{ "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)
\n", "ToDo:
\n", "* DuckDB with Polars\n", "* MongoDB\n", "* Kdb+\n", "\n", " \n", "Deve-se relevar:\n", "é uma \"cold-storage\" ou \"frezze-storage\"?
\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": 11, "id": "ab6c6c81-6ac1-4668-a79b-a9a0341fb35a", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import configparser\n", "import time\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", "\n", "# import pymongo\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": 12, "id": "55c3cd57-0996-4723-beb5-8f3196c96009", "metadata": { "tags": [] }, "outputs": [], "source": [ "# Variables\n", "dbname = \"EURUSDtest\"" ] }, { "cell_type": "code", "execution_count": 13, "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": 14, "id": "3634a4ec-04c2-4f1e-8659-5d22eb17a254", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idfromattoopencloseminmaxvolume
077308012023-01-02 15:58:4516726751400000000002023-01-02 15:59:001.0659951.0660351.0659301.06607057
177308022023-01-02 15:59:0016726751550000000002023-01-02 15:59:151.0660551.0660851.0660051.06611552
277308032023-01-02 15:59:1516726751700000000002023-01-02 15:59:301.0660801.0660251.0660251.06611057
377308042023-01-02 15:59:3016726751850000000002023-01-02 15:59:451.0659801.0659851.0658851.06604564
477308052023-01-02 15:59:4516726752000000000002023-01-02 16:00:001.0659751.0660551.0658301.06605550
\n", "
" ], "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": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# %%time\n", "# Load Dataset\n", "df = pd.read_csv(\"out.csv\", index_col=0)\n", "df.head()" ] }, { "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": null, "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": 15, "id": "c3202bbb-2655-45b2-b166-9f45a3ef854c", "metadata": { "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Failed to connect to 192.168.1.142:9000\n", "Traceback (most recent call last):\n", " File \"/home/sandman/dev/pipenv/lib/python3.10/site-packages/clickhouse_driver/connection.py\", line 395, in connect\n", " return self._init_connection(host, port)\n", " File \"/home/sandman/dev/pipenv/lib/python3.10/site-packages/clickhouse_driver/connection.py\", line 325, in _init_connection\n", " self.socket = self._create_socket(host, port)\n", " File \"/home/sandman/dev/pipenv/lib/python3.10/site-packages/clickhouse_driver/connection.py\", line 297, in _create_socket\n", " raise err\n", " File \"/home/sandman/dev/pipenv/lib/python3.10/site-packages/clickhouse_driver/connection.py\", line 288, in _create_socket\n", " sock.connect(sa)\n", "TimeoutError: timed out\n" ] }, { "ename": "SocketTimeoutError", "evalue": "Code: 209. (192.168.1.142:9000)", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mSocketTimeoutError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[15], line 44\u001b[0m\n\u001b[1;32m 40\u001b[0m client\u001b[38;5;241m.\u001b[39mdisconnect()\n\u001b[1;32m 41\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m dfQuery\n\u001b[0;32m---> 44\u001b[0m \u001b[43mcHouseCreateDb\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdbname\u001b[49m\u001b[43m)\u001b[49m\n", "Cell \u001b[0;32mIn[15], line 17\u001b[0m, in \u001b[0;36mcHouseCreateDb\u001b[0;34m(databasename)\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcHouseCreateDb\u001b[39m(databasename):\n\u001b[1;32m 16\u001b[0m client \u001b[38;5;241m=\u001b[39m cHouseConnect()\n\u001b[0;32m---> 17\u001b[0m \u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 18\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mCREATE TABLE IF NOT EXISTS \u001b[39;49m\u001b[38;5;132;43;01m{}\u001b[39;49;00m\u001b[38;5;124;43m (id UInt32,\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m 19\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfrom DateTime, at UInt64, to DateTime, open Float64,\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m 20\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mclose Float64, min Float64, max Float64, volume UInt32)\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m 21\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mENGINE MergeTree ORDER BY to\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mformat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdatabasename\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 22\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 23\u001b[0m client\u001b[38;5;241m.\u001b[39mdisconnect()\n\u001b[1;32m 24\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDatabase created\u001b[39m\u001b[38;5;124m\"\u001b[39m\n", "File \u001b[0;32m~/dev/pipenv/lib/python3.10/site-packages/clickhouse_driver/client.py:361\u001b[0m, in \u001b[0;36mClient.execute\u001b[0;34m(self, query, params, with_column_types, external_tables, query_id, settings, types_check, columnar)\u001b[0m\n\u001b[1;32m 318\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 319\u001b[0m \u001b[38;5;124;03mExecutes query.\u001b[39;00m\n\u001b[1;32m 320\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 356\u001b[0m \u001b[38;5;124;03m and types.\u001b[39;00m\n\u001b[1;32m 357\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 359\u001b[0m start_time \u001b[38;5;241m=\u001b[39m time()\n\u001b[0;32m--> 361\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdisconnect_on_error(query, settings):\n\u001b[1;32m 362\u001b[0m \u001b[38;5;66;03m# INSERT queries can use list/tuple/generator of list/tuples/dicts.