{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7f1f5a4a-ae34-4a27-9efa-399edc0e384a",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Benchmark: ClickHouse Vs. InfluxDB Vs. Postgresql Vs. Parquet \n",
    "\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 50.000 lines) to four diferent databases:\n",
    "* ClickHouse\n",
    "* InfluxDB\n",
    "* Postgresql\n",
    "* Parquet (in a S3 Minio Storage) <br>\n",
    "ToDo: <br>\n",
    "* DuckDB with Polars\n",
    "* MongoDB\n",
    "* Kdb+\n",
    "\n",
    " \n",
    "Deve-se relevar:\n",
    "é uma \"cold-storage\" ou  \"frezze-storage\"? <br>\n",
    "influxdb: alta leitura e possui  a vantagem da indexaçõa para vizualização de dados em gráficos.\n",
    "\n",
    "notas: \n",
    "* comparar tamanho do csv com parquet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bb26ce7-1e84-4665-accd-916bb977f95d",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Imports "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ab6c6c81-6ac1-4668-a79b-a9a0341fb35a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 1,
     "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",
    "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 sqlalchemy import create_engine\n",
    "\n",
    "load_dotenv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "55c3cd57-0996-4723-beb5-8f3196c96009",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Variables\n",
    "dbname = \"EURUSDtest\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 5,
   "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": 5,
     "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": 6,
   "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": 20,
   "id": "c3202bbb-2655-45b2-b166-9f45a3ef854c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Database created'"
      ]
     },
     "execution_count": 20,
     "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",
    "# 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(\n",
    "        \"SELECT * FROM default.{}\".format(databaseName)\n",
    "    )  # LIMIT 10000\n",
    "    client.disconnect()\n",
    "    return dfQuery\n",
    "\n",
    "\n",
    "cHouseCreateDb(dbname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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": 22,
   "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": 23,
   "id": "597ae7bd-2eea-44d7-b379-f0eb7e745c15",
   "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>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>2999995</th>\n",
       "      <td>8230798</td>\n",
       "      <td>2023-05-03 10:35:00</td>\n",
       "      <td>1683110115000000000</td>\n",
       "      <td>2023-05-03 10:35:15</td>\n",
       "      <td>1.103340</td>\n",
       "      <td>1.10330</td>\n",
       "      <td>1.103275</td>\n",
       "      <td>1.103340</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2999996</th>\n",
       "      <td>8230799</td>\n",
       "      <td>2023-05-03 10:35:15</td>\n",
       "      <td>1683110130000000000</td>\n",
       "      <td>2023-05-03 10:35:30</td>\n",
       "      <td>1.103300</td>\n",
       "      <td>1.10341</td>\n",
       "      <td>1.103300</td>\n",
       "      <td>1.103410</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2999997</th>\n",
       "      <td>8230799</td>\n",
       "      <td>2023-05-03 10:35:15</td>\n",
       "      <td>1683110130000000000</td>\n",
       "      <td>2023-05-03 10:35:30</td>\n",
       "      <td>1.103300</td>\n",
       "      <td>1.10341</td>\n",
       "      <td>1.103300</td>\n",
       "      <td>1.103410</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2999998</th>\n",
       "      <td>8230800</td>\n",
       "      <td>2023-05-03 10:35:30</td>\n",
       "      <td>1683110145000000000</td>\n",
       "      <td>2023-05-03 10:35:45</td>\n",
       "      <td>1.103415</td>\n",
       "      <td>1.10351</td>\n",
       "      <td>1.103385</td>\n",
       "      <td>1.103515</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2999999</th>\n",
       "      <td>8230800</td>\n",
       "      <td>2023-05-03 10:35:30</td>\n",
       "      <td>1683110145000000000</td>\n",
       "      <td>2023-05-03 10:35:45</td>\n",
       "      <td>1.103415</td>\n",
       "      <td>1.10351</td>\n",
       "      <td>1.103385</td>\n",
       "      <td>1.103515</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              id                from                   at                  to  \\\n",
       "2999995  8230798 2023-05-03 10:35:00  1683110115000000000 2023-05-03 10:35:15   \n",
       "2999996  8230799 2023-05-03 10:35:15  1683110130000000000 2023-05-03 10:35:30   \n",
       "2999997  8230799 2023-05-03 10:35:15  1683110130000000000 2023-05-03 10:35:30   \n",
       "2999998  8230800 2023-05-03 10:35:30  1683110145000000000 2023-05-03 10:35:45   \n",
       "2999999  8230800 2023-05-03 10:35:30  1683110145000000000 2023-05-03 10:35:45   \n",
       "\n",
       "             open    close       min       max  volume  \n",
       "2999995  1.103340  1.10330  1.103275  1.103340      61  \n",
       "2999996  1.103300  1.10341  1.103300  1.103410      44  \n",
       "2999997  1.103300  1.10341  1.103300  1.103410      44  \n",
       "2999998  1.103415  1.10351  1.103385  1.103515      51  \n",
       "2999999  1.103415  1.10351  1.103385  1.103515      51  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfCh.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "86794e47-611f-4ca8-a7e8-07e71afafe67",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.64297915700081\n"
     ]
    }
   ],
   "source": [
    "print(cHouse_read_execution_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e7926062-8e84-4d3f-90a9-32807ce4f3d4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.190685558998666\n"
     ]
    }
   ],
   "source": [
    "print(cHouse_write_execution_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8faa5683-a204-461d-80c3-67644aa714ce",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.21 s, sys: 383 ms, total: 2.6 s\n",
      "Wall time: 10.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "dfCh = cHouseQueryDf(dbname)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d389546-911f-43f7-aad1-49f7bcc83503",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### InfluxDB\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3e7ebfd-76f1-4ac4-9833-312eb1a531af",
   "metadata": {},
   "outputs": [],
   "source": [
    "client = influxdb_client.InfluxDBClient(\n",
    "    url=InfluxDBUrl, token=InfluxDBKey, org=InfluxDBUser\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbf61f12-830b-4c57-804a-2257d8b3599a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Read data from CSV without index and parse 'TimeStamp' as date.\n",
    "df = pd.read_csv(\"out.csv\", sep=\",\", index_col=False, parse_dates=[\"from\"])\n",
    "# Set 'TimeStamp' field as index of dataframe # test another indexs\n",
    "df.set_index(\"from\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54342a28-ba2b-4ade-a692-00566b53a639",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f861fab2-f1b1-49dd-b758-12d10aef3462",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "# gravando... demorou... mas deu certo\n",
    "with client.write_api() as writer:\n",
    "    writer.write(\n",
    "        bucket=InfluxDBBucket,\n",
    "        record=df,\n",
    "        data_frame_measurement_name=\"id\",\n",
    "        data_frame_tag_columns=[\"volume\"],\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0bb2563d-68e2-4ff4-8842-70ac730dc6b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data\n",
    "#   |> pivot(\n",
    "#     rowKey:[\"_time\"],\n",
    "#     columnKey: [\"_field\"],\n",
    "#     valueColumn: \"_value\"\n",
    "#   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb1596f9-4cee-4642-803a-ee61c9dddf64",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9ddfdc6-c899-4f6c-9b4e-8ec6ab6d7e05",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Postgresql"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "16cd8eb7-333d-43fd-88e0-ee983645d3fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Engine(postgresql+psycopg2://postgres:***@192.168.1.133:5432/postgres)"
      ]
     },
     "execution_count": 24,
     "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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",
    "\n",
    "    cur.copy_from(output, \"comparedbs\")  # , 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 \"comparedbs\"', con=engine)\n",
    "    return df\n",
    "\n",
    "\n",
    "# testar função"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 27,
   "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": 29,
   "id": "6d1b7480-5bc7-4f08-8cf3-b9590802d8f7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "126.40752380799677\n"
     ]
    }
   ],
   "source": [
    "print(psql_read_execution_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": 15,
   "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": 13,
   "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": null,
   "id": "a803e870-1970-496f-b678-a7f8866870cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# falta read (parquet to df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "390918c8-c88f-404a-96c4-685d578fdad0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.8389689489995362\n"
     ]
    }
   ],
   "source": [
    "print(s3_write_execution_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": 17,
   "id": "573b10c3-5dfe-420a-a988-688d4ebcde24",
   "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>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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pq.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fefb54f6-9040-4fd7-9861-20322c202d92",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5066086639999412\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": null,
   "id": "d104d9af-fa34-4261-8478-329a28ee4f2e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Load csv dataset\n",
    "data = pd.read_csv(\"out.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0af8f72c-5b58-4dfc-af36-c5b4bc79f127",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Connect to MongoDB\n",
    "client = MongoClient(\n",
    "    # \"mongodb://192.168.1.133:27017\"\n",
    "    \"mongodb://{}:{}@{}/EURUSDtest?retryWrites=true&w=majority\".format(\n",
    "        MongoUser, MongoKey, MongoUrl\n",
