标签:cat 文件 string 0.12 world ppa ++ hash 忽略
环境介绍:
主服务器ip:192.168.80.128(master) NameNode SecondaryNameNode ResourceManager
从服务器ip:192.168.80.129(slave1) DataNode NodeManager
从服务器ip: 192.168.80.130(slave2) DataNode NodeManager
1.文件准备
1)在HDFS上创建文件夹
hadoop fs -mkdir /user/joe/wordcount/input
2)在本地创建文件夹
mkdir /home/chenyun/data/mapreduce
3)创建file01
cd /home/chenyun/data/mapreduce
touch file01
vi file01
往file01写入内容:
Hello World, Bye World!
4)创建file02
cd /home/chenyun/data/mapreduce touch file02 vi file02
往file02写入内容:
Hello Hadoop, Goodbye to hadoop.
5)把本地文件file01、file02上传到hdfs的/user/joe/wordcount/input目录
hadoop fs -put /home/chenyun/data/mapreduce/file01 /user/joe/wordcount/input hadoop fs -put /home/chenyun/data/mapreduce/file02 /user/joe/wordcount/input
2.编写mapreduce程序
1)在Eclipse编写Mapreduce程序
import java.io.BufferedReader; import java.io.FileReader; import java.io.IOException; import java.net.URI; import java.util.ArrayList; import java.util.HashSet; import java.util.List; import java.util.Set; 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.Counter; 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; import org.apache.hadoop.util.StringUtils; public class WordCount { public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable> { static enum CountersEnum { INPUT_WORDS } private final static IntWritable one = new IntWritable(1); private Text word = new Text(); private boolean caseSensitive; private Set<String> patternsToSkip = new HashSet<String>(); private Configuration conf; private BufferedReader fis; @Override public void setup(Context context) throws IOException, InterruptedException { conf = context.getConfiguration(); caseSensitive = conf.getBoolean("wordcount.case.sensitive", true); if (conf.getBoolean("wordcount.skip.patterns", false)) { URI[] patternsURIs = Job.getInstance(conf).getCacheFiles(); for (URI patternsURI : patternsURIs) { Path patternsPath = new Path(patternsURI.getPath()); String patternsFileName = patternsPath.getName().toString(); parseSkipFile(patternsFileName); } } } private void parseSkipFile(String fileName) { try { fis = new BufferedReader(new FileReader(fileName)); String pattern = null; while ((pattern = fis.readLine()) != null) { patternsToSkip.add(pattern); } } catch (IOException ioe) { System.err .println("Caught exception while parsing the cached file ‘" + StringUtils.stringifyException(ioe)); } } @Override public void map(Object key, Text value, Context context) throws IOException, InterruptedException { String line = (caseSensitive) ? value.toString() : value.toString() .toLowerCase(); for (String pattern : patternsToSkip) { line = line.replaceAll(pattern, ""); } StringTokenizer itr = new StringTokenizer(line); while (itr.hasMoreTokens()) { word.set(itr.nextToken()); context.write(word, one); Counter counter = context.getCounter( CountersEnum.class.getName(), CountersEnum.INPUT_WORDS.toString()); counter.increment(1); } } } 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(); GenericOptionsParser optionParser = new GenericOptionsParser(conf, args); String[] remainingArgs = optionParser.getRemainingArgs(); if ((remainingArgs.length != 2) && (remainingArgs.length != 4)) { System.err .println("Usage: wordcount <in> <out> [-skip skipPatternFile]"); System.exit(2); } 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); List<String> otherArgs = new ArrayList<String>(); for (int i = 0; i < remainingArgs.length; ++i) { if ("-skip".equals(remainingArgs[i])) { job.addCacheFile(new Path(remainingArgs[++i]).toUri()); job.getConfiguration().setBoolean("wordcount.skip.patterns", true); } else { otherArgs.add(remainingArgs[i]); } } FileInputFormat.addInputPath(job, new Path(otherArgs.get(0))); FileOutputFormat.setOutputPath(job, new Path(otherArgs.get(1))); System.exit(job.waitForCompletion(true) ? 0 : 1); } }
2)导出mapreduce.jar
3) 上传到master的目录
/home/chenyun/project/mapreduce
3.运行wordCount
hadoop jar /home/chenyun/project/mapreduce/mapreduce.jar com.accp.mapreduce.WordCount /user/joe/wordcount/input /user/joe/wordcount/output
4)查看运行结果
hadoop fs -cat /user/joe/wordcount/output/part-r-00000
=======================================================================================================================
4.过滤不需要统计的字符
1)在本地创建/home/chenyun/data/mapreduce/patterns.txt ,在文件里加入
\. \, \! to
2)把文件上传到hdfs上
hadoop fs -put /home/chenyun/data/mapreduce/patterns.txt /user/joe/wordcount
3)运行
hadoop jar /home/chenyun/project/mapreduce/mapreduce.jar com.accp.mapreduce.WordCount -Dwordcount.case.sensitive=true /user/joe/wordcount/input /user/joe/wordcount/output1 -skip /user/joe/wordcount/patterns.txt
4)查看运行结果
hadoop fs -cat /user/joe/wordcount/output1/part-r-00000
======================================================================================================================
5.忽略大小写,进行统计
1)运行
hadoop jar /home/chenyun/project/mapreduce/mapreduce.jar com.accp.mapreduce.WordCount -Dwordcount.case.sensitive=false /user/joe/wordcount/input /user/joe/wordcount/output5 -skip /user/joe/wordcount/patterns.txt
2)查看运行结果
hadoop fs -cat /user/joe/wordcount/output5/part-r-00000
Hadoop实战-MapReduce之WordCount(五)
标签:cat 文件 string 0.12 world ppa ++ hash 忽略
原文地址:http://www.cnblogs.com/qq27271609/p/6822829.html