Roaring Bitmap in Cython
A roaring bitmap is an efficient compressed datastructure to store a set of integers. A Roaring bitmap stores a set of 32-bit integers in a series of arrays and bitmaps, whichever takes the least space (which is always 2 ** 16 bits or less).
This datastructure is useful for storing a large number of integers, e.g., for an inverted index used by search engines and databases. In particular, it is possible to quickly compute the intersection of a series of sets, which can be used to implement a query as the conjunction of subqueries.
This implementation is based on the Java and C implementations at https://github.com/lemire/RoaringBitmap and https://github.com/lemire/CRoaring
Additional features of this implementation:
Inverted list representation: blocks that are mostly full are stored compactly as an array of non-members (instead of as an array of members or a fixed-size bitmap).
Collections of immutable roaring bitmaps can be efficiently serialized with mmap in a single file.
Missing features w.r.t. CRoaring:
Run-length encoded blocks
Various AVX2 / SSE optimizations
See also PyRoaringBitmap, a Python wrapper of CRoaring: https://github.com/Ezibenroc/PyRoaringBitMap
License, requirements
The code is licensed under GNU GPL v2, or any later version at your option.
Python 2.7+/3.3+ http://www.python.org (headers required, e.g. python-dev package)
Cython 3.0+ http://www.cython.org
Installation
- ::
$ pip install roaringbitmap
For Linux and Mac, there are binary wheels. Binary wheels for x86-64 require the POPCNT CPU instruction and raise ImportError on unsupported CPUs. Builds from source use -march=native by default and are optimized for the build machine.
To compile from source:
$ git clone https://github.com/andreasvc/roaringbitmap.git $ cd roaringbitmap $ make
For Python 2, build with a Cython version that supports Python 2.7:
$ python2 -m pip install 'Cython>=3,<3.1' $ make py2
The C sources published on PyPI are generated with the current Cython release and support Python 3 only.
Usage
A RoaringBitmap() can be used as a replacement for a normal (mutable) Python set containing (unsigned) 32-bit integers:
>>> from roaringbitmap import RoaringBitmap
>>> RoaringBitmap(range(10)) & RoaringBitmap(range(5, 15))
RoaringBitmap({5, 6, 7, 8, 9})
ImmutableRoaringBitmap is an immutable variant (analogous to frozenset) which is stored compactly as a contiguous block of memory.
A sequence of immutable RoaringBitmaps can be stored in a single file and accessed efficiently with mmap, without needing to copy or deserialize:
>>> from roaringbitmap import MultiRoaringBitmap
>>> mrb = MultiRoaringBitmap([range(n, n + 5) for n in range(10)], filename='index')
>>> mrb = MultiRoaringBitmap.fromfile('index')
>>> mrb[5]
ImmutableRoaringBitmap({5, 6, 7, 8, 9})
For API documentation cf. http://roaringbitmap.readthedocs.io
Benchmarks
Output of $ make bench:
small sparse set
100 runs with sets of 200 random elements n s.t. 0 <= n < 40000
set() RoaringBitmap() ratio
init 0.000383 0.000627 0.61
initsort 0.000992 0.000258 3.85
and 0.000499 0.000125 3.98
or 0.000507 8.9e-05 5.7
xor 0.000479 8.14e-05 5.88
sub 0.000335 7.57e-05 4.42
iand 1.18e-05 2.77e-06 4.26
ior 8.39e-06 2.75e-06 3.05
ixor 8.12e-06 2.81e-06 2.89
isub 6.54e-06 2.87e-06 2.28
eq_identity 0.000183 4.78e-06 38.2
eq_equal 0.000169 7.33e-06 23
neq_card 4.62e-06 5.43e-06 0.85
neq_early 1.79e-05 6.21e-06 2.89
neq_late 0.000167 6.64e-06 25.2
jaccard 0.000973 4.99e-05 19.5
medium load factor
100 runs with sets of 59392 random elements n s.t. 0 <= n < 118784
set() RoaringBitmap() ratio
init 0.451 0.138 3.26
initsort 0.349 0.0995 3.51
and 0.362 0.000271 1336
or 0.482 0.000248 1942
xor 0.46 0.000258 1780
sub 0.381 0.000444 857
iand 0.00421 5.33e-06 790
ior 0.00581 5.49e-06 1058
ixor 0.00243 5.05e-06 481
isub 0.00229 4.88e-06 469
eq_identity 0.123 1.64e-05 7501
eq_equal 0.137 3.75e-05 3643
neq_card 7.49e-06 7.06e-06 1.06
neq_early 8.08e-06 2.22e-05 0.364
neq_late 0.139 5.42e-05 2571
jaccard 0.88 0.000183 4806
dense set / high load factor
100 runs with sets of 39800 random elements n s.t. 0 <= n < 40000
set() RoaringBitmap() ratio
init 0.206 0.0967 2.13
initsort 0.179 0.0531 3.37
and 0.149 0.000134 1117
or 0.209 0.000112 1862
xor 0.186 0.000112 1653
sub 0.12 0.000109 1092
iand 0.00174 3.75e-06 463
ior 0.000865 3.58e-06 241
ixor 0.00112 3.6e-06 310
isub 0.00102 3.5e-06 292
eq_identity 0.052 5.39e-06 9644
eq_equal 0.0559 1.68e-05 3318
neq_card 7.18e-06 7.53e-06 0.954
neq_early 7.17e-06 7.73e-06 0.928
neq_late 0.0437 1.36e-05 3209
jaccard 0.342 9.84e-05 3472
See https://github.com/Ezibenroc/roaring_analysis/ for a performance comparison of PyRoaringBitmap and this library.
References
Chambi, S., Lemire, D., Kaser, O., & Godin, R. (2016). Better bitmap performance with Roaring bitmaps. Software: practice and experience, 46(5), pp. 709-719. http://arxiv.org/abs/1402.6407
The idea of using the inverted list representation is based on https://issues.apache.org/jira/browse/LUCENE-5983
Release files for roaringbitmap 0.7.4
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