{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7f1f5a4a-ae34-4a27-9efa-399edc0e384a",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Benchmark Fastest Timeseries alternatives: ClickHouse Vs. InfluxDB Vs. Postgresql Vs. Parquet  Vs. MongoDB Vs. DuckDB Vs Kdb+\n",
    "\n",
    "-----\n",
    "#### How to use:\n",
    "* Rename the file \"properties-model.ini\" to \"properties.ini\"\n",
    "* Fill with your own credentials\n",
    "----\n",
    "\n",
    "The proposal of this work is to compare the speed in read/writing a midle level of data ( a dataset with 9 columns and 1.000.000 lines) to four diferent databases:\n",
    "\n",
    "* [ClickHouse](#ClickHouse)\n",
    "* [InfluxDB](#InfluxDB)\n",
    "* [Postgresql](#Postgresql)\n",
    "* [Parquet (in a S3 Minio Storage)](#S3-Parquet))\n",
    "* [MongoDB](#MongoDB)\n",
    "* [DuckDB](#DuckDB)\n",
    "* [Kdb+](#Kdb+)\n",
    "\n",
    "Jump to:\n",
    "\n",
    "* [Table with Results](#Table)\n",
    "* [Graph with Read/Write result](#Graph)\n",
    "* [Graph with the best results](#Graph-(bests))\n",
    "* [Size Ocuppied on Disk](#Size)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bb26ce7-1e84-4665-accd-916bb977f95d",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Imports "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "ab6c6c81-6ac1-4668-a79b-a9a0341fb35a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import configparser\n",
    "import io\n",
    "import timeit\n",
    "from datetime import datetime\n",
    "\n",
    "import duckdb\n",
    "import influxdb_client\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pdmongo as pdm\n",
    "from clickhouse_driver import Client\n",
    "from dotenv import load_dotenv\n",
    "from minio import Minio\n",
    "from pymongo import MongoClient\n",
    "from pytz import timezone\n",
    "from qbstyles import mpl_style\n",
    "from qpython import qconnection\n",
    "from sqlalchemy import create_engine\n",
    "\n",
    "load_dotenv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "55c3cd57-0996-4723-beb5-8f3196c96009",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Variables\n",
    "dbname = \"EURUSDtest\"\n",
    "collection = \"finance\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "968403e3-2e5e-4834-b969-be4600e2963a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "arq = configparser.RawConfigParser()\n",
    "arq.read(\"properties.ini\")\n",
    "ClickHouseUser = arq.get(\"CLICKHOUSE\", \"user\")\n",
    "ClickHouseKey = arq.get(\"CLICKHOUSE\", \"key\")\n",
    "ClickHouseUrl = arq.get(\"CLICKHOUSE\", \"url\")\n",
    "\n",
    "InfluxDBUser = arq.get(\"INFLUXDB\", \"user\")\n",
    "InfluxDBKey = arq.get(\"INFLUXDB\", \"key\")\n",
    "InfluxDBUrl = arq.get(\"INFLUXDB\", \"url\")\n",
    "InfluxDBBucket = arq.get(\"INFLUXDB\", \"bucket\")\n",
    "\n",
    "PostgresqlUser = arq.get(\"POSTGRESQL\", \"user\")\n",
    "PostgresqlKey = arq.get(\"POSTGRESQL\", \"key\")\n",
    "PostgresqlUrl = arq.get(\"POSTGRESQL\", \"url\")\n",
    "PostgresqlDB = arq.get(\"POSTGRESQL\", \"database\")\n",
    "\n",
    "S3MinioUser = arq.get(\"S3MINIO\", \"user\")\n",
    "S3MinioKey = arq.get(\"S3MINIO\", \"key\")\n",
    "S3MinioUrl = arq.get(\"S3MINIO\", \"url\")\n",
    "S3MinioRegion = arq.get(\"S3MINIO\", \"region\")\n",
    "\n",
    "MongoUser = arq.get(\"MONGODB\", \"user\")\n",
    "MongoKey = arq.get(\"MONGODB\", \"key\")\n",
    "MongoUrl = arq.get(\"MONGODB\", \"url\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "3634a4ec-04c2-4f1e-8659-5d22eb17a254",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>id</th>\n",
       "      <th>from</th>\n",
       "      <th>at</th>\n",
       "      <th>to</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>999995</th>\n",
       "      <td>7984748</td>\n",
       "      <td>2023-03-03 18:13:30</td>\n",
       "      <td>1677867225000000000</td>\n",
       "      <td>2023-03-03 18:13:45</td>\n",
       "      <td>1.062695</td>\n",
       "      <td>1.062635</td>\n",
       "      <td>1.062630</td>\n",
       "      <td>1.062700</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999996</th>\n",
       "      <td>7984749</td>\n",
       "      <td>2023-03-03 18:13:45</td>\n",
       "      <td>1677867240000000000</td>\n",
       "      <td>2023-03-03 18:14:00</td>\n",
       "      <td>1.062645</td>\n",
       "      <td>1.062650</td>\n",
       "      <td>1.062625</td>\n",
       "      <td>1.062650</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999997</th>\n",
       "      <td>7984750</td>\n",
       "      <td>2023-03-03 18:14:00</td>\n",
       "      <td>1677867255000000000</td>\n",
       "      <td>2023-03-03 18:14:15</td>\n",
       "      <td>1.062640</td>\n",
       "      <td>1.062625</td>\n",
       "      <td>1.062620</td>\n",
       "      <td>1.062665</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999998</th>\n",
       "      <td>7984751</td>\n",
       "      <td>2023-03-03 18:14:15</td>\n",
       "      <td>1677867270000000000</td>\n",
       "      <td>2023-03-03 18:14:30</td>\n",
       "      <td>1.062625</td>\n",
       "      <td>1.062535</td>\n",
       "      <td>1.062535</td>\n",
       "      <td>1.062645</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999999</th>\n",
       "      <td>7984752</td>\n",
       "      <td>2023-03-03 18:14:30</td>\n",
       "      <td>1677867285000000000</td>\n",
       "      <td>2023-03-03 18:14:45</td>\n",
       "      <td>1.062535</td>\n",
       "      <td>1.062520</td>\n",
       "      <td>1.062520</td>\n",
       "      <td>1.062580</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             id                 from                   at  \\\n",
       "999995  7984748  2023-03-03 18:13:30  1677867225000000000   \n",
       "999996  7984749  2023-03-03 18:13:45  1677867240000000000   \n",
       "999997  7984750  2023-03-03 18:14:00  1677867255000000000   \n",
       "999998  7984751  2023-03-03 18:14:15  1677867270000000000   \n",
       "999999  7984752  2023-03-03 18:14:30  1677867285000000000   \n",
       "\n",
       "                         to      open     close       min       max  volume  \n",
       "999995  2023-03-03 18:13:45  1.062695  1.062635  1.062630  1.062700      64  \n",
       "999996  2023-03-03 18:14:00  1.062645  1.062650  1.062625  1.062650      43  \n",
       "999997  2023-03-03 18:14:15  1.062640  1.062625  1.062620  1.062665      47  \n",
       "999998  2023-03-03 18:14:30  1.062625  1.062535  1.062535  1.062645      43  \n",
       "999999  2023-03-03 18:14:45  1.062535  1.062520  1.062520  1.062580      59  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# %%time\n",
    "# Load Dataset\n",
    "df = pd.read_csv(\"out.csv\", index_col=0)\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76199f91-31d6-416b-9f15-5d435b3792c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"from\"] = pd.to_datetime(df[\"from\"], unit=\"s\")\n",
    "df[\"to\"] = pd.to_datetime(df[\"to\"], unit=\"s\")\n",
    "# Optional use when not transoformed yet\n",
    "# Transform Datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "274cc026-2f48-4e38-b80f-b1a9ff982060",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Funçoes\n",
    "\n",
