Redis实现布隆过滤器解析

布隆过滤器原理介绍

  【1】概念说明

    1)布隆过滤器(Bloom Filter)是1970年由布隆提出的。它实际上是一个很长的二进制向量和一系列随机映射函数。布隆过滤器可以用于检索一个元素是否在一个集合中。它的优点是空间效率和查询时间都远远超过一般的算法,缺点是有一定的误识别率和删除困难。

  【2】设计思想

    1)BF是由一个长度为m比特的位数组(bit array)与k个哈希函数(hash function)组成的数据结构。位数组均初始化为0,所有哈希函数都可以分别把输入数据尽量均匀地散列。

    2)当要插入一个元素时,将其数据分别输入k个哈希函数,产生k个哈希值。以哈希值作为位数组中的下标,将所有k个对应的比特置为1。

    3)当要查询(即判断是否存在)一个元素时,同样将其数据输入哈希函数,然后检查对应的k个比特。如果有任意一个比特为0,表明该元素一定不在集合中。如果所有比特均为1,表明该集合有(较大的)可能性在集合中。为什么不是一定在集合中呢?因为一个比特被置为1有可能会受到其他元素的影响,这就是所谓“假阳性”(false positive)。相对地,“假阴性”(false negative)在BF中是绝不会出现的。

  【3】图示

          

  【4】优缺点

    1)优点

      1.不需要存储数据本身,只用比特表示,因此空间占用相对于传统方式有巨大的优势,并且能够保密数据;

      2.时间效率也较高,插入和查询的时间复杂度均为O(k);

      3.哈希函数之间相互独立,可以在硬件指令层面并行计算。

 

    2)缺点

      1.存在假阳性的概率,不适用于任何要求100%准确率的情境;

      2.只能插入和查询元素,不能删除元素,这与产生假阳性的原因是相同的。我们可以简单地想到通过计数(即将一个比特扩展为计数值)来记录元素数,但仍然无法保证删除的元素一定在集合中。(因此只能进行重建

 

guava框架如何实现布隆过滤器

  【1】引入依赖

<dependency>
   <groupId>com.google.guava</groupId>
   <artifactId>guava</artifactId>
   <version>28.0-jre</version>
</dependency>

 

  【2】简单使用

//布隆过滤器-数字指纹存储在当前jvm当中
public class LocalBloomFilter {

    private static final BloomFilter<String> bloomFilter = BloomFilter.create(Funnels.stringFunnel(StandardCharsets.UTF_8),1000000,0.01);
    /**
     * 谷歌guava布隆过滤器
     * @param id
     * @return
     */
    public static boolean match(String id){
        return bloomFilter.mightContain(id);
    }

    public static void put(Long id){
        bloomFilter.put(id+"");
    }
}

 

  【3】源码分析(由上面的三个主要方法看起,create方法,mightContain方法,put方法)

    1)create方法深入分析

@VisibleForTesting
static <T> BloomFilter<T> create(Funnel<? super T> funnel, long expectedInsertions, double fpp, Strategy strategy) {
    //检测序列化器
    checkNotNull(funnel);
    //检测存储容量
    checkArgument(expectedInsertions >= 0, "Expected insertions (%s) must be >= 0", expectedInsertions);
    //容错率应该在0-1之前
    checkArgument(fpp > 0.0, "False positive probability (%s) must be > 0.0", fpp);
    checkArgument(fpp < 1.0, "False positive probability (%s) must be < 1.0", fpp);
    //检测策略
    checkNotNull(strategy);

    if (expectedInsertions == 0) {
      expectedInsertions = 1;
    }

    // 这里numBits即底下LockFreeBitArray位数组的长度,可以看到计算方式就是外部传入的期待数和容错率
    long numBits = optimalNumOfBits(expectedInsertions, fpp);
    int numHashFunctions = optimalNumOfHashFunctions(expectedInsertions, numBits);
    try {
      return new BloomFilter<T>(new BitArray(numBits), numHashFunctions, funnel, strategy);
    } catch (IllegalArgumentException e) {
      throw new IllegalArgumentException("Could not create BloomFilter of " + numBits + " bits", e);
    }
}

private BloomFilter(BitArray bits, int numHashFunctions, Funnel<? super T> funnel, Strategy strategy) {
    //检测hash函数个数应该在0-255之间
    checkArgument(numHashFunctions > 0, "numHashFunctions (%s) must be > 0", numHashFunctions);
    checkArgument(numHashFunctions <= 255, "numHashFunctions (%s) must be <= 255", numHashFunctions);
    this.bits = checkNotNull(bits);
    this.numHashFunctions = numHashFunctions;
    this.funnel = checkNotNull(funnel);
    this.strategy = checkNotNull(strategy);
}


//计算容量大小
@VisibleForTesting
static long optimalNumOfBits(long n, double p) {
    if (p == 0) {
      p = Double.MIN_VALUE;
    }
    return (long) (-n * Math.log(p) / (Math.log(2) * Math.log(2)));
}

//计算满足条件时,应进行多少次hash函数
@VisibleForTesting
static int optimalNumOfHashFunctions(long n, long m) {
    // (m / n) * log(2), but avoid truncation due to division!
    return Math.max(1, (int) Math.round((double) m / n * Math.log(2)));
}

 

