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USE WITH CAUTION
As of now this crate has not yet been battle tested or benchmarked to an extend where the author would recommend general production usage. Please file bugs, suggestions or enhancements to the issue tracker of this repository.
A vector-like data structure that organizes its elements into a set of buckets of fixed-capacity in order to guarantee that mutations to the bucket vector never moves elements and thus invalidates references to them.
This is comparable to a Vec<Box<T>> but a lot more efficient.
The BucketVecConfig trait allows to customize the internal structure of your
BucketVec. This allows users to fine-tune their BucketVec for particular
use cases.
The trait mainly controls the capacity of the first bucket and the growth rate of the capacity of new buckets.
The default DefaultConfig tries to balance out the different interests
between start capacity and growth rate.
The BucketVec is really just a vector of Bucket instances.
Whenever an element is pushed to the BucketVec the element is pushed onto
the last Bucket if it isn't filled, yet.
If the last Bucket is filled a new Bucket is pushed onto the BucketVec
with a new capacity determined by the used bucket vector configuration.
This way the BucketVec never moves elements around upon inserting new elements
in order to preserve references. When a normal Vec is modified it can potentially
invalidate references because of reallocation of the internal buffer which
might cause severe bugs if references to the internal elements are stored
outside the Vec. Note that normally Rust prevents such situations so the
BucketVec is mainly used in the area of unsafe Rust where a developer
actively decides that they want or need pinned references into another data
structure.
For the same reasons as stated above the BucketVec does not allow to remove
or swap elements.
Looking at an example BucketVec<i32> with the following configuration:
start_capacity := 1growth_rate := 2We have already pushed the elements A,.., K onto it.
[ [A], [B, C], [D, E, F, G], [H, I, J, K, _, _, _, _] ]
Where _ refers to a vacant bucket entry.
Pushing another L,.., O onto the same BucketVec results in:
[ [A], [B, C], [D, E, F, G], [H, I, J, K, L, M, N, O] ]
So we are full on capacity for all buckets.
The next time we push another element onto the BucketVec it will create a new Bucket with a capacity of 16 since growth_rate == 2 and our current latest bucket already has a capacity of 8.
[ [A], [B, C], [D, E, F, G], [H, I, J, K, L, M, N, O], [P, 15 x _] ]
Where 15 x _ denotes 15 consecutive vacant entries.
BucketVec fullfills its role in being a replacement for situations where
a Vec<Box<T>> is the naive go-to solution.
The benchmark suite is still small and not super expressive but already provides
some insights in where BucketVec already performs pretty well and where it
could improve.
Benchmarks have been run on a Intel(R) Core(TM) i7-6700HQ CPU @ 2.60GHz.
Note that for every of the benchmark groups (push, get and iter) the
most efficient configuration for the BucketVec has been chosen.
Some benchmarks (get) have shown significant difference between configs.
The following is the output of a benchmark run:
``` bucketvec::push/10000 time: [43.650 us 43.818 us 43.995 us] vecbox::push/10000 time: [405.64 us 411.44 us 418.03 us] vec_value::push/10000 time: [28.840 us 28.957 us 29.096 us]
bucketvec::get/10000 time: [20.260 us 20.307 us 20.360 us] vecbox::get/10000 time: [14.472 us 14.503 us 14.538 us] vec_value::get/10000 time: [8.8010 us 8.8174 us 8.8365 us]
bucketvec::iter/10000 time: [5.9917 us 6.0059 us 6.0212 us] vecbox::iter/10000 time: [9.8053 us 9.8318 us 9.8626 us] vec_value::iter/10000 time: [3.6165 us 3.6297 us 3.6445 us]
bucketvec::iter.rev()/10000 time: [5.2279 us 5.2400 us 5.2536 us] vecbox::iter.rev()/10000 time: [10.021 us 10.042 us 10.065 us] vec_value::iter.rev()/10000 time: [3.5831 us 3.5945 us 3.6074 us] ```
It can be seen that BucketVec greatly outperforms Vec<Box<_>> on
push, iter and iter().rev() benchmarks.
Also BucketVec is approximately 50% slower than the Vec<_> which is the
theoretical optimum that unfortunately doesn't solve the underlying problem.
Author: Robin Freyler (github.com/Robbepop)
Special thanks to Niklas Tittjung (github.com/lugino-emeritus) who helped me a lot with some internal formulae.
Licensed under either of
at your option.