    "-> Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "27de1ec8-4de1-440a-b555-b4a46c5ef7ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "def timestamp2dataHora(x, timezone_=\"America/Sao_Paulo\"):\n",
    "    d = datetime.fromtimestamp(x, tz=timezone(timezone_))\n",
    "    return d"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a8d5703-9bc9-4d38-83ff-457159304d58",
   "metadata": {
    "tags": []
   },
   "source": [
    "### ClickHouse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c3202bbb-2655-45b2-b166-9f45a3ef854c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Database created'"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# !! driver tcp.\n",
    "def cHouseConnect():\n",
    "    client = Client(\n",
    "        host=ClickHouseUrl,\n",
    "        user=ClickHouseUser,\n",
    "        password=ClickHouseKey,\n",
    "        settings={\"use_numpy\": True},\n",
    "    )\n",
    "    return client\n",
    "\n",
    "\n",
    "def cHouseDropTables(databasename):\n",
    "    client = cHouseConnect()\n",
    "    client.execute(\"DROP TABLE {}\".format(databasename))\n",
    "    client.disconnect()\n",
    "    return \"Database deleted\"\n",
    "\n",
    "\n",
    "# Create Tables in ClickHouse\n",
    "# !! ALTERAR TIPOS !!\n",
    "# ENGINE: 'Memory' desaparece quando server é reiniciado\n",
    "def cHouseCreateDb(databasename):\n",
    "    client = cHouseConnect()\n",
    "    client.execute(\n",
    "        \"CREATE TABLE IF NOT EXISTS {} (id UInt32,\"\n",
    "        \"from DateTime, at UInt64, to DateTime, open Float64,\"\n",
    "        \"close Float64, min Float64, max  Float64, volume UInt32)\"\n",
    "        \"ENGINE MergeTree ORDER BY to\".format(databasename)\n",
    "    )\n",
    "    client.disconnect()\n",
    "    return \"Database created\"\n",
    "\n",
    "\n",
    "# Write dataframe to db\n",
    "def cHouseInsertDf(dbName, dataframe):\n",
    "    client = cHouseConnect()\n",
    "    client.insert_dataframe(\"INSERT INTO {} VALUES\".format(dbName), dataframe)\n",
    "    client.disconnect()\n",
    "    return \" dataframe {} inserted in clickhouse database\".format(dataframe)\n",
    "\n",
    "\n",
    "def cHouseQueryDf(databaseName):\n",
    "    client = cHouseConnect()\n",
    "    dfQuery = client.query_dataframe(\"SELECT * FROM default.{}\".format(databaseName))\n",
    "    client.disconnect()\n",
    "    return dfQuery\n",
    "\n",
    "\n",
    "cHouseCreateDb(dbname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "cc4865b3-a1bc-4a35-9624-15334754b3a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Insert to db and benchmark time\n",
    "start = timeit.default_timer()\n",
    "cHouseInsertDf(dbname, df)\n",
    "stop = timeit.default_timer()\n",
    "cHouse_write_execution_time = stop - start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "1fac82c1-2d04-44ef-893a-dc13b755e6d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read from db and benchmark time\n",
    "start = timeit.default_timer()\n",
    "dfCh = cHouseQueryDf(dbname)\n",
    "stop = timeit.default_timer()\n",
    "cHouse_read_execution_time = stop - start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "597ae7bd-2eea-44d7-b379-f0eb7e745c15",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>to</th>\n",
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       "      <th>close</th>\n",
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       "    <tr>\n",
       "      <th>1999995</th>\n",
       "      <td>8083054</td>\n",
       "      <td>2023-03-28 18:54:00</td>\n",
       "      <td>1680029655003247667</td>\n",
       "      <td>2023-03-28 18:54:15</td>\n",
       "      <td>1.084015</td>\n",
       "      <td>1.084015</td>\n",
       "      <td>1.083995</td>\n",
       "      <td>1.084040</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999996</th>\n",
       "      <td>8083055</td>\n",
       "      <td>2023-03-28 18:54:15</td>\n",
       "      <td>1680029670000000000</td>\n",
       "      <td>2023-03-28 18:54:30</td>\n",
       "      <td>1.084020</td>\n",
       "      <td>1.083950</td>\n",
       "      <td>1.083940</td>\n",
       "      <td>1.084045</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999997</th>\n",
       "      <td>8083055</td>\n",
       "      <td>2023-03-28 18:54:15</td>\n",
       "      <td>1680029670000000000</td>\n",
       "      <td>2023-03-28 18:54:30</td>\n",
       "      <td>1.084020</td>\n",
       "      <td>1.083950</td>\n",
       "      <td>1.083940</td>\n",
       "      <td>1.084045</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999998</th>\n",
       "      <td>8083056</td>\n",
       "      <td>2023-03-28 18:54:30</td>\n",
       "      <td>1680029685000000000</td>\n",
       "      <td>2023-03-28 18:54:45</td>\n",
       "      <td>1.083965</td>\n",
       "      <td>1.083955</td>\n",
       "      <td>1.083945</td>\n",
       "      <td>1.083975</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999999</th>\n",
       "      <td>8083056</td>\n",
       "      <td>2023-03-28 18:54:30</td>\n",
       "      <td>1680029685000000000</td>\n",
       "      <td>2023-03-28 18:54:45</td>\n",
       "      <td>1.083965</td>\n",
       "      <td>1.083955</td>\n",
       "      <td>1.083945</td>\n",
       "      <td>1.083975</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "              id                from                   at                  to  \\\n",
       "1999995  8083054 2023-03-28 18:54:00  1680029655003247667 2023-03-28 18:54:15   \n",
       "1999996  8083055 2023-03-28 18:54:15  1680029670000000000 2023-03-28 18:54:30   \n",
       "1999997  8083055 2023-03-28 18:54:15  1680029670000000000 2023-03-28 18:54:30   \n",
       "1999998  8083056 2023-03-28 18:54:30  1680029685000000000 2023-03-28 18:54:45   \n",
       "1999999  8083056 2023-03-28 18:54:30  1680029685000000000 2023-03-28 18:54:45   \n",
       "\n",
       "             open     close       min       max  volume  \n",
       "1999995  1.084015  1.084015  1.083995  1.084040      42  \n",
       "1999996  1.084020  1.083950  1.083940  1.084045      55  \n",
       "1999997  1.084020  1.083950  1.083940  1.084045      55  \n",
       "1999998  1.083965  1.083955  1.083945  1.083975      40  \n",
       "1999999  1.083965  1.083955  1.083945  1.083975      40  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfCh.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "86794e47-611f-4ca8-a7e8-07e71afafe67",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.571660671004793\n"
     ]
    }
   ],
   "source": [
    "print(cHouse_read_execution_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e7926062-8e84-4d3f-90a9-32807ce4f3d4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.804937635999522\n"
     ]
    }
   ],
   "source": [
    "print(cHouse_write_execution_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "8faa5683-a204-461d-80c3-67644aa714ce",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Database deleted'"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Utils\n",
    "# %%time\n",
    "# dfCh = cHouseQueryDf(dbname)\n",
    "# drop table\n",
    "cHouseDropTables(dbname)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d389546-911f-43f7-aad1-49f7bcc83503",
   "metadata": {
    "tags": []
