{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Small Benchmark\n",
    "This notebook runs a small benchmark for  ParquetDB, PyArrow, and SQLite across comparable read/write/query operations. This was first conducted by Christopher Körber and adapated by Logan Lang for this notebook\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",
    "- **ParquetDB 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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: parquetdb in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (0.25.1)\n",
      "Requirement already satisfied: python-dotenv in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from parquetdb) (1.1.0)\n",
      "Requirement already satisfied: pyarrow==17.0.0 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from parquetdb) (17.0.0)\n",
      "Requirement already satisfied: pandas in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from parquetdb) (2.2.3)\n",
      "Requirement already satisfied: variconfig in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from parquetdb) (0.0.3)\n",
      "Requirement already satisfied: matplotlib in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from parquetdb) (3.10.1)\n",
      "Requirement already satisfied: beautifulsoup4 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from parquetdb) (4.13.4)\n",
      "Requirement already satisfied: requests in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from parquetdb) (2.32.3)\n",
      "Requirement already satisfied: dill in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from parquetdb) (0.4.0)\n",
      "Requirement already satisfied: pathos in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from parquetdb) (0.3.4)\n",
      "Requirement already satisfied: dask in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from parquetdb) (2025.4.1)\n",
      "Requirement already satisfied: distributed in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from parquetdb) (2025.4.1)\n",
      "Requirement already satisfied: platformdirs in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from parquetdb) (4.3.7)\n",
      "Requirement already satisfied: numpy>=1.16.6 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from pyarrow==17.0.0->parquetdb) (2.2.5)\n",
      "Requirement already satisfied: soupsieve>1.2 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from beautifulsoup4->parquetdb) (2.7)\n",
      "Requirement already satisfied: typing-extensions>=4.0.0 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from beautifulsoup4->parquetdb) (4.13.2)\n",
      "Requirement already satisfied: click>=8.1 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from dask->parquetdb) (8.1.8)\n",
      "Requirement already satisfied: cloudpickle>=3.0.0 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from dask->parquetdb) (3.1.1)\n",
      "Requirement already satisfied: fsspec>=2021.09.0 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from dask->parquetdb) (2025.3.2)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from dask->parquetdb) (25.0)\n",
      "Requirement already satisfied: partd>=1.4.0 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from dask->parquetdb) (1.4.2)\n",
      "Requirement already satisfied: pyyaml>=5.3.1 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from dask->parquetdb) (6.0.2)\n",
      "Requirement already satisfied: toolz>=0.10.0 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from dask->parquetdb) (1.0.0)\n",
      "Requirement already satisfied: importlib_metadata>=4.13.0 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from dask->parquetdb) (8.6.1)\n",
      "Requirement already satisfied: jinja2>=2.10.3 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from distributed->parquetdb) (3.1.6)\n",
      "Requirement already satisfied: locket>=1.0.0 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from distributed->parquetdb) (1.0.0)\n",
      "Requirement already satisfied: msgpack>=1.0.2 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from distributed->parquetdb) (1.1.0)\n",
