redis中使用bloomfilter的白名单功能解决缓存穿透问题

public class WhiteList {
    public static void main(String[] args) {
        testGuavaWithBloomFilter();
    }
    public static void testGuavaWithBloomFilter()
    {
        //1 创建guava版布隆过滤器
        BloomFilter<Integer> bloomFilter = BloomFilter.create(Funnels.integerFunnel(), 100);
        //2 判断指定的元素是否存在
        System.out.println(bloomFilter.mightContain(1));
        System.out.println(bloomFilter.mightContain(2));
        System.out.println();
        //3 讲元素新增进入bloomfilter
        bloomFilter.put(1);
        bloomFilter.put(2);
        System.out.println(bloomFilter.mightContain(1));
        System.out.println(bloomFilter.mightContain(2));
    }
}

@RestController
@Slf4j
public class GuvaFilter {
    @Resource
    private GuavaBloomFilterService guavaBloomFilterService;
    @ApiOperation("guava布隆过滤器插入100万样本数据并额外10W测试是否存在")
    @RequestMapping(value = "/guavafilter",method = RequestMethod.GET)
    public void guavaBloomFilter()
    {
        guavaBloomFilterService.guavaBloomFilter();
    }
}

@Service
@Slf4j
public class GuavaBloomFilterService
{
    //1 定义一个常量
    public static final int _1W = 10000;
    //2 定义我们guava布隆过滤器，初始容量
    public static final int SIZE = 100 * _1W;
    //3 误判率，它越小误判的个数也就越少(思考，是否可以是无限小？？没有误判岂不是更好)
    public static double fpp = 0.01;//0.01 0.000000000000001
    //4 创建guava布隆过滤器
    private static BloomFilter<Integer> bloomFilter = BloomFilter.create(Funnels.integerFunnel(), SIZE,fpp);
    public void guavaBloomFilter()
    {
        //1 先让bloomFilter加入100W白名单数据
        for (int i = 1; i <= SIZE  ; i++) {
            bloomFilter.put(i);
        }
        //2 故意取10W个不在合法范围内的数据,来进行误判率的演示
        ArrayList<Integer> list = new ArrayList<>(10 * _1W);
        //3 验证
        for (int i = SIZE+1; i <= SIZE+(10 * _1W) ; i++)
        {
            if(bloomFilter.mightContain(i))
            {
                log.info("被误判了:{}",i);
                list.add(i);
            }
        }
        log.info("误判总数量:{}",list.size());
    }
}