{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fe31575a",
   "metadata": {},
   "source": [
    "# Read/Write Benchmark Notebook\n",
    "This notebook runs read/write benchmarks for SQLite, MongoDB, and ParquetDB across various row counts, then plots the results.\n",
    "\n",
    "## Benchmark Details\n",
    "- **Data Generation:** Generates 1,000,000 rows × 100 columns of integers (0–1,000,000). Integers are chosen as a basic primitive type—byte size is the main factor, so these results represent a **lower bound** on performance; more complex or larger types will incur higher cost.\n",
    "- **Parquet Normalization (defaults):** row-group size 50,000–100,000 rows, max rows per file 10,000,000. Tuning these can shift performance between inserts, reads, and updates.\n",
    "\n",
    "## System Specifications\n",
    "- **Operating System:** Windows 10  \n",
    "- **Processor:** AMD Ryzen 7 3700X 8‑Core @ 3.6 MHz (8 cores, 16 logical processors)   \n",
    "- **Memory:** 128 GB DDR4‑3600 MHz (4×32 GB DIMMs) \n",
    "- **Storage**: SATA HDD 2TB (Model: ST2000DM008-2FR102)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24df2f78",
   "metadata": {},
   "source": [
    "## 1. Setup\n",
    "Import libraries and define directories and parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b198edba",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install parquetdb\n",
    "!pip install pymongo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a71b2d9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import time\n",
    "import random\n",
    "import shutil\n",
    "import sqlite3\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "from pymongo import MongoClient\n",
    "import pyarrow.compute as pc\n",
    "\n",
    "from parquetdb import ParquetDB, config\n",
    "\n",
    "# Directories\n",
    "bench_dir = os.path.join(config.data_dir, 'benchmarks')\n",
    "sqlite_dir = os.path.join(bench_dir, 'sqlite')\n",
    "mongo_dir = os.path.join(bench_dir, 'mongodb')\n",
    "pq_dir = os.path.join(bench_dir, 'parquetdb')\n",
    "for d in (sqlite_dir, mongo_dir, pq_dir):\n",
    "    os.makedirs(d, exist_ok=True)\n",
    "\n",
    "row_counts = [1, 10, 100, 1_000, 10_000, 100_000, 1_000_000]\n",
    "n_cols = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb84c182",
   "metadata": {},
   "source": [
    "## 2. SQLite Read/Write Benchmark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ac39ee49",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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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>n_rows</th>\n",
       "      <th>insert</th>\n",
       "      <th>read</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.107998</td>\n",
       "      <td>0.000998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>0.116234</td>\n",
       "      <td>0.002001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100</td>\n",
       "      <td>0.105669</td>\n",
       "      <td>0.003000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>0.190102</td>\n",
       "      <td>0.017001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10000</td>\n",
       "      <td>0.849536</td>\n",
       "      <td>0.168004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   n_rows    insert      read\n",
       "0       1  0.107998  0.000998\n",
       "1      10  0.116234  0.002001\n",
       "2     100  0.105669  0.003000\n",
       "3    1000  0.190102  0.017001\n",
       "4   10000  0.849536  0.168004"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def generate_data_sqlite(n_rows, n_cols=100):\n",
    "    return [tuple(random.randint(0, 1_000_000) for _ in range(n_cols)) for _ in range(n_rows)]\n",
    "\n",
    "def benchmark_read_write_sqlite(n_rows, db_file):\n",
    "    if os.path.exists(db_file): os.remove(db_file)\n",
    "    # Insert\n",
    "    start = time.time()\n",
    "    conn = sqlite3.connect(db_file)\n",
    "    cols = ', '.join(f'col{i} INTEGER' for i in range(n_cols))\n",
    "    conn.execute(f'CREATE TABLE t ({cols})')\n",
    "    conn.execute('PRAGMA synchronous = OFF')\n",
    "    conn.execute('PRAGMA journal_mode = MEMORY')\n",
    "    placeholders = ', '.join('?' for _ in range(n_cols))\n",
    "    conn.executemany(f'INSERT INTO t VALUES ({placeholders})', generate_data_sqlite(n_rows, n_cols))\n",
    "    conn.commit()\n",
    "    conn.close()\n",
    "    insert_time = time.time() - start\n",
    "\n",
    "    # Read (with index on col0)\n",
    "    conn = sqlite3.connect(db_file)\n",
    "    conn.execute('CREATE INDEX IF NOT EXISTS idx_col0 ON t(col0)')\n",
    "    conn.commit()\n",
    "    conn.close()\n",
    "    start = time.time()\n",
    "    conn = sqlite3.connect(db_file)\n",
    "    cur = conn.cursor()\n",
    "    cur.execute('SELECT * FROM t')\n",
    "    _ = cur.fetchall()\n",
    "    conn.close()\n",
    "    read_time = time.time() - start\n",
    "    return insert_time, read_time\n",
    "\n",
    "# Run SQLite benchmark\n",
    "results_sql = {'n_rows': [], 'insert': [], 'read': []}\n",
    "db_file = os.path.join(sqlite_dir, 'benchmark.db')\n",
    "for n in row_counts:\n",
    "    it, rt = benchmark_read_write_sqlite(n, db_file)\n",
    "    results_sql['n_rows'].append(n)\n",
    "    results_sql['insert'].append(it)\n",
    "    results_sql['read'].append(rt)\n",
    "df_sql = pd.DataFrame(results_sql)\n",
    "df_sql.to_csv(os.path.join(sqlite_dir, 'sqlite_benchmark.csv'), index=False)\n",
    "df_sql.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5e987d5",
   "metadata": {},
   "source": [
    "## 3. MongoDB Read/Write Benchmark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c4e7986a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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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>n_rows</th>\n",
       "      <th>insert</th>\n",
       "      <th>read</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.011999</td>\n",
       "      <td>0.002002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>0.011999</td>\n",
       "      <td>0.001001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100</td>\n",
       "      <td>0.024998</td>\n",
       "      <td>0.003001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>0.122996</td>\n",
       "      <td>0.020001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10000</td>\n",
       "      <td>1.148678</td>\n",
       "      <td>0.294522</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   n_rows    insert      read\n",
       "0       1  0.011999  0.002002\n",
       "1      10  0.011999  0.001001\n",
       "2     100  0.024998  0.003001\n",
       "3    1000  0.122996  0.020001\n",
       "4   10000  1.148678  0.294522"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def generate_data_mongo(n_rows, n_cols=100):\n",
    "    return [{f'col_{i}': random.randint(0, 1_000_000) for i in range(n_cols)} for _ in range(n_rows)]\n",
    "\n",
    "def benchmark_read_write_mongo(n_rows, client, db_name='benchmark'):\n",
    "    client.drop_database(db_name)\n",
    "    start = time.time()\n",
    "    coll = client[db_name].t\n",
    "    coll.insert_many(generate_data_mongo(n_rows, n_cols))\n",
    "    insert_time = time.time() - start\n",
    "\n",
    "    start = time.time()\n",
    "    list(coll.find({}))\n",
    "    read_time = time.time() - start\n",
    "    return insert_time, read_time\n",
    "\n",
    "# Run MongoDB benchmark\n",
    "client = MongoClient('mongodb://localhost:27017/')\n",
    "results_mg = {'n_rows': [], 'insert': [], 'read': []}\n",
    "for n in row_counts:\n",
    "    it, rt = benchmark_read_write_mongo(n, client)\n",
    "    results_mg['n_rows'].append(n)\n",
    "    results_mg['insert'].append(it)\n",
    "    results_mg['read'].append(rt)\n",
    "df_mg = pd.DataFrame(results_mg)\n",
    "df_mg.to_csv(os.path.join(mongo_dir, 'mongodb_benchmark.csv'), index=False)\n",
    "client.close()\n",
    "df_mg.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "121c67d0",
   "metadata": {},
   "source": [
    "## 4. ParquetDB Read/Write Benchmark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c23a826e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] 2025-04-19 13:11:42 - parquetdb.core.parquetdb[205][__init__] - Initializing ParquetDB with db_path: Z:\\data\\parquetdb\\data\\benchmarks\\parquetdb\\BenchmarkDB\n",
      "[INFO] 2025-04-19 13:11:42 - parquetdb.core.parquetdb[207][__init__] - verbose: 1\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\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>n_rows</th>\n",
       "      <th>insert</th>\n",
       "      <th>read</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.082995</td>\n",
       "      <td>0.015001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>0.058997</td>\n",
       "      <td>0.015002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100</td>\n",
       "      <td>0.132612</td>\n",
       "      <td>0.013999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>0.199732</td>\n",
       "      <td>0.014002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10000</td>\n",
       "      <td>1.186683</td>\n",
       "      <td>0.017001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   n_rows    insert      read\n",
       "0       1  0.082995  0.015001\n",
       "1      10  0.058997  0.015002\n",
       "2     100  0.132612  0.013999\n",
       "3    1000  0.199732  0.014002\n",
       "4   10000  1.186683  0.017001"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from parquetdb.utils import general_utils\n",
    "\n",
    "def benchmark_read_write_pq(n_rows, db_path):\n",
    "    if os.path.exists(db_path): shutil.rmtree(db_path)\n",
    "    db = ParquetDB(db_path)\n",
    "    start = time.time()\n",
    "    data = general_utils.generate_pylist_data(n_rows=n_rows, min_value=0, max_value=1_000_000)\n",
    "    db.create(data)\n",
    "    insert_time = time.time() - start\n",
    "\n",
    "    start = time.time()\n",
    "    _ = db.read()\n",
    "    read_time = time.time() - start\n",
    "    return insert_time, read_time\n",
    "\n",
    "# Run ParquetDB benchmark\n",
    "results_pq = {'n_rows': [], 'insert': [], 'read': []}\n",
    "db_path = os.path.join(pq_dir, 'BenchmarkDB')\n",
    "for n in row_counts:\n",
    "    it, rt = benchmark_read_write_pq(n, db_path)\n",
    "    results_pq['n_rows'].append(n)\n",
    "    results_pq['insert'].append(it)\n",
    "    results_pq['read'].append(rt)\n",
    "df_pq = pd.DataFrame(results_pq)\n",
    "df_pq.to_csv(os.path.join(pq_dir, 'parquetdb_benchmark.csv'), index=False)\n",
    "df_pq.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dfe167e",
   "metadata": {},
   "source": [
    "## 5. Load & Preview Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1a3da546",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(   n_rows    insert      read\n",
       " 0       1  0.107998  0.000998\n",
       " 1      10  0.116234  0.002001\n",
       " 2     100  0.105669  0.003000\n",
       " 3    1000  0.190102  0.017001\n",
       " 4   10000  0.849536  0.168004,\n",
       "    n_rows    insert      read\n",
       " 0       1  0.011999  0.002002\n",
       " 1      10  0.011999  0.001001\n",
       " 2     100  0.024998  0.003001\n",
       " 3    1000  0.122996  0.020001\n",
       " 4   10000  1.148678  0.294522,\n",
       "    n_rows    insert      read\n",
       " 0       1  0.082995  0.015001\n",
       " 1      10  0.058997  0.015002\n",
       " 2     100  0.132612  0.013999\n",
       " 3    1000  0.199732  0.014002\n",
       " 4   10000  1.186683  0.017001)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sql = pd.read_csv(os.path.join(sqlite_dir, 'sqlite_benchmark.csv'))\n",
    "df_mg  = pd.read_csv(os.path.join(mongo_dir, 'mongodb_benchmark.csv'))\n",
    "df_pq  = pd.read_csv(os.path.join(pq_dir, 'parquetdb_benchmark.csv'))\n",
    "df_sql.head(), df_mg.head(), df_pq.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bad2f97a",
   "metadata": {},
   "source": [
    "## 6. Plot Create & Read Times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ae411ba0",
   "metadata": {
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lllang\\AppData\\Local\\Temp\\ipykernel_17352\\263859993.py:204: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({\n",
    "    'axes.labelsize': 18, 'axes.titlesize': 18,\n",
    "    'xtick.labelsize': 14, 'ytick.labelsize': 14\n",
    "})\n",
    "\n",
    "benchmark_dirs=[sqlite_dir,mongo_dir,pq_dir]\n",
    "n_rows=df_pq['n_rows']\n",
    "\n",
    "fig, ax1 = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "# colors, styles\n",
    "colors = {\"sqlite\": \"#e52207\", \"mongodb\": \"#e5a000\", \"parquetdb\": \"#59b9de\"}\n",
    "line_styles = {\"create_times\": \"solid\", \"read_times\": \"dashed\"}\n",
    "marker_styles = {\"sqlite\": \"o\", \"mongodb\": \"o\", \"parquetdb\": \"o\"}\n",
    "marker_fill = {\n",
    "    \"sqlite\": {\"create_times\": \"full\", \"read_times\": \"none\"},\n",
    "    \"mongodb\": {\"create_times\": \"full\", \"read_times\": \"none\"},\n",
    "    \"parquetdb\": {\"create_times\": \"full\", \"read_times\": \"none\"},\n",
    "}\n",
    "\n",
    "# # primary: create times\n",
    "# for benchmark_dir in benchmark_dirs:\n",
    "#     name = os.path.basename(benchmark_dir)\n",
    "#     df = pd.read_csv(os.path.join(benchmark_dir, f\"{name}_benchmark.csv\"))\n",
    "ax1.plot(\n",
    "    n_rows,\n",
    "    df_sql[\"insert\"],\n",
    "    label=f\"sqlite create\",\n",
    "    color=colors[\"sqlite\"],\n",
    "    linestyle=line_styles[\"create_times\"],\n",
    "    marker=marker_styles[\"sqlite\"],\n",
    "    fillstyle=marker_fill[\"sqlite\"][\"create_times\"],\n",
    ")\n",
    "\n",
    "ax1.plot(\n",
    "    n_rows,\n",
    "    df_mg[\"insert\"],\n",
    "    label=f\"mongodb create\",\n",
    "    color=colors[\"mongodb\"],\n",
    "    linestyle=line_styles[\"create_times\"],\n",
    "    marker=marker_styles[\"mongodb\"],\n",
    "    fillstyle=marker_fill[\"mongodb\"][\"create_times\"],\n",
    ")\n",
    "\n",
    "ax1.plot(\n",
    "    n_rows,\n",
    "    df_pq[\"insert\"],\n",
    "    label=f\"parquetdb create\",\n",
    "    color=colors[\"parquetdb\"],\n",
    "    linestyle=line_styles[\"create_times\"],\n",
    "    marker=marker_styles[\"parquetdb\"],\n",
    "    fillstyle=marker_fill[\"parquetdb\"][\"create_times\"],\n",
    ")\n",
    "\n",
    "# secondary: read times\n",
    "ax2 = ax1.twinx()\n",
    "\n",
    "ax2.plot(\n",
    "    n_rows,\n",
    "    df_sql[\"read\"],\n",
    "    label=f\"sqlite read\",\n",
    "    color=colors[\"sqlite\"],\n",
    "    linestyle=line_styles[\"read_times\"],\n",
    "    marker=marker_styles[\"sqlite\"],\n",
    "    fillstyle=marker_fill[\"sqlite\"][\"read_times\"],\n",
    ")\n",
    "ax2.plot(\n",
    "    n_rows,\n",
    "    df_mg[\"read\"],\n",
    "    label=f\"mongodb read\",\n",
    "    color=colors[\"mongodb\"],\n",
    "    linestyle=line_styles[\"read_times\"],\n",
    "    marker=marker_styles[\"mongodb\"],\n",
    "    fillstyle=marker_fill[\"mongodb\"][\"read_times\"]\n",
    "    )\n",
    "ax2.plot(\n",
    "    n_rows,\n",
    "    df_pq[\"read\"],\n",
    "    label=f\"parquetdb read\",\n",
    "    color=colors[\"parquetdb\"],\n",
    "    linestyle=line_styles[\"read_times\"],\n",
    "    marker=marker_styles[\"parquetdb\"],\n",
    "    fillstyle=marker_fill[\"parquetdb\"][\"read_times\"],\n",
    ")\n",
    "\n",
    "\n",
    "\n",
    "# labels & styling\n",
    "ax1.set_xlabel(\"Number of Rows\")\n",
    "ax1.set_ylabel(\"Create Times (s)\")\n",
    "ax2.set_ylabel(\"Read Times (s)\")\n",
    "ax1.spines[\"left\"].set_linestyle(line_styles[\"create_times\"])\n",
    "ax1.spines[\"left\"].set_linewidth(2.5)\n",
    "ax2.spines[\"right\"].set_linestyle(line_styles[\"read_times\"])\n",
    "ax2.spines[\"right\"].set_linewidth(2.5)\n",
    "ax1.spines[\"right\"].set_visible(False)\n",
    "ax2.spines[\"left\"].set_visible(False)\n",
    "ax1.tick_params(axis=\"both\", which=\"major\", length=10, width=2, direction=\"out\")\n",
    "ax2.tick_params(axis=\"both\", which=\"major\", length=10, width=2, direction=\"out\")\n",
    "ax1.grid(True)\n",
    "\n",
    "# inset log–log\n",
    "scale = 36\n",
    "ax_inset = inset_axes(\n",
    "    ax1,\n",
    "    width=f\"{scale}%\",\n",
    "    height=f\"{scale}%\",\n",
    "    loc=\"upper left\",\n",
    "    bbox_to_anchor=(0.06, -0.03, 1, 1),\n",
    "    bbox_transform=ax1.transAxes,\n",
    "    borderpad=2,\n",
    ")\n",
    "ax_inset.grid(True)\n",
    "\n",
    "# inset create\n",
    "ax_inset.plot(\n",
    "    n_rows,\n",
    "    df_sql[\"insert\"],\n",
    "    label=f\"sqlite create\",\n",
    "    color=colors[\"sqlite\"],\n",
    "    linestyle=line_styles[\"create_times\"],\n",
    "    marker=marker_styles[\"sqlite\"],\n",
    "    fillstyle=marker_fill[\"sqlite\"][\"create_times\"],\n",
    ")\n",
    "\n",
    "ax_inset.plot(\n",
    "    n_rows,\n",
    "    df_mg[\"insert\"],\n",
    "    label=f\"mongodb create\",\n",
    "    color=colors[\"mongodb\"],\n",
    "    linestyle=line_styles[\"create_times\"],\n",
    "    marker=marker_styles[\"mongodb\"],\n",
    "    fillstyle=marker_fill[\"mongodb\"][\"create_times\"],\n",
    ")\n",
    "\n",
    "ax_inset.plot(\n",
    "    n_rows,\n",
    "    df_pq[\"insert\"],\n",
    "    label=f\"parquetdb create\",\n",
    "    color=colors[\"parquetdb\"],\n",
    "    linestyle=line_styles[\"create_times\"],\n",
    "    marker=marker_styles[\"parquetdb\"],\n",
    "    fillstyle=marker_fill[\"parquetdb\"][\"create_times\"],\n",
    ")\n",
    "# inset read\n",
    "ax_inset2 = ax_inset.twinx()\n",
    "ax_inset2.plot(\n",
    "    n_rows,\n",
    "    df_sql[\"read\"],\n",
    "    label=f\"sqlite read\",\n",
    "    color=colors[\"sqlite\"],\n",
    "    linestyle=line_styles[\"read_times\"],\n",
    "    marker=marker_styles[\"sqlite\"],\n",
    "    fillstyle=marker_fill[\"sqlite\"][\"read_times\"],\n",
    ")\n",
    "ax_inset2.plot(\n",
    "    n_rows,\n",
    "    df_mg[\"read\"],\n",
    "    label=f\"mongodb read\",\n",
    "    color=colors[\"mongodb\"],\n",
    "    linestyle=line_styles[\"read_times\"],\n",
    "    marker=marker_styles[\"mongodb\"],\n",
    "    fillstyle=marker_fill[\"mongodb\"][\"read_times\"]\n",
    "    )\n",
    "ax_inset2.plot(\n",
    "    n_rows,\n",
    "    df_pq[\"read\"],\n",
    "    label=f\"parquetdb read\",\n",
    "    color=colors[\"parquetdb\"],\n",
    "    linestyle=line_styles[\"read_times\"],\n",
    "    marker=marker_styles[\"parquetdb\"],\n",
    "    fillstyle=marker_fill[\"parquetdb\"][\"read_times\"],\n",
    ")\n",
    "\n",
    "\n",
    "# log scales & labels\n",
    "ax_inset.set_xscale(\"log\")\n",
    "ax_inset.set_yscale(\"log\")\n",
    "ax_inset2.set_yscale(\"log\")\n",
    "ax_inset.set_xlabel(\"Number of Rows (log)\", fontsize=8)\n",
    "ax_inset.set_ylabel(\"Create Time (log)\", fontsize=8, labelpad=-2)\n",
    "ax_inset2.set_ylabel(\"Read Time (log)\", fontsize=8)\n",
    "\n",
    "maj = ticker.LogLocator(numticks=9)\n",
    "minr = ticker.LogLocator(subs=\"all\", numticks=9)\n",
    "ax_inset.xaxis.set_major_locator(maj)\n",
    "ax_inset.xaxis.set_minor_locator(minr)\n",
    "ax_inset.spines[\"left\"].set_linestyle(line_styles[\"create_times\"])\n",
    "ax_inset.spines[\"left\"].set_linewidth(2.5)\n",
    "ax_inset2.spines[\"right\"].set_linestyle(line_styles[\"read_times\"])\n",
    "ax_inset2.spines[\"right\"].set_linewidth(2.5)\n",
    "ax_inset.spines[\"right\"].set_visible(False)\n",
    "ax_inset2.spines[\"left\"].set_visible(False)\n",
    "ax_inset.tick_params(axis=\"both\", which=\"major\", length=6, width=1.5, direction=\"out\")\n",
    "ax_inset.tick_params(axis=\"x\", which=\"minor\", length=3, width=1, direction=\"out\")\n",
    "ax_inset.tick_params(axis=\"y\", which=\"minor\", length=3, width=1, direction=\"out\")\n",
    "ax_inset2.tick_params(axis=\"both\", which=\"major\", length=6, width=1.5, direction=\"out\")\n",
    "\n",
    "# legend & title\n",
    "lines1, labels1 = ax1.get_legend_handles_labels()\n",
    "lines2, labels2 = ax2.get_legend_handles_labels()\n",
    "ax1.legend(lines1 + lines2, labels1 + labels2, loc=\"upper center\", bbox_to_anchor=(0.15, 0, 1, 1))\n",
    "ax1.set_title(\"Benchmark for Create and Read:\\nSQLite, MongoDB, and ParquetDB with 100 integer columns\")\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig(os.path.join(bench_dir,\"create-read_benchmark.pdf\"))\n",
    "plt.show()\n"
   ]
  },
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   "id": "7010a9bd",
   "metadata": {},
   "source": [
    "## 6. Dicussion\n",
    "\n",
    "1. **Create performance hierarchy**  \n",
    "   - **SQLite** remains the fastest writer across all scales, thanks to its optimized C‑API and lightweight on‑disk format.  \n",
    "   - **ParquetDB** comes in **2nd**, with linear growth in create time driven by Arrow serialization and file I/O.  \n",
    "   - **MongoDB** trails slightly behind ParquetDB, reflecting the overhead of BSON encoding and network/driver layers.\n",
    "\n",
    "2. **Read (full‑table scan) performance**  \n",
    "   - **ParquetDB** is the clear winner on raw reads: it returns a ready‑to‑use PyArrow table in one shot, avoiding any Python‑level loops.  \n",
    "   - **SQLite** and **MongoDB** both require iterating over each row in Python (via a cursor or cursor-like API), accumulating values into a list, this per‑row overhead dominates their read times, resulting in roughly linear growth up to ~16 s at 1 M rows.\n",
    "\n",
    "3. **Input format caveat**  \n",
    "   - All ParquetDB create times above were measured using a **Python list of row‑dicts (pylist)**, our worst‑case input. As shown in the Input Update benchmark, switching to columnar inputs (pandas or native PyArrow tables) can significantly reduce ParquetDB’s create latency.\n",
    "\n",
    "\n",
    "4. **Key takeaways**  \n",
    "   - If your workload is **write‑heavy**, SQLite still leads for small to moderate datasets—but ParquetDB’s throughput is competitive and gains further speed with columnar inputs.  \n",
    "   - If you need **fast full‑table reads** into analytical pipelines, ParquetDB outperforms both row‑stores by avoiding Python‑loop overhead and leveraging Arrow’s zero‑copy data structures.\n"
   ]
  }
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