   },
   "source": [
    "### InfluxDB\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "ecd217ab-0e16-40a6-9b92-9212b9bb20e9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "query = \"\"\"\n",
    "from(bucket: \"EURUSDtest\")\n",
    "|> range(start:2023-03-03T18:14:30Z, stop: now())\n",
    "|> filter(fn: (r) => r._measurement == \"id\")\n",
    "|> pivot(rowKey:[\"_time\"], columnKey: [\"_field\"], valueColumn: \"_value\")\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "c3e7ebfd-76f1-4ac4-9833-312eb1a531af",
   "metadata": {},
   "outputs": [],
   "source": [
    "def influxdbConnect():\n",
    "    client = influxdb_client.InfluxDBClient(\n",
    "        url=InfluxDBUrl, token=InfluxDBKey, org=InfluxDBUser\n",
    "    )\n",
    "    return client\n",
    "\n",
    "\n",
    "def influxdbLoadCsv(csv=\"out.csv\", dictDates=[\"from\", \"to\"], index=\"from\"):\n",
    "    # Read data from CSV without index and parse 'TimeStamp' as date.\n",
    "    df = pd.read_csv(csv, sep=\",\", index_col=False, parse_dates=dictDates)\n",
    "    # Set 'TimeStamp' field as index of dataframe # test another indexs\n",
    "    df.set_index(index, inplace=True)\n",
    "    return df\n",
    "\n",
    "\n",
    "def influxdbWriteCsv(dataFrame, bucket, measurement=\"id\", tag=\"volume\"):\n",
    "    client = influxdbConnect()\n",
    "    # write_options=SYNCHRONOUS\n",
    "    with client.write_api() as writer:\n",
    "        writer.write(\n",
    "            bucket=bucket,\n",
    "            record=dataFrame,\n",
    "            data_frame_measurement_name=measurement,\n",
    "            data_frame_tag_columns=[tag],\n",
    "        )\n",
    "    writer.__del__()\n",
    "    client.__del__()\n",
    "    return 0\n",
    "\n",
    "\n",
    "def influxdRead(org=InfluxDBUrl, query=query):\n",
    "    client = influxdbConnect()\n",
    "    InfluxDf = client.query_api().query_data_frame(org=\"librography\", query=query)\n",
    "    # display(InfluxDf.head())\n",
    "    return InfluxDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "4be399bb-6b69-4f2b-aac0-9eab03cebfff",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def delete_data(measurement=\"id\", bucket=InfluxDBBucket, org=\"librography\"):\n",
    "    client = influxdbConnect()\n",
    "    delete_api = client.delete_api()\n",
    "    start = \"1970-01-01T00:00:00Z\"\n",
    "    stop = \"2024-10-30T00:00:00Z\"\n",
    "    delete_api.delete(start, stop, f'_measurement=\"{measurement}\"', bucket, org)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "baab1b7c-8f09-4d5e-8275-8588b4b5dd9d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "delete_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e05266b8-ff32-462c-b059-325a40a53d25",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "dafr = influxdbLoadCsv()\n",
    "# dafr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "95191283-f11e-456f-8395-36981ab1ac51",
   "metadata": {},
   "outputs": [],
   "source": [
    "start = timeit.default_timer()\n",
    "influxdbWriteCsv(dafr, InfluxDBBucket)\n",
    "# dfIdw = cHouseQueryDf(dbname)\n",
    "stop = timeit.default_timer()\n",
    "influxdb_write_execution_time = stop - start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "3e5bb029-4988-4692-bc5f-9a6f7fcc2159",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "87.55419652500132\n"
     ]
    }
   ],
   "source": [
    "print(influxdb_write_execution_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "850c6921-5e1c-417a-bea6-ea18be642008",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# read from db and benchmark time\n",
    "start = timeit.default_timer()\n",
    "influxdRead()\n",
    "stop = timeit.default_timer()\n",
    "influxdb_read_execution_time = stop - start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "3ee3c0dd-cb70-4124-a0fb-db8dd2c134c0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69.66793818100268\n"
     ]
    }
   ],
   "source": [
    "print(influxdb_read_execution_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "54342a28-ba2b-4ade-a692-00566b53a639",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>Write Time</th>\n",
       "      <th>Read Time</th>\n",
       "      <th>Total Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Click House</th>\n",
       "      <td>5.80 sec</td>\n",
       "      <td>7.57 sec</td>\n",
       "      <td>13.38 sec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kdb+</th>\n",
       "      <td>2.87 sec</td>\n",
       "      <td>4.15 sec</td>\n",
       "      <td>7.03 sec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r2</th>\n",
       "      <td>fill</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>r3</th>\n",
       "      <td>fill</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Write Time Read Time Total Time\n",
       "Click House   5.80 sec  7.57 sec  13.38 sec\n",
       "Kdb+          2.87 sec  4.15 sec   7.03 sec\n",
       "r2                fill        15          0\n",
       "r3                fill        14          0"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9ddfdc6-c899-4f6c-9b4e-8ec6ab6d7e05",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Postgresql"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "16cd8eb7-333d-43fd-88e0-ee983645d3fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>id</th>\n",
       "      <th>from</th>\n",
       "      <th>at</th>\n",
       "      <th>to</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>999995</th>\n",
       "      <td>7984748</td>\n",
       "      <td>2023-03-03 18:13:30</td>\n",
       "      <td>1677867225000000000</td>\n",
       "      <td>2023-03-03 18:13:45</td>\n",
       "      <td>1.062695</td>\n",
       "      <td>1.062635</td>\n",
       "      <td>1.062630</td>\n",
       "      <td>1.062700</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999996</th>\n",
       "      <td>7984749</td>\n",
       "      <td>2023-03-03 18:13:45</td>\n",
       "      <td>1677867240000000000</td>\n",
       "      <td>2023-03-03 18:14:00</td>\n",
       "      <td>1.062645</td>\n",
       "      <td>1.062650</td>\n",
       "      <td>1.062625</td>\n",
       "      <td>1.062650</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999997</th>\n",
       "      <td>7984750</td>\n",
       "      <td>2023-03-03 18:14:00</td>\n",
       "      <td>1677867255000000000</td>\n",
       "      <td>2023-03-03 18:14:15</td>\n",
       "      <td>1.062640</td>\n",
       "      <td>1.062625</td>\n",
       "      <td>1.062620</td>\n",
       "      <td>1.062665</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999998</th>\n",
       "      <td>7984751</td>\n",
       "      <td>2023-03-03 18:14:15</td>\n",
       "      <td>1677867270000000000</td>\n",
       "      <td>2023-03-03 18:14:30</td>\n",
       "      <td>1.062625</td>\n",
       "      <td>1.062535</td>\n",
       "      <td>1.062535</td>\n",
       "      <td>1.062645</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999999</th>\n",
       "      <td>7984752</td>\n",
       "      <td>2023-03-03 18:14:30</td>\n",
       "      <td>1677867285000000000</td>\n",
       "      <td>2023-03-03 18:14:45</td>\n",
       "      <td>1.062535</td>\n",
       "      <td>1.062520</td>\n",
