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Hadoop读书笔记(五)MapReduce统计单词demo
Hadoop读书笔记(一)Hadoop介绍:http://blog.csdn.net/caicongyang/article/details/39898629
Hadoop读书笔记(二)HDFS的shell操作:http://blog.csdn.net/caicongyang/article/details/41253927
Hadoop读书笔记(三)Java API操作HDFS:http://blog.csdn.net/caicongyang/article/details/41290955
Hadoop读书笔记(四)HDFS体系结构 :http://blog.csdn.net/caicongyang/article/details/41322649
1.demo说明
功能:统计文章中每一个单词出现的次数
步骤:
1.1读取hdfs中的文件。每一行解析成一个<k,v>。每一个键值对调用一次map函数
1.2 覆盖map(),接收1.1产生的<k,v>,进行处理,转换为新的<k,v>输出
1.3 对1.2输出的<k,v>进行分区。默认分为1个区
1.4 对不同分区中的数据进行排序(按照k)、分组。分组指的是相同key的value放到一个集合中
1.5 (可选)对分组后的数据进行规约。
2.1 多个map任务的输出,按照不同的分区,通过网络copy到不同的reduce节点上。
2.2 对多个map的输出进行合并、排序。覆盖reduce函数,接收的是分组后的数据,实现自己的业务逻辑,处理后,产生新的<k,v>输出。
2.3 对reduce输出的<k,v>写到hdfs中。
2.代码
package mapReduce; import java.io.IOException; import java.net.URI; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.FileSystem; import org.apache.hadoop.fs.Path; 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.input.TextInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat; import org.apache.hadoop.mapreduce.lib.partition.HashPartitioner; public class WordCount { private static final String INPUT_PATH="hdfs://192.168.80.100:9000/hello"; private static final String OUT_PATH="hdfs://192.168.80.100:9000/out"; public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); FileSystem fileSystem = FileSystem.get(new URI(INPUT_PATH),conf); Path outPath = new Path(OUT_PATH); if(fileSystem.exists(outPath)){ fileSystem.delete(outPath,true); } //创建作业 Job job = new Job(conf, WordCount.class.getSimpleName()); //1.1读取指定的文件 FileInputFormat.setInputPaths(job, INPUT_PATH); //输入文件格式化 job.setInputFormatClass(TextInputFormat.class); //1.2指定自定义Mapper类 job.setMapperClass(MyMapper.class); //指定输出类型 job.setOutputKeyClass(Text.class); job.setOutputValueClass(LongWritable.class); //1.3分区 job.setPartitionerClass(HashPartitioner.class); //设置reduce个数 job.setNumReduceTasks(1); //1.4排序、分组 //1.5归约 //2.2指定Reducer类 job.setReducerClass(MyReducer.class); //设定输出类型 job.setOutputKeyClass(Text.class); job.setOutputValueClass(LongWritable.class); //输出地址 FileOutputFormat.setOutputPath(job, new Path(OUT_PATH)); //输出文件格式化类 job.setOutputFormatClass(TextOutputFormat.class); //job交给JobTracker执行 job.waitForCompletion(true); } static class MyReducer extends Reducer<Text, LongWritable, Text, LongWritable>{ @Override protected void reduce(Text key, Iterable<LongWritable> value, Context context) throws IOException, InterruptedException { long count = 0L; for(LongWritable times :value ){ count += times.get(); } context.write(key, new LongWritable(count)); } } static class MyMapper extends Mapper<LongWritable ,Text,Text,LongWritable>{ @Override protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { String[] splited = value.toString().split("\t"); for(String word:splited){ context.write(new Text(word), new LongWritable(1)); } } } }
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Hadoop读书笔记(五)MapReduce统计单词demo