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; public class WordCount { public static void main(String[] args) throws Exception { if (args.length != 2) { System.err.println("Usage: WordCount <input path> <output path>"); System.exit(1); } 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); } }
4、编译并打包Java代码为jar文件:
$ javac classpathhadoop classpath
d wordcount_classes WordCount*.java
$ jar cvf wordcount.jar C wordcount_classes/ .
5、在Hadoop集群上运行MapReduce作业:
$ hadoop jar wordcount.jar WordCount /input/path /output/path
/input/path
是包含输入数据的HDFS路径,/output/path
是要将结果写入的HDFS路径。
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