public_benchmark.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Serialization formats comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import xray\n",
    "import numpy as np\n",
    "import h5py as h5\n",
    "import bcolz\n",
    "\n",
    "\n",
    "def load_buf(path, shape, dtype='float64'):\n",
    "    return np.fromfile(path, dtype=dtype).reshape(shape)\n",
    "\n",
    "def load_h5(path):\n",
    "    with h5.File(path, \"r\") as f:\n",
    "        dset = f['band']\n",
    "        return dset.value\n",
    "\n",
    "def load_bcolz(path):\n",
    "    return bcolz.open('./data/bcolz', mode='r')\n",
    "\n",
    "def load_protobuf(path):\n",
    "    pb_data = ndarray_pb2.NDArray()\n",
    "    pb_data.ParseFromString(open(path, 'r').read())\n",
    "    return np.array(pb_data.array, dtype=pb_data.dtype).reshape(tuple(pb_data.shape))\n",
    "\n",
    "\n",
    "remote_data = xray.open_dataset('http://iridl.ldeo.columbia.edu/SOURCES/.OSU/.PRISM/.monthly/dods', decode_times=False)\n",
    "arr1 = remote_data['tmax'][0, :, :].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Serialize Numpy buffer to disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 76 µs, sys: 5.23 ms, total: 5.31 ms\n",
      "Wall time: 7.43 ms\n"
     ]
    }
   ],
   "source": [
    "#Band written as numpy buffer to file\n",
    "arr1.tofile(\"./data/test.buf\")\n",
    "\n",
    "%time arr2 = load_buf(\"./data/test.buf\", arr1.shape, arr1.dtype.name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###HDF5 raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 931 µs, sys: 5.24 ms, total: 6.17 ms\n",
      "Wall time: 6.13 ms\n"
     ]
    }
   ],
   "source": [
    "#Band written as hdf5 to file\n",
    "\n",
    "with h5.File(\"./data/test1.h5\", \"w\") as f:\n",
    "    dset = f.create_dataset(\"band\", data=arr1)\n",
    "    \n",
    "%time arr3 = load_h5(\"./data/test1.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###HDF5 with LZF compression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 10 ms, sys: 3.33 ms, total: 13.4 ms\n",
      "Wall time: 14.6 ms\n"
     ]
    }
   ],
   "source": [
    "#Band written as hdf5 to file using lzf compression\n",
    "\n",
    "with h5.File(\"./data/test2.h5\", \"w\") as f:\n",
    "    dset = f.create_dataset(\"band\", data=arr1, compression=\"lzf\")\n",
    "\n",
    "%time arr4 = load_h5(\"./data/test2.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###HDF5 with LZF compression and chunking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 9.44 ms, sys: 2.51 ms, total: 12 ms\n",
      "Wall time: 12.9 ms\n"
     ]
    }
   ],
   "source": [
    "with h5.File(\"./data/test3.h5\", \"w\") as f:\n",
    "    dset = f.create_dataset(\"band\", data=arr1, chunks=(128, 128), compression=\"lzf\")\n",
    "\n",
    "%time arr5 = load_h5(\"./data/test3.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###HDF5 with GZIP compression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 56.9 ms, sys: 2.52 ms, total: 59.5 ms\n",
      "Wall time: 59.9 ms\n"
     ]
    }
   ],
   "source": [
    "with h5.File(\"./data/test4.h5\", \"w\") as f:\n",
    "    dset = f.create_dataset(\"band\", data=arr1, compression=\"gzip\")\n",
    "\n",
    "%time arr6 = load_h5(\"./data/test4.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###BCOLZ concurrent block compression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 702 µs, sys: 126 µs, total: 828 µs\n",
      "Wall time: 834 µs\n",
      "CPU times: user 11.6 ms, sys: 12.1 ms, total: 23.7 ms\n",
      "Wall time: 12.8 ms\n"
     ]
    }
   ],
   "source": [
    "with bcolz.carray(arr1, rootdir='data/bcolz', mode='w') as _:  \n",
