使用springboot集成redis实现一个简单的热搜功能。
实例
@Transactional @Service("redisService") public class RedisService { @Resource private StringRedisTemplate redisSearchTemplate; /** * 新增一条该userId用户在搜索栏的历史记录,searchKey代表输入的关键词 * * @param userId * @param searchKey * @return */ public int addSearchHistoryByUserId(String userId, String searchKey) { String searchHistoryKey = RedisKeyUtil.getSearchHistoryKey(userId); boolean flag = redisSearchTemplate.hasKey(searchHistoryKey); if (flag) { Object hk = redisSearchTemplate.opsForHash().get(searchHistoryKey, searchKey); if (hk != null) { return 1; } else { redisSearchTemplate.opsForHash().put(searchHistoryKey, searchKey, "1"); } } else { redisSearchTemplate.opsForHash().put(searchHistoryKey, searchKey, "1"); } return 1; } /** * 删除个人历史数据 * * @param userId * @param searchKey * @return */ public long delSearchHistoryByUserId(String userId, String searchKey) { String searchHistoryKey = RedisKeyUtil.getSearchHistoryKey(userId); return redisSearchTemplate.opsForHash().delete(searchHistoryKey, searchKey); } /** * 获取个人历史数据列表 * * @param userId * @return */ public List<String> getSearchHistoryByUserId(String userId) { List<String> history = new ArrayList<>(); String searchHistoryKey = RedisKeyUtil.getSearchHistoryKey(userId); boolean flag = redisSearchTemplate.hasKey(searchHistoryKey); if (flag) { Cursor<Map.Entry<Object, Object>> cursor = redisSearchTemplate.opsForHash().scan(searchHistoryKey, ScanOptions.NONE); while (cursor.hasNext()) { Map.Entry<Object, Object> map = cursor.next(); String key = map.getKey().toString(); history.add(key); } return history; } return null; } /** * 新增一条热词搜索记录,将用户输入的热词存储下来 * * @param searchKey * @return */ public int addHot(String searchKey) { Long now = System.currentTimeMillis(); ZSetOperations zSetOperations = redisSearchTemplate.opsForZSet(); ValueOperations<String, String> valueOperations = redisSearchTemplate.opsForValue(); List<String> title = new ArrayList<>(); title.add(searchKey); for (int i = 0, length = title.size(); i < length; 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 * @return */ public List<String> getHotList(String searchKey) { String key = searchKey; Long now = System.currentTimeMillis(); List<String> result = new ArrayList<>(); ZSetOperations zSetOperations = redisSearchTemplate.opsForZSet(); ValueOperations<String, String> valueOperations = redisSearchTemplate.opsForValue(); Set<String> value = 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)) { //只返回最热的前十名 if (result.size() > 9) { 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 * @return */ public int incrementHot(String searchKey) { String key = searchKey; Long now = System.currentTimeMillis(); ZSetOperations zSetOperations = redisSearchTemplate.opsForZSet(); ValueOperations<String, String> valueOperations = redisSearchTemplate.opsForValue(); zSetOperations.incrementScore("title", key, 1); valueOperations.getAndSet(key, String.valueOf(now)); return 1; } }
在向redis添加搜索词汇时需要过滤不雅文字,合法时再去存储到redis中,下面是过滤不雅文字的过滤器。
public class SensitiveFilter { /** * 敏感词库 */ 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; /** * 单例 */ private static SensitiveFilter instance = null; /** * 构造函数,初始化敏感词库 * * @throws IOException */ private SensitiveFilter() throws IOException { sensitiveWordMap = new SensitiveWordInit().initKeyWord(); } /** * 获取单例 * * @return * @throws IOException */ public static SensitiveFilter getInstance() throws IOException { if (null == instance) { instance = new SensitiveFilter(); } return instance; } /** * 获取文字中的敏感词 * * @param txt * @param matchType * @return */ public Set<String> getSensitiveWord(String