wesm/Feather-Perf-Test.ipynb

## Feather-Perf-Test.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gc\n",
    "import os\n",
    "import time\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import feather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_floats(n, pct_null, repeats=1):\n",
    "    nunique = int(n / repeats)\n",
    "    unique_values = np.random.randn(nunique)\n",
    "    \n",
    "    num_nulls = int(nunique * pct_null)\n",
    "    null_indices = np.random.choice(nunique, size=num_nulls, replace=False)\n",
    "    unique_values[null_indices] = np.nan\n",
    "    \n",
    "    return unique_values.repeat(repeats)\n",
    "\n",
    "def generate_data(nrows, ncols, pct_null=0.1, repeats=1, dtype='float64'):\n",
    "    type_ = np.dtype('float64')\n",
    "    \n",
    "    data = {\n",
    "        'c' + str(i): generate_floats(nrows, pct_null, repeats)\n",
    "        for i in range(ncols)\n",
    "    }\n",
    "    return pd.DataFrame(data)\n",
    "\n",
    "  \n",
    "MEGABYTE = 1 << 20\n",
    "DATA_SIZE = 512 * MEGABYTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.17156705660745503\n",
      "0.17317785640479996\n",
      "0.17500421280274167\n"
     ]
    }
   ],
   "source": [
    "def get_timing(f, path, niter):\n",
    "    start = time.clock_gettime(time.CLOCK_MONOTONIC)\n",
    "    for i in range(niter):\n",
    "        f(path)\n",
    "    elapsed = time.clock_gettime(time.CLOCK_MONOTONIC) - start\n",
    "    return elapsed\n",
    "\n",
    "NITER = 5\n",
    "\n",
    "NCOLS = [10, 50, 500]\n",
    "\n",
    "results = {}\n",
    "for ncols in NCOLS:\n",
    "    NROWS = DATA_SIZE / ncols / np.dtype('float64').itemsize\n",
    "    df = generate_data(NROWS, ncols)\n",
    "\n",
    "    path = 'test.feather'\n",
    "\n",
    "    feather.write_dataframe(df, path)\n",
    "        \n",
    "    # prime the OS cache\n",
    "    feather.read_dataframe(path)\n",
    "    \n",
    "    gc.disable()\n",
    "    elapsed = get_timing(feather.read_dataframe, path, NITER) / NITER\n",
    "    gc.enable()\n",
    "    gc.collect()\n",
    "    print(elapsed)\n",
    "    results[ncols] = elapsed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{10: 0.853481687803287, 50: 0.3072799387970008, 500: 0.7237378041958437}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feather 0.3.1\n",
    "results = {10: 0.853481687803287, 50: 0.3072799387970008, 500: 0.7237378041958437}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{10: 0.17156705660745503, 50: 0.17317785640479996, 500: 0.17500421280274167}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feather 0.4.0\n",
    "results = {10: 0.17156705660745503, 50: 0.17317785640479996, 500: 0.17500421280274167}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = {\n",
    "    'Feather 0.4.0': {10: 0.17156705660745503, 50: 0.17317785640479996, 500: 0.17500421280274167},\n",
    "    'Feather 0.3.1': {10: 0.853481687803287, 50: 0.3072799387970008, 500: 