sigmod.07.30.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from collections import Counter\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import numpy as np\n",
    "import probscale\n",
    "import itertools\n",
    "import csv\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "class Result:\n",
    "    def __init__(self, path):\n",
    "        self.path = Path(f\"results/{path}/flamestream\")\n",
    "    \n",
    "    def lat_data(self):\n",
    "        with open(self.path.joinpath(\"latency/lat.data\"), 'r') as file:\n",
    "            return list(map(int, file.read().split(\", \")))\n",
    "    \n",
    "    def notification_await_times(self):\n",
    "        with open(self.path.joinpath(\"notification_await_times.csv\"), 'r') as file:\n",
    "            return list(map(lambda row: int(row[0]) / 1E6, csv.reader(file)))\n",
    "        \n",
    "    def duration(self):\n",
    "        with open(self.path.joinpath(\"duration.txt\"), 'r') as file:\n",
    "            return int(file.read())\n",
    "        \n",
    "    def durations(self):\n",
    "        with open(self.path.joinpath(\"durations.txt\"), 'r') as file:\n",
    "            return list(map(int, file.read().split(\", \")))\n",
    "    \n",
    "    def throughput_hz(self, skip=0):\n",
    "        durations = self.durations()\n",
    "        return (len(durations) - skip) / (durations[-1] - durations[skip]) * 1E9\n",
    "\n",
    "    def plot_latencies_evolution(self, **kwargs):\n",
    "        plot_quantiles_evolution(self.lat_data(), **kwargs)\n",
    "        \n",
    "def plot_quantiles_evolution(data, window_size=100, q=0.95, stabilize=True):\n",
    "    quantiles = []\n",
    "    prev_quantile = None\n",
    "    is_stable = not stabilize\n",
    "    for window in each_slice(data, window_size):\n",
    "        quantile = np.quantile(window, q)\n",
    "        if is_stable:\n",
    "            quantiles.append(quantile)\n",
    "        elif prev_quantile and prev_quantile < quantile:\n",
    "            is_stable = True\n",
    "            quantiles.append(prev_quantile)\n",
    "            quantiles.append(quantile)\n",
    "        else:\n",
    "            prev_quantile = quantile\n",
    "        prev_window = window\n",
    "    if not is_stable:\n",
