{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "2e7d8525",
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"from pathlib import Path\n",
"\n",
"import pandas\n",
"import plotly.express as px"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "0f420fe2",
"metadata": {},
"outputs": [],
"source": [
"benchmark_path = Path('/path/to/sqliteviz/project/sqliteviz/lib/sql-js/benchmark')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "b7d79c6c",
"metadata": {},
"outputs": [],
"source": [
"df_dict = {}\n",
"stat_dict = {}\n",
"for p in benchmark_path.glob('build-*-result.json'):\n",
" build_name = p.stem.split('-', 2)[1]\n",
" for browser_data in json.loads(p.read_text()):\n",
" browser_name = f'{browser_data[\"browser\"][\"name\"]} {browser_data[\"browser\"][\"major\"]}'\n",
" browser_name = browser_name.lower().replace('chrome headless', 'chromium')\n",
" for result in (r for i, r in browser_data['result'].items() if i.isdigit()):\n",
" key = build_name, browser_name, result['name']\n",
" df_dict[key] = result['stats']['sample']\n",
" stat_dict[key] = result['stats']\n",
"\n",
"min_sample_size = min(len(v) for v in df_dict.values())\n",
"df_dict = {k: v[:min_sample_size] for k, v in df_dict.items()}\n",
" \n",
"wide_df = pandas.DataFrame(df_dict).reset_index()\n",
"df = pandas.melt(\n",
" wide_df, \n",
" id_vars='index', \n",
" var_name=['build', 'browser', 'test'], \n",
" value_name='duration',\n",
")\n",
"df.sort_values(['build', 'index'], inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "bc655c11",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"pr\tfirefox 88\tselect\t0.20 s ± 1.5%\n",
"master\tfirefox 88\tselect\t0.22 s ± 1.3%\n",
"pr\tchromium 90\tselect\t0.18 s ± 1.1%\n",
"master\tchromium 90\tselect\t0.18 s ± 0.5%\n",
"pr\tfirefox 88\timport\t2.37 s ± 4.3%\n",
"master\tfirefox 88\timport\t2.64 s ± 2.6%\n",
"pr\tchromium 90\timport\t1.67 s ± 0.7%\n",
"master\tchromium 90\timport\t1.74 s ± 1.8%\n"
]
}
],
"source": [
"stat_df = pandas.DataFrame(stat_dict)\n",
"stat_df = stat_df.loc[['mean', 'rme']].transpose()\n",
"stat_df.index = stat_df.index.set_names(['build', 'browser', 'test'])\n",
"stat_df = stat_df.reset_index().sort_values(['test', 'browser'], ascending=False)\n",
"for index, row in stat_df.iterrows():\n",
" print('\\t'.join([\n",
" row['build'],\n",
" row['browser'],\n",
" row['test'],\n",
" f'{row[\"mean\"]:.2f} s ± {row[\"rme\"]:.1f}%'\n",
" ]))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "5a3ab654",
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = px.box(df, x='browser', y='duration', points='all', color='build', facet_row='test')\n",
"fig.update_yaxes(matches=None)\n",
"fig.show('svg')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "1619018c",
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = px.line(\n",
" df, x='index', y='duration', color='build', facet_col='browser', facet_row='test'\n",
")\n",
"fig.update_yaxes(matches=None)\n",
"fig.show('svg')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "397b848c",
"metadata": {},
"outputs": [
{
"data": {
"image/svg+xml": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_df = df.groupby(['browser', 'build', 'test'])['duration'].mean().reset_index()\n",
"plot_df['pct'] = (\n",
" plot_df\n",
" .groupby(['browser', 'test'])['duration']\n",
" .pct_change()\n",
" .fillna('')\n",
" .map(lambda v: '{:.2%}'.format(v) if v else v)\n",
")\n",
"fig = px.bar(\n",
" plot_df, \n",
" x='browser', \n",
" y='duration', \n",
" color='build', \n",
" text='pct', \n",
" barmode='overlay', \n",
" facet_row='test',\n",
")\n",
"fig.update_yaxes(matches=None)\n",
"fig.show('svg')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Stats Python 3",
"language": "python",
"name": "stats"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.9"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {},
"version_major": 2,
"version_minor": 0
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}