sigmod.10.06.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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 last_notification_await_times(self, parallelism):\n",
    "      [min(slice) for slice in each_slice(\n",
    "        self.notification_await_times(),\n",
    "        parallelism,\n",
    "      )]\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,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x11c20a110>"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 2
    },
    {
     "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",
    "        (\n",
    "          float(rate),\n",
    "          np.quantile([np.min(slice) for slice in each_slice(Result(\n",
    "            path=f\"sigmod.06.10/local_acker_flush_delay=5/iterations=100/rate={rate}/tracking_frequency=1/tracking=acking/distributed=False\"\n",
    "          ).notification_await_times(), 100)], 0.5),\n",
    "        )\n",
    "        for rate in sorted(set([\n",
    "            re.search(\"rate=(\\d+(?:\\.\\d*)?)\", path)[1]\n",
    "            for path in glob(f'results/sigmod.06.10/local_acker_flush_delay=5/iterations=100/rate=*/tracking_frequency=1/tracking=acking/distributed=False')\n",
    "        ]), key=float)\n",
    "    )), marker='o') and \"Centralized acker\",\n",
    "    plt.plot(*zip(*(\n",
    "        (\n",
    "          float(rate),\n",
    "          np.quantile([np.min(slice) for slice in each_slice(Result(\n",
    "            path=f\"sigmod.06.10/local_acker_flush_delay=5/iterations=100/rate={rate}/tracking_frequency=1/tracking=acking/distributed=True\"\n",
    "          ).notification_await_times(), 100)], 0.5),\n",
    "        )\n",
    "        for rate in sorted(set([\n",
    "            re.search(\"rate=(\\d+(?:\\.\\d*)?)\", path)[1]\n",
    "            for path in glob(f'results/sigmod.06.10/local_acker_flush_delay=5/iterations=100/rate=*/tracking_frequency=1/tracking=acking/distributed=True')\n",
    "        ]), key=float)\n",
    "  )), marker='o') and \"Distributed acker\",\n",
    "], title=\"Tracking\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x11d1f8750>"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 3
    },
    {
     "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",
    "        (\n",
    "          1000 / float(rate),\n",
    "          np.quantile([np.min(slice) for slice in each_slice(Result(\n",
    "            path=f\"sigmod.06.10/local_acker_flush_delay=5/iterations=100/rate={rate}/tracking_frequency=1/tracking=acking/distributed=False\"\n",
    "          ).notification_await_times(), 100)], 0.5),\n",
    "        )\n",
    "        for rate in sorted(set([\n",
    "            re.search(\"rate=(\\d+(?:\\.\\d*)?)\", path)[1]\n",
    "            for path in glob(f'results/sigmod.06.10/local_acker_flush_delay=5/iterations=100/rate=*/tracking_frequency=1/tracking=acking/distributed=False')\n",
    "        ]), key=float)\n",
    "    )), marker='o') and \"Centralized acker\",\n",
    "    plt.plot(*zip(*(\n",
    "        (\n",
    "          1000 / float(rate),\n",
    "          np.quantile([np.min(slice) for slice in each_slice(Result(\n",
    "            path=f\"sigmod.06.10/local_acker_flush_delay=5/iterations=100/rate={rate}/tracking_frequency=1/tracking=acking/distributed=True\"\n",
    "          ).notification_await_times(), 100)], 0.5),\n",
    "        )\n",
    "        for rate in sorted(set([\n",
    "            re.search(\"rate=(\\d+(?:\\.\\d*)?)\", path)[1]\n",
    "            for path in glob(f'results/sigmod.06.10/local_acker_flush_delay=5/iterations=100/rate=*/tracking_frequency=1/tracking=acking/distributed=True')\n",
    "        ]), key=float)\n",
    "  )), marker='o') and \"Distributed acker\",\n",
    "], title=\"Tracking\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x11f4119d0>"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 4
    },
    {
     "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",
    "        (\n",
    "          float(rate),\n",
    "          np.quantile([np.min(slice) for slice in each_slice(Result(\n",
    "            path=f\"sigmod.06.10/local_acker_flush_delay=10/iterations=100/rate={rate}/tracking_frequency=1/tracking=acking/distributed=False\"\n",
    "          ).notification_await_times(), 100)], 0.5),\n",
    "        )\n",
    "        for rate in sorted(set([\n",
    "            re.search(\"rate=(\\d+(?:\\.\\d*)?)\", path)[1]\n",
    "            for path in glob(f'results/sigmod.06.10/local_acker_flush_delay=10/iterations=100/rate=*/tracking_frequency=1/tracking=acking/distributed=False')\n",
    "        ]), key=float)\n",
    "    )), marker='o') and \"Centralized acker\",\n",
    "    plt.plot(*zip(*(\n",
    "        (\n",
    "          float(rate),\n",
    "          np.quantile([np.min(slice) for slice in each_slice(Result(\n",
    "            path=f\"sigmod.06.10/local_acker_flush_delay=10/iterations=100/rate={rate}/tracking_frequency=1/tracking=acking/distributed=True\"\n",
    "          ).notification_await_times(), 100)], 0.5),\n",
    "        )\n",
    "        for rate in sorted(set([\n",
    "            re.search(\"rate=(\\d+(?:\\.\\d*)?)\", path)[1]\n",
    "            for path in glob(f'results/sigmod.06.10/local_acker_flush_delay=10/iterations=100/rate=*/tracking_frequency=1/tracking=acking/distributed=True')\n",
    "        ]), key=float)\n",
    "  )), marker='o') and \"Distributed acker\",\n",
    "], title=\"Tracking\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n",
     "is_executing": false
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x11f457290>"
     },
     "metadata": {},
     "output_type": "execute_result",
     "execution_count": 5
    },
    {
     "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",
    "        (\n",
    "          float(rate),\n",
    "          np.quantile([np.min(slice) for slice in each_slice(Result(\n",
    "            path=f\"sigmod.06.10/local_acker_flush_delay=10/partition-acks-by-fronts/iterations=100/rate={rate}/tracking_frequency=1/tracking=acking/distributed=False\"\n",
    "          ).notification_await_times(), 98)], 0.5),\n",
    "        )\n",
    "        for rate in sorted(set([\n",
    "            re.search(\"rate=(\\d+(?:\\.\\d*)?)\", path)[1]\n",
    "            for path in glob(f'results/sigmod.06.10/local_acker_flush_delay=10/partition-acks-by-fronts/iterations=100/rate=*/tracking_frequency=1/tracking=acking/distributed=False')\n",
    "        ]), key=float)\n",
    "    )), marker='o') and \"Centralized acker\",\n",
    "    plt.plot(*zip(*(\n",
    "        (\n",
    "          float(rate),\n",
    "          np.quantile([np.min(slice) for slice in each_slice(Result(\n",
    "            path=f\"sigmod.06.10/local_acker_flush_delay=10/partition-acks-by-fronts/iterations=100/rate={rate}/tracking_frequency=1/tracking=acking/distributed=True\"\n",
    "          ).notification_await_times(), 97)], 0.5),\n",
    "        )\n",
    "        for rate in sorted(set([\n",
    "            re.search(\"rate=(\\d+(?:\\.\\d*)?)\", path)[1]\n",
    "            for path in glob(f'results/sigmod.06.10/local_acker_flush_delay=10/partition-acks-by-fronts/iterations=100/rate=*/tracking_frequency=1/tracking=acking/distributed=True')\n",
    "        ]), key=float)\n",
    "  )), marker='o') and \"Distributed acker\",\n",
    "], title=\"Tracking\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "is_executing": false
    }
   },
   "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
}