误判率的推导
- 前提：
- 数组长度 m
- 有 k 个 hash 函数，每个 hash 函数彼此独立（老实说，彼此独立这个条件怎么达到我也不太清楚，以及或许有其他的前提条件我也不太清楚）
- 用 n 个样本空间
- 推导过程
第一部分：
经过一个 hash 函数以后某一位置为 0 的概率是
经过 k 个 hash 函数以后某一位置为 0 的概率是
经过 n 个样本以后某一位置为 0 的概率是
因此经过 n 个样本以后某一位为 1 的概率是
现在再来一个新的样本，全选到 1 的概率是
第二部分，上面先推导到这里接下来需要推导一个别的：
- 这是 e 的推导：
- 将 -x 替换 x
我们再从第一部分的第五步继续向后：
- 变形得：
- 对于大 m 约等于：
所以针对大 m，误报率约为：
我们通常要根据 n 和 m 推导合适的 hash 个数，为：。
如果需要根据误报率来推导，此时 ，此时误报率 。可以简写为：
这导致：
所以 m 和 n 的最佳比值此时为：
后面的部分我都是摘自 wiki：https://en.wikipedia.org/wiki/Bloom_filter。根据这些我们就可以实现自己的 Bloom filter。
- 参考
https://en.wikipedia.org/wiki/Bloom_filter
实现
我们在实现的时候前提条件通常是：
- 假阳性 p 概率是多少
- 要存的样本空间多大
要求的就是上面公式里的 k 和 m。
- m 告诉我们需要多少的 bit 位
- k 告诉我们需要多少个 hash 函数
按照公式：
大概实现如下：
// bloom.go
// Copyright 2021 hardcore-os Project Authors
//
// Licensed under the Apache License, Version 2.0 (the "License")
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
package utils
import "math"
// Filter is an encoded set of []byte keys.
type Filter []byte
// MayContainKey _
func (f Filter) MayContainKey(k []byte) bool {
return f.MayContain(Hash(k))
}
func (f Filter) K() uint8 {
return f[len(f) - 1]
}
// get 根据 hash 值得到 filter 中某一位的值
func (f Filter) get(h uint32) uint8 {
x, y := posInFilter(h, len(f) - 1)
return uint8((f[x] >> y) & 1)
}
// set 根据 hash 值将某一位置 1
func (f Filter) set(h uint32) {
x, y := posInFilter(h, len(f) - 1)
f[x] = f[x] | 1 << y
}
// MayContain returns whether the filter may contain given key. False positives
// are possible, where it returns true for keys not in the original set.
func (f Filter) MayContain(h uint32) bool {
//Implement me here!!!
//在这里实现判断一个数据是否在bloom过滤器中
//思路大概是经过K个Hash函数计算，判读对应位置是否被标记为1
delta, k := h >> 17 | h << 15, f.K()
for j := uint8(0); j < k; j ++ {
if f.get(h) == 0 {
return false
}
h += delta
}
return true
}
// posInFilter 根据 hash 值计算此 hash 在 pos 的哪一个位置
// h 是 hash 值，filterLen 就是用byte数组中真正做做filter的长度
func posInFilter(h uint32, filterLen int) (x, y int) {
nBits := uint32(filterLen * 8)
bitPos := h % nBits
return int(bitPos / 8), int(bitPos % 8)
}
// NewFilter returns a new Bloom filter that encodes a set of []byte keys with
// the given number of bits per key, approximately.
//
// A good bitsPerKey value is 10, which yields a filter with ~ 1% false
// positive rate.
func NewFilter(keys []uint32, bitsPerKey int) Filter {
return appendFilter(keys, bitsPerKey)
}
// BloomBitsPerKey returns the bits per key required by bloomfilter based on
// the false positive rate.
func BloomBitsPerKey(numEntries int, fp float64) int {
//Implement me here!!!
//阅读bloom论文实现，并在这里编写公式
//传入参数numEntries是bloom中存储的数据个数，fp是false positive假阳性率
// 计算 m/n 根据：https://en.wikipedia.org/wiki/Bloom_filter
return int(-1.44 * math.Log2(fp) + 1)
}
func appendFilter(keys []uint32, bitsPerKey int) Filter {
//Implement me here!!!
