springboot+redis实现简单的热搜功能

@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;
    }
}

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;
    }
}

@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";
    }