mirror of
https://github.com/DioxusLabs/dioxus
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120 lines
3.2 KiB
Rust
120 lines
3.2 KiB
Rust
#![allow(non_snake_case, non_upper_case_globals)]
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//! This benchmark tests just the overhead of Dioxus itself.
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//!
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//! For the JS Framework Benchmark, both the framework and the browser is benchmarked together. Dioxus prepares changes
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//! to be made, but the change application phase will be just as performant as the vanilla wasm_bindgen code. In essence,
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//! we are measuring the overhead of Dioxus, not the performance of the "apply" phase.
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//!
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//! On my MBP 2019:
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//! - Dioxus takes 3ms to create 1_000 rows
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//! - Dioxus takes 30ms to create 10_000 rows
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//!
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//! As pure "overhead", these are amazing good numbers, mostly slowed down by hitting the global allocator.
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//! These numbers don't represent Dioxus with the heuristic engine installed, so I assume it'll be even faster.
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use criterion::{criterion_group, criterion_main, Criterion};
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use dioxus_core as dioxus;
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use dioxus_core::prelude::*;
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use dioxus_core_macro::*;
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use dioxus_html as dioxus_elements;
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use rand::prelude::*;
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fn main() {
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static App: Component<()> = |cx, _| {
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let mut rng = SmallRng::from_entropy();
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let rows = (0..10_000_usize).map(|f| {
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let label = Label::new(&mut rng);
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rsx! {
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Row {
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row_id: f,
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label: label
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}
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}
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});
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cx.render(rsx! {
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table {
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tbody {
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{rows}
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}
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}
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})
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};
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let mut dom = VirtualDom::new(App);
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let g = dom.rebuild();
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assert!(g.edits.len() > 1);
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}
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#[derive(PartialEq, Props)]
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struct RowProps {
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row_id: usize,
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label: Label,
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}
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fn Row(cx: Context, props: &RowProps) -> Element {
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let [adj, col, noun] = props.label.0;
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cx.render(rsx! {
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tr {
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td { class:"col-md-1", "{props.row_id}" }
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td { class:"col-md-1", onclick: move |_| { /* run onselect */ }
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a { class: "lbl", "{adj}" "{col}" "{noun}" }
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}
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td { class: "col-md-1"
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a { class: "remove", onclick: move |_| {/* remove */}
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span { class: "glyphicon glyphicon-remove remove" aria_hidden: "true" }
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}
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}
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td { class: "col-md-6" }
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}
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})
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}
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#[derive(PartialEq)]
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struct Label([&'static str; 3]);
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impl Label {
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fn new(rng: &mut SmallRng) -> Self {
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Label([
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ADJECTIVES.choose(rng).unwrap(),
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COLOURS.choose(rng).unwrap(),
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NOUNS.choose(rng).unwrap(),
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])
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}
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}
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static ADJECTIVES: &[&str] = &[
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"pretty",
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"large",
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"big",
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"small",
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"tall",
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"short",
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"long",
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"handsome",
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"plain",
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"quaint",
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"clean",
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"elegant",
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"easy",
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"angry",
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"crazy",
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"helpful",
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"mushy",
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"odd",
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"unsightly",
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"adorable",
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"important",
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"inexpensive",
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"cheap",
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"expensive",
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"fancy",
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];
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static COLOURS: &[&str] = &[
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"red", "yellow", "blue", "green", "pink", "brown", "purple", "brown", "white", "black",
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"orange",
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];
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static NOUNS: &[&str] = &[
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"table", "chair", "house", "bbq", "desk", "car", "pony", "cookie", "sandwich", "burger",
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"pizza", "mouse", "keyboard",
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];
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