springboot+redis实现简单的热搜功能
短信预约 -IT技能 免费直播动态提醒
使用springboot集成redis实现一个简单的热搜功能。
- 搜索栏展示当前登录的个人用户的搜索历史记录；
- 删除个人用户的搜索历史记录；
- 插入个人用户的搜索历史记录；
- 用户在搜索栏输入某字符，则将该字符记录下来以zset格式存储在redis中，记录该字符被搜索的个数；
- 当用户再次查询了已在redis存储了的字符时，则直接累加个数；
- 搜索相关最热的前十条数据；
实例
@Transactional
@Service("redisService")
public class RedisService {
@Resource
private StringRedisTemplate redisSearchTemplate;
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;
}
public long delSearchHistoryByUserId(String userId, String searchKey) {
String searchHistoryKey = RedisKeyUtil.getSearchHistoryKey(userId);
return redisSearchTemplate.opsForHash().delete(searchHistoryKey, searchKey);
}
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;
}
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;
}
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;
}
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 {
private Map sensitiveWordMap = null;
public static int minMatchType = 1;
public static int maxMatchType = 2;
private static SensitiveFilter instance = null;
private SensitiveFilter() throws IOException {
sensitiveWordMap = new SensitiveWordInit().initKeyWord();
}
public static SensitiveFilter getInstance() throws IOException {
if (null == instance) {
instance = new SensitiveFilter();
}
return instance;
}
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;
}
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;
}
private String getReplaceChars(String replaceChar, int length) {
String resultReplace = replaceChar;
for (int i = 1; i < length; i++) {
resultReplace += replaceChar;
}
return resultReplace;
}
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 {
private String ENCODING = "UTF-8";
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);
}
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;
}
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";
}
到此这篇关于springboot+redis实现热搜的文章就介绍到这了,更多相关springboot redis热搜内容请搜索编程网以前的文章或继续浏览下面的相关文章希望大家以后多多支持编程网！
免责声明：
① 本站未注明“稿件来源”的信息均来自网络整理。其文字、图片和音视频稿件的所属权归原作者所有。本站收集整理出于非商业性的教育和科研之目的，并不意味着本站赞同其观点或证实其内容的真实性。仅作为临时的测试数据，供内部测试之用。本站并未授权任何人以任何方式主动获取本站任何信息。
② 本站未注明“稿件来源”的临时测试数据将在测试完成后最终做删除处理。有问题或投稿请发送至: 邮箱/279061341@qq.com QQ/279061341
