SpringBoot+Redis 搜索栏热搜、不雅文字过滤功能 springboot

需求：

搜索栏展示当前登陆的个人用户的搜索历史记录，删除个人历史记录
用户在搜索栏输入某字符，则将该字符记录下来，记录该字符被搜索的个数以及当前的时间戳
每当用户查询了已在redis存在了的字符时，则直接累加个数，用来获取平台上最热查询的十条数据。
不雅文字过滤功能。

【SpringBoot+Redis 搜索栏热搜、不雅文字过滤功能】首先配置好redis数据源等等基础
最后贴上核心的服务层的代码：

import org.apache.commons.lang.StringUtils; import org.springframework.data.redis.core.*; import org.springframework.stereotype.Service; import javax.annotation.Resource; import java.util.*; import java.util.concurrent.TimeUnit; @Transactional @Service("redisService") public class RedisServiceImpl implements RedisService { //导入数据源 @Resource(name = "redisSearchTemplate") private StringRedisTemplate redisSearchTemplate; /** * 新增一条userid用户在搜索栏的历史记录 * @param searchkey输入的关键词 * @param userid */ @Override public int insertSearchHistoryByUserId(String userid, String searchkey) { String shistory = RedisKeyUtils.getSearchHistoryKey(userid); boolean b = redisSearchTemplate.hasKey(shistory); if (b) { Object hk = redisSearchTemplate.opsForHash().get(shistory, searchkey); if (hk != null) { return 1; }else{ redisSearchTemplate.opsForHash().put(shistory, searchkey, "1"); } }else{ redisSearchTemplate.opsForHash().put(shistory, searchkey, "1"); } return 1; } /** * 删除个人历史数据 * @param searchkey输入的关键词 * @param userid */ @Override public Long delSearchHistoryByUserId(String userid, String searchkey) { String shistory = RedisKeyUtils.getSearchHistoryKey(userid); return redisSearchTemplate.opsForHash().delete(shistory, searchkey); } /** * 获取个人历史数据列表 * @param userid */ @Override public List getSearchHistoryByUserId(String userid) { List stringList = null; String shistory = RedisKeyUtils.getSearchHistoryKey(userid); boolean b = redisSearchTemplate.hasKey(shistory); if(b){ Cursor> cursor = redisSearchTemplate.opsForHash().scan(shistory, ScanOptions.NONE); while (cursor.hasNext()) { Map.Entry map = cursor.next(); String key = map.getKey().toString(); stringList.add(key); } return stringList; } return null; } /** * 新增热词搜索记录，将用户输入的热词存储 * @param searchkey输入的关键词 */ @Override public int insertScoreByUserId(String searchkey) { Long now = System.currentTimeMillis(); ZSetOperations zSetOperations = redisSearchTemplate.opsForZSet(); ValueOperations valueOperations = redisSearchTemplate.opsForValue(); List title = new ArrayList<>(); title.add(searchkey); for (int i = 0, lengh = title.size(); i < lengh; i++) { String tle = title.get(i); try { if (zSetOperations.score("title", tle) <= 0) { zSetOperations.add("title", tle, 0); valueOperations.set(tle, String.valueOf(now)); } } catch (Exception e) { zSetOperations.add("title", tle, 0); valueOperations.set(tle, String.valueOf(now)); } } return 1; }/** * 根据searchkey搜索其相关最热的前十名 * (如果searchkey为null空，则返回redis存储的前十最热词条) * @param searchkey输入的关键词 */ @Override public List getHotList(String searchkey) { String key = searchkey; Long now = System.currentTimeMillis(); List result = new ArrayList<>(); ZSetOperations zSetOperations = redisSearchTemplate.opsForZSet(); ValueOperations valueOperations = redisSearchTemplate.opsForValue(); Set value = https://www.it610.com/article/zSetOperations.reverseRangeByScore("title", 0, Double.MAX_VALUE); //key不为空的时候推荐相关的最热前十名 if(StringUtils.isNotEmpty(searchkey)){ for (String val : value) { if (StringUtils.containsIgnoreCase(val, key)) { if (result.size() > 9) {//只返回最热的前十名 break; } Long time = Long.valueOf(valueOperations.get(val)); if ((now - time) < 2592000000L) {//返回最近一个月的数据 result.add(val); } else {//时间超过一个月没搜索就把这个词热度归0 zSetOperations.add("title", val, 0); } } } }else{ for (String val : value) { if (result.size() > 9) {//只返回最热的前十名 break; } Long time = Long.valueOf(valueOperations.get(val)); if ((now - time) < 2592000000L) {//返回最近一个月的数据 result.add(val); } else {//时间超过一个月没搜索就把这个词热度归0 zSetOperations.add("title", val, 0); } } } return result; } /** * 每次点击给相关词searchkey热度 +1 * @param searchkey输入的关键词 */ @Override public int incrementScore(String searchkey) { String key = searchkey; Long now = System.currentTimeMillis(); ZSetOperations zSetOperations = redisSearchTemplate.opsForZSet(); ValueOperations valueOperations = redisSearchTemplate.opsForValue(); zSetOperations.incrementScore("title", key, 1); valueOperations.getAndSet(key, String.valueOf(now)); return 1; } }

