https://github.com/arpitbbhayani/abloom
A pure Go implementation of Bloom Filter.
https://github.com/arpitbbhayani/abloom
bloom-filter
Last synced: 5 months ago
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A pure Go implementation of Bloom Filter.
- Host: GitHub
- URL: https://github.com/arpitbbhayani/abloom
- Owner: arpitbbhayani
- License: apache-2.0
- Created: 2022-10-19T05:12:12.000Z (about 3 years ago)
- Default Branch: master
- Last Pushed: 2022-11-13T16:06:21.000Z (about 3 years ago)
- Last Synced: 2024-06-21T19:08:42.505Z (over 1 year ago)
- Topics: bloom-filter
- Language: Go
- Homepage:
- Size: 37.1 KB
- Stars: 37
- Watchers: 3
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
abloom
===
`abloom` is a pure Go implementation of Bloom Filter. The implementation supports the following
Bloom Filters
- Simple Bloom Filter
- Deletable Bloom Filter
## Installation
```
$ go get github.com/arpitbbhayani/abloom
```
## How to use
A simple example of using bloom filter is as shown below. More examples can be found
in the [examples directory](https://github.com/arpitbbhayani/abloom/tree/master/examples/) of the package.
```go
package main
import (
"log"
"github.com/arpitbbhayani/abloom"
)
func main() {
b := abloom.NewSimpleBF(512, nil)
b.Put([]byte("apple"))
b.Put([]byte("banana"))
b.Put([]byte("cat"))
v, err := b.Check([]byte("apple"))
if err != nil {
log.Fatal("error while computing the hash")
}
log.Println("is apple present?", v)
}
```
## Running Tests
To run all the tests fire the following command
```sh
$ go test ./...
```
## Behaviour
The behaviour of bloom filter on various parameters is evalated
on the usecase of profanity detector as documented in the
[examples directory](https://github.com/arpitbbhayani/abloom/tree/master/examples/profanity-detector). Please look at the source code to understand
what and how we have benchmarked.
### Bloom Filter Size vs False Positive Rate
We can see how the size of bloom filter is related to the observed false
positivity rate. Larger the size, smaller the false positivity rate.
### Number of Hash Functions vs False Positive Rate and Time
Here we see how the number of hash functions used in a bloom filter
affects the false positivity rate and also time. The FP rate decreases
initially but after a certain stage it saturates and then increases.
The time takes for the operation increases almost linearly with the
number of hash functions given how expensive hash computation can get.
### Deletable Bloom Filter Number of Regions vs False Positive Rate
### When not to use BF
## Benchmark
Preliminary benchmark results on [profanity usecase](https://github.com/arpitbbhayani/abloom/tree/master/examples/perftest) are shown below. Please refer to the perf test code to see what has been
tested and benchmarked.
```
goos: linux
goarch: amd64
pkg: github.com/arpitbbhayani/abloom/examples/perftest
cpu: AMD Ryzen 7 4800U with Radeon Graphics
BenchmarkBFCheck-16 381 3172999 ns/op
BenchmarkBFFirstCheck-16 20828637 51.24 ns/op
BenchmarkBFOneRandomCheck-16 11330626 112.2 ns/op
BenchmarkSetCheck-16 230 4906682 ns/op
BenchmarkSetFirstCheck-16 82293306 14.12 ns/op
BenchmarkSetOneRandomCheck-16 6412029 178.6 ns/op
```
## Citations
- Bloom, Burton H. “Space/Time Trade-Offs in Hash Coding with Allowable Errors.” Communications of the ACM 13, no. 7 (July 1970): 422–26. https://doi.org/10.1145/362686.362692.
- Rothenberg, Christian Esteve, Carlos A. B. Macapuna, Fabio L. Verdi, and Mauricio F. Magalhaes. “The Deletable Bloom Filter: A New Member of the Bloom Family.” arXiv, May 3, 2010. http://arxiv.org/abs/1005.0352.
## Contributors
## License
Abloom is open-sourced under [Apache License, Version 2.0](LICENSE.md).