dioxus/packages/core/examples/rows.rs
2021-12-09 21:19:31 -05:00

120 lines
3.2 KiB
Rust

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