    "    c = _  \n",
    " \n",
    "%time arr7 = load_bcolz(\"'data/bcolz'\")\n",
    "%time arr7 = load_bcolz(\"'data/bcolz'\")[:, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Protocol Buffers \n",
    "\n",
    "#####Used Schema: ndarray.proto\n",
    "\n",
    "```\n",
    "message NDArray {\n",
    "  repeated double array = 1;\n",
    "  repeated int32 shape = 2;\n",
    "  required string dtype = 3;\n",
    "}\n",
    "\n",
    "message NDArrayPacked {\n",
    "  repeated double array = 1 [packed=true];\n",
    "  repeated int32 shape = 2;\n",
    "  required string dtype = 3;\n",
    "}```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.9 s, sys: 31.3 ms, total: 1.94 s\n",
      "Wall time: 1.94 s\n",
      "CPU times: user 1.72 s, sys: 18.3 ms, total: 1.74 s\n",
      "Wall time: 1.74 s\n"
     ]
    }
   ],
   "source": [
    "# Create this lib by compiling the previous schema with:\n",
    "# protoc -I=./ --python_out=./ ./ndarray.proto\n",
    "import data.ndarray_pb2 as ndarray_pb2\n",
    "\n",
    "parray = list(arr1.flatten().astype(int))\n",
    "\n",
    "with open(\"data/test1.pbf\", \"w\") as pbf:\n",
    "    arr = ndarray_pb2.NDArray()\n",
    "    arr.array.extend(parray)\n",
    "    arr.shape.extend(list(arr1.shape))\n",
    "    arr.dtype = arr1.dtype.name\n",
    "    pbf.write(arr.SerializeToString())\n",
    "\n",
    "with open(\"data/test2.pbf\", \"w\") as pbf:\n",
    "    arr = ndarray_pb2.NDArrayPacked()\n",
    "    arr.array.extend(parray)\n",
    "    arr.shape.extend(list(arr1.shape))\n",
    "    arr.dtype = arr1.dtype.name\n",
    "    pbf.write(arr.SerializeToString())\n",
    "\n",
    "%time arr8 = load_protobuf(\"data/test1.pbf\")\n",
    "%time arr8 = load_protobuf(\"data/test2.pbf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###Comparison of resulting file sizes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of numpy buff: 6.66 MB\n",
      "Size of hdf5: 6.66 MB\n",
      "Size of hdf5 (lzf compressed): 1.55 MB\n",
      "Size of hdf5 (128 chunk & lzf compressed): 1.49 MB\n",
      "Size of hdf5 (gzip compressed): 1.13 MB\n",
      "Size of bcolz: 2.49 MB\n",
      "Size of protobuf: 7.49 MB\n",
      "Size of protobuf (packed): 6.66 MB\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "MB = 1024*1024.0\n",
    "\n",
    "print(\"Size of numpy buff: {0:.2f} MB\".format(os.stat(\"./data/test.buf\").st_size/MB))\n",
    "print(\"Size of hdf5: {0:.2f} MB\".format(os.stat(\"./data/test1.h5\").st_size/MB))\n",
    "print(\"Size of hdf5 (lzf compressed): {0:.2f} MB\".format(os.stat(\"./data/test2.h5\").st_size/MB))\n",
    "print(\"Size of hdf5 (128 chunk & lzf compressed): {0:.2f} MB\".format(os.stat(\"./data/test3.h5\").st_size/MB))\n",
    "print(\"Size of hdf5 (gzip compressed): {0:.2f} MB\".format(os.stat(\"./data/test4.h5\").st_size/MB))\n",
    "print(\"Size of bcolz: {0:.2f} MB\".format(sum(os.path.getsize('data/bcolz/data/' + f) for f in os.listdir('data/bcolz/data') if os.path.isfile('data/bcolz/data/' + f))/MB))\n",
    "print(\"Size of protobuf: {0:.2f} MB\".format(os.stat(\"./data/test1.pbf\").st_size/MB))\n",
    "print(\"Size of protobuf (packed): {0:.2f} MB\".format(os.stat(\"./data/test2.pbf\").st_size/MB))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/pablo/anaconda/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
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      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104db5b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pylab as plt\n",
    "\n",
    "im = plt.imshow(arr1, cmap='hot')\n",
    "plt.colorbar(im, orientation='horizontal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}