txt, int matchType) { Set<String> 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)); // 减1的原因,是因为for会自增 i = i + length - 1; } } return sensitiveWordList; } /** * 替换敏感字字符 * * @param txt * @param matchType * @param replaceChar * @return */ public String replaceSensitiveWord(String txt, int matchType, String replaceChar) { String resultTxt = txt; // 获取所有的敏感词 Set<String> set = getSensitiveWord(txt, matchType); Iterator<String> 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:<br> * 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) { //长度必须大于等于1,为词 if (matchFlag < 2 || !flag) { matchFlag = 0; } } if (SensitiveFilter.minMatchType == matchType) { //长度必须大于等于1,为词 if (matchFlag < 2 && !flag) { matchFlag = 0; } } return matchFlag; } }
@Configuration @SuppressWarnings({"rawtypes", "unchecked"}) public class SensitiveWordInit { /** * 字符编码 */ private String ENCODING = "UTF-8"; /** * 初始化敏感字库 * * @return * @throws IOException */ public Map initKeyWord() throws IOException { // 读取敏感词库,存入Set中 Set<String> 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 * * @return * @throws IOException */ private Set<String> readSensitiveWordFile() throws IOException { Set<String> wordSet = null; ClassPathResource classPathResource = new ClassPathResource("static/sensitiveWord.txt"); InputStream inputStream = classPathResource.getInputStream(); // 敏感词库 try { // 读取文件输入流 InputStreamReader read = new InputStreamReader(inputStream, ENCODING); // 文件是否是文件 和 是否存在 wordSet = new HashSet<>(); // BufferedReader是包装类,先把字符读到缓存里,到缓存满了,再读入内存,提高了读的效率。 BufferedReader br = new BufferedReader(read); String txt = null; // 读取文件,将文件内容放入到set中 while ((txt = br.readLine()) != null) { wordSet.add(txt); } br.close(); // 关闭文件流 read.close(); } catch (Exception e) { e.printStackTrace(); } return wordSet; } /** * 将HashSet中的敏感词,存入HashMap中 * * @param wordSet * @return */ private Map addSensitiveWordToHashMap(Set<String> wordSet) { // 初始化敏感词容器,减少扩容操作 Map wordMap = new HashMap(wordSet.size()); for (String word : wordSet) { Map nowMap = wordMap; for (int i = 0; i < word.length(); i++) { // 转换成char型 char keyChar = word.charAt(i); // 获取 Object tempMap = nowMap.get(keyChar); // 如果存在该key,直接赋值 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<String, String> newMap = new HashMap<>(); newMap.put("isEnd", "0"); // 添加到集合 nowMap.put(keyChar, newMap); nowMap = newMap; } // 最后一个 if (i == word.length() - 1) { nowMap.put("isEnd", "1"); } } } return wordMap; } }
其中用到的sensitiveWord.txt文件在resources目录下的static目录中,这个文件是不雅文字大全,需要与时俱进,不断进步的。
测试
@GetMapping("/add") public Object add() { int num = redisService.addSearchHistoryByUserId("001", "hello"); return num; } @GetMapping("/delete") public Object delete() { long num = redisService.delSearchHistoryByUserId("001", "hello"); return num; } @GetMapping("/get") public Object get() { List<String> history = redisService.getSearchHistoryByUserId("001"); return history; } @GetMapping("/incrementHot") public Object incrementHot() { int num = redisService.addHot("母亲节礼物"); return num; } @GetMapping("/getHotList") public Object getHotList() { List<String> hotList = redisService.getHotList("母亲节礼物"); return hotList; } @GetMapping("/incrementScore") public Object incrementScore() { int num = redisService.incrementHot("母亲节礼物"); return num; } @GetMapping("/sensitive") public Object sensitive() throws IOException { //非法敏感词汇判断 SensitiveFilter filter = SensitiveFilter.getInstance(); int n = filter.checkSensitiveWord("hello", 0, 1); if (n > 0) { //存在非法字符 System.out.printf("这个人输入了非法字符--> %s,不知道他到底要查什么~ userid--> %s","hello","001"); return "exist sensitive word"; } return "ok"; }