ycsb.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from common import collect_results, normalize_region_name\n",
    "from pathlib import Path\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "ROOT = Path(\"./ycsb\")\n",
    "\n",
    "def get_matcher(prefix):\n",
    "    def matcher(p: Path):   \n",
    "        import re\n",
    "        pattern = re.compile(prefix + r\"-hot(\\d+)-(\\w)-mr(\\d+)-([\\d-]+)\")\n",
    "        match = pattern.match(p.name)\n",
    "        if not match:\n",
    "            return None\n",
    "\n",
    "        print(\"Found result:\", p.name)\n",
    "        hot = int(match.group(1))\n",
    "        return {\n",
    "            \"hot\": 1000000 if hot == 0 else hot,\n",
    "            \"workload\": str(match.group(2)),\n",
    "            \"mr\": int(match.group(3)),\n",
    "            \"suffix\": str(match.group(4)),\n",
    "        }\n",
    "    \n",
    "    return matcher"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Throughput & Error\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Collect the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "SYSTEMS = [\"sunstorm\", \"aurora-global\", \"aurora-eventual\", \"yugabyte\"]\n",
    "# SYSTEMS = [\"sunstorm\", \"aurora\", \"yugabyte\"]\n",
    "HOTS = [100000, 1000]\n",
    "RENEW_CACHE = False\n",
    "\n",
    "# RMW\n",
    "\n",
    "\n",
    "aurora_matcher = get_matcher(\"ycsb-throughput-aurora\")\n",
    "aurora_throughput_df = collect_results(\n",
    "    ROOT / \"throughput-aurora\",\n",
    "    aurora_matcher,\n",
    "    RENEW_CACHE,\n",
    ")\n",
    "# aurora_throughput_df[\"system\"] = \"aurora\"\n",
    "aurora_throughput_df[\"system\"] = \"aurora-global\"\n",
    "\n",
    "aurora_matcher = get_matcher(\"ycsb-throughput-aurora-eventual\")\n",
    "aurora_eventual_throughput_df = collect_results(\n",
    "    ROOT / \"throughput-aurora-eventual\",\n",
    "    aurora_matcher,\n",
    "    RENEW_CACHE,\n",
    ")\n",
    "aurora_eventual_throughput_df[\"system\"] = \"aurora-eventual\"\n",
    "\n",
    "\n",
    "rmw_aurora_matcher = get_matcher(\"ycsb-throughput-aurora\")\n",
    "rmw_aurora_throughput_df = collect_results(\n",
    "    ROOT / \"rmw-aurora\",\n",
    "    rmw_aurora_matcher,\n",
    "    RENEW_CACHE,\n",
    ")\n",
    "\n",
    "rmw_aurora_matcher = get_matcher(\"ycsb-throughput-aurora\")\n",
    "rmw_aurora_eventual_throughput_df = collect_results(\n",
    "    ROOT / \"rmw-aurora-eventual\",\n",
    "    rmw_aurora_matcher,\n",
    "    RENEW_CACHE,\n",
    ")\n",
    "\n",
    "rmw_yugabyte_matcher = get_matcher(\"ycsb-throughput-yugabyte\")\n",
    "rmw_yugabyte_throughput_df = collect_results(\n",
    "    ROOT / \"rmw-yugabyte\",\n",
    "    rmw_yugabyte_matcher,\n",
    "    RENEW_CACHE,\n",
    ")\n",
    "\n",
    "# Read-Heavy\n",
    "\n",
    "read_heavy_sunstorm_matcher = get_matcher(\"ycsb-mixed-sunstorm\")\n",
    "read_heavy_sunstorm_throughput_df = collect_results(\n",
    "    ROOT / \"read-heavy-sunstorm\",\n",
    "    read_heavy_sunstorm_matcher,\n",
    "    RENEW_CACHE,\n",
    ")\n",
    "\n",
    "read_heavy_aurora_matcher = get_matcher(\"ycsb-mixed-aurora\")\n",
    "read_heavy_aurora_throughput_df = collect_results(\n",
    "    ROOT / \"read-heavy-aurora\",\n",
    "    read_heavy_aurora_matcher,\n",
    "    RENEW_CACHE,\n",
    ")\n",
    "\n",
    "read_heavy_aurora_matcher = get_matcher(\"ycsb-mixed-aurora\")\n",
    "read_heavy_aurora_eventual_throughput_df = collect_results(\n",
    "    ROOT / \"read-heavy-aurora-eventual\",\n",
    "    read_heavy_aurora_matcher,\n",
    "    RENEW_CACHE,\n",
    ")\n",
    "\n",
    "read_heavy_yugabyte_matcher = get_matcher(\"ycsb-mixed-yugabyte\")\n",
    "read_heavy_yugabyte_throughput_df = collect_results(\n",
    "    ROOT / \"read-heavy-yugabyte\",\n",
    "    read_heavy_yugabyte_matcher,\n",
    "    RENEW_CACHE,\n",
    ")\n",
    "\n",
    "# Read-Only\n",
    "\n",
    "read_only_sunstorm_matcher = get_matcher(\"ycsb-read-only-sunstorm\")\n",
    "read_only_sunstorm_throughput_df = collect_results(\n",
    "    ROOT / \"read-only-sunstorm\",\n",
    "    read_only_sunstorm_matcher,\n",
    "    RENEW_CACHE,\n",
    ")\n",
    "\n",
    "read_only_aurora_matcher = get_matcher(\"ycsb-read-only-aurora\")\n",
    "read_only_aurora_throughput_df = collect_results(\n",
    "    ROOT / \"read-only-aurora\",\n",
    "    read_only_aurora_matcher,\n",
    "    RENEW_CACHE,\n",
    ")\n",
    "\n",
    "read_only_aurora_matcher = get_matcher(\"ycsb-read-only-aurora\")\n",
    "read_only_aurora_eventual_throughput_df = collect_results(\n",
    "    ROOT / \"read-only-aurora-eventual\",\n",
    "    read_only_aurora_matcher,\n",
    "    RENEW_CACHE,\n",
    ")\n",
    "\n",
    "read_only_yugabyte_matcher = get_matcher(\"ycsb-read-only-yugabyte\")\n",
    "read_only_yugabyte_throughput_df = collect_results(\n",
    "    ROOT / \"read-only-yugabyte\",\n",
    "    read_only_yugabyte_matcher,\n",
    "    RENEW_CACHE,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combine the results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Throughput"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>path</th>\n",
       "      <th>region</th>\n",
       "      <th>hot</th>\n",
       "      <th>workload</th>\n",
       "      <th>mr</th>\n",
       "      <th>suffix</th>\n",
       "      <th>rejected</th>\n",
       "      <th>rejected.WorkloadF1</th>\n",
       "      <th>rejected.WorkloadF2</th>\n",
       "      <th>rejected.WorkloadF3</th>\n",
       "      <th>...</th>\n",
       "      <th>other_aborts</th>\n",
       "      <th>deadlock</th>\n",
       "      <th>throughput</th>\n",
       "      <th>throughput.WorkloadF3</th>\n",
       "      <th>throughput.WorkloadF1</th>\n",
       "      <th>unexpected.WorkloadF2</th>\n",
       "      <th>unexpected.WorkloadF3</th>\n",
       "      <th>system</th>\n",
       "      <th>unexpected.WorkloadF1</th>\n",
       "      <th>HOT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ycsb/throughput-sunstorm/ycsb-throughput-sunst...</td>\n",
       "      <td>1-us-east-1-0</td>\n",
       "      <td>100000</td>\n",
       "      <td>100% Writes</td>\n",
       "      <td>10</td>\n",
       "      <td>20240430-153607</td>\n",
       "      <td>1064</td>\n",
       "      <td>560</td>\n",
       "      <td>419.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>...</td>\n",
       "      <td>598</td>\n",
       "      <td>0</td>\n",
       "      <td>5208.286832</td>\n",
       "      <td>74.0</td>\n",
       "      <td>4698.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sunstorm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ycsb/throughput-sunstorm/ycsb-throughput-sunst...</td>\n",
       "      <td>2-eu-west-1-0</td>\n",
       "      <td>100000</td>\n",
       "      <td>100% Writes</td>\n",
       "      <td>10</td>\n",
       "      <td>20240430-153607</td>\n",
       "      <td>1064</td>\n",
       "      <td>584</td>\n",
       "      <td>388.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>...</td>\n",
       "      <td>617</td>\n",
       "      <td>0</td>\n",
       "      <td>4575.755323</td>\n",
       "      <td>63.4</td>\n",
       "      <td>4103.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sunstorm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ycsb/throughput-sunstorm/ycsb-throughput-sunst...</td>\n",
       "      <td>3-ap-northeast-1-0</td>\n",
       "      <td>100000</td>\n",
       "      <td>100% Writes</td>\n",
       "      <td>10</td>\n",
       "      <td>20240430-153607</td>\n",
       "      <td>1174</td>\n",
       "      <td>560</td>\n",
       "      <td>507.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>...</td>\n",
       "      <td>598</td>\n",
       "      <td>0</td>\n",
       "      <td>3934.176440</td>\n",
       "      <td>55.4</td>\n",
