feat: add serde
Adds serialization/deserialization support via serde and serde_with. Also: - remove `license-file` in Cargo.toml (superfluous) - update README with Getting Started section - add `serde` and `serde_with` dependencies to README - add tests to `HeightVector` - tweak assertion in `NetworkCoordinate` - refactor `Vector` to be a struct witn an `inner` instead of a tuple struct so that we can use `serde_as` (from `serde_with`) to help us derive SerDe implementations for the const generic sized array - moved trait requirements (which are getting long) into where clauses Signed-off-by: Stephen Waits <steve@waits.net>
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@@ -11,11 +11,13 @@ documentation = "https://docs.rs/vivaldi-nc/"
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readme = "README.md"
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homepage = "https://sr.ht/~swaits/vivaldi-nc/"
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repository = "https://git.sr.ht/~swaits/vivaldi-nc"
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license-file = "LICENSE.txt"
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[dev-dependencies]
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assert_approx_eq = "1.1.0"
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serde_json = "1.0.91"
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[dependencies]
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nanorand = {version = "0.7.0", features = ["wyrand", "getrandom"]}
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num-traits = "0.2.15"
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serde = {version = "1.0.152", features = ["serde_derive"]}
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serde_with = "2.2.0"
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@@ -10,7 +10,7 @@ other will be close to each other. Nodes with high latency between them will be
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far from each other.
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This is an implementation of Vivaldi Network Coordinates, a specific NC
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algorithm, with a public simple interface and few dependencies. Vivaldi
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algorithm, with a simple simple interface and few dependencies. Vivaldi
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coordinates are typically used in distributed networking applications, like p2p
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networks. They allow each node in the network to estimate its position in a
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latency space in a distributed way, with no central authority. This enables
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@@ -62,6 +62,51 @@ Regarding the two drawbacks mentioned by the Wikiepedia article, quoted above:
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### Getting Started
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Add the crate to your project:
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```bash
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cargo add vivaldi-nc
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```
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Each node in the network should create a `NetworkCoordinate` (NC). The node
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uses this structure to track its latency position in the entire network.
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```rust
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use vivaldi_nc::network_coordinate::NetworkCoordinate;
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// create a 2-dimensional NetworkCoordinate to track my position
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let mut my_position = NetworkCoordinate::<2>::new();
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```
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Normally 2 or 3-dimensions is plenty. Higher dimensions may add some accuracy,
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but not enough to be worth the extra costs.
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Each node occasionally sends its NC to other nodes. Upon receiving a NC from a
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remote node, update your local node's position with the actual measured ping
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time to that node.
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```rust
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my_position.update(&remote_position, Duration::from_millis(measured_rtt));
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```
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Over time your NC will get more and more accurate as it's updated against more
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nodes in the network.
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You can estimate your ping time with any other NC you receive, even if it was
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forwarded to you indirectly.
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```rust
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let rtt_estimate = my_position.estimate_rtt(&remote_position);
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```
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That's the entire interface for creating and iteratively updating NCs.
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If you want to save/restore NCs, or send/receive them over a network, you'll
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want to serialize/deserialize them. `NetworkCoordinate` supports
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[Serde](https://crates.io/crates/serde) by default. Many formats are supported
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by Serde, including text formats like [JSON](https://github.com/serde-rs/json)
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and compact binary formats like [bincode](https://crates.io/crates/bincode) and
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[MessagePack](https://github.com/3Hren/msgpack-rust).
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### Features
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### Examples
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@@ -72,13 +117,19 @@ One design goal of this crate is to minimize dependencies. When dependencies
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are required, I try to be very selective about them (re: bloat and licensing).
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This crate depends on:
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- `nanorand`: A fast PRNG based on WyRand. It pulls in `getrandom` as a
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portable source of entropy. I chose this over the more commonly used, but
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heavyweight `rand` because it's significantly smaller and does just what I
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need, and not much more.
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- `num-traits`: A brilliant crate which makes it easier to operate on numbers
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in generics; like using `Float` as a constraint on a generic type. Its
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convenience outweighs its cost.