      "Requirement already satisfied: psutil>=5.8.0 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from distributed->parquetdb) (7.0.0)\n",
      "Requirement already satisfied: sortedcontainers>=2.0.5 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from distributed->parquetdb) (2.4.0)\n",
      "Requirement already satisfied: tblib>=1.6.0 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from distributed->parquetdb) (3.1.0)\n",
      "Requirement already satisfied: tornado>=6.2.0 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from distributed->parquetdb) (6.4.2)\n",
      "Requirement already satisfied: urllib3>=1.26.5 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from distributed->parquetdb) (2.4.0)\n",
      "Requirement already satisfied: zict>=3.0.0 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from distributed->parquetdb) (3.0.0)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from matplotlib->parquetdb) (1.3.2)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from matplotlib->parquetdb) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from matplotlib->parquetdb) (4.57.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from matplotlib->parquetdb) (1.4.8)\n",
      "Requirement already satisfied: pillow>=8 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from matplotlib->parquetdb) (11.2.1)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from matplotlib->parquetdb) (3.2.3)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from matplotlib->parquetdb) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from pandas->parquetdb) (2025.2)\n",
      "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from pandas->parquetdb) (2025.2)\n",
      "Requirement already satisfied: ppft>=1.7.7 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from pathos->parquetdb) (1.7.7)\n",
      "Requirement already satisfied: pox>=0.3.6 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from pathos->parquetdb) (0.3.6)\n",
      "Requirement already satisfied: multiprocess>=0.70.18 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from pathos->parquetdb) (0.70.18)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from requests->parquetdb) (3.4.1)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from requests->parquetdb) (3.10)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from requests->parquetdb) (2025.4.26)\n",
      "Requirement already satisfied: toml in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from variconfig->parquetdb) (0.10.2)\n",
      "Requirement already satisfied: colorama in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from click>=8.1->dask->parquetdb) (0.4.6)\n",
      "Requirement already satisfied: zipp>=3.20 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from importlib_metadata>=4.13.0->dask->parquetdb) (3.21.0)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from jinja2>=2.10.3->distributed->parquetdb) (3.0.2)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\lllang\\miniconda3\\envs\\parquetdb\\lib\\site-packages (from python-dateutil>=2.7->matplotlib->parquetdb) (1.17.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install parquetdb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] 2025-04-28 13:20:18 - parquetdb.utils.config[37][load_config] - Config file: C:\\Users\\lllang\\AppData\\Local\\parquetdb\\parquetdb\\config.yml\n",
      "[INFO] 2025-04-28 13:20:18 - parquetdb.utils.config[41][load_config] - Setting data_dir to Z:\\data\\parquetdb\\data\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import time\n",
    "import shutil\n",
    "\n",
    "import numpy as np\n",
    "import pyarrow as pa\n",
    "import pyarrow.parquet as pq\n",
    "import pyarrow.dataset as ds\n",
    "\n",
    "from parquetdb import config\n",
    "from parquetdb.utils import general_utils\n",
    "\n",
    "bench_dir = os.path.join(config.data_dir, 'benchmarks')\n",
    "sqlite_dir = os.path.join(bench_dir, 'sqlite')\n",
    "pa_dir = os.path.join(bench_dir, 'pyarrow')\n",
    "pq_dir = os.path.join(bench_dir, 'parquetdb')\n",
    "\n",
    "for d in (sqlite_dir, pa_dir, pq_dir):\n",
    "    os.makedirs(d, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "orders=np.arange(7)\n",
