使用jdk 1.7的新api来统计jdk中英语单词的分布情况

2024-05-15 14:58
文章标签 分布 jdk 统计 使用 api 情况 英语单词 1.7

本文主要是介绍使用jdk 1.7的新api来统计jdk中英语单词的分布情况，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

       如何统计JDK源码中英语单词的分布情况呢？大概的思路就是：遍历JDK源码中的所有.java文件，分别抽取出每个.java文件中的英语单词，然后将这些单词存储在TreeMap中。选择TreeMap的好处就是可以将数据按照a-z的字典排序起来，有利于观察最终的结果。下面就按照总体的思路去做，第一步就是先建立一个递归遍历文件夹的框架。下面我们介绍一种jdk1.7的新api来递归遍历文件夹。这个新的api位于java.nio.file.*下面，里面添加了更加符合语义的Path，也就是路径这个对象。然后为了更好遍历目录，JDK1.7之后提供了一种访问者设计模式来访问递归访问目录。客户端只需要继承SimpleFileVisitor类即可，然后覆写（Override）里面的访问文件，访问目录，访问失败三个方法。在每个方法中，需要返回一个常亮，这里面统一返回CONTINUE，具体的常亮，大家可以查看下API。主要的框架代码如下：  

public static class PrintFilesextends SimpleFileVisitor<Path> {// Print information about// each type of file.@Overridepublic FileVisitResult visitFile(Path file,BasicFileAttributes attr) {if (attr.isSymbolicLink()) {System.out.format("Symbolic link: %s ", file);} else if (attr.isRegularFile()) {System.out.format("Regular file: %s ", file);} else {System.out.format("Other: %s ", file);}System.out.println("(" + attr.size() + "bytes)");return CONTINUE;}// Print each directory visited.@Overridepublic FileVisitResult postVisitDirectory(Path dir,IOException exc) {System.out.format("Directory: %s%n", dir);return CONTINUE;}// If there is some error accessing// the file, let the user know.// If you don't override this method// and an error occurs, an IOException // is thrown.@Overridepublic FileVisitResult visitFileFailed(Path file,IOException exc) {System.err.println(exc);return CONTINUE;}
}
       这个类主要覆写（Override）三个方法，访问文件、访问目录、访问失败。很明显，我们只需要将统计单词的任务放在visitFile方法中，实现相关业务逻辑即可。当需要遍历某个目录是，直接调用Files.walkFileTree(startingDir, SimpleFileVistor)方法即可。其中上面的例子什么也不做，只是简单的打印出一些相关信息。

       进入正题，怎么统计JDK源码中的英语单词分布呢？下面先分解下我们的任务。在这里面，需要一个类用于解析一个.java文件，并且将里面的单词都抽取出来，一个类用于存储单词的汇总信息，一个类继承SimpleFileVisitor。主要就是这三个类，如下面的UML类图所示：

package net.itaem.luohong.honghong;public class Word implements Comparable<Word> {private String word;public Word(String word){this.word = word;}//需要排序，所以这里需要override hashCode & equals@Overridepublic int hashCode() {final int prime = 31;int result = 1;result = prime * result + ((word == null) ? 0 : word.hashCode());return result;}@Overridepublic boolean equals(Object obj) {if (this == obj)return true;if (obj == null)return false;if (getClass() != obj.getClass())return false;Word other = (Word) obj;if (word == null) {if (other.word != null)return false;} else if (!word.equals(other.word))return false;return true;}@Overridepublic String toString() {return word;}public String getWord(){return word;}public int compareTo(Word o) {return this.getWord().compareTo(o.getWord());}
}



