Description
When rendering a scatter trace, we run rgb(color_string) and parse(color_string) four times for every mark. For the vast majority of scatter charts, this is wasteful re-computation.
Surprisingly, this string parsing appears to be a serious bottleneck. Removing it seems to provide a 2x speed improvement for charts with large numbers of marks.
Screenshots/Video
Steps to reproduce
Run npm start.
In the the console run:
const gd = document.getElementById('graph');
const n = 1e5, x = new Float64Array(n), y = new Float64Array(n);
for (let i = 0; i < n; i++) { x[i] = i; y[i] = Math.sin(i / 500); }
const runs = [];
for (let k = 0; k < 5; k++) {
await Plotly.purge(gd);
const t = performance.now();
await Plotly.newPlot(gd, [{type: 'scatter', mode: 'markers', x, y}],
{width: 900, height: 600}, {displayModeBar: false});
runs.push(+(performance.now() - t).toFixed(1));
}
runs.sort((a, b) => a - b);
console.log('median', runs[2], runs);
See the numbers. Then test with the memoization PR I've put up and notice the lower numbers.
Description
When rendering a scatter trace, we run
rgb(color_string)andparse(color_string)four times for every mark. For the vast majority of scatter charts, this is wasteful re-computation.Surprisingly, this string parsing appears to be a serious bottleneck. Removing it seems to provide a 2x speed improvement for charts with large numbers of marks.
Screenshots/Video
Steps to reproduce
Run
npm start.In the the console run:
See the numbers. Then test with the memoization PR I've put up and notice the lower numbers.