       "      <td>3535.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sunstorm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ycsb/throughput-sunstorm/ycsb-throughput-sunst...</td>\n",
       "      <td>1-us-east-1-0</td>\n",
       "      <td>10000</td>\n",
       "      <td>100% Writes</td>\n",
       "      <td>50</td>\n",
       "      <td>20240430-161215</td>\n",
       "      <td>3402</td>\n",
       "      <td>502</td>\n",
       "      <td>2246.0</td>\n",
       "      <td>654.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1032</td>\n",
       "      <td>0</td>\n",
       "      <td>1726.062443</td>\n",
       "      <td>123.4</td>\n",
       "      <td>856.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sunstorm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ycsb/throughput-sunstorm/ycsb-throughput-sunst...</td>\n",
       "      <td>2-eu-west-1-0</td>\n",
       "      <td>10000</td>\n",
       "      <td>100% Writes</td>\n",
       "      <td>50</td>\n",
       "      <td>20240430-161215</td>\n",
       "      <td>3229</td>\n",
       "      <td>477</td>\n",
       "      <td>2112.0</td>\n",
       "      <td>640.0</td>\n",
       "      <td>...</td>\n",
       "      <td>959</td>\n",
       "      <td>0</td>\n",
       "      <td>1434.814092</td>\n",
       "      <td>100.4</td>\n",
       "      <td>719.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sunstorm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>ycsb/throughput-yugabyte/ycsb-throughput-yugab...</td>\n",
       "      <td>2-eu-west-1-0</td>\n",
       "      <td>1000</td>\n",
       "      <td>100% Writes</td>\n",
       "      <td>0</td>\n",
       "      <td>20240501-110250</td>\n",
       "      <td>45280</td>\n",
       "      <td>45280</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>45280</td>\n",
       "      <td>0</td>\n",
       "      <td>1863.080996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2361.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yugabyte</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>ycsb/throughput-yugabyte/ycsb-throughput-yugab...</td>\n",
       "      <td>3-ap-northeast-1-0</td>\n",
       "      <td>1000</td>\n",
       "      <td>100% Writes</td>\n",
       "      <td>0</td>\n",
       "      <td>20240501-110250</td>\n",
       "      <td>37823</td>\n",
       "      <td>37823</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>37823</td>\n",
       "      <td>0</td>\n",
       "      <td>1907.240574</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2326.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yugabyte</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>ycsb/throughput-yugabyte/ycsb-throughput-yugab...</td>\n",
       "      <td>1-us-east-1-0</td>\n",
       "      <td>1000</td>\n",
       "      <td>100% Writes</td>\n",
       "      <td>5</td>\n",
       "      <td>20240501-110250</td>\n",
       "      <td>41530</td>\n",
       "      <td>35286</td>\n",
       "      <td>5246.0</td>\n",
       "      <td>998.0</td>\n",
       "      <td>...</td>\n",
       "      <td>41530</td>\n",
       "      <td>0</td>\n",
       "      <td>1725.680609</td>\n",
       "      <td>15.5</td>\n",
       "      <td>2077.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yugabyte</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>ycsb/throughput-yugabyte/ycsb-throughput-yugab...</td>\n",
       "      <td>2-eu-west-1-0</td>\n",
       "      <td>1000</td>\n",
       "      <td>100% Writes</td>\n",
       "      <td>5</td>\n",
       "      <td>20240501-110250</td>\n",
       "      <td>30664</td>\n",
       "      <td>24826</td>\n",
       "      <td>4950.0</td>\n",
       "      <td>888.0</td>\n",
       "      <td>...</td>\n",
       "      <td>30664</td>\n",
       "      <td>0</td>\n",
       "      <td>1642.895334</td>\n",
       "      <td>13.1</td>\n",
       "      <td>1853.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yugabyte</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>ycsb/throughput-yugabyte/ycsb-throughput-yugab...</td>\n",
       "      <td>3-ap-northeast-1-0</td>\n",
       "      <td>1000</td>\n",
       "      <td>100% Writes</td>\n",
       "      <td>5</td>\n",
       "      <td>20240501-110250</td>\n",
       "      <td>33705</td>\n",
       "      <td>27531</td>\n",
       "      <td>5221.0</td>\n",
       "      <td>953.0</td>\n",
       "      <td>...</td>\n",
       "      <td>33705</td>\n",
       "      <td>0</td>\n",
       "      <td>1687.406320</td>\n",
       "      <td>14.3</td>\n",
       "      <td>1947.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yugabyte</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>210 rows × 87 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 path              region  \\\n",
       "0   ycsb/throughput-sunstorm/ycsb-throughput-sunst...       1-us-east-1-0   \n",
       "1   ycsb/throughput-sunstorm/ycsb-throughput-sunst...       2-eu-west-1-0   \n",
       "2   ycsb/throughput-sunstorm/ycsb-throughput-sunst...  3-ap-northeast-1-0   \n",
       "3   ycsb/throughput-sunstorm/ycsb-throughput-sunst...       1-us-east-1-0   \n",
       "4   ycsb/throughput-sunstorm/ycsb-throughput-sunst...       2-eu-west-1-0   \n",
       "..                                                ...                 ...   \n",
       "55  ycsb/throughput-yugabyte/ycsb-throughput-yugab...       2-eu-west-1-0   \n",
       "56  ycsb/throughput-yugabyte/ycsb-throughput-yugab...  3-ap-northeast-1-0   \n",
       "57  ycsb/throughput-yugabyte/ycsb-throughput-yugab...       1-us-east-1-0   \n",
       "58  ycsb/throughput-yugabyte/ycsb-throughput-yugab...       2-eu-west-1-0   \n",
       "59  ycsb/throughput-yugabyte/ycsb-throughput-yugab...  3-ap-northeast-1-0   \n",
       "\n",
       "       hot     workload  mr           suffix  rejected  rejected.WorkloadF1  \\\n",
       "0   100000  100% Writes  10  20240430-153607      1064                  560   \n",
       "1   100000  100% Writes  10  20240430-153607      1064                  584   \n",
       "2   100000  100% Writes  10  20240430-153607      1174                  560   \n",
       "3    10000  100% Writes  50  20240430-161215      3402                  502   \n",
       "4    10000  100% Writes  50  20240430-161215      3229                  477   \n",
       "..     ...          ...  ..              ...       ...                  ...   \n",
       "55    1000  100% Writes   0  20240501-110250     45280                45280   \n",
       "56    1000  100% Writes   0  20240501-110250     37823                37823   \n",
       "57    1000  100% Writes   5  20240501-110250     41530                35286   \n",
       "58    1000  100% Writes   5  20240501-110250     30664                24826   \n",
       "59    1000  100% Writes   5  20240501-110250     33705                27531   \n",
       "\n",
       "    rejected.WorkloadF2  rejected.WorkloadF3  ...  other_aborts  deadlock  \\\n",
       "0                 419.0                 85.0  ...           598         0   \n",
       "1                 388.0                 92.0  ...           617         0   \n",
       "2                 507.0                107.0  ...           598         0   \n",
       "3                2246.0                654.0  ...          1032         0   \n",
       "4                2112.0                640.0  ...           959         0   \n",
       "..                  ...                  ...  ...           ...       ...   \n",
       "55                  NaN                  NaN  ...         45280         0   \n",
       "56                  NaN                  NaN  ...         37823         0   \n",
       "57               5246.0                998.0  ...         41530         0   \n",
       "58               4950.0                888.0  ...         30664         0   \n",
       "59               5221.0                953.0  ...         33705         0   \n",
       "\n",
       "     throughput  throughput.WorkloadF3  throughput.WorkloadF1  \\\n",
