parse_log.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('M1', 1e-06), ('M2', 0.00027499999999999996), ('T1', 0.002559), ('T2', 0.034852999999999745), ('T3', 0.0014069999999999987), ('T4', 0.1081019999999984), ('T5', 0.07100399999999948), ('T6', 0.013037999999999852), ('T7', 0.19797199999999648), ('W1', 0.000163), ('W2', 1.025765), ('W3', 0.001071)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "times = {}\n",
    "df = {\"timestamp\": [], \"time\": []}\n",
    "lines = open('/mnt/ramdisk/log_deferred_commit.txt', 'r').readlines()\n",
    "for line in lines:\n",
    "    line = line.split(\"|\")\n",
    "    t = line[0]\n",
    "    #print(line)\n",
    "    time = float(line[1].split(\",\")[0].split(\":\")[1])\n",
    "    if t not in times.keys():\n",
    "        times[t] = [time]\n",
    "    else:\n",
    "        times[t].append(time)\n",
    "    df[\"timestamp\"].append(t)\n",
    "    df[\"time\"].append(time)\n",
    "print([(key, sum(times[key])) for key in times.keys()])\n",
    "print([(key, len(times[key])) for key in times.keys()])\n",
    "draw(df)\n",
    "#detached thread"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('M1', 0.0), ('M2', 0.00015700000000000002), ('T1', 0.002652999999999997), ('T2', 0.028792999999999996), ('T3', 0.0011819999999999992), ('T4', 0.08995499999999958), ('T5', 0.07567499999999988), ('T6', 0.04010100000000058), ('T7', 0.16415300000000058), ('W1', 0.0005), ('W2', 1.887063), ('W3', 0.0016020000000000001)]\n",
      "[('M1', 7925), ('M2', 7925), ('T1', 5984), ('T2', 5983), ('T3', 5983), ('T4', 5983), ('T5', 5983), ('T6', 5983), ('T7', 5983), ('W1', 2), ('W2', 2), ('W3', 2)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py:2241: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  \"Data has no positive values, and therefore cannot be \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "times = {}\n",
    "df = {\"timestamp\": [], \"time\": []}\n",
    "lines = open('/mnt/ramdisk/log_deferred_commit.txt', 'r').readlines()\n",
    "for line in lines:\n",
    "    line = line.split(\"|\")\n",
    "    t = line[0]\n",
    "    #print(line)\n",
    "    time = float(line[1].split(\",\")[0].split(\":\")[1])\n",
    "    if t not in times.keys():\n",
    "        times[t] = [time]\n",
    "    else:\n",
    "        times[t].append(time)\n",
    "    df[\"timestamp\"].append(t)\n",
    "    df[\"time\"].append(time)\n",
    "print([(key, sum(times[key])) for key in times.keys()])\n",
    "print([(key, len(times[key])) for key in times.keys()])\n",
    "draw(df)\n",
    "#detached thread without duplicate write inodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('M1', 0.0), ('M2', 0.9624349999999999), ('T1', 0.005534000000000166), ('T2', 0.04785399999999909), ('T3', 0.0025290000000000035), ('T4', 0.13699200000000006), ('T5', 0.085491999999998), ('T6', 0.014955999999999216), ('T7', 1.2267239999999988), ('W1', 0.00019700000000000002), ('W2', 0.9602200000000001), ('W3', 0.001411)]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "times = {}\n",
    "df = {\"timestamp\": [], \"time\": []}\n",
    "lines = open('/mnt/ramdisk/log_deferred_commit.txt', 'r').readlines()\n",
    "for line in lines:\n",
    "    line = line.split(\"|\")\n",
    "    t = line[0]\n",
    "    time = float(line[1].split(\",\")[0].split(\":\")[1])\n",
    "    if t not in times.keys():\n",
    "        times[t] = [time]\n",
    "    else:\n",
    "        times[t].append(time)\n",
    "    df[\"timestamp\"].append(t)\n",
    "    df[\"time\"].append(time)\n",
    "print([(key, sum(times[key])) for key in times.keys()])\n",
    "draw(df)\n",
