rbloom 1.5.1

Description:

rbloom 1.5.1

rBloom



A fast, simple and lightweight
Bloom filter library for
Python, implemented in Rust. It's designed to be as pythonic as possible,
mimicking the built-in set type where it can, and works with any
hashable object. While it's a new library (this project was started in
2023), it's currently the fastest option for Python by
a long shot (see the section
Benchmarks). Releases are published on
PyPI.
Quickstart
This library defines only one class, which can be used as follows:
>>> from rbloom import Bloom
>>> bf = Bloom(200, 0.01) # 200 items max, false positive rate of 1%
>>> bf.add("hello")
>>> "hello" in bf
True
>>> "world" in bf
False
>>> bf.update(["hello", "world"]) # "hello" and "world" now in bf
>>> other_bf = Bloom(200, 0.01)

### add some items to other_bf

>>> third_bf = bf | other_bf # third_bf now contains all items in
# bf and other_bf
>>> third_bf = bf.copy()
... third_bf.update(other_bf) # same as above
>>> bf.issubset(third_bf) # bf <= third_bf also works
True

For the full API, see the section Documentation.
Installation
On almost all platforms, simply run:
pip install rbloom

If you're on an uncommon platform, this may cause pip to build the library
from source, which requires the Rust
toolchain. You can also build
rbloom by cloning this repository and running
maturin:
maturin build --release

This will create a wheel in the target/wheels/ directory, which can
subsequently also be passed to pip.
Why rBloom?
Why should you use this library instead of one of the other
Bloom filter libraries on PyPI?

Simple: Almost all important methods work exactly like their
counterparts in the built-in set
type.
Fast: rbloom is implemented in Rust, which makes it
blazingly fast. See section Benchmarks for more
information.
Lightweight: rbloom has no dependencies of its own.
Maintainable: This library is very concise, and it's written
in idiomatic Rust. Even if I were to stop maintaining rbloom (which I
don't intend to), it would be trivially easy for you to fork it and keep
it working for you.

I started rbloom because I was looking for a simple Bloom filter
dependency for a project, and the only sufficiently fast option
(pybloomfiltermmap3) was segfaulting on recent Python versions. rbloom
ended up being twice as fast and has grown to encompass a more complete
API (e.g. with set comparisons like issubset). Do note that it doesn't
use mmapped files, however. This shouldn't be an issue in most cases, as
the random access heavy nature of a Bloom filter negates the benefits of
mmap after very few operations, but it is something to keep in mind for
edge cases.
Benchmarks
I implemented the following simple benchmark in the respective API of
each library:
bf = Bloom(10_000_000, 0.01)

for i in range(10_000_000):
bf.add(i + 0.5) # floats because ints are hashed as themselves

for i in range(10_000_000):
assert i + 0.5 in bf

This resulted in the following runtimes:



Library
Time
Notes




rBloom
5.956 s
works out-of-the-box


pybloomfiltermmap3
11.280 s
surprisingly hard to get working [1]


pybloom3
75.871 s
works out-of-the-box


Flor
128.837 s
doesn't work on arbitrary objects [2]


bloom-filter2
325.044 s
doesn't work on arbitrary objects [2]



[1] It refused to install on Python 3.11 and kept segfaulting on 3.10 (on Linux
as of January 2023), so I installed 3.7 on my machine for this benchmark.
[2] I tested both converting to bytes and pickling, and chose the faster time.
The benchmark was run on a 2019 Dell XPS 15 7590 with an Intel Core
i5-9300H. It was run 5 times for each library, and the average time was
used.
Also note that rbloom is compiled against a stable ABI for
portability, and that you can get a small but measurable speedup by
removing the "abi3-py37" flag from Cargo.toml and building
it yourself.
Documentation
This library defines only one class, the signature of which should be
thought of as follows. Note that only the first few methods differ from
the built-in set type:
class Bloom:

# expected_items: max number of items to be added to the filter
# false_positive_rate: max false positive rate of the filter
# hash_func: optional argument, see section "Cryptographic security"
def __init__(self, expected_items: int, false_positive_rate: float,
hash_func=__builtins__.hash)

@property
def size_in_bits(self) -> int # number of buckets in the filter

@property
def hash_func(self) -> Callable[[Any], int] # retrieve the hash_func
# given to __init__

@property
def approx_items(self) -> float # estimated number of items in
# the filter