       "0   5208.286832                   74.0                 4698.3   \n",
       "1   4575.755323                   63.4                 4103.3   \n",
       "2   3934.176440                   55.4                 3535.2   \n",
       "3   1726.062443                  123.4                  856.2   \n",
       "4   1434.814092                  100.4                  719.0   \n",
       "..          ...                    ...                    ...   \n",
       "55  1863.080996                    NaN                 2361.2   \n",
       "56  1907.240574                    NaN                 2326.4   \n",
       "57  1725.680609                   15.5                 2077.5   \n",
       "58  1642.895334                   13.1                 1853.5   \n",
       "59  1687.406320                   14.3                 1947.2   \n",
       "\n",
       "    unexpected.WorkloadF2  unexpected.WorkloadF3    system  \\\n",
       "0                     NaN                    NaN  sunstorm   \n",
       "1                     NaN                    NaN  sunstorm   \n",
       "2                     NaN                    NaN  sunstorm   \n",
       "3                     NaN                    NaN  sunstorm   \n",
       "4                     NaN                    NaN  sunstorm   \n",
       "..                    ...                    ...       ...   \n",
       "55                    NaN                    NaN  yugabyte   \n",
       "56                    NaN                    NaN  yugabyte   \n",
       "57                    NaN                    NaN  yugabyte   \n",
       "58                    NaN                    NaN  yugabyte   \n",
       "59                    NaN                    NaN  yugabyte   \n",
       "\n",
       "    unexpected.WorkloadF1     HOT  \n",
       "0                     NaN  100000  \n",
       "1                     NaN  100000  \n",
       "2                     NaN  100000  \n",
       "3                     NaN   10000  \n",
       "4                     NaN   10000  \n",
       "..                    ...     ...  \n",
       "55                    NaN    1000  \n",
       "56                    NaN    1000  \n",
       "57                    NaN    1000  \n",
       "58                    NaN    1000  \n",
       "59                    NaN    1000  \n",
       "\n",
       "[210 rows x 87 columns]"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "throughput_df = pd.concat([\n",
    "    sunstorm_throughput_df,\n",
    "    aurora_throughput_df,\n",
    "    aurora_eventual_throughput_df,\n",
    "    yugabyte_throughput_df,\n",
    "])\n",
    "throughput_df[\"HOT\"] = throughput_df[\"hot\"]\n",
    "throughput_df[\"workload\"] = \"100% Writes\"\n",
    "normalize_region_name(throughput_df)\n",
    "throughput_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the error rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "                            ood_index_page  ood_tuple  other_aborts  \\\n",
       "hot     system          mr                                            \n",
       "1000    aurora-eventual 0                0          0         26212   \n",
       "                        5                0          0         26315   \n",
       "                        10               0          0         26231   \n",
       "                        15               0          0         25482   \n",
       "                        50               0          0         31336   \n",
       "        aurora-global   0                0          0         28098   \n",
       "                        5                0          0         20204   \n",
       "                        10               0          0         19779   \n",
       "                        15               0          0         16248   \n",
       "                        50               0          0         13589   \n",
       "        sunstorm        0                0          0         44314   \n",
       "                        5             9254      31234        102399   \n",
       "                        10            1035      30880         81177   \n",
       "                        15            1301      26738         66856   \n",
       "                        50               3      30241         69584   \n",
       "        yugabyte        0                0          0        126698   \n",
       "                        5                0          0        105899   \n",
       "                        10               0          0        100615   \n",
       "                        15               0          0         94674   \n",
       "                        50               0          0         86896   \n",
       "10000   aurora-eventual 0                0          0          1956   \n",
       "                        5                0          0          1951   \n",
       "                        10               0          0          2167   \n",
       "                        15               0          0          2278   \n",
       "                        50               0          0          3146   \n",
       "        yugabyte        0                0          0         20688   \n",
       "                        5                0          0         20828   \n",
       "                        10               0          0         22108   \n",
       "                        15               0          0         23140   \n",
       "                        50               0          0         19993   \n",
       "100000  aurora-eventual 0                0          0           698   \n",
       "                        5                0          0           760   \n",
       "                        10               0          0           718   \n",
       "                        15               0          0           708   \n",
       "                        50               0          0           778   \n",
       "        aurora-global   0                0          0          2471   \n",
       "                        5                0          0          1496   \n",
       "                        10               0          0          1416   \n",
       "                        15               0          0          1378   \n",
       "                        50               0          0          1115   \n",
       "        sunstorm        0                0          0          2731   \n",
       "                        5              253       1848          4642   \n",
       "                        10              30       1455          3340   \n",
       "                        15               8       1296          2612   \n",
       "                        50               7        943          1878   \n",
       "        yugabyte        0                0          0          7900   \n",
       "                        5                0          0          7572   \n",
       "                        10               0          0          7380   \n",
       "                        15               0          0          6967   \n",
       "                        50               0          0          4499   \n",
       "1000000 aurora-eventual 0                0          0           830   \n",
       "                        5                0          0           713   \n",
       "                        10               0          0           750   \n",
       "                        15               0          0           726   \n",
       "                        50               0          0           690   \n",
       "        yugabyte        0                0          0          8002   \n",
       "                        5                0          0          7898   \n",
       "                        10               0          0          7502   \n",
       "                        15               0          0          7287   \n",
       "                        50               0          0          4088   \n",