//在这里实现将多个Key值放入到bloom过滤器中
// TODO：系统检查 bitsPerKey
if bitsPerKey < 0 {
bitsPerKey = 0
}
keyLen := len(keys)
k := uint8(float64(bitsPerKey) * 0.69)
if k < 1 {
k = 1
}
if k > 30 {
k = 30
}
nBits := bitsPerKey * keyLen
// 如果 nBits 太小会有很高的 false positive
if nBits < 64 {
nBits = 64
}
// TODO：检查 nBits 的上界
nBytes := (nBits + 7) / 8
// 最后一位
filter := Filter(make([]byte, nBytes + 1))
// 向 filter 中放入所有的 key
for _, h := range keys {
delta := h >> 17 | h << 15
for j := uint8(0); j < k; j ++ {
filter.set(h)
h += delta
}
}
filter[nBytes] = k
return filter
}
// Hash implements a hashing algorithm similar to the Murmur hash.
func Hash(b []byte) uint32 {
const (
seed = 0xbc9f1d34
m = 0xc6a4a793
)
h := uint32(seed) ^ uint32(len(b))*m
for ; len(b) >= 4; b = b[4:] {
h += uint32(b[0]) | uint32(b[1])<<8 | uint32(b[2])<<16 | uint32(b[3])<<24
h *= m
h ^= h >> 16
}
switch len(b) {
case 3:
h += uint32(b[2]) << 16
fallthrough
case 2:
h += uint32(b[1]) << 8
fallthrough
case 1:
h += uint32(b[0])
h *= m
h ^= h >> 24
}
return h
}
// bloom_test.go
// Copyright 2021 hardcore-os Project Authors
//
// Licensed under the Apache License, Version 2.0 (the "License")
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
package utils
import (
"testing"
)
func (f Filter) String() string {
s := make([]byte, 8*len(f))
for i, x := range f {
for j := 0; j < 8; j++ {
if x&(1<> 0)
b[1] = uint8(uint32(i) >> 8)
b[2] = uint8(uint32(i) >> 16)
b[3] = uint8(uint32(i) >> 24)
return b
}
nMediocreFilters, nGoodFilters := 0, 0
loop:
for length := 1; length <= 10000; length = nextLength(length) {
keys := make([][]byte, 0, length)
for i := 0; i < length; i++ {
keys = append(keys, le32(i))
}
var hashes []uint32
for _, key := range keys {
hashes = append(hashes, Hash(key))
}
f := NewFilter(hashes, 10)
if len(f) > (length*10/8)+40 {
t.Errorf("length=%d: len(f)=%d is too large", length, len(f))
continue
}
// All added keys must match.
for _, key := range keys {
if !f.MayContainKey(key) {
t.Errorf("length=%d: did not contain key %q", length, key)
continue loop
}
}
// Check false positive rate.
nFalsePositive := 0
for i := 0; i < 10000; i++ {
if f.MayContainKey(le32(1e9 + i)) {
nFalsePositive++
}
}
if nFalsePositive > 0.02*10000 {
t.Errorf("length=%d: %d false positives in 10000", length, nFalsePositive)
continue
}
if nFalsePositive > 0.0125*10000 {
nMediocreFilters++
} else {
nGoodFilters++
}
}
if nMediocreFilters > nGoodFilters/5 {
t.Errorf("%d mediocre filters but only %d good filters", nMediocreFilters, nGoodFilters)
}
}
func TestHash(t *testing.T) {
// The magic want numbers come from running the C++ leveldb code in hash.cc.
testCases := []struct {
s string
want uint32
}{
{"", 0xbc9f1d34},
{"g", 0xd04a8bda},
{"go", 0x3e0b0745},
{"gop", 0x0c326610},
{"goph", 0x8c9d6390},
{"gophe", 0x9bfd4b0a},
{"gopher", 0xa78edc7c},
{"I had a dream it would end this way.", 0xe14a9db9},
}
for _, tc := range testCases {
if got := Hash([]byte(tc.s)); got != tc.want {
t.Errorf("s=%q: got 0x%08x, want 0x%08x", tc.s, got, tc.want)
}
}
}
- 参考
测试代码和实现代码的框架来自：https://github.com/hardcore-os/corekv