不雅文字功能实现，代码如下：

import org.springframework.context.annotation.Configuration; import org.springframework.core.io.ClassPathResource; import java.io.*; import java.util.HashMap; import java.util.HashSet; import java.util.Map; import java.util.Set; //屏蔽敏感词初始化 @Configuration @SuppressWarnings({ "rawtypes", "unchecked" }) public class SensitiveWordInit { // 字符编码 private String ENCODING = "UTF-8"; // 初始化敏感字库 public Map initKeyWord() throws IOException { // 读取敏感词库 ,存入Set中 Set wordSet = readSensitiveWordFile(); // 将敏感词库加入到HashMap中 return addSensitiveWordToHashMap(wordSet); } // 读取敏感词库 ,存入HashMap中 private Set readSensitiveWordFile() throws IOException { Set wordSet = null; ClassPathResource classPathResource = new ClassPathResource("static/censorword.txt"); InputStream inputStream = classPathResource.getInputStream(); try { InputStreamReader read = new InputStreamReader(inputStream, ENCODING); wordSet = new HashSet(); BufferedReader br = new BufferedReader(read); String txt = null; while ((txt = br.readLine()) != null) { wordSet.add(txt); } br.close(); read.close(); } catch (Exception e) { e.printStackTrace(); } return wordSet; }private Map addToHashMap(Set wordSet) { // 初始化敏感词容器 Map wordMap = new HashMap(wordSet.size()); for (String word : wordSet) { Map nowMap = wordMap; for (int i = 0; i < word.length(); i++) { char keyChar = word.charAt(i); Object tempMap = nowMap.get(keyChar); if (tempMap != null) { nowMap = (Map) tempMap; } // 不存在则，则构建一个map，同时将isEnd设置为0，因为他不是最后一个 else { // 设置标志位 Map newMap = new HashMap(); newMap.put("isEnd", "0"); nowMap.put(keyChar, newMap); nowMap = newMap; } // 最后一个 if (i == word.length() - 1) { nowMap.put("isEnd", "1"); } } } return wordMap; } }

工具类代码：

import java.io.IOException; import java.util.HashSet; import java.util.Iterator; import java.util.Map; import java.util.Set; //敏感词过滤器：DFA算法进行敏感词过滤 public class SensitiveFilter { //敏感词过滤器：利用DFA算法进行敏感词过滤 private Map sensitiveWordMap = null; // 最小匹配规则 public static int minMatchType = 1; // 最大匹配规则 public static int maxMatchType = 2; private static SensitiveFilter instance = null; // 构造函数，初始化敏感词库 private SensitiveFilter() throws IOException { sensitiveWordMap = new SensitiveWordInit().initKeyWord(); } // 获取单例 public static SensitiveFilter getInstance() throws IOException { if (null == instance) { instance = new SensitiveFilter(); } return instance; } // 获取文字中的敏感词 public Set getSensitiveWord(String txt, int matchType) { Set sensitiveWordList = new HashSet(); for (int i = 0; i < txt.length(); i++) { // 判断是否包含敏感字符 int length = CheckSensitiveWord(txt, i, matchType); // 存在,加入list中 if (length > 0) { sensitiveWordList.add(txt.substring(i, i + length)); i = i + length - 1; } } return sensitiveWordList; } // 替换敏感字字符 public String replaceSensitiveWord(String txt, int matchType, String replaceChar) { String resultTxt = txt; // 获取所有的敏感词 Set set = getSensitiveWord(txt, matchType); Iterator iterator = set.iterator(); String word = null; String replaceString = null; while (iterator.hasNext()) { word = iterator.next(); replaceString = getReplaceChars(replaceChar, word.length()); resultTxt = resultTxt.replaceAll(word, replaceString); } return resultTxt; } /** * 获取替换字符串 * * @param replaceChar * @param length * @return */ private String getReplaceChars(String replaceChar, int length) { String resultReplace = replaceChar; for (int i = 1; i < length; i++) { resultReplace += replaceChar; } return resultReplace; } /** * 检查文字中是否包含敏感字符，检查规则如下： * 如果存在，则返回敏感词字符的长度，不存在返回0 * @param txt * @param beginIndex * @param matchType * @return */ public int CheckSensitiveWord(String txt, int beginIndex, int matchType) { // 敏感词结束标识位：用于敏感词只有1位的情况 boolean flag = false; // 匹配标识数默认为0 int matchFlag = 0; Map nowMap = sensitiveWordMap; for (int i = beginIndex; i < txt.length(); i++) { char word = txt.charAt(i); // 获取指定key nowMap = (Map) nowMap.get(word); // 存在，则判断是否为最后一个 if (nowMap != null) { // 找到相应key，匹配标识+1 matchFlag++; // 如果为最后一个匹配规则,结束循环，返回匹配标识数 if ("1".equals(nowMap.get("isEnd"))) { // 结束标志位为true flag = true; // 最小规则，直接返回,最大规则还需继续查找 if (SensitiveFilter.minMatchType == matchType) { break; } } } // 不存在，直接返回 else { break; } } if (SensitiveFilter.maxMatchType == matchType){ if(matchFlag < 2 || !flag){//长度必须大于等于1，为词 matchFlag = 0; } } if (SensitiveFilter.minMatchType == matchType){ if(matchFlag < 2 && !flag){//长度必须大于等于1，为词 matchFlag = 0; } } return matchFlag; } }

controller层直接调用方法判断：

//非法敏感词汇判断 SensitiveFilter filter = SensitiveFilter.getInstance(); int n = filter.CheckSensitiveWord(searchkey,0,1); if(n > 0){ //存在非法字符 logger.info("这个人输入了非法字符--> {},不知道他到底要查什么~ userid--> {}",searchkey,userid); return null; }

敏感文字替换*等字符：