package net.itaem.luohong.honghong;import java.io.BufferedReader;
import java.io.File;
import java.io.FileInputStream;
import java.io.FileNotFoundException;
import java.io.IOException;
import java.io.InputStreamReader;
import java.util.ArrayList;
import java.util.List;
import java.util.StringTokenizer;/*** 读取一个文件的内容* */
public class WordReader {public static List<Word> wordInFile(String path){return stringToWords(read(path));}/*** 读取一个文件的内容，然后将内容返回* * */private static String read(String path){StringBuilder sb = new StringBuilder();try {BufferedReader reader = new BufferedReader(new InputStreamReader(new FileInputStream(new File(path))));String line = null;while((line = reader.readLine()) != null){sb.append(line);}reader.close();} catch (FileNotFoundException e) {e.printStackTrace();} catch (IOException e) {e.printStackTrace();}return sb.toString();}/*** 将字符串变成单词数组* */private static List<Word> stringToWords(String source){StringTokenizer strToken = new StringTokenizer(source, " \t\n\r\f\\.");List<Word> wordList = new ArrayList<Word>();while(strToken.hasMoreTokens()){String wordStr = strToken.nextToken();if(!isInvalidString(wordStr)){wordStr = removeInvalidStr(wordStr);if(wordStr != null && !"".equals(wordStr)){if(needSplitToWord(wordStr)){ recurSplitToWord(wordStr, wordList);}else{  //不需要切割wordStr = removeInvalidStr(wordStr);if(!isInvalidString(wordStr)){							wordList.add(new Word(wordStr));}}}}}return wordList;}/*** 判断字符串是否需要分割为多个单词* @param wordStr* @return* */private static boolean needSplitToWord(String wordStr){if(wordStr == null || "".equals(wordStr)) return false;if(wordStr.contains(".") || wordStr.contains("/") || wordStr.contains("-")  || wordStr.contains("&") || wordStr.contains("#") || wordStr.contains("]") || wordStr.contains("|") || wordStr.contains(",") || wordStr.contains("(") || wordStr.contains("+") || wordStr.contains("_") || wordStr.contains("[") || wordStr.contains(">") || wordStr.contains(";") || wordStr.contains("{")|| wordStr.contains("<") || wordStr.contains("=") || wordStr.contains(")") || wordStr.contains("}")){return true;}else{return false;}}/*** 递归切割字符串为单词列表* 因为一个字符串可能同时包含多个分割单词的字符，所以需要递归切割* * @param wordStr 要切割的字符串* @param wordList 将切割的单词放入该list* */private static void recurSplitToWord(String wordStr, List<Word> wordList){if(wordStr == null) return;if(needSplitToWord(wordStr)){String[] words = splitToWords0(wordStr);	for(String word: words){if(needSplitToWord(word)){recurSplitToWord(word, wordList);}else{if(word != null && !"".equals(word)  && !isInvalidString(word)){word = removeInvalidStr(word);if(word != null && !word.equals("")){if(!isInvalidString(word))wordList.add(new Word(word));}}}}}}/*** 将一个字符串切割为单词数组* @param str* @return* */private static String[] splitToWords0(String wordStr){String[] words = null;//split wordif(wordStr.contains(".")){words = wordStr.split("\\.");}else if(wordStr.contains("/")){words = wordStr.split("/");}else if(wordStr.contains("-")){words = wordStr.split("-");}else if(wordStr.contains("#")){words = wordStr.split("#");}else if(wordStr.contains("]")){words = wordStr.split("]");}else if(wordStr.contains("[")){words = wordStr.split("\\[");}else if(wordStr.contains(",")){words = wordStr.split(",");}else if(wordStr.contains("(")){words = wordStr.split("\\(");}else if(wordStr.contains(">")){words = wordStr.split(">");}else if(wordStr.contains("<")){words = wordStr.split("<");}else if(wordStr.contains("=")){words = wordStr.split("=");}else if(wordStr.contains(")")){words = wordStr.split("\\)");}else if(wordStr.contains(";")){words = wordStr.split(";");}else if(wordStr.contains("+")){words = wordStr.split("\\+");}else if(wordStr.contains("{")){words = wordStr.split("\\{");}else if(wordStr.contains("}")){words = wordStr.split("}");}else if(wordStr.contains("_")){words = wordStr.split("_");}else if(wordStr.contains("|")){words = wordStr.split("\\|");}else if(wordStr.contains("&")){words = wordStr.split("&");}return words;}/*** 去掉 + - * / >= <= =!