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monkeybutter/public_benchmark.ipynb

Last active September 12, 2022 13:46
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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
}
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