0.7237378041958437}\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = pd.DataFrame(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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fnOSC4e0LkrxlIo8BAACTbdst0OaLW2v3J0lr7f6q2mO8jarqfUnelyQzdtk9\nc544fwuUwlbh9H+Y6goAgEmy9Ozjp7qEkUzZhXuttc+21ua11ubN2GHWVJUBAADr2BIh+YGq2jNJ\nht8f3AKPAQAAW8yWCMmXJzlpePukJIu2wGMAAMAWM9G3gPtCkuuS7FdVy6rq3UnOTvK6qlqS5HXD\nZQAA2GpM6MK91to71nPXMRNpFwAAppJP3AMAgI6QDAAAHSEZAAA6QjIAAHSEZAAA6AjJAADQEZIB\nAKAjJAMAQEdIBgCAjpAMAAAdIRkAADpCMgAAdIRkAADoCMkAANARkgEAoCMkAwBAR0gGAICOkAwA\nAB0hGQAAOkIyAAB0hGQAAOgIyQAA0Nl2qgtIkoNeMis3nX38VJcBAABJjCQDAMA6hGQAAOgIyQAA\n0BGSAQCgIyQDAEBHSAYAgI6QDAAAHSEZAAA6QjIAAHSEZAAA6AjJAADQEZIBAKAjJAMAQEdIBgCA\njpAMAAAdIRkAADpCMgAAdIRkAADoCMkAANARkgEAoCMkAwBAR0gGAICOkAwAAB0hGQAAOkIyAAB0\nhGQAAOhUa22qa0hVPZrku1Ndx1biRUkemuoithL6ajT6aXT6anT6ajT6aXT6anT6asNe2lrbfWMb\nbTsZlYzgu621eVNdxNagqm7SV6PRV6PRT6PTV6PTV6PRT6PTV6PTV5uH6RYAANARkgEAoDNdQvJn\np7qArYi+Gp2+Go1+Gp2+Gp2+Go1+Gp2+Gp2+2gymxYV7AAAwnUyXkWQAAJg2JjUkV9Ubquq7VXVX\nVZ0+zv3bV9VFw/tvqKo5k1nfdDFCP/27qrq5qlZV1QlTUeN0MUJf/UFVfbuqbquqr1XVS6eizulg\nhL46uar+Z1XdWlXfrKpXTUWd08HG+mrMdidUVauq5+RV5CM8p95ZVSuGz6lbq+o9U1HndDDKc6qq\n3jr8fXVHVf3tZNc4XYzwvPrYmOfU96rqkamoczoYoa9+paquqqpbhn8Hj5uKOrdarbVJ+UoyI8nd\nSV6W5HlJvpXkVd02709yzvD225NcNFn1TZevEftpTpKDk1yY5ISprnma99WvJ9lhePuU5+JzahP6\napcxtxck+ceprnu69tVwu52TXJPk+iTzprru6dhPSd6Z5C+mutap/hqxr16e5JYkLxgu7zHVdU/X\nvuq2/0CS86a67unaVxnMTT5lePtVSZZOdd1b09dkjiQfnuSu1tr3W2s/T/LFJG/utnlzkguGty9J\nckxV1STWOB1stJ9aa0tba7cl+cVUFDiNjNJXV7XWfjpcvD7J7EmucboYpa9+MmZxxyTP1QsWRvld\nlST/Kcn/k+SJySxuGhm1nxiAUSeFAAADYklEQVStr96b5C9baz9Oktbag5Nc43Sxqc+rdyT5wqRU\nNv2M0lctyS7D27OS3DeJ9W31JjMkvyTJvWOWlw3XjbtNa21VkpVJdpuU6qaPUfqJgU3tq3cn+fIW\nrWj6Gqmvqur3quruDMLfqZNU23Sz0b6qql9Lsndr7b9PZmHTzKg/f781fJn3kqrae3JKm3ZG6atX\nJHlFVV1bVddX1RsmrbrpZeTf68Ppc/sk+fok1DUdjdJXZyX5napaluSKDEbeGdFkhuTxRoT7kapR\ntnm20wejG7mvqup3ksxL8qdbtKLpa6S+aq39ZWvtV5P8YZIPb/GqpqcN9lVVbZPkY0k+NGkVTU+j\nPKf+Psmc1trBSf4pv3yl8LlmlL7aNoMpF0dnMDr611W16xauazralL+Bb09ySWvt6S1Yz3Q2Sl+9\nI8nnW2uzkxyX5G+Gv8MYwWR21LIkY0cRZmfdYf8121TVthm8NPCjSalu+hilnxgYqa+q6jeTnJlk\nQWvtyUmqbbrZ1OfVF5O8ZYtWNH1trK92TnJgkqurammSI5Jc/hy8eG+jz6nW2sNjfub+KsmrJ6m2\n6WbUv3+LWmtPtdb+Ncl3MwjNzzWb8rvq7XnuTrVIRuurdye5OElaa9clmZnkRZNS3bPAZIbkG5O8\nvKr2qarnZfDkvrzb5vIkJw1vn5Dk62042/w5ZJR+YmCjfTV8WfzcDALyc3WOXzJaX439g3x8kiWT\nWN90ssG+aq2tbK29qLU2p7U2J4O57gtaazdNTblTZpTn1J5jFhckuXMS65tORvm9flkGFxqnql6U\nwfSL709qldPDSH8Dq2q/JC9Ict0k1zedjNJXP0hyTJJU1SszCMkrJrXKrdikheThHOPfT/KVDH5R\nXtxau6Oq/u+qWjDc7HNJdququ5L8QZL1vvXSs9Uo/VRVhw3nF/12knOr6o6pq3jqjPic+tMkOyX5\n0vDtgp6T/3CM2Fe/P3zrqVsz+Pk7aT3NPauN2FfPeSP206nD59S3Mpjj/s6pqXZqjdhXX0nycFV9\nO8lVSU5rrT08NRVPnU34+XtHki8+BwfS1hixrz6U5L3Dn8EvJHnnc7nPNpVP3AMAgI7J2wAA0BGS\nAQCgIyQDAEBHSAYAgI6QDAAAHSEZAAA6QjIAAHSEZAAA6Pz/Q7bzOt7wXokAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb5706cb828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results.plot.barh(figsize=(12, 6))\n",
    "plt.title('Time to read 512 MB (shorter is faster)')\n",
    "plt.savefig('feather-perf.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 2,
	"metadata": {},
	"outputs": [],
	"source": [
	"import gc\n",
	"import os\n",
	"import time\n",
	"\n",
	"import numpy as np\n",
	"import pandas as pd\n",
	"import feather"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 2,
	"metadata": {},
	"outputs": [],
	"source": [
	"def generate_floats(n, pct_null, repeats=1):\n",
	" nunique = int(n / repeats)\n",
	" unique_values = np.random.randn(nunique)\n",
	" \n",
	" num_nulls = int(nunique * pct_null)\n",
	" null_indices = np.random.choice(nunique, size=num_nulls, replace=False)\n",
	" unique_values[null_indices] = np.nan\n",
	" \n",
	" return unique_values.repeat(repeats)\n",
	"\n",
	"def generate_data(nrows, ncols, pct_null=0.1, repeats=1, dtype='float64'):\n",
	" type_ = np.dtype('float64')\n",
	" \n",
	" data = {\n",
	" 'c' + str(i): generate_floats(nrows, pct_null, repeats)\n",