    2)mightContain方法深入分析

public boolean mightContain(T object) {
    return strategy.mightContain(object, funnel, numHashFunctions, bits);
}

public <T> boolean mightContain(T object, Funnel<? super T> funnel, int numHashFunctions, BloomFilterStrategies.BitArray bits) {
    long bitSize = bits.bitSize();
    long hash64 = Hashing.murmur3_128().hashObject(object, funnel).asLong();
    int hash1 = (int)hash64;
    int hash2 = (int)(hash64 >>> 32);

    for(int i = 1; i <= numHashFunctions; ++i) {
        int combinedHash = hash1 + i * hash2;
        if (combinedHash < 0) {
            combinedHash = ~combinedHash;
        }

        if (!bits.get((long)combinedHash % bitSize)) {
            return false;
        }
    }

    return true;
}

 

    3)put方法深入分析

@CanIgnoreReturnValue
public boolean put(T object) {
    return strategy.put(object, funnel, numHashFunctions, bits);
}

//策略实现填入bits
public <T> boolean put(T object, Funnel<? super T> funnel, int numHashFunctions, BloomFilterStrategies.BitArray bits) {
    long bitSize = bits.bitSize();
    long hash64 = Hashing.murmur3_128().hashObject(object, funnel).asLong();
    int hash1 = (int)hash64;
    int hash2 = (int)(hash64 >>> 32);
    boolean bitsChanged = false;

    for(int i = 1; i <= numHashFunctions; ++i) {
        int combinedHash = hash1 + i * hash2;
        if (combinedHash < 0) {
            combinedHash = ~combinedHash;
        }

        bitsChanged |= bits.set((long)combinedHash % bitSize);
    }

    return bitsChanged;
}

 

采用Redis实现布隆过滤器

  【1】抽出guava框架中部分核心逻辑方法形成工具类

/**
 * 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。
 * @description: 布隆过滤器,摘录自Google-guava包
 **/
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;
        // 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[] murmurHashOffset(T value) {
        int[] offset = new int[numHashFunctions];

        //有点类似于hashmap中采用高32位与低32位相与获得hash值
        long hash64 = Hashing.murmur3_128().hashObject(value, funnel).asLong();
        int hash1 = (int) hash64;
        int hash2 = (int) (hash64 >>> 32);
        //采用对低32进行变更以达到随机哈希函数的效果
        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
     * Math.log(2) = 0.6931471805599453;(取0.693147来用)
     * (Math.log(2) * Math.log(2)) = 0.48045237;
     * 假设传入n为1,000,000  , p为0.01;
     * Math.log(0.01) = -4.605170185988091;
     * 则返回值为9,585,071 ,即差不多是预设容量的10倍
     * 
     * 要知道 1MB = 1024KB , 1KB = 1024B ,1B=8bit。
     * 也就是对一百万数据预计花费的内存为1.143MB的内存
     */
    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)));
    }
}

 

  【2】构建Redis实现布隆过滤器的服务类

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;
    }

    /**
     * 根据给定的布隆过滤器添加值
     * 这里可以考虑LUA脚本进行优化,减少传输次数
     * 如 eval "redis.call('setbit',KEYS[1],ARGV[1],1) redis.call('setbit',KEYS[1],ARGV[2],1) " 1 mybool  243 5143 
     * 但是又需要衡量操作的时间,与如果次数很多导致的传输的数据量很大容易阻塞问题
     */
    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);
        }
    }

    /**
     * 根据给定的布隆过滤器判断值是否存在
     */
    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】编辑配置类

@Slf4j
@Configuration
public class BloomFilterConfig implements InitializingBean{

    @Autowired
    private PmsProductService productService;

    @Autowired
    private RedisTemplate template;

    @Bean
    public BloomFilterHelper<String> initBloomFilterHelper() {
        return new BloomFilterHelper<>((Funnel<String>) (from, into) -> into.putString(from, Charsets.UTF_8)
                .putString(from, Charsets.UTF_8), 1000000, 0.01);
    }

    // 布隆过滤器bean注入
    @Bean
    public BloomRedisService bloomRedisService(){
        BloomRedisService bloomRedisService = new BloomRedisService();
        bloomRedisService.setBloomFilterHelper(initBloomFilterHelper());
        bloomRedisService.setRedisTemplate(template);
        return bloomRedisService;
    }

    @Override
    public void afterPropertiesSet() throws Exception {
        List<Long> list = productService.getAllProductId();
        log.info("加载产品到布隆过滤器当中,size:{}",list.size());
        if(!CollectionUtils.isEmpty(list)){
            list.stream().forEach(item->{
                //LocalBloomFilter.put(item);
                bloomRedisService().addByBloomFilter(RedisKeyPrefixConst.PRODUCT_REDIS_BLOOM_FILTER,item+"");
            });
        }
    }
}

 

  【4】构建布隆过滤器的拦截器

//拦截器,所有需要查看商品详情的请求必须先过布隆过滤器
@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();
        //解析出pathvariable
        Map<String, String> pathVariable = matcher.extractUriTemplateVariables("/pms/productInfo/{id}", currentUrl);
        //布隆过滤器存储在redis中
        if(bloomRedisService.includeByBloomFilter(RedisKeyPrefixConst.PRODUCT_REDIS_BLOOM_FILTER,pathVariable.get("id"))){
            return true;
        }

        /*
         * 不在布隆过滤器当中,直接返回验证失败
         * 设置响应头
         */
        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;
    }

}

 

  【5】将拦截器注册进SpringMVC中

@Configuration
public class IntercepterConfiguration implements WebMvcConfigurer {

    @Override
    public void addInterceptors(InterceptorRegistry registry) {
        //注册拦截器
        registry.addInterceptor(authInterceptorHandler())
                .addPathPatterns("/pms/productInfo/**");
    }

    @Bean
    public BloomFilterInterceptor authInterceptorHandler(){
        return new BloomFilterInterceptor();
    }
}

 

posted @ 2022-10-07 01:16  忧愁的chafry  阅读(1168)  评论(0编辑  收藏  举报