file_size.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from django.db.models.functions import Length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_bytes = JobEvent.objects.filter(\n",
    "    job_id=41, end_line__gte=4, start_line__lte=9\n",
    ").annotate(byte_est=Length('stdout')).aggregate(Sum('byte_est'))['byte_est__sum']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "408"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_bytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bytes_in_job(end_line):\n",
    "    return JobEvent.objects.filter(\n",
    "        job_id=41, end_line__gte=0, start_line__lte=end_line\n",
    "    ).annotate(byte_est=Length('stdout')).aggregate(Sum('byte_est'))['byte_est__sum']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "205"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bytes_in_job(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "291"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bytes_in_job(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "351"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bytes_in_job(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[35,\n",
       " 86,\n",
       " 205,\n",
       " 291,\n",
       " 351,\n",
       " 459,\n",
       " 515,\n",
       " 529,\n",
       " 568,\n",
       " 613,\n",
       " 695,\n",
       " 838,\n",
       " 920,\n",
       " 920,\n",
       " 967,\n",
       " 1049,\n",
       " 1049,\n",
       " 1078,\n",
       " 1265,\n",
       " 1265]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[bytes_in_job(x) for x in t]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa4b54d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "t = range(100)\n",
    "s = [bytes_in_job(x) for x in t]\n",
    "plt.plot(t, s)\n",
    "\n",
    "plt.xlabel('End Line Number of Request')\n",
    "plt.ylabel('Characters returned (aspirationally bytes)')\n",
    "plt.title('ORM stdout size calculation')\n",
    "plt.grid(True)\n",
    "plt.savefig(\"test.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Django Shell-Plus",
   "language": "python",
   "name": "django_extensions"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}