    "        raise \"has not stabilized\"\n",
    "    plt.plot(quantiles)\n",
    "        \n",
    "def results_path(parallelism=3, rate=5, watermarks=False):\n",
    "    return Path(f\"results/sigmod/parallelism={parallelism}/distributed_acker={False}/rate={rate}/watermarks={watermarks}/iterations=10/flamestream\")\n",
    "\n",
    "def plot_probabilities(results_by_legends, legend_title=None, plot_title=None):\n",
    "    fig, ax = plt.subplots(figsize=(10,10))\n",
    "    ax.legend(results_by_legends.keys(), title=legend_title)\n",
    "    for result in results_by_legends.values():\n",
    "        plt.plot(*probscale.plot_pos(result.lat_data()))\n",
    "    plt.xlabel('Percentile')\n",
    "    plt.ylabel('Latency, ns')\n",
    "    plt.title(plot_title)\n",
    "    plt.show()\n",
    "    \n",
    "def plot_percentile(percentile, results_by_rates):\n",
    "    fig, ax = plt.subplots(figsize=(10,10))\n",
    "    plt.plot(\n",
    "        results_by_rates.keys(),\n",
    "        [np.percentile(result.lat_data(), percentile) for result in results_by_rates.values()],\n",
    "        marker='o'\n",
    "    )\n",
    "    plt.xlabel('Rate')\n",
    "    plt.ylabel(f\"Latencies {percentile} percentile, ns\")\n",
    "    \n",
    "def plot_percentile_by_throughput(percentile, results):\n",
    "    plt.plot(*plottable_percentile_by_throughput(percentile, results))\n",
    "    \n",
    "def plottable_percentile_by_throughput(percentile, results):\n",
    "    return (\n",
    "        [result.throughput() for result in results],\n",
    "        [np.percentile(result.lat_data(), percentile) for result in results]\n",
    "    )\n",
    "\n",
    "def plot_throughput_by_number_of_iterations(results):\n",
    "    plt.plot(\n",
    "        [result.iterations for result in results],\n",
    "        [result.throughput() for result in results],\n",
    "    )\n",
    "    \n",
    "def show_max_throughput(result_factory, bad_rate, max_rate):\n",
    "    pass\n",
    "\n",
    "def plot_throughput_by_parallelism(results):\n",
    "    plt.plot(\n",
    "        [result.parallelism for result in results],\n",
    "        [result.throughput_hz() for result in results],\n",
    "    )\n",
    "    \n",
    "def plot_latencies_barh(results_by_labels, ax):\n",
    "    w = .70\n",
