Redis 工具类 与 Redis 布隆过滤器

Redis 工具类

1. 核心依赖

<!--redis-->
<dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-data-redis</artifactId>
</dependency>
<dependency><groupId>com.google.guava</groupId><artifactId>guava</artifactId><version>31.1-jre</version>
</dependency>
<dependency><groupId>com.alibaba</groupId><artifactId>fastjson</artifactId><version>1.2.31</version>
</dependency>

2. 序列化

public class FastJsonRedisSerializer<T> implements RedisSerializer<T> {public static final Charset DEFAULT_CHARSET = Charset.forName("UTF-8");private Class<T> clazz;static {ParserConfig.getGlobalInstance().setAutoTypeSupport(true);}public FastJsonRedisSerializer(Class<T> clazz) {super();this.clazz = clazz;}@Overridepublic byte[] serialize(T t) throws SerializationException {if (t == null) {return new byte[0];}return JSON.toJSONString(t, SerializerFeature.WriteClassName).getBytes(DEFAULT_CHARSET);}@Overridepublic T deserialize(byte[] bytes) throws SerializationException {if (bytes == null || bytes.length <= 0) {return null;}String str = new String(bytes, DEFAULT_CHARSET);return JSON.parseObject(str, clazz);}protected JavaType getJavaType(Class<?> clazz){return TypeFactory.defaultInstance().constructType(clazz);}
}

@Configuration
public class RedisSerializeConfig {@Bean@SuppressWarnings(value = { "unchecked", "rawtypes" })public RedisTemplate<Object, Object> redisTemplate(RedisConnectionFactory connectionFactory) {RedisTemplate<Object, Object> template = new RedisTemplate<Object, Object>();template.setConnectionFactory(connectionFactory);FastJsonRedisSerializer serializer = new FastJsonRedisSerializer(Object.class);// 使用StringRedisSerializer来序列化和反序列化redis的key值template.setKeySerializer(new StringRedisSerializer());template.setValueSerializer(serializer);// Hash的key也采用StringRedisSerializer的序列化方式template.setHashKeySerializer(new StringRedisSerializer());template.setHashValueSerializer(serializer);template.afterPropertiesSet();return template;}
}

3. 布隆过滤器

/*** 算法过程：* 1. 首先需要k个hash函数，每个函数可以把key散列成为1个整数* 2. 初始化时，需要一个长度为n比特的数组，每个比特位初始化为0* 3. 某个key加入集合时，用k个hash函数计算出k个散列值，并把数组中对应的比特位置为1* 4. 判断某个key是否在集合时，用k个hash函数计算出k个散列值，并查询数组中对应的比特位，如果所有的比特位都是1，认为在集合中。**/
@Component
public class BloomFilterHelper<T> {private int numHashFunctions;private int bitSize;private Funnel<T> funnel;private static final int NUM_BITS = (int) 1e4;private static final double RATE = 0.03;//不存在误判为存在的概率private static void funnel(@Nullable Object o, PrimitiveSink primitiveSink) {primitiveSink.putBytes(o.toString().getBytes());}public BloomFilterHelper() {this((Funnel) BloomFilterHelper::funnel, NUM_BITS, RATE);}public BloomFilterHelper(Funnel<T> funnel, int expectedInsertions, double fpp) {this.funnel = funnel;// 计算bit数组长度bitSize = optimalNumOfBits(expectedInsertions, fpp);// 计算hash方法执行次数numHashFunctions = optimalNumOfHashFunctions(expectedInsertions, bitSize);}public int[] getHashOffset() {return new int[numHashFunctions];}public int[] murmurHashOffset(T value) {int[] offset = new int[numHashFunctions];long hash64 = Hashing.murmur3_128().hashObject(value, funnel).asLong();int hash1 = (int) hash64;int hash2 = (int) (hash64 >>> 32);for (int i = 1; i <= numHashFunctions; i++) {int nextHash = hash1 + i * hash2;if (nextHash < 0) {nextHash = ~nextHash;}offset[i - 1] = nextHash % bitSize;}return offset;}/*** 计算bit数组长度*/private int optimalNumOfBits(long n, double p) {if (p == 0) {// 设定最小期望长度p = Double.MIN_VALUE;}return (int) (-n * Math.log(p) / (Math.log(2) * Math.log(2)));}/*** 计算hash方法执行次数*/private int optimalNumOfHashFunctions(long n, long m) {return Math.max(1, (int) Math.round((double) m / n * Math.log(2)));}
}