       "\n",
       "                            ood_table  deadlock  completed  \\\n",
       "hot     system          mr                                   \n",
       "1000    aurora-eventual 0           0  0.000000     646983   \n",
       "                        5           0  0.000000     630521   \n",
       "                        10          0  0.000000     614136   \n",
       "                        15          0  0.000000     607221   \n",
       "                        50          0  0.000000     564379   \n",
       "        aurora-global   0           0  0.000000     500212   \n",
       "                        5           0  0.000000     396004   \n",
       "                        10          0  0.000000     386791   \n",
       "                        15          0  0.000000     382549   \n",
       "                        50          0  0.000000     264103   \n",
       "        sunstorm        0           0  0.000000    2931750   \n",
       "                        5           0  0.010116    1245535   \n",
       "                        10          0  0.010557     824069   \n",
       "                        15          0  0.037153     584069   \n",
       "                        50          0  0.000000     309921   \n",
       "        yugabyte        0           0  0.000000     512773   \n",
       "                        5           0  0.000000     455041   \n",
       "                        10          0  0.000000     408703   \n",
       "                        15          0  0.000000     363685   \n",
       "                        50          0  0.000000     167699   \n",
       "10000   aurora-eventual 0           0  0.000000     738907   \n",
       "                        5           0  0.000000     739819   \n",
       "                        10          0  0.000000     736848   \n",
       "                        15          0  0.000000     737991   \n",
       "                        50          0  0.000000     724610   \n",
       "        yugabyte        0           0  0.000000     631357   \n",
       "                        5           0  0.000000     584158   \n",
       "                        10          0  0.000000     537718   \n",
       "                        15          0  0.000000     495650   \n",
       "                        50          0  0.000000     259231   \n",
       "100000  aurora-eventual 0           0  0.000000     750620   \n",
       "                        5           0  0.000000     743572   \n",
       "                        10          0  0.000000     735768   \n",
       "                        15          0  0.000000     744286   \n",
       "                        50          0  0.000000     745021   \n",
       "        aurora-global   0           0  0.000000     732279   \n",
       "                        5           0  0.000000     748509   \n",
       "                        10          0  0.000000     747139   \n",
       "                        15          0  0.000000     743925   \n",
       "                        50          0  0.000000     755534   \n",
       "        sunstorm        0           0  0.000000    2961639   \n",
       "                        5           0  0.000000    1934309   \n",
       "                        10          0  0.000000    1252923   \n",
       "                        15          0  0.000000     928348   \n",
       "                        50          0  0.000000     418939   \n",
       "        yugabyte        0           0  0.000000     644558   \n",
       "                        5           0  0.000000     595379   \n",
       "                        10          0  0.000000     557525   \n",
       "                        15          0  0.000000     519938   \n",
       "                        50          0  0.000000     275025   \n",
       "1000000 aurora-eventual 0           0  0.000000     745659   \n",
       "                        5           0  0.000000     742062   \n",
       "                        10          0  0.000000     740907   \n",
       "                        15          0  0.000000     743219   \n",
       "                        50          0  0.000000     737904   \n",
       "        yugabyte        0           0  0.000000     654582   \n",
       "                        5           0  0.000000     607262   \n",
       "                        10          0  0.000000     564253   \n",
       "                        15          0  0.000000     527167   \n",
       "                        50          0  0.000000     276436   \n",
       "\n",
       "                            outdated index page  outdated table  \\\n",
       "hot     system          mr                                        \n",
       "1000    aurora-eventual 0              0.000000             0.0   \n",
       "                        5              0.000000             0.0   \n",
       "                        10             0.000000             0.0   \n",
       "                        15             0.000000             0.0   \n",
       "                        50             0.000000             0.0   \n",
       "        aurora-global   0              0.000000             0.0   \n",
       "                        5              0.000000             0.0   \n",
       "                        10             0.000000             0.0   \n",
       "                        15             0.000000             0.0   \n",
       "                        50             0.000000             0.0   \n",
       "        sunstorm        0              0.000000             0.0   \n",
       "                        5              0.742974             0.0   \n",
       "                        10             0.125596             0.0   \n",
       "                        15             0.222748             0.0   \n",
       "                        50             0.000968             0.0   \n",
       "        yugabyte        0              0.000000             0.0   \n",
       "                        5              0.000000             0.0   \n",
       "                        10             0.000000             0.0   \n",
       "                        15             0.000000             0.0   \n",
       "                        50             0.000000             0.0   \n",
       "10000   aurora-eventual 0              0.000000             0.0   \n",
       "                        5              0.000000             0.0   \n",
       "                        10             0.000000             0.0   \n",
       "                        15             0.000000             0.0   \n",
       "                        50             0.000000             0.0   \n",
       "        yugabyte        0              0.000000             0.0   \n",
       "                        5              0.000000             0.0   \n",
       "                        10             0.000000             0.0   \n",
       "                        15             0.000000             0.0   \n",
       "                        50             0.000000             0.0   \n",
       "100000  aurora-eventual 0              0.000000             0.0   \n",
       "                        5              0.000000             0.0   \n",
       "                        10             0.000000             0.0   \n",
       "                        15             0.000000             0.0   \n",
       "                        50             0.000000             0.0   \n",
       "        aurora-global   0              0.000000             0.0   \n",
       "                        5              0.000000             0.0   \n",
       "                        10             0.000000             0.0   \n",
       "                        15             0.000000             0.0   \n",
       "                        50             0.000000             0.0   \n",
       "        sunstorm        0              0.000000             0.0   \n",