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- [`nanorand`](https://crates.io/crates/nanorand): A fast PRNG based on WyRand.
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It pulls in `getrandom` as a portable source of entropy. I chose this over
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the more commonly used, but heavyweight `rand` because it's significantly
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smaller and does just what I need, and not much more.
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- [`num-traits`](https://crates.io/crates/num-traits): A brilliant crate which
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makes it easier to operate on numbers in generics; like using `Float` as a
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constraint on a generic type. Its convenience outweighs its cost.
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- [`serde`](https://crates.io/crates/serde): I think NCs are usually meant to
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be shared across a network. That requires serialization/deserialization and
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serde is *the* choice for that. It might be big, but it's efficient.
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- [`serde_with`](https://crates.io/crates/serde_with): Because the inner
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`Vector<T,N>` uses a const generic length, we use this to help derive
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`Deserialize`.
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## Alternative implementations
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@@ -98,12 +149,12 @@ I had several design goals which the existing crates didn't satisfy:
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rigamarole. Support `serde` traits by default.
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4. Well documented. Well tested. This varies by the crates.
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All of that said, those other implementations might work best for you. Here are
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the ones I know of today:
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All of that said, those other rust implementations might work best for you.
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Here are the ones I know of today:
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- TODO: list crates
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- TODO: list crates
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- TODO: list crates
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- [netloc](https://github.com/dgtony/netloc)
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- [violin](https://crates.io/crates/violin)
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- [vivaldi](https://crates.io/crates/vivaldi)
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## Other Algorithms
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+66
-1
@@ -45,6 +45,7 @@
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use std::ops::{Add, Mul, Sub};
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use nanorand::{Rng, WyRand};
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use serde::{Deserialize, Serialize};
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use crate::vector::Vector;
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@@ -61,7 +62,7 @@ use crate::vector::Vector;
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/// ## Generic Parameters
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///
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/// - `N`: the dimensionality of the vector portion (i.e. non-height) of the Vivaldi height vector
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#[derive(Copy, Clone, Debug)]
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#[derive(Copy, Clone, Debug, Serialize, Deserialize)]
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pub(crate) struct HeightVector<const N: usize>(Vector<f32, N>, f32);
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//
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@@ -160,4 +161,68 @@ mod tests {
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let a = HeightVector::<3>::from(([1.0, 2.0, 3.0], 4.0));
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assert_approx_eq!(a.len(), 7.741_657, 0.00001);
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}
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#[test]
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fn test_new() {
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// new gives us a random unit length vec
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let a = HeightVector::<3>::new();
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assert_approx_eq!(a.len(), 1.0);
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}
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#[test]
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fn test_default() {
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// default() should give us a zero vec
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let a = HeightVector::<3>::default();
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assert_approx_eq!(a.len(), 0.0);
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}
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#[test]
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fn test_from() {
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let a = HeightVector::<2>::from(([1.0, 2.0], 3.0));
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assert_eq!(a.0[0], 1.0);
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assert_eq!(a.0[1], 2.0);
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assert_eq!(a.1, 3.0);
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}
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#[test]
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fn test_add() {
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let a = HeightVector::<2>::from(([1.0, 2.0], 3.0));
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let b = HeightVector::<2>::from(([3.0, 2.0], 1.0));
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let c = a + b;
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assert_eq!(c.0[0], 4.0);
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assert_eq!(c.0[1], 4.0);
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assert_eq!(c.1, 4.0);
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}
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#[test]
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fn test_sub() {
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let a = HeightVector::<2>::from(([1.0, 2.0], 3.0));
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let b = HeightVector::<2>::from(([3.0, 2.0], 1.0));
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let c = a - b;
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assert_eq!(c.0[0], -2.0);
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assert_eq!(c.0[1], 0.0);
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assert_eq!(c.1, 4.0);
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}
