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simplebloom is a (probably) dumb but fast bloom filter.
To quote Wikipedia <https://en.wikipedia.org/wiki/Bloom_filter>
_:
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?
~1.4 million elements/s on an i7-6700HQ, both adding and checking.
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))
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)
Extension code is generated by Cython. Install Cython to make and build changes to the extension.
FAQs
A dumb but fast bloom filter.
We found that simplebloom demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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