    "#async async"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('M', 0.0), ('T1', 0.005381000000000061), ('T2', 0.0520380000000006), ('T3', 0.003222000000000004), ('T4', 0.1464900000000013), ('T5', 0.09155099999999722), ('T6', 0.0163259999999992), ('T7', 1.625920000000001), ('W1', 0.000127), ('W2', 1.407481), ('W3', 0.010043)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py:2241: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  \"Data has no positive values, and therefore cannot be \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "times = {}\n",
    "df = {\"timestamp\": [], \"time\": []}\n",
    "lines = open('/mnt/ramdisk/log_deferred_put.txt', 'r').readlines()\n",
    "for line in lines:\n",
    "    line = line.split(\"|\")\n",
    "    t = line[0]\n",
    "    time = float(line[1].split(\",\")[0].split(\":\")[1])\n",
    "    if t not in times.keys():\n",
    "        times[t] = [time]\n",
    "    else:\n",
    "        times[t].append(time)\n",
    "    df[\"timestamp\"].append(t)\n",
    "    df[\"time\"].append(time)\n",
    "print([(key, sum(times[key])) for key in times.keys()])\n",
    "draw(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('M', 0.0), ('T1', 0.0025880000000000096), ('T2', 0.028404999999999767), ('T3', 0.0012069999999999993), ('T4', 0.08512500000000083), ('T5', 0.07473800000000011), ('T6', 0.010369000000000033), ('T7', 1.1402069999999875), ('W1', 0.000138), ('W2', 1.0233349999999999), ('W3', 0.001769)]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/matplotlib/ticker.py:2241: UserWarning: Data has no positive values, and therefore cannot be log-scaled.\n",
      "  \"Data has no positive values, and therefore cannot be \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "times = {}\n",
    "df = {\"timestamp\": [], \"time\": []}\n",
    "lines = open('/mnt/ramdisk/log_simple_put.txt', 'r').readlines()\n",
    "for line in lines:\n",
    "    line = line.split(\"|\")\n",
    "    t = line[0]\n",
    "    time = float(line[1].split(\",\")[0].split(\":\")[1])\n",
    "    if t not in times.keys():\n",
    "        times[t] = [time]\n",
    "    else:\n",
    "        times[t].append(time)\n",
    "    df[\"timestamp\"].append(t)\n",
    "    df[\"time\"].append(time)\n",
    "print([(key, sum(times[key])) for key in times.keys()])\n",
    "draw(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw(df):\n",
    "    sns.set(style=\"ticks\")\n",
    "\n",
    "    # Initialize the figure with a logarithmic x axis\n",
    "    f, ax = plt.subplots(figsize=(7, 6))\n",
    "    ax.set_xscale(\"log\")\n",
    "\n",
    "    # Load the example planets dataset\n",
    "    df = pd.DataFrame.from_dict(df)\n",
    "\n",
    "    # Plot the orbital period with horizontal boxes\n",
    "    sns.boxplot(x=\"time\", y=\"timestamp\", data=df,\n",
    "                whis=[0, 100], width=.6, palette=\"vlag\")\n",
    "\n",
    "    # Add in points to show each observation\n",
    "    sns.stripplot(x=\"time\", y=\"timestamp\", data=df,\n",
    "                size=4, color=\".3\", linewidth=0)\n",
    "\n",
    "    # Tweak the visual presentation\n",
    "    ax.xaxis.grid(True)\n",
    "    ax.set(ylabel=\"\")\n",
    "    sns.despine(trim=True, left=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('M', 0.0), ('T1', 0.0025880000000000096), ('T2', 0.028404999999999767), ('T3', 0.0012069999999999993), ('T4', 0.08512500000000083), ('T5', 0.07473800000000011), ('T6', 0.010369000000000033), ('T7', 1.1402069999999875), ('W1', 0.000138), ('W2', 1.0233349999999999), ('W3', 0.001769)]\n",
      "[('M', 6925), ('T1', 5865), ('T2', 5865), ('T3', 5865), ('T4', 5865), ('T5', 5864), ('T6', 5864), ('T7', 5864), ('W1', 3), ('W2', 3), ('W3', 3)]\n"
     ]
    }
   ],
   "source": [
    "print([(key, sum(times[key])) for key in times.keys()])\n",
    "print([(key, len(times[key])) for key in times.keys()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6.9 64-bit",
   "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": "3.6.9"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}