       "                        5              0.013080             0.0   \n",
       "                        10             0.002394             0.0   \n",
       "                        15             0.000862             0.0   \n",
       "                        50             0.001671             0.0   \n",
       "        yugabyte        0              0.000000             0.0   \n",
       "                        5              0.000000             0.0   \n",
       "                        10             0.000000             0.0   \n",
       "                        15             0.000000             0.0   \n",
       "                        50             0.000000             0.0   \n",
       "1000000 aurora-eventual 0              0.000000             0.0   \n",
       "                        5              0.000000             0.0   \n",
       "                        10             0.000000             0.0   \n",
       "                        15             0.000000             0.0   \n",
       "                        50             0.000000             0.0   \n",
       "        yugabyte        0              0.000000             0.0   \n",
       "                        5              0.000000             0.0   \n",
       "                        10             0.000000             0.0   \n",
       "                        15             0.000000             0.0   \n",
       "                        50             0.000000             0.0   \n",
       "\n",
       "                            outdated tuple  other aborts        pct  \n",
       "hot     system          mr                                           \n",
       "1000    aurora-eventual 0         0.000000      4.051420   4.051420  \n",
       "                        5         0.000000      4.173533   4.173533  \n",
       "                        10        0.000000      4.271204   4.271204  \n",
       "                        15        0.000000      4.196495   4.196495  \n",
       "                        50        0.000000      5.552297   5.552297  \n",
       "        aurora-global   0         0.000000      5.617218   5.617218  \n",
       "                        5         0.000000      5.101969   5.101969  \n",
       "                        10        0.000000      5.113614   5.113614  \n",
       "                        15        0.000000      4.247299   4.247299  \n",
       "                        50        0.000000      5.145341   5.145341  \n",
       "        sunstorm        0         0.000000      1.511520   1.511520  \n",
       "                        5         2.507677      8.221286  11.482054  \n",
       "                        10        3.747259      9.850753  13.734165  \n",
       "                        15        4.577884     11.446593  16.284377  \n",
       "                        50        9.757648     22.452173  32.210789  \n",
       "        yugabyte        0         0.000000     24.708399  24.708399  \n",
       "                        5         0.000000     23.272408  23.272408  \n",
       "                        10        0.000000     24.618121  24.618121  \n",
       "                        15        0.000000     26.031868  26.031868  \n",
       "                        50        0.000000     51.816648  51.816648  \n",
       "10000   aurora-eventual 0         0.000000      0.264715   0.264715  \n",
       "                        5         0.000000      0.263713   0.263713  \n",
       "                        10        0.000000      0.294091   0.294091  \n",
       "                        15        0.000000      0.308676   0.308676  \n",
       "                        50        0.000000      0.434165   0.434165  \n",
       "        yugabyte        0         0.000000      3.276752   3.276752  \n",
       "                        5         0.000000      3.565474   3.565474  \n",
       "                        10        0.000000      4.111449   4.111449  \n",
       "                        15        0.000000      4.668617   4.668617  \n",
       "                        50        0.000000      7.712426   7.712426  \n",
       "100000  aurora-eventual 0         0.000000      0.092990   0.092990  \n",
       "                        5         0.000000      0.102209   0.102209  \n",
       "                        10        0.000000      0.097585   0.097585  \n",
       "                        15        0.000000      0.095125   0.095125  \n",
       "                        50        0.000000      0.104427   0.104427  \n",
       "        aurora-global   0         0.000000      0.337440   0.337440  \n",
       "                        5         0.000000      0.199864   0.199864  \n",
       "                        10        0.000000      0.189523   0.189523  \n",
       "                        15        0.000000      0.185234   0.185234  \n",
       "                        50        0.000000      0.147578   0.147578  \n",
       "        sunstorm        0         0.000000      0.092212   0.092212  \n",
       "                        5         0.095538      0.239982   0.348600  \n",
       "                        10        0.116128      0.266577   0.385099  \n",
       "                        15        0.139603      0.281360   0.421825  \n",
       "                        50        0.225092      0.448275   0.675039  \n",
       "        yugabyte        0         0.000000      1.225646   1.225646  \n",
       "                        5         0.000000      1.271795   1.271795  \n",
       "                        10        0.000000      1.323707   1.323707  \n",
       "                        15        0.000000      1.339967   1.339967  \n",
       "                        50        0.000000      1.635851   1.635851  \n",
       "1000000 aurora-eventual 0         0.000000      0.111311   0.111311  \n",
       "                        5         0.000000      0.096084   0.096084  \n",
       "                        10        0.000000      0.101227   0.101227  \n",
       "                        15        0.000000      0.097683   0.097683  \n",
       "                        50        0.000000      0.093508   0.093508  \n",
       "        yugabyte        0         0.000000      1.222460   1.222460  \n",
       "                        5         0.000000      1.300592   1.300592  \n",
       "                        10        0.000000      1.329545   1.329545  \n",
       "                        15        0.000000      1.382294   1.382294  \n",
       "                        50        0.000000      1.478823   1.478823  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = ['ood_index_page', 'ood_tuple', 'other_aborts', 'ood_table', 'deadlock', 'completed'] \n",
    "error_df = throughput_df.groupby([\"workload\",\"hot\", \"system\", \"mr\"]).sum()[columns]\n",
    "error_df[\"outdated index page\"] = error_df[\"ood_index_page\"] / error_df[\"completed\"] * 100\n",
    "error_df[\"outdated table\"] = error_df[\"ood_table\"] / error_df[\"completed\"] * 100\n",
    "error_df[\"outdated tuple\"] = error_df[\"ood_tuple\"] / error_df[\"completed\"] * 100\n",
    "error_df[\"other aborts\"] = error_df[\"other_aborts\"] / error_df[\"completed\"] * 100\n",
    "error_df[\"deadlock\"] = error_df[\"deadlock\"] / error_df[\"completed\"] * 100\n",
    "error_df[\"pct\"] = error_df[\"deadlock\"] + error_df[\"outdated index page\"] + error_df[\"outdated table\"] + error_df[\"outdated tuple\"] + error_df[\"other aborts\"]\n",
    "error_df.loc[(\"RMW\")] # type: ignore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create plots for RMW and Read-Heavy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x250 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import ScalarFormatter\n",
    "from matplotlib.gridspec import GridSpec\n",
    "\n",
    "for hot in HOTS:\n",
    "    hot_df = throughput_df[throughput_df.hot == hot]\n",
    "    hot_label = hot\n",
    "    # Print hot_df when \"mr\" is 0\n",
    "    ax = hot_df.pivot_table(\n",
