Important: Zeroth, consider if a Cuckoo filter could be right for your use-case.
Face-meltingly fast, thread-safe, marshalable, unionable, probability- and optimal-size-calculating Bloom filter in go
Copyright © 2014-2016,2018 Barry Allard
WTF is a bloom filter
**TL;DR: **Probabilistic, extra lookup table to track a set of elements kept elsewhere to reduce expensive, unnecessary set element retrieval and/or iterator operations when an element is not present in the set. It's a classic time-storage tradeoff algoritm.
Properties
See wikipedia for algorithm details
| Impact | What | Description |
|---|---|---|
| Good | No false negatives | know for certain if a given element is definitely NOT in the set |
| Bad | False positives | uncertain if a given element is in the set |
| Bad | Theoretical potential for hash collisions | in very large systems and/or badly hash.Hash64-conforming implementations |
| Bad | Add only | Cannot remove an element, it would destroy information about other elements |
| Good | Constant storage | uses only a fixed amount of memory |
Naming conventions
(Similar to algorithm)
| Variable/function | Description | Range |
|---|---|---|
| m/M() | number of bits in the bloom filter (memory representation is about m/8 bytes in size) | >=2 |
| n/N() | number of elements present | >=0 |
| k/K() | number of keys to use (keys are kept private to user code but are de/serialized to Marshal and file I/O) | >=0 |
| maxN | maximum capacity of intended structure | >0 |
| p | maximum allowed probability of collision (for computing m and k for optimal sizing) | >0..<1 |
- Memory representation should be exactly
24 + 8*(k + (m+63)/64) + unsafe.Sizeof(RWMutex)bytes. - Serialized (
BinaryMarshaler) representation should be exactly72 + 8*(k + (m+63)/64)bytes. (Disk format is less due to compression.)
Binary serialization format
All values in Little-endian format
| Offset | Offset (Hex) | Length (bytes) | Name | Type |
|---|---|---|---|---|
| 0 | 00 | 8 | k | uint64 |
| 8 | 08 | 8 | n | uint64 |
| 16 | 10 | 8 | m | uint64 |
| 24 | 18 | k | (keys) | [k]uint64 |
| 24+8*k | ... | (m+63)/64 | (bloom filter) | [(m+63)/64]uint64 |
| 24+8*k+8*((m+63)/64) | ... | 48 | (SHA384 of all previous fields, hashed in order) | [48]byte |
bloomfilter.Filterconforms toencoding.BinaryMarshalerand `encoding.BinaryUnmarshaler'
Usage
import "github.com/steakknife/bloomfilter"
const (
maxElements = 100000
probCollide = 0.0000001
)
bf, err := bloomfilter.NewOptimal(maxElements, probCollide)
if err != nil {
panic(err)
}
someValue := ... // must conform to hash.Hash64
bf.Add(someValue)
if bf.Contains(someValue) { // probably true, could be false
// whatever
}
anotherValue := ... // must also conform to hash.Hash64
if bf.Contains(anotherValue) {
panic("This should never happen")
}
err := bf.WriteFile("1.bf.gz") // saves this BF to a file
if err != nil {
panic(err)
}
bf2, err := bloomfilter.ReadFile("1.bf.gz") // read the BF to another var
if err != nil {
panic(err)
}
Design
Where possible, branch-free operations are used to avoid deep pipeline / execution unit stalls on branch-misses.
Get
go get -u github.com/steakknife/bloomfilter # master is always stable
Source
-
On the web: https://github.com/steakknife/bloomfilter
-
Git:
git clone https://github.com/steakknife/bloomfilter
Contact
License
Copyright © 2014-2016 Barry Allard