# see section "Persistence" for more information on these four methods
@classmethod
def load(cls, filepath: str, hash_func) -> Bloom
def save(self, filepath: str)
@classmethod
def load_bytes(cls, data: bytes, hash_func) -> Bloom
def save_bytes(self) -> bytes

#####################################################################
# ALL SUBSEQUENT METHODS ARE #
# EQUIVALENT TO THE CORRESPONDING METHODS #
# OF THE BUILT-IN SET TYPE #
#####################################################################

def add(self, obj) # add obj to self
def __contains__(self, obj) -> bool # check if obj in self
def __bool__(self) -> bool # False if empty
def __repr__(self) -> str # basic info

def __or__(self, other: Bloom) -> Bloom # self | other
def __ior__(self, other: Bloom) # self |= other
def __and__(self, other: Bloom) -> Bloom # self & other
def __iand__(self, other: Bloom) # self &= other

# these extend the functionality of __or__, __ior__, __and__, __iand__
def union(self, *others: Union[Iterable, Bloom]) -> Bloom # __or__
def update(self, *others: Union[Iterable, Bloom]) # __ior__
def intersection(self, *others: Union[Iterable, Bloom]) -> Bloom # __and__
def intersection_update(self, *others: Union[Iterable, Bloom]) # __iand__

# these implement <, >, <=, >=, ==, !=
def __lt__, __gt__, __le__, __ge__, __eq__, __ne__(self,
other: Bloom) -> bool
def issubset(self, other: Bloom) -> bool # self <= other
def issuperset(self, other: Bloom) -> bool # self >= other

def clear(self) # remove all items
def copy(self) -> Bloom # duplicate self

To prevent death and destruction, the bitwise set operations only work on
filters where all parameters are equal (including the hash functions being
the exact same object). Because this is a Bloom filter, the __contains__
and approx_items methods are probabilistic, as are all the methods that
compare two filters (such as __le__ and issubset).
Cryptographic security
Python's built-in hash function is designed to be fast, not maximally
collision-resistant, so if your program depends on the false positive rate
being perfectly correct, you may want to supply your own hash function.
This is especially the case when working with very large filters (more
than a few tens of millions of items) or when false positives are very
costly and could be exploited by an adversary. Just make sure that your
hash function returns an integer between -2^127 and 2^127 - 1. Feel free
to use the following example in your own code:
from rbloom import Bloom
from hashlib import sha256
from pickle import dumps

def hash_func(obj):
h = sha256(dumps(obj)).digest()
return int.from_bytes(h[:16], "big") - 2**127

bf = Bloom(100_000_000, 0.01, hash_func)

When you throw away Python's built-in hash function and start hashing
serialized representations of objects, however, you open up a breach into
the scary realm of the unpythonic:

Numbers like 1, 1.0, 1 + 0j and True will suddenly no longer
be equal.
Instances of classes with custom hashing logic (e.g. to stop
caches inside instances from affecting their hashes) will suddenly
display undefined behavior.
Objects that can't be serialized simply won't be hashable at all.

Making you supply your own hash function in this case is a deliberate
design decision intended to show you what you're doing and prevent
you from shooting yourself in the foot.
Also note that using a custom hash will incur a performance penalty over
using the built-in hash.
Persistence
The save and load methods, along with their byte-oriented counterparts
save_bytes and load_bytes, allow you to save and load filters to and
from disk/Python bytes objects. However, as the built-in hash function's
salt changes between invocations of Python, they only work on filters with
custom hash functions. Note that it is your responsibility to ensure that
the hash function you supply to the loading functions is the same as the
one originally used by the filter you're loading!
bf = Bloom(10_000, 0.01, some_hash_func)
bf.add("hello")
bf.add("world")

# saving to a file
bf.save("bf.bloom")

# loading from a file
loaded_bf = Bloom.load("bf.bloom", some_hash_func)
assert loaded_bf == bf

# saving to bytes
bf_bytes = bf.save_bytes()

# loading from bytes
loaded_bf_from_bytes = Bloom.load_bytes(bf_bytes, some_hash_func)
assert loaded_bf_from_bytes == bf

The size of the file is bf.size_in_bits / 8 + 8 bytes.

Statement of attribution: Bloom filters were originally proposed in
(Bloom, 1970). Furthermore, this
implementation makes use of a constant recommended by
(L'Ecuyer, 1999) for
redistributing the entropy of a single hash over multiple integers using a
linear congruential generator.