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#[test]
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fn test_mul_scalar() {
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let a = HeightVector::<2>::from(([1.0, 2.0], 3.0)) * 10.0;
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assert_eq!(a.0[0], 10.0);
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assert_eq!(a.0[1], 20.0);
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assert_eq!(a.1, 30.0);
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}
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#[test]
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fn test_serde() {
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// start with JSON, deserialize it
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let s = "[{\"inner\":[1.0,2.0,3.0]},4.0]";
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let a: HeightVector<3> = serde_json::from_str(s).unwrap();
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// make sure it's the right length and works like we expect a normal NC
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assert_approx_eq!(a.len(), 7.741_657, 0.001);
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// serialize it into a new JSON string and make sure it matches the original
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let t = serde_json::to_string(&a);
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assert_eq!(t.as_ref().unwrap(), s);
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}
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}
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@@ -27,6 +27,8 @@
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use std::time::Duration;
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use serde::{Deserialize, Serialize};
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use crate::height_vector::HeightVector;
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//
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@@ -47,7 +49,7 @@ const MIN_ERROR: f32 = f32::EPSILON;
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// **** Structs ****
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//
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#[derive(Debug)]
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#[derive(Debug, Serialize, Deserialize)]
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pub struct NetworkCoordinate<const N: usize> {
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position: HeightVector<N>,
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error: f32,
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@@ -274,6 +276,22 @@ mod tests {
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(slc.error.powf(2.0) + nyc.error.powf(2.0) + lax.error.powf(2.0) + mad.error.powf(2.0))
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.sqrt();
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println!("error = {error}");
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assert!(error < 0.07);
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assert!(error < 0.2);
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}
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#[test]
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fn test_serde() {
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// start with JSON, deserialize it
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let s = "{\"position\":[{\"inner\":[1.5,0.5,2.0]},0.1],\"error\":1.0}";
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let a: NetworkCoordinate<3> = serde_json::from_str(s).unwrap();
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// make sure it's the right length and works like we expect a normal NC
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assert_approx_eq!(a.position.len(), 2.649_509, 0.001);
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assert_eq!(a.error, 1.0);
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assert_eq!(a.estimated_rtt(&a).as_millis(), 0);
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// serialize it into a new JSON string and make sure it matches the original
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let t = serde_json::to_string(&a);
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assert_eq!(t.as_ref().unwrap(), s);
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}
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}
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+60
-17
@@ -4,6 +4,8 @@
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//! Vivaldi `HeightVector`.
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use num_traits::Float;
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use serde::{Deserialize, Serialize};
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use serde_with::serde_as;
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use std::ops::{Add, Div, Index, Mul, Sub};
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@@ -16,14 +18,24 @@ use std::ops::{Add, Div, Index, Mul, Sub};
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/// ## Generic parameters:
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/// - `T`: the type to use, must satisfy the `Float` trait (i.e. `f32` or `f64`)
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/// - `N`: the number of dimensions (i.e. 2 for 2D vectors, 3 for 3D, etc)
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#[derive(Copy, Clone, Debug, PartialEq, Eq)]
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pub(crate) struct Vector<T: Float, const N: usize>([T; N]);
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#[serde_as]
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#[derive(Copy, Clone, Debug, PartialEq, Eq, Serialize, Deserialize)]
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pub(crate) struct Vector<T, const N: usize>
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where
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T: Float + Serialize + for<'d> Deserialize<'d>,
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{
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#[serde_as(as = "[_; N]")]
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inner: [T; N],
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}
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//
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// **** Implementations ****
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//
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impl<T: Float, const N: usize> Vector<T, N> {
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impl<T, const N: usize> Vector<T, N>
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where
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T: Float + Serialize + for<'d> Deserialize<'d>,
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{
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/// Create a new Zero vector.
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pub(crate) fn new() -> Self {
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Self::default()
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@@ -32,8 +44,8 @@ impl<T: Float, const N: usize> Vector<T, N> {
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/// Compute magnitude of vector.