\u001b[39;00m\n\u001b[1;32m 363\u001b[0m \u001b[38;5;66;03m# For SELECT parameters can be passed in only in dict right now.\u001b[39;00m\n\u001b[1;32m 364\u001b[0m is_insert \u001b[38;5;241m=\u001b[39m \u001b[38;5;28misinstance\u001b[39m(params, (\u001b[38;5;28mlist\u001b[39m, \u001b[38;5;28mtuple\u001b[39m, types\u001b[38;5;241m.\u001b[39mGeneratorType))\n\u001b[1;32m 366\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_insert:\n", "File \u001b[0;32m~/dev/anaconda3/lib/python3.10/contextlib.py:135\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 133\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwds, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc\n\u001b[1;32m 134\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 135\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgen\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 136\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[1;32m 137\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgenerator didn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt yield\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", "File \u001b[0;32m~/dev/pipenv/lib/python3.10/site-packages/clickhouse_driver/client.py:305\u001b[0m, in \u001b[0;36mClient.disconnect_on_error\u001b[0;34m(self, query, settings)\u001b[0m\n\u001b[1;32m 302\u001b[0m \u001b[38;5;129m@contextmanager\u001b[39m\n\u001b[1;32m 303\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdisconnect_on_error\u001b[39m(\u001b[38;5;28mself\u001b[39m, query, settings):\n\u001b[1;32m 304\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 305\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mestablish_connection\u001b[49m\u001b[43m(\u001b[49m\u001b[43msettings\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 307\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m\n\u001b[1;32m 309\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrack_current_database(query)\n", "File \u001b[0;32m~/dev/pipenv/lib/python3.10/site-packages/clickhouse_driver/client.py:292\u001b[0m, in \u001b[0;36mClient.establish_connection\u001b[0;34m(self, settings)\u001b[0m\n\u001b[1;32m 290\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconnection \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_connection()\n\u001b[1;32m 291\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmake_query_settings(settings)\n\u001b[0;32m--> 292\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnection\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforce_connect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 293\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlast_query \u001b[38;5;241m=\u001b[39m QueryInfo()\n\u001b[1;32m 295\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (errors\u001b[38;5;241m.\u001b[39mSocketTimeoutError, errors\u001b[38;5;241m.\u001b[39mNetworkError):\n", "File \u001b[0;32m~/dev/pipenv/lib/python3.10/site-packages/clickhouse_driver/connection.py:254\u001b[0m, in \u001b[0;36mConnection.force_connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 251\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcheck_query_execution()\n\u001b[1;32m 253\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconnected:\n\u001b[0;32m--> 254\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconnect\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 256\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mping():\n\u001b[1;32m 257\u001b[0m logger\u001b[38;5;241m.\u001b[39mwarning(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mConnection was closed, reconnecting.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", "File \u001b[0;32m~/dev/pipenv/lib/python3.10/site-packages/clickhouse_driver/connection.py:416\u001b[0m, in \u001b[0;36mConnection.connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 413\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhosts\u001b[38;5;241m.\u001b[39mrotate(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 415\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m err \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 416\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m err\n", "\u001b[0;31mSocketTimeoutError\u001b[0m: Code: 209. (192.168.1.142:9000)" ] } ], "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": 47, "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": 48, "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": 49, "id": "597ae7bd-2eea-44d7-b379-f0eb7e745c15", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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idfromattoopencloseminmaxvolume
077308012023-01-02 15:58:4516726751400000000002023-01-02 15:59:001.0659951.0660351.0659301.06607057
177308012023-01-02 15:58:4516726751400000000002023-01-02 15:59:001.0659951.0660351.0659301.06607057
277308022023-01-02 15:59:0016726751550000000002023-01-02 15:59:151.0660551.0660851.0660051.06611552
377308022023-01-02 15:59:0016726751550000000002023-01-02 15:59:151.0660551.0660851.0660051.06611552
477308032023-01-02 15:59:1516726751700000000002023-01-02 15:59:301.0660801.0660251.0660251.06611057
\n", "