	" for i in range(ncols)\n",
	" }\n",
	" return pd.DataFrame(data)\n",
	"\n",
	" \n",
	"MEGABYTE = 1 << 20\n",
	"DATA_SIZE = 512 * MEGABYTE"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 3,
	"metadata": {},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"0.17156705660745503\n",
	"0.17317785640479996\n",
	"0.17500421280274167\n"
	]
	}
	],
	"source": [
	"def get_timing(f, path, niter):\n",
	" start = time.clock_gettime(time.CLOCK_MONOTONIC)\n",
	" for i in range(niter):\n",
	" f(path)\n",
	" elapsed = time.clock_gettime(time.CLOCK_MONOTONIC) - start\n",
	" return elapsed\n",
	"\n",
	"NITER = 5\n",
	"\n",
	"NCOLS = [10, 50, 500]\n",
	"\n",
	"results = {}\n",
	"for ncols in NCOLS:\n",
	" NROWS = DATA_SIZE / ncols / np.dtype('float64').itemsize\n",
	" df = generate_data(NROWS, ncols)\n",
	"\n",
	" path = 'test.feather'\n",
	"\n",
	" feather.write_dataframe(df, path)\n",
	" \n",
	" # prime the OS cache\n",
	" feather.read_dataframe(path)\n",
	" \n",
	" gc.disable()\n",
	" elapsed = get_timing(feather.read_dataframe, path, NITER) / NITER\n",
	" gc.enable()\n",
	" gc.collect()\n",
	" print(elapsed)\n",
	" results[ncols] = elapsed"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 8,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"{10: 0.853481687803287, 50: 0.3072799387970008, 500: 0.7237378041958437}"
	]
	},
	"execution_count": 8,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"# Feather 0.3.1\n",
	"results = {10: 0.853481687803287, 50: 0.3072799387970008, 500: 0.7237378041958437}"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 4,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"{10: 0.17156705660745503, 50: 0.17317785640479996, 500: 0.17500421280274167}"
	]
	},
	"execution_count": 4,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"# Feather 0.4.0\n",
	"results = {10: 0.17156705660745503, 50: 0.17317785640479996, 500: 0.17500421280274167}"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 3,
	"metadata": {},
	"outputs": [],
	"source": [
	"results = {\n",
	" 'Feather 0.4.0': {10: 0.17156705660745503, 50: 0.17317785640479996, 500: 0.17500421280274167},\n",
	" 'Feather 0.3.1': {10: 0.853481687803287, 50: 0.3072799387970008, 500: 0.7237378041958437}\n",
	"}"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {},
	"outputs": [],
	"source": [
	"import matplotlib"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 4,
	"metadata": {},
	"outputs": [],
	"source": [
	"results = pd.DataFrame(results)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 10,
	"metadata": {},
	"outputs": [],
	"source": [
	"%matplotlib inline"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 11,
	"metadata": {},
	"outputs": [],
	"source": [
	"import matplotlib.pyplot as plt"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 16,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"image/png": 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fnOSC4e0LkrxlIo8BAACTbdst0OaLW2v3J0lr7f6q2mO8jarqfUnelyQzdtk9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\n",
	"text/plain": [
	"<matplotlib.figure.Figure at 0x7fb5706cb828>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"results.plot.barh(figsize=(12, 6))\n",
	"plt.title('Time to read 512 MB (shorter is faster)')\n",
	"plt.savefig('feather-perf.png')"
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {},
	"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": "3.6.3"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 2
	}