create-plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(os.path.join('results', 'results-summary.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bson', 'cbor', 'json', 'msgpack', 'parquet', 'pickle', 'protobuf', 'ujson']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(set(df.name.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# parquet & protobuf requires tuples\n",
    "df = df[(df['dtype'] != 'tuples') | (df['name'] == 'parquet') | (df['name'] == 'protobuf')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# single serde results\n",
    "single_df = df[df['items'] == 1].copy()\n",
    "\n",
    "# 1M objects serde results\n",
    "million_df = df[df['items'] == 1_000_000].copy()\n",
    "million_df['avg_object'] = million_df['avg'] / 1_000_000\n",
    "million_df['avg_serde_object'] = million_df['avg_serde'] / 1_000_000\n",
    "million_df['avg_serialized_size'] = million_df['serialized_size'] / 1_000_000\n",
    "\n",
    "# 1M serialization results\n",
    "ser_mil_df = million_df[million_df['fn'] == 'dump']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>dtype</th>\n",
       "      <th>fn</th>\n",
       "      <th>items</th>\n",
       "      <th>avg</th>\n",
       "      <th>avg_serde</th>\n",
       "      <th>baseline-ratio</th>\n",
       "      <th>baseline-speedup</th>\n",
       "      <th>serialized_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bson</td>\n",
       "      <td>dicts</td>\n",
       "      <td>dump</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bson</td>\n",
       "      <td>dicts</td>\n",
       "      <td>load</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>44.4444</td>\n",
       "      <td>2.25</td>\n",
       "      <td>498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>cbor</td>\n",
       "      <td>dicts</td>\n",
       "      <td>dump</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>23.5294</td>\n",
       "      <td>4.25</td>\n",
       "      <td>435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>cbor</td>\n",
       "      <td>dicts</td>\n",
       "      <td>load</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>22.2222</td>\n",
       "      <td>4.50</td>\n",
       "      <td>435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>json</td>\n",
       "      <td>dicts</td>\n",
       "      <td>dump</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>1.00</td>\n",
       "      <td>934</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name  dtype    fn  items       avg  avg_serde  baseline-ratio  \\\n",
       "0   bson  dicts  dump      1  0.000017   0.000013        100.0000   \n",
       "5   bson  dicts  load      1  0.000008   0.000013         44.4444   \n",
       "10  cbor  dicts  dump      1  0.000004   0.000004         23.5294   \n",
       "15  cbor  dicts  load      1  0.000004   0.000004         22.2222   \n",
       "30  json  dicts  dump      1  0.000017   0.000017        100.0000   \n",
       "\n",
       "    baseline-speedup  serialized_size  \n",
       "0               1.00              498  \n",
       "5               2.25              498  \n",
       "10              4.25              435  \n",
       "15              4.50              435  \n",
       "30              1.00              934  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>dtype</th>\n",
       "      <th>fn</th>\n",
       "      <th>items</th>\n",
       "      <th>avg</th>\n",
       "      <th>avg_serde</th>\n",
       "      <th>baseline-ratio</th>\n",
       "      <th>baseline-speedup</th>\n",
       "      <th>serialized_size</th>\n",
       "      <th>avg_object</th>\n",
       "      <th>avg_serde_object</th>\n",
       "      <th>avg_serialized_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bson</td>\n",
       "      <td>dicts</td>\n",
       "      <td>dump</td>\n",
       "      <td>1000000</td>\n",
       "      <td>9.722888</td>\n",
       "      <td>7.926932</td>\n",
       "      <td>200.1367</td>\n",
       "      <td>0.4997</td>\n",
       "      <td>446883139</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>446.883139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>bson</td>\n",
       "      <td>dicts</td>\n",
       "      <td>load</td>\n",
       "      <td>1000000</td>\n",
       "      <td>6.130976</td>\n",
       "      <td>7.926932</td>\n",
       "      <td>54.9966</td>\n",
       "      <td>1.8183</td>\n",
       "      <td>446883139</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>446.883139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>cbor</td>\n",
       "      <td>dicts</td>\n",
       "      <td>dump</td>\n",
       "      <td>1000000</td>\n",
       "      <td>1.803696</td>\n",
       "      <td>3.801880</td>\n",
       "      <td>37.1274</td>\n",
       "      <td>2.6934</td>\n",
       "      <td>372329891</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>372.329891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>cbor</td>\n",
       "      <td>dicts</td>\n",
       "      <td>load</td>\n",
       "      <td>1000000</td>\n",
       "      <td>5.800064</td>\n",
       "      <td>3.801880</td>\n",
       "      <td>52.0282</td>\n",
       "      <td>1.9220</td>\n",
       "      <td>372329891</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>372.329891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>json</td>\n",
       "      <td>dicts</td>\n",
       "      <td>dump</td>\n",
       "      <td>1000000</td>\n",
       "      <td>4.858123</td>\n",
       "      <td>8.003020</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>781815927</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>781.815927</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name  dtype    fn    items       avg  avg_serde  baseline-ratio  \\\n",
       "4   bson  dicts  dump  1000000  9.722888   7.926932        200.1367   \n",
       "9   bson  dicts  load  1000000  6.130976   7.926932         54.9966   \n",
       "14  cbor  dicts  dump  1000000  1.803696   3.801880         37.1274   \n",
       "19  cbor  dicts  load  1000000  5.800064   3.801880         52.0282   \n",
       "34  json  dicts  dump  1000000  4.858123   8.003020        100.0000   \n",
       "\n",
       "    baseline-speedup  serialized_size  avg_object  avg_serde_object  \\\n",
       "4             0.4997        446883139    0.000010          0.000008   \n",
       "9             1.8183        446883139    0.000006          0.000008   \n",
       "14            2.6934        372329891    0.000002          0.000004   \n",
       "19            1.9220        372329891    0.000006          0.000004   \n",
       "34            1.0000        781815927    0.000005          0.000008   \n",
       "\n",
       "    avg_serialized_size  \n",
       "4            446.883139  \n",
       "9            446.883139  \n",
       "14           372.329891  \n",
       "19           372.329891  \n",
       "34           781.815927  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "million_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>dtype</th>\n",
       "      <th>fn</th>\n",
       "      <th>items</th>\n",
       "      <th>avg</th>\n",
       "      <th>avg_serde</th>\n",
       "      <th>baseline-ratio</th>\n",
       "      <th>baseline-speedup</th>\n",
       "      <th>serialized_size</th>\n",
       "      <th>avg_object</th>\n",
       "      <th>avg_serde_object</th>\n",
       "      <th>avg_serialized_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bson</td>\n",
       "      <td>dicts</td>\n",
       "      <td>dump</td>\n",
       "      <td>1000000</td>\n",
       "      <td>9.722888</td>\n",
       "      <td>7.926932</td>\n",
       "      <td>200.1367</td>\n",
       "      <td>0.4997</td>\n",
       "      <td>446883139</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>446.883139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>cbor</td>\n",
       "      <td>dicts</td>\n",
       "      <td>dump</td>\n",
       "      <td>1000000</td>\n",
       "      <td>1.803696</td>\n",
       "      <td>3.801880</td>\n",
       "      <td>37.1274</td>\n",
       "      <td>2.6934</td>\n",
       "      <td>372329891</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>372.329891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>json</td>\n",
       "      <td>dicts</td>\n",
       "      <td>dump</td>\n",
       "      <td>1000000</td>\n",
       "      <td>4.858123</td>\n",
       "      <td>8.003020</td>\n",
       "      <td>100.0000</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>781815927</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>781.815927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>msgpack</td>\n",
       "      <td>dicts</td>\n",
       "      <td>dump</td>\n",
       "      <td>1000000</td>\n",
       "      <td>1.234061</td>\n",
       "      <td>3.246158</td>\n",
       "      <td>25.4020</td>\n",
       "      <td>3.9367</td>\n",
       "      <td>373773160</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>373.773160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>parquet</td>\n",
       "      <td>tuples</td>\n",
       "      <td>dump</td>\n",
       "      <td>1000000</td>\n",
       "      <td>3.360880</td>\n",
       "      <td>2.132360</td>\n",
       "      <td>95.7149</td>\n",
       "      <td>1.0448</td>\n",
       "      <td>294309649</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>294.309649</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       name   dtype    fn    items       avg  avg_serde  baseline-ratio  \\\n",
       "4      bson   dicts  dump  1000000  9.722888   7.926932        200.1367   \n",
       "14     cbor   dicts  dump  1000000  1.803696   3.801880         37.1274   \n",
       "34     json   dicts  dump  1000000  4.858123   8.003020        100.0000   \n",
       "54  msgpack   dicts  dump  1000000  1.234061   3.246158         25.4020   \n",
       "74  parquet  tuples  dump  1000000  3.360880   2.132360         95.7149   \n",
       "\n",
       "    baseline-speedup  serialized_size  avg_object  avg_serde_object  \\\n",
       "4             0.4997        446883139    0.000010          0.000008   \n",
       "14            2.6934        372329891    0.000002          0.000004   \n",
       "34            1.0000        781815927    0.000005          0.000008   \n",
       "54            3.9367        373773160    0.000001          0.000003   \n",
       "74            1.0448        294309649    0.000003          0.000002   \n",
       "\n",
       "    avg_serialized_size  \n",
       "4            446.883139  \n",
       "14           372.329891  \n",
       "34           781.815927  \n",
       "54           373.773160  \n",
       "74           294.309649  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser_mil_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import altair as alt\n",
    "\n",
    "\n",
    "def make_bars_chart(df, title, scale_title=None, avg_col='avg', sqrt_scale=True,\n",
    "                   width=50, height=300, multi_bar=True, legend=True, labels=True):\n",
    "    source = df.copy()\n",
    "\n",
    "    if source[avg_col].median() > 1:\n",
    "        avg_scale = 'secs'\n",
    "        avg_coef = 1\n",
    "    elif source[avg_col].median() > 0.001:\n",
    "        avg_scale = 'ms'\n",
    "        avg_coef = 1e3\n",
    "    else:\n",
    "        avg_scale = 'µs'\n",
    "        avg_coef = 1e6\n",
    "\n",
    "    source[avg_scale] = (df[avg_col] * avg_coef).round(2)\n",
    "    \n",
    "    if not scale_title:\n",
    "        scale_titles = {\n",
    "            'secs': 'seconds',\n",
    "            'ms': 'milliseconds (1e−3 secs)',\n",
    "            'µs': 'microseconds (1e−6 secs)',\n",
    "        }\n",
    "        scale_title = scale_titles[avg_scale]\n",
    "    \n",
    "    if sqrt_scale:\n",
    "        y_scale = alt.Scale(type='sqrt')\n",
    "    else:\n",
    "        y_scale = alt.Scale()\n",
    "    \n",
    "    if multi_bar:\n",
    "        x_val = 'fn:N'\n",
    "        facet_kwds = {'column':'name:N'}\n",
    "    else:\n",
    "        x_val = 'name:N'\n",
    "        facet_kwds = {}\n",
    "\n",
    "    if legend:\n",
    "        legend = alt.Legend()\n",
    "    else:\n",
    "        legend = None\n",
    "\n",
    "    chart = alt.Chart(\n",
    "        width=width,\n",
    "        height=height,\n",
    "    ).mark_bar(\n",
    "        stroke='transparent',\n",
    "        size=20,\n",
    "    ).encode(\n",
    "        alt.X(x_val, scale=alt.Scale(), axis=alt.Axis(title='', labels=labels)),\n",
    "        alt.Y(f'{avg_scale}:Q', scale=y_scale, axis=alt.Axis(title=scale_title, grid=False)),\n",
    "        color=alt.Color(x_val, scale=alt.Scale(range=[\"#FF7B06\", \"#094AFB\"]), legend=legend),\n",
    "    )\n",
    "\n",
    "    text = chart.mark_text(\n",
    "        color='black',\n",
    "        dx = 0,\n",
    "        dy = -2,\n",
    "    ).encode(\n",
    "        text=f'{avg_scale}:Q'\n",
    "    )\n",
    "\n",
    "    return alt.layer(chart, text, data=source).facet(\n",
    "        **facet_kwds\n",
    "    ).configure_axis(\n",
    "        domainWidth=0.8\n",
    "    ).configure_view(\n",
    "        stroke='transparent'\n",
    "    ).properties(\n",
    "        title=title\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v2+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v2.6.0.json",
       "config": {
        "axis": {
         "domainWidth": 0.8
        },
        "view": {
         "height": 300,
         "stroke": "transparent",
         "width": 400
        }
       },
       "data": {
        "name": "data-85e238ea4c3369047bb4d4f33725406d"
       },
       "datasets": {
        "data-85e238ea4c3369047bb4d4f33725406d": [
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",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make_bars_chart(\n",
    "    single_df,\n",
    "    title='Serialization Time of a Single Object',\n",
    "    sqrt_scale=True,\n",
    "    labels=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v2+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v2.6.0.json",
       "config": {
        "axis": {
         "domainWidth": 0.8
        },
        "view": {
         "height": 300,
         "stroke": "transparent",
         "width": 400
        }
       },
       "data": {
        "name": "data-a794f04c8c482747d6aac612932af153"
       },
       "datasets": {
        "data-a794f04c8c482747d6aac612932af153": [
         {
          "avg": 9.722888000000001,
          "avg_object": 9.722888000000002e-06,
          "avg_serde": 7.926932000000001,
          "avg_serde_object": 7.926932000000001e-06,
          "avg_serialized_size": 446.883139,
          "baseline-ratio": 200.1367,
          "baseline-speedup": 0.4997,
          "dtype": "dicts",
          "fn": "dump",
          "items": 1000000,
          "name": "bson",
          "serialized_size": 446883139,
          "µs": 9.72
         },
         {
          "avg": 6.130976,
          "avg_object": 6.1309760000000004e-06,
          "avg_serde": 7.926932000000001,
          "avg_serde_object": 7.926932000000001e-06,
          "avg_serialized_size": 446.883139,
          "baseline-ratio": 54.9966,
          "baseline-speedup": 1.8183,
          "dtype": "dicts",
          "fn": "load",
          "items": 1000000,
          "name": "bson",
          "serialized_size": 446883139,
          "µs": 6.13
         },
         {
          "avg": 1.803696,
          "avg_object": 1.8036959999999999e-06,
          "avg_serde": 3.80188,
          "avg_serde_object": 3.8018800000000003e-06,
          "avg_serialized_size": 372.329891,
          "baseline-ratio": 37.1274,
          "baseline-speedup": 2.6934,
          "dtype": "dicts",
          "fn": "dump",
          "items": 1000000,
          "name": "cbor",
          "serialized_size": 372329891,
          "µs": 1.8
         },
         {
          "avg": 5.800064,
          "avg_object": 5.800064e-06,
          "avg_serde": 3.80188,
          "avg_serde_object": 3.8018800000000003e-06,
          "avg_serialized_size": 372.329891,
          "baseline-ratio": 52.0282,
          "baseline-speedup": 1.922,
          "dtype": "dicts",
          "fn": "load",
          "items": 1000000,
          "name": "cbor",
          "serialized_size": 372329891,
          "µs": 5.8
         },
         {
          "avg": 4.858123,
          "avg_object": 4.858123e-06,
          "avg_serde": 8.00302,
          "avg_serde_object": 8.003019999999999e-06,
          "avg_serialized_size": 781.815927,
          "baseline-ratio": 100,
          "baseline-speedup": 1,
          "dtype": "dicts",
          "fn": "dump",
          "items": 1000000,
          "name": "json",
          "serialized_size": 781815927,
          "µs": 4.86
         },
         {
          "avg": 11.147917,
          "avg_object": 1.1147917e-05,
          "avg_serde": 8.00302,
          "avg_serde_object": 8.003019999999999e-06,
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          "µs": 2.7
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         {
          "avg": 6.852906,
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          "µs": 6.85
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       },
       "facet": {
        "column": {
         "field": "name",
         "type": "nominal"
        }
       },
       "spec": {
        "layer": [
         {
          "encoding": {
           "color": {
            "field": "fn",
            "legend": {},
            "scale": {
             "range": [
              "#FF7B06",
              "#094AFB"
             ]
            },
            "type": "nominal"
           },
           "x": {
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             "title": ""
            },
            "field": "fn",
            "scale": {},
            "type": "nominal"
           },
           "y": {
            "axis": {
             "grid": false,
             "title": "microseconds (1e−6 secs)"
            },
            "field": "µs",
            "scale": {
             "type": "sqrt"
            },
            "type": "quantitative"
           }
          },
          "height": 300,
          "mark": {
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           "stroke": "transparent",
           "type": "bar"
          },
          "width": 50
         },
         {
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            "scale": {
             "range": [
              "#FF7B06",
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            },
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           },
           "text": {
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           },
           "x": {
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            },
            "field": "fn",
            "scale": {},
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           },
           "y": {
            "axis": {
             "grid": false,
             "title": "microseconds (1e−6 secs)"
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            "scale": {
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           "color": "black",
           "dx": 0,
           "dy": -2,
           "type": "text"
          },
          "width": 50
         }
        ]
       },
       "title": "Average Object Serialization Time (1M objects)"
      },
      "image/png": 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",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make_bars_chart(\n",
    "    million_df,\n",
    "    avg_col='avg_object',\n",
    "    title='Average Object Serialization Time (1M objects)',\n",
    "    sqrt_scale=True,\n",
    "    labels=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v2+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v2.6.0.json",
       "config": {
        "axis": {
         "domainWidth": 0.8
        },
        "view": {
         "height": 300,
         "stroke": "transparent",
         "width": 400
        }
       },
       "data": {
        "name": "data-b382fde39e804e5ea942a440fcd03c51"
       },
       "datasets": {
        "data-b382fde39e804e5ea942a440fcd03c51": [
         {
          "avg": 9.722888000000001,
          "avg_object": 9.722888000000002e-06,
          "avg_serde": 7.926932000000001,
          "avg_serde_object": 7.926932000000001e-06,
          "avg_serialized_size": 446.883139,
          "baseline-ratio": 200.1367,
          "baseline-speedup": 0.4997,
          "dtype": "dicts",
          "fn": "dump",
          "items": 1000000,
          "name": "bson",
          "serialized_size": 446883139,
          "µs": 7.93
         },
         {
          "avg": 1.803696,
          "avg_object": 1.8036959999999999e-06,
          "avg_serde": 3.80188,
          "avg_serde_object": 3.8018800000000003e-06,
          "avg_serialized_size": 372.329891,
          "baseline-ratio": 37.1274,
          "baseline-speedup": 2.6934,
          "dtype": "dicts",
          "fn": "dump",
          "items": 1000000,
          "name": "cbor",
          "serialized_size": 372329891,
          "µs": 3.8
         },
         {
          "avg": 4.858123,
          "avg_object": 4.858123e-06,
          "avg_serde": 8.00302,
          "avg_serde_object": 8.003019999999999e-06,
          "avg_serialized_size": 781.815927,
          "baseline-ratio": 100,
          "baseline-speedup": 1,
          "dtype": "dicts",
          "fn": "dump",
          "items": 1000000,
          "name": "json",
          "serialized_size": 781815927,
          "µs": 8
         },
         {
          "avg": 1.234061,
          "avg_object": 1.2340610000000002e-06,
          "avg_serde": 3.2461575000000003,
          "avg_serde_object": 3.2461575000000004e-06,
          "avg_serialized_size": 373.77316,
          "baseline-ratio": 25.401999999999997,
          "baseline-speedup": 3.9367,
          "dtype": "dicts",
          "fn": "dump",
          "items": 1000000,
          "name": "msgpack",
          "serialized_size": 373773160,
          "µs": 3.25
         },
         {
          "avg": 3.3608800000000003,
          "avg_object": 3.36088e-06,
          "avg_serde": 2.13236,
          "avg_serde_object": 2.13236e-06,
          "avg_serialized_size": 294.309649,
          "baseline-ratio": 95.7149,
          "baseline-speedup": 1.0448,
          "dtype": "tuples",
          "fn": "dump",
          "items": 1000000,
          "name": "parquet",
          "serialized_size": 294309649,
          "µs": 2.13
         },
         {
          "avg": 3.087295,
          "avg_object": 3.087295e-06,
          "avg_serde": 3.755861,
          "avg_serde_object": 3.755861e-06,
          "avg_serialized_size": 389.496209,
          "baseline-ratio": 63.5491,
          "baseline-speedup": 1.5736,
          "dtype": "dicts",
          "fn": "dump",
          "items": 1000000,
          "name": "pickle",
          "serialized_size": 389496209,
          "µs": 3.76
         },
         {
          "avg": 52.989596999999996,
          "avg_object": 5.2989597e-05,
          "avg_serde": 48.664047,
          "avg_serde_object": 4.8664046999999995e-05,
          "avg_serialized_size": 299.458025,
          "baseline-ratio": 1509.0964,
          "baseline-speedup": 0.0663,
          "dtype": "tuples",
          "fn": "dump",
          "items": 1000000,
          "name": "protobuf",
          "serialized_size": 299458025,
          "µs": 48.66
         },
         {
          "avg": 2.698661,
          "avg_object": 2.698661e-06,
          "avg_serde": 4.7757835,
          "avg_serde_object": 4.7757835e-06,
          "avg_serialized_size": 759.753782,
          "baseline-ratio": 55.5495,
          "baseline-speedup": 1.8002,
          "dtype": "dicts",
          "fn": "dump",
          "items": 1000000,
          "name": "ujson",
          "serialized_size": 759753782,
          "µs": 4.78
         }
        ]
       },
       "facet": {},
       "spec": {
        "layer": [
         {
          "encoding": {
           "color": {
            "field": "name",
            "legend": null,
            "scale": {
             "range": [
              "#FF7B06",
              "#094AFB"
             ]
            },
            "type": "nominal"
           },
           "x": {
            "axis": {
             "labels": true,
             "title": ""
            },
            "field": "name",
            "scale": {},
            "type": "nominal"
           },
           "y": {
            "axis": {
             "grid": false,
             "title": "microseconds (1e−6 secs)"
            },
            "field": "µs",
            "scale": {
             "type": "sqrt"
            },
            "type": "quantitative"
           }
          },
          "height": 250,
          "mark": {
           "size": 20,
           "stroke": "transparent",
           "type": "bar"
          },
          "width": 500
         },
         {
          "encoding": {
           "color": {
            "field": "name",
            "legend": null,
            "scale": {
             "range": [
              "#FF7B06",
              "#094AFB"
             ]
            },
            "type": "nominal"
           },
           "text": {
            "field": "µs",
            "type": "quantitative"
           },
           "x": {
            "axis": {
             "labels": true,
             "title": ""
            },
            "field": "name",
            "scale": {},
            "type": "nominal"
           },
           "y": {
            "axis": {
             "grid": false,
             "title": "microseconds (1e−6 secs)"
            },
            "field": "µs",
            "scale": {
             "type": "sqrt"
            },
            "type": "quantitative"
           }
          },
          "height": 250,
          "mark": {
           "color": "black",
           "dx": 0,
           "dy": -2,
           "type": "text"
          },
          "width": 500
         }
        ]
       },
       "title": "Average Object Serialize & Deserialize Time (1M objects)"
      },
      "image/png": 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",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make_bars_chart(\n",
    "    ser_mil_df,\n",
    "    avg_col='avg_serde_object',\n",
    "    title='Average Object Serialize & Deserialize Time (1M objects)',\n",
    "    sqrt_scale=True,\n",
    "    width=500,\n",
    "    height=250,\n",
    "    multi_bar=False,\n",
    "    legend=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v2+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v2.6.0.json",
       "config": {
        "axis": {
         "domainWidth": 0.8
        },
        "view": {
         "height": 300,
         "stroke": "transparent",
         "width": 400
        }
       },
       "data": {
        "name": "data-5041d64827229ec19697139d0a343fe5"
       },
       "datasets": {
        "data-5041d64827229ec19697139d0a343fe5": [
         {
          "avg": 9.722888000000001,
          "avg_object": 9.722888000000002e-06,
          "avg_serde": 7.926932000000001,
          "avg_serde_object": 7.926932000000001e-06,
          "avg_serialized_size": 446.883139,
          "baseline-ratio": 200.1367,
          "baseline-speedup": 0.4997,
          "dtype": "dicts",
          "fn": "dump",
          "items": 1000000,
          "name": "bson",
          "secs": 446.88,
          "serialized_size": 446883139
         },
         {
          "avg": 1.803696,
          "avg_object": 1.8036959999999999e-06,
          "avg_serde": 3.80188,
          "avg_serde_object": 3.8018800000000003e-06,
          "avg_serialized_size": 372.329891,
          "baseline-ratio": 37.1274,
          "baseline-speedup": 2.6934,
          "dtype": "dicts",
          "fn": "dump",
          "items": 1000000,
          "name": "cbor",
          "secs": 372.33,
          "serialized_size": 372329891
         },
         {
          "avg": 4.858123,
          "avg_object": 4.858123e-06,
          "avg_serde": 8.00302,
          "avg_serde_object": 8.003019999999999e-06,
          "avg_serialized_size": 781.815927,
          "baseline-ratio": 100,
          "baseline-speedup": 1,
          "dtype": "dicts",
          "fn": "dump",
          "items": 1000000,
          "name": "json",
          "secs": 781.82,
          "serialized_size": 781815927
         },
         {
          "avg": 1.234061,
          "avg_object": 1.2340610000000002e-06,
          "avg_serde": 3.2461575000000003,
          "avg_serde_object": 3.2461575000000004e-06,
          "avg_serialized_size": 373.77316,
          "baseline-ratio": 25.401999999999997,
          "baseline-speedup": 3.9367,
          "dtype": "dicts",
          "fn": "dump",
          "items": 1000000,
          "name": "msgpack",
          "secs": 373.77,
          "serialized_size": 373773160
         },
         {
          "avg": 3.3608800000000003,
          "avg_object": 3.36088e-06,
          "avg_serde": 2.13236,
          "avg_serde_object": 2.13236e-06,
          "avg_serialized_size": 294.309649,
          "baseline-ratio": 95.7149,
          "baseline-speedup": 1.0448,
          "dtype": "tuples",
          "fn": "dump",
          "items": 1000000,
          "name": "parquet",
          "secs": 294.31,
          "serialized_size": 294309649
         },
         {
          "avg": 3.087295,
          "avg_object": 3.087295e-06,
          "avg_serde": 3.755861,
          "avg_serde_object": 3.755861e-06,
          "avg_serialized_size": 389.496209,
          "baseline-ratio": 63.5491,
          "baseline-speedup": 1.5736,
          "dtype": "dicts",
          "fn": "dump",
          "items": 1000000,
          "name": "pickle",
          "secs": 389.5,
          "serialized_size": 389496209
         },
         {
          "avg": 52.989596999999996,
          "avg_object": 5.2989597e-05,
          "avg_serde": 48.664047,
          "avg_serde_object": 4.8664046999999995e-05,
          "avg_serialized_size": 299.458025,
          "baseline-ratio": 1509.0964,
          "baseline-speedup": 0.0663,
          "dtype": "tuples",
          "fn": "dump",
          "items": 1000000,
          "name": "protobuf",
          "secs": 299.46,
          "serialized_size": 299458025
         },
         {
          "avg": 2.698661,
          "avg_object": 2.698661e-06,
          "avg_serde": 4.7757835,
          "avg_serde_object": 4.7757835e-06,
          "avg_serialized_size": 759.753782,
          "baseline-ratio": 55.5495,
          "baseline-speedup": 1.8002,
          "dtype": "dicts",
          "fn": "dump",
          "items": 1000000,
          "name": "ujson",
          "secs": 759.75,
          "serialized_size": 759753782
         }
        ]
       },
       "facet": {},
       "spec": {
        "layer": [
         {
          "encoding": {
           "color": {
            "field": "name",
            "legend": null,
            "scale": {
             "range": [
              "#FF7B06",
              "#094AFB"
             ]
            },
            "type": "nominal"
           },
           "x": {
            "axis": {
             "labels": true,
             "title": ""
            },
            "field": "name",
            "scale": {},
            "type": "nominal"
           },
           "y": {
            "axis": {
             "grid": false,
             "title": "bytes"
            },
            "field": "secs",
            "scale": {},
            "type": "quantitative"
           }
          },
          "height": 250,
          "mark": {
           "size": 20,
           "stroke": "transparent",
           "type": "bar"
          },
          "width": 500
         },
         {
          "encoding": {
           "color": {
            "field": "name",
            "legend": null,
            "scale": {
             "range": [
              "#FF7B06",
              "#094AFB"
             ]
            },
            "type": "nominal"
           },
           "text": {
            "field": "secs",
            "type": "quantitative"
           },
           "x": {
            "axis": {
             "labels": true,
             "title": ""
            },
            "field": "name",
            "scale": {},
            "type": "nominal"
           },
           "y": {
            "axis": {
             "grid": false,
             "title": "bytes"
            },
            "field": "secs",
            "scale": {},
            "type": "quantitative"
           }
          },
          "height": 250,
          "mark": {
           "color": "black",
           "dx": 0,
           "dy": -2,
           "type": "text"
          },
          "width": 500
         }
        ]
       },
       "title": "Average Object Serialized Size (1M objects)"
      },
      "image/png": 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",
      "text/plain": [
       "<VegaLite 2 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make_bars_chart(\n",
    "    ser_mil_df,\n",
    "    avg_col='avg_serialized_size',\n",
    "    scale_title='bytes',\n",
    "    title='Average Object Serialized Size (1M objects)',\n",
    "    sqrt_scale=False,\n",
    "    width=500,\n",
    "    height=250,\n",
    "    multi_bar=False,\n",
    "    legend=False,\n",
    ")"
   ]
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}