    "    ind_fs = [1, 2.0, 3.0]\n",
    "\n",
    "    def do_plot(q, c):\n",
    "        ax.barh(range(len(results_by_labels)), [np.percentile(result.lat_data(), q) for result in results_by_labels.values()], color=c, height=w, left=0)\n",
    "\n",
    "    do_plot(99, '.8')\n",
    "    do_plot(90, '.6')\n",
    "    do_plot(75, '.4')\n",
    "    do_plot(50, '.2')\n",
    "\n",
    "    plt.gca().invert_yaxis()\n",
    "\n",
    "    for tick in ax.xaxis.get_major_ticks(): tick.label.set_fontsize(60)\n",
    "\n",
    "    ax.set_yticklabels([])\n",
    "#     plt.xlim((0, np.percentile(latencies[8], 99) + 100))\n",
    "\n",
    "    ax.set_xlabel('Latency(ms)', fontsize=60)\n",
    "\n",
    "    for i, label in enumerate(results_by_labels):\n",
    "        ax.text(np.percentile(results_by_labels[label].lat_data(), 99) + 10, i, label, fontsize = 60)\n",
    "\n",
    "    patch_99 = mpatches.Patch(color='.8', label='99 %-ile')\n",
    "    patch_90 = mpatches.Patch(color='.6', label='95 %-ile')\n",
    "    patch_75 = mpatches.Patch(color='.4', label='75 %-ile')\n",
    "    patch_50 = mpatches.Patch(color='.2', label='50 %-ile')\n",
    "    ax.legend(handles=[patch_99, patch_90, patch_75, patch_50][::-1], fontsize = 60, frameon=False, bbox_to_anchor=(1, 1), ncol=1)\n",
    "\n",
    "    for spine in plt.gca().spines.values():\n",
    "        spine.set_visible(False)\n",
    "        \n",
    "def each_slice(generator, n):\n",
    "    group = []\n",
    "    for element in generator:\n",
    "        group.append(element)\n",
    "        if len(group) == n:\n",
    "            yield group\n",
    "            group = []\n",
    "\n",
    "    if group:\n",
    "        yield group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f4f27b96400>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(*zip(*(\n",
    "    (LOCAL_ACKER_FLUSH_COUNT, np.quantile(Result(\n",
    "        path=f\"sigmod.07.30.idle-cpu-level=5/iterations=100/rate=2/LOCAL_ACKER_FLUSH_COUNT={LOCAL_ACKER_FLUSH_COUNT}/LOCAL_ACKER_FLUSH_DELAY_IN_MILLIS=5\"\n",
    "    ).lat_data(), 0.5) / 1E6)\n",
    "    for LOCAL_ACKER_FLUSH_COUNT in [500, 1000, 2000]\n",
    ")), marker='o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Idle-cpu-level=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f4f252ebe10>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.legend([\n",