    "        index=[\"mr\"], columns=[\"system\"], values=\"throughput\", aggfunc=\"sum\"\n",
    "    )[SYSTEMS].plot(\n",
    "        title=f\"HOT = {hot_label}\",\n",
    "        xlabel=\"% multi-region transactions\",\n",
    "        ylabel=\"txn/sec\",\n",
    "        figsize=(5, 2.5),\n",
    "        style=[\"o-\", \"^-\", \"^--\", \"x-\"],\n",
    "        color=[\"C0\", \"C1\", \"C1\", \"C2\"],\n",
    "        ylim=(0, 40000),\n",
    "        rot=0,\n",
    "    )\n",
    "    ax.set_xscale(\"symlog\", linthresh=20)\n",
    "    ax.set_xticks([0, 5, 10, 15, 50])\n",
    "    ax.set_xticklabels([0, 5, 10, 15, 50])\n",
    "    ax.margins(x=0.05)\n",
    "    ax.grid(axis=\"y\")\n",
    "    ax.legend(title=\"\", ncol=4, handletextpad=0.3)\n",
    "    ax.invert_xaxis()\n",
    "    #plt.savefig(f\"output/micro-throughput-hot{hot}.pdf\", bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot for Read-only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x220 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x220 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.lines import Line2D\n",
    "\n",
    "FONT_SIZE_AXES = 13\n",
    "FONT_SIZE_LEGEND = 12\n",
    "\n",
    "    hot_label = hot\n",
    "    ax = ratio_df[reversed(SYSTEMS)].plot.bar(\n",
    "        title=f\"HOT = {hot_label} (SR only)\",\n",
    "        xlabel=\"% multi-region\",\n",
    "        ylabel=\"% aborted\",\n",
    "        figsize=(5, 2.2),\n",
    "        rot=0,\n",
    "        width=0.7,\n",
    "        color=[\"C2\", \"C1\", \"C0\"],\n",
    "    )\n",
    "    bars = ax.patches\n",
    "    patterns = [\"oo\", \"\", \"xx\"]\n",
    "    hatches = [p for p in patterns for _ in range(len(bars) // len(patterns))]\n",
    "    for bar, hatch in zip(bars, hatches):\n",
    "        bar.set_hatch(hatch)\n",
    "\n",
    "    legend_handles, legend_labels = ax.get_legend_handles_labels()\n",
    "    ax.legend(reversed(legend_handles), reversed(legend_labels), title=\"\", ncol=3, handletextpad=0.3)\n",
    "    ax.invert_xaxis()\n",
    "    \n",
    "    ax.margins(y=0.4)\n",
    "    # plt.savefig(f\"output/micro-abort-sr-hot{hot}.pdf\", bbox_inches=\"tight\")\n",
    "\n",
    "    ax.set_yscale(\"log\")\n",
    "    ax.set_ylim(0.01, 150)\n",
    "    # plt.savefig(f\"output/micro-abort-sr-hot{hot}-logy.pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Latency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x220 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x220 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(4, 3, figsize=(10, 8), sharey=True)\n",
    "\n",
    "for hot in HOTS:\n",
    "    hot_df = throughput_df[throughput_df.hot == hot]\n",
    "    rejected_2_df = hot_df.pivot_table(\n",
    "        index=[\"mr\"], columns=[\"system\"], values=\"rejected.WorkloadF2\", aggfunc=\"sum\"\n",
    "    )\n",
    "    rejected_3_df = hot_df.pivot_table(\n",
    "        index=[\"mr\"], columns=[\"system\"], values=\"rejected.WorkloadF3\", aggfunc=\"sum\"\n",
    "    )\n",
    "    completed_df = hot_df.pivot_table(\n",
    "        index=[\"mr\"], columns=[\"system\"], values=\"completed\", aggfunc=\"sum\"\n",
    "    )\n",
    "    aborted_df = rejected_2_df + rejected_3_df\n",
    "    ratio_df = aborted_df / (aborted_df + completed_df) * 100\n",
    "    hot_label = hot\n",
    "    ax = ratio_df[reversed(SYSTEMS)].plot.bar(\n",
    "        title=f\"HOT = {hot_label} (MR only)\",\n",
    "        xlabel=\"% multi-region\",\n",
    "        ylabel=\"% aborted\",\n",
    "        figsize=(5, 2.2),\n",
    "        rot=0,\n",
    "        width=0.7,\n",
    "        color=[\"C2\", \"C1\", \"C1\", \"C0\"],\n",
    "    )\n",
    "    bars = ax.patches\n",
    "    patterns = [\"oo\", \"\", \"++\", \"xx\"]\n",
    "    hatches = [p for p in patterns for _ in range(len(bars) // len(patterns))]\n",
    "    for bar, hatch in zip(bars, hatches):\n",
    "        bar.set_hatch(hatch, )\n",
    "\n",
    "    legend_handles, legend_labels = ax.get_legend_handles_labels()\n",
    "    ax.legend(reversed(legend_handles), reversed(legend_labels), title=\"\", ncol=3, handletextpad=0.3)\n",
    "    ax.invert_xaxis()\n",
    "\n",
    "    ax.margins(y=0.3)\n",
    "    # plt.savefig(f\"output/micro-abort-mr-hot{hot}.pdf\", bbox_inches=\"tight\")\n",
    "\n",
    "    ax.set_yscale(\"log\")\n",
    "    ax.set_ylim(0.005, 300)\n",
    "    # plt.savefig(f\"output/micro-abort-mr-hot{hot}-logy.pdf\", bbox_inches=\"tight\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SunStorm error types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rc('axes', labelsize=12) \n",
    "plt.rc('legend', fontsize=11)\n",
    "\n",
    "sunstorm_df = throughput_df[throughput_df.system == \"sunstorm\"]\n",
    "for hot in HOTS:\n",
    "    hot_df = sunstorm_df[sunstorm_df.hot == hot]\\\n",
    "        .groupby([\"mr\"])\\\n",
    "        .sum()\n",
    "    mh_aborts = hot_df[\"ood_tuple\"] + hot_df[\"ood_index_page\"]  + hot_df[\"ood_table\"]\n",
    "    hot_df[\"ood_index_page_pct\"] = hot_df[\"ood_index_page\"] / mh_aborts * 100\n",
    "    hot_df[\"ood_tuple_pct\"] = hot_df[\"ood_tuple\"] / mh_aborts * 100\n",
    "    hot_df[\"ood_table_pct\"] = hot_df[\"ood_table\"] / mh_aborts * 100\n",
    "    ax = hot_df[[\"ood_index_page_pct\", \"ood_tuple_pct\", \"ood_table_pct\"]].plot.bar( \n",
    "        title=\"Validation errors HOT = \" + str(hot),\n",
    "        ylabel=\"validation errors\",\n",
    "        figsize=(10, 3),\n",
    "        stacked=True,\n",
    "    )\n",
    "    ax.legend([\"Index page\", \"Tuple\", \"Table\"], title=\"\", ncol=1, handletextpad=0.3)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>system</th>\n",
       "      <th>sunstorm</th>\n",
       "      <th>aurora-global</th>\n",
       "      <th>aurora-eventual</th>\n",
       "      <th>yugabyte</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mr</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.493502</td>\n",
       "      <td>5.617218</td>\n",
       "      <td>4.051420</td>\n",
       "      <td>24.708399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.005957</td>\n",
       "      <td>5.101969</td>\n",
       "      <td>4.173533</td>\n",
       "      <td>23.272408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8.957032</td>\n",
       "      <td>5.113614</td>\n",
       "      <td>4.271204</td>\n",
       "      <td>24.618121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10.627359</td>\n",
       "      <td>4.247299</td>\n",
       "      <td>4.196495</td>\n",
       "      <td>26.031868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>51.217132</td>\n",
       "      <td>5.145341</td>\n",
       "      <td>5.552297</td>\n",
       "      <td>51.816648</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "system   sunstorm  aurora-global  aurora-eventual   yugabyte\n",
       "mr                                                          \n",
       "0        1.493502       5.617218         4.051420  24.708399\n",
       "5        6.005957       5.101969         4.173533  23.272408\n",
       "10       8.957032       5.113614         4.271204  24.618121\n",
       "15      10.627359       4.247299         4.196495  26.031868\n",
       "50      51.217132       5.145341         5.552297  51.816648"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_columns = ['ood_index_page', 'ood_tuple', 'other_aborts'] \n",
    "error_df = throughput_df.groupby([\"hot\", \"system\", \"mr\"]).sum()[error_columns + ['ood_table', 'deadlock', 'completed']]\n",
    "error_df[\"ood_index_page\"] = error_df[\"ood_index_page\"] / error_df[\"completed\"] * 100\n",
    "error_df[\"ood_table\"] = error_df[\"ood_table\"] / error_df[\"completed\"] * 100\n",
    "error_df[\"ood_tuple\"] = error_df[\"ood_tuple\"] / error_df[\"completed\"] * 100\n",
    "error_df[\"other_aborts\"] = error_df[\"other_aborts\"] / error_df[\"completed\"] * 100\n",
    "error_df[\"deadlock\"] = error_df[\"deadlock\"] / error_df[\"completed\"] * 100\n",