@Component
@RequiredArgsConstructor
public class RedisBloomFilter {private final RedisTemplate redisTemplate;private final BloomFilterHelper bloomFilterHelper;public void init(String bloomFilterName) {int[] offset = bloomFilterHelper.getHashOffset();for (int i : offset) {redisTemplate.opsForValue().setBit(bloomFilterName, i, true);}}/*** 根据给定的布隆过滤器添加值*/public <T> void add(String bloomFilterName, T value) {int[] offset = bloomFilterHelper.murmurHashOffset(value);for (int i : offset) {redisTemplate.opsForValue().setBit(bloomFilterName, i, true);}}/*** 根据给定的布隆过滤器判断值是否存在*/public <T> boolean contains(String bloomFilterName, T value) {int[] offset = bloomFilterHelper.murmurHashOffset(value);for (int i : offset) {if (!redisTemplate.opsForValue().getBit(bloomFilterName, i)) {return false;}}return true;}}

4. Redis工具类

@Component
@RequiredArgsConstructor
@Slf4j
@SuppressWarnings(value = { "unchecked", "rawtypes" })
public class RedisCache {private final RedisTemplate redisTemplate;private final RedisBloomFilter redisBloomFilter;/*** 设置有效时间** @param key Redis键* @param timeout 超时时间* @return true=设置成功；false=设置失败*/public Boolean expire(final String key, final long timeout, final TimeUnit timeUnit) {log.info("为 Redis 的键值设置超时时间\t[{}]-[{}  {}]", key, timeout, timeUnit.name());return redisTemplate.expire(key, timeout, timeUnit);}/*** 原子设置过期时间* @param key* @param value* @param timeout*/public <T> void execute(final String key, final T value, final long timeout, final TimeUnit timeUnit) {log.info("尝试存入 Redis\t[{}]-[{}]，超时时间:[{}  {}]", key, value, timeout, timeUnit.name());redisTemplate.execute(new SessionCallback() {@Overridepublic Object execute(RedisOperations redisOperations) throws DataAccessException {redisOperations.multi();redisOperations.opsForValue().set(key, value);redisOperations.expire(key, timeout, timeUnit);return redisOperations.exec();}});}/***  获得对象的剩余存活时间* @param key 键* @return 剩余存活时间*/public long getKeyTTL(final String key, final TimeUnit timeUnit) {int ttl = Math.toIntExact(redisTemplate.opsForValue().getOperations().getExpire(key));String message = null;switch (ttl) {case -1:message = "没有设置过期时间";break;case -2:message = "key不存在";break;default:message = ttl + "  " + TimeUnit.SECONDS.name();break;}log.info("查询 Redis key[{}] 剩余存活时间:{}", key, message);return TimeUnit.SECONDS.convert(ttl, timeUnit);}/*** 缓存基本的对象，Integer、String、实体类等** @param key 缓存的键值* @param value 缓存的值*/public <T> void setCacheObject(final String key, final T value) {log.info("存入 Redis\t[{}]-[{}]", key, value);redisTemplate.opsForValue().set(key, value);}/*** 缓存基本的对象，Integer、String、实体类等** @param key 缓存的键值* @param value 缓存的值* @param timout 超时时间*/public <T> void setCacheObject(final String key, final T value, final long timout, final TimeUnit timeUnit) {log.info("存入 Redis\t[{}]-[{}]，超时时间:[{}  {}]", key, value, timout, timeUnit.name());redisTemplate.opsForValue().set(key, value, timout, timeUnit);}/*** 获取键值* @param key 键* @return 键对应的值，并封装成 Optional 对象* @param <T>*/public <T> Optional<T> getCacheObject(final String key) {T value = (T) redisTemplate.opsForValue().get(key);log.info("查询 Redis\t[{}]-[{}]", key, value);return Optional.ofNullable(value);}/*** 让指定 Redis 键值进行自减* @param key 键* @return 自减后的值*/public long decrementCacheNumber(final String key) {long number = redisTemplate.opsForValue().decrement(key);log.info("Redis key[{}] 自减后：{}", key, number);return number;}/*** 让指定 Redis 键值进行自增* @param key 键* @return 自增后的值*/public long incrementCacheNumber(final String key) {long number = redisTemplate.opsForValue().increment(key);log.info("Redis key[{}] 自增后：{}", key, number);return number;}/*** 初始化布隆过滤器* @param bloomFilterName*/public void initBloomFilter(final String bloomFilterName) {log.info("初始化布隆过滤器[{}]", bloomFilterName);redisTemplate.execute(new SessionCallback() {@Overridepublic Object execute(RedisOperations redisOperations) throws DataAccessException {redisOperations.multi();redisBloomFilter.init(bloomFilterName);return redisOperations.exec();}});}/*** 初始化布隆过滤器* @param bloomFilterName* @param timeout* @param timeUnit*/public void initBloomFilter(final String bloomFilterName, final long