    "    plt.plot(*zip(*(\n",
    "        (rate, np.quantile(Result(\n",
    "            path=f\"sigmod.07.30.idle-cpu-level=5/iterations=100/rate={rate}/tracking_frequency=0/tracking=disabled\"\n",
    "        ).lat_data(), 0.5) / 1E6)\n",
    "        for rate in [2]\n",
    "    )), marker='o') and \"Disabled\",\n",
    "    plt.plot(*zip(*(\n",
    "        (rate, np.quantile(Result(\n",
    "            path=f\"sigmod.07.30.idle-cpu-level=5/iterations=100/rate={rate}/tracking_frequency=1/tracking=acking/distributed=False\"\n",
    "        ).lat_data(), 0.5) / 1E6)\n",
    "        for rate in [2]\n",
    "    )), marker='o') and \"Centralized acker\",\n",
    "    plt.plot(*zip(*(\n",
    "        (rate, np.quantile(Result(\n",
    "            path=f\"sigmod.07.30.idle-cpu-level=5/iterations=100/rate={rate}/tracking_frequency=1/tracking=acking/distributed=True\"\n",
    "        ).lat_data(), 0.5) / 1E6)\n",
    "        for rate in [2]\n",
    "    )), marker='o') and \"Distributed acker\",\n",
    "], title=\"Tracking\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f4f25421550>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7vxYzM7pwV3pjpizdRrcRmby7JFsnXKVUKTbczWysmeWY2aoC4/eb2TozW21mwwosa2BmB83s4VAXLBIKFRLieLT3BXwwuDMNa1Tg4XeW03/MQjbmHPS7NJGQOJU993FAr/wDZtYV6Au0cs41B54r8D0jgI9CUaBIOJ1fuzLv3n0JT13dkrU79tN75ByGf7Kew8c0+5NEt2LD3Tk3B/ihwPAg4Gnn3BFvnX9Pk2Nm/YCvgdUhrFMkbGJijBvaN+DThy+lz4V1ePHTjfR8YQ5zN+zyuzSRoAV7zD0FSDOzRWaWaWbtAMysEvD/gP8t7gnM7E4zyzKzrF279CIS/yVVSuT561vz5sCLiTHjN68tZvCEpeQc0OxPEn2CDfc4oDrQARgKTDIzA54AnnfOFXvg0jk3xjmX6pxLTU5ODrIMkdDr3CSJj4akMbhbEz5e9R3dhmfy1qJvOKnZnySKBBvu2cBkF7AYOAkkARcDw8xsC/AA8Fszuy8klYqUoHLxsWT0SOGjB9JoXqcyj01ZxbWvfM7aHfv9Lk3klAQb7lOBrgBmlgIkALudc2nOuYbOuYbAC8CfnXOjQlKpiA/OTa7EhDs6MPxXrdjy/SH6vDiPp6av5dBRzf4kke1ULoWcACwAmppZtpkNBMYCjb3LIycCtzhdJCyllJlxzUX1mJ2RzrVt6/HXOZvpMWIOs9fu9Ls0kSJZJGRyamqqy8rK8rsMkVOy+OsfeGzKSjbkHKRX89r84RfNOLtKeb/LkjLIzJY451ILW6ZPqIqcpvaNqvPh4DSG9mzKv9bn0H14JmPnfc0JnXCVCKJwFwlCQlwM93Y9j5kPppPasDp//GANfV+ax4rsvX6XJgIo3EXOSIMaFRh3aztG3diGnfuP0O+l+TwxbTUHDmv2J/GXwl3kDJkZfS6sw+yH0rmpwzmMX7CF7iMymb5yh5qRiW8U7iIhUrlcPH/s24Ip93SiRsVE7nnrS24b9wXf/nDI79KkDFK4i4RY6/pVmXZfJx6/8gIWff0DPZ7PZPRnmzh2QrM/SclRuIuEQVxsDLenNWZWRjpdmiTzzMfr6POXeSz5pmAPPpHwULiLhFGdquUZc3Mqr96cyoHDx7hm9AIenbyCvYc0+5OEl8JdpAT0aFaLmRnp3JHWiElZ2XQbnsmUpZr9ScJH4S5SQiomxvHYlc2Ydl8n6lWvwIP/WM5Nry1i8y7N/iShp3AXKWHN61Rh8qBLeLJvc1Z8u49eI+cyctYGjhzX7E8SOgp3ER/Exhi/6diQ2Q+lc3mzWjw/6yt6j5zL55t2+12alBIKdxEf1axcjlE3tmX8be05fsJx46uLyJi0jO8PHvG7NIlyCneRCJCekswnD3bhvq7n8f7y7Vw2PJOJi7dq9icJmsJdJEKUi4/l4Z5NmT44jaa1zuKRySu5fswCvtp5wO/SJAop3EUiTJNaZzHxzg4Mu+ZCNuQc5IqRc3nm43XkHtUJVzl1CneRCBQTY1zXrj6zM9Lp27ouoz/bxOUvZPLZ+hy/S5MooXAXiWA1KiUy/LpWTLijA/GxMQz4+xfc+/aX5Ow/7HdpEuEU7iJRoOO5NfhoSBoZPVKYuWYn3YZn8vqCLZr9SYqkcBeJEolxsQzu1oQZD3ShVf2q/P691Vz98nxWbdvnd2kSgRTuIlGmUVJF3hjYnpH9W7Ntby6/GDWPJz9Yw49HjvtdmkQQhbtIFDIz+rauy+yMS+nfvgGvzfua7iMymbH6O79LkwihcBeJYlUqxPPnX7bkn4MuoUr5eO56Ywm3j89i295cv0sTnyncRUqBi86pxvv3d+bR3uczf+NueozI5NU5mzmu2Z/KLIW7SCkRHxvDXennMjOjCx0b1+BP09dy1aj5LN26x+/SxAcKd5FSpl61CvztllReuakte348ytWjP+fxqSvZl3vM79KkBBUb7mY21sxyzGxVgfH7zWydma02s2HeWHszW+bdlpvZL8NVuIgUzczo1eJsZj2UzoBLGvL2oq10H5HJtOXbNftTGWHF/UebWRfgIPC6c66FN9YVeAy40jl3xMxqOudyzKwCcNQ5d9zMzgaWA3Wccz97jVZqaqrLysoKxb9HRAqxMnsfv52ykpXb9pHWJIn/69eCc2pU9LssOUNmtsQ5l1rYsmL33J1zc4CCU7YPAp52zh3x1snx7g/lC/JygHYRRCJAy3pVmHpvJ564qhlLt+7l8ufnMOrTDRw9rhOupVWwx9xTgDQzW2RmmWbWLm+BmV1sZquBlcDdRe21m9mdZpZlZlm7du0KsgwROVWxMcaATo2YlZFOtwtq8twnX3HFX+ayaPP3fpcmYRBsuMcB1YEOwFBgkpkZgHNukXOuOdAOeNTMyhX2BM65Mc65VOdcanJycpBliMjpql2lHC//+iLGDkgl9+gJrh+zkKHvLGfPj0f9Lk1CKNhwzwYmu4DFwEkgKf8Kzrm1BI7VtzizEkUkHC47vxYzM7pwd/q5TFm6jW4jMnl3SbZOuJYSwYb7VKArgJmlAAnAbjNrZGZx3vg5wPnAlhDUKSJhUCEhjkd6n88HgzvTKKkiD7+znP5jFrIx56DfpckZOpVLIScAC4CmZpZtZgOBsUBj7/LIicAtLvDnvjOw3MyWAVOAe5xzms5dJMKdX7sy79zVkaeubsnaHfvpPXIOwz9Zz+Fjmv0pWhV7KWRJ0KWQIpFj98Ej/OnDtUxZuo1zalTg//q1IK2JzotFojO6FFJEypakSok8f31r3hx4MTFm/Oa1xQyesJScA5r9KZoo3EWkUJ2bJPHRkDQGd2vCx6u+o9vwTN5a9A0nNftTVFC4i0iRysXHktEjhY8eSKN5nco8NmUV177yOWt37Pe7NCmGwl1EinVuciUm3NGB4b9qxZbvD9HnxXk8NX0th45q9qdIpXAXkVNiZlxzUT1mZ6Rzbdt6/HXOZnqMmMPstTv9Lk0KoXAXkdNSrWICz1x7IZPu6kiFhFgGjs/i7jeWsGOfZn+KJAp3EQlK+0bV+XBwGkN7NuVf63PoPjyTsfO+5oROuEYEhbuIBC0hLoZ7u57HzAfTSW1YnT9+sIa+L81jRfZev0sr8xTuInLGGtSowLhb2zHqxjbs3H+Efi/N54lpqzlwWLM/+UXhLiIhYWb0ubAOsx9K56YO5zB+wRa6j8hk+sodakbmA4W7iIRU5XLx/LFvC6bc04kaFRO5560vuW3cF3z7wyG/SytTFO4iEhat61dl2n2dePzKC1j09Q/0eD6T0Z9t4tgJzf5UEhTuIhI2cbEx3J7WmFkZ6XRpkswzH6+jz1/mseSbgjN3Sqgp3EUk7OpULc+Ym1N59eZUDhw+xjWjF/Do5BXsPaTZn8JF4S4iJaZHs1rMzEjnjrRGTMrKptvwTKYs1exP4aBwF5ESVTExjseubMa0+zpRr3oFHvzHcm56bRGbd2n2p1BSuIuIL5rXqcLkQZfwZL8WrMjeR6+Rcxk5awNHjmv2p1BQuIuIb2JjjN90OIfZD6XTs3ltnp/1Fb1HzuXzTZqd80wp3EXEdzXPKseLN7Rh/G3tOX7CceOri8iYtIzvDx7xu7SopXAXkYiRnpLMJw924b6u5/H+8u1cNjyTiYu3avanICjcRSSilIuP5eGeTZk+OI2mtc7ikckruX7MAr7aecDv0qKKwl1EIlKTWmcx8c4ODLvmQjbkHOSKkXN55uN15B7VCddToXAXkYgVE2Nc164+szPS6du6LqM/28TlL2Ty2focv0uLeAp3EYl4NSolMvy6Vky4owPxsTEM+PsX3Pv2l+TsP+x3aRFL4S4iUaPjuTX4aEgaGT1SmLlmJ92GZ/L6gi2a/akQCncRiSqJcbEM7taEGQ90oVX9qvz+vdVc/fJ8Vm3b53dpEaXYcDezsWaWY2arCozfb2brzGy1mQ3zxnqY2RIzW+ndXxauwkWkbGuUVJE3BrZnZP/WbNubyy9GzePJD9bw45HjfpcWEU5lz30c0Cv/gJl1BfoCrZxzzYHnvEW7gauccy2BW4A3QleqiMhPmRl9W9dldsal9G/fgNfmfU33EZnMWP2d36X5rthwd87NAQo2Xx4EPO2cO+Ktk+PdL3XObffWWQ2UN7PEENYrIvJfqlSI58+/bMk/B11ClfLx3PXGEm4fn8W2vbl+l+abYI+5pwBpZrbIzDLNrF0h61wDfJn3B6AgM7vTzLLMLGvXrl1BliEi8h8XnVON9+/vzKO9z2f+xt30GJHJq3M2c7wMzv4UbLjHAdWBDsBQYJKZWd5CM2sOPAPcVdQTOOfGOOdSnXOpycnJQZYhIvJT8bEx3JV+LjMzutCxcQ3+NH0tV42az9Kte/wurUQFG+7ZwGQXsBg4CSQBmFk9YApws3NuU2jKFBE5PfWqVeBvt6Tyyk1t2fPjUa4e/TmPT13JvtxjfpdWIoIN96lAVwAzSwESgN1mVhX4EHjEOTc/NCWKiATHzOjV4mxmPZTOgEsa8vairXQfkcm05dtL/exPp3Ip5ARgAdDUzLLNbCAwFmjsXR45EbjFBbbUfcB5wO/NbJl3qxnG+kVEilUpMY4/XNWc9+7tTO3K5Rg8YSk3j13MN9//6HdpYWOR8NcrNTXVZWVl+V2GiJQBJ0463liwhec++YpjJ05y/2XncWeXc0mIi77PdJrZEudcamHLou9fIyJyBmJjjAGdGjErI51uF9TkuU++4oq/zGXR5u/9Li2kFO4iUibVrlKOl399EWMHpHL42AmuH7OQoe8sZ8+PR/0uLSQU7iJSpl12fi1mPpjO3ennMmXpNrqNyOTdJdlRf8JV4S4iZV75hFge6X0+HwzuTKOkijz8znL6j1nIxpyDfpcWNIW7iIjn/NqVeeeujjx1dUvW7thP75FzGP7Jeg4fi77ZnxTuIiL5xMQYN7RvwKcPX0qfC+vw4qcb6fnCHOZuiK42KQp3EZFCJFVK5PnrW/PmwIuJMeM3ry1m8ISl5ByIjtmfFO4iIj+jc5MkPhqSxuBuTfh41Xd0G57JW4u+4WSEz/6kcBcRKUa5+FgyeqTw0QNpNK9TmcemrOLaVz5n7Y79fpdWJIW7iMgpOje5EhPu6MDwX7Viy/eH6PPiPJ6avpZDRyNv9ieFu4jIaTAzrrmoHrMz0rm2bT3+OmczPUbMYfbanX6X9hMKdxGRIFSrmMAz117IpLs6UiEhloHjs7j7jSXs2BcZsz8p3EVEzkD7RtX5cHAaQ3s25V/rc+g+PJOx877mhM8nXBXuIiJnKCEuhnu7nsfMB9NJbVidP36whr4vzWNF9l7falK4i4iESIMaFRh3aztG3diGnfuP0O+l+TwxbTUHDpf87E8KdxGREDIz+lxYh9kPpXNTh3MYv2AL3UdkMn3ljhJtRqZwFxEJg8rl4vlj3xZMuacTNSomcs9bX3LbuC/49odDJfLzNROTiEiYHT9xknGfb2HEzK846RxDuqVQ86wERszcwPa9udSpWp6hPZvSr03d03ren5uJSeEuIlJCtu/N5Ylpq/lkzU4MyJ++5eNjeerqlqcV8JpmT0QkAtSpWp4xN6dSvWICBXerc4+d4NkZ60P2sxTuIiIlrKip/LbvDd0HoBTuIiIlrE7V8qc1HgyFu4hICRvasynl42N/MlY+PpahPZuG7GfEheyZRETklOSdNH12xvozulrm5yjcRUR80K9N3ZCGeUE6LCMiUgoVG+5mNtbMcsxsVYHx+81snZmtNrNh3lgNM/uXmR00s1HhKlpERH7eqRyWGQeMAl