mirror of
https://github.com/boostorg/mysql.git
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303b9f0b59
Added one_small_row, one_big_row, many_rows, stmt_params benchmarks against libmysqlclient and libmariadb Added a CI build to compile and run benchmarks Added a Python script to run the benchmarks Refactored the connection_pool benchmark to be use data independent from examples close #458
88 lines
2.6 KiB
Plaintext
88 lines
2.6 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Benchmark data processing\n",
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"\n",
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"Contains the code required to plot the figures in the docs.\n",
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"\n",
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"* Build the benchmarks with CMake, using the `BOOST_MYSQL_BENCH` CMake option and the `boost_mysql_bench` CMake target.\n",
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"* Run the benchmarks with `tools/ci/run_benchmarks.py`, which leaves a .txt file with the results in the current directory.\n",
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"* Run this notebook to generate the graphs.\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"from matplotlib import pyplot as plt\n",
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"\n",
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"df = pd.read_csv('../private/benchmark-results.txt', header=None) # Update file path as required\n",
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"df = df.set_index(df.index.map(lambda x: x % df.groupby(0).size()[0]))\n",
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"df = df.pivot(columns=[0], values=[1])\n",
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"df.columns = df.columns.get_level_values(0)\n",
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"df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def plot_result(df: pd.DataFrame, bench: str, title: str, ax):\n",
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" cols = [f'{bench}_boost', f'{bench}_libmysqlclient', f'{bench}_libmariadb']\n",
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" df = df[cols].rename(columns={\n",
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" f'{bench}_boost': 'Boost.MySQL',\n",
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" f'{bench}_libmysqlclient': 'libmysqlclient',\n",
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" f'{bench}_libmariadb': 'libmariadb'\n",
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" })\n",
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" mean_val = round(df.mean().mean())\n",
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" df.plot.box(ylim=(mean_val-150, mean_val+150), ax=ax, title=title, ylabel='Time (ms)')\n",
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"\n",
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"\n",
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"fig, _ = plt.subplots(2, 2, figsize=(15, 15))\n",
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"\n",
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"plot_result(df, bench='one_small_row', title='Reading one small row', ax=fig.axes[0])\n",
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"plot_result(df, bench='one_big_row', title='Reading one big row', ax=fig.axes[1])\n",
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"plot_result(df, bench='many_rows', title='Reading 5k rows', ax=fig.axes[2])\n",
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"plot_result(df, bench='stmt_params', title='Statement with params', ax=fig.axes[3])\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "base",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.3"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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