" ], "text/plain": [ " id from at to \\\n", "0 7730801 2023-01-02 15:58:45 1672675140000000000 2023-01-02 15:59:00 \n", "1 7730801 2023-01-02 15:58:45 1672675140000000000 2023-01-02 15:59:00 \n", "2 7730802 2023-01-02 15:59:00 1672675155000000000 2023-01-02 15:59:15 \n", "3 7730802 2023-01-02 15:59:00 1672675155000000000 2023-01-02 15:59:15 \n", "4 7730803 2023-01-02 15:59:15 1672675170000000000 2023-01-02 15:59:30 \n", "\n", " open close min max volume \n", "0 1.065995 1.066035 1.065930 1.066070 57 \n", "1 1.065995 1.066035 1.065930 1.066070 57 \n", "2 1.066055 1.066085 1.066005 1.066115 52 \n", "3 1.066055 1.066085 1.066005 1.066115 52 \n", "4 1.066080 1.066025 1.066025 1.066110 57 " ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dfCh.head()" ] }, { "cell_type": "code", "execution_count": 50, "id": "86794e47-611f-4ca8-a7e8-07e71afafe67", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5.175396532999002\n" ] } ], "source": [ "print(cHouse_read_execution_time)" ] }, { "cell_type": "code", "execution_count": 51, "id": "e7926062-8e84-4d3f-90a9-32807ce4f3d4", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6.163630739996734\n" ] } ], "source": [ "print(cHouse_write_execution_time)" ] }, { "cell_type": "code", "execution_count": 52, "id": "8faa5683-a204-461d-80c3-67644aa714ce", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.15 s, sys: 216 ms, total: 1.36 s\n", "Wall time: 5.24 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": 23, "id": "16cd8eb7-333d-43fd-88e0-ee983645d3fd", "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax. Perhaps you forgot a comma? (1520588424.py, line 4)", "output_type": "error", "traceback": [ "\u001b[0;36m Cell \u001b[0;32mIn[23], line 4\u001b[0;36m\u001b[0m\n\u001b[0;31m \"postgresql+psycopg2://{}:{}@{}:5432/{}\".format(\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax. Perhaps you forgot a comma?\n" ] } ], "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": null, "id": "e173a45b-60a1-4c33-946e-ccf98bf8e97f", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 26, "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", " # disconnect()\n", " return 0\n", "\n", "\n", "psqlCreateTables(dbname)\n", "# testar função" ] }, { "cell_type": "code", "execution_count": 27, "id": "a7883c4d-4609-4380-8a45-246b7ca2f9c5", "metadata": { "tags": [] }, "outputs": [ { "ename": "AttributeError", "evalue": "'int' object has no attribute 'raw_connection'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "File \u001b[0;32m:2\u001b[0m\n", "\u001b[0;31mAttributeError\u001b[0m: 'int' object has no attribute 'raw_connection'" ] } ], "source": [ "%%time\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()" ] }, { "cell_type": "code", "execution_count": null, "id": "73de4294-1284-49b0-b31e-45db6e835877", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "e37a93e1-fc0e-4d27-9e16-dca6c8aea324", "metadata": {}, "outputs": [], "source": [ "start = time.time()\n", "# %%time\n", "# Read\n", "df = pd.read_sql_query('select * from \"comparedbs\"', con=engine)\n", "end = time.time()\n", "postgresql_read_time = exec_time(start, end)" ] }, { "cell_type": "code", "execution_count": null, "id": "6d1b7480-5bc7-4f08-8cf3-b9590802d8f7", "metadata": { "tags": [] }, "outputs": [], "source": [ "print(postgresql_read_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": null, "id": "60a990e2-4607-4654-84ec-17d4985adae2", "metadata": { "tags": [] }, "outputs": [], "source": [ "# fazer sem funçao para ver se melhora\n", "# verifique se esta no ssd os arquivos da pasta git\n", "def main():\n", " client = Minio(\n", " S3MinioUrl,\n", " secure=False,\n", " region=S3MinioRegion,\n", " access_key=\"MatMPA7NyHltz7DQ\",\n", " secret_key=\"SO1IG5iBPSjNPZanYUaHCLcoSbjphLCP\",\n", " )\n", "\n", " # Make bucket if not exist.\n", " found = client.bucket_exists(\"data\")\n", " if not found:\n", " client.make_bucket(\"data\")\n", " else:\n", " print(\"Bucket 'data' already exists\")\n", "\n", " # Upload\n", " client.fput_object(\n", " \"data\",\n", " \"data.parquet\",\n", " \"data/data.parquet\",\n", " )\n", " # print(\n", " # \"'data/data.parquet' is successfully uploaded as \"\n", " # \"object 'data.parquet' to bucket 'data'.\"\n", " # )" ] }, { "cell_type": "code", "execution_count": null, "id": "390918c8-c88f-404a-96c4-685d578fdad0", "metadata": { "tags": [] }, "outputs": [], "source": [ "%%time\n", "df.to_parquet(\"data/data.parquet\")\n", "if __name__ == \"__main__\":\n", " try:\n", " main()\n", " except S3Error as exc:\n", " print(\"error occurred.\", exc)" ] }, { "cell_type": "code", "execution_count": null, "id": "a9e07143-8c11-4b68-a869-c3922cda9092", "metadata": { "tags": [] }, "outputs": [], "source": [ "pq = pd.read_parquet(\"data/data.parquet\", engine=\"pyarrow\")\n", "pq.head()" ] }, { "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": { "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": { "tags": [] }, "source": [ "### Kdb+" ] }, { "cell_type": "code", "execution_count": null, "id": "14f63810-1943-4e28-9bce-2148be6be02d", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "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": 66, "id": "a9740731-6077-4bf1-bb65-b4c4225ac79b", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.arange(1)\n", "width = 0.40\n", "y1 = [cHouse_read_execution_time]\n", "y2 = [cHouse_write_execution_time]\n", "plt.bar(x - 0.2, y1, width)\n", "plt.bar(x + 0.2, y2, width)\n", "plt.xticks(x, [\"Click House\"])\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 }