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https://github.com/gyson/blex
Fast Bloom filter with concurrent accessibility, powered by :atomics module.
https://github.com/gyson/blex
atomics bloom bloom-filter elixir erlang filter probabilistic-data-structures
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Fast Bloom filter with concurrent accessibility, powered by :atomics module.
- Host: GitHub
- URL: https://github.com/gyson/blex
- Owner: gyson
- License: mit
- Created: 2019-01-05T20:35:31.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2020-12-08T04:17:26.000Z (almost 4 years ago)
- Last Synced: 2024-09-28T13:04:11.820Z (about 2 months ago)
- Topics: atomics, bloom, bloom-filter, elixir, erlang, filter, probabilistic-data-structures
- Language: Elixir
- Homepage:
- Size: 23.4 KB
- Stars: 38
- Watchers: 1
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Blex
Blex is a fast Bloom filter with **concurrent accessibility**, powered by [`:atomics`](http://erlang.org/doc/man/atomics.html) module.
## Features
* Fixed size Bloom filter
* Concurrent reads & writes
* Serialization
* Merge multiple Bloom filters into one
* Only one copy of data because data is saved in either `:atomics` or binary (if > 64 bytes)
* Custom hash functions
## Example
```elixir
iex> b = Blex.new(1000, 0.01)
iex> Task.async(fn -> Blex.put(b, "hello") end) |> Task.await()
iex> Task.async(fn -> Blex.put(b, "world") end) |> Task.await()
iex> Blex.member?(b, "hello")
true
iex> Blex.member?(b, "world")
true
iex> Blex.member?(b, "others")
false
```
## Installation
**Note**: it requires OTP-21.2.1 or later. OTP-21.2 is not good due to a [issue](https://github.com/erlang/otp/pull/2061).
It can be installed by adding `blex` to your list of dependencies in `mix.exs`:
```elixir
def deps do
[
{:blex, "~> 0.2"}
]
end
```
## Documentation
Documentation can be found at [hexdocs.pm/blex/Blex.html](https://hexdocs.pm/blex/Blex.html).
## Benchmarking
Compare to alternative Bloom filter powered by `:array` module,
Blex is faster with read operation:
```
Operating System: macOS"
CPU Information: Intel(R) Core(TM) i7-3720QM CPU @ 2.60GHz
Number of Available Cores: 8
Available memory: 16 GB
Elixir 1.7.4
Erlang 21.2.2
Benchmark suite executing with the following configuration:
warmup: 2 s
time: 5 s
memory time: 0 μs
parallel: 1
inputs: none specified
Estimated total run time: 21 s
Benchmarking Blex.members?...
Benchmarking Blex.members? with binary format...
Benchmarking Bloomex.members?...
Name ips average deviation median 99th %
Blex.members? with binary format 0.69 1.44 s ±0.23% 1.44 s 1.44 s
Blex.members? 0.63 1.58 s ±0.61% 1.58 s 1.58 s
Bloomex.members? 0.40 2.51 s ±0.00% 2.51 s 2.51 s
Comparison:
Blex.members? with binary format 0.69
Blex.members? 0.63 - 1.09x slower
Bloomex.members? 0.40 - 1.74x slower
```
Blex is much faster with write operation:
```
Operating System: macOS"
CPU Information: Intel(R) Core(TM) i7-3720QM CPU @ 2.60GHz
Number of Available Cores: 8
Available memory: 16 GB
Elixir 1.7.4
Erlang 21.2.2
Benchmark suite executing with the following configuration:
warmup: 2 s
time: 10 s
memory time: 0 μs
parallel: 1
inputs: none specified
Estimated total run time: 24 s
Benchmarking Blex.put...
Benchmarking Bloomex.add...
Name ips average deviation median 99th %
Blex.put 0.44 2.25 s ±3.98% 2.30 s 2.33 s
Bloomex.add 0.126 7.91 s ±0.22% 7.91 s 7.92 s
Comparison:
Blex.put 0.44
Bloomex.add 0.126 - 3.51x slower
```
Above benchmarking script is available at `bench/comparison.exs`.
## Implementation
Instead of traditional Bloom filter, partitioned Bloom filter (a variant Bloom filter described in section 3 of
[the paper](http://gsd.di.uminho.pt/members/cbm/ps/dbloom.pdf)) is used for performance benefits. The partitioned
Bloom filter would partition bits array into **k** parts where **k** is number of hash functions. Each hash functions
would only read & write bits from its own partitioned space. This would bring following benefits:
* Reduce hash function (`:erlang.phash2`) calls for some cases.
* Speed up `Blex.estimate_size` by scanning only part of bits.
## License
MIT