7PGzCzrkBfoJVz7oiZ1fQWHQZ+B7TwbiIi4oNi99ydc3OAHwoMDwKeds4d8dbJ8e5/dM7NIxDyIiLik2CPuacAaWa2yMwyzazd6T6Bmd1pZllmlrVr164gyxARkcIEe7VMHFAd6AC0AyaZWWN3Go1qnHNjgDEAZrbLzL4JspZwSAJ2+13Ez4j0+kA1hkKk1weRX2Ok1wdnVuM5RS0INtyzgclemC82s5MECgxqF9w5lxxkHWFhZllFNeOJBJFeH6jGUIj0+iDya4z0+iB8NQZ7WGYq0BXAzFKABCL/r6OISJlR7J67mU0ALgWSzCwb+AMwFhjrXR55FLgl75CMmW0BKgMJZtYPuNw5tyY85YuISGGKDYX4ftoAAAVgSURBVHfn3A1FLLqpiPUbnklBEWKM3wUUI9LrA9UYCpFeH0R+jZFeH4SpxoiYrENEREJL7QdEREohhbuISClUJsPdzKqa2bteb5y1ZtbRzKqb2Uwz2+DdV/PWNTP7i5ltNLMVZta2hGp80Ovbs8rMJphZOTNr5H1wbKOZ/cPMErx1E72vN3rLG4appv/qMxTMdjOzW7z1N5jZLWGu71nv/3mFmU0xs6r5lj3q1bfezHrmG+/ljW00s0dCVV9RNeZb9pCZOTNL8r6OiG3ojf9XLylvPCK2oZm1NrOFZrbMAh+ObO+N+7EN61ugx9Yab3sN8cZL9rXinCtzN2A8cLv3OAGoCgwDHvHGHgGe8R5fAXwEGIEPbS0qgfrqAl8D5b2vJwEDvPv+3tgrwCDv8T3AK97j/sA/wlRXF6AtsCrf2GltNwIfftvs3VfzHlcLY32XA3He42fy1dcMWA4kAo2ATUCsd9sENPZ+N5YDzcK5Db3x+sAM4BsgKcK2YVdgFpDofV0z0rYh8AnQO992+8zHbXg20NZ7fBbwlbetSvS1Uub23M2sCoFfjtcAnHNHnXN7CTRCG++tNh7o5z3uC7zuAhYCVc3s7BIoNQ4ob2ZxQAVgB3AZ8G4RNebV/i7Qzcws1AW5wvsMne526wnMdM794JzbA8wEeoWrPufcJ865496XC4F6+eqb6Jw74pz7GtgItPduG51zm51zR4GJ3rohUcQ2BHge+B/4ybzJEbENKaKXFJG1DR2BS7ABqgDb89VY0ttwh3PuS+/xAWAtgR22En2tlLlwJ7CHsQv4u5ktNbO/mVlFoJZzboe3zndALe9xXeDbfN+f7Y2FjXNuG/AcsJVAqO8DlgB78wVV/jr+XaO3fB9QI5w15nO6263Et2c+txHYQ+Jn6ijx+sysL7DNObe8wKJIqbGoXlKRUh/AA8CzZvYtgdfOo5FQowUOkbYBFlHCr5WyGO5xBN7SjXbOtQF+JPAW6d9c4D2Rb9eIesfi+hL4Q1QHqEiI9irCye/t9nPM7DHgOPCW37XkZ2YVgN8Cv/e7lp+Rv5fUUAK9pEL+zvAMDQIedM7VBx7Ee2fuJzOrBPwTeMA5tz//spJ4rZTFcM8Gsp1zi7yv3yUQ9jvzDrd493lvPbcROB6ap543Fk7dga+dc7ucc8eAyUAnAm/X8j54lr+Of9foLa8CfB/mGvOc7nYr8e1pZgOAPsCvvRdVJNV3LoE/4sst8OnuesCXZlY7gmr8dy8p59xiIK+XVKTUB3ALgdcJwDsEDg3hV41mFk8g2N9yzuXVVaKvlTIX7s6574BvzSxvmvFuwBpgGoFfELz797zH04CbvTPaHYB9+d5ahctWoIOZVfD2kPJq/BdwbRE15tV+LfBpvhALt9PdbjOAy82smvcO5XJvLCzMrBeBY9m/cM4dKlB3fwtcadQIaAIsBr4AmljgyqQEAieop4WrPufcSudcTedcQxf4dHc2gZNx3xEh25Cie0lFxDb0bAfSvceXARu8xyW+Db3X7GvAWufciHyLSva1cqZnhqPxBrQGsoAVBH5xqxE4Rj2bwC/FLKC6t64BLxE4+78SSC2hGv8XWAesAt4gcEVCYwIvno0E9k7yrl4o53290VveOEw1TSBwDuAYgRAaGMx2I3Dse6N3uzXM9W0kcNxymXd7Jd/6j3n1rce70sIbv4LAFQ6bgMfCvQ0LLN/Cf66WiZRtmAC86f0ufglcFmnbEOhM4LzUcgLHty/ycRt2JnDIZUW+37srSvq1ovYDIiKlUJk7LCMiUhYo3EVESiGFu4hIKaRwFxEphRTuIiKlkMJdRKQUUriLiJRC/x/PqMbNdkRS6AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(*zip(*(\n",
    "    (LOCAL_ACKER_FLUSH_COUNT, np.quantile(Result(\n",
