scopatz/CSI Timings.

## CSI Timings.
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  "name": ""
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from tables import *\n",
      "\n",
      "class KeyValue(IsDescription):\n",
      "    key = StringCol(itemsize=30, dflt=\" \", pos=0)  # character String\n",
      "    value = Int64Col(dflt=0, pos=1)\n",
      "\n",
      "f = open_file(\"keyvalue.h5\", \"w\")    \n",
      "filters = Filters(complevel=1, complib='blosc')\n",
      "kv = f.create_table(\"/\", \"keyvalues\", KeyValue, filters=filters)\n",
      "\n",
      "import string\n",
      "import random\n",
      "\n",
      "for j in range(20):\n",
      "    values = []\n",
      "    for i in xrange(100000):\n",
      "        key = \"\".join(random.sample(string.uppercase, 5))\n",
      "        value = random.randint(0, 1000000)\n",
      "        values.append((key, value))\n",
      "    kv.append(values)\n",
      "\n",
      "f.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = open_file(\"keyvalue.h5\", \"a\")\n",
      "kv = f.root.keyvalues\n",
      "\n",
      "kv.cols.value.remove_index()\n",
      "\n",
      "print \"without index\"\n",
      "%timeit a = kv.read_where('value < 10')\n",
      "%timeit a = kv.read_where('value < 1000')\n",
      "\n",
      "kv.cols.value.create_csindex()\n",
      "\n",
      "print \"with index\"\n",
      "%timeit a = kv.read_where('value < 10')\n",
      "%timeit a = kv.read_where('value < 1000')\n",
      "\n",
      "#f.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "without index\n",
        "10 loops, best of 3: 139 ms per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "1 loops, best of 3: 175 ms per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "with index"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "10000 loops, best of 3: 201 \u00b5s per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "10 loops, best of 3: 88.6 ms per loop"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from timeit import timeit, repeat"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = []\n",
      "for i in range(7):\n",
      "    x = repeat(lambda: kv.read_where('value < {0}'.format(10**i)), number=10)\n",
      "    data.append(min(x)/10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "plt.loglog([10**i for i in range(7)], data)\n",
      "plt.xlabel(\"max value\")\n",
      "plt.ylabel(\"execution time [sec]\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "<matplotlib.text.Text at 0x92db9d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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169crHY7FqD6Mae5cICRE6WiIyB5ZfI1Do9EgLCwMKSkpj/yZvdU4qg9j6t0b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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x9287a90>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}
	{
	"metadata": {
	"name": ""
	},
	"nbformat": 3,
	"nbformat_minor": 0,
	"worksheets": [
	{
	"cells": [
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"from tables import *\n",
	"\n",
	"class KeyValue(IsDescription):\n",
	" key = StringCol(itemsize=30, dflt=\" \", pos=0) # character String\n",
	" value = Int64Col(dflt=0, pos=1)\n",
	"\n",
	"f = open_file(\"keyvalue.h5\", \"w\") \n",
	"filters = Filters(complevel=1, complib='blosc')\n",
	"kv = f.create_table(\"/\", \"keyvalues\", KeyValue, filters=filters)\n",
	"\n",
	"import string\n",
	"import random\n",
	"\n",
	"for j in range(20):\n",
	" values = []\n",
	" for i in xrange(100000):\n",
	" key = \"\".join(random.sample(string.uppercase, 5))\n",
	" value = random.randint(0, 1000000)\n",
	" values.append((key, value))\n",
	" kv.append(values)\n",
	"\n",
	"f.close()"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 1
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"f = open_file(\"keyvalue.h5\", \"a\")\n",
	"kv = f.root.keyvalues\n",
	"\n",
	"kv.cols.value.remove_index()\n",
	"\n",
	"print \"without index\"\n",
	"%timeit a = kv.read_where('value < 10')\n",
	"%timeit a = kv.read_where('value < 1000')\n",
	"\n",
	"kv.cols.value.create_csindex()\n",
	"\n",
	"print \"with index\"\n",
	"%timeit a = kv.read_where('value < 10')\n",
	"%timeit a = kv.read_where('value < 1000')\n",
	"\n",
	"#f.close()"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"output_type": "stream",
	"stream": "stdout",
	"text": [
	"without index\n",
	"10 loops, best of 3: 139 ms per loop"
	]
	},
	{
	"output_type": "stream",
	"stream": "stdout",
	"text": [
	"\n",
	"1 loops, best of 3: 175 ms per loop"
	]
	},
	{
	"output_type": "stream",
	"stream": "stdout",
	"text": [
	"\n",
	"with index"
	]
	},
	{
	"output_type": "stream",
	"stream": "stdout",
	"text": [
	"\n",
	"10000 loops, best of 3: 201 \u00b5s per loop"
	]
	},
	{
	"output_type": "stream",
	"stream": "stdout",
	"text": [
	"\n",
	"10 loops, best of 3: 88.6 ms per loop"
	]
	},
	{
	"output_type": "stream",
	"stream": "stdout",
	"text": [
	"\n"
	]
	}
	],
	"prompt_number": 2
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"from timeit import timeit, repeat"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 8
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"data = []\n",
	"for i in range(7):\n",
	" x = repeat(lambda: kv.read_where('value < {0}'.format(10**i)), number=10)\n",
	" data.append(min(x)/10)"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 13
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"import matplotlib.pyplot as plt\n",
	"plt.loglog([10**i for i in range(7)], data)\n",
	"plt.xlabel(\"max value\")\n",
	"plt.ylabel(\"execution time [sec]\")"
	],
	"language": "python",
	"metadata": {},
	"outputs": [
	{
	"metadata": {},
	"output_type": "pyout",
	"prompt_number": 17,
	"text": [
	"<matplotlib.text.Text at 0x92db9d0>"
	]
	},
	{
	"metadata": {},
	"output_type": "display_data",
	"png": 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169crHY7FqD6Mae5cICRE6WiIyB5ZfI1Do9EgLCwMKSkpj/yZvdU4qg9j6t0b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\n",
	"text": [
	"<matplotlib.figure.Figure at 0x9287a90>"
	]
	}
	],
	"prompt_number": 17
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [
	"f.close()"
	],
	"language": "python",
	"metadata": {},
	"outputs": [],
	"prompt_number": 18
	},
	{
	"cell_type": "code",
	"collapsed": false,
	"input": [],
	"language": "python",
	"metadata": {},
	"outputs": []
	}
	],
	"metadata": {}
	}
	]
	}