Speed-optimized BitSet implementation for modern browsers and JavaScript engines, uses typed arrays
A BitSet (also called Bitmap or bit vector) is an ideal data structure to implement a set when values being stored are reasonably small integers. It can be orders of magnitude faster than a generic set implementation. In particular, a BitSet has fast support for set operations (union, difference, intersection).
The TypedFastBitSet.js implementation optimizes for speed, leveraging commonly available features like typed arrays. It can be several times faster than competitive alternatives. It is also entirely dynamic, and has functions to minimize the memory usage. It should be supported by most of the modern browsers and JavaScript engines. It is ideal for maintaining sets of integers when performance matters.
License: Apache License 2.0
const b = new TypedFastBitSet();// initially empty
b.add(1);// add the value "1"
b.has(1); // check that the value is present! (will return true)
b.add(2);
console.log(""+b);// should display {1,2}
b.add(10);
b.addRange(11, 13)
b.array(); // would return [1, 2, 10, 11, 12, 13]
let c = new TypedFastBitSet([1,2,3,10]); // create bitset initialized with values 1,2,3,10
c.difference(b); // from c, remove elements that are in b (modifies c)
c.difference2(b) // from c, remove elements that are in b (modifies b)
c.change(b); // c will contain all elements that are in b or in c, but not both (elements that changed)
const su = c.union_size(b);// compute the size of the union (bitsets are unchanged)
c.union(b); // c will contain all elements that are in c and b
const out1 = c.new_union(b); // creates a new bitmap that contains everything in c and b
const out2 = c.new_intersection(b); // creates a new bitmap that contains everything that is in both c and b
const out3 = c.new_change(b); // creates a new bitmap that contains everything in b or in c, but not both
const s1 = c.intersection_size(b);// compute the size of the intersection (bitsets are unchanged)
const s2 = c.difference_size(b);// compute the size of the difference (bitsets are unchanged)
const s3 = c.change_size(b); // compute the number of elements that are in b but not c, or vice versa
c.intersects(b); // return true if c intersects with b
c.intersection(b); // c will only contain elements that are in both c and b
c = b.clone(); // create a (deep) copy of b and assign it to c.
c.equals(b); // checks whether c and b are equal
c.forEach(fnc); // execute fnc on each value stored in c
for (const x of c) fnc(x); // execute fnc on each value stored in c (allows early exit with break)
c.trim(); // reduce the memory usage of the bitmap if possible, the content remains the sameIf you are using node.js, you need to import the module:
const TypedFastBitSet = require("typedfastbitset");
const b = new TypedFastBitSet();// initially empty
b.add(1);// add the value "1"Performance tip: in-place functions such as intersection, union and difference can be much faster than functions generating a new bitmap (new_intersection, new_union and new_difference) because they avoid creating a new object, a potentially expensive process in JavaScript. For faster code, use as few FastBitSet objects as you can.
$ npm install typedfastbitset
Using node.js (npm), you can test the code as follows...
$ npm install mocha
$ npm test
To run benchmarks, please use the instructions in the benchmark folder.
TypedFastBitSet can be quite fast compared to competitive alternatives :
$ node test.js
Benchmarking against:
TypedFastBitSet.js: https://github.com/lemire/TypedFastBitSet.js
roaring: https://www.npmjs.com/package/roaring
infusion.BitSet.js from https://github.com/infusion/BitSet.js 5.0.0
tdegrunt.BitSet from https://github.com/tdegrunt/bitset 5.0.3
mattkrick.fast-bitset from https://github.com/mattkrick/fast-bitset 1.3.2
standard Set object from JavaScript
Not all libraries support all operations. We benchmark what is available.
Platform: linux 4.4.0-135-generic x64
Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz
Node version 10.9.0, v8 version 6.8.275.24-node.14
We proceed with the logical operations generating new bitmaps:
starting union query benchmark
roaring x 4,006 ops/sec ±0.07% (103 runs sampled)
FastBitSet (creates new bitset) x 1,140 ops/sec ±16.91% (90 runs sampled)
TypedFastBitSet (creates new bitset) x 3,153 ops/sec ±0.99% (80 runs sampled)
mattkrick.fast-bitset (creates new bitset) x 2,869 ops/sec ±2.08% (87 runs sampled)
Set x 3.38 ops/sec ±0.23% (13 runs sampled)
starting intersection query benchmark
FastBitSet (creates new bitset) x 2,413 ops/sec ±1.69% (90 runs sampled)
TypedFastBitSet (creates new bitset) x 5,340 ops/sec ±0.84% (81 runs sampled)
mattkrick.fast-bitset (creates new bitset) x 495 ops/sec ±1.09% (96 runs sampled)
roaring x 16,553 ops/sec ±9.27% (79 runs sampled)
Set x 5.03 ops/sec ±17.84% (18 runs sampled)
starting difference query benchmark
FastBitSet (creates new bitset) x 1,972 ops/sec ±2.52% (89 runs sampled)
TypedFastBitSet (creates new bitset) x 3,732 ops/sec ±1.36% (60 runs sampled)
Set x 3.71 ops/sec ±0.12% (13 runs sampled)
We benchmark the in-place logical operations:
(Notice how much faster they are.)