    "        path=f\"sigmod.07.30.idle-cpu-level=5/iterations=100/rate=2/distributed=True/LOCAL_ACKER_FLUSH_COUNT={LOCAL_ACKER_FLUSH_COUNT}/LOCAL_ACKER_FLUSH_DELAY_IN_MILLIS=2\"\n",
    "    ).lat_data(), 0.5) / 1E6)\n",
    "    for LOCAL_ACKER_FLUSH_COUNT in [500, 1000, 2000]\n",
    ")), marker='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f4f252c37f0>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(*zip(*(\n",
    "    (LOCAL_ACKER_FLUSH_DELAY_IN_MILLIS, np.quantile(Result(\n",
    "        path=f\"sigmod.07.30.idle-cpu-level=5/iterations=100/rate=2/distributed=True/LOCAL_ACKER_FLUSH_COUNT=500/LOCAL_ACKER_FLUSH_DELAY_IN_MILLIS={LOCAL_ACKER_FLUSH_DELAY_IN_MILLIS}\"\n",
    "    ).lat_data(), 0.5) / 1E6)\n",
    "    for LOCAL_ACKER_FLUSH_DELAY_IN_MILLIS in [2, 5]\n",
    ")), marker='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f4f2507bb00>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.legend([\n",
    "    plt.plot(*zip(*(\n",
    "        (rate, np.quantile(Result(\n",
    "            path=f\"sigmod.07.30.idle-cpu-level=5/iterations=100/rate={rate}/tracking_frequency=0/tracking=disabled\"\n",
    "        ).lat_data(), 0.5) / 1E6)\n",
    "        for rate in [2]\n",
    "    )), marker='o') and \"Disabled\",\n",
    "    plt.plot(*zip(*(\n",
    "        (rate, np.quantile(Result(\n",
    "            path=f\"sigmod.07.30.idle-cpu-level=5/iterations=100/rate={rate}/LOCAL_ACKER_FLUSH_DELAY_IN_MILLIS=2/tracking_frequency=1/tracking=acking/distributed=False\"\n",
    "        ).lat_data(), 0.5) / 1E6)\n",
    "        for rate in [2]\n",
    "    )), marker='o') and \"Centralized acker\",\n",
    "    plt.plot(*zip(*(\n",
    "        (rate, np.quantile(Result(\n",
    "            path=f\"sigmod.07.30.idle-cpu-level=5/iterations=100/rate={rate}/distributed=True/LOCAL_ACKER_FLUSH_COUNT=1000/LOCAL_ACKER_FLUSH_DELAY_IN_MILLIS=2\"\n",
    "        ).lat_data(), 0.5) / 1E6)\n",
    "        for rate in [2]\n",
    "    )), marker='o') and \"Distributed acker\",\n",
    "], title=\"Tracking\")"
   ]
  },
  {
   "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": "3.6.8"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "source": [],
    "metadata": {
     "collapsed": false
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}