= ||等无效字符* @param str 字符串* @return * */private static boolean isInvalidString(String str){if(str == null || str.equals("") || str.equals("*") || str.equals("{") ||str.equals("}")  || str.equals("+")  ||str.equals("-")  || str.equals("/")|| str.equals("=") || str.equals("!=") || str.equals(">") || str.equals("<") || str.equals(">=") || str.equals("<=")|| str.equals("||") || str.equals("}}") || str.equals("/*") || str.equals("};") || str.equals("+=") || str.matches("\\d+")|| str.equals(".") || str.equals(":") || str.equals("/**") || str.equals("//") || str.equals("?") || str.equals("!")|| (str.contains("==") && str.contains("&") && str.contains(";")) || str.equals("&&") || str.equals("&") || str.equals("'") || str.equals("'$'")|| str.equals("$") || str.equals(")") || str.equals(")}")|| str.equals("%") || str.matches("0[xX][0-9a-zA-Z]+")|| str.equals(">") || str.equals("<") || str.equals("$%") || str.matches("[x]\\d+[a-zA-Z]*")|| str.matches("[uA-Fa-f]\\d+[a-fA-F]")|| str.matches("\\*+") || str.matches("[\\d+$][a-zA-Z]*[*a-zA-Z0-9]*") || str.matches("\\d{1}[A-Za-z]{1}")){return true;}else{return false;}}/*** 判断一个字符串是否是数字* */private static boolean isNumber(String str){return str.matches("^[-+]?(([0-9]+)([.]([0-9]+))?|([.]([0-9]+))?)$");}/*** 去掉一个字符串中的无效字符* @param* @return* */private static String removeInvalidStr(String wordStr){if(isInvalidString(wordStr)){return null;}//去掉结尾;if(wordStr.endsWith(";")){wordStr = wordStr.substring(0, wordStr.length() - 1);}//去掉尾部,if(wordStr.endsWith(",")){wordStr = wordStr.substring(0, wordStr.length() - 1);}//去掉结尾()if(wordStr.endsWith("()")){wordStr = wordStr.substring(0, wordStr.length() - 2);}//去掉开头(if(wordStr.startsWith("(")){wordStr = wordStr.substring(1);}//去掉开头'if(wordStr.startsWith("'")){wordStr = wordStr.substring(1);}//去掉结尾'if(wordStr.endsWith("'")){wordStr = wordStr.substring(0, wordStr.length() - 1);}//去掉结尾)if(wordStr.endsWith(")")){wordStr = wordStr.substring(0, wordStr.length() - 1);}if(wordStr.startsWith("`")){wordStr = wordStr.substring(1);}//去掉结尾:if(wordStr.endsWith(":")){wordStr = wordStr.substring(0, wordStr.length() - 1);}if(wordStr.startsWith(":")){wordStr = wordStr.substring(1);}//去掉开头"if(wordStr.startsWith("\"")){wordStr = wordStr.substring(1);}if(wordStr.startsWith("^")){wordStr = wordStr.substring(1);}//去掉 if(wordStr.startsWith(" ")){wordStr = wordStr.substring(6);}//去掉> < >, &if(wordStr.startsWith("&")){wordStr = wordStr.substring(5);}if(wordStr.startsWith(">")){wordStr = wordStr.substring(4);}if(wordStr.startsWith(">")){wordStr = wordStr.substring(3);}if(wordStr.startsWith("<")){wordStr = wordStr.substring(4);}if(wordStr.startsWith("<")){wordStr = wordStr.substring(3);}if(wordStr.startsWith("&")){wordStr = wordStr.substring(1);}if(wordStr.startsWith(")")){wordStr = wordStr.substring(1);}if(wordStr.startsWith(")*")){wordStr = wordStr.substring(2);}if(wordStr.startsWith(")+")){wordStr = wordStr.substring(2);}if(wordStr.startsWith(")}")){wordStr = wordStr.substring(2);}if(wordStr.startsWith("++")){wordStr = wordStr.substring(2);}if(wordStr.startsWith("+")){wordStr = wordStr.substring(1);}if(wordStr.endsWith("+")){wordStr = wordStr.substring(0, wordStr.length() - 1);}if(wordStr.startsWith("*?")){wordStr = wordStr.substring(2);}//去掉结尾"if(wordStr.endsWith("\"")){wordStr = wordStr.substring(0, wordStr.length() - 1);}//去掉结尾处.if(wordStr.endsWith(".")){wordStr = wordStr.substring(0, wordStr.length() - 1);}//去掉开头*/if(wordStr.startsWith("*/")){wordStr = wordStr.substring(2);}//去掉java.util;/**结尾处的四个字符if(wordStr.endsWith(";/**")){wordStr = wordStr.substring(0, wordStr.length() - 4);}//去掉开头的{@if(wordStr.startsWith("{@")){wordStr = wordStr.substring(2);}//去掉开头的@if(wordStr.startsWith("@")){wordStr = wordStr.substring(1);}//取出下面该格式的单词，比如：<tt>hello</tt>取出hello，<li>world</li>取出为world，<T>取出为T，</pre>取出为pre，<pre>取出为preif(wordStr.startsWith("<") && wordStr.endsWith(">")){if(wordStr.startsWith("<") && !wordStr.contains("</")){ //格式为<T>wordStr = wordStr.substring(wordStr.indexOf("<") + 1, wordStr.lastIndexOf(">"));}else if(wordStr.contains("</") && !wordStr.startsWith("</")){  //格式为：<tt>hello</tt>wordStr = wordStr.substring(wordStr.indexOf(">") + 1, wordStr.lastIndexOf("</"));}else if(wordStr.startsWith("</")){  //格式为</pre>wordStr = wordStr.substring(2, wordStr.lastIndexOf(">"));}	}//去掉<li>time中的<li>if(wordStr.contains("<") && wordStr.contains(">") && !wordStr.contains("/") && wordStr.startsWith("<")){wordStr = wordStr.substring(wordStr.lastIndexOf(">") + 1);}//去掉time<li>中的<li>if(wordStr.contains("<") && wordStr.contains(">") && !wordStr.contains("/") && wordStr.endsWith(">")){wordStr = wordStr.substring(0, wordStr.lastIndexOf("<"));}//去掉time</li>if(wordStr.contains("</") && wordStr.contains(">")){wordStr = wordStr.substring(0, wordStr.lastIndexOf("</"));}//去掉开头的<if(wordStr.startsWith("<")){wordStr = wordStr.substring(1);}//去掉结尾的>if(wordStr.endsWith(">")){wordStr = wordStr.substring(0, wordStr.length() -1);}//去掉结尾的[if(wordStr.startsWith("{")){wordStr = wordStr.substring(1);}//去掉开头的[if(wordStr.endsWith("}")){wordStr = wordStr.substring(0, wordStr.length() - 1);}//去掉开头的==if(wordStr.startsWith("==")){wordStr = wordStr.substring(2);}//去掉结尾的=if(wordStr.endsWith("=")){wordStr = wordStr.substring(0, wordStr.length() - 1);}//去掉结尾的{//去掉开头的[if(wordStr.endsWith("{")){wordStr = wordStr.substring(0, wordStr.length() - 1);}//去掉结尾的[if(wordStr.endsWith("[]")){wordStr = wordStr.substring(0, wordStr.length() - 2);}//去掉结尾的[if(wordStr.endsWith("]")){wordStr = wordStr.substring(0, wordStr.length() - 1);}//去掉结尾的[if(wordStr.endsWith("[")){wordStr = wordStr.substring(0, wordStr.length() - 1);}//去掉开头的[if(wordStr.startsWith("]")){wordStr = wordStr.substring(1);}//去掉开头的+if(wordStr.startsWith("+")){wordStr = wordStr.substring(1);}//去掉<?if(wordStr.endsWith("+")){wordStr = wordStr.substring(0, wordStr.length() - 1);}//去掉<?if(wordStr.endsWith("<?")){wordStr = wordStr.substring(0, wordStr.length() - 2);}//去掉"if(wordStr.contains("\"")){wordStr = wordStr.replace("\"", "");}//去掉开头的!if(wordStr.startsWith("!")){wordStr = wordStr.substring(1);}if(wordStr.startsWith("?")){wordStr = wordStr.substring(1);}if(wordStr.startsWith("@")){wordStr = wordStr.substring(1);}if(wordStr.startsWith("~")){wordStr = wordStr.substring(1);}if(wordStr.startsWith("|")){wordStr = wordStr.substring(1);}if(wordStr.startsWith("|+")){wordStr = wordStr.substring(2);}if(wordStr.startsWith("}?")){wordStr = wordStr.substring(2);}if(wordStr.startsWith("}~")){wordStr = wordStr.substring(2);}if(wordStr.startsWith("} ")){wordStr = wordStr.substring(6);}if(wordStr.contains(" ")){wordStr.replaceAll(" ", "");}//去掉开头的[//去掉数字if(isNumber(wordStr)){return null;}return wordStr;}public static void main(String[] args) {WordCount wc = new WordCount();wc.count(wordInFile("E:\\jar集合\\java source code\\java\\util\\regex\\ASCII.java"));wc.count(wordInFile("E:\\jar集合\\java source code\\java\\util\\regex\\Matcher.java"));wc.count(wordInFile("E:\\jar集合\\java source code\\java\\util\\regex\\MatchResult.java"));wc.count(wordInFile("E:\\jar集合\\java source code\\java\\util\\regex\\Pattern.java"));wc.count(wordInFile("E:\\jar集合\\java source code\\java\\util\\regex\\PatternSyntaxException.java"));wc.count(wordInFile("E:\\jar集合\\java source code\\java\\util\\regex\\UnicodeProp.java"));wc.printResult();}
}