       "      <td>1.062520</td>\n",
       "      <td>1.062580</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             id                 from                   at  \\\n",
       "999995  7984748  2023-03-03 18:13:30  1677867225000000000   \n",
       "999996  7984749  2023-03-03 18:13:45  1677867240000000000   \n",
       "999997  7984750  2023-03-03 18:14:00  1677867255000000000   \n",
       "999998  7984751  2023-03-03 18:14:15  1677867270000000000   \n",
       "999999  7984752  2023-03-03 18:14:30  1677867285000000000   \n",
       "\n",
       "                         to      open     close       min       max  volume  \n",
       "999995  2023-03-03 18:13:45  1.062695  1.062635  1.062630  1.062700      64  \n",
       "999996  2023-03-03 18:14:00  1.062645  1.062650  1.062625  1.062650      43  \n",
       "999997  2023-03-03 18:14:15  1.062640  1.062625  1.062620  1.062665      47  \n",
       "999998  2023-03-03 18:14:30  1.062625  1.062535  1.062535  1.062645      43  \n",
       "999999  2023-03-03 18:14:45  1.062535  1.062520  1.062520  1.062580      59  "
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Connect / Create Tables\n",
    "def psqlConnect():\n",
    "    engine = create_engine(\n",
    "        \"postgresql+psycopg2://{}:{}@{}:5432/{}\".format(\n",
    "            PostgresqlUser, PostgresqlKey, PostgresqlUrl, PostgresqlDB\n",
    "        )\n",
    "    )\n",
    "    return engine\n",
    "\n",
    "\n",
    "psqlConnect()\n",
    "# testar função\n",
    "df = pd.read_csv(\"out.csv\", index_col=0)\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "be31f3a0-b7ed-48e6-9b65-dc16319fb8d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop old table and create new empty table\n",
    "def psqlCreateTables(databaseName):\n",
    "    engine = psqlConnect()\n",
    "    df.head(0).to_sql(databaseName, engine, if_exists=\"replace\", index=False)\n",
    "    # Write\n",
    "    conn = engine.raw_connection()\n",
    "    cur = conn.cursor()\n",
    "    output = io.StringIO()\n",
    "    df.to_csv(output, sep=\"\\t\", header=False, index=False)\n",
    "    output.seek(0)\n",
    "    contents = output.getvalue()\n",
    "    cur.copy_from(output, \"EURUSDtest\")  # , null=\"\")  # null values become ''\n",
    "    conn.commit()\n",
    "    cur.close()\n",
    "    conn.close()\n",
    "    # disconnect()\n",
    "    return 0\n",
    "\n",
    "\n",
    "# funcao read sql\n",
    "def psqlReadTables():\n",
    "    engine = psqlConnect()\n",
    "    df = pd.read_sql_query('select * from \"EURUSDtest\"', con=engine)\n",
    "    return df\n",
    "\n",
    "\n",
    "# testar função"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "98cc9360-4b84-43e4-b23b-b32d0c50c3b9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Insert to db and benchmark time\n",
    "start = timeit.default_timer()\n",
    "psqlCreateTables(dbname)\n",
    "stop = timeit.default_timer()\n",
    "psql_write_execution_time = stop - start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "75b7a130-92e3-4af1-9e1c-a285622c8996",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24.818096771996352\n"
     ]
    }
   ],
   "source": [
    "print(psql_write_execution_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "82e1b44e-35af-403f-9936-5e1561fd5abf",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "start = timeit.default_timer()\n",
    "psqlReadTables()\n",
    "stop = timeit.default_timer()\n",
    "psql_read_execution_time = stop - start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "6d1b7480-5bc7-4f08-8cf3-b9590802d8f7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18.282347426000342\n"
     ]
    }
   ],
   "source": [
    "print(psql_read_execution_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "6acb2959-3255-43bd-aea5-9ef70acc8902",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9e0393d-7d1d-406a-a068-9dbf4968e977",
   "metadata": {
    "tags": []
   },
   "source": [
    "### S3-Parquet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "7c7022bf-9c3b-400a-9045-b089483f05ad",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# fazer sem funçao para ver se melhora\n",
    "# verifique se esta no ssd os arquivos da pasta git\n",
    "def s3Connect():\n",
    "    client = Minio(\n",
    "        S3MinioUrl,\n",
    "        secure=False,\n",
    "        region=S3MinioRegion,\n",
    "        access_key=S3MinioUser,\n",
    "        secret_key=S3MinioKey,\n",
    "    )\n",
    "    return client\n",
    "\n",
    "\n",
    "def s3CreateBucket(bucketName=\"data\"):\n",
    "    client = s3Connect()\n",
    "    found = client.bucket_exists(bucketName)\n",
    "    if not found:\n",
    "        return client.make_bucket(bucketName)\n",
    "    else:\n",
    "        return \"Bucket '{}' already exists\".format(bucketName)\n",
    "\n",
    "\n",
    "def s3uploadCsv():\n",
    "    client = s3Connect()\n",
    "    client.fput_object(\n",
    "        \"data\",\n",
    "        \"data.parquet\",\n",
    "        \"data/data.parquet\",\n",
    "    )\n",
    "    return (\n",
    "        \"'data/data.parquet' is successfully uploaded as \"\n",
    "        \"object 'data.parquet' to bucket 'data'.\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "cd7fe012-9eee-4f91-8c07-8e0148633766",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Insert to db and benchmark time\n",
    "df.to_parquet(\"data/data.parquet\")\n",
    "s3CreateBucket()\n",
    "start = timeit.default_timer()\n",
    "s3uploadCsv()\n",
    "stop = timeit.default_timer()\n",
    "s3_write_execution_time = stop - start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "390918c8-c88f-404a-96c4-685d578fdad0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.160801975995128\n"
     ]
    }
   ],
   "source": [
    "print(s3_write_execution_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "666d4bc7-933d-4e6e-87c1-72fd37e61e0d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "start = timeit.default_timer()\n",
    "pq = pd.read_parquet(\"data/data.parquet\", engine=\"pyarrow\")\n",
    "stop = timeit.default_timer()\n",
    "s3_read_execution_time = stop - start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "573b10c3-5dfe-420a-a988-688d4ebcde24",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>id</th>\n",
       "      <th>from</th>\n",
       "      <th>at</th>\n",
       "      <th>to</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7730801</td>\n",
       "      <td>2023-01-02 15:58:45</td>\n",
       "      <td>1672675140000000000</td>\n",
       "      <td>2023-01-02 15:59:00</td>\n",
       "      <td>1.065995</td>\n",
       "      <td>1.066035</td>\n",
       "      <td>1.065930</td>\n",
       "      <td>1.066070</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7730802</td>\n",
       "      <td>2023-01-02 15:59:00</td>\n",
       "      <td>1672675155000000000</td>\n",
       "      <td>2023-01-02 15:59:15</td>\n",
       "      <td>1.066055</td>\n",
       "      <td>1.066085</td>\n",
       "      <td>1.066005</td>\n",
       "      <td>1.066115</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7730803</td>\n",
       "      <td>2023-01-02 15:59:15</td>\n",
       "      <td>1672675170000000000</td>\n",
       "      <td>2023-01-02 15:59:30</td>\n",