    "error_df[\"pct\"] = error_df[\"deadlock\"] + error_df[\"ood_index_page\"] + error_df[\"ood_table\"] + error_df[\"ood_tuple\"] + error_df[\"other_aborts\"]\n",
    "error_df.loc[(1000, \"sunstorm\",)]\n",
    "\n",
    "error_df.unstack(level=1)[\"pct\"].loc[1000][SYSTEMS]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x17dd204d0>"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import ScalarFormatter\n",
    "from matplotlib.gridspec import GridSpec\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "plt.rc('axes', labelsize=11) \n",
    "plt.rc('legend', fontsize=11)\n",
    "\n",
    "xticks = [0, 5, 10, 15, 50]\n",
    "xticklabels = [0, 5, 10, 15, 50]\n",
    "legend_handles = []\n",
    "legend_labels = []\n",
    "abort_colors = [\"C0\", \"C1\", \"C2\"]\n",
    "abort_patterns = [\"oo\", \"++\", \"xx\"]\n",
    "\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(10, 7))\n",
    "gs = GridSpec(3, 2, figure=fig, height_ratios=[4, 2, 2], wspace= 0.1, hspace=0.1)\n",
    "\n",
    "for h,hot in enumerate([100000, 1000]):\n",
    "    ax = plt.subplot(gs[0, h])\n",
    "    hot_throughput_df = throughput_df[throughput_df.hot == hot]\n",
    "    hot_throughput_df.pivot_table(\n",
    "        index=[\"mr\"], columns=[\"system\"], values=\"throughput\", aggfunc=\"sum\"\n",
    "    ).plot(\n",
    "        ax=ax,\n",
    "        title=f\"HOT = {hot}\",\n",
    "        ylabel=\"txn/sec\",\n",
    "        style=[\"^--\", \"^-\", \"o-\", \"x-\"],\n",
    "        color=[\"C1\", \"C1\", \"C0\", \"C2\"],\n",
    "        ylim=(0, 40000),\n",
    "        # logy = True,\n",
    "        rot=0,\n",
    "        legend=False,\n",
    "    )\n",
    "    ax.set_xscale(\"symlog\", linthresh=20)\n",
    "    ax.set_xticks(xticks)\n",
    "    ax.set_xticklabels([])\n",
    "    ax.margins(x=0.05)\n",
    "    ax.grid(axis=\"y\")\n",
    "    ax.invert_xaxis()\n",
    "    ax.set_xlabel(\"\")\n",
    "    \n",
    "    if h == 1:\n",
    "        handles, labels = ax.get_legend_handles_labels()\n",
    "        legend_handles += handles\n",
    "        legend_labels += labels\n",
    "        ax.set_yticklabels([])\n",
    "        ax.set_ylabel(\"\")\n",
    "    \n",
    "\n",
    "    ax = plt.subplot(gs[1, h]) \n",
    "\n",
    "    error_df.unstack(level=1)[\"pct\"].loc[hot][SYSTEMS]\\\n",
    "        .set_index(np.array(xticks))\\\n",
    "        .plot(\n",
    "            ax=ax,\n",
    "            ylabel=\"% aborted txns\",\n",
    "            style=[\"o-\", \"^-\", \"^--\", \"x-\"],\n",
    "            color=[\"C0\", \"C1\", \"C1\", \"C2\"],\n",
    "            rot=0,\n",
    "            legend=False,\n",
    "        )\n",
    "\n",
    "    ax.set_xscale(\"symlog\", linthresh=20)\n",
    "    ax.set_xticks(xticks)\n",
    "    ax.set_xticklabels([])\n",
    "    ax.margins(x=0.05)\n",
    "    ax.grid(axis=\"y\")\n",
    "    ax.invert_xaxis()\n",
    "    ax.get_yaxis().set_major_formatter(ScalarFormatter())\n",
    "    if h == 1:\n",
    "        ax.set_ylabel(\"\")   \n",
    "\n",
    "    xticks_hack = [0, 5, 10, 15, 28]\n",
    "    ax = plt.subplot(gs[2, h])\n",
    "    sunstorm_error_df = error_df.loc[(hot, \"sunstorm\",)].copy()\n",
    "    error_columns_pct = [f\"{col}\" for col in error_columns]\n",
    "    bottom = None\n",
    "    for col, color, pattern in zip(error_columns_pct, abort_colors, abort_patterns):\n",
    "        error = sunstorm_error_df[col] / sunstorm_error_df[\"pct\"] * 100\n",
    "        ax.bar(\n",
    "            xticks_hack,\n",
    "            error,\n",
    "            bottom=bottom,\n",
    "            label=col,\n",
    "            width=1.5,\n",
    "            facecolor=color,\n",
    "            hatch=pattern,\n",
    "        )\n",
    "        if bottom is None:\n",
    "            bottom = error\n",
    "        else:\n",
    "            bottom += error\n",
    "    ax.set_ylim(0, 100)\n",
    "    ax.margins(x=0.05)\n",
    "    ax.set_xticks(xticks_hack)\n",
    "    ax.set_xticklabels(xticklabels)\n",
    "    ax.set_ylabel(\"% aborts\")\n",
    "    ax.invert_xaxis()\n",
    "    if h == 1:\n",
    "        handles, labels = ax.get_legend_handles_labels()\n",
    "        legend_handles += handles\n",
    "        legend_labels += labels\n",
    "        ax.set_ylabel(\"\")\n",
    "        ax.set_yticklabels([])\n",
    "\n",
    "fig.supxlabel(\"% multi-region transactions\")\n",
    "plt.legend(legend_handles, legend_labels, ncol=7, loc='lower center', handletextpad=0.3,\n",
    "           bbox_to_anchor=(0,-1)) \n",
    "\n",
    "\n",
    "    #plt.savefig(f\"output/micro-throughput-hot{hot}.pdf\", bbox_inches=\"tight\")\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Latency\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.lines import Line2D\n",
    "from matplotlib.gridspec import GridSpec\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(10, 5))\n",
    "gs = GridSpec(2, 3, figure=fig)\n",
    "\n",
    "plt.rc('axes', labelsize=12) \n",
    "plt.rc('legend', fontsize=11)\n",
    "\n",
    "labels = [\n",
    "    \"1 region\\n\",\n",
    "    \"2 regions\\n\",\n",
    "    \"3 regions\\n\",\n",
    "]\n",
    "ticks = np.arange(len(labels))\n",
    "\n",
    "width = 0.5\n",
    "\n",
    "def to_box(df, n):\n",
    "    def extract(col_name):\n",
    "        col = df[col_name]\n",
    "        col = col[col.notna()]\n",
    "        assert len(col) == 1, f\"Exactly one value expected, but {col_name} =\\n{col}\"\n",
    "        return col.iloc[0]\n",
    "\n",
    "    return {\n",
    "        'whislo': extract(f\"p0.WorkloadF{n}\"),\n",
    "        'q1': extract(f\"p25.WorkloadF{n}\"),\n",
    "        'med': extract(f\"p50.WorkloadF{n}\"),\n",
    "        'q3': extract(f\"p75.WorkloadF{n}\"),\n",
    "        'whishi': extract(f\"p99.WorkloadF{n}\"),\n",
    "        'fliers': []\n",
    "    }\n",
    "\n",
    "legends = []\n",
    "patterns = [\"xx\", \"||\", \"++\", \"oo\"]\n",
    "colors = [\"C0\", \"C1\", \"C3\", \"C2\"]\n",
    "\n",
    "for h, hot in enumerate([100000, 1000]):\n",
    "    hot_df = throughput_df[throughput_df.hot == hot]\n",
    "    hot_label = hot\n",
    "    for r, region in enumerate([\"1-us-east-1\", \"2-eu-west-1\", \"3-ap-northeast-1\"]):\n",
    "        region_df = hot_df[hot_df.region.str.startswith(region)]\n",
    "\n",
    "        ax = plt.subplot(gs[h, r])\n",
    "        for i, system in enumerate(SYSTEMS):\n",
    "            system_df = region_df[region_df.system == system]\n",
    "            boxes = [\n",
    "                to_box(system_df[system_df.mr == 5], 1),\n",
    "                to_box(system_df[system_df.mr == 5], 2),\n",
    "                to_box(system_df[system_df.mr == 5], 3),\n",
    "            ]\n",
    "            comps = ax.bxp(\n",
    "                boxes,\n",
    "                positions=ticks - width / 2 + i * width / (len(SYSTEMS) - 1),\n",
    "                showfliers=False,\n",
    "                manage_ticks=False,\n",
    "                patch_artist=True,\n",
    "                widths=0.15,\n",
    "                boxprops={\n",
    "                    \"facecolor\": colors[i],\n",
    "                    \"linewidth\": 0,\n",
    "                    \"hatch\": patterns[i],\n",
    "                },\n",
    "                medianprops={\n",
    "                    \"color\": \"yellow\",\n",
    "                    \"linewidth\": 1,\n",
    "                },\n",
    "            )\n",
    "            if (h==0 and r==0):\n",
    "                legends.append(comps[\"boxes\"][0])\n",
    "            # legends.append(comps[\"boxes\"][0])\n",
    "\n",
    "        # ax.legend(legends, SYSTEMS, ncol=3)\n",
    "\n",
    "\n",
    "        ax.set_xticks(labels=labels, ticks=ticks)\n",
    "        ax.set_yscale(\"log\")\n",
    "        ax.set_ylim(0.99, 15000)\n",
    "        # ax.set_ylabel(\"latency (ms)\")\n",