       当然，已经可以将每个文件的单词抽取出来了，下面就使用一个类汇总单词信息即可。这里面为了最后结果可以从a-z按照字典排序，使用了TreeMap来统计结果，不多说，见代码： 

package net.itaem.luohong.honghong;import java.util.List;
import java.util.Map;
import java.util.TreeMap;/*** 用于统计每个单词出现的次数* * @author luohong 846705189@qq.com* */
public class WordCount {private Map<Word, Integer> wordCount;public WordCount(){wordCount = new TreeMap<Word, Integer>();}public int size(){return wordCount.size();}/*** 统计一个单词出现的次数* @param word 要统计的单词* @return 该单词出现的次数* */public Integer count(Word word){if(wordCount.containsKey(word)){return wordCount.put(word, wordCount.get(word) + 1);}else{return wordCount.put(word, 1);}}public void count(List<Word> wordList){for(Word word: wordList){count(word);}}/*** 输出结果* */public void printResult(){for(Word word: wordCount.keySet()){System.out.println(word + ":" + wordCount.get(word));}}
}



       基本上整个架构代码就写完了，最后将这些业务逻辑添加到visitFile方法中即可。 

package net.itaem.luohong.honghong;
import static java.nio.file.FileVisitResult.CONTINUE;import java.io.File;
import java.io.IOException;
import java.nio.file.FileVisitResult;
import java.nio.file.Files;
import java.nio.file.Path;
import java.nio.file.SimpleFileVisitor;
import java.nio.file.attribute.BasicFileAttributes;
import java.util.List;/*** 递归遍历目录* * */
public class PrintFiles extends SimpleFileVisitor<Path> {WordCount wordCount = new WordCount();// Print information about// each type of file.@Overridepublic FileVisitResult visitFile(Path file,BasicFileAttributes attr) {if (attr.isSymbolicLink()) {} else if (attr.isRegularFile()) {List<Word> words = WordReader.wordInFile(file.toString());System.out.println(file);for(Word word: words){wordCount.count(word);}//System.out.println(words);} else {//System.out.format("Other: %s ", file);}return CONTINUE;}// Print each directory visited.@Overridepublic FileVisitResult postVisitDirectory(Path dir,IOException exc) {//System.out.format("Directory: %s%n", dir);return CONTINUE;}// If there is some error accessing// the file, let the user know.// If you don't override this method// and an error occurs, an IOException // is thrown.@Overridepublic FileVisitResult visitFileFailed(Path file, IOException exc) {//System.err.println(exc);return CONTINUE;}public void count(String path){Path startingDir = new File(path).toPath();PrintFiles printFiles = new PrintFiles();try {Files.walkFileTree(startingDir, printFiles);wordCount.printResult();} catch (IOException e) {e.printStackTrace();}}<span style="font-family: Arial, Helvetica, sans-serif;">    public static void main(String[] args) {</span>
    	String path = "E:\\jar集合\\java source code";System.out.println("enter the dictionary you want to count");path = new CommandReader().readCommand();new PrintFiles().count(path);}}

 

 
       这里面使用了一个读取命令行的类，非常简单，如下 

 

 

package net.itaem.luohong.honghong;import java.io.BufferedReader;
import java.io.IOException;
import java.io.InputStreamReader;public class CommandReader{public CommandReader(){}public String readCommand(){BufferedReader reader = new BufferedReader(new InputStreamReader(System.in));String line = null;try {line = reader.readLine();} catch (IOException e) {e.printStackTrace();}finally{try {reader.close();} catch (IOException e) {}}return line;}
}
 


 
 

输出部分结果：
 

 

