SIMD-accelerated implementations of various streaming algorithms.
This library is a work in progress. PRs are very welcome! Currently implemented algorithms include:
- Count–min sketch
- Top k (Count–min sketch plus a doubly linked hashmap to track heavy hitters / top k keys when ordered by aggregated value)
- Reservoir sampling
A goal of this library is to enable composition of these algorithms; for example Top k + HyperLogLog to enable an approximate version of something akin to
SELECT key FROM table GROUP BY key ORDER BY COUNT(DISTINCT value) DESC LIMIT k.
Run your application with
RUSTFLAGS="-C target-cpu=native" and the
nightly feature to benefit from the SIMD-acceleration like so:
RUSTFLAGS="-C target-cpu=native" cargo run --features "streaming_algorithms/nightly" --release
See this gist for a good list of further algorithms to be implemented. Other resources are Probabilistic data structures – Wikipedia, DataSketches – A similar Java library originating at Yahoo, and Algebird – A similar Java library originating at Twitter.
As these implementations are often in hot code paths, unsafe is used, albeit only when necessary to a) achieve the asymptotically optimal algorithm or b) mitigate an observed bottleneck.
Licensed under either of
- Apache License, Version 2.0, (LICENSE-APACHE.txt or http://www.apache.org/licenses/LICENSE-2.0)
- MIT license (LICENSE-MIT.txt or http://opensource.org/licenses/MIT)
at your option.
Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.