simplebloom is a (probably) dumb but fast bloom filter. To quote Wikipedia:
A Bloom filter is a space-efficient probabilistic data structure, conceived by Burton Howard Bloom in 1970, that is used to test whether an element is a member of a set. False positive matches are possible, but false negatives are not – in other words, a query returns either “possibly in set” or “definitely not in set”. Elements can be added to the set, but not removed […]; the more items added, the larger the probability of false positives.
The included BloomFilter class is quite dumb as it’s fixed size, only supports strings, and always uses the blake2s hash function included with Python 3.6+. But at least it’s fast, hey?
Speed
~1.4 million elements/s on an i7-6700HQ, both adding and checking.
Usage
Note that around 98% of the execution time is spent creating UUIDs.
import uuid
from simplebloom import BloomFilter
keys = [uuid.uuid4().hex for _ in range(100000)]
bf = BloomFilter(len(keys))
for k in keys:
bf += k
with open('test.filter', 'wb') as fp:
bf.dump(fp)
with open('test.filter', 'rb') as fp:
bf = BloomFilter.load(fp)
for k in keys:
assert k in bf
other_keys = [uuid.uuid4().hex for _ in range(1000000)]
fp = 0
for k in other_keys:
fp += k in bf
print(bf.false_positive_prob, fp / len(other_keys))
The BloomFilter class
A simple but fast bloom filter. Elements must be strings.
Add an element and check whether it is contained:
bf = BloomFilter(1000) bf += 'hellobloom' assert 'hellobloom' in bf
false_positive_prob defaults to 1 / num_elements.
The number of bits in the filter is num_bits = num_elements * log(false_positive_prob) / log(1 / 2**log(2)), rounded to the next highest multiple of 8.
The number of hash functions used is num_hashes = round(num_bits / num_elements * log(2)) .
- Parameters:
num_elements: expected max number of elements in the filter false_positive_prob: desired approximate false positive probability
BloomFilter.__iadd__ / add element
Use the “inplace add” syntax to add elements bf += k, where bf is the BloomFilter and k a string.
BloomFilter.__contains__ / contains element
Use the “contains” syntax to check if an element is (probably) in the filter k in bf, where bf is the BloomFilter and k a string.
BloomFilter.load
Load a filter from a path or file-like:
bf = BloomFilter.load('bloom.filter')
with open('bloom.filter', 'rb') as fp:
bf = BloomFilter.load(fp)
- Parameters:
fp: path or file-like
BloomFilter.loads
Load a filter from a buffer:
data = bf.dumps() bf = BloomFilter.loads(data)
- Parameters:
data: filter data
BloomFilter.dump
Dump filter to a path or file-like:
bf.dump('bloom.filter')
with open('bloom.filter', 'wb') as fp:
bf.dump(fp)
- Parameters:
fp: path or file-like
BloomFilter.dumps
Returns filter data as buffer:
data = bf.dumps() bf = BloomFilter.loads(data)
Developing
Extension code is generated by Cython. Install Cython to make and build changes to the extension.
1 maintainer
Metadata
1 maintainer
Release files for simplebloom 1.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| simplebloom-1.1.0.tar.gz | 74.0 kB | Details |
Built distributions (wheels)
Total release size: 1.5 MB
Release files / simplebloom-1.1.0.tar.gz
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