<span style="font-size:18px;">addExceptionDetailMessage:2
addFirst:2
addGuardedAction:2
addIDAtPosition:3
addIORComponentToProfileInternal:3
addInvocationHandler:3
addLast:14
addNodes:3
addObject:7
addPOA:2
addPoaInactive:9
addPoaManager:2
addPrefix:2
addRemoteContactInfos:2
addReply:12
addReplyServiceContextQueue:11
addServiceContext:3
addServiceContexts:2
addTaggedProfiles:3
addToErrorSet:7
addToIORTemplate:3
addToParser:2
addToProperties:2
addTypeCodeAtPosition:4
addWork:7
addXXX:1
add_client_request_interceptor:2
add_client_request_interceptor_with_policy:1
add_component:1
add_component_to_profile:1
add_exception:1
add_in_arg:1
add_inout_arg:1
add_ior_component:1
add_ior_component_to_profile:1
add_ior_interceptor:2
add_ior_interceptor_with_policy:1
add_item:4
add_named_in_arg:1
add_named_inout_arg:1
add_named_out_arg:1
add_out_arg:1
add_reply_service_context:2
add_request_service_context:1
add_server_request_interceptor:2
add_server_request_interceptor_with_policy:1
add_value:1
added:31
addedSerialOrExtern:2
adding:15
addition:5
additional:10
addr:29
addrDisp:12
addrDisposition:12
address:30
addresses:2
addressing:17
addressingDisposition:14
adds:9
adheres:1
adjust:3
admap:2
admin:1
advance:2
advancement:1
advancing:1
advertise:1
advertised:1
ae:10
af:1
affect:3
affected:1
affecting:2
affiliates:438
after:125
afterwards:8
again:23
against:10
againt:1
aggregate:1
agree:7
agreed:2
ahead:2
ai:1
aids:2
aka:1
alert:2
algorithm:11
alias:8
aliased:4
aliases:5
aliasing:2
align:50
alignAndCheck:15
alignAndReserve:16
alignIncr:2
alignOnBoundary:12
alignResult:2
aligned:17
alignment:43
alignmentForEncoding:2
alive:3
all:295
all*:1
all?:3
allMethodInfo:11
allMethodNames:3
allMethodsThrowRemoteException:2
allocComponents:7
allocate:15
allocateDirect:1
allocateServerRequestId:4
allocateSlotId:2
allocate_slot_id:4
allocated:13
allocating:3
allocation:3
allocations:2
allocator:1
allow:32
allowIndirection:2
allowLocalOptimization:3
allowed:23
allowing:8
allows:29
almost:3</span>
 

 
  

 
       总结：查阅最新的JDK文档新特性是必要的，不然每次都自己写一个递归去访问目录，那肯定会带来不必要的麻烦。并且使用JDK的这个观察者模式去访问目录，非常简单... 

 


 

                                    

                                        这篇关于使用jdk 1.7的新api来统计jdk中英语单词的分布情况的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
                                    

                                    

                                

                            

                            

                        

                    

                    

                        

                            

                            http://www.chinasem.cn/article/992132。
                            
                        

                    

                    
                    

                        
                            
                        
                    

                

                

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                                        中文分词jieba库的使用与实景应用（一）
                                    
                                

                            

                            

                                

                                    中文分词jieba库的使用与实景应用（一）
                                

                                

                                    知识星球：https://articles.zsxq.com/id_fxvgc803qmr2.html 目录 一.定义： 精确模式（默认模式）： 全模式： 搜索引擎模式： paddle 模式（基于深度学习的分词模式）： 二 自定义词典 三.文本解析   调整词出现的频率 四. 关键词提取 A. 基于TF-IDF算法的关键词提取 B. 基于TextRank算法的关键词提取
                                

                                

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                                        使用SecondaryNameNode恢复NameNode的数据
                                    
                                

                            

                            

                                

                                    使用SecondaryNameNode恢复NameNode的数据
                                

                                

                                    1）需求： NameNode进程挂了并且存储的数据也丢失了，如何恢复NameNode 此种方式恢复的数据可能存在小部分数据的丢失。  2）故障模拟 （1）kill -9 NameNode进程 [lytfly@hadoop102 current]$ kill -9 19886 （2）删除NameNode存储的数据（/opt/module/hadoop-3.1.4/data/tmp/dfs/na
                                

                                

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                                        Hadoop数据压缩使用介绍
                                    
                                

                            

                            

                                

                                    Hadoop数据压缩使用介绍
                                

                                

                                    一、压缩原则 （1）运算密集型的Job，少用压缩 （2）IO密集型的Job，多用压缩  二、压缩算法比较  三、压缩位置选择  四、压缩参数配置 1）为了支持多种压缩/解压缩算法，Hadoop引入了编码/解码器  2）要在Hadoop中启用压缩，可以配置如下参数
                                

                                

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                                        Makefile简明使用教程
                                    
                                

                            

                            

                                

                                    Makefile简明使用教程
                                

                                

                                    文章目录 规则makefile文件的基本语法：加在命令前的特殊符号：.PHONY伪目标: Makefilev1 直观写法v2 加上中间过程v3 伪目标v4 变量 make 选项-f-n-C     Make 是一种流行的构建工具，常用于将源代码转换成可执行文件或者其他形式的输出文件(如库文件、文档等)。Make 可以自动化地执行编译、链接等一系列操作。  规则 makefile文件
                                

                                

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                                        hdu1496（用hash思想统计数目）
                                    
                                

                            

                            

                                

                                    hdu1496（用hash思想统计数目）
                                

                                

                                    作为一个刚学hash的孩子，感觉这道题目很不错，灵活的运用的数组的下标。 解题步骤：如果用常规方法解，那么时间复杂度为O（n^4）,肯定会超时，然后参考了网上的解题方法，将等式分成两个部分，a*x1^2+b*x2^2和c*x3^2+d*x4^2， 各自作为数组的下标，如果两部分相加为0，则满足等式； 代码如下：  #include<iostream>#include<algorithm
                                

                                

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                                        使用opencv优化图片（画面变清晰）
                                    
                                

                            

                            

                                

                                    使用opencv优化图片（画面变清晰）
                                

                                

                                    文章目录 需求影响照片清晰度的因素 实现降噪测试代码 锐化空间锐化Unsharp Masking频率域锐化对比测试 对比度增强常用算法对比测试     需求  对图像进行优化，使其看起来更清晰，同时保持尺寸不变，通常涉及到图像处理技术如锐化、降噪、对比度增强等  影响照片清晰度的因素 影响照片清晰度的因素有很多，主要可以从以下几个方面来分析 1. 拍摄设备 相机传感器：相机传
                                

                                

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                                        常用的jdk下载地址
                                    
                                

                            

                            

                                

                                    常用的jdk下载地址
                                

                                

                                    jdk下载地址    安装方式可以看之前的博客： mac安装jdk  oracle 版本：https://www.oracle.com/java/technologies/downloads/  Eclipse Temurin版本：https://adoptium.net/zh-CN/temurin/releases/  阿里版本： github：https://github.com/
                                

                                

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                                        pdfmake生成pdf的使用
                                    
                                

                            

                            

                                

                                    pdfmake生成pdf的使用
                                

                                

                                    实际项目中有时会有根据填写的表单数据或者其他格式的数据，将数据自动填充到pdf文件中根据固定模板生成pdf文件的需求   文章目录 利用pdfmake生成pdf文件1.下载安装pdfmake第三方包2.封装生成pdf文件的共用配置3.生成pdf文件的文件模板内容4.调用方法生成pdf    利用pdfmake生成pdf文件 1.下载安装pdfmake第三方包  npm i pdfma
                                

                                

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                                        零基础学习Redis(10) -- zset类型命令使用
                                    
                                

                            

                            

                                

                                    零基础学习Redis(10) -- zset类型命令使用
                                

                                

                                    zset是有序集合，内部除了存储元素外，还会存储一个score，存储在zset中的元素会按照score的大小升序排列，不同元素的score可以重复，score相同的元素会按照元素的字典序排列。 1. zset常用命令 1.1 zadd   zadd key [NX | XX] [GT | LT]   [CH] [INCR] score member [score member ...]
                                

                                

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                                        [英语单词] paste
                                    
                                

                            

                            

                                

                                    [英语单词] paste
                                

                                

                                    这个单词的意思，有时候可能会被，被copy/paste误导了，会有一种假象，只有copy了才可以paste。 其实还可以将两个东西paste在一起。比如下面的： /kernel/include/linux/compiler_types.h /* Indirect macros required for expanded argument pasting, eg. __LINE__. */#d
                                

                                

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