       "      <td>1.066080</td>\n",
       "      <td>1.066025</td>\n",
       "      <td>1.066025</td>\n",
       "      <td>1.066110</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7730804</td>\n",
       "      <td>2023-01-02 15:59:30</td>\n",
       "      <td>1672675185000000000</td>\n",
       "      <td>2023-01-02 15:59:45</td>\n",
       "      <td>1.065980</td>\n",
       "      <td>1.065985</td>\n",
       "      <td>1.065885</td>\n",
       "      <td>1.066045</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7730805</td>\n",
       "      <td>2023-01-02 15:59:45</td>\n",
       "      <td>1672675200000000000</td>\n",
       "      <td>2023-01-02 16:00:00</td>\n",
       "      <td>1.065975</td>\n",
       "      <td>1.066055</td>\n",
       "      <td>1.065830</td>\n",
       "      <td>1.066055</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                 from                   at                   to  \\\n",
       "0  7730801  2023-01-02 15:58:45  1672675140000000000  2023-01-02 15:59:00   \n",
       "1  7730802  2023-01-02 15:59:00  1672675155000000000  2023-01-02 15:59:15   \n",
       "2  7730803  2023-01-02 15:59:15  1672675170000000000  2023-01-02 15:59:30   \n",
       "3  7730804  2023-01-02 15:59:30  1672675185000000000  2023-01-02 15:59:45   \n",
       "4  7730805  2023-01-02 15:59:45  1672675200000000000  2023-01-02 16:00:00   \n",
       "\n",
       "       open     close       min       max  volume  \n",
       "0  1.065995  1.066035  1.065930  1.066070      57  \n",
       "1  1.066055  1.066085  1.066005  1.066115      52  \n",
       "2  1.066080  1.066025  1.066025  1.066110      57  \n",
       "3  1.065980  1.065985  1.065885  1.066045      64  \n",
       "4  1.065975  1.066055  1.065830  1.066055      50  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pq.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "fefb54f6-9040-4fd7-9861-20322c202d92",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5090432809956837\n"
     ]
    }
   ],
   "source": [
    "print(s3_read_execution_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50d1fc58-89a7-4507-aff0-6e943656cfe0",
   "metadata": {
    "tags": []
   },
   "source": [
    "### MongoDB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "d104d9af-fa34-4261-8478-329a28ee4f2e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "port = \"27017\"\n",
    "\n",
    "\n",
    "def mongoLoadCsv(csvfile):\n",
    "    data = pd.read_csv(\"out.csv\")\n",
    "    return data\n",
    "\n",
    "\n",
    "def mongoConnect():\n",
    "    client = MongoClient(\n",
    "        \"mongodb://{}:{}@{}/EURUSDtest?retryWrites=true&w=majority\".format(\n",
    "            MongoUser, MongoKey, MongoUrl\n",
    "        ),\n",
    "        authSource=\"admin\",\n",
    "    )\n",
    "    return client\n",
    "\n",
    "\n",
    "def mongoWriteDict(dados, dbs, collection):\n",
    "    client = mongoConnect()\n",
    "    db = client[dbs]\n",
    "    collection = db[collection]\n",
    "    # data.reset_index(inplace=True)\n",
    "    data_dict = dados.to_dict(\"records\")\n",
    "    # return data_dict\n",
    "    collection.insert_many(data_dict)\n",
    "    return 0\n",
    "\n",
    "\n",
    "def mongoRead():\n",
    "    df = pdm.read_mongo(\n",
    "        collection, [], \"mongodb://192.168.1.162:27017/{}\".format(dbname)\n",
    "    )\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "739de6aa-313f-4ccd-96c8-fa22d0cc687e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>Unnamed: 0</th>\n",
       "      <th>id</th>\n",
       "      <th>from</th>\n",
       "      <th>at</th>\n",
       "      <th>to</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7730801</td>\n",
       "      <td>2023-01-02 15:58:45</td>\n",
       "      <td>1672675140000000000</td>\n",
       "      <td>2023-01-02 15:59:00</td>\n",
       "      <td>1.065995</td>\n",
       "      <td>1.066035</td>\n",
       "      <td>1.065930</td>\n",
       "      <td>1.066070</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>7730802</td>\n",
       "      <td>2023-01-02 15:59:00</td>\n",
       "      <td>1672675155000000000</td>\n",
       "      <td>2023-01-02 15:59:15</td>\n",
       "      <td>1.066055</td>\n",
       "      <td>1.066085</td>\n",
       "      <td>1.066005</td>\n",
       "      <td>1.066115</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>7730803</td>\n",
       "      <td>2023-01-02 15:59:15</td>\n",
       "      <td>1672675170000000000</td>\n",
       "      <td>2023-01-02 15:59:30</td>\n",
       "      <td>1.066080</td>\n",
       "      <td>1.066025</td>\n",
       "      <td>1.066025</td>\n",
       "      <td>1.066110</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7730804</td>\n",
       "      <td>2023-01-02 15:59:30</td>\n",
       "      <td>1672675185000000000</td>\n",
       "      <td>2023-01-02 15:59:45</td>\n",
       "      <td>1.065980</td>\n",
       "      <td>1.065985</td>\n",
       "      <td>1.065885</td>\n",
       "      <td>1.066045</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>7730805</td>\n",
       "      <td>2023-01-02 15:59:45</td>\n",
       "      <td>1672675200000000000</td>\n",
       "      <td>2023-01-02 16:00:00</td>\n",
       "      <td>1.065975</td>\n",
       "      <td>1.066055</td>\n",
       "      <td>1.065830</td>\n",
       "      <td>1.066055</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0       id                 from                   at  \\\n",
       "0           0  7730801  2023-01-02 15:58:45  1672675140000000000   \n",
       "1           1  7730802  2023-01-02 15:59:00  1672675155000000000   \n",
       "2           2  7730803  2023-01-02 15:59:15  1672675170000000000   \n",
       "3           3  7730804  2023-01-02 15:59:30  1672675185000000000   \n",
       "4           4  7730805  2023-01-02 15:59:45  1672675200000000000   \n",
       "\n",
       "                    to      open     close       min       max  volume  \n",
       "0  2023-01-02 15:59:00  1.065995  1.066035  1.065930  1.066070      57  \n",
       "1  2023-01-02 15:59:15  1.066055  1.066085  1.066005  1.066115      52  \n",
       "2  2023-01-02 15:59:30  1.066080  1.066025  1.066025  1.066110      57  \n",
       "3  2023-01-02 15:59:45  1.065980  1.065985  1.065885  1.066045      64  \n",
       "4  2023-01-02 16:00:00  1.065975  1.066055  1.065830  1.066055      50  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = mongoLoadCsv(\"out.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "0af8f72c-5b58-4dfc-af36-c5b4bc79f127",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "start = timeit.default_timer()\n",
    "dfCh = mongoWriteDict(data, dbname, \"finance\")\n",
    "stop = timeit.default_timer()\n",
    "mongo_write_execution_time = stop - start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "0757f14c-4677-41d3-90d8-63b884e24e7e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54.566006569999445\n"
     ]
    }
   ],
   "source": [
    "print(mongo_write_execution_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "e7922312-74cb-4df3-8dea-e5ee0d99fab7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "start = timeit.default_timer()\n",
    "dfMongo = mongoRead()\n",
    "stop = timeit.default_timer()\n",