starting inplace union benchmark
FastBitSet (inplace) x 4,850 ops/sec ±0.05% (104 runs sampled)
TypedFastBitSet (inplace) x 4,489 ops/sec ±0.03% (104 runs sampled)
infusion.BitSet.js (inplace) x 68.75 ops/sec ±1.21% (73 runs sampled)
tdegrunt.BitSet (inplace) x 69.55 ops/sec ±1.29% (74 runs sampled)
roaring x 24,969 ops/sec ±0.09% (100 runs sampled)
Set (inplace) x 12.05 ops/sec ±0.12% (35 runs sampled)
starting inplace intersection benchmark
FastBitSet (inplace) x 12,260 ops/sec ±2.71% (103 runs sampled)
TypedFastBitSet (inplace) x 10,744 ops/sec ±0.96% (101 runs sampled)
infusion.BitSet.js (inplace) x 1,340 ops/sec ±2.06% (91 runs sampled)
tdegrunt.BitSet (inplace) x 1,358 ops/sec ±2.19% (92 runs sampled)
roaring x 41,172 ops/sec ±0.07% (104 runs sampled)
Set (inplace) x 6.88 ops/sec ±0.08% (22 runs sampled)
starting inplace difference benchmark
FastBitSet (inplace) x 12,434 ops/sec ±0.39% (101 runs sampled)
TypedFastBitSet (inplace) x 10,631 ops/sec ±0.24% (100 runs sampled)
infusion.BitSet.js (inplace) x 597 ops/sec ±2.15% (87 runs sampled)
tdegrunt.BitSet (inplace) x 601 ops/sec ±2.37% (88 runs sampled)
roaring x 49,582 ops/sec ±0.06% (104 runs sampled)
Set (inplace) x 6.70 ops/sec ±0.10% (21 runs sampled)
We benchmark the operations computing the set sizes:
starting cardinality benchmark
FastBitSet x 5,160 ops/sec ±0.71% (102 runs sampled)
TypedFastBitSet x 5,165 ops/sec ±0.70% (102 runs sampled)
infusion.BitSet.js x 5,293 ops/sec ±0.01% (104 runs sampled)
tdegrunt.BitSet x 5,295 ops/sec ±0.02% (104 runs sampled)
mattkrick.fast-bitset x 4,753 ops/sec ±0.02% (104 runs sampled)
starting union cardinality query benchmark
FastBitSet (creates new bitset) x 563 ops/sec ±1.29% (92 runs sampled)
FastBitSet (fast way) x 2,369 ops/sec ±0.01% (103 runs sampled)
TypedFastBitSet (creates new bitset) x 1,248 ops/sec ±1.68% (84 runs sampled)
TypedFastBitSet (fast way) x 2,454 ops/sec ±0.11% (102 runs sampled)
roaring x 59,798 ops/sec ±0.08% (101 runs sampled)
mattkrick.fast-bitset (creates new bitset) x 1,232 ops/sec ±1.43% (84 runs sampled)
Set x 9.09 ops/sec ±0.16% (27 runs sampled)
starting intersection cardinality query benchmark
FastBitSet (creates new bitset) x 1,098 ops/sec ±1.61% (96 runs sampled)
FastBitSet (fast way) x 4,568 ops/sec ±0.02% (104 runs sampled)
TypedFastBitSet (creates new bitset) x 2,440 ops/sec ±1.57% (84 runs sampled)
TypedFastBitSet (fast way) x 4,812 ops/sec ±0.61% (101 runs sampled)
roaring x 60,769 ops/sec ±0.08% (102 runs sampled)
mattkrick.fast-bitset (creates new bitset) x 400 ops/sec ±1.66% (91 runs sampled)
Set x 9.12 ops/sec ±0.09% (27 runs sampled)
starting difference cardinality query benchmark
FastBitSet (creates new bitset) x 567 ops/sec ±1.50% (93 runs sampled)
FastBitSet (fast way) x 4,287 ops/sec ±0.03% (103 runs sampled)
TypedFastBitSet (creates new bitset) x 1,246 ops/sec ±1.59% (84 runs sampled)
TypedFastBitSet (fast way) x 4,581 ops/sec ±0.05% (103 runs sampled)
roaring x 60,438 ops/sec ±0.07% (101 runs sampled)
Set x 9.12 ops/sec ±0.07% (27 runs sampled)
We conclude with other benchmarks:
starting dynamic bitmap creation benchmark
FastBitSet x 268 ops/sec ±1.27% (88 runs sampled)
TypedFastBitSet x 328 ops/sec ±1.84% (90 runs sampled)
infusion.BitSet.js x 235 ops/sec ±1.46% (87 runs sampled)
tdegrunt.BitSet x 232 ops/sec ±1.46% (87 runs sampled)
Set x 10.05 ops/sec ±1.39% (30 runs sampled)
starting query benchmark
FastBitSet x 177,278,318 ops/sec ±0.11% (104 runs sampled)
TypedFastBitSet x 174,176,611 ops/sec ±0.16% (99 runs sampled)
infusion.BitSet.js x 167,893,143 ops/sec ±0.62% (100 runs sampled)
tdegrunt.BitSet x 181,343,910 ops/sec ±0.07% (99 runs sampled)
mattkrick.fast-bitset x 145,328,815 ops/sec ±0.16% (100 runs sampled)
Set x 76,784,958 ops/sec ±0.01% (104 runs sampled)If you like this library, you might also like
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent