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eiri/Effect of variable compressor on database size in CouchDB.ipynb

Last active August 29, 2015 14:23
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Effect of variable compressor on database size in CouchDB
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Effect of variable compressor on database size in CouchDB.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Effect of variable compressor on database size in CouchDB\n",
"\n",
"\n",
"## What is it\n",
"\n",
"CouchDB allows to configure a compression library used to store data on disk. The choises are [snappy] vs zlib's [deflate]. As an optimization at compression phase the data compressed with both libraries and smaller one is used even if it is not a configured option.\n",
"\n",
"This document is an attempt to find out the real effect of this optimization on used disk size.\n",
"\n",
"\n",
"## How the data was gathered\n",
"\n",
"For the measurments used a standard three nodes CouchDB 2.0 cluster configured as:\n",
"\n",
"---\n",
"```\n",
"[cluster]\n",
"q=8\n",
"r=2\n",
"w=2\n",
"n=3\n",
"```\n",
"---\n",
"\n",
"The new test database created for each run and propagated with 1024 documents through individual POST on `/db/doc` end-point. Each document is exactly 1024 byte big as per `Content-Length` header. The total database data size expected to be around 1M. Pull of `_all_docs` with `include_docs=true` confirms that by returning 1.2M JSON document where additional size is an overhead of the description fields.\n",
"\n",
"After the propogation, the data directories of the cluster recursivly walked and the size of each test database shard file sums up. At the next step a compaction run on each shard and the sum of all shard files calculated again.\n",
"\n",
"The test ran 8 times for each case with and without the compression optimization.\n",
"\n",
"\n",
"## Result\n",
"\n",
"This naïve testing demonstrate that on cluster with `n=3` the compression optimization allows to save less than one percent of the total disk space.\n",
"\n",
"[deflate]: https://en.wikipedia.org/wiki/DEFLATE\n",
"[snappy]: https://en.wikipedia.org/wiki/Snappy_(software)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.ticker import FuncFormatter"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"byte_func = lambda x, pos: '%1.1fM' % (x * 1e-6) if x >= 1e6 else '%1.1fK' % (x * 1e-3) if x >= 1e3 else '%1.1f' % x\n",
"majorFormatter = FuncFormatter(byte_func)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Shards sizes without compaction"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"with_optimization = (61158008, 61534840, 61993592, 61612664, 61457016, 61813368, 61604472, 61788792)\n",
"\n",
"without_optimization = (61993592, 61616760, 61948536, 61641336, 61588088, 61203064, 61837944, 61981304)\n",
"\n",
"data1 = {\n",
" 'with optimization' : with_optimization,\n",
" 'without optimization' : without_optimization\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x109b54e90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots()\n",
"ax.boxplot(data1.values())\n",
"ax.set_xticklabels(data1.keys())\n",
"ax.yaxis.set_major_formatter(majorFormatter)\n",
"plt.title('Database size')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>with optimization</th>\n",
" <th>without optimization</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>8.000000</td>\n",
" <td>8.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>61620344.000000</td>\n",
" <td>61726328.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>254389.889754</td>\n",
" <td>270032.17559</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>61158008.000000</td>\n",
" <td>61203064.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>61515384.000000</td>\n",
" <td>61609592.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>61608568.000000</td>\n",
" <td>61739640.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>61794936.000000</td>\n",
" <td>61956728.00000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>61993592.000000</td>\n",
" <td>61993592.00000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" with optimization without optimization\n",
"count 8.000000 8.00000\n",
"mean 61620344.000000 61726328.00000\n",
"std 254389.889754 270032.17559\n",
"min 61158008.000000 61203064.00000\n",
"25% 61515384.000000 61609592.00000\n",
"50% 61608568.000000 61739640.00000\n",
"75% 61794936.000000 61956728.00000\n",
"max 61993592.000000 61993592.00000"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1 = pd.DataFrame(data1)\n",
"df1.describe()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Average increase of used space: 0.17%\n"
]
}
],
"source": [
"mean_with = df1.mean()['with optimization']\n",
"mean_without = df1.mean()['without optimization']\n",
"print \"Average increase of used space: {:.2%}\".format((mean_without - mean_with) / mean_with)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Shards sizes after compaction"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with_optimization_compacted = (3678840, 3666552, 3666552, 3666552, 3666552, 3678840, 3678840, 3678840)\n",
"\n",
"without_optimization_compacted = (3691128, 3715704, 3703416, 3691128, 3740280, 3703416, 3703416, 3691128)\n",
"\n",
"data2 = {\n",
" 'with optimization' : with_optimization_compacted,\n",
" 'without optimization' : without_optimization_compacted\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x109bf0350>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots()\n",
"ax.boxplot(data2.values())\n",
"ax.set_xticklabels(data2.keys())\n",
"ax.yaxis.set_major_formatter(majorFormatter)\n",
"plt.title('Compacted database size')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>with optimization</th>\n",
" <th>without optimization</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>8.000000</td>\n",
" <td>8.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>3672696.000000</td>\n",
" <td>3704952.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>6568.212281</td>\n",
" <td>16665.018555</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>3666552.000000</td>\n",
" <td>3691128.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>3666552.000000</td>\n",
" <td>3691128.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>3672696.000000</td>\n",
" <td>3703416.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>3678840.000000</td>\n",
" <td>3706488.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>3678840.000000</td>\n",
" <td>3740280.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" with optimization without optimization\n",
"count 8.000000 8.000000\n",
"mean 3672696.000000 3704952.000000\n",
"std 6568.212281 16665.018555\n",
"min 3666552.000000 3691128.000000\n",
"25% 3666552.000000 3691128.000000\n",
"50% 3672696.000000 3703416.000000\n",
"75% 3678840.000000 3706488.000000\n",
"max 3678840.000000 3740280.000000"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2 = pd.DataFrame(data2)\n",
"df2.describe()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Average increase of used space: 0.88%\n"
]
}
],
"source": [
"mean_with = df2.mean()['with optimization']\n",
"mean_without = df2.mean()['without optimization']\n",
"print \"Average increase of used space: {:.2%}\".format((mean_without - mean_with) / mean_with)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Data size growth rate"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Setup\n",
"\n",
"The documents of fixed size 1024 byte added in groups by 1024 up to total 10240 documents or 10MB of data size. After each batch compaction ran on each shard and sum of the shards recorded.\n",
"\n",
"The measurments done on a fresh built cluster for both version with and without compaction."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x109c08e50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"columns = ['with optimization', 'without optimization']\n",
"\n",
"num_docs = (1024, 2048, 3072, 4096, 5120, 6144, 7168, 8192, 9216, 10240)\n",
"\n",
"sizes_with_optimization = (3666552, 7070436, 10486680, 13890456, 17294232,\n",
" 20722584, 24126360, 27567000, 30970776, 34386840)\n",
"\n",
"sizes_without_optimization = (3691128, 7144164, 10572696, 14037912, 17466264,\n",
" 20870040, 24335256, 27788184, 31204248, 34657176)\n",
"\n",
"df3 = pd.DataFrame(data = zip(sizes_with_optimization, sizes_without_optimization), index=num_docs, columns=columns)\n",
"\n",
"df3.plot(kind='bar')\n",
"plt.title('Size growth')\n",
"plt.xticks(rotation=35)\n",
"plt.gca().set_ylim([0, 41943040])\n",
"plt.gca().yaxis.set_major_formatter(majorFormatter)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x109c0b290>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def delta(x, y):\n",
" return abs(x - y)\n",
"\n",
"df3['delta'] = delta(df3['with optimization'], df3['without optimization'])\n",
"df3['delta'].plot()\n",
"plt.title('Delta of size change')\n",
"plt.xticks(rotation=35)\n",
"plt.gca().yaxis.set_major_formatter(majorFormatter)\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": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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