如何在springboot项目中redis使用布隆过滤器防止缓存穿透

上一篇博客讲到了布隆过滤器在java中的应用，这一篇说
如何在springboot项目中redis使用布隆过滤器防止缓存穿透。

先引入依赖

        <!--使用Redis-->
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-data-redis</artifactId>
        </dependency>
        <!--借助guava的布隆过滤器-->
        <dependency>
            <groupId>com.google.guava</groupId>
            <artifactId>guava</artifactId>
            <version>19.0</version>
        </dependency>

yml redis配置

spring:
  redis:
    database: 3
    host: 127.0.0.1
    port: 6379
    password: 12345
    jedis.pool.max-idle: 100
    jedis.pool.max-wait: -1ms
    jedis.pool.min-idle: 2
    timeout: 2000ms

两个工具类

BloomFilterHelper

package com.whrfjd.rescenter.utis;

import com.google.common.base.Preconditions;
import com.google.common.hash.Funnel;
import com.google.common.hash.Hashing;

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];

        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;
        }
        int sizeOfBitArray = (int) (-n * Math.log(p) / (Math.log(2) * Math.log(2)));
        return sizeOfBitArray;
    }

    /**
     * 计算hash方法执行次数
     */
    private int optimalNumOfHashFunctions(long n, long m) {
        int countOfHash = Math.max(1, (int) Math.round((double) m / n * Math.log(2)));
        return countOfHash;
    }
}

RedisBloomFilter

package com.whrfjd.rescenter.utis;

import com.google.common.base.Preconditions;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.data.redis.core.RedisTemplate;
import org.springframework.stereotype.Service;

/**
 * @Author : JCccc
 * @CreateTime : 2020/4/23
 * @Description :
 **/
@Service
public class RedisBloomFilter {
    @Autowired
    private RedisTemplate redisTemplate;

    /**
     * 根据给定的布隆过滤器添加值
     */
    public <T> void addByBloomFilter(BloomFilterHelper<T> bloomFilterHelper, String key, T value) {
        Preconditions.checkArgument(bloomFilterHelper != null, "bloomFilterHelper不能为空");
        int[] offset = bloomFilterHelper.murmurHashOffset(value);
        for (int i : offset) {
            System.out.println("key : " + key + " " + "value : " + i);
            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(BloomFilterHelper<T> bloomFilterHelper, String key, T value) {
        Preconditions.checkArgument(bloomFilterHelper != null, "bloomFilterHelper不能为空");
        int[] offset = bloomFilterHelper.murmurHashOffset(value);
        for (int i : offset) {
            System.out.println("key : " + key + " " + "value : " + i);
            if (!redisTemplate.opsForValue().getBit(key, i)) {
                return false;
            }
        }

        return true;
    }

}

配置完成现在可以测试了。
redis布隆过滤器数据添加

 @GetMapping("/redis/bloomFilter")
    @ApiOperation("redis布隆过滤器数据添加")
    public ResponseResult redisBloomFilter(){
        List<String> allResourceId = resCenterDao.getAllResourceId();
        for (String id : allResourceId) {
            //将所有的资源id放入到布隆过滤器中
            redisBloomFilter.addByBloomFilter(bloomFilterHelper,"bloom",id);
        }
        return new ResponseResult(ResponseEnum.SUCCESS);
    }

redis布隆过滤器资源测试

 @GetMapping("/redis/bloomFilter/resourceId")
    @ApiOperation("redis布隆过滤器资源测试")
    public ResponseResult redisBloomFilterResourceId(@RequestParam("resourceId")String resourceId){
        boolean mightContain = redisBloomFilter.includeByBloomFilter(bloomFilterHelper,"bloom",resourceId);
        if (!mightContain){
            return new QueryResult<>(ResCenterEnum.RESOURCE_EXSIT,"");
        }
        return new ResponseResult(ResponseEnum.SUCCESS);
    }

完成！

本文地址：https://blog.csdn.net/weixin_43748936/article/details/110225696