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v0.3.0
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Cargo.toml

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[package]
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name = "streaming_algorithms"
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version = "0.2.0"
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version = "0.3.0"
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license = "MIT OR Apache-2.0"
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authors = ["Alec Mocatta <alec@mocatta.net>"]
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categories = ["data-structures","algorithms","science"]
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"""
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repository = "https://github.com/alecmocatta/streaming_algorithms"
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homepage = "https://github.com/alecmocatta/streaming_algorithms"
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documentation = "https://docs.rs/streaming_algorithms/0.2.0"
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documentation = "https://docs.rs/streaming_algorithms"
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readme = "README.md"
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edition = "2018"
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README.md

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[![MIT / Apache 2.0 licensed](https://img.shields.io/crates/l/streaming_algorithms.svg?maxAge=2592000)](#License)
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[![Build Status](https://dev.azure.com/alecmocatta/streaming_algorithms/_apis/build/status/tests?branchName=master)](https://dev.azure.com/alecmocatta/streaming_algorithms/_build?definitionId=16)
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[📖 Docs](https://docs.rs/streaming_algorithms/0.2.0/streaming_algorithms/) | [💬 Chat](https://constellation.zulipchat.com/#narrow/stream/213236-subprojects)
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[📖 Docs](https://docs.rs/streaming_algorithms) | [💬 Chat](https://constellation.zulipchat.com/#narrow/stream/213236-subprojects)
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SIMD-accelerated implementations of various [streaming algorithms](https://en.wikipedia.org/wiki/Streaming_algorithm).
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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`.
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Run your application with `RUSTFLAGS="-C target-cpu=native"` to benefit from the SIMD-acceleration like so:
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Run your application with `RUSTFLAGS="-C target-cpu=native"` and the `nightly` feature to benefit from the SIMD-acceleration like so:
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```bash
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RUSTFLAGS="-C target-cpu=native" cargo run --release
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RUSTFLAGS="-C target-cpu=native" cargo run --features "streaming_algorithms/nightly" --release
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```
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See [this gist](https://gist.github.com/debasishg/8172796) for a good list of further algorithms to be implemented. Other resources are [Probabilistic data structures – Wikipedia](https://en.wikipedia.org/wiki/Category:Probabilistic_data_structures), [DataSketches – A similar Java library originating at Yahoo](https://datasketches.github.io/), and [Algebird – A similar Java library originating at Twitter](https://github.com/twitter/algebird).

src/lib.rs

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//!
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//! 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`.
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//!
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//! Run your application with `RUSTFLAGS="-C target-cpu=native"` to benefit from the SIMD-acceleration like so:
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//! Run your application with `RUSTFLAGS="-C target-cpu=native"` and the `nightly` feature to benefit from the SIMD-acceleration like so:
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//!
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//! ```bash
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//! RUSTFLAGS="-C target-cpu=native" cargo run --release
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//! RUSTFLAGS="-C target-cpu=native" cargo run --features "streaming_algorithms/nightly" --release
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//! ```
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//!
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//! See [this gist](https://gist.github.com/debasishg/8172796) for a good list of further algorithms to be implemented. Other resources are [Probabilistic data structures – Wikipedia](https://en.wikipedia.org/wiki/Category:Probabilistic_data_structures), [DataSketches – A similar Java library originating at Yahoo](https://datasketches.github.io/), and [Algebird – A similar Java library originating at Twitter](https://github.com/twitter/algebird).
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//!
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//! 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.
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#![doc(html_root_url = "https://docs.rs/streaming_algorithms/0.2.0")]
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#![doc(html_root_url = "https://docs.rs/streaming_algorithms/0.3.0")]
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#![warn(
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missing_copy_implementations,
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missing_debug_implementations,

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