| Name | simplebloom JSON |
| Version |
1.0.5
JSON |
| download |
| home_page | |
| Summary | A dumb but fast bloom filter. |
| upload_time | 2023-09-08 22:29:25 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.7 |
| license | MIT License Copyright (c) 2021 Joachim Folz Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. From hereon included are the license texts of all bundled software. |
| keywords |
bloom
filter
bloomfilter
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
simplebloom
===========
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?
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.
Raw data
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"description": "simplebloom\n===========\n\nsimplebloom is a (probably) dumb but fast bloom filter.\nTo quote `Wikipedia <https://en.wikipedia.org/wiki/Bloom_filter>`_:\n\n A Bloom filter is a space-efficient probabilistic data structure,\n conceived by Burton Howard Bloom in 1970, that is used to test\n whether an element is a member of a set.\n False positive matches are possible, but false negatives are not\n \u2013 in other words, a query returns either \"possibly in set\" or\n \"definitely not in set\".\n Elements can be added to the set, but not removed [...];\n the more items added, the larger the probability of false positives.\n\nThe included ``BloomFilter`` class is quite dumb as it's fixed size,\nonly supports strings, and always uses the blake2s hash function included\nwith Python 3.6+.\nBut at least it's fast, hey?\n\n\nSpeed\n-----\n\n~1.4 million elements/s on an i7-6700HQ, both adding and checking.\n\n\nUsage\n-----\n\nNote that around 98% of the execution time is spent creating UUIDs.\n\n::\n\n import uuid\n from simplebloom import BloomFilter\n\n keys = [uuid.uuid4().hex for _ in range(100000)]\n bf = BloomFilter(len(keys))\n\n for k in keys:\n bf += k\n\n with open('test.filter', 'wb') as fp:\n bf.dump(fp)\n\n with open('test.filter', 'rb') as fp:\n bf = BloomFilter.load(fp)\n\n for k in keys:\n assert k in bf\n\n other_keys = [uuid.uuid4().hex for _ in range(1000000)]\n fp = 0\n for k in other_keys:\n fp += k in bf\n print(bf.false_positive_prob, fp / len(other_keys))\n\n\nThe BloomFilter class\n---------------------\n\nA simple but fast bloom filter.\nElements must be strings.\n\nAdd an element and check whether it is contained::\n\n bf = BloomFilter(1000)\n bf += 'hellobloom'\n assert 'hellobloom' in bf\n\n``false_positive_prob`` defaults to ``1 / num_elements``.\n\nThe number of bits in the filter is\n``num_bits = num_elements * log(false_positive_prob) / log(1 / 2**log(2))``,\nrounded to the next highest multiple of 8.\n\nThe number of hash functions used is\n``num_hashes = round(num_bits / num_elements * log(2))`` .\n\nParameters:\n num_elements: expected max number of elements in the filter\n false_positive_prob: desired approximate false positive probability\n\n\n``BloomFilter.__iadd__`` / add element\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nUse the \"inplace add\" syntax to add elements ``bf += k``,\nwhere bf is the ``BloomFilter`` and ``k`` a string.\n\n\n``BloomFilter.__contains__`` / contains element\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nUse the \"contains\" syntax to check if an element is (probably)\nin the filter ``k in bf``,\nwhere bf is the ``BloomFilter`` and ``k`` a string.\n\n\n``BloomFilter.load``\n~~~~~~~~~~~~~~~~~~~~\n\nLoad a filter from a path or file-like::\n\n bf = BloomFilter.load('bloom.filter')\n\n with open('bloom.filter', 'rb') as fp:\n bf = BloomFilter.load(fp)\n\nParameters:\n - fp: path or file-like\n\n\n``BloomFilter.loads``\n~~~~~~~~~~~~~~~~~~~~~\n\nLoad a filter from a buffer::\n\n data = bf.dumps()\n bf = BloomFilter.loads(data)\n\nParameters:\n data: filter data\n\n\n``BloomFilter.dump``\n~~~~~~~~~~~~~~~~~~~~\n\nDump filter to a path or file-like::\n\n bf.dump('bloom.filter')\n\n with open('bloom.filter', 'wb') as fp:\n bf.dump(fp)\n\nParameters:\n - fp: path or file-like\n\n\n``BloomFilter.dumps``\n~~~~~~~~~~~~~~~~~~~~~\n\nReturns filter data as buffer::\n\n data = bf.dumps()\n bf = BloomFilter.loads(data)\n\n\nDeveloping\n----------\n\nExtension code is generated by Cython.\nInstall Cython to make and build changes to the extension.\n",
"bugtrack_url": null,
"license": "MIT License Copyright (c) 2021 Joachim Folz Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. From hereon included are the license texts of all bundled software.",
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