1 unstable release
Uses old Rust 2015
|0.1.1||Nov 12, 2018|
#10 in #broadcast
Used in stocker
The main use case of this library is to simplify creating efficient computational DAGs (or computational trees, to be precise) that operate on streams of values. It does not aim to replicate the entire galaxy of ReactiveX operators, nor does it attempt to delve into futures/concurrency territory.
What is a computational tree? First, there's the root at the top, that's
where the input values get fed into continuously. Then, we perform
computations on these values – each of which may yield zero,
one or more values that are sent further down. Some downstream
nodes may share their parents – for instance,
the input and
f is the intermediate transformation; in this case, we want
to make sure we don't have to recompute
f(x) twice. Moreover, this
being Rust, we'd like to ensure we're not copying and cloning any values
needlessly, and we generally prefer things to be zero-cost/inlineable
when possible. Finally, there are leaves – these are observers, functions
that receive transform values and do something with them, likely recording
them somewhere or mutating the environment in some other way.
Streams, broadcasts and observers in this crate operate on pairs of
values: the context and the element. Context can be viewed as
optional metadata attached to the original value. Closures required in
.map() only take one argument (the element) and are
expected to return a single value; this way, the element can be changed
without touching the context. This can be extremely convenient if you
need to access the original input value (or any "upstream" value) way
down the computation chain – this way you don't have to propagate
Most stream/broadcast methods have an alternative "full" version that
operates on both context/element, with
Consider the following problem: we have an incoming stream of buy/sell price pairs, and for each incoming event we would like to compute how the current mid-price (the average between the two) compares relatively to the minimum buy price and the maximum sell price over the last three observations. Moreover, we would like to skip the first few events in order to allow the buffer to fill up.
Here's one way we could do it (not the most ultimately efficient way of solving this particular problem, but it serves quite well to demonstrate the basic functionality of the crate):
use std::cell::Cell; use std::f64; use reactive_rs::*; let min_rel = Cell::new(0.); let max_rel = Cell::new(0.); // create a broadcast of (buy, sell) pairs let quotes = SimpleBroadcast::new(); // clone the broadcast so we can feed values to it later let last = quotes.clone() // save the mid-price for later use .with_ctx_map(|_, &(buy, sell)| (buy + sell) / 2.) // cache the last three observations .last_n(3) // wait until the queue fills up .filter(|quotes| quotes.len() > 2) // share the output (slices of values) .broadcast(); // subscribe to the stream of slices let min = last.clone() // compute min buy price .map(|p| p.iter().map(|q| q.0).fold(1./0., f64::min)); // subscribe to the stream of slices let max = last.clone() // compute max sell price .map(|p| p.iter().map(|q| q.1).fold(-1./0., f64::max)); // finally, attach observers min.subscribe_ctx(|p, min| min_rel.set(min / p)); max.subscribe_ctx(|p, max| max_rel.set(max / p)); quotes.send((100., 102.)); quotes.send((101., 103.)); assert_eq!((min_rel.get(), max_rel.get()), (0., 0.)); quotes.send((99., 101.)); assert_eq!((min_rel.get(), max_rel.get()), (0.99, 1.03)); quotes.send((97., 103.)); assert_eq!((min_rel.get(), max_rel.get()), (0.97, 1.03));
The MIT License (MIT)
Copyright (c) 2018 Ivan Smirnov
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