    "    ),\n",
    "    authSource=\"admin\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1b20d15-f5af-463c-813f-ffae61119de1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "db = client[\"EUROUSDtest\"]\n",
    "collection = db[\"finance\"]\n",
    "# data.reset_index(inplace=True)\n",
    "data_dict = data.to_dict(\"records\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70674d23-f375-4659-87ec-c745dec96d54",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "# Insert collection\n",
    "collection.insert_many(data_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81a4a33d-5914-45d8-af4e-2b0aabd2ac38",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# read"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97405e42-61dc-42c7-8220-237a312c0ec7",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### DuckDB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbcdb883-d6dc-46db-88db-4c90b84522ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "cursor = duckdb.connect()\n",
    "print(cursor.execute(\"SELECT 42\").fetchall())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35025a6e-9dc7-46cf-a792-76b3d84f1ac0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "conn = duckdb.connect()\n",
    "data = pd.read_csv(\"out.csv\")\n",
    "conn.register(\"EURUSDtest\", data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6abdaaa-3ac2-425b-9208-d6cb79afe966",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "display(conn.execute(\"SHOW TABLES\").df())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2acce0f3-f0b2-47d0-8e0d-f9e9687efc18",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "df = conn.execute(\"SELECT * FROM EURUSDtest\").df()\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4409cc89-ed14-4313-ac89-65b826038533",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Kdb+"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14f63810-1943-4e28-9bce-2148be6be02d",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.bool = np.bool_\n",
    "from qpython import qconnection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ff6c090-7e02-435a-a179-f2aab81da972",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read csv\n",
    "data = pd.read_csv(\"out.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4eb8ab9-81e8-4732-8cf7-51f0981d3d57",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# open connection\n",
    "q = qconnection.QConnection(host=\"localhost\", port=5001)\n",
    "q.open()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97cb6b5b-65a5-46a0-a4ee-e5c535a716ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "# send df to kd+ in memory bank\n",
    "q.sendSync(\"{t::x}\", data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2ed2d51-bc8e-4207-892a-35fc55d43570",
   "metadata": {},
   "outputs": [],
   "source": [
    "# write to on disk table\n",
    "q.sendSync(\"`:/home/sandman/q/tab1 set t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c055a95-f73f-43a3-8fbd-61e42235117e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "# read from on disk table\n",
    "df2 = q.sendSync(\"tab2: get `:/home/sandman/q/tab1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9760de38-9f04-4322-bfff-c7ee12d5dee5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# print(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c06c9222-c69d-4872-9d21-052281a013e2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "# load to variable df2\n",
    "df2 = q.sendSync(\"tab2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8815f01c-fd0a-4f94-ab7f-f8ede84ba4e7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# df2(type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6ed3927-4395-45cd-9a28-88c5db01f2e5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "# converto to dataframe\n",
    "df = pd.DataFrame(q(\"t\"))  # , pandas=True))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fc7f16b-6c39-4ebe-88d2-ff857e30ab62",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "# select\n",
    "df3 = q.sendSync(\"select from t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c88646ca-3d25-4a85-80b5-f9e559f568dd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "q.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7baf1fd1-2afd-41b5-a579-33f053e4ddfc",
   "metadata": {},
   "source": [
    "## Graph\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a9740731-6077-4bf1-bb65-b4c4225ac79b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(3)  # change here\n",
    "width = 0.40\n",
    "y1 = [\n",
    "    cHouse_read_execution_time,\n",
    "    psql_read_execution_time,\n",
    "    s3_read_execution_time,\n",
    "]  # change here\n",
    "y2 = [\n",
    "    cHouse_write_execution_time,\n",
    "    psql_write_execution_time,\n",
    "    s3_write_execution_time,\n",
    "]  # change here\n",
    "plt.bar(x - 0.2, y1, width)\n",
    "plt.bar(x + 0.2, y2, width)\n",
    "plt.xticks(x, [\"Click House\", \"Postgresql\", \"S3 Parquet\"])\n",
    "plt.xlabel(\"Databases\")\n",
    "plt.ylabel(\"Seconds\")\n",
    "plt.legend([\"Read\", \"Write\"])  # ver\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cac1c82a-bcab-47b1-b302-77d1341e5304",
   "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
}