    "data_dict = {}\n",
    "\n",
    "col_prefix = \"col\"\n",
    "for order in orders:\n",
    "    data_dict[order] = general_utils.generate_pylist_data(n_rows=10**order, min_value=0, max_value=1_000_000, prefix=col_prefix) \n",
    "    \n",
    "parquetdb_filters = [pa.compute.field(f\"{col_prefix}1\") > 100, pa.compute.field(f\"{col_prefix}97\") < 1000]\n",
    "pyarrow_filter = (pa.compute.field(f\"{col_prefix}1\") > 100) & (pa.compute.field(f\"{col_prefix}97\") < 1000)\n",
    "sql_query = f\"{col_prefix}1 > 100 and {col_prefix}97 < 1000\"\n",
    "\n",
    "CREATE_TIMES={\n",
    "    \"parquetdb\":{\"mean\":[], \"std\":[]},\n",
    "    \"pyarrow\":{\"mean\":[], \"std\":[]},\n",
    "    \"sqlite\":{\"mean\":[], \"std\":[]}\n",
    "}\n",
    "\n",
    "READ_TIMES={\n",
    "    \"parquetdb\":{\"mean\":[], \"std\":[]},\n",
    "    \"pyarrow\":{\"mean\":[], \"std\":[]},\n",
    "    \"sqlite\":{\"mean\":[], \"std\":[]}\n",
    "}\n",
    "\n",
    "QUERY_TIMES={\n",
    "    \"parquetdb\":{\"mean\":[], \"std\":[]},\n",
    "    \"pyarrow\":{\"mean\":[], \"std\":[]},\n",
    "    \"sqlite\":{\"mean\":[], \"std\":[]}\n",
    "}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using PyArrow Directly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pyarrow_dir = os.path.join(pa_dir, \"pyarrow\")\n",
    "\n",
    "def pyarrow_benchmark_experiment(data):\n",
    "    \n",
    "    if os.path.exists(pyarrow_dir):\n",
    "        shutil.rmtree(pyarrow_dir)\n",
    "    os.makedirs(pyarrow_dir, exist_ok=True)\n",
    "    \n",
    "    \n",
    "    create_time = time.time()\n",
    "    start = 0 \n",
    "    table = pa.Table.from_pylist(data).add_column(0, 'id', [range(start, start + len(data))])\n",
    "    temp_file_path=os.path.join(pyarrow_dir, \"0.parquet\")\n",
    "    pq.write_table(table, temp_file_path)\n",
    "    create_time=time.time() - create_time\n",
    "    del table\n",
    "    \n",
    "    read_time = time.time()\n",
    "    dataset = ds.dataset(pyarrow_dir, format=\"parquet\")\n",
    "    table=dataset.to_table(filter=None)\n",
    "    read_time = time.time() - read_time\n",
    "    del table\n",
    "    del dataset\n",
    "\n",
    "    query_time = time.time()\n",
    "    dataset = ds.dataset(pyarrow_dir, format=\"parquet\")\n",
    "    table=dataset.to_table(filter=pyarrow_filter)\n",
    "    query_time = time.time() - query_time\n",
    "    \n",
    "    return create_time, read_time, query_time\n",
    "    \n",
    "    \n",
    "experiment_dict = { key: {} for key in range(5)}\n",
    "for run_name, benchmark_dict in experiment_dict.items():\n",
    "    tmp_dict={}\n",
    "          \n",
    "    create_times = []\n",
    "    read_times = []\n",
    "    query_times=[]\n",
    "    for order, data in data_dict.items():\n",
    "        create_time, read_time, query_time = pyarrow_benchmark_experiment(data)\n",
    "        create_times.append(create_time)\n",
    "        read_times.append(read_time)\n",
    "        query_times.append(query_time)\n",
    "        \n",
    "    tmp_dict = {\n",
    "        \"create_times\": create_times,\n",
    "        \"read_times\": read_times,\n",
    "        \"query_times\": query_times\n",
    "    }\n",
    "    benchmark_dict[run_name] = tmp_dict\n",
    "\n",
    "mean_create_times=[]\n",
    "mean_read_times=[]\n",
    "mean_query_times=[]\n",
    "\n",
    "std_create_times=[]\n",
    "std_read_times=[]\n",
    "std_query_times=[]\n",
    "for order, data in data_dict.items():\n",
    "    tmp_create_times=[]\n",
    "    tmp_read_times=[]\n",
    "    tmp_query_times=[]\n",
    "    for run_name, benchmark_dict in experiment_dict.items():\n",
    "        tmp_create_times.append(benchmark_dict[run_name]['create_times'][order])\n",
    "        tmp_read_times.append(benchmark_dict[run_name]['read_times'][order])\n",
    "        tmp_query_times.append(benchmark_dict[run_name]['query_times'][order])\n",
    "    mean_create_times.append(float(np.mean(tmp_create_times)))\n",
    "    mean_read_times.append(float(np.mean(tmp_read_times)))\n",
    "    mean_query_times.append(float(np.mean(tmp_query_times)))\n",
    "    std_create_times.append(float(np.std(tmp_create_times)))\n",
    "    std_read_times.append(float(np.std(tmp_read_times)))\n",
    "    std_query_times.append(float(np.std(tmp_query_times)))\n",
    "\n",
    "\n",
    "CREATE_TIMES['pyarrow'][\"mean\"] = mean_create_times\n",
    "CREATE_TIMES['pyarrow'][\"std\"] = std_create_times\n",
    "\n",
    "READ_TIMES['pyarrow'][\"mean\"] = mean_read_times\n",
    "READ_TIMES['pyarrow'][\"std\"] = std_read_times\n",
    "\n",
    "QUERY_TIMES['pyarrow'][\"mean\"] = mean_query_times\n",
    "QUERY_TIMES['pyarrow'][\"std\"] = std_query_times\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Create Times:\n",
      "[0.007800912857055664, 0.009195232391357422, 0.01038656234741211, 0.037519407272338864, 0.3952972888946533, 5.838534593582153, 55.53361873626709]\n",
      "Read Times:\n",
      "[0.012827444076538085, 0.012448263168334962, 0.011999797821044923, 0.011917877197265624, 0.014612627029418946, 0.047410774230957034, 0.3932796478271484]\n",
      "Query Times:\n",
      "[0.004358243942260742, 0.004599761962890625, 0.004617071151733399, 0.005998802185058594, 0.00899968147277832, 0.057991552352905276, 0.3307355403900146]\n"
     ]
    }
   ],
   "source": [
    "print(\"Create Times:\")\n",
    "print(CREATE_TIMES['pyarrow'][\"mean\"])\n",
    "print(\"Read Times:\")\n",
    "print(READ_TIMES['pyarrow'][\"mean\"])\n",
    "print(\"Query Times:\")\n",
    "print(QUERY_TIMES['pyarrow'][\"mean\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SQLite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "import traceback\n",
    "import sys\n",
    "sqlite_db = os.path.join(sqlite_dir, \"benchmark.sqlite\")\n",
    "\n",
    "def sqlite_benchmark_experiment(data):\n",
    "    if os.path.exists(sqlite_db): os.remove(sqlite_db)\n",
    "    \n",
    "    sql_data_keys = data[0].keys()\n",
    "    sql_data_values=[]\n",
    "    for record in data:\n",
    "        # for key,value in record.items():\n",
    "        sql_data_values.append(tuple(record.values()))\n",
    "    n_cols = len(data[0])\n",
    "\n",
    "    try:\n",
    "        create_time = time.time()\n",
    "        conn = sqlite3.connect(sqlite_db)\n",
    "        cursor = conn.cursor()\n",
    "        cursor.execute(\"PRAGMA synchronous = OFF\")\n",
    "        cursor.execute(\"PRAGMA journal_mode = MEMORY\")\n",
    "        cols = ', '.join(f'{data_key} INTEGER' for data_key in sql_data_keys)\n",
    "        \n",
    "        conn.execute(f'CREATE TABLE benchmark ({cols})')\n",
    "        placeholders = ', '.join('?' for _ in range(n_cols))\n",
    "        sql= f'INSERT INTO benchmark VALUES ({placeholders})'\n",
    "        \n",
    "        cursor.executemany(sql, sql_data_values)  \n",
    "        conn.commit()\n",
    "        create_time=time.time() - create_time\n",
    "    except Exception as e:\n",
    "        # Get the traceback information as a formatted string\n",
    "        tb_str = traceback.format_exc()\n",
    "        print(tb_str)\n",
    "        # Or, get the traceback object and work with it directly\n",
    "        tb = sys.exc_info()[2]\n",
    "        traceback.print_tb(tb)\n",
    "    conn.close()\n",
    "    \n",
    "    \n",
    "    try:\n",
    "        read_time = time.time()\n",
    "        conn = sqlite3.connect(sqlite_db)\n",
    "        cursor = conn.cursor()\n",
    "        cursor.execute(\"SELECT * FROM benchmark\")\n",
    "        results = cursor.fetchall()\n",
    "        read_time = time.time() - read_time\n",
    "\n",
    "    except Exception as e:\n",
    "        # Get the traceback information as a formatted string\n",
    "        tb_str = traceback.format_exc()\n",
    "        print(tb_str)\n",
    "        # Or, get the traceback object and work with it directly\n",
    "        tb = sys.exc_info()[2]\n",
    "        traceback.print_tb(tb)\n",
    "    conn.close()\n",
    "    \n",
    "    try:\n",
    "        query_time = time.time()\n",
    "        conn = sqlite3.connect(sqlite_db)\n",
    "        cursor = conn.cursor()\n",
    "        cursor.execute(f\"SELECT * FROM benchmark WHERE {sql_query}\")\n",
    "        results = cursor.fetchall()\n",
    "        query_time = time.time() - query_time\n",
    "    except Exception as e:\n",
    "        # Get the traceback information as a formatted string\n",
    "        tb_str = traceback.format_exc()\n",
    "        print(tb_str)\n",
    "        # Or, get the traceback object and work with it directly\n",
    "        tb = sys.exc_info()[2]\n",
    "        traceback.print_tb(tb)\n",
    "    conn.close()\n",
    "                \n",
    "    \n",
    "    return create_time, read_time, query_time\n",
    "\n",
    "\n",
    "experiment_dict = { key: {} for key in range(5)}\n",
    "for run_name, benchmark_dict in experiment_dict.items():\n",
    "    tmp_dict={}\n",
    "          \n",
    "    create_times = []\n",
    "    read_times = []\n",
    "    query_times=[]\n",
    "    for order, data in data_dict.items():\n",
    "        create_time, read_time, query_time = sqlite_benchmark_experiment(data)\n",
    "        create_times.append(create_time)\n",
    "        read_times.append(read_time)\n",
    "        query_times.append(query_time)\n",
    "        \n",
    "    tmp_dict = {\n",
    "        \"create_times\": create_times,\n",
    "        \"read_times\": read_times,\n",
    "        \"query_times\": query_times\n",
    "    }\n",
    "    benchmark_dict[run_name] = tmp_dict\n",
    "\n",
    "mean_create_times=[]\n",
    "mean_read_times=[]\n",
    "mean_query_times=[]\n",
    "\n",
    "std_create_times=[]\n",
    "std_read_times=[]\n",
    "std_query_times=[]\n",
    "for order, data in data_dict.items():\n",
    "    tmp_create_times=[]\n",
    "    tmp_read_times=[]\n",
    "    tmp_query_times=[]\n",
    "    for run_name, benchmark_dict in experiment_dict.items():\n",
    "        tmp_create_times.append(benchmark_dict[run_name]['create_times'][order])\n",
    "        tmp_read_times.append(benchmark_dict[run_name]['read_times'][order])\n",
    "        tmp_query_times.append(benchmark_dict[run_name]['query_times'][order])\n",
    "\n",
    "    mean_create_times.append(float(np.mean(tmp_create_times)))\n",
    "    mean_read_times.append(float(np.mean(tmp_read_times)))\n",
    "    mean_query_times.append(float(np.mean(tmp_query_times)))\n",
    "    std_create_times.append(float(np.std(tmp_create_times)))\n",
    "    std_read_times.append(float(np.std(tmp_read_times)))\n",
    "    std_query_times.append(float(np.std(tmp_query_times)))\n",
    "\n",
    "\n",
    "CREATE_TIMES['sqlite'][\"mean\"] = mean_create_times\n",
    "CREATE_TIMES['sqlite'][\"std\"] = std_create_times\n",
    "\n",
    "READ_TIMES['sqlite'][\"mean\"] = mean_read_times\n",
    "READ_TIMES['sqlite'][\"std\"] = std_read_times\n",
    "\n",
    "QUERY_TIMES['sqlite'][\"mean\"] = mean_query_times\n",
    "QUERY_TIMES['sqlite'][\"std\"] = std_query_times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Create Times:\n",
      "[0.001200389862060547, 0.0008005142211914062, 0.0013998985290527345, 0.006399631500244141, 0.04707293510437012, 0.47698025703430175, 4.630354404449463]\n",
      "Read Times:\n",
      "[0.0013997554779052734, 0.001400327682495117, 0.0022001743316650392, 0.013914346694946289, 0.12802090644836425, 1.288858985900879, 13.262772560119629]\n",
      "Query Times:\n",
      "[0.0003994941711425781, 0.0001998424530029297, 0.0009997367858886718, 0.0017994403839111327, 0.01177358627319336, 0.11495437622070312, 1.3606952667236327]\n"
     ]
    }
   ],
   "source": [
    "print(\"Create Times:\")\n",
    "print(CREATE_TIMES['sqlite'][\"mean\"])\n",
    "print(\"Read Times:\")\n",
    "print(READ_TIMES['sqlite'][\"mean\"])\n",
    "print(\"Query Times:\")\n",
    "print(QUERY_TIMES['sqlite'][\"mean\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ParquetDB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] 2025-04-28 13:43:28 - parquetdb.core.parquetdb[200][__init__] - Initializing ParquetDB with db_path: Z:\\data\\parquetdb\\data\\benchmarks\\parquetdb\\parquetdb\\BenchmarkDB\n",
      "[INFO] 2025-04-28 13:43:28 - parquetdb.core.parquetdb[202][__init__] - verbose: 1\n"
     ]
    }
   ],
   "source": [
    "from parquetdb import ParquetDB\n",
    "\n",
    "parquetdb_dir = os.path.join(pq_dir, \"parquetdb\", \"BenchmarkDB\")\n",
    "\n",
    "def parquetdb_benchmark_experiment(data):\n",
    "    \n",
    "    time.sleep(1)\n",
    "    if os.path.exists(parquetdb_dir):\n",
    "        shutil.rmtree(parquetdb_dir)\n",
    "    os.makedirs(parquetdb_dir, exist_ok=True)\n",
    "    \n",
    "    db = ParquetDB(db_path=parquetdb_dir)\n",
    "    \n",
    "    \n",
    "    create_time = time.time()\n",
    "    db.create(data)\n",
    "    create_time=time.time() - create_time\n",
    "\n",
    "    \n",
    "    read_time = time.time()\n",
    "    table=db.read(filters=None)\n",
    "    read_time = time.time() - read_time\n",
    "    del table\n",
    "\n",
    "\n",
    "    query_time = time.time()\n",
    "    table=db.read(filters=parquetdb_filters)\n",
    "    query_time = time.time() - query_time\n",
    "    \n",
    "    return create_time, read_time, query_time\n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "\n",
    "experiment_dict = { key: {} for key in range(5)}\n",
    "for run_name, benchmark_dict in experiment_dict.items():\n",
    "    tmp_dict={}\n",
    "          \n",
    "    create_times = []\n",
    "    read_times = []\n",
    "    query_times=[]\n",
    "    for order, data in data_dict.items():\n",
    "        create_time, read_time, query_time = parquetdb_benchmark_experiment(data)\n",
    "        create_times.append(create_time)\n",
    "        read_times.append(read_time)\n",
    "        query_times.append(query_time)\n",
    "        \n",
    "    tmp_dict = {\n",
    "        \"create_times\": create_times,\n",
    "        \"read_times\": read_times,\n",
    "        \"query_times\": query_times\n",
    "    }\n",
    "    benchmark_dict[run_name] = tmp_dict\n",
    "\n",
    "mean_create_times=[]\n",
    "mean_read_times=[]\n",
    "mean_query_times=[]\n",
    "\n",
    "std_create_times=[]\n",
    "std_read_times=[]\n",
    "std_query_times=[]\n",
    "for order, data in data_dict.items():\n",
    "    tmp_create_times=[]\n",
    "    tmp_read_times=[]\n",
    "    tmp_query_times=[]\n",
    "    for run_name, benchmark_dict in experiment_dict.items():\n",
    "        tmp_create_times.append(benchmark_dict[run_name]['create_times'][order])\n",
    "        tmp_read_times.append(benchmark_dict[run_name]['read_times'][order])\n",
    "        tmp_query_times.append(benchmark_dict[run_name]['query_times'][order])\n",
    "\n",
    "    mean_create_times.append(float(np.mean(tmp_create_times)))\n",
    "    mean_read_times.append(float(np.mean(tmp_read_times)))\n",
    "    mean_query_times.append(float(np.mean(tmp_query_times)))\n",
    "    std_create_times.append(float(np.std(tmp_create_times)))\n",
    "    std_read_times.append(float(np.std(tmp_read_times)))\n",
    "    std_query_times.append(float(np.std(tmp_query_times)))\n",
    "\n",
    "\n",
    "CREATE_TIMES['parquetdb'][\"mean\"] = mean_create_times\n",
    "CREATE_TIMES['parquetdb'][\"std\"] = std_create_times\n",
    "\n",
    "READ_TIMES['parquetdb'][\"mean\"] = mean_read_times\n",
    "READ_TIMES['parquetdb'][\"std\"] = std_read_times\n",
    "\n",
    "QUERY_TIMES['parquetdb'][\"mean\"] = mean_query_times\n",
    "QUERY_TIMES['parquetdb'][\"std\"] = std_query_times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Create Times:\n",
      "[0.0775458812713623, 0.07558975219726563, 0.08175263404846192, 0.14939537048339843, 0.4466542720794678, 3.535001277923584, 30.60030345916748]\n",
      "Read Times:\n",
      "[0.006400966644287109, 0.00680084228515625, 0.006400394439697266, 0.007201433181762695, 0.009401369094848632, 0.037060880661010744, 0.30539579391479493]\n",
      "Query Times:\n",
      "[0.005598592758178711, 0.005934333801269532, 0.005599403381347656, 0.007399559020996094, 0.010915565490722656, 0.04081435203552246, 0.3793008327484131]\n"
     ]
    }
   ],
   "source": [
    "print(\"Create Times:\")\n",
    "print(CREATE_TIMES['parquetdb'][\"mean\"])\n",
    "print(\"Read Times:\")\n",
    "print(READ_TIMES['parquetdb'][\"mean\"])\n",
    "print(\"Query Times:\")\n",
    "print(QUERY_TIMES['parquetdb'][\"mean\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "\n",
    "def plotting_experiments(benchmark_times: dict):\n",
    "    \n",
    "    plt.rcParams.update({\n",
    "        'axes.labelsize': 18, 'axes.titlesize': 18,\n",
    "        'xtick.labelsize': 14, 'ytick.labelsize': 14\n",
    "    })\n",
    "\n",
    "    fig, axes = plt.subplots(figsize=(10, 6))\n",
    "    \n",
    "\n",
    "    # colors, styles\n",
    "    colors = [\"#e52207\", \"#e5a000\",\"#59b9de\"]\n",
    "    linestyle=\"solid\"\n",
    "    markerstyle=\"o\"\n",
    "    marker_fill=\"none\"\n",
    "\n",
    "\n",
    "    n_rows = [10**order for order in orders]\n",
    "    for i, (label, time_dict)  in enumerate(benchmark_times.items()):\n",
    "        mean_times=time_dict['mean']\n",
    "        std_times=time_dict['std']\n",
    "        axes.plot(\n",
    "            n_rows,\n",
    "            mean_times,\n",
    "            label=label,\n",
    "            color=colors[i],\n",
    "            linestyle=linestyle,\n",
    "            marker=markerstyle,\n",
    "            fillstyle=marker_fill,\n",
    "        )\n",
    "        # Add error bars for standard deviation\n",
    "        axes.errorbar(\n",
    "            n_rows,\n",
    "            mean_times,\n",
    "            yerr=std_times,\n",
    "            fmt='none',  # No line connecting error bars\n",
    "            ecolor=colors[i],\n",
    "            elinewidth=1.5,\n",
    "            capsize=3\n",
    "        )\n",
    "        \n",
    "        \n",
    "    scale = 36\n",
    "    ax_inset = inset_axes(\n",
    "        axes,\n",
    "        width=f\"{scale}%\",\n",
    "        height=f\"{scale}%\",\n",
    "        loc=\"upper left\",\n",
    "        bbox_to_anchor=(0.05, -0.03, 1, 1),\n",
    "        bbox_transform=axes.transAxes,\n",
    "        borderpad=2,\n",
    "    )\n",
    "    \n",
    "    for i, (label, time_dict)  in enumerate(benchmark_times.items()):\n",
    "        mean_times=time_dict['mean']\n",
    "        std_times=time_dict['std']\n",
    "        ax_inset.plot(\n",
    "            n_rows,\n",
    "            mean_times,\n",
    "            label=label,\n",
    "            color=colors[i],\n",
    "            markersize=8,\n",
    "            linestyle=linestyle,\n",
    "            marker=markerstyle,\n",
    "            fillstyle=marker_fill,\n",
    "        )\n",
    "        \n",
    "        axes.errorbar(\n",
    "            n_rows,\n",
    "            mean_times,\n",
    "            yerr=std_times,\n",
    "            fmt='none',  # No line connecting error bars\n",
    "            ecolor=colors[i],\n",
    "            elinewidth=1.5,\n",
    "            capsize=3\n",
    "        )\n",
    "    \n",
    "    axes.set_xlabel(\"Number of Rows\")\n",
    "    \n",
    "    axes.spines[\"left\"].set_linestyle(linestyle)\n",
    "    axes.spines[\"left\"].set_linewidth(2.5)\n",
    "    axes.spines[\"right\"].set_visible(False)\n",
    "    axes.tick_params(axis=\"both\", which=\"major\", length=10, width=2, direction=\"out\")\n",
    "    axes.grid(True)\n",
    "    \n",
    "    \n",
    "    ax_inset.grid(True)\n",
    "    ax_inset.set_xscale(\"log\")\n",
    "    ax_inset.set_yscale(\"log\")\n",
    "    ax_inset.set_xlabel(\"Number of Rows (log)\", fontsize=8)\n",
    "    \n",
    "\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(linestyle)\n",
    "    ax_inset.spines[\"left\"].set_linewidth(2.5)\n",
    "\n",
    "    ax_inset.spines[\"right\"].set_visible(False)\n",
    "\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",
    "    \n",
    "    lines1, labels1 = axes.get_legend_handles_labels()\n",
    "    axes.legend(lines1, labels1, loc=\"upper center\", bbox_to_anchor=(0.15, 0, 1, 1))\n",
    "    return axes, ax_inset\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lllang\\AppData\\Local\\Temp\\ipykernel_33172\\488313717.py:5: 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 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes,ax_inset=plotting_experiments(benchmark_times=CREATE_TIMES)\n",
    "axes.set_title(\"Benchmark for Create Times\")\n",
    "axes.set_ylabel(\"Create Times (s)\")\n",
    "ax_inset.set_ylabel(\"Create Time (log)\", fontsize=8, labelpad=-2)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read Times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lllang\\AppData\\Local\\Temp\\ipykernel_33172\\662491483.py:5: 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 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes,ax_inset= plotting_experiments(benchmark_times=READ_TIMES)\n",
    "axes.set_title(\"Benchmark for Read Times\")\n",
    "axes.set_ylabel(\"Read Times (s)\")\n",
    "ax_inset.set_ylabel(\"Read Time (log)\", fontsize=8, labelpad=-2)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Query Times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lllang\\AppData\\Local\\Temp\\ipykernel_33172\\211292255.py:5: 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 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes,ax_inset= plotting_experiments(benchmark_times=QUERY_TIMES)\n",
    "axes.set_title(\"Benchmark for Query Times\")\n",
    "axes.set_ylabel(\"Query Times (s)\")\n",
    "ax_inset.set_ylabel(\"Query Time (log)\", fontsize=8, labelpad=-2)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dicussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. **Create Times**  \n",
    "   - **SQLite** scales best for raw inserts: its lightweight B‑tree writer in C outpaces both columnar solutions at every scale (≤ 1 M rows).  \n",
    "   - **ParquetDB** is next.\n",
    "   - **PyArrow** the worst perfromance: this can mainly be attributed to a difference in how the indices are generated.\n",
    "   \n",
    "\n",
    "2. **Read Times**  \n",
    "   - **ParquetDB** & **PyArrow** are effectively identical: both return zero‑copy Arrow tables in ~0.1 s for 1 M rows, thanks to their native columnar layout and batch I/O.  \n",
    "   - **SQLite** is ~40 × slower on full table scans, since it must fetch each row via a cursor and append into Python lists in a tight loop.\n",
    "\n",
    "3. **Query Times**  \n",
    "   - **ParquetDB** & **PyArrow** again match each other closely: filtering 1 M rows takes ~0.3–0.4 s, as Arrow applies vectorized predicates.  \n",
    "   - **SQLite** requires ~1.8 s for the same filter, because every row check is a separate Python‑C transition and Python boolean append.\n",
    "\n",
    "4. **Developer experience & boilerplate**  \n",
    "   - A raw **PyArrow** workflow requires explicit directory management, manual ID generation, repeated calls to `pa.Table.from_pylist()`, `pq.write_table()`, and rebuilding the dataset for each operation—boilerplate that can be tedious to write, maintain, and debug.  \n",
    "   - **ParquetDB** abstracts away all of that: you simply call `db.create(data)`, `db.read()`, or `db.read(filters=…)`. It manages file layouts, row‑group boundaries, indexing, and state under the hood, reducing cognitive load and speeding up development.\n",
    "\n",
    "5. **Key takeaways**  \n",
    "   - ParquetDB’s performance is dominated by the underlying Arrow I/O path—it inherits PyArrow’s blazing read/query speed, with only a small additional cost on table creation.  \n",
    "   - For **write‑heavy** workloads up to ~1 M rows, SQLite still leads on raw insert throughput.  \n",
    "   - For **analytics‑style** workloads (full scans or vectorized filters), the columnar engines (ParquetDB/PyArrow) deliver **an order of magnitude** better performance by avoiding Python‑level loops.  \n",
    "   - For **developer productivity**, ParquetDB’s simple API can save hours of boilerplate code and eliminate error‑prone state management.\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "parquetdb",
   "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": "3.10.17"
  },
  "nbsphinx": {
   "execute": "never"
  },
  "nbsphinx-thumbnail": {
   "tooltip": "In this example, we benchmark the performance of creates, reads, and queries in ParquetDB, SQLite, and PyArrow."
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}