    "        # ax.grid()\n",
    "        if h == 0:\n",
    "            ax.set_title(region[2:])\n",
    "\n",
    "        if r == 0:\n",
    "            ax.set_ylabel(f\"HOT = {hot_label}\")\n",
    "        # ax.set_title(f\"HOT = {hot_label} ({region[2:]})\")\n",
    "\n",
    "fig.legend(legends, SYSTEMS, ncol=4, bbox_to_anchor=(0.85, 0.01), handletextpad=0.3)\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"./ycsb-latency.pdf\", bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_latency(mr, hot):\n",
    "    # Remove the \"-0\" suffix in region name\n",
    "    throughput_df['region'] = throughput_df['region'].str.replace(r'-0', '', regex=True)\n",
    "    regions = sorted([r for r in set(throughput_df.region.unique()) if not r.endswith(\"-0\")])\n",
    "    for system in throughput_df.system.unique():\n",
    "        fig, axes = plt.subplots(1, len(regions), figsize=(20, 4), sharey=True)\n",
    "        fig.suptitle(f\"{system}, hot = {hot}, mr = {mr}%\")\n",
    "        workload_df = throughput_df[\n",
    "            (throughput_df.system == system)\n",
    "            & (throughput_df.mr == mr)\n",
    "            & (throughput_df.hot == hot)\n",
    "        ]\n",
    "        # Plot for each region\n",
    "        for i, region in enumerate(regions):\n",
    "            region_df = workload_df[workload_df.region.str.startswith(region)]\n",
    "            if region_df.empty:\n",
    "                continue\n",
    "            # Seperate the tran sactions by the number of regions they access\n",
    "            max_nregion = 3\n",
    "            index = range(1, max_nregion + 1)\n",
    "            nregion_latency_df = {\n",
    "                \"p0\": [region_df[f\"p0.WorkloadF{n}\"].iloc[0] for n in index],\n",
    "                \"p25\": [region_df[f\"p25.WorkloadF{n}\"].iloc[0] for n in index],\n",
    "                \"p50\": [region_df[f\"p50.WorkloadF{n}\"].iloc[0] for n in index],\n",
    "                \"p75\": [region_df[f\"p75.WorkloadF{n}\"].iloc[0] for n in index],\n",
    "                \"p95\": [region_df[f\"p95.WorkloadF{n}\"].iloc[0] for n in index],\n",
    "                \"p99\": [region_df[f\"p99.WorkloadF{n}\"].iloc[0] for n in index],\n",
    "            }\n",
    "            pd.DataFrame(nregion_latency_df, index=index).plot.bar(\n",
    "                title=region,\n",
    "                ylabel=\"latency (ms)\",\n",
    "                ax=axes[i],\n",
    "                rot=0,\n",
    "                ylim=(1, 1200),\n",
    "                logy=True,\n",
    "            )\n",
    "            axes[i].set_xlabel(\"number of regions\")\n",
    "            axes[i].grid(axis=\"y\")\n",
    "        plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/lib/python3.11/site-packages/pandas/plotting/_matplotlib/tools.py:318: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  for t in axis.get_majorticklabels():\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_latency(0, 1000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/lib/python3.11/site-packages/pandas/plotting/_matplotlib/tools.py:318: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  for t in axis.get_majorticklabels():\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_latency(0, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/lib/python3.11/site-packages/pandas/plotting/_matplotlib/tools.py:318: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  for t in axis.get_majorticklabels():\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_latency(10, 1000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/lib/python3.11/site-packages/pandas/plotting/_matplotlib/tools.py:318: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  for t in axis.get_majorticklabels():\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_latency(10, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/lib/python3.11/site-packages/pandas/plotting/_matplotlib/tools.py:318: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  for t in axis.get_majorticklabels():\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_latency(50, 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>path</th>\n",
       "      <th>region</th>\n",
       "      <th>hot</th>\n",
       "      <th>workload</th>\n",
       "      <th>mr</th>\n",
       "      <th>suffix</th>\n",
       "      <th>rejected</th>\n",
       "      <th>rejected.WorkloadF1</th>\n",
       "      <th>rejected.WorkloadF2</th>\n",
       "      <th>rejected.WorkloadF3</th>\n",
       "      <th>...</th>\n",
       "      <th>avg.WorkloadF3</th>\n",
       "      <th>p0.WorkloadF3</th>\n",
       "      <th>p25.WorkloadF3</th>\n",
       "      <th>p50.WorkloadF3</th>\n",
       "      <th>p75.WorkloadF3</th>\n",
       "      <th>p90.WorkloadF3</th>\n",
       "      <th>p95.WorkloadF3</th>\n",
       "      <th>p99.WorkloadF3</th>\n",
       "      <th>p100.WorkloadF3</th>\n",
       "      <th>system</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>ycsb/throughput-yugabyte/ycsb-throughput-yugab...</td>\n",
       "      <td>2-eu-west-1-0</td>\n",
       "      <td>100000</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>20240501-110250</td>\n",
       "      <td>2689</td>\n",
       "      <td>2689</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>yugabyte</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 83 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 path         region     hot  \\\n",
       "52  ycsb/throughput-yugabyte/ycsb-throughput-yugab...  2-eu-west-1-0  100000   \n",
       "\n",
       "   workload  mr           suffix  rejected  rejected.WorkloadF1  \\\n",
       "52        F   0  20240501-110250      2689                 2689   \n",
       "\n",
       "    rejected.WorkloadF2  rejected.WorkloadF3  ...  avg.WorkloadF3  \\\n",
       "52                  NaN                  NaN  ...             NaN   \n",
       "\n",
       "    p0.WorkloadF3  p25.WorkloadF3  p50.WorkloadF3  p75.WorkloadF3  \\\n",
       "52            NaN             NaN             NaN             NaN   \n",
       "\n",
       "    p90.WorkloadF3  p95.WorkloadF3  p99.WorkloadF3  p100.WorkloadF3    system  \n",
       "52             NaN             NaN             NaN              NaN  yugabyte  \n",
       "\n",
       "[1 rows x 83 columns]"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yugabyte_throughput_df[(yugabyte_throughput_df.mr==0) & (yugabyte_throughput_df.region=='2-eu-west-1-0') & (yugabyte_throughput_df.hot==100000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 200x250 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(2, 2.5))\n",
    "\n",
    "medianprops = dict(linestyle='-', linewidth=1, color='black')\n",
    "\n",
    "\n",
    "aurora_eu_df = aurora_throughput_df[(aurora_throughput_df.mr==0) & (aurora_throughput_df.region=='2-eu-west-1-0') & (aurora_throughput_df.hot==100000)]\n",
    "ax.bxp(\n",
    "    [to_box(aurora_eu_df, 1)],\n",
    "    positions=[0],\n",
    "    patch_artist=True,\n",
    "    widths=0.6,\n",
    "    boxprops={\n",
    "        \"facecolor\": \"C0\",\n",
    "        \"linewidth\": 0,\n",
    "    },\n",
    "    medianprops=medianprops,\n",
    ")\n",
    "\n",
    "yugabyte_eu_df = yugabyte_throughput_df[(yugabyte_throughput_df.mr==0) & (yugabyte_throughput_df.region=='2-eu-west-1-0') & (yugabyte_throughput_df.hot==100000)]\n",
    "ax.bxp(\n",
    "    [to_box(yugabyte_eu_df, 1)],\n",
    "    positions=[1],\n",
    "    patch_artist=True,\n",
    "    widths=0.6,\n",
    "    boxprops={\n",
    "        \"facecolor\": \"C1\",\n",
    "        \"linewidth\": 0,\n",
    "    },\n",
    "    medianprops=medianprops,\n",
    ")\n",
    "\n",
    "ax.set_ylabel('latency (ms)')\n",
    "ax.yaxis.set_label_position(\"right\")\n",
    "ax.yaxis.tick_right()\n",
    "ax.set_xticklabels([\"Aurora\", \"YugabyteDB\"])\n",
    "plt.savefig('intro-latency.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}