一、RedisTemplate
1、首先将guava实现的本地的布隆过滤器的算法代码拿过来
public class BloomFilterHelper<T> {
private int numHashFunctions;
private int bitSize;
private Funnel<T> funnel;
public BloomFilterHelper(Funnel<T> funnel, int expectedInsertions, double fpp) {
Preconditions.checkArgument(funnel != null, "funnel不能为空");
this.funnel = funnel;
bitSize = optimalNumOfBits(expectedInsertions, fpp);
numHashFunctions = optimalNumOfHashFunctions(expectedInsertions, bitSize);
}
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;
}
private int optimalNumOfBits(long n, double p) {
if (p == 0) {
p = Double.MIN_VALUE;
}
return (int) (-n * Math.log(p) / (Math.log(2) * Math.log(2)));
}
private int optimalNumOfHashFunctions(long n, long m) {
return Math.max(1, (int) Math.round((double) m / n * Math.log(2)));
}
}
2、结合redis将数据存到bitMap中
public class BloomRedisService {
private RedisTemplate<String, Object> redisTemplate;
private BloomFilterHelper bloomFilterHelper;
public void setBloomFilterHelper(BloomFilterHelper bloomFilterHelper) {
this.bloomFilterHelper = bloomFilterHelper;
}
public void setRedisTemplate(RedisTemplate<String, Object> redisTemplate) {
this.redisTemplate = redisTemplate;
}
public <T> void addByBloomFilter(String key, T value) {
Preconditions.checkArgument(bloomFilterHelper != null, "bloomFilterHelper不能为空");
int[] offset = bloomFilterHelper.murmurHashOffset(value);
for (int i : offset) {
redisTemplate.opsForValue().setBit(key, i, true);
}
}
public <T> boolean includeByBloomFilter(String key, T value) {
Preconditions.checkArgument(bloomFilterHelper != null, "bloomFilterHelper不能为空");
int[] offset = bloomFilterHelper.murmurHashOffset(value);
for (int i : offset) {
if (!redisTemplate.opsForValue().getBit(key, i)) {
return false;
}
}
return true;
}
}
3、可以拦截查询方法,对查询数据是否存在进行处理,如果存在则继续,否则不执行查询操作
@Slf4j
public class BloomFilterInterceptor implements HandlerInterceptor {
@Autowired
private BloomRedisService bloomRedisService;
@Override
public boolean preHandle(HttpServletRequest request, HttpServletResponse response, Object handler) throws Exception {
String currentUrl = request.getRequestURI();
PathMatcher matcher = new AntPathMatcher();
Map<String, String> pathVariable = matcher.extractUriTemplateVariables("/pms/productInfo/{id}", currentUrl);
if(bloomRedisService.includeByBloomFilter(RedisKeyPrefixConst.PRODUCT_REDIS_BLOOM_FILTER,pathVariable.get("id"))){
return true;
}
response.setHeader("Content-Type","application/json");
response.setCharacterEncoding("UTF-8");
String result = new ObjectMapper().writeValueAsString(CommonResult.validateFailed("产品不存在!"));
response.getWriter().print(result);
return false;
}
}
二、Redission
Redission对Redis做了很多封装,除了常见的分布式锁之外,对布隆过滤器也同样有实现,简单好用
public class RedissonBloomFilter {
public static void main(String[] args) {
Config config = new Config();
config.useSingleServer().setAddress("redis://ip:port");
RedissonClient redisson = Redisson.create(config);
RBloomFilter<String> bloomFilter = redisson.getBloomFilter("phoneList");
bloomFilter.tryInit(100000000L,0.03);
bloomFilter.add("aa");
System.out.println(bloomFilter.contains("bb"));
System.out.println(bloomFilter.contains("aa"));
}
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