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pub(crate) fn len(&self) -> T {
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(0..N)
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.map(|i| self.0[i] * self.0[i])
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.fold(T::zero(), |acc, x| acc + x)
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.map(|i| self.inner[i] * self.inner[i])
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.fold(T::zero(), |sum_sq, x| sum_sq + x)
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.sqrt()
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}
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}
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@@ -42,21 +54,32 @@ impl<T: Float, const N: usize> Vector<T, N> {
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// **** Trait Implementations ****
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//
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impl<T: Float, const N: usize> Default for Vector<T, N> {
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impl<T, const N: usize> Default for Vector<T, N>
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where
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T: Float + Serialize + for<'d> Deserialize<'d>,
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{
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/// A default vector is all zeroes
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fn default() -> Self {
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Self([T::zero(); N])
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Self {
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inner: [T::zero(); N],
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}
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}
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}
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impl<T: Float, const N: usize> From<[T; N]> for Vector<T, N> {
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impl<T, const N: usize> From<[T; N]> for Vector<T, N>
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where
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T: Float + Serialize + for<'d> Deserialize<'d>,
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{
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/// Create a new Vector from an array of type T, N dimensions.
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fn from(value: [T; N]) -> Self {
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Self(value)
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Self { inner: value }
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}
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}
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impl<T: Float, const N: usize> Add for Vector<T, N> {
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impl<T, const N: usize> Add for Vector<T, N>
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where
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T: Float + Serialize + for<'d> Deserialize<'d>,
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{
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type Output = Self;
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/// Vector+Vector addition.
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@@ -69,7 +92,10 @@ impl<T: Float, const N: usize> Add for Vector<T, N> {
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}
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}
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impl<T: Float, const N: usize> Sub for Vector<T, N> {
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impl<T, const N: usize> Sub for Vector<T, N>
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where
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T: Float + Serialize + for<'d> Deserialize<'d>,
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{
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type Output = Self;
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/// Vector-Vector subtraction.
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@@ -82,38 +108,47 @@ impl<T: Float, const N: usize> Sub for Vector<T, N> {
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}
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}
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impl<T: Float, const N: usize> Mul<T> for Vector<T, N> {
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impl<T, const N: usize> Mul<T> for Vector<T, N>
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where
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T: Float + Serialize + for<'d> Deserialize<'d>,
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{
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type Output = Self;
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/// Vector*scalar multiplication.
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fn mul(self, rhs: T) -> Self::Output {
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let mut ret = Self::Output::new();
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for i in 0..N {
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ret.0[i] = self.0[i] * rhs
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ret.inner[i] = self.inner[i] * rhs
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}
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ret
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}
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}
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impl<T: Float, const N: usize> Div<T> for Vector<T, N> {
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impl<T, const N: usize> Div<T> for Vector<T, N>
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where
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T: Float + Serialize + for<'d> Deserialize<'d>,
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{
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type Output = Self;
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/// Vector/scalar division.
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fn div(self, rhs: T) -> Self::Output {
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let mut ret = Self::Output::new();
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for i in 0..N {
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ret.0[i] = self.0[i] / rhs
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ret.inner[i] = self.inner[i] / rhs
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}
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ret
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}
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}
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impl<T: Float, const N: usize> Index<usize> for Vector<T, N> {
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impl<T, const N: usize> Index<usize> for Vector<T, N>
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where
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T: Float + Serialize + for<'d> Deserialize<'d>,
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{
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type Output = T;
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/// Nicer indexing [] for read only references.
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fn index(&self, index: usize) -> &Self::Output {
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&self.0[index]
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&self.inner[index]
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}
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}
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@@ -183,4 +218,12 @@ mod tests {
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let b = a * 10.0;
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assert_approx_eq!(b.len(), 37.416_57, 0.0001);
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}
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#[test]
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fn test_serde() {
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let a = Vector::<f32, 3>::from([1.0, 2.0, 3.0]);
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let s = serde_json::to_string(&a);
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assert_eq!(s.as_ref().unwrap(), "{\"inner\":[1.0,2.0,3.0]}");
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assert!(s.is_ok());
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}
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}
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Reference in new issue
Block a user