裁减了原仓库的核心代码，增加了 redis bitset 支持，删除了原代码的某些方法，引入 mini redis 保留了原有的一些单元测试代码，移除或注释了目前不支持的测试代码。 目前基础的 Test 和 Add 都已支持，也尽量采用管道批量操作，目前还缺失一个 Count 方法统计总共插入的数量。根据布隆过滤器的原理，如果插入数量超过预设，准确率会 下降，所以要避免这种情况必须知道插入的元素数量是否已经超过了，不过因为目前我自己没有这个需求，同时也是破坏性的修改源码，没打算提 pr，暂时就这样了。
A Bloom filter has two parameters: m, the number of bits used in storage, and k, the number of hashing functions on elements of the set. (The actual hashing functions are important, too, but this is not a parameter for this implementation). A Bloom filter is backed by a BitSet; a key is represented in the filter by setting the bits at each value of the hashing functions (modulo m). Set membership is done by testing whether the bits at each value of the hashing functions (again, modulo m) are set. If so, the item is in the set. If the item is actually in the set, a Bloom filter will never fail (the true positive rate is 1.0); but it is susceptible to false positives. The art is to choose k and m correctly.
In this implementation, the hashing functions used is murmurhash, a non-cryptographic hashing function.
Given the particular hashing scheme, it's best to be empirical about this. Note that estimating the FP rate will clear the Bloom filter.