sigmod.09.21.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "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": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-e963ab90b0f0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m         \u001b[0mpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mf\"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\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     ).lat_data(), 0.5) / 1E6)\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mLOCAL_ACKER_FLUSH_COUNT\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m500\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m )), marker='o')\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-2-e963ab90b0f0>\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m      4\u001b[0m         \u001b[0mpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mf\"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\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     ).lat_data(), 0.5) / 1E6)\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mLOCAL_ACKER_FLUSH_COUNT\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m500\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m )), marker='o')\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'Result' is not defined"
     ],
     "ename": "NameError",
     "evalue": "name 'Result' is not defined",
     "output_type": "error"
    }
   ],
   "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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\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": null,
   "outputs": [],
   "source": [
    "plt.legend([\n",
    "    plt.plot(*zip(*(\n",
    "        (rate, np.quantile(Result(\n",
    "            path=f\"sigmod.09.17/iterations=50/rate={rate}/tracking_frequency=1/tracking=acking/distributed=False\"\n",
    "        ).notification_await_times(), 0.5))\n",
    "        for rate in [0.5, 0.6, 0.7, 0.8, 0.9, 1.0]\n",
    "    )), marker='o') and \"Centralized acker\",\n",
    "    plt.plot(*zip(*(\n",
    "        (rate, np.quantile(Result(\n",
    "            path=f\"sigmod.09.17/iterations=50/rate={rate}/tracking_frequency=1/tracking=acking/distributed=True\"\n",
    "        ).notification_await_times(), 0.5))\n",
    "        for rate in [0.5, 0.6, 0.7, 0.8, 0.9, 1.0]\n",
    "  )), marker='o') and \"Distributed acker\",\n",
    "], title=\"Tracking\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x1171a6dd0>"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 41
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from glob import glob\n",
    "import re\n",
    "\n",
    "plt.legend([\n",
    "    plt.plot(*zip(*(\n",
    "        (float(rate), np.quantile(Result(\n",
    "            path=f\"sigmod.09.21/iterations=10/rate={rate}/tracking_frequency=1/tracking=acking/distributed=False\"\n",
    "        ).notification_await_times(), 0.5))\n",
    "        for rate in sorted(set([\n",
    "          re.search(\"rate=(\\d+(?:\\.\\d*)?)\", path)[1]\n",
    "          for path in glob(f'results/sigmod.09.21/iterations=10/rate=*/tracking_frequency=1/tracking=acking/distributed=False')\n",
    "        ]) - {'0.1'}, key=float)\n",
    "    )), marker='o') and \"Centralized acker\",\n",
    "    plt.plot(*zip(*(\n",
    "        (float(rate), np.quantile(Result(\n",
    "            path=f\"sigmod.09.21/iterations=10/rate={rate}/tracking_frequency=1/tracking=acking/distributed=True\"\n",
    "        ).notification_await_times(), 0.5))\n",
    "        for rate in sorted(set([\n",
    "          re.search(\"rate=(\\d+(?:\\.\\d*)?)\", path)[1]\n",
    "          for path in glob(f'results/sigmod.09.21/iterations=10/rate=*/tracking_frequency=1/tracking=acking/distributed=True')\n",
    "        ]) - {'0.09', '0.101', '0.19', '0.145', '0.14', '0.13'}, key=float)\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
}