timeout, final TimeUnit timeUnit) {redisTemplate.execute(new SessionCallback() {@Overridepublic Object execute(RedisOperations redisOperations) throws DataAccessException {redisOperations.multi();redisBloomFilter.init(bloomFilterName);expire(bloomFilterName, timeout, timeUnit);return redisOperations.exec();}});}/*** 加入布隆过滤器* @param bloomFilterName 隆过滤器的名字* @param key key 键*/public <T> void addToBloomFilter(final String bloomFilterName, final T key) {log.info("加入布隆过滤器[{}]\tkey[{}]", bloomFilterName, key);redisTemplate.execute(new SessionCallback() {@Overridepublic Object execute(RedisOperations redisOperations) throws DataAccessException {redisOperations.multi();redisBloomFilter.add(bloomFilterName, key);return redisOperations.exec();}});}/*** 布隆过滤器是否存在该键值* @param bloomFilterName 布隆过滤器的名字* @param key 键* @return 键是否存在*/public <T> boolean containsInBloomFilter(final String bloomFilterName, final T key) {boolean flag = redisBloomFilter.contains(bloomFilterName, key);log.info("key[{}]\t是否存在于布隆过滤器[{}]:\t{}", key, bloomFilterName, flag);return flag;}/*** 缓存Map** @param key* @param data*/public <K, T> void setCacheMap(final String key, final Map<K, T> data) {if (Objects.nonNull(data)) {log.info("Map 存入 Redis\t[{}]-[{}]", key, data);redisTemplate.opsForHash().putAll(key, data);}}/*** 缓存Map** @param key* @param data*/public <K, T> void setCacheMap(final String key, final Map<K, T> data, long timeout, final TimeUnit timeUnit) {if (Objects.nonNull(data)) {Map<String, T> map = new HashMap<>();data.entrySet().stream().parallel().forEach(entry -> {map.put(entry.getKey().toString(), entry.getValue());});log.info("尝试存入 Redis\t[{}]-[{}] 超时时间:[{}  {}]", key, map, timeout, timeUnit.name());redisTemplate.execute(new SessionCallback() {@Overridepublic Object execute(RedisOperations redisOperations) throws DataAccessException {redisOperations.multi();redisTemplate.opsForHash().putAll(key, map);expire(key, timeout, timeUnit);return redisOperations.exec();}});}}/*** 获得缓存的Map** @param key* @return*/public <K, T> Optional<Map<K, T>> getCacheMap(final String key) {Map<K, T> data = redisTemplate.opsForHash().entries(key);data = data.size() == 0 ? null: data;log.info("获取 Redis 中的 Map 缓存\t[{}]-[{}]", key, data);return Optional.ofNullable(data);}/*** 往Hash中存入数据** @param key Redis键* @param hashKey Hash键* @param value 值*/public <K, T> void setCacheMapValue(final String key, final K hashKey, final T value) {log.info("存入 Redis 的某个 Map\t[{}.{}]-[{}]", key, hashKey, value);redisTemplate.opsForHash().put(key, hashKey.toString(), value);}/*** 获取Hash中的数据** @param key Redis键* @param hashKey Hash键* @return Hash中的对象*/public <K, T> Optional<T> getCacheMapValue(final String key, final K hashKey) {T value = (T) redisTemplate.opsForHash().get(key, hashKey.toString());log.info("获取 Redis 中的 Map 的键值\t[{}.{}]-[{}]", key, hashKey, value);return Optional.ofNullable(value);}/*** 删除Hash中的数据** @param key* @param hashKey*/public <K> void delCacheMapValue(final String key, final K hashKey) {log.info("删除 Redis 中的 Map 的键值\tkey[{}.{}]", key, hashKey);redisTemplate.opsForHash().delete(key, hashKey.toString());}/*** 让指定 HashMap 的键值进行自减* @param key HashMap的名字* @param hashKey HashMap的一个键* @return 自减后的值*/public <K> long decrementCacheMapNumber(final String key, final K hashKey) {long number = redisTemplate.opsForHash().increment(key, hashKey.toString(), -1);log.info("Redis key[{}.{}] 自减后：{}", key, hashKey, number);return number;}/*** 让指定 HashMap 的键值进行自增* @param key HashMap的名字* @param hashKey HashMap的一个键* @return 自增后的值*/public <K> long incrementCacheMapNumber(final String key, final K hashKey) {long number = redisTemplate.opsForHash().increment(key, hashKey.toString(), +1);log.info("Redis key[{}.{}] 自增后：{}", key, hashKey, number);return number;}/*** 删除单个对象* @param key*/public boolean deleteObject(final String key) {log.info("删除 Redis 的键值\tkey[{}]", key);return redisTemplate.delete(key);}}

5. 查询Redis与Redis设置缓存的技巧

伪代码：

redisCache.getCacheObject(redisKey).orElseGet(() -> {/* balabala ....*/// 可能查询有误，所以这里也可能没法获取到data（为null），也就可能没必要设置缓存redisCache.setCacheObject(redisKey, data, ttl, unit); /* balabala ....*/return data;}));

现实示例：