    "mongo_read_execution_time = stop - start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "93fb22ea-b283-4447-b774-fe755a782223",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34.66374846499821\n"
     ]
    }
   ],
   "source": [
    "print(mongo_read_execution_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97405e42-61dc-42c7-8220-237a312c0ec7",
   "metadata": {
    "tags": []
   },
   "source": [
    "### DuckDB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "bbcdb883-d6dc-46db-88db-4c90b84522ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://duckdb.org/2022/07/27/art-storage.html\n",
    "\n",
    "\n",
    "def duckdbConnect():\n",
    "    cursor = duckdb.connect()\n",
    "    return cursor\n",
    "\n",
    "\n",
    "def duckdbLoadCsv(csvFile=\"out.csv\"):\n",
    "    data = pd.read_csv(csvFile)\n",
    "    return data\n",
    "\n",
    "\n",
    "# write\n",
    "def duckdbWrite(file, dataframe, tableName):\n",
    "    conn = duckdbConnect()\n",
    "    conn = duckdb.connect(file)\n",
    "    # conn.execute(\"DROP TABLE EURUSDtest\")\n",
    "    # conn.sql('CREATE TABLE EURUSDtest(i INTEGER)')\n",
    "    conn.register(\"tempTable\", dataframe)\n",
    "    conn.execute(\"CREATE TABLE {} AS SELECT * FROM tempTable\".format(tableName))\n",
    "    conn.close()\n",
    "    return 0\n",
    "\n",
    "\n",
    "def duckdbRead(dbfile):\n",
    "    conn = duckdbConnect()\n",
    "    conn = duckdb.connect(dbfile)\n",
    "    conn.execute(\"SELECT * FROM {}\".format(dbname)).fetchdf()\n",
    "    conn.close()\n",
    "    return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "1c787f48-5640-4eb5-9456-be8f0a8211eb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "data = duckdbLoadCsv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f07f03d0-021e-4dc3-bfa8-efc029a9797a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "conn = duckdbConnect()\n",
    "conn.execute(\"DROP TABLE EURUSDtest\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "45620be8-34c6-4a3a-89f7-09337f665c90",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<duckdb.DuckDBPyConnection at 0x7f925fb29b30>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conn.register(\"EURUSDtest\", data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "9eb19431-fbad-43b4-84f7-0043e65de162",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EURUSDtest</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         name\n",
       "0  EURUSDtest"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conn = duckdb.connect(\"file.db\")\n",
    "# conn.execute(\"DROP TABLE EURUSDtest\")\n",
    "# conn.sql(\"CREATE TABLE EURUSDtest(i INTEGER)\")\n",
    "display(conn.execute(\"SHOW TABLES\").df())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32037939-def0-48e7-9a47-46bcf1a5883c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# conn = duckdbConnect()\n",
    "conn.execute(\"SELECT * FROM EURUSDtest\")\n",
    "conn.fetchall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "c6f53d67-684b-4b34-a573-472986ee3e47",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "start = timeit.default_timer()\n",
    "dfDuckdb = duckdbWrite(\"file.db\", data, dbname)\n",
    "stop = timeit.default_timer()\n",
    "duckdb_write_execution_time = stop - start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "102f363a-b35d-433c-8752-7acc85c27bdc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.02947735300404\n"
     ]
    }
   ],
   "source": [
    "print(duckdb_write_execution_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "f630fc1a-0d52-4e3a-9dfe-1ec60d188033",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "start = timeit.default_timer()\n",
    "dfDuckdb = duckdbRead(\"file.db\")\n",
    "stop = timeit.default_timer()\n",
    "duckdb_read_execution_time = stop - start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "c6abdaaa-3ac2-425b-9208-d6cb79afe966",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5635835029970622\n"
     ]
    }
   ],
   "source": [
    "print(duckdb_read_execution_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4409cc89-ed14-4313-ac89-65b826038533",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Kdb+"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "14f63810-1943-4e28-9bce-2148be6be02d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# numpy version boolean fix\n",
    "np.bool = np.bool_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bbd217e3-695f-4fa6-ae42-83db1dde8311",
   "metadata": {},
   "outputs": [],
   "source": [
    "# functions\n",
    "# cd ~ && q/l64/q -p 5001\n",
    "\n",
    "\n",
    "def kdbConnect():\n",
    "    q = qconnection.QConnection(host=\"localhost\", port=5001)\n",
    "    q.open()\n",
    "    return q\n",
    "\n",
    "\n",
    "def kdbLoadCsv(file=\"out.csv\"):\n",
    "    data = pd.read_csv(file)\n",
    "    return data\n",
    "\n",
    "\n",
    "def kdbWrite():\n",
    "    q = kdbConnect()\n",
    "    data = kdbLoadCsv()\n",
    "    q.sendSync(\"{t::x}\", data)\n",
    "    q.sendSync(\"`:/home/sandman/q/tab1 set t\")\n",
    "    q.close()\n",
    "    return 0\n",
    "\n",
    "\n",
    "def kdbRead():\n",
    "    q = kdbConnect()\n",
    "    # df2 = q.sendSync(\"tab2: get `:/home/sandman/q/tab1\")\n",
    "    # df2 = q.sendSync(\"tab2\")\n",
    "    df = pd.DataFrame(q(\"t\"))  # , pandas=True))\n",
    "    # df3 = q.sendSync(\"select from t\")\n",
    "    # ver todos esses loads\n",
    "    q.close()\n",
    "    return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "67f0c26e-44fb-40b0-a147-5d97bfbbded2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# write\n",
    "start = timeit.default_timer()\n",
    "dfKdb = kdbWrite()\n",
    "stop = timeit.default_timer()\n",
    "kdb_write_execution_time = stop - start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "dcb200be-ffc9-4bcc-8554-5740fb420ab5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.8739770100000896\n"
     ]
    }
   ],
   "source": [
    "# print write time\n",
    "print(kdb_write_execution_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d4ce0203-b0c7-440b-a3ca-d7b2a7682474",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read\n",
    "start = timeit.default_timer()\n",
    "dfKdb = kdbRead()\n",
    "stop = timeit.default_timer()\n",
    "kdb_read_execution_time = stop - start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a16fd76-2158-40fe-9285-c53791f8ed51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.153738381999574\n"
     ]
    }
   ],
   "source": [
    "# print read time\n",
    "print(kdb_read_execution_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c923c16-d741-4903-8493-81ff3c4d300e",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "3a09558c-73e6-4324-9fc5-782fcd0d12e5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\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>Write Time</th>\n",
       "      <th>Read Time</th>\n",
       "      <th>Total Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Click House</th>\n",
       "      <td>5.80 sec</td>\n",
       "      <td>7.57 sec</td>\n",
       "      <td>13.38 sec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InfluxDb</th>\n",
       "      <td>87.55 sec</td>\n",
       "      <td>69.67 sec</td>\n",
       "      <td>157.22 sec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Postgresql</th>\n",
       "      <td>24.82 sec</td>\n",
       "      <td>18.28 sec</td>\n",
       "      <td>43.10 sec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S3</th>\n",
       "      <td>4.16 sec</td>\n",
       "      <td>0.51 sec</td>\n",
       "      <td>4.67 sec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MongoDb</th>\n",
       "      <td>54.57 sec</td>\n",
       "      <td>34.66 sec</td>\n",
       "      <td>89.23 sec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DuckDb</th>\n",
       "      <td>2.03 sec</td>\n",
       "      <td>0.56 sec</td>\n",
       "      <td>2.59 sec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kdb+</th>\n",
       "      <td>2.87 sec</td>\n",
       "      <td>4.15 sec</td>\n",
       "      <td>7.03 sec</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Write Time  Read Time  Total Time\n",
       "Click House   5.80 sec   7.57 sec   13.38 sec\n",
       "InfluxDb     87.55 sec  69.67 sec  157.22 sec\n",
       "Postgresql   24.82 sec  18.28 sec   43.10 sec\n",
       "S3            4.16 sec   0.51 sec    4.67 sec\n",
       "MongoDb      54.57 sec  34.66 sec   89.23 sec\n",
       "DuckDb        2.03 sec   0.56 sec    2.59 sec\n",
       "Kdb+          2.87 sec   4.15 sec    7.03 sec"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# duckdb_write_execution_time\n",
    "s = \" sec\"\n",
    "data = [\n",
    "    [\n",
    "        \"{:.2f}\".format(cHouse_write_execution_time) + s,\n",
    "        \"{:.2f}\".format(cHouse_read_execution_time) + s,\n",
    "        \"{:.2f}\".format(cHouse_write_execution_time + cHouse_read_execution_time) + s,\n",
    "    ],\n",
    "    [\n",
    "        \"{:.2f}\".format(influxdb_write_execution_time) + s,\n",
    "        \"{:.2f}\".format(influxdb_read_execution_time) + s,\n",
    "        \"{:.2f}\".format(influxdb_write_execution_time + influxdb_read_execution_time)\n",
    "        + s,\n",
    "    ],\n",
    "    [\n",
    "        \"{:.2f}\".format(psql_write_execution_time) + s,\n",
    "        \"{:.2f}\".format(psql_read_execution_time) + s,\n",
    "        \"{:.2f}\".format(psql_write_execution_time + psql_read_execution_time) + s,\n",
    "    ],\n",
    "    [\n",
    "        \"{:.2f}\".format(s3_write_execution_time) + s,\n",
    "        \"{:.2f}\".format(s3_read_execution_time) + s,\n",
    "        \"{:.2f}\".format(s3_write_execution_time + s3_read_execution_time) + s,\n",
    "    ],\n",
    "    [\n",
    "        \"{:.2f}\".format(mongo_write_execution_time) + s,\n",
    "        \"{:.2f}\".format(mongo_read_execution_time) + s,\n",
    "        \"{:.2f}\".format(mongo_write_execution_time + mongo_read_execution_time) + s,\n",
    "    ],\n",
    "    [\n",
    "        \"{:.2f}\".format(duckdb_write_execution_time) + s,\n",
    "        \"{:.2f}\".format(duckdb_read_execution_time) + s,\n",
    "        \"{:.2f}\".format(duckdb_write_execution_time + duckdb_read_execution_time) + s,\n",
    "    ],\n",
    "    [\n",
    "        \"{:.2f}\".format(kdb_write_execution_time) + s,\n",
    "        \"{:.2f}\".format(kdb_read_execution_time) + s,\n",
    "        \"{:.2f}\".format(kdb_write_execution_time + kdb_read_execution_time) + s,\n",
    "    ],\n",
    "]\n",
    "\n",
    "index_labels = [\n",
    "    \"Click House\",\n",
    "    \"InfluxDb\",\n",
    "    \"Postgresql\",\n",
    "    \"S3\",\n",
    "    \"MongoDb\",\n",
    "    \"DuckDb\",\n",
    "    \"Kdb+\",\n",
    "]\n",
    "dff = pd.DataFrame(\n",
    "    data, columns=[\"Write Time\", \"Read Time\", \"Total Time\"], index=index_labels\n",
    ")\n",
    "dff"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7baf1fd1-2afd-41b5-a579-33f053e4ddfc",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Graph\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "a9740731-6077-4bf1-bb65-b4c4225ac79b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Yu956rGzpd3T33n3NWbTEuqJj9YZNqlX1yRuz46dOa/3mrbJYLFqxdoOSJXNS9mxZJUnVKlfSZO85unXnjgIePtS4/1YKPFajSmVNmO6t6zduKig4WL+PGqPSJUs8d8uBm363bP4oFhQcrLFTpis8PFxHjh7TqvUbCbVfMTdv2f4+8iz1a1bX2k2btXHLNpnNZgU8fKh/jx23HjcZjfruqy9UqnhRtfusl/xi8Ds/vwslLSWLFtWde/e0fddua1tMfy9Zu3GzzetV6lSpVLhA/pdZPuIZm2gjTu7df7Q5X/q0aWK1GWimDBmee7s2zZpo6eq1On323HP7Gzl+UrSbaD+WPl06Xb1+3fp1WFiY/G7fVoZ0aZ/b975DhzVmyjR1addGv+bIrt37D+iP0eN19fp1Zc6YQd07tlPndk9WujiZTEqfNs1z+0XsBQQGWi8jkaSg4BDrX2Hjw6F/j+rK1WvKlDGDFq9cHaPbpE+XVof/PWr9OvJlnFev31C6NMyLlyXyc0LB/PkUEhqq23efvNm/fPWa9Wd12ux5cnF21v9++l4ebm5a+/dmjRw/KdqVZ+nSpFFIaKj10l1JunbjRpTzrt/0s/4/c8aM8nR31+YVTz5gwCCDTCaj3fE93NyVzMnJZj5di+YSYcSP23fvatjocRo2epxSeHqoQ6sW+v3nH1WtUfMogeK5Cxd16sxZDejztTr3eva+Rv1+HqLN23dEaW/RqL7q1aim9OnSKUIRckvuqlQpU1iPPwwMkn/AkzHTpUljszrAbDbr1p07Mb5vvF453u79B9W/95fq2Lqljvgcs3k+ypAuna5dt30euXL1moo/tZrj6fMlKTgkRO5uj1770qVNoxtPPefcuOFnc26GdGl19an+b92+o9CwMGVIl063bj97HqVPl1b3H/hbv/a7fdtmk+TrN27EaLULXp70adPqwVPfs2fJnDGDDhz599n9pE+nmlk/Us++/W2ei+yOze9CScqkGbNUtFABjR8+VJ16fq179x/IK2XKGP1e8vTz2ePXq/RpH73v6tOzh3U1ppPJJOdkzjZ//P/82+906Kl5hFcDK5AQJxcuXdbV6zf08VPX57+IazduPHcJa/dv+qla5Ur6pGnjWI3xtJt+fjaXBjg5OSldmjS64XdL0qO/qLm4uFiPR35xm79kuT7p2kPVG7dQWFiYevfoKkm64XdLf4wZrw9q1LP+e79qbU2ZNTfONSNhBAUFK/lT+xklT+4iDzc3m3NaN2kk52TJdO7CRX3Wsd1z+8yUMYPy581js4ou8uUvmTKkl9/t2K3YQ9zd8POTi7OzzcaimTNmsK6iDAoO1ohxE1WvZVt90vVzvVu8mBrXrS1JioiIsOnL7/ZtuTg727zZz5QhQ5QxLRbLk/Fv+unuvfs2zxXla9TV+1Vr2x3/7v37Cg8328ynjOnTxcMjgud54B+gsVOmyzV5cmXOFP3rVTInJ2XLkuWF+y5aqIA6tWmt7wb9qg9qPpoPZ86fl8FgsJ5jibDY3Mbv9m3rfjbSoz1t0qZ+snrkec9tz3u9ijzPEf8iIiK0fM06tW3eRMvW2O5jdsPPz+b7K0mZMmbUDT/bIOhZ/G7dVoannhsyZLB9nrjhd0uZn+o/jZeXnJMls9u/h7u73ilezOa1LV2aNDabumfMkEE3b92KUY1IeEajURXLvqf9hw7rYVCQkidPbnP86dfAq9dvKHvWZz9/Xbt+Q19+96N++b6PzV5qz8LvQklPaFiovuo/QFevX9eE4UPllSpljH8vie716vFzxeA//rS+DvX49jsd/NfH5rWJ8OjVRICEOPvfiL/UtnlTNa1f17q8OXvWLPrhmy+fuXfEY9t27pazs7O6tPtEyZO7yMnJSSUj/QXr6rXrat+jlxrWqfnMTf1iatX6jWpSv47ezJlDyZIlU7f2bXTz1i35HH+0eeXxU6dVo0plGY1GvZU7l2pUqWy97dv58qpwgbfl5OSk4JAQBQUHy/zfG8O5i5eqdZNGypc3j6RHv7C/W6KY0sdgZRMc4/ip0ypcoIByZs8mZ2dnde/YzuaNU768edShdQv1GTBIfQcOVq1qVVSmVIlo+zKZTCpWuKCGDuivA4f/1T+791qP5ciaVfVqVpfRaFTZ0u+qVPGiWrvx7wS/f4ie363b2nvwsHp2/VTJk7soY4b06tCquZb/9yauXJnSyp41iwwGgx4GPlS42Wz9aPY7d+8q61MbJd+46adDPkfVvWM7ubg4K1uWzKpXq7rd8Y+eOKnrN/3UtX0b62q5jBnS6/13S9kd32KxaP3mLeravo08PTyUNk1qfdIs7qE6ovL08FDX9m2UM3s2GY1GJU/uopaNG+q+v7/OX7wo1+TJVbvax/L08JAk5X4jp9q3aq6de/e98Fju7u6yRFh07/59GQwG1a728XP3M1qzYZOqVa6kgvnzycnJSZ9+0lKuT705fN5z2/NeryLPcyQM7/kL1fWrb7Xln5027dt37VFqr1RqVLeWjEajihUuqOoffWhzmZs9azb+rTbNmiiNl5c83N3VsXULm+Or1m1U+1bNlSF9OrkmT64vu3fW7v0Hol19ZDAYlC9vHv3603e6ffeu9XlSklyTJ9enn7SUk5OTCubPp2qVK2nVuo2xeCQQ33Jmz6aBfXvL3d1dM+ct1CnfM8qSMaOKFS4oo9GoT5o2VqqUTzZOX7xytT6uVEEVy70vo9EoD3d3FXrb9hKjf3bvVb+Bg/XbgP56p3ixaMfld6GkLTw8XF99P0AXr1zV+D9+U6oUKWL0e0mVSh/YvF7dvXdPR44ej2YEJBZcwoY427Zzl7r37qOOrVuqS/tHS+Jv3PDTynUbdOv2HaWzsyw+KDhYnXv11lfdu2j1/FmSpL0HDkXZB+n6jZvq8PmXGv/HbzKZTJo43TtWtS5fs16pU3lpxOCB8vT00NHjJ/VFn++tKwR+HfGXBvTtrW2rlujQv0e1fO1664uoh5u7enXrpKyZMyvcHK4jPsc06I8/JUlbd+ySs7Oz+n/dU1kyZ1JYaJh8jp/QkBGjYlUnEt7eg4e0cNkKTR09QsHBwRo7ZYYeBgVJevSL8ZAf+umviVOs+x4N/O0PDejTW03addKde/ckSVP+Gi6LJULh5nBduHhZq9Zv1JxFS23G2bFnrwoXyK9eXTvpzt276vfzEF26cvVl3lVE0nfAIH3zRXetmuetkJBQrd6wUVP/W32RLUsm9e7RVam9UikoKFgbt27X/KUrJEne8xfppz5fa8vKxTp0xEef9/lefQcO1o/ffKkNi+fr4uXLWrVuo6p99Ox9HSwWiz7/9jv16NRBi2ZMkrubu67fuKGFy1dJ2mt3/CHDR+n7r3tq5dyZunXnjuYvWRZve37hibDwMKVPm1Yj//eLUnulUkhoqE6cOq3uX/dVcHCIkid3UbXKFdWz66dyTpZMd+7e08Yt2zR26vQXHmvH7r3asHmr5k0er7CwMK1ct0GHfOz/xXX3/oMaM3mqhg7sLxcXFy1YukJnzp+3Hrf33CY9//UqunmO+PfAP0C79x+M0u4fEKDuX/fVV5910Wcd28vv9m0NGjYixn+JnzhjltKk9tLCGZMUEPBQU2fP1QfvlVFYaJgkabL3bLkmd9G00SPk7OysfQcP67uf/2fTx+D+fWU2W2SxWHTl2jVt3r5D0+fMt7mU98z58zKZTFq/eK6Cg0P018QpUX53w8vzeeeO6tq+rSwWi27euqUdu/eq5afddOfePd2+e1cjxk3Qbz/1l9Fo1KyFi22eM06cOq2v+w9Q1/ZtNKBPbwUFBWnWgsU2+yBJ0s69+9Xnp1805Md+6jdwsHbu3S+J34VeJ2azWb1/GKjB/ftq/Iih6tyzt3p/3s3u7yVLV65Rj04dVCBfXvmePa9e/X6wWZmNxMfgkTUXa5UBAIgHbVs01TvFi6rLl/Y/1Sg+vJU7l2ZPHKPiFao8/2QkaXMmjdWs+YuiXA4FFC7wtiaMGKp3K9tfHQkAQExwCRsAALGUL28e5cyezfr/ZvXrav3mrQ6uCsDrKnWqVCpZtIiMRqPSpkmt7h3baeOWbY4uCwCQRHAJGwAAseSVMoX6Deiv1F6pdPfefS1asUpLVq5xdFkAXlNGo1FffdZF2bJkVnBwiHbt269f//zL0WUBAJIILmEDAAAAAACAXVzCBgAAAAAAALsIkAAAAAAAAGAXARIAAAAAAADsIkACAAAAAACAXQRIAAAAAAAAsIsACQAAAAAAAHYRIAEAAAAAAMAuAiQAAAAAAADYRYAEAAAAAAAAuwiQAAAAAAAAYBcBEgAAwCtg5dyZGjF4oKPLAAAAiJaTowsAAAB4WWpXraIfv/3K+nVoWJgePPDX6bPntH3Xbi1dtVaBQUEv3G/hAm+rTKkSmrVgsfwDAuKzZAAAgFcCARIAAHjtjJ48TVevXZeTk5PSpPZSyaJF9FX3LmrZuKG+6PO9Tp8990L9FS1YQJ3atNKyNesIkAAAQJJEgAQAAF47/+zeo+MnT1u/nuI9R6WKFdWIIQM1fPBA1W/dTiEhoQ6sEAAA4NVCgAQAACBp78FDmjDdW591bKfqH1XW4hWrlOfNN9SycUMVL1JI6dKmkb9/gLbv3qPhY8br/gN/SVKnNq3UqU0rSdLKOTOs/dVo2krXrt9Q7Wofq0aVysr9Rk55eLjr0pWrmrNoiRYsXRFtHWVKldDnnTsqZ/Zsunz1mkZPmqpNW7dbj6fw9FD7ls313jsllTlTRlksETrsc1R/jpuoU2fO2vTVtH5dNaxdQ5kzZVRoWJiuXL2mmfMWavWGTdZz0qVNo67t26hc6Xfl6emhS5evaPrcBVq2eu0L9wUAAJIuAiQAAID/rFy3QZ91bKcypUpo8YpVKl2qhLJkzqilq9fq9p27yvVGDtWvWV25cuZU6y6fSZI2bd2uHNmyquqHFTV01Bjdu/9AknT33j1JUqM6tXTm3Hlt+WeHzGaLyr9XWn179pDRYNS8Jct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      "text/plain": [
       "<Figure size 1400x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot(dark):\n",
    "    mpl_style(dark)\n",
    "    # plt.subplots(figsize=(16, 8))\n",
    "    plt.figure().set_figwidth(14)\n",
    "    x = np.arange(7)  # change here\n",
    "    width = 0.40\n",
    "    y1 = [\n",
    "        cHouse_read_execution_time,\n",
    "        influxdb_read_execution_time,\n",
    "        psql_read_execution_time,\n",
    "        s3_read_execution_time,\n",
    "        mongo_read_execution_time,\n",
    "        duckdb_read_execution_time,\n",
    "        kdb_read_execution_time,\n",
    "    ]  # change here\n",
    "    y2 = [\n",
    "        cHouse_write_execution_time,\n",
    "        influxdb_write_execution_time,\n",
    "        psql_write_execution_time,\n",
    "        s3_write_execution_time,\n",
    "        mongo_write_execution_time,\n",
    "        duckdb_write_execution_time,\n",
    "        kdb_write_execution_time,\n",
    "    ]  # change here\n",
    "    plt.bar(x - 0.2, y2, width)\n",
    "    plt.bar(x + 0.2, y1, width)\n",
    "    plt.xticks(\n",
    "        x,\n",
    "        [\n",
    "            \"Click House\",\n",
    "            \"InfluxDb\",\n",
    "            \"Postgresql\",\n",
    "            \"S3 Parquet\",\n",
    "            \"MongoDb\",\n",
    "            \"DuckDb\",\n",
    "            \"Kdb+\",\n",
    "        ],\n",
    "    )\n",
    "    plt.xlabel(\"Databases\")\n",
    "    plt.ylabel(\"Seconds\")\n",
    "    plt.legend([\"Write\", \"Read\"])  # ver\n",
    "    plt.rcParams.update({\"font.size\": 18})\n",
    "    plt.title(\"Fastest Databases  Comparation (Write/Read)\")\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "plot(dark=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aeda3a71-f57d-419d-98b4-b711ea2e61af",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Graph-(bests)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "810e8a81-0a20-40aa-bb07-7bb71094b8d2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot(dark):\n",
    "    mpl_style(dark)\n",
    "    plt.figure().set_figwidth(15, 10)\n",
    "    x = np.arange(4)  # change here\n",
    "    width = 0.40\n",
    "    y1 = [\n",
    "        cHouse_read_execution_time,\n",
    "        s3_read_execution_time,\n",
    "        duckdb_read_execution_time,\n",
    "        kdb_read_execution_time,\n",
    "    ]  # change here\n",
    "    y2 = [\n",
    "        cHouse_write_execution_time,\n",
    "        s3_write_execution_time,\n",
    "        duckdb_write_execution_time,\n",
    "        kdb_write_execution_time,\n",
    "    ]  # change here\n",
    "    plt.bar(x - 0.2, y2, width)\n",
    "    plt.bar(x + 0.2, y1, width)\n",
    "    plt.xticks(\n",
    "        x,\n",
    "        [\n",
    "            \"Click House\",\n",
    "            \"S3 Parquet\",\n",
    "            \"DuckDb\",\n",
    "            \"Kdb+\",\n",
    "        ],\n",
    "    )\n",
    "    plt.xlabel(\"Databases\")\n",
    "    plt.ylabel(\"Seconds\")\n",
    "    plt.legend([\"Write\", \"Read\"])  # ver\n",
    "    plt.rcParams.update({\"font.size\": 8})\n",
    "    plt.style.use(\"dark_background\")\n",
    "    plt.title(\"Top 4 fastest Databases on Benchmark -  Graph Comparation\")\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "plot(dark=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcfa53b5-137b-4c26-91f2-740cc2e93df8",
   "metadata": {},
   "source": [
    "## Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "e58920d6-2a99-4ce4-9503-05873d61ffc3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "originalCsv = 106.8\n",
    "clickHouse_size_on_disk = 20\n",
    "influxDb_size_on_disk = 84\n",
    "postgresql_size_on_disk = 128\n",
    "S3_size_size_on_disk = 31.8\n",
    "mongoDb_size_on_disk = 174.5\n",
    "DuckDb_size_on_disk = 36.5\n",
    "Kdb_size_on_disk = 99.2\n",
    "\n",
    "\n",
    "def plot(dark):\n",
    "    mpl_style(dark)\n",
    "    plt.figure().set_figwidth(12, 10)\n",
    "    x = np.arange(8)  # change here\n",
    "    width = 0.50\n",
    "    y1 = [\n",
    "        originalCsv,\n",
    "        clickHouse_size_on_disk,\n",
    "        influxDb_size_on_disk,\n",
    "        postgresql_size_on_disk,\n",
    "        S3_size_size_on_disk,\n",
    "        mongoDb_size_on_disk,\n",
    "        DuckDb_size_on_disk,\n",
    "        Kdb_size_on_disk,\n",
    "    ]  # change here\n",
    "\n",
    "    # plt.bar(x - 0.2, y2, width)\n",
    "    plt.bar(x + 0.0, y1, width, color=(0.2, 0.4, 0.6, 0.6))\n",
    "    plt.xticks(\n",
    "        x,\n",
    "        [\n",
    "            \"Original CSV\",\n",
    "            \"Click House\",\n",
    "            \"InfluxDb\",\n",
    "            \"Postgresql\",\n",
    "            \"S3 Parquet\",\n",
    "            \"MongoDb\",\n",
    "            \"DuckDb\",\n",
    "            \"Kdb+\",\n",
    "        ],\n",
    "    )\n",
    "    plt.xlabel(\"Databases\")\n",
    "    plt.ylabel(\"Megabytes\")\n",
    "    plt.legend([\"Size Ocupied on Disk In MB\"])  # ver\n",
    "    plt.rcParams.update({\"font.size\": 8})\n",
    "    plt.style.use(\"dark_background\")\n",
    "    plt.title(\"Size On Disk Occupied with the Same Archive on different Databases\")\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "plot(dark=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "428abef6-c1df-4abf-83f8-a74cb8af4208",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.11"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}