Roaring Bitmap
Project description
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:
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). Based on https://issues.apache.org/jira/browse/LUCENE-5983
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 0.20+ http://www.cython.org
Installation, usage
$ git clone https://github.com/andreasvc/roaringbitmap.git $ cd roaringbitmap $ make
(or make py2 for Python 2)
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 0.000834 0.00138 0.603 initsort 0.00085 0.000394 2.16 and 0.00102 8.49e-05 12.1 or 0.00171 0.000169 10.1 xor 0.00152 0.000213 7.11 sub 0.000934 0.000197 4.74 iand 1.29e-05 2.97e-06 4.35 ior 9.7e-06 3.26e-06 2.98 ixor 8.98e-06 3.43e-06 2.62 isub 6.83e-06 3.3e-06 2.07 eq 0.000438 1.17e-05 37.6 neq 6.37e-06 7.81e-06 0.816 jaccard 0.0029 0.000126 23.1 medium load factor 100 runs with sets of 59392 random elements n s.t. 0 <= n < 118784 set() RoaringBitmap() ratio init 0.564 0.324 1.74 initsort 0.696 0.273 2.55 and 0.613 0.000418 1466 or 0.976 0.000292 3344 xor 0.955 0.000294 3250 sub 0.346 0.000316 1092 iand 0.00658 1.14e-05 575 ior 0.00594 1.08e-05 548 ixor 0.00434 1.12e-05 385 isub 0.00431 1.09e-05 397 eq 0.0991 0.000116 851 neq 9.62e-06 1.29e-05 0.743 jaccard 1.62 0.00025 6476 dense set / high load factor 100 runs with sets of 39800 random elements n s.t. 0 <= n < 40000 set() RoaringBitmap() ratio init 0.33 0.0775 4.26 initsort 0.352 0.148 2.38 and 0.24 0.000223 1078 or 0.45 0.000165 2734 xor 0.404 0.000161 2514 sub 0.169 0.000173 973 iand 0.00287 6.02e-06 477 ior 0.00179 6.34e-06 282 ixor 0.00195 5.53e-06 353 isub 0.0017 6.35e-06 267 eq 0.0486 4.65e-05 1045 neq 1.01e-05 1.13e-05 0.888 jaccard 0.722 0.000118 6136
See https://github.com/Ezibenroc/roaring_analysis/ for a performance comparison of PyRoaringBitmap and this library.
References
Samy Chambi, Daniel Lemire, Owen Kaser, Robert Godin (2014), Better bitmap performance with Roaring bitmaps, http://arxiv.org/abs/1402.6407
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