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README.rst

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.

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