大数据成长之路—— Hadoop 词频统计
HDFS项目实战
需求分析
使用HDFS Java API 才完成HDFS文件系统上的额文件的词频统计
例子 /test/1.txt ==> ' hello world'
/test/2.txt ==> ' hello world world'
得出 hello 两个, world 三个
代码框架编写
1:读取HDFS上的文件
2:词频统计
3:将处理的结果混存起来 Map
4:将结果输出到HDFS
上下文
package com.bigdata.hadoop.hdfs; import java.util.HashMap; import java.util.Map; //自定义上下文,缓存处理 public class Context { private Map<Object,Object> cacheMap = new HashMap<Object, Object>(); public Map<Object,Object> getCacheMap (){ return cacheMap; } /** * 写数据到缓存中 * @param key 单词 * @param value 次数 */ public void write(Object key,Object value){ cacheMap.put(key,value); } /** * 从缓存中获取数据 * @param key 单词 * @return 词频 */ public Object get(Object key){ return cacheMap.get(key); } } 复制代码处理类实现
package com.bigdata.hadoop.hdfs; public interface Mapper { /** * 自定义上下文 * @param line 读取到每一行数据 * @param context 上下文/缓存 */ public void map(String line, Context context); } 复制代码package com.bigdata.hadoop.hdfs; public class WordCount implements Mapper{ @Override public void map(String line, Context context) { String[] words = line.split("\t"); for(String word: words){ Object value = context.get(word); if(value==null){//没有出现该单词 context.write(word,1); }else{//已经有了,取出value再+1 int v =Integer.parseInt(value.toString()); context.write(word,v+1); } } } } 复制代码功能实现
package com.bigdata.hadoop.hdfs; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.*; import java.io.BufferedReader; import java.io.File; import java.io.InputStreamReader; import java.net.URI; import java.util.HashMap; import java.util.Map; import java.util.Set; public class HDFSWCApp01 { public static void main(String[] args) throws Exception{ //1:读取HDFS上的文件 Path input= new Path("/hdfsapi/local.txt"); FileSystem fs = FileSystem.get(new URI("hdfs://hadoop000:8020"),new Configuration(),"hadoop"); //?迭代器 RemoteIterator<LocatedFileStatus> iterator = fs.listFiles(input,false); Mapper mapper =new WordCount(); //创建上下文 Context context= new Context(); while(iterator.hasNext()){ LocatedFileStatus file = iterator.next(); FSDataInputStream in = fs.open(file.getPath()); BufferedReader reader = new BufferedReader(new InputStreamReader(in)); String line=""; while ((line =reader.readLine())!=null){ //2:词频统计 //将业务逻辑处理完成后再返回給cache中 mapper.map(line,context); } reader.close(); in.close(); } //3:将处理的结果混存起来 Map Map<Object,Object> contextMap = context.getCacheMap(); //Map<Object,Object> contextMap = new HashMap<Object,Object>(); //4:将结果输出到HDFS Path output =new Path("/hdfsapi/output/"); FSDataOutputStream out = fs.create(new Path(output,new Path("wc.out"))); Set<Map.Entry<Object,Object>> entries = contextMap.entrySet(); //迭代循环 for(Map.Entry<Object,Object> entry : entries){ out.write((entry.getKey().toString()+ "\t"+entry.getValue()+"\n").getBytes()); } out.close(); fs.close(); System.out.println("统计完毕"); } } 复制代码/Library/Java/JavaVirtualMachines/jdk1.8.0_181.jdk/Contents/Home/bin/java -javaagent:/Applications/IntelliJ IDEA.app/Contents/lib/idea_rt.jar=54308:/Applications/IntelliJ IDEA.app/Contents/bin -Dfile.encoding=UTF-8 -classpath 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com.bigdata.hadoop.hdfs.HDFSWCApp01 log4j:WARN No appenders could be found for logger (org.apache.hadoop.metrics2.lib.MutableMetricsFactory). log4j:WARN Please initialize the log4j system properly. log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for more info. 统计完毕 Process finished with exit code 0 复制代码
hello world[hadoop@hadoop000 sbin]$ hadoop fs -text /hdfsapi/local.txt hello world hello hello hello world [hadoop@hadoop000 sbin]$ hadoop fs -cat /hdfsapi/output/wc.out 1 world 2 hello 4 [hadoop@hadoop000 sbin]$
作者：孙中明
链接：https://juejin.cn/post/7020587170565980197