rdflib store comparison

rdflib-all-queries-8000

{
 "metadata": {
  "name": "allqueries"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import scipy"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def data(s): \n",
      "    return map(float, s.split(\"\\t\"))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 102
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "headers=\"q01.sparql\tq02.sparql\tq03a.sparql\tq03b.sparql\tq03c.sparql\tq04.sparql\tq05a.sparql\tq05b.sparql\tq06.sparql\tq07.sparql\tq08.sparql\tq09.sparql\tq10.sparql\tq11.sparql\tq12a.sparql\tq12b.sparql\tq12c.sparql\".split(\"\\t\")\n",
      "default=data(\"\"\"0.01\t0.24\t0.52\t0.5\t0.5\t361.22\t79.74\t10.73\t17.89\t0.77\t45.77\t0.31\t0.01\t0.15\t81.92\t45.64\t0\"\"\")\n",
      "smallmem=data(\"\"\"0.01\t0.26\t0.53\t0.51\t0.51\t406.12\t86.41\t12.91\t17.75\t0.83\t47.39\t0.33\t0.01\t0.15\t86.62\t47.41\t0\"\"\")\n",
      "bsddb=data(\"\"\"0.01\t0.38\t0.79\t0.77\t0.79\t821.44\t150.94\t33.52\t18.28\t1.47\t67.9\t0.54\t0.02\t0.18\t150.28\t67.64\t0\"\"\")\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 111
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f=pylab.figure(figsize=(12,8))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 112
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "w=0.3\n",
      "ax=f.add_subplot(111)\n",
      "ind=scipy.arange(len(headers))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 113
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "r1=ax.bar(ind, default, w)\n",
      "r2=ax.bar(ind+w, bsddb, w, color='red')\n",
      "r3=ax.bar(ind+2*w, smallmem, w, color='yellow')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 114
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ax.legend((r1,r2,r3), (\"IOMemory\", \"Sleepycat\", \"SmallMemory\"))\n",
      "ax.set_title(\"All queries done over 8000 triples\")\n",
      "ax.set_xticklabels(headers, rotation=90)\n",
      "ax.set_xticks(ind+1.5*w)\n",
      "ax.set_ylabel(\"seconds\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 115,
       "text": [
        "<matplotlib.text.Text at 0x3353310>"
       ]
      }
     ],
     "prompt_number": 115
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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NKiws1KBBg5SbmysXl9M5n3lMgPMwZxoAyBpXokY7Z9rT01Nubm6qqqrSiRMn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